diff --git a/.env b/.env new file mode 100644 index 000000000..f785615cf --- /dev/null +++ b/.env @@ -0,0 +1,5 @@ +FRONTEND="https://encord-active-frontend.web.app" +STREAMLIT_RUNNER_MAGIC_ENABLED="false" +STREAMLIT_BROWSER_GATHER_USAGE_STATS="false" +STREAMLIT_THEME_BASE="light" +STREAMLIT_THEME_PRIMARY_COLOR="#5658dd" diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 000000000..d31078e82 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1 @@ +* @encord-team/ml-team diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md new file mode 100644 index 000000000..6db61ff6f --- /dev/null +++ b/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +- Using welcoming and inclusive language +- Being respectful of differing viewpoints and experiences +- Gracefully accepting constructive criticism +- Focusing on what is best for the community +- Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +- The use of sexualized language or imagery and unwelcome sexual attention or + advances +- Trolling, insulting/derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or electronic + address, without explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at active@encord.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 000000000..7188403e3 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,31 @@ +name: 🐞 Bug Report +description: Create a bug report to help us reproduce and fix the bug +title: "bug: " +labels: ["🐞❔ unconfirmed bug"] +body: + - type: textarea + attributes: + label: Provide environment information + description: | + Run this command in your project root and paste the results: + ```bash + encord-active print system-info + ``` + validations: + required: true + - type: textarea + attributes: + label: Describe the bug + description: A clear and concise description of the bug, as well as what you expected to happen when encountering it. + validations: + required: true + - type: textarea + attributes: + label: To reproduce + description: Describe how to reproduce your bug. Steps, code snippets etc. + validations: + required: true + - type: textarea + attributes: + label: Additional information + description: Add any other information related to the bug here, screenshots/videos if applicable. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..6c1278221 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: true +contact_links: + - name: ❓ Simple question - Slack Chat + url: https://join.slack.com/t/encordactive/shared_invite/zt-1hc2vqur9-Fzj1EEAHoqu91sZ0CX0A7Q + about: This issue tracker is not for technical support. Please use our Slack chat and ask the community for help. diff --git a/.github/ISSUE_TEMPLATE/documentation_issue.yml b/.github/ISSUE_TEMPLATE/documentation_issue.yml new file mode 100644 index 000000000..ba4ddcf76 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation_issue.yml @@ -0,0 +1,27 @@ +name: 📚 Documentation +description: Help us improve our documentation +title: "docs:" +labels: ["📚 documentation"] +body: + - type: textarea + attributes: + label: Documentation issue + description: Is this issue related to an existing part of the documentation? If so, please provide a link + placeholder: > + Helpful info about this issue. + validations: + required: true + + - type: textarea + attributes: + label: Describe the thing to improve + description: A clear and concise description of problems and what you would like to see + validations: + required: true + + - type: textarea + attributes: + label: Describe the solution (optional) + description: Ideas about what we should do to improve experience of beginners and readers. It may be new pages to add, grammar fixes etc. + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 000000000..a1738bae0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,27 @@ +name: 🧑‍💻 Feature Request +description: Suggest an idea for this project +title: "feat: " +labels: ["🌟 enhancement"] +body: + - type: textarea + attributes: + label: Is your feature request related to a problem? Please describe. + description: A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + validations: + required: true + - type: textarea + attributes: + label: Describe the solution you'd like to see + description: A clear and concise description of what you want to happen. + validations: + required: true + - type: textarea + attributes: + label: Describe alternate solutions + description: A clear and concise description of any alternative solutions or features you've considered. + validations: + required: true + - type: textarea + attributes: + label: Additional information + description: Add any other information related to the feature here. If your feature request is related to any issues or discussions, link them here. diff --git a/.github/actions/setup-root-poetry-environment/action.yml b/.github/actions/setup-root-poetry-environment/action.yml new file mode 100644 index 000000000..5f142bd53 --- /dev/null +++ b/.github/actions/setup-root-poetry-environment/action.yml @@ -0,0 +1,39 @@ +name: "Setup test environment" +description: "Sets up Python, Poetry and dependencies" + +inputs: + python: + description: "Python version to use" + default: 3.9.14 + required: false + poetry: + description: "Poetry version to use" + default: 1.1.12 + required: false + +runs: + using: "composite" + + steps: + - uses: actions/setup-python@v4 + with: + python-version: ${{ inputs.python }} + + - uses: snok/install-poetry@v1 + with: + version: ${{ inputs.poetry }} + virtualenvs-in-project: true + + - id: cached-poetry + uses: actions/cache@v2 + with: + path: .venv + key: > + ${{ hashFiles('pyproject.toml') }}-${{ hashFiles('poetry.lock') }} + -${{ inputs.python }}-${{ inputs.poetry }} + + - if: steps.cached-poetry.outputs.cache-hit != 'true' + run: | + poetry lock --no-update + poetry install --no-interaction + shell: bash diff --git a/.github/workflows/docs-deploy.yml b/.github/workflows/docs-deploy.yml new file mode 100644 index 000000000..42dee78c7 --- /dev/null +++ b/.github/workflows/docs-deploy.yml @@ -0,0 +1,79 @@ +name: Docs build, deploy + +on: + push: + branches: + - main + release: + types: [published] + workflow_dispatch: + +env: + NODE_VERSION: 18 + SRC_FOLDER: docs + BUILD_ARTIFACT_NAME: build_artifact + BUILD_ARTIFACT_PATH: docs/build +concurrency: + group: docs-${{ github.ref }} + cancel-in-progress: true + +jobs: + build: + name: Build docs + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Setup Node + uses: actions/setup-node@v3 + with: + node-version: ${{ env.NODE_VERSION }} + cache: "npm" + cache-dependency-path: "docs/package-lock.json" + + - name: Install dependencies + working-directory: ${{ env.SRC_FOLDER }} + run: | + npm ci + + - name: Build docs + working-directory: ${{ env.SRC_FOLDER }} + run: | + npm run-script build + + - name: Upload build folder + uses: actions/upload-artifact@v3 + with: + name: ${{ env.BUILD_ARTIFACT_NAME }} + path: ${{ env.BUILD_ARTIFACT_PATH }} + if-no-files-found: error + retention-days: 1 + + deploy: + name: Deploy + needs: [build] + runs-on: ubuntu-latest + steps: + - name: Checkout Repo + uses: actions/checkout@v3 + + - name: Download build artifact + uses: actions/download-artifact@v3 + with: + name: ${{ env.BUILD_ARTIFACT_NAME }} + path: ${{ env.BUILD_ARTIFACT_PATH }} + + - name: Create deployment message + id: vars + run: | + echo "sha=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT + echo "message=$(git log -1 --format=%s | cut -c -64)" >> $GITHUB_OUTPUT + + - name: Deploy to Firebase + uses: w9jds/firebase-action@master + with: + args: deploy --only hosting:prod -m \"${{ steps.vars.outputs.sha }} ${{ steps.vars.outputs.message }}\" + env: + GCP_SA_KEY: ${{ secrets.FIREBASE_SERVICE_ACCOUNT_ENCORD_ACTIVE_FRONTEND }} + PROJECT_PATH: ./${{ env.SRC_FOLDER }} diff --git a/.github/workflows/firebase-hosting-merge.yml b/.github/workflows/firebase-hosting-merge.yml new file mode 100644 index 000000000..21768d53d --- /dev/null +++ b/.github/workflows/firebase-hosting-merge.yml @@ -0,0 +1,23 @@ +# This file was auto-generated by the Firebase CLI +# https://github.com/firebase/firebase-tools + +name: Deploy to Firebase Hosting on merge +"on": + push: + branches: + - main +jobs: + build_and_deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Install and Build + working-directory: frontend + run: npm ci && npm run build + - uses: FirebaseExtended/action-hosting-deploy@v0 + with: + repoToken: "${{ secrets.GITHUB_TOKEN }}" + firebaseServiceAccount: "${{ secrets.FIREBASE_SERVICE_ACCOUNT_ENCORD_ACTIVE_FRONTEND }}" + channelId: live + projectId: encord-active-frontend + entryPoint: frontend diff --git a/.github/workflows/firebase-hosting-pull-request.yml b/.github/workflows/firebase-hosting-pull-request.yml new file mode 100644 index 000000000..808ac2335 --- /dev/null +++ b/.github/workflows/firebase-hosting-pull-request.yml @@ -0,0 +1,20 @@ +# This file was auto-generated by the Firebase CLI +# https://github.com/firebase/firebase-tools + +name: Deploy to Firebase Hosting on PR +"on": pull_request +jobs: + build_and_preview: + if: "${{ github.event.pull_request.head.repo.full_name == github.repository }}" + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Install and Build + working-directory: frontend + run: npm ci && npm run build + - uses: FirebaseExtended/action-hosting-deploy@v0 + with: + repoToken: "${{ secrets.GITHUB_TOKEN }}" + firebaseServiceAccount: "${{ secrets.FIREBASE_SERVICE_ACCOUNT_ENCORD_ACTIVE_FRONTEND }}" + projectId: encord-active-frontend + entryPoint: frontend diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 000000000..fb11153f6 --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,25 @@ +name: Pre-commit workflow + +on: + pull_request: + workflow_dispatch: + +env: + PYTHON: 3.9.14 + +jobs: + pre-commit: + name: Linting and type checking + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON }} + - name: Setup root poetry environment + uses: ./.github/actions/setup-root-poetry-environment + + - name: Run linting, type checking and testing + uses: pre-commit/action@v2.0.3 + with: + extra_args: "--all-files --hook-stage=push" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 000000000..4dec4b6e7 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,56 @@ +name: Publish + +on: + release: + types: [published] + +env: + PYTHON: 3.9.14 + POETRY: 1.1.12 + PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + +concurrency: + group: encord-active-${{ github.ref }}-publish + cancel-in-progress: true + +jobs: + publish: + name: Publish Encord.Active + runs-on: ubuntu-latest + steps: + - name: Checkout repo + uses: actions/checkout@v3 + with: + token: ${{ secrets.AUTOMATION_BOT_ACCESS_TOKEN }} + ref: main + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON }} + + - name: Setup Poetry + uses: snok/install-poetry@v1 + with: + version: ${{ env.POETRY }} + virtualenvs-in-project: true + + - name: Version Bump + run: "poetry version ${{ github.ref_name }}" + + - name: Version Bump commit + uses: stefanzweifel/git-auto-commit-action@v4 + with: + commit_message: "Version bump to ${{ github.ref_name }}" + push_options: --force + + - name: Install dependencies + run: | + poetry install --no-interaction + - name: Build + run: poetry build + + - name: Publish + run: | + poetry config pypi-token.pypi ${{ env.PYPI_TOKEN }} + poetry publish diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..86eb869ff --- /dev/null +++ b/.gitignore @@ -0,0 +1,356 @@ +# Created by https://www.toptal.com/developers/gitignore/api/macos,linux,vim,python,pycharm +# Edit at https://www.toptal.com/developers/gitignore?templates=macos,linux,vim,python,pycharm + +### Linux ### +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### macOS Patch ### +# iCloud generated files +*.icloud + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# SonarLint plugin +.idea/sonarlint/ + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +### PyCharm Patch ### +# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 + +# *.iml +# modules.xml +# .idea/misc.xml +# *.ipr + +# Sonarlint plugin +# https://plugins.jetbrains.com/plugin/7973-sonarlint +.idea/**/sonarlint/ + +# SonarQube Plugin +# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin +.idea/**/sonarIssues.xml + +# Markdown Navigator plugin +# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced +.idea/**/markdown-navigator.xml +.idea/**/markdown-navigator-enh.xml +.idea/**/markdown-navigator/ + +# Cache file creation bug +# See https://youtrack.jetbrains.com/issue/JBR-2257 +.idea/$CACHE_FILE$ + +# CodeStream plugin +# https://plugins.jetbrains.com/plugin/12206-codestream +.idea/codestream.xml + +# Azure Toolkit for IntelliJ plugin +# https://plugins.jetbrains.com/plugin/8053-azure-toolkit-for-intellij +.idea/**/azureSettings.xml + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env.local +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Vim ### +# Swap +[._]*.s[a-v][a-z] +!*.svg # comment out if you don't need vector files +[._]*.sw[a-p] +[._]s[a-rt-v][a-z] +[._]ss[a-gi-z] +[._]sw[a-p] + +# Session +Session.vim +Sessionx.vim + +# Temporary +.netrwhist +# Auto-generated tag files +tags +# Persistent undo +[._]*.un~ + +# End of https://www.toptal.com/developers/gitignore/api/macos,linux,vim,python,pycharm +/conf/config.yaml + +.idea + +/viewer/outputs/ +/outputs/ +/viewer/pages/outputs/ +/local_tests/ + +imports.prof diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..90927d690 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,27 @@ +repos: + - repo: local + hooks: + - id: black + name: black + entry: poetry run black --config=pyproject.toml + types_or: [ python, pyi ] + language: system + + - id: isort + name: isort + entry: poetry run isort --sp=pyproject.toml + types_or: [ python, pyi ] + language: system + + - id: mypy + name: mypy + entry: poetry run mypy . --config-file=pyproject.toml + types_or: [ python, pyi ] + language: system + pass_filenames: false + + - id: pylint + name: pylint + entry: poetry run pylint -j 0 --rcfile pyproject.toml + types_or: [ python, pyi ] + language: system diff --git a/ADOPTERS.md b/ADOPTERS.md new file mode 100644 index 000000000..aaebfeb34 --- /dev/null +++ b/ADOPTERS.md @@ -0,0 +1,13 @@ +# Adopters + +[!["Join us on +Slack"](https://img.shields.io/badge/Slack-4A154B?logo=slack&logoColor=white)](https://join.slack.com/t/encordactive/shared_invite/zt-1hc2vqur9-Fzj1EEAHoqu91sZ0CX0A7Q) [![Twitter +Follow](https://img.shields.io/twitter/follow/encord_team?label=%40encord_team&style=social)](https://twitter.com/encord_team) + +🦩 _If you're using Encord Active in your organization, please try to add your company name to this list. It really helps the project to gain momentum and credibility. It's a small contribution back to the project with a big impact. You can do this by by editing this file and contributing your changes via a pull-request on GitHub._ + +> 👋 If you are considering using Encord Active in your organization please introduce yourself via this [typeform](https://l3yr0zko9ed.typeform.com/to/FLp6fV1R?typeform-source=landing_home) 🙇🏻‍♂️ + +| Organization | Contact | Description of Use | +| -------------------------------- | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------- | +| [Encord](https://www.encord.com) | [Twitter](https://twitter.com/encord_team) [Email](mailto:active@encord.com) | Encord uses Encord Active to improve data, labels and by extension models. | diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..b5b120219 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,3 @@ +If you're using Encord Active in your organization, please try to add your company name to the [ADOPTERS.md](./ADOPTERS.md). It really helps the project to gain momentum and credibility. It's a small contribution back to the project with a big impact. + +Read the [contributing docs](https://encord-active-docs.web.app/contributing). diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..0ad25db4b --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +our General Public Licenses are intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + Developers that use our General Public Licenses protect your rights +with two steps: (1) assert copyright on the software, and (2) offer +you this License which gives you legal permission to copy, distribute +and/or modify the software. + + A secondary benefit of defending all users' freedom is that +improvements made in alternate versions of the program, if they +receive widespread use, become available for other developers to +incorporate. Many developers of free software are heartened and +encouraged by the resulting cooperation. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 000000000..7ca54c996 --- /dev/null +++ b/README.md @@ -0,0 +1,108 @@ +

+

Encord Active

+ Encord logo +

+ +[!["Join us on +Slack"](https://img.shields.io/badge/Slack-4A154B?logo=slack&logoColor=white)][join-slack] [![Twitter +Follow](https://img.shields.io/twitter/follow/encord_team?label=%40encord_team&style=social)][twitter-url] +[![PRs-Welcome][contribute-image]][contribute-url] + +## Documentation + +Please refer to our [documentation][encord-active-docs]. + +## Installation + +The simplest way to install the CLI is using `pip` in a suitable virtual environment: + +```shell +pip install encord-active +``` + +We recommend using a virtual environment, such as `venv`: + +```shell +$ python3.9 -m venv ea-venv +$ source ea-venv/bin/activate +$ pip install encord-active +``` + +> `encord-active` requires [python3.9](https://www.python.org/downloads/release/python-3915/). +> If you have trouble installing `encord-active`, you find more detailed instructions on +> installing it [here][encord-active-docs]. + +## Downloading a pre-built project + +The quickest way to get started is by downloading an existing dataset. +The download command will setup a directory where the project will be stored and will ask which pre-built dataset to use. + +```shell +$ encord-active download +$ encord-active visualise /path/to/project +``` + +The app should automatically open in the browser. If not, navigate to `localhost:8501`. +Our [Docs][encord-active-docs] contain more information about what you can see in the page. + +## Importing an Encord Project + +This section requires setting up an ssh key with Encord, so slightly more technical. + +> If you haven't set up an ssh key with Encord, you can follow the tutorial in this [link](https://docs.encord.com/admins/settings/public-keys/#set-up-public-key-authentication) + +To import an Encord project, use this script: + +```shell +$ encord-active import project +``` + +## Development + +### Write your own metrics + +Encord Active isn't limited to the metrics we provided, it is actually quite easy to write your own 🔧 +See the [Writing Your Own Metric](https://docs.encord.com/admins/settings/public-keys/#set-up-public-key-authentication) page on the WIKI for details on this. + +### Pre-commit hooks + +If you have installed the dependencies with poetry, then you can install pre-commit hooks by running the following command: + +```shell +$ pre-commit install +``` + +The effect of this will be that `black`, `isort`, `mypy`, and `pylint` needs to run without finding issues with the code before you are allowed to commit. +When you commit and either `black` or `isort` fails, committing again is enough, as the side effect of committing the first time is to reformat files. + +Running each of the tools individually on your code can be done as follows: + +```shell +$ poetry run black --config=pyproject.toml . +$ poetry run isort --sp=pyproject.toml . +$ poetry run mypy . --ignore-missing-imports +$ poetry run pylint -j 0 --rcfile pyproject.toml [subdir] +``` + +## Community and Support + +Join our community on [Slack][join-slack] and [Twitter][twitter-url]! + +[Suggest improvements and report problems][new-issue] + +# Contributions + +If you're using Encord Active in your organization, please try to add your company name to the [ADOPTERS.md](./ADOPTERS.md). It really helps the project to gain momentum and credibility. It's a small contribution back to the project with a big impact. + +Read the [contributing docs][contribute-url]. + +# Licence + +This project is licensed under the terms of the AGPL-3.0 license. + +[encord-active-docs]: https://encord-active-docs.web.app/ +[contribute-url]: https://encord-active-docs.web.app/contributing +[contribute-image]: https://img.shields.io/badge/PRs-welcome-blue.svg +[join-slack]: https://join.slack.com/t/encordactive/shared_invite/zt-1hc2vqur9-Fzj1EEAHoqu91sZ0CX0A7Q +[twitter-url]: https://twitter.com/encord_team +[new-issue]: https://github.com/encord-team/encord-active/issues/new diff --git a/docs/.firebaserc b/docs/.firebaserc new file mode 100644 index 000000000..6ad886351 --- /dev/null +++ b/docs/.firebaserc @@ -0,0 +1,14 @@ +{ + "projects": { + "encord-active-frontend": "encord-active-frontend" + }, + "targets": { + "encord-active-frontend": { + "hosting": { + "prod": [ + "encord-active-docs" + ] + } + } + } +} diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 000000000..b2d6de306 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,20 @@ +# Dependencies +/node_modules + +# Production +/build + +# Generated files +.docusaurus +.cache-loader + +# Misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 000000000..767f7cb87 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,25 @@ +# Encord Active Docs + +This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. + +### Installation + +``` +$ npm i +``` + +### Local Development + +``` +$ npm start +``` + +This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. + +### Build + +``` +$ npm run build +``` + +This command generates static content into the `build` directory and can be served using any static contents hosting service. diff --git a/docs/babel.config.js b/docs/babel.config.js new file mode 100644 index 000000000..e00595dae --- /dev/null +++ b/docs/babel.config.js @@ -0,0 +1,3 @@ +module.exports = { + presets: [require.resolve('@docusaurus/core/lib/babel/preset')], +}; diff --git a/docs/builders/build_metrics_pages.py b/docs/builders/build_metrics_pages.py new file mode 100644 index 000000000..02ba2dc86 --- /dev/null +++ b/docs/builders/build_metrics_pages.py @@ -0,0 +1,94 @@ +""" +Script for building markdown pages with information from the metrics. +When run this will build pages directly into the `docs/docs/metrics directory. +""" +from importlib import import_module +from pathlib import Path +from typing import Any, List, Union, cast + +from tabulate import tabulate + +import encord_active.lib.common.metric as metrics +import encord_active.lib.metrics.run_all as run_all + +github_url = "https://github.com/encord-team/encord-active" +descriptions = { + "heuristic": "Work on images or individual video frames and are heuristic in the sense that they mostly depend on the image content without labels.", + "geometric": "Operate on the geometries of objects like bounding boxes, polygons, and polylines.", + "semantic": "Operates with the semantic information of images or individual video frames.", +} + +metric_path = Path(run_all.__file__).parent +docs_root = metric_path.parents[1] + +submodules = [p.name for p in metric_path.iterdir() if p.name[0] != "_" and p.is_dir()] + +ms = cast( # rename to avoid conflict with metrics module + List[List[metrics.Metric]], + [ + sorted( + [import_module(m1).__getattribute__(m2)() for m1, m2 in run_all.get_metrics(submodule)], + key=lambda m: m.TITLE, + ) + for submodule in submodules + ], +) + + +def fix_strings(row: List[Any]) -> List[str]: + for i, val in enumerate(row): + val = row[i] + if not isinstance(val, list): + val = [val] + val2: List[Union[str, int, float]] = [] + for v in val: + if hasattr(v, "value"): + v = getattr(v, "value") + elif hasattr(v, "__name__"): + v = "Any" + elif v is None: + v = "Any" + + if i in {2, 3,} and isinstance( + v, str + ): # image and annotation type, so replace "_" with space and let every type be a tag + v = v.replace("_", " ") + v = f"`{v}`" + val2.append(v) + try: + row[i] = ", ".join(map(str, sorted(set(val2)))) + except TypeError: + print(row) + print(val2) + + row[0] = f'[{row[0]}]({row[0].lower().replace(" ", "-")}) - {row[1]}' + row.pop(1) + return row + + +for submodule, metrics_list in zip(submodules, ms): + rows = [] + for m in metrics_list: + rows.append(fix_strings([m.TITLE, m.SHORT_DESCRIPTION, m.DATA_TYPE, m.ANNOTATION_TYPE])) + markdown_table = tabulate( + rows, + headers=["Title", "Metric Type", "Data Type", "Annotation Type"], + tablefmt="github", + ) + + md = "" + for metric in metrics_list: + url = f"{github_url}/blob/main/{type(metric).__module__.replace('.', '/')}.py" + md += f"\n## {metric.TITLE} \n" f"{metric.LONG_DESCRIPTION} \n\n" f"Implementation on [GitHub]({url})\n" + + markdown_file = docs_root / "docs" / "metrics" / f"{submodule}.md" + with markdown_file.open("w", encoding="utf-8") as f: + + def w(line): + f.write(f"{line}") + f.write("\n\n") + + w(f"# {submodule.title()}") + w(descriptions[submodule]) + w(markdown_table) + w(md) diff --git a/docs/docs/cli/_category_.json b/docs/docs/cli/_category_.json new file mode 100644 index 000000000..bc548e372 --- /dev/null +++ b/docs/docs/cli/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Command Line Interface", + "position": 5, + "collapsed": true, + "link": { + "type": "generated-index", + "description": "How to use the command line interface (CLI)." + } +} diff --git a/docs/docs/cli/configuration.md b/docs/docs/cli/configuration.md new file mode 100644 index 000000000..a201a8525 --- /dev/null +++ b/docs/docs/cli/configuration.md @@ -0,0 +1,57 @@ +--- +sidebar_position: 5 +--- + +# Configuration + +Encord Active keeps some configurable properties to prevent repetitive input prompts. + +The config file is stored at: + +- Linux: `~/.config/encord-active/config.toml` +- MacOS: `~/Library/Application Support/encord-active/config.toml` +- Windows: `%APPDATA%/encord-active/config.toml` + +And these are the configurable properties. + +```toml +ssh_key_path = "/absolute/path/to/ssh-key" # A key to use when accessing Encord projects +projects_dir = "/absolute/path/to/projects/dir" # A directory where all projects should be stored +``` + +All properties are empty by default and are saved after the first time the user is prompted to provide them. + +The CLI also provides the `config` command which allows `list`/`get`/`set`/`unset` configurations. + +### Usage examples + +```shell +encord-active config list + +# output (only if already set) +ssh_key_path = "/Users/foo/.ssh/encord" +projects_dir = "/Users/foo/projects" +``` + +```shell +encord-active config get ssh_key_path + +# output +ssh_key_path = "/Users/foo/.ssh/encord" +``` + +```shell +encord-active config set ssh_key_path ~/.ssh/encord + +# output +Property `ssh_key_path` has been set. +``` + +```shell +encord-active config unset ssh_key_path + +# output +Property `ssh_key_path` was unset. + +# note: next the property is needed a prompt will appear +``` diff --git a/docs/docs/cli/download-sandbox-data.md b/docs/docs/cli/download-sandbox-data.md new file mode 100644 index 000000000..c303cb300 --- /dev/null +++ b/docs/docs/cli/download-sandbox-data.md @@ -0,0 +1,37 @@ +--- +sidebar_position: 1 +--- + +# Download Sandbox Dataset + +To get started quickly with a sandbox dataset, you run the following command. + +```shell +(ea-venv)$ encord-active download +``` + +The script will ask you to + +1. `Where should we store the data?`: specify a directory in which the data should be stored. +2. (potentially) `Directory not existing, want to create it? [y/N]` type y then enter. +3. `[?] Choose a project:` choose a project with and and hit enter + +:::tip + +If you plan on using multiple datasets, it may be worth first creating an empty directory for all the datasets. + +``` +$ mkdir /path/to/data/root +# or windows +$ md /path/to/data/root +``` + +In step 1. above, specify, e.g., `/path/to/data/root/sandbox1` + +::: + +When the download process is complete, you visualise the results by following the printed instructions. +For more details, see the [Open Encord Active](/cli/open-encord-active) page. + + + diff --git a/docs/docs/cli/import-coco-predictions.md b/docs/docs/cli/import-coco-predictions.md new file mode 100644 index 000000000..80705eafb --- /dev/null +++ b/docs/docs/cli/import-coco-predictions.md @@ -0,0 +1,25 @@ +--- +sidebar_position: 4 +--- + +# Importing Coco Predictions + +:::info +Make sure you have installed Encord Active with the `coco` [extras](/installation#coco-extras). +::: + +:::note +This command assume that you have imported you project useing the [COCO importer](/cli/import-coco-project). +::: + +Importing COCO predictions is currently the easiest way to import predictions to Encord Active. + +You need to have a results JSON file following the [COCO results format](https://cocodataset.org/#format-results) and run the following command on it: + +```shell +encord-active import predictions --coco results.json +``` + +This command will ask you to which project you would want to import the predications. + +After the execution is done you should be ready to view your [model assertions metrics](/category/model-assertions). diff --git a/docs/docs/cli/import-coco-project.md b/docs/docs/cli/import-coco-project.md new file mode 100644 index 000000000..695a89ed9 --- /dev/null +++ b/docs/docs/cli/import-coco-project.md @@ -0,0 +1,55 @@ +--- +sidebar_position: 3 +--- + +# Importing Coco Project + +:::info +Make sure you have installed Encord Active with the `coco` [extras](/installation#coco-extras). +::: + +If you already have a project on your local machine using the COCO data format, you can import that project with the following command: + +```shell +(ea-venv)$ encord-active import project --coco ./images ./annotations.json +``` + +:::info +The command above assumes the following structure but is not limited to it. + +``` + +coco-project-foo +├── images +│   ├── 00000001.jpeg +│   ├── 00000002.jpeg +│   ├── ... +└── annotations.json + +``` + +You can provide any path to each of the arguments as long as the first one is a directory of images and the second is an annotations file following the [COCO data format](https://cocodataset.org/#format-data). +::: + +If this is your first time using Encord Active, you will need the path to your private `ssh-key` associated with Encord and a projects directory where all projects should be stored. +Don't worry! The script will guide you on the way if you don't know it already. + +Running the importer will do the following things. + +1. Create a dataset. +2. Create an ontology. +3. Create a project. +4. Create a local Encord Active project. +5. Compute all the [metrics](category/metrics). + +:::info + +- Steps 1-3 are on the Encord platform. This is a dependency that we intend to make optional in the future. +- Step 4 will make a hard copy of the images used in your dataset. In the future, we expect to support symlinks or just pointers to the original images. + +::: + +The whole flow might take a while depending on the size of your dataset. +Bare with us, it is worth the wait. + +When the process is done, follow the printed instructions to open the app or see more details in the [Open Encord Active](/cli/open-encord-active) page. diff --git a/docs/docs/cli/import-encord-project.md b/docs/docs/cli/import-encord-project.md new file mode 100644 index 000000000..d74cf27a1 --- /dev/null +++ b/docs/docs/cli/import-encord-project.md @@ -0,0 +1,33 @@ +--- +sidebar_position: 2 +--- + +# Importing Encord Project + +If you already have a project on the Encord platform, you can import that project with the following command: + +``` +(ea-venv)$ encord-active import project +``` + +:::tip + +If you don't have an Encord project ready, you can find your next steps in the SDK section [Migrating Data to Encord](/sdk/migrating-data). +Otherwise, you can [download one of our sandbox datasets](/cli/download-sandbox-data). + +::: +To be able to do this, you will need the path to your private `ssh-key` associated with Encord and a `project_hash`. +Don't worry! The script will guide you on the way if you don't know it already. +The script will ask you: + +1. `Where is your private ssh key stored?`: type the path to your private ssh key +2. `Where should your project directory be located?`: use, e.g., `/path/to/data/root/your_project_title` from above. +3. `Are you sure you want to use /path/you/chose? [y/N]`: hit y then enter to indicate the specified path is correct. +4. `Specify project hash`: paste / type the `project_hash`. If you don't know the id, you can type `?` and hit enter to get all your projects listed with their `project_hash`s before being prompted with the question again. Now you can copy paste the id. + +Next, `encord-active` will fetch your data and labels before computing all the [metrics](/category/metrics) available in `encord-active`. + +Downloading the data and computing the metrics may take a while. +Bare with us, it is worth the wait. + +When the process is done, follow the printed instructions to open the app or see more details in the [Open Encord Active](/cli/open-encord-active) page. diff --git a/docs/docs/cli/open-encord-active.md b/docs/docs/cli/open-encord-active.md new file mode 100644 index 000000000..17a362d50 --- /dev/null +++ b/docs/docs/cli/open-encord-active.md @@ -0,0 +1,38 @@ +--- +sidebar_position: 4 +--- + +import Link from '@docusaurus/Link'; + +# Open Encord Active + +:::info + +If you haven't [downloaded](download-sandbox-data) or [imported](import-encord-project) a project yet, you need to do that first. + +::: + +To run the Encord Active app, you need the data path that you specified when you +[downloaded](download-sandbox-data) or [imported](import-encord-project) your project. + +Run the following command: + +```shell +(ea-venv)$ encord-active visualise +``` + +Now, you will be able to select either of the projects you have previously imported or downloaded. +Upon hitting enter, your browser should open a new window with Encord Active. + +:::info + +If it is the first time you run the app, you may be asked for an email. +If you don't feel like sharing it, just hit enter to ignore it. + +::: + +:::caution + +If the script just seems to hang and nothing happens in your browser, try visiting http://localhost:8501. + +::: diff --git a/docs/docs/contributing.mdx b/docs/docs/contributing.mdx new file mode 100644 index 000000000..31364a43e --- /dev/null +++ b/docs/docs/contributing.mdx @@ -0,0 +1,236 @@ +--- +sidebar_position: 10 +--- + +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; + +# Contributing + +We follow a [code of conduct][code_of_conduct] when participating in the community. Please read it before you make any contributions. + +- If you plan to work on an issue, mention so in the issue page before you start working on it. +- If you plan to work on a new feature, create an issue and discuss it with other community members/maintainers. +- Ask for help in our [slack community][join-slack]. + +## Ways to contribute + +- **Stars on GitHub**: If you're an Encord Active user and enjoy using our platform, don't forget to star it on [GitHub][repo]! 🌟 +- **Improve documentation**: Good documentation is imperative to the success of any project. You can make our documents the best they need to be by improving their quality or adding new ones. +- **Give feedback**: We're always looking for ways to make Encord Active better, please share how you use Encord Active, what features are missing and what is done good via [Slack](http://discord.gg/refine). +- **Share refine**: Help us reach people. Share [Encrod Active repository][repo] with everyone who can be interested. +- **Contribute to codebase**: Your help is needed to make this project the best it can be! You could develop new features or fix [existing issues][repo-issues] - every contribution will be welcomed with great pleasure! + +## Environment setup + +Make sure you have `python3.9` installed on your system. + +To install the correct version of python you can use [pyenv](https://github.com/pyenv/pyenv), [brew (mac only)](https://formulae.brew.sh/formula/python@3.9) or simply [download](https://www.python.org/downloads) it. + +You'll also need to have [poetry installed](https://python-poetry.org/docs/#installation). + +After forking and cloning the repository, run: + +```shell +poetry install + +# If you intend to work on coco related things, run this instead: +poetry install --extras "coco" +``` + +:::info +You might need to install `xcode-select` if you are on Mac or `C++ build tools` if you are on Windows. +::: + +After the installation is done, you can activate the created virtual environment with: + +```shell +poetry shell +``` + +You're all set up, now you should be able to run your locally installed `encord-active`. + +:::info +Make sure you are always running `encord-active` from the activated virtual environment to not conflict with a globally installed version. +::: + +### Running the frontend + +Our frontend is build with [React](https://reactjs.org/). To start it in development mode, run: + +:::info +Running the frontend locally is only required if you intend to work on our React frontend components +::: + +```shell +cd frontend && npm i && npm start +``` + +In order to point `encord-active` to your locally running frontend, you'll need to change the `FRONTEND` environment variable in the `.env` file. Make sure you point it to the correct port, by default it should be running on `http://localhost:5173/` + +### Running the documentation + +Our documentation is built with [Docusaurus][docusaurus]. To start it in development mode, run: + +```shell +cd docs && npm i && npm start +``` + +## Commit convention + +Commit messages are essential to make changes clear and concise. We use [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/) to keep our commit messages consistent and easy to understand. + +``` +(optional scope): +``` + +Examples: + +- `feat: allow provided config object to extend other configs` +- `fix: array parsing issue when multiple spaces were contained in string` +- `docs: correct spelling of CHANGELOG` + +## Contribution guide + +You need to follow these steps below to make contribution to the main repository via pull request. You can learn about the details of pull request [here](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests). + +### 1. Fork the official repository + + + + +Firstly, you need to visit the [Encord Active repository][repo] and fork into your own account. The `fork` button is at the right top corner of the web page alongside with buttons such as `watch` and `star` (highly appreciated if you click this one as well 🌟). + +Now, you can clone your own forked repository into your local environment. + +```shell +git clone https://github.com//encord-active.git +``` + + + + +If you have Github CLI installed this will create a fork, if you haven't, consider [installing it](https://github.com/cli/cli#installation). + +```shell +gh repo fork encord-team/encord-active +``` + + + + +### 2. Configure git + +You need to set the official repository as your upstream so that you can synchronize with the latest updates in the official repository. You can learn about syncing forks [here](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/configuring-a-remote-for-a-fork). + + + + +```shell +cd encord-active +git remote add upstream https://github.com/encord-team/encord-active.git +``` + + + + +> If you use the Github CLI, this step is done automatically 🪄 + + + + +You can use the following command to verify that the remote is set. You should see both `origin` and `upstream` in the output. + +```shell +git remote -v +> origin https://github.com//encord-active.git (fetch) +> origin https://github.com//encord-active.git (push) +> upstream https://github.com/encord-team/encord-active.git (fetch) +> upstream https://github.com/encord-team/encord-active.git (push) +``` + +### 3. Synchronize + +Before you make changes to the codebase, it is always good to fetch the latest updates in the official repository. In order to do so, you can use the commands below. + + + + +```shell +git fetch upstream +git checkout main +git merge upstream/main +git push origin main +``` + +Otherwise, you can click the `fetch upstream` button on the github webpage of the main branch of your forked repository. Then, use these commands to sync. + +```shell +git checkout main +git fetch main +``` + + + + +To sync your remote fork: + +```shell +gh repo sync /encord-active +``` + +And then to sync you local clone: + +```shell +gh repo sync +``` + + + + +### 4. Pull request issue + +In order to not waste your time implementing a change that has already been declined, or is generally not needed, start by opening an [issue][repo-issues] describing the problem you would like to solve. Make sure you use appropriate title and description and be as descriptive as possible. + +Generally, your code change should be only targeting one problem in order to make the review process as simple as possible. + +### 5. Make changes + +You should not make changes to the `main` branch of your forked repository as this might make upstream synchronization difficult. You can create a new branch with the appropriate name. Generally, branch names should start with a conventional commit type, e.g. `fix/` / `docs/` / `feat` followed by the scope. + +```shell +git checkout -b +``` + +It is finally time to implement your change! + +You can commit and push the changes to your local repository. The changes should be kept logical, modular and atomic. + +```shell +git add -A +git commit -m ": " +git push -u origin +``` + +### 7. Open a pull request + +You can now create a pull request on the GitHub webpage of your repository. The source branch is `` of your repository and the target branch should be `main` of `encord-team/encord-active`. After creating this pull request, you should be able to see it [here](https://github.com/encord-team/encord-active/pulls). + +If you are using Github CLI you can run: + +```shell +gh pr create --web +``` + +Fill out the title and body appropriately trying to be as clear as possible. And again, make sure to follow the conventional commit guidelines for your title. + +Do write a clear description of your pull request and [link the pull request to your target issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue). This will automatically close the issue when the pull request is approved. + +In case of code conflict, you should rebase your branch and resolve the conflicts manually. + +[docusaurus]: https://docusaurus.io/ +[issues]: https://github.com/encord-team/encord-active/issues +[code_of_conduct]: https://github.com/encord-team/encord-active/blob/main/CODE_OF_CONDUCT.md +[join-slack]: https://join.slack.com/t/encordactive/shared_invite/zt-1hc2vqur9-Fzj1EEAHoqu91sZ0CX0A7Q +[repo]: https://github.com/encord-team/encord-active +[repo-issues]: https://github.com/encord-team/encord-active/issues diff --git a/docs/docs/getting-started.md b/docs/docs/getting-started.md new file mode 100644 index 000000000..9aa082d62 --- /dev/null +++ b/docs/docs/getting-started.md @@ -0,0 +1,142 @@ +--- +sidebar_position: 1 +slug: / +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Link from '@docusaurus/Link'; + +# Getting Started + +**Encord Active** is an open-source Python library that enables users to improve machine learning models in an active learning fashion via +[data quality](category/data-quality), [label quality](category/label-quality), and [model assertions](category/model-assertions). + +## Install `encord-active` + +Install `encord-active` in your favorite Python environment with the following commands: + +```shell +python3.9 -m venv ea-venv +source ea-venv/bin/activate +# within venv +pip install encord-active +``` + +:::tip + +`encord-active` requires [python3.9](https://www.python.org/downloads/release/python-3915/). +If you have trouble installing `encord-active`, you find more detailed instructions on installing it [here](./installation). + +::: + +## Check the Installation + +To check that Encord Active is installed, run: + +```shell +# within venv +encord-active --help +``` + +This must be run in the same virtual environment where you installed your package. + +The `--help` option provides some context to what you can do with `encord-active`. + +To learn more about how to use the command line interface, see the [Command Line Interface section](category/command-line-interface). + +## Sandbox Data + +To get started quickly with a sandbox dataset, you run the following command: + +```shell +# within venv +encord-active download +``` + +The script will ask you to + +1. `Where should we store the data?`: specify a directory in which the data should be stored. +2. (potentially) `Directory not existing, want to create it? [y/N]` type y then enter. +3. `[?] Choose a project:` choose a project with and and hit enter + +:::tip + +If you plan on using multiple datasets, it may be worth first creating an empty directory for all the datasets. + +```shell +mkdir /path/to/data/root +# or windows +md /path/to/data/root +``` + +In step 1. above, specify, e.g., `/path/to/data/root/sandbox1` + +::: + +When the download process is complete, you visualise the results by following the printed instructions or following the steps in [Running the App](#running-the-app) section below. + +## Importing Data from Encord + +If you already have a project on the Encord platform, you can import that project with the following command: + +``` +(ea-venv)$ encord-active import project +``` + +:::tip + +If you don't have an Encord project ready, you can find your next steps in the SDK section [Migrating Data to Encord](sdk/migrating-data). +Otherwise, you can [download one of our sandbox datasets](/cli/download-sandbox-data). + +::: + +To be able to do this, you will need the path to your private `ssh-key` associated with Encord and a `project_hash`. +Don't worry! The script will guide you on the way if you don't know it already. + +The script will ask you: + +1. `Where is your private ssh key stored?`: type the path to your private ssh key +2. `Where should your project directory be located?`: use, e.g., `/path/to/data/root/your_project_title` from above. +3. `Are you sure you want to use /path/you/chose? [y/N]`: hit y then enter to indicate the specified path is correct. +4. `Specify project hash`: paste / type the `project_hash`. If you don't know the id, you can type `?` and hit enter to get all your projects listed with their `project_hash`s before being prompted with the question again. Now you can copy paste the id. + +Next, `encord-active` will fetch your data and labels before computing all the [metrics](category/metrics) available in `encord-active`. + +Downloading the data and computing the metrics may take a while. +Bare with us, it is worth the wait. + +When the process is done, follow the printed instructions to open the app or see more details in the [Open Encord Active](/cli/open-encord-active) page. + + +## Running the App + +To run the Encord Active app, you need the data path that you specified in either of the two sections above. +Run the following command: + +```shell +# within venv +encord-active visualise /the/path/you/chose +``` + +Now, your browser should open a new window with Encord Active. + +:::caution + +If the script just seems to hang and nothing happens in your browser, try visiting http://localhost:8501. + +::: + +### What's Up Next? + +We recommend having a look at the [workflows](category/workflows) section to learn about importing your model predictions and improving your model performance. +A couple of example references are: + +1. [Import your model predictions](workflows/import-predictions) +2. Find outliers in your [data](workflows/improve-your-data/identify-outliers-edge-cases) or your [labels](workflows/improve-your-labels/identify-outliers) +3. [Identify metrics](workflows/improve-your-models/metric-importance) that are important for your model performance + +You can also have a look at how to [write custom metrics](/metrics/write-your-own) and how to use the [command line interface](https://encord-active-docs.web.app/category/command-line-interface). + + + diff --git a/docs/docs/images/clock_vs_person_performance.png b/docs/docs/images/clock_vs_person_performance.png new file mode 100644 index 000000000..93d9b7b52 Binary files /dev/null and b/docs/docs/images/clock_vs_person_performance.png differ diff --git a/docs/docs/images/export_balance.png b/docs/docs/images/export_balance.png new file mode 100644 index 000000000..409cafe1b Binary files /dev/null and b/docs/docs/images/export_balance.png differ diff --git a/docs/docs/images/export_filter.png b/docs/docs/images/export_filter.png new file mode 100644 index 000000000..bb064ed93 Binary files /dev/null and b/docs/docs/images/export_filter.png differ diff --git a/docs/docs/images/export_filter_encord.png b/docs/docs/images/export_filter_encord.png new file mode 100644 index 000000000..3859d79f8 Binary files /dev/null and b/docs/docs/images/export_filter_encord.png differ diff --git a/docs/docs/images/index_importance.png b/docs/docs/images/index_importance.png new file mode 100644 index 000000000..62c91ea06 Binary files /dev/null and b/docs/docs/images/index_importance.png differ diff --git a/docs/docs/images/object_area_relative_performance.png b/docs/docs/images/object_area_relative_performance.png new file mode 100644 index 000000000..9899e86aa Binary files /dev/null and b/docs/docs/images/object_area_relative_performance.png differ diff --git a/docs/docs/installation.mdx b/docs/docs/installation.mdx new file mode 100644 index 000000000..5bc831a75 --- /dev/null +++ b/docs/docs/installation.mdx @@ -0,0 +1,56 @@ +--- +sidebar_position: 2 +--- + +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; + +# Installation + +:::info + +Make sure you have `python3.9` installed on your system. + +To install the correct version of python you can use [pyenv](https://github.com/pyenv/pyenv), [brew (mac only)](https://formulae.brew.sh/formula/python@3.9) or simply [download](https://www.python.org/downloads) it. + +::: + +## From PyPi + +Install `encord-active` in your favorite Python environment with the following commands: + +```shell +$ python3.9 -m venv ea-venv +$ # On Linux/MacOS +$ source ea-venv/bin/activate +$ # On Windows +$ ea-venv\Scripts\activate +(ea-venv)$ python -m pip install encord-active + +``` + +#### Coco extras + +If you intend to work on coco related things you'll have to install extra dependencies this way: + +```shell +(ea-venv)$ python -m pip install encord-active[coco] +``` + +:::info +You might need to install `xcode-select` if you are on Mac or `C++ build tools` if you are on Windows. +::: + +## Check the Installation + +To check that Encord Active is installed, run: + +```shell +$ encord-active --help +``` + +This must be run in the same virtual environment where you installed your package. + +The `--help` option provides some context to what you can do with `encord-active`. + +To learn more about how to use the command line interface, see the [Command Line Interface section](category/command-line-interface). diff --git a/docs/docs/known-issues.md b/docs/docs/known-issues.md new file mode 100644 index 000000000..0d748f2d8 --- /dev/null +++ b/docs/docs/known-issues.md @@ -0,0 +1,11 @@ +--- +sidebar_position: 11 +--- + +# Known Issues + +Encord Active is in beta phase, therefore, errors and unexpected behaviours may sometimes be encountered. + +- In some projects/metrics we have observed that the UI freezes and eventually crashes in the browser. + +If you encounter any errors, we would love to hear from you so we can take care of them. You can get in touch with us at [active@encord.com](mailto:active@encord.com). diff --git a/docs/docs/metrics/_category_.json b/docs/docs/metrics/_category_.json new file mode 100644 index 000000000..b1637393f --- /dev/null +++ b/docs/docs/metrics/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Metrics", + "position": 6, + "collapsed": true, + "link": { + "type": "generated-index", + "description": "Detailed descriptions of the pre-existing metrics." + } +} diff --git a/docs/docs/metrics/geometric.md b/docs/docs/metrics/geometric.md new file mode 100644 index 000000000..369a5780a --- /dev/null +++ b/docs/docs/metrics/geometric.md @@ -0,0 +1,77 @@ +# Geometric + +Operate on the geometries of objects like bounding boxes, polygons, and polylines. + +| Title | Metric Type | Data Type | +| ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------------------------------------------------------- | +| [Annotation Duplicates](#annotation-duplicates) - Ranks annotations by how likely they are to represent the same object | `image` | `bounding box`, `polygon` | +| [Annotation closeness to image borders](#annotation-closeness-to-image-borders) - Ranks annotations by how close they are to image borders. | `image` | `bounding box`, `point`, `polygon`, `polyline`, `skeleton` | +| [Detect Occlusion in Video](#detect-occlusion-in-video) - Tracks objects and detect outliers | `sequence` | `bounding box` | +| [Frame object density](#frame-object-density) - Computes the percentage of image area that's occupied by objects | `image` | `bounding box`, `polygon` | +| [Object Area - Absolute](#object-area---absolute) - Computes object area in amount of pixels | `image` | `bounding box`, `polygon` | +| [Object Area - Relative](#object-area---relative) - Computes object area as a percentage of total image area | `image` | `bounding box`, `polygon` | +| [Object Aspect Ratio](#object-aspect-ratio) - Computes aspect ratios of objects | `image` | `bounding box`, `polygon` | +| [Polygon Shape Similarity](#polygon-shape-similarity) - Ranks objects by how similar they are to their instances in previous frames. | `sequence` | `polygon` | +| [Shape outlier detection](#shape-outlier-detection) - Calculates potential outliers by polygon shape. | `image` | `polygon` | + +## Annotation Duplicates + +Ranks annotations by how likely they are to represent the same object. + +> [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) +> is used to measure closeness of two annotations. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/annotation_duplicates.py) + +## Annotation closeness to image borders + +This metric ranks annotations by how close they are to image borders. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/image_border_closeness.py) + +## Detect Occlusion in Video + +This metric collects information related to object size and aspect ratio for each track +and find outliers among them. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/occlusion_detection_video.py) + +## Frame object density + +Computes the percentage of image area that's occupied by objects. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/object_size.py) + +## Object Area - Absolute + +Computes object area in amount of pixels. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/object_size.py) + +## Object Area - Relative + +Computes object area as a percentage of total image area. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/object_size.py) + +## Object Aspect Ratio + +Computes aspect ratios ($width/height$) of objects. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/object_size.py) + +## Polygon Shape Similarity + +This metric ranks objects by how similar they are to their instances in previous frames +based on [Hu moments](https://en.wikipedia.org/wiki/Image_moment). The more an object's shape changes, +the lower its score will be. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/hu_temporal.py) + +## Shape outlier detection + +Computes the Euclidean distance between the polygons' +[Hu moments](https://en.wikipedia.org/wiki/Image_moment) for each class and +the prototypical class moments. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/geometric/hu_static.py) diff --git a/docs/docs/metrics/heuristic.md b/docs/docs/metrics/heuristic.md new file mode 100644 index 000000000..c6034adcd --- /dev/null +++ b/docs/docs/metrics/heuristic.md @@ -0,0 +1,191 @@ +# Heuristic + +Work on images or individual video frames and are heuristic in the sense that they mostly depend on the image content without labels. + +| Title | Metric Type | Data Type | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ----------- | ------------------------- | +| [Area](#area) - Ranks images by their area (width\*height). | `image` | `Any` | +| [Aspect Ratio](#aspect-ratio) - Ranks images by their aspect ratio (width/height). | `image` | `Any` | +| [Blue Values](#blue-values) - Ranks images by how much blue is present in the image. | `image` | `Any` | +| [Blur](#blur) - Ranks images by their blurriness. | `image` | `Any` | +| [Brightness](#brightness) - Ranks images by their brightness. | `image` | `Any` | +| [Contrast](#contrast) - Ranks images by their contrast. | `image` | `Any` | +| [Green Values](#green-values) - Ranks images by how much green is present in the image. | `image` | `Any` | +| [Inconsistent Object Classification and Track IDs](#inconsistent-object-classification-and-track-ids) - Looks for overlapping objects with different classes (across frames). | `sequence` | `bounding box`, `polygon` | +| [Missing Objects and Broken Tracks](#missing-objects-and-broken-tracks) - Identifies missing objects and broken tracks based on object overlaps. | `sequence` | `bounding box`, `polygon` | +| [Object Count](#object-count) - Counts number of objects in the image | `image` | `Any` | +| [Red Values](#red-values) - Ranks images by how much red is present in the image. | `image` | `Any` | +| [Sharpness](#sharpness) - Ranks images by their sharpness. | `image` | `Any` | + +## Area + +This metric ranks images by their area (width\*height). + +Area is computed as the product of image width and image height. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Aspect Ratio + +This metric ranks images by their aspect ratio (width/height). + +Aspect ratio is computed as the ratio of image width to image height. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Blue Values + +This metric ranks images by how much blue is present in the image. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Blur + +This metric ranks images by their blurriness. + +Blurriness is computed by applying a Laplacian filter to each image and computing the +variance of the output. In short, the score computes "the amount of edges" in each +image. Note that this is $1 - \text{sharpness}$. + +```python +score = 1 - cv2.Laplacian(image, cv2.CV_64F).var() +``` + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Brightness + +This metric ranks images by their brightness. + +Brightness is computed as the average (normalized) pixel value across each image. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Contrast + +This metric ranks images by their contrast. + +Contrast is computed as the standard deviation of the pixel values. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Green Values + +This metric Ranks images by how much blue is present in the image. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Inconsistent Object Classification and Track IDs + +This algorithm looks for overlapping objects in consecutive +frames that have different classes. Furthermore, if classes are the same for overlapping objects but have different +track-ids, they will be flagged as potential inconsistencies in tracks. + +**Example 1:** + +``` + Frame 1 Frame 2 +┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ DOG:1 │ │ +│ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ +│ │ │ │ +└───────────────────┘ └───────────────────┘ +``` + +`Dog:1` will be flagged as potentially wrong class, because it overlaps with `CAT:1`. + +**Example 2:** + +``` + Frame 1 Frame 2 +┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ CAT:2 │ │ +│ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ +│ │ │ │ +└───────────────────┘ └───────────────────┘ +``` + +`Cat:2` will be flagged as potentially having a broken track, because track ids `1` and `2` doesn't match. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/high_iou_changing_classes.py) + +## Missing Objects and Broken Tracks + +Identifies missing objects by comparing object overlaps based +on a running window. + +**Case 1:** +If an intermediate frame (frame $i$) doesn't include an object in the same +region as in the two surrounding frames ($i-1$ and $i+1$), it is flagged. + +``` + Frame i-1 Frame i Frame i+1 +┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ │ │ +│ ┌───────┐ │ │ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ │ │ CAT:1 │ │ +│ │ │ │ │ │ │ │ │ │ +│ └───────┘ │ │ │ │ └───────┘ │ +│ │ │ │ │ │ +│ │ │ │ │ │ +└───────────────────┘ └───────────────────┘ └───────────────────┘ +``` + +Frame $i$ will be flagged as potentially missing an object. + +**Case 2:** +If objects of the same class overlap in three consecutive frames ($i-1$, $i$, and $i+1$) but do not share object +hash, they will be flagged as a potentially broken track. + +``` + Frame i-1 Frame i Frame i+1 +┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ CAT:2 │ │ │ │ CAT:1 │ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ │ └───────┘ │ +│ │ │ │ │ │ +│ │ │ │ │ │ +└───────────────────┘ └───────────────────┘ └───────────────────┘ +``` + +`CAT:2` will be marked as potentially having a wrong track id. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/missing_objects_and_wrong_tracks.py) + +## Object Count + +Counts number of objects in the image. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/object_counting.py) + +## Red Values + +This metric ranks images by how much red is present in the image. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) + +## Sharpness + +This metric ranks images by their sharpness. + +Sharpness is computed by applying a Laplacian filter to each image and computing the +variance of the output. In short, the score computes "the amount of edges" in each +image. + +```python +score = cv2.Laplacian(image, cv2.CV_64F).var() +``` + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/heuristic/img_features.py) diff --git a/docs/docs/metrics/overview.md b/docs/docs/metrics/overview.md new file mode 100644 index 000000000..903b0e062 --- /dev/null +++ b/docs/docs/metrics/overview.md @@ -0,0 +1,14 @@ +--- +sidebar_position: 1 +--- + +# Overview + +Metrics constitute the foundation of Encord Active. +In short, they serve the purpose of indexing data, labels, and model predictions based on various properties. + +We have split the metrics into three main categories: + +- **Heuristic:** operate on images or individual video frames and are heuristic in the sense that they mostly depend on the image content without labels. +- **Geometric:** operate on the geometries of objects like bounding boxes, polygons, and polylines. +- **Semantic:** operate with the semantic information of images or individual video frames. diff --git a/docs/docs/metrics/semantic.md b/docs/docs/metrics/semantic.md new file mode 100644 index 000000000..013cc88c1 --- /dev/null +++ b/docs/docs/metrics/semantic.md @@ -0,0 +1,23 @@ +# Semantic + +Operates with the semantic information of images or individual video frames. + +| Title | Metric Type | Data Type | +| ---------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ------------------------- | +| [Image-level Annotation Quality](#image-level-annotation-quality) - Compares image classifications against similar images | `image` | `radio` | +| [Object Annotation Quality](#object-annotation-quality) - Compares object annotations against similar image crops | `image` | `bounding box`, `polygon` | + +## Image-level Annotation Quality + +This metric creates embeddings from images. Then, these embeddings are used to build +nearest neighbor graph. Similar embeddings' classifications are compared against each other. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/semantic/img_classification_quality.py) + +## Object Annotation Quality + +This metric transforms polygons into bounding boxes +and an embedding for each bounding box is extracted. Then, these embeddings are compared +with their neighbors. If the neighbors are annotated differently, a low score is given to it. + +Implementation on [GitHub](https://github.com/encord-team/encord-active/blob/main/encord_active.metrics/semantic/img_object_quality.py) diff --git a/docs/docs/metrics/write-your-own.md b/docs/docs/metrics/write-your-own.md new file mode 100644 index 000000000..6a48e7610 --- /dev/null +++ b/docs/docs/metrics/write-your-own.md @@ -0,0 +1,80 @@ +--- +sidebar_position: 5 +--- + +# Write Your Own Metric + +Create a new python file in the `` directory and use the template provided in +`libs/encord_active/metrics/example.py`. The subdirectory within `libs/encord_active/metrics` is dictated by what +information the metric employs: + +- **Geometric:** Metrics related to the geometric properties of annotations. + This includes size, shape, location etc. +- **Semantic:** Metrics based on the _contents_ of some image, video or annotation. + This includes embedding distances, image uncertainties etc. +- **Heuristic:** Any other metrics. For example, brightness, sharpness, object counts, etc. + +You can use the following template to get started with writing your own metric. +Your implementation should call `writer.write(, )` for every object in the iterator or use `writer.write()` for every data unit in the iterator. + +```python +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, MetricType, Metric +from encord_active.lib.common.writer import CSVMetricWriter + +class ExampleMetric(Metric): + TITLE = "Example Title" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "A short description of your metric." + LONG_DESCRIPTION = "A longer and more detailed description. " \ + "I can use Markdown to _format_ the text." + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + + for data_unit, img_pth in iterator.iterate(desc="Progress bar description"): + # Frame level score (data quality) + writer.write(1337, description="Your description for the frame [can be omitted]") + for obj in data_unit["labels"].get("objects", []): + # Label (object/classification) level score (label / model prediction quality) + if not obj["shape"] in valid_annotation_types: + continue + + # This is where you do the actual inference. + # Some convenient properties associated with the current data. + # ``iterator.label_hash`` the label hash of the current data unit + # ``iterator.du_hash`` the data unit hash of + # ``iterator.frame`` the frame of the current data unit hash of + # ``iterator.num_frame`` the total number of frames in the label row. + + # Do your thing (inference) + # ... + # Then + writer.write(42, labels=obj, description="Your description of the score [can be omitted]") + +if __name__ == "__main__": + import sys + from pathlib import Path + from encord_active.lib.common.tester import perform_test + + path = sys.argv[1] + perform_test(ExampleMetric(), data_dir=Path(path)) +``` +Before running your own custom metric, make sure that you have `project_meta.yaml` file in the project data folder. + +To run your metric from the root directory, use: + +```shell +(ea-venv)$ python your_metric_file.py /path/to/your/data/dir +``` + +You can check the generated metric file in your `/metrics`, its name should be `_example_title.csv` . +When you have run your metric, you can visualise your results by running: + +```shell +(ea-venv)$ encord-active visualise /path/to/your/data/dir +``` + +Now, you can improve your data/labels/model by choosing your own custom metric in the app. diff --git a/docs/docs/pages/_category_.json b/docs/docs/pages/_category_.json new file mode 100644 index 000000000..38377325d --- /dev/null +++ b/docs/docs/pages/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Pages", + "position": 4, + "collapsible": false, + "link": { + "type": "generated-index", + "description": "A description of the pages you can see in the Encord Active app." + } +} diff --git a/docs/docs/pages/data-quality/_category_.json b/docs/docs/pages/data-quality/_category_.json new file mode 100644 index 000000000..4c73f7fc2 --- /dev/null +++ b/docs/docs/pages/data-quality/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Data Quality", + "position": 1, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "All the components that you can see on the Data Quality page of the Encord Active app." + } +} diff --git a/docs/docs/pages/data-quality/explorer.md b/docs/docs/pages/data-quality/explorer.md new file mode 100644 index 000000000..06798387b --- /dev/null +++ b/docs/docs/pages/data-quality/explorer.md @@ -0,0 +1,16 @@ +--- +sidebar_position: 2 +--- + +# Explorer +On the Explorer page, images are ranked according to their scores. + +On the top bar, you can choose the metric to order +your data by, classes you want to explore, and annotator to get annotator-specific information such as scores and +the number of annotations by each annotator. + +According to the chosen metric, data distribution will be shown in the main view. Under the distribution plot, +a double-ended scroll bar filters data to only visualize the selected range. In default, images are ranked in +descending order; however, this behavior can be changed by sorting combobox. + +[Tagging](../../tags.md) feature is available on this page to tag images to take action on them later. \ No newline at end of file diff --git a/docs/docs/pages/data-quality/summary.md b/docs/docs/pages/data-quality/summary.md new file mode 100644 index 000000000..22e33ac63 --- /dev/null +++ b/docs/docs/pages/data-quality/summary.md @@ -0,0 +1,13 @@ +--- +sidebar_position: 1 +--- + +# Summary +On the summary page, you will find outliers based on **frame-level** metrics. For each metric, we identify outliers +based on its score distribution. + +Box-plot outlier technique is used to identify outliers and according to its distance to the IQR, outliers are divided +into two category: **Severe** outlier and **moderate** outlier. + +On this page, images are ranked according to their distance to the IQR (between 25th and 75th percentiles). +[Tagging](../../tags.md) feature is available on this page to tag images of interest and take action on them. \ No newline at end of file diff --git a/docs/docs/pages/export/_category_.json b/docs/docs/pages/export/_category_.json new file mode 100644 index 000000000..0fe28c16a --- /dev/null +++ b/docs/docs/pages/export/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Export", + "position": 5, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "Export data from Encord Active." + } +} diff --git a/docs/docs/pages/export/balance_export.md b/docs/docs/pages/export/balance_export.md new file mode 100644 index 000000000..87efd8e9f --- /dev/null +++ b/docs/docs/pages/export/balance_export.md @@ -0,0 +1,21 @@ +--- +sidebar_position: 2 +description: Export balanced data partitions over a series of metrics. +--- + +# Balance & Export + +## Export to COCO file + +The Balance & Export feature allows you to create and export balanced data partitions over a series of metrics. +There are three main sections in the Balance & Export page: +- **Metrics**: Select the metrics to balance the data over. You can also select the random seed for the data partitioning. +- **Partitions**: Create data partitions and specify their percentage size. +- **Distribution visualiser**: Displays the distribution of each partition for each metric. + +After you have selected a set of metrics and created data partitions, click on **Generate COCO file** and sit tight, this can take up to a few minutes depending on the number of samples. + +Once the COCO file is generated, the **Download filtered data** button will unlock. Click it to download the resulting file. + + +![export_balance.png](../../images/export_balance.png) diff --git a/docs/docs/pages/export/filter_export.md b/docs/docs/pages/export/filter_export.md new file mode 100644 index 000000000..94390ab58 --- /dev/null +++ b/docs/docs/pages/export/filter_export.md @@ -0,0 +1,24 @@ +--- +sidebar_position: 1 +description: Filter data according to metrics and tags. +--- + +# Filter & Export + +## Export to COCO file + +The Filter & Export feature allows you to export data according to specific metric values or the tags created during the data exploration process. + +After you have created a filtering rule, click on **Generate COCO file** and sit tight, this can take up to a few minutes depending on the number of samples. + +Once the COCO file is generated, the **Download filtered data** button will unlock. Click it to download the resulting file. + +![export_filter.png](../../images/export_filter.png) + +## Export to Encord + +Additionally, you can also export the data directly to the Encord labelling platform. For this you will need to specify dataset and project names, and optionally a description. + +Once you are satisfied, press the **Create** button and the data will be exported to Encord. + +![export_encord.png](../../images/export_filter_encord.png) \ No newline at end of file diff --git a/docs/docs/pages/label-quality/_category_.json b/docs/docs/pages/label-quality/_category_.json new file mode 100644 index 000000000..e887ec0a1 --- /dev/null +++ b/docs/docs/pages/label-quality/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Label Quality", + "position": 2, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "All the components that you can see on the Label Quality page of the Encord Active app." + } +} diff --git a/docs/docs/pages/label-quality/explorer.md b/docs/docs/pages/label-quality/explorer.md new file mode 100644 index 000000000..9a5929d1a --- /dev/null +++ b/docs/docs/pages/label-quality/explorer.md @@ -0,0 +1,17 @@ +--- +sidebar_position: 2 +--- + +# Explorer +On the Explorer page, images are ranked according to their scores. + +On the top bar, you can choose the metric to order +your data by, classes you want to explore, and annotator to get annotator-specific information such as scores and the +number of annotations by each annotator. + +According to the chosen metric, data distribution will be shown in the main view. Under the distribution plot, +a double-ended scroll bar filters data to only visualize the selected range. In default, images are ranked in +descending order: however, this behavior can be changed by sorting combobox. + +[Tagging](../../tags.md) feature is available on this page to tag images to take action on them later. + diff --git a/docs/docs/pages/label-quality/summary.md b/docs/docs/pages/label-quality/summary.md new file mode 100644 index 000000000..bd73a54ea --- /dev/null +++ b/docs/docs/pages/label-quality/summary.md @@ -0,0 +1,13 @@ +--- +sidebar_position: 1 +--- + +# Summary +On the summary page, you will find outliers based on **object-level** metrics. For each metric, we identify outliers +based on its score distribution. + +Box-plot outlier technique is used to identify outliers and according to its distance to the IQR, outliers are divided +into two category: **Severe** outlier and **moderate** outlier. + +On this page, images are ranked according to their distance to the IQR (between 25th and 75th percentiles). +[Tagging](../../tags.md) feature is available on this page to tag images of interest and take action on them. \ No newline at end of file diff --git a/docs/docs/pages/model-assertions/_category_.json b/docs/docs/pages/model-assertions/_category_.json new file mode 100644 index 000000000..762d02519 --- /dev/null +++ b/docs/docs/pages/model-assertions/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Model Assertions", + "position": 3, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "All the components that you can see on the Model Assertions page of the Encord Active app." + } +} diff --git a/docs/docs/pages/model-assertions/false-negatives.md b/docs/docs/pages/model-assertions/false-negatives.md new file mode 100644 index 000000000..f6f149245 --- /dev/null +++ b/docs/docs/pages/model-assertions/false-negatives.md @@ -0,0 +1,11 @@ +--- +sidebar_position: 5 +--- + +# False Negatives + +These are the labels that were not matched with any predictions. + +The purple boxes mark the false negatives. That is the labels that were not matched to any predictions. The remaining +objects are predictions, where colors correspond to their predicted class (identical colors to labels objects in the editor). + diff --git a/docs/docs/pages/model-assertions/false-positives.md b/docs/docs/pages/model-assertions/false-positives.md new file mode 100644 index 000000000..8648026bb --- /dev/null +++ b/docs/docs/pages/model-assertions/false-positives.md @@ -0,0 +1,15 @@ +--- +sidebar_position: 4 +--- + +# False Positives + +These are the predictions for which either of the following is true: + +1. The IOU between the prediction and the best matching label was too low +2. There was another prediction with higher model confidence which matched the label already +3. The predicted class didn't match + +The red boxes mark the false positive predictions. The remaining colors correspond to the dataset labels with the +colors you are used to from the label editor. + diff --git a/docs/docs/pages/model-assertions/metrics.md b/docs/docs/pages/model-assertions/metrics.md new file mode 100644 index 000000000..f1a84d5aa --- /dev/null +++ b/docs/docs/pages/model-assertions/metrics.md @@ -0,0 +1,18 @@ +--- +sidebar_position: 1 +--- + +# Metrics + +**Metric importance**: Measures the *strength* of the dependency between the metric and model + performance. A high value means that the model performance would be strongly affected by + a change in the metric. For example, high importance in 'Brightness' implies that a change + in that quantity would strongly affect model performance. Values range from 0 (no dependency) + to 1 (perfect dependency, one can completely predict model performance simply by looking + at this metric). + +**Metric [correlation](https://en.wikipedia.org/wiki/Correlation)**: Measures the *linearity + and direction* of the dependency between a metric and model performance. + Crucially, this metric tells us whether a positive change in a metric + will lead to a positive change (positive correlation) or a negative change (negative correlation) + in model performance. Values range from -1 to 1. diff --git a/docs/docs/pages/model-assertions/performance-by-metric.md b/docs/docs/pages/model-assertions/performance-by-metric.md new file mode 100644 index 000000000..95a83f575 --- /dev/null +++ b/docs/docs/pages/model-assertions/performance-by-metric.md @@ -0,0 +1,14 @@ +--- +sidebar_position: 2 +--- + +# Performance by Metric + +On this page, your model scores are displayed as a function of the metric that you selected in the sidebar. +Samples are discretized into n equally sized buckets and the middle point of each bucket is displayed as the x-value in the plots. +Bars indicate the number of samples in each bucket, while lines indicate the true positive rate and false negative rate of each bucket, respectively. + +Metrics marked with (P) are metrics computed on your predictions. +Metrics marked with (F) are frame level metrics, which depends on the frame that each prediction is associated with. + +For metrics that are computed on predictions (P) in the "True Positive Rate" plot, the corresponding "label metrics" (O/F) _computed on your labels_ are used for the "False Negative Rate" plot. diff --git a/docs/docs/pages/model-assertions/true-positives.md b/docs/docs/pages/model-assertions/true-positives.md new file mode 100644 index 000000000..f190d5d8f --- /dev/null +++ b/docs/docs/pages/model-assertions/true-positives.md @@ -0,0 +1,12 @@ +--- +sidebar_position: 3 +--- + +# True Positives + +These are the predictions for which the IOU was sufficiently high and the confidence score was the highest amongst +predictions that overlap with the label. + +The purple boxes mark the true positive predictions. +The remaining colors correspond to the dataset labels with the colors you are used to from the label editor. + diff --git a/docs/docs/sdk/_category_.json b/docs/docs/sdk/_category_.json new file mode 100644 index 000000000..1aaae46f1 --- /dev/null +++ b/docs/docs/sdk/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Python SDK", + "position": 9, + "collapsed": true, + "link": { + "type": "generated-index", + "description": "How to use the `encord_active` python SDK." + } +} diff --git a/docs/docs/sdk/importing-model-predictions.mdx b/docs/docs/sdk/importing-model-predictions.mdx new file mode 100644 index 000000000..171904366 --- /dev/null +++ b/docs/docs/sdk/importing-model-predictions.mdx @@ -0,0 +1,486 @@ +--- +sidebar_position: 2 +--- +import TOCInline from '@theme/TOCInline'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Importing Model Predictions + +This page shows you how to import model predictions **with code**. + +:::caution + +Everytime you run any of these importers, previously imported predictions will be overwritten! +We're working on fixing this. + +::: + +:::tip + +There is also a workflow description on importing model predictions [here](/workflows/import-predictions). + +::: + +### Prerequisites +Before you can import your predictions you need to have a couple of prerequisites in place: + +1. You should have [imported a project](cli/import-encord-project) - and taken note of the `/path/to/the/data` +2. In your code, you need to have an `encord.Project` initialised. + +You can do this with the following code - only the highlighted line should need to change: + +```python +from pathlib import Path +import yaml + +from encord import EncordUserClient + +# highlight-next-line +data_dir = Path("/path/to/the/data") + +meta = yaml.save_load((data_dir / "project_meta.yaml").read_text()) +private_key = Path(meta["ssh_key_path"]).read_text() + +client = EncordUserClient.create_with_ssh_private_key(private_key) +project = client.get_project(project_hash=meta["project_hash"]) +``` + +:::note + +The code examples from this point on assume that you have the `data_dir` and the `project` variables available. + +::: + +When you have these things in place, there are a couple of options for importing your predictions into Encord Active: + + node.level === 2 && index <= 10)} /> + +## Prepare a `.pkl` File to be Imported with the CLI + +You can prepare a pickle file (`.pkl`) to be imported with the Encord Active CLI as well. +You do this by building a list of `Prediction` objects. +A prediction object holds a unique identifier of the data unit (the `data_hash` and potentially a `frame`), the `class_id`, a model `confidence` score, the actual prediction `data`, and the `format` of that data. + +#### Creating a `Prediction` Object +Below, you find examples of how to create an object of each of the three supported types. + + + + +You should specify your `BoundingBox` with relative coordinates and dimensions. +That is: + +- `x`: x-coordinate of the top-left corner of the box divided by the image width +- `y`: y-coordinate of the top-left corner of the box divided by the image height +- `w`: box pixel width / image width +- `h`: box pixel height / image height + +```python +from encord_active.app.db.predictions import BoundingBox, Prediction, Format + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + format = Format.BOUNDING_BOX, + # highlight-start + # Your bounding box coordinates in relative terms (% of image width/height). + data = BoundingBox(x=0.2, y=0.3, w=0.1, h=0.4) + # highlight-end +) +``` + +:::tip + +If you don't have your bounding box represented in relative terms, you can convert it from pixel values like this: + +```python +img_h, img_w = 720, 1280 # the image size in pixels +BoundingBox(x=10/img_w, y=25/img_h, w=200/img_w, h=150/img_h) +``` + +::: + + + + +You specify masks as binary `numpy` arrays of size [height, width] and with `dtype` `np.uint8`. + +```python +from encord_active.app.db.predictions import Prediction, Format + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + # highlight-start + format = Format.MASK, + data = mask # _binary_ np.ndarray of shape [h, w] and dtype np.uint8 + # highlight-end +) +``` + + + + +You should specify your `Polygon` with relative coordinates as a numpy array of shape `[num_points, 2]`. +That is, an array of relative (`x`, `y`) coordinates: + +- `x`: relative x-coordinate of each point of the polygon (pixel coordinate / image width) +- `y`: relative y-coordinate of each point of the polygon (pixel coordinate / image height) + +```python +from encord_active.app.db.predictions import Prediction, Format +import numpy as np + +polygon = np.array([ + # x y + [0.2, 0.1], + [0.2, 0.4], + [0.3, 0.4], + [0.3, 0.1], +]) + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + # highlight-start + format = Format.POLYGON, + data = polygon # np.ndarray of shape [n, 2] and dtype float in range [0,1] + # highlight-end +) +``` + +:::tip + +If you have your polygon represented in absolute terms of pixel locations, you can convert it to relative terms like this: + +```python +img_h, img_w = 720, 1280 # the image size in pixels +polygon = polygon / np.array([[img_h, img_w]]) +``` + +Notice the double braces `[[img_h, img_w]]` to get an array of shape `[1, 2]`. + +::: + + + + + +#### Preparing the Pickle File + +Now you're ready to prepare the file. +You can copy the appropriate snippet based on your prediction format from above and paste it in the code below. +Note the highlighted line, which defines where the `.pkl` file will be stored. + +```python showLineNumbers +from encord_active.app.db.predictions import Prediction, Format + +predictions_to_store = [] + +for prediction in my_predictions: # Iterate over your predictions + predictions_to_store.append( + # PASTE appropriate prediction snippet from above + ) + +# highlight-next-line +with open("/path/to/predictions.pkl", "wb") as f: + pickle.dump(predictions_to_store, f) +``` + +In the above code snippet, you will have to fetch the `data_hash`, `class_id`, etc. ready from the for loop in line 5. + +#### Import Your Predictions via the CLI + +To import the predictions into Encord Active, you run the following command from the command line: + +```shell +encord-active import predictions /path/to/predictions.pkl +``` + +This will import your predictions into Encord Active and run all the [metrics](/category/metrics) on your predictions. +With the `.pkl` approach, you are done after this step. + + + +## Predictions from Your Prediction Loop + +You probably have a prediction loop, which looks similar to this: + +```python +def predict(test_loader): + for imgs, img_ids in test_loader: + predictions = model(imgs) +``` + +You can directly import your predictions into Encord Active by the use of an `encord_active.model_predictions.prediction_writer.PredictionWriter`. +The code would change to something similar to this: + +```python +# highlight-next-line +from encord_active.lib.model_predictions.prediction_writer import PredictionWriter + +def predict(test_loader): + # highlight-next-line + with PredictionWriter(data_dir, project) as writer: # project is defined above. + for imgs, img_ids in test_loader: + predictions = model(imgs) + # highlight-start + for img_id, img_preds in zip(img_ids, predictions) + for pred in img_preds: + writer.add_prediction( + data_hash = img_id, + class_uid = pred.class_id, + confidence_score = pred.confidence, + # either bbox + bbox = pred.bbox # dict with x, y, w, h normalized + # or segmentation (mask or normalized polygon points) + polygon = pred.mask + frame = 0 # If video indicate what frame of the video + ) + # highlight-end +``` + +In the code example above, the arguments to `add_prediction` +are: + +- `data_hash`: The ``data_hash`` of the data unit that the prediction belongs to. +- `class_uid`: The ``featureNodeHash`` of the ontology object corresponding to the class of the prediction. +- `confidence_score`: The model confidence score. +- `bbox`: A bounding box prediction. This should be a dict with the format: + +```python +{ + 'x': 0.1 # normalized x-coordinate of the top-left corner of the bounding box. + 'y': 0.2 # normalized y-coordinate of the top-left corner of the bounding box. + 'w': 0.3 # normalized width of the bounding box. + 'h': 0.1 # normalized height of the bounding box. +} +``` + +- `polygon`: A polygon represented either as a list of normalized `[x, y]` points or a mask of size `[h, w]`. +- `frame`: If predictions are associated with a video, then the frame number should be provided. + +:::note + +Only one bounding box or polygon can be specified in any given call to this function. + +::: + +## Predictions from KITTI Files + +:::caution + +This works for bounding boxes only. + +::: + + +If you have KITTI labels stored in CSV files, there is a utility function to import the predictions from those files. +For this, the files must be associated with one image each and their file names must contain the `data_hash` of the associated image. + +The file structure needs to be as follows: + +``` +labels_root +├── labels +│   ├── aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee__whatever_you_may_need.txt +│   ├── ... +│   └── aaaaaaaa-bbbb-cccc-dddd-ffffffffffff__whatever_you_may_need.csv +└── ontology_label_map.json +``` + +That is, a root directory with two components: + +1. A subdirectory named "labels" that contains text files with names that start with the `data_hash` followed by two underscores +2. A json file which maps class names to Encord ontology classes + +We cover the two components below. + +### Text File Format +The KITTI importer supports the format described [here](https://docs.nvidia.com/tao/archive/tlt-20/tlt-user-guide/text/preparing_data_input.html#label-files) with the addition of an additional column corresponding to the model confidence. + +An example: + +``` +car 0.00 0 0.00 587.01 173.33 614.12 200.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 97.85 +cyclist 0.00 0 0.00 665.45 160.00 717.93 217.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 32.65 +pedestrian 0.00 0 0.00 423.17 173.67 433.17 224.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.183 +``` + +Columns are: + +- `class_name`: str +- ~~`truncation`: float~~ ignored +- ~~`occlusion`: int~~ ignored +- ~~`alpha`: float~~ ignored +- `xmin`: float +- `ymin`: float +- `xmax`: float +- `ymax`: float +- ~~`height`: float~~ ignored +- ~~`width`: float~~ ignored +- ~~`length`: float~~ ignored +- ~~`location_x`: float~~ ignored +- ~~`location_y`: float~~ ignored +- ~~`location_z`: float~~ ignored +- ~~`rotation_y`: float~~ ignored +- `confidence`: float + +Note, the ignored items need to be there but will be ignored. + +### The JSON Class Map + +The JSON class map needs to follow the following structure: + +```json +{ + "OTk2MzM3": "pedestrian", + "NzYyMjcx": "cyclist", + "Nzg2ODEx": "car" +} +``` + +The keys should correspond to the `featureNodeHash` of a bounding box object in the project ontology. +To list the available hashes from your project, you can do this in your script: + +```python +# NB: Remember to include the first code snippet on this page. +print({o["featureNodeHash"]: o["name"] for o in project.ontology["objects"]}) +# Outputs somtihing similar to +# {'OTk2MzM3': 'Pedestrian', 'NzYyMjcx': 'Cyclist', 'Nzg2ODEx': 'Car'} +``` + +The values of the JSON file should be the values that can appear in the first column of text files described above. + + +### Importing the Predictions +To import the predictions, you do the following + +```python +import json + +from encord_active.lib.model_predictions.importers import import_KITTI_labels +from encord_active.lib.model_predictions.prediction_writer import PredictionWriter + +# highlight-next-line +predictions_root = Path("/path/to/your/predictions") +object_map = json.loads((predictions_root / "ontology_label_map.json").read_text()) + +with PredictionWriter(cache_dir=data_dir, project=project, custom_object_map=object_map) as writer: + import_KITTI_labels(project, data_root=predictions_root, prediction_writer=writer) +``` + + +## Predictions from Masks +:::caution + +This works for segmentation/polygons only. + +::: + + +If you have your predictions stored as png masks of shape `[height, width]`, where each pixel value correspond to a class, +then you can use the `import_mask_predictions` function from `encord_active.model_predictions.importers`. +It requires that you can provide a mapping between file name and data hashes. + +Assuming you have predictions stored in a directory like this: + +``` +predictions +├── aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee.png +├── ... +└── aaaaaaaa-bbbb-cccc-dddd-ffffffffffff.png +``` + +or in a nested structure like + +``` +predictions +├── dir1 +│   ├── aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee.png +│   ├── ... +│   └── aaaaaaaa-bbbb-cccc-dddd-ffffffffffff.png +└── dir2 +    ├── bbbbbbbb-bbbb-cccc-dddd-eeeeeeeeeeee.png +    ├── ... +    └── bbbbbbbb-bbbb-cccc-dddd-ffffffffffff.png +``` + +You can use this template where the highlighted lined are what you need to change: + +```python +from encord_active.lib.model_predictions.importers import import_mask_predictions +from encord_active.lib.model_predictions.prediction_writer import PredictionWriter + +# highlight-start +class_map = { + # featureNodeHash: pixel_value + "OTk2MzM3": 1, # "pedestrian" + "NzYyMjcx": 2, # "cyclist", + "Nzg2ODEx": 3, # "car" + # Note: value: 0 is reserved for "background" +} +predictions_root = Path("/path/to/predictions") +# highlight-end +with PredictionWriter(cache_dir=data_dir, project=project) as writer: + import_mask_predictions( + project, + data_root=predictions_root, + cache_dir=data_dir, + prediction_writer=writer, + # this is what provides the mapping between file names and data hashes: + du_hash_name_lookup=lambda file_pth: (file_pth.stem, 0), + ) +``` + +:::caution + +1. The script will look recursively for files with a `.png` extension and import them. +2. For each file, every "self-contained" contour will be interpreted as an individual prediction. + For example, This mask will be treated as three objects. Two from class 1 and one from class 2. +``` +┌───────────────────┐ +│0000000000000000000│ +│0011100000000000000│ +│0011100000002222000│ +│0000000000002222000│ +│0000111000002200000│ +│0000111000002200000│ +│0000111000000000000│ +│0000000000000000000│ +└───────────────────┘ +``` +3. **NB**: model confidence scores will be set to 1... we're working on fixing this! + +::: + + +## Running Metrics on Your Predictions +When you have imported your predictions, it is time to run all the metrics on them. + +For this, you can use these lines of code: + +```python +from encord_active.lib.model_predictions.iterator import PredictionIterator +from encord_active.lib.metrics.run_all import run_metrics + +run_metrics(data_dir=data_dir, iterator_cls=PredictionIterator) +``` + +This will compute all the metrics for your predictions. +Next time you run + +```shell +(ea-venv)$ encord-active visualise /path/to/your/data +``` + +You should be able to see the performance of your model based on the metrics. + diff --git a/docs/docs/sdk/migrating-data.mdx b/docs/docs/sdk/migrating-data.mdx new file mode 100644 index 000000000..4b52aa8f7 --- /dev/null +++ b/docs/docs/sdk/migrating-data.mdx @@ -0,0 +1,44 @@ +--- +sidebar_position: 1 +--- + +import TOCInline from "@theme/TOCInline"; + +# Migrating Data to Encord + +Although we plan to fix this very soon, Encord Active is currently dependent on [Encord](https://www.encord.com) Projects. +If you haven't got such a project, you have a couple of options to migrate your data to the Encord platform: + + + +:::info + +If you do not have an Encord account, we would be happy to help you getting +started! Please contact us via the [Slack community](https://encordactive.slack.com) +or by shooting us an email on [active@encord.com](mailto:active@encord.com). We +will be happy to provide you with an account to get you started. + +::: + +## Import data from CVAT/COCO + +If you have a CVAT/COCO project already, you can import your data into Encord easily by following these steps: + +1. [Set up public key authentication with Encord](https://docs.encord.com/admins/settings/public-keys/#set-up-public-key-authentication) +2. [Import CVAT project via the SDK](https://python.docs.encord.com/tutorials/projects.html#cvat-integration) or [Import COCO using Encord Active](/cli/import-coco-project) + +Again, if you need help, don't be shy to reach out on the [Slack community](https://encordactive.slack.com)! + +## Migrating Data from Your Own Project + +:::caution + +This is less straight forward and will require some coding! + +::: + +The steps you need to take to import your local data into the Encord platform is: + +1. [Set up public key authentication with Encord](https://docs.encord.com/admins/settings/public-keys/#set-up-public-key-authentication) +2. [Follow step 1-3 in this tutorial](https://python.docs.encord.com/tutorials/end-to-end/preparing_data_for_labelling.html) to set up a project with Encord. +3. (optional: if you want to also import labels) [Follow this tutorial](https://python.docs.encord.com/tutorials/end-to-end/saving_labels_to_project.html) to import your labels into Encord. diff --git a/docs/docs/tags.md b/docs/docs/tags.md new file mode 100644 index 000000000..fb6f8f7e7 --- /dev/null +++ b/docs/docs/tags.md @@ -0,0 +1,37 @@ +--- +sidebar_position: 5 +--- + +# Create Tags + + +In Encord Active, you can tag your data to be able to [export](pages/export/filter_export) it. +Whether you are looking to relabel, augment, export, or delete data, the tagging feature is used in almost all Encord Active workflows. + + + `Prerequisites:` Dataset (Labels are optional) + +### Steps: +**Individual tagging:** +How to tag individual images: +1. Use the tagging feature from the _Data Quality_ and _Label Quality_ _Explorer_ tab. +2. Access tags at the bottom of the left sidebar. +3. Create tags of interest that you want group your data by (e.g., outliers, send to relabeling, delete) +4. Tag images by clicking the dropdown menu located below the image (A single image can have multiple tags). +5. View all tagged images in the _Actions_ tab by clicking tags within the _Add filter_ box. + + +**Bulk tagging:** +How to tag images in bulk: +1. Use the tagging feature from the _Data Quality_ and _Label Quality_ _Explorer_ tab. +2. Access tags at the bottom of the left sidebar. +3. Create tags of interest that you want to group your data by (e.g., outliers, send to relabeling, delete). Consider the example of finding outliers. +4. Use the bulk tagging menu bar to select _Outlier - Brightness_ and chose whether to perform bulk tagging on a page level or range level. + * If range level, chose the range using the sliders. + * If page level, all images on the current page will be tagged. +6. Select _Add_ and _Submit_ bulk tagging (To undo tagging, select _Remove_ and resubmit). +7. View all tagged images in the _Actions_ tab within the _Add filter_ box. + + + + diff --git a/docs/docs/tutorials/_category_.json b/docs/docs/tutorials/_category_.json new file mode 100644 index 000000000..dec6484fa --- /dev/null +++ b/docs/docs/tutorials/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Tutorials", + "position": 3.5, + "collapsed": true, + "link": { + "type": "generated-index", + "description": "Case studies that show how to improve your data." + } +} diff --git a/docs/docs/tutorials/touring-the-coco-dataset.mdx b/docs/docs/tutorials/touring-the-coco-dataset.mdx new file mode 100644 index 000000000..2c1393818 --- /dev/null +++ b/docs/docs/tutorials/touring-the-coco-dataset.mdx @@ -0,0 +1,223 @@ +--- +sidebar_position: 1 +--- +import Link from '@docusaurus/Link'; +import TOCInline from '@theme/TOCInline'; +import getSubsectionNodes from '@site/src/js/toc-filter-functions'; + +# Touring the Coco Sandbox Dataset +In this tutorial, you will see some cool features of Encord Active based on the Coco sandbox dataset. +You will go through the following steps: + + node.level === 2)} /> + +:::note + +This tutorial assumes that you have [installed](/installation) `encord-active`. + +::: + +## 1. Downloading the Data + +Download the data by running this command + +```shell +$ encord-active download +``` + +The script will ask you +1. `Where should we store the data?`: specify a directory in which the data should be stored +2. (potentially) `Directory not existing, want to create it? [y/N]` type y then enter +2. `[?] Choose a project:` choose the [open-source][validation]-coco-2017-dataset project by hitting enter + +Now `encord-active` will download your data. + +## 2. Opening Encord Active in Your Browser + +When the download process is done, `encord-active` will print how to run the app: + +```shell +encord-active visualise /tmp/test1 +``` + +:::caution + +If the command hangs and nothing happens in your browser, go to http://localhost:8501. + +::: + + +## 3. Finding and Flagging Label Errors {#find-and-flag-label-errors} + +You will cary out this process in two steps. + + + + +### Identifying Metrics with Label Errors +When you open Encord Active, you should see a page like this + +![Encord Active Landing page](/img/tutorials/landing-page.png) + +Expand the "Label Quality" tab in the left sidebar and click the "Summary" page. +The page should look like this: + +![Label Quality Summary Page](/img/tutorials/data-quality-summary-entry.png) + +In the summary page, you will find all the outliers that Encord Active automatically found based on all the [metrics](category/metrics) that were computed for the labels. + +Scroll down on the main view and expand the "Annotation Duplicates Outliers" section and expand it. +The page should look similar to this: + +![Annotation Duplicates Outliers](/img/tutorials/annotation-duplicate-outlier-summary.png) + +The page will show you how this metric was computed, how many outliers were found and some of the most severe outliers. + +If you hover the image with the oranges, you can click the expand button as indicated here: + +![Expand the image](/img/tutorials/expand-image-oranges.png) + +When clicking the button and taking a closer look at the image, you will see that there are two overlapping annotations on the orange. + +
+ Duplicated annotations +

Notice the duplicated annotations.

+
+ +Hit esc to exit the full screen view. + +If you look at the other images displayed, you will notice similar issues. + +
+
+ Duplicated annotations +
+
+ Duplicated annotations +
+
+ Duplicated annotations +
+
+ +:::tip + +You can find other sources of label errors by inspecting the other tabs. Good places to start could be the "Object Area" and "Object Aspect Ratio" sections in the App. + +::: + +### Tagging Label Errors + +To tag the images with label errors, you first add a new tag in the left sidebar: + +1. Give the tag a className +2. Click the "+" button. + +![Add new tag](/img/tutorials/label_duplicate_tag_add.png) + +Afterwards, you can add the tag to the images you see in the outlier pane with "Annotation duplicated" by selecting the tag from the drop-down below each image: + +![Tag an image](/img/tutorials/tag-added-for-oranges.png) + + +### Exporting Labels for Additional Annotation + +Suppose you have now tagged all the duplicated annotations that you would like to export for additional annotations. +Then you go to the _Actions_ tab in the left sidebar and check the "Add filters" checkbox in the top of the page. + +![Select images with the `label_duplication` tag](/img/tutorials/export-duplication-tag.png) + +_Note how your data has been filtered to only the rows that you tagged._ + +Now you can _Download_, _Delete_, _Edit_, or _Augment_ your data based on your selection. + + +## 4. Figuring Out What Metrics Influence Model Performance {#figuring-out-what-metrics-influence-model-performance} + +Encord Active also allows you to figure out which metrics influence your model performance the most. +In this section, we'll go through a subset of those: + + + +### The High Level View of Model Performance +#### mAP and mAR scores +First, navigate to the _Model Assertions_ > _Metrics_ page to find sections that automatically give you multiple insights into your model performance. + +The first section will give you the _mean Average Precision (mAP)_ and the _mean Average Recall (mAR)_ of your model based on the IOU threshold set in the top of the page. + +![mAP and mAR scored](/img/tutorials/map-mar-fragment.png) + +If you drag the slider, you will notice how the score changes. +You can also choose to see the aggregate score for certain classes by selecting them in the drop-down to the left. + + +#### Metric Importance and Correlation + +In the next section (the _Metric Importance_), you will find importances and correlations of your model performance as function of metrics. + +![Metric Importances](/img/tutorials/importance-and-correlations.png) + +From this section, you quickly find that, e.g., "Object Area - Absolute (P)" has a high importance for the model performance. + +:::note + +The "(P)" and the "(F)" in the labels of the plots indicate whether the metric was run on predictions or frames, respectively. + +::: + +A next step from here would be to jump to the _Performance by Metric_ page and have a look at exactly how the true-positive rate of the model is affected by this metric. +However, we want to show you the rest of this page prior to doing this. +You can skip straight ahead to the [Inspecting Model Performance for a Specific Metric](#inspecting-model-performance-for-a-specific-metric) if you are too curious to wait. + +#### Subset Scores +Before jumping into specific metrics, we want to show you the decomposition of the model performance based on individual classes. +If you look in the _Subset selection scores_ section, you will find the average precision (AP) and average recall (AR) scores for each individual class. + +![AP and AR scores decomposed based on classes](/img/tutorials/precision-recall-coco.png) + +:::note + +We've choosen a subset of classes [`airplane`, `bus`, `cat`, `dog`, `person`] in the settings in the top of the page to make the plots more digestible. + +::: + +From the left plot (Mean scores), we can see the AP and the AR for each individual class. +The right plot shows the precision-recall curves for each of the classes. +From these plots, we learn that the model performs better on the `cat` class than it does on, e.g., the `person` class. +To learn for which instances the model works and for which it doesn't, you can look in the _True Positives_, _False Positives_, and _False Negatives_ tabs to see concrete instances of the three success/failure modes. + +### Inspecting Model Performance for a Specific Metric + +Above, we noticed that the "Object Area - Absolute (P)" metric has a high importance on the model performance. +To learn more about how the specific metric affects the model performance you: + +1. Click the _Performance By Metric_ tab in the sidebar. +2. Select the "Object Area - Absolute (P)" metric in the settings in the top. + +You should now see a page similar to this: + +![Performance by Metric page](/img/tutorials/performance-by-object-area-coco.png) + +The plot shows the true positive rate and the false negative rate as a function of the selected metric; the "Object Area - Absolute (P)" in this case. +We can see how when the model predictions are small in terms of the absolute area, then the true-positive rate is low (bad), while larger predictions are more often correct (good). +Similarly, when labels are small, the false negative rate is high (bad), while larger labels are less likely to be issed by the model (good). + +:::tip + +If you click and drag on the top-most plot "Select to Zoom," you can zoom in on a specific subsection of the x-axis on the two plots below. + +::: + + +## 5. Conclusion + +This concludes the tour around Encord Active. +By now, you should have a good idea about how you can improve both your data, labels, and models by the insights you get from Encord Active. + +*Further reading:* + +- We've only covered each page in the app briefly in this tutorial. To learn more about what information you can find in each page, please refer to the [Pages](/category/pages) section. +- To learn more about concrete actionable steps you can take to improve your model performance, we suggest that you have a look at the [Workflow section](/category/workflows). +- If you want to learn more about the existing metrics or want to build your own metric function, the [Metrics section](/category/metrics) is where you should continue reading. +- Finally, we have also included some in-depth descriptions the [Command Line Interface](/category/command-line-interface). + diff --git a/docs/docs/workflows/Improve-your-data/_category_.json b/docs/docs/workflows/Improve-your-data/_category_.json new file mode 100644 index 000000000..94ea7995f --- /dev/null +++ b/docs/docs/workflows/Improve-your-data/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Improve Your Data", + "position": 3, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "Workflows for improving your data." + } +} diff --git a/docs/docs/workflows/Improve-your-data/identify-outliers-edge-cases.md b/docs/docs/workflows/Improve-your-data/identify-outliers-edge-cases.md new file mode 100644 index 000000000..9f65886d8 --- /dev/null +++ b/docs/docs/workflows/Improve-your-data/identify-outliers-edge-cases.md @@ -0,0 +1,28 @@ +--- +sidebar_position: 3 +--- + +# Find Outliers + +**Find outliers in your dataset using Encord Active's Data Quality tab** + + +With Encord Active, you can quickly find image outliers for pre-defined metrics, custom metrics, and label classes. +Encord Active finds outliers using precomputed [Interquartile ranges](/pages/data-quality/summary). + + `Prerequisites:` Dataset + +### Steps: +1. Navigate to the _Data Quality_ > _Summary_ tab. Here, the [metrics](/category/metrics) will be presented as expandable panes. +2. Click on a metric to get deeper insight into _moderate outliers_ and _severe outliers_. The most severe outliers are presented first in the pane. +3. Use the slider to navigate your data from most severe outlier to least severe. +4. When you have identified outliers of interest, use the [tagging](/tags) or [bulk tagging](/tags) feature to save a group of images. +5. After creating a tagged image group, you can access it at the bottom of the left sidebar in the _Actions_ tab. +6. Within the _Actions_ tab, click _add filters_, scroll the bottom, and select _tags_. Next, choose the tags you would like to export, relabel, or delete from your dataset. + + * To export images in CSV click _download filtered data_. + * To delete images from your dataset click _Delete_. + * To relabel images please contact Encord to hear more. + * To augment similar images please contact Encord to hear more. + + \ No newline at end of file diff --git a/docs/docs/workflows/Improve-your-data/similar-images.md b/docs/docs/workflows/Improve-your-data/similar-images.md new file mode 100644 index 000000000..59a534359 --- /dev/null +++ b/docs/docs/workflows/Improve-your-data/similar-images.md @@ -0,0 +1,35 @@ +--- +sidebar_position: 2 +--- + +# Find Similar Images + +**Mine edge cases, duplicates, and check the quality of your labels with visual similarity search** + +Often, when you find characteristics of interest in your dataset you may want to identify other similar images (e.g., to send for relabelling or deletion). +Identifying these cases can help you understand where your data is, e.g., underrepresented or mislabelled. +As your dataset grows, finding such cases manually becomes increasingly difficult. Using Encord Active's **similarity search** you can easily find semantically similar images in your dataset. +When have identified an edge case or a duplicate, you can tag it and export or delete it in the _Actions tab_. + + `Prerequisites:` Dataset & Embeddings + +:::tip + + There are two tabs where you can utilize similarity search: [Summary tab](/pages/data-quality/summary) and [Explorer tab](/pages/data-quality/explorer). + +::: + +### Steps: +1. Navigate to the _Data Quality_ > _Summary_ or _Explorer_ tab. +2. Select a metric in the top left menu to order your data by. +3. Select an image of interest and click _Show Similar Images_. + * Encord Active shows the most semantically similar images below the image in a new tab. +4. If you have not yet created any tags, create a tag on the left sidebar. +5. Tag images of interest. +6. Repeat step 2-5 until satisfied. +7. When you are satisfied with your selection navigate to the _Actions_ tab to act. +8. Within the _Actions_ tab Click _Add filters_, scroll the bottom, and select _tags_. Next, choose the tags you would like to export, relabel, delete, or augment from your dataset. + * To export images in CSV click _Download filtered data_. + * To delete images from your dataset click _Delete_. + * To relabel images please contact Encord to hear more. + * To augment similar images please contact Encord to hear more. diff --git a/docs/docs/workflows/Improve-your-data/understand-data-distribution.md b/docs/docs/workflows/Improve-your-data/understand-data-distribution.md new file mode 100644 index 000000000..4225464c1 --- /dev/null +++ b/docs/docs/workflows/Improve-your-data/understand-data-distribution.md @@ -0,0 +1,24 @@ +--- +sidebar_position: 1 +--- + +# Understand your Data Distribution + +**Get insights into the distribution of your visual data with Encord Active** + + Encord Active enables you to visually explore your data distribution by pre-defined metrics, custom metrics, and label classes. Understanding your data distribution by different metrics helps you uncover areas where you might be missing data that could improve your models performance on different outliers or edge cases. + + + `Prerequisites:` Dataset + +:::tip + +If you have uploaded your model predictions you can combine this workflow with [Find Important Metrics](/workflows/improve-your-models/metric-importance) to better prioritise what metrics to look at. + +::: +### Steps: +1. Navigate to the _Data Quality_ > _Explorer_ tab. +2. Select a metric to order your data by in the dropdown menu in the top of the page (e.g., Brightness or Aspect Ratio). +3. In the dashboard you can see the distribution of your data according to the chosen metric. +4. Use the slider to navigate the dataset ordered by the chosen metric. + diff --git a/docs/docs/workflows/_category_.json b/docs/docs/workflows/_category_.json new file mode 100644 index 000000000..e5a775e0e --- /dev/null +++ b/docs/docs/workflows/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Workflows", + "position": 3, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "Workflows for improving your data, labels, and models." + } +} diff --git a/docs/docs/workflows/import-data.mdx b/docs/docs/workflows/import-data.mdx new file mode 100644 index 000000000..7a3bbaf30 --- /dev/null +++ b/docs/docs/workflows/import-data.mdx @@ -0,0 +1,27 @@ +--- +sidebar_position: 1 +--- + +# Importing Data and Labels + +:::info + +There are two ways your can import a project, from an existing Encord project or from a local project in a COCO data format that will be uploaded to Encord. +If you do not have an Encord account and data on the Encord platform, we would be happy to help you getting started! Please contact us via the Slack or by shooting us an email on [active@encord.com](mailto:active@encord.com). +We will be happy to provide you with an account to get you started. + +::: + +To use Encord Active, you need data. +This page describes how you can get data into Encord Active. + +Overall, there are two steps you need to take: + +1. Get your data on the Encord Platform. + If you have an Encord project already, that's great - you can skip to the next step. + If not, please see the SDK section on [Migrating data to Encord](/sdk/migrating-data) for more instructions. +2. [Import the project into Encord Active](/cli/import-encord-project) + +If you want to use a local project in a COCO format, you can use our [COCO importer](/cli/import-coco-project) which will do steps 1 and 2 automatically. + +When you have done this, you can [run the app](/cli/open-encord-active). diff --git a/docs/docs/workflows/import-predictions.mdx b/docs/docs/workflows/import-predictions.mdx new file mode 100644 index 000000000..e4ac4d471 --- /dev/null +++ b/docs/docs/workflows/import-predictions.mdx @@ -0,0 +1,255 @@ +--- +sidebar_position: 2 +--- +import TOCInline from '@theme/TOCInline'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Import Model Predictions + +To import your model predictions into Encord Active, there are a couple of steps you need to follow: + + node.level === 2)} /> + +If you are already familiar with `data_hash`es and `featureNodeHash`es from the Encord platform, you can safely skip to [2. Prepare a `.pkl` File to be Imported](#2-prepare-a-pkl-file-to-be-imported). +Just note that when specifying the `class_id` for a prediction, Encord Active expects the associated `featureNodeHash` from the Encord ontology as id. + +:::tip + +In the [SDK section](/sdk/importing-model-predictions), you will also find ways to import predictions directly from your [prediction loop](/sdk/importing-model-predictions#predictions-from-your-prediction-loop), [KITTI files](/sdk/importing-model-predictions#predictions-from-kitti-files), and [directories containing mask files](/sdk/importing-model-predictions#predictions-from-masks). + +::: + + +## 1. Covering the Bases + +Before we can really dive into the details, we have a couple of thing that we need to cover. + +### Uniquiely Identifying Data Units + +At Encord, every data unit has a `data_hash` which uniquely defines it. +To see the mapping between the `data_hash`es in your Encord project and the filenames that you uploaded, you can use: + +```shell +encord-active print data-mapping +``` + +After selecting the project you want to print mapping for via the command line, it will display a JSON object similar to the following with key-value pairs of (`data_hash`, data file name): + +``` +{ + "c115344f-6869-4608-a4b8-644241fea10c": "image_1.jpg", + "5973f5b6-d284-4a71-9e7e-6576aa3e56cb": "image_2.jpg", + "9f4dae86-cad4-42f8-bb47-b179eb2e4886": "video_1.mp4" + ... +} + +``` + + +:::tip + +To store the data mapping as `data_mapping.json` in the current working directory, you can do + +```shell +encord-active print --json data-mapping +``` + +::: + + +Note that for `image_groups`, each image in the group has it's own `data_hash`, while +videos have one `data_hash` for the entire video. +As a consequence, predictions for videos will also need a `frame` to uniquely define where the prediction belongs. + +:::caution + +When you are preparing predictions for import, you need to have the `data_hash` and potentially the `frame` available. + +::: + +### Uniquely Identifying Predicted Classes + +The second thing you will need when you are preparing predictions for import, is the `class_id` for each prediction. +The class id tells Encord Active which class the prediction is associated with. + +The `class_id`s of an Encord project is defined by the `featureNodeHash` attribute on objects in the encord ontology. +You can easily print the class names and `class_id`s from your project ontology: + +```shell +encord-active print ontology +``` + +After selecting the project you want to print mapping for via the command line, it will display a JSON object similar to the following with key-value pairs of (object name, `class_id`): + +``` +{ + "cat": "OTK8MrM3", + "dog": "Nr52O8Ex", + "horse": "MjkXn2Mx" +} +``` + + +:::tip + +To store the ontology as `ontology.json` in the current working directory, you can do + +```shell +encord-active print --json ontology +``` + +::: + + +## 2. Prepare a `.pkl` File to be Imported + +Now, you can prepare a pickle file (`.pkl`) to be imported by Encord Active. +You do this by building a list of `Prediction` objects. +A prediction object holds a unique identifier of the data unit (the `data_hash` and potentially a `frame`), the `class_id`, a model `confidence` score, the actual prediction `data`, and the `format` of that data. + +#### Creating a `Prediction` Object +Below, you find examples of how to create an object of each of the three supported types. + + + + +You should specify your `BoundingBox` with relative coordinates and dimensions. +That is: + +- `x`: x-coordinate of the top-left corner of the box divided by the image width +- `y`: y-coordinate of the top-left corner of the box divided by the image height +- `w`: box pixel width / image width +- `h`: box pixel height / image height + +```python +from encord_active.app.db.predictions import BoundingBox, Prediction, Format + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + format = Format.BOUNDING_BOX, + # highlight-start + # Your bounding box coordinates in relative terms (% of image width/height). + data = BoundingBox(x=0.2, y=0.3, w=0.1, h=0.4) + # highlight-end +) +``` + +:::tip + +If you don't have your bounding box represented in relative terms, you can convert it from pixel values like this: + +```python +img_h, img_w = 720, 1280 # the image size in pixels +BoundingBox(x=10/img_w, y=25/img_h, w=200/img_w, h=150/img_h) +``` + +::: + + + + +You specify masks as binary `numpy` arrays of size [height, width] and with `dtype` `np.uint8`. + +```python +from encord_active.app.db.predictions import Prediction, Format + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + # highlight-start + format = Format.MASK, + data = mask # _binary_ np.ndarray of shape [h, w] and dtype np.uint8 + # highlight-end +) +``` + + + + +You should specify your `Polygon` with relative coordinates as a numpy array of shape `[num_points, 2]`. +That is, an array of relative (`x`, `y`) coordinates: + +- `x`: relative x-coordinate of each point of the polygon (pixel coordinate / image width) +- `y`: relative y-coordinate of each point of the polygon (pixel coordinate / image height) + +```python +from encord_active.app.db.predictions import Prediction, Format +import numpy as np + +polygon = np.array([ + # x y + [0.2, 0.1], + [0.2, 0.4], + [0.3, 0.4], + [0.3, 0.1], +]) + +prediction = Prediction( + data_hash = "", + frame = 3, # optional frame for videos + class_id = "", + confidence = 0.8, + # highlight-start + format = Format.POLYGON, + data = polygon # np.ndarray of shape [n, 2] and dtype float in range [0,1] + # highlight-end +) +``` + +:::tip + +If you have your polygon represented in absolute terms of pixel locations, you can convert it to relative terms like this: + +```python +img_h, img_w = 720, 1280 # the image size in pixels +polygon = polygon / np.array([[img_h, img_w]]) +``` + +Notice the double braces `[[img_h, img_w]]` to get an array of shape `[1, 2]`. + +::: + + + + + +#### Preparing the Pickle File + +Now you're ready to prepare the file. +You can copy the appropriate snippet based on your prediction format from above and paste it in the code below. +Note the highlighted line, which defines where the `.pkl` file will be stored. + +```python showLineNumbers +from encord_active.app.db.predictions import Prediction, Format + +predictions_to_store = [] + +for prediction in my_predictions: # Iterate over your predictions + predictions_to_store.append( + # PASTE appropriate prediction snippet from above + ) + +# highlight-next-line +with open("/path/to/predictions.pkl", "wb") as f: + pickle.dump(predictions_to_store, f) +``` + +In the above code snippet, you will have to fetch the `data_hash`, `class_id`, etc. ready from the for loop in line 5. + +## 3. Import Your Predictions via the CLI + +To import the predictions into Encord Active, you run the following command from the command line: + +```shell +encord-active import predictions /path/to/predictions.pkl +``` + +This will import your predictions into Encord Active and run all the [metrics](/category/metrics) on your predictions. + + diff --git a/docs/docs/workflows/improve-your-labels/_category_.json b/docs/docs/workflows/improve-your-labels/_category_.json new file mode 100644 index 000000000..354077fbf --- /dev/null +++ b/docs/docs/workflows/improve-your-labels/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Improve Your Labels", + "position": 4, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "Workflows for improving your labels" + } +} diff --git a/docs/docs/workflows/improve-your-labels/identify-outliers.md b/docs/docs/workflows/improve-your-labels/identify-outliers.md new file mode 100644 index 000000000..d717a0509 --- /dev/null +++ b/docs/docs/workflows/improve-your-labels/identify-outliers.md @@ -0,0 +1,27 @@ +--- +sidebar_position: 2 +--- + +# Find Outliers + +**Use Encord Active to find label outliers in your dataset** + +With Encord Active, you can quickly find image outliers for pre-defined metrics, custom metrics, and label classes. +Encord Active finds outliers using precomputed [Interquartile ranges](/pages/data-quality/summary). + + `Prerequisites:` Dataset & Labels + +### Steps: +1. Navigate to the _Label Quality_ > _Summary_ tab. Here each [metric](/category/metrics) will be presented as an expandable panes. +2. Click on a metric to get a deeper insight into _moderate outliers_ and _severe outliers_. Severe outliers are presented first in the pane. +3. Use the slider to navigate your data from most severe outlier to least severe. +4. When you have identified outliers of interest use the [tagging](/tags) or [bulk tagging](/tags) feature to select a group of images. +5. After creating a tagged image group, you can access it at the bottom of the left sidebar in the _Actions_ tab. +6. Within the _Actions_ tab, click _Add filters_, scroll to the bottom, and select _tags_. +7. Next choose the tags you would like to export, relabel, delete or augment from your dataset: + * To export images in CSV click _download filtered data_. + * To delete images from your dataset click _Delete_. + * To relabel images please contact Encord to hear more. + * To augment similar images please contact Encord to hear more. + + \ No newline at end of file diff --git a/docs/docs/workflows/improve-your-labels/understand-data-distribution.md b/docs/docs/workflows/improve-your-labels/understand-data-distribution.md new file mode 100644 index 000000000..4066c679d --- /dev/null +++ b/docs/docs/workflows/improve-your-labels/understand-data-distribution.md @@ -0,0 +1,34 @@ +--- +sidebar_position: 1 +--- + +# Understand your label Distribution + +**Get insights into the distribution of your labels with Encord Active** + + +Encord Active enables you to visually explore your label distribution by pre-defined metrics, custom metrics, and label classes. +Understanding your label distribution by different metrics can help you discover gaps in your dataset to prioritise for further data collection and labelling to improve your models' performance. + +`Prerequisites:` Dataset & Labels + +:::tip + +If you have uploaded your model predictions you can combine this workflow with [Find Important Metrics](/workflows/improve-your-models/metric-importance) to better prioritise what metrics to look at. + +::: + +### Steps: +1. Navigate to the _Label Quality_ > _Explorer_ tab. +2. Select a metric to order your data by in the dropdown menu top left (e.g. Brightness or Aspect Ratio). +3. [Optional] filter your data by class or by annotator using the middle and right dropdown menu. +4. In the dashboard, you can see three different data distributions. + * Distribution by class: Shows the number of samples by class in the dataset. + * Distribution by annotator: Shows the number of samples by annotator and their annotator score. + * Distribution by metric: Shows the number of samples by metric score. + * Use the slider to navigate the dataset ordered by the chosen metric. + + + + + diff --git a/docs/docs/workflows/improve-your-models/_category_.json b/docs/docs/workflows/improve-your-models/_category_.json new file mode 100644 index 000000000..17353712e --- /dev/null +++ b/docs/docs/workflows/improve-your-models/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "Improve Your Models", + "position": 5, + "collapsible": true, + "link": { + "type": "generated-index", + "description": "Workflows for improving your models" + } +} diff --git a/docs/docs/workflows/improve-your-models/identify-false-negative.md b/docs/docs/workflows/improve-your-models/identify-false-negative.md new file mode 100644 index 000000000..6bd204adf --- /dev/null +++ b/docs/docs/workflows/improve-your-models/identify-false-negative.md @@ -0,0 +1,13 @@ +--- +sidebar_position: 4 +--- + +# Identifying False Negatives + +Using the false negatives tab in Encord Active, you can quickly find out which objects the model misses. + +`Prerequisites:` Dataset, Labels, Predictions + +### Steps: +1. Navigate to the _Model Assertions_ > _False Negatives_ tab on the left sidebar. +2. Observe the ground-truth objects (purple boxed objects) that are missed by the model and get insights on where the model fails. The remaining objects in the image are the model predictions for that image. \ No newline at end of file diff --git a/docs/docs/workflows/improve-your-models/identify-false-positive.md b/docs/docs/workflows/improve-your-models/identify-false-positive.md new file mode 100644 index 000000000..5fed0e52a --- /dev/null +++ b/docs/docs/workflows/improve-your-models/identify-false-positive.md @@ -0,0 +1,28 @@ +--- +sidebar_position: 3 +--- +# Identifying False Positives + +Using the false positive tab in Encord you can quickly identify in which areas your model fails. With this +functionality you can, for example: +- Detect missing ground-truth labels +- Diagnose annotation errors +- Learn which classes your model confuses + +and more, depending on your use cases. + +`Prerequisites:` Dataset, Labels, Predictions + +### Steps: +1. Navigate to the _Model Assertions_ > _False Positives_ tab on the left sidebar. +2. Visualise predictions and try to get insights on where model fails. +3. Under each image, an explanation is given for why the prediction is false positive. The three reasons are: + - No overlapping with the ground-truth object. This means that there is no label with the same class as the predicted +class which overlaps with the prediction. + - IoU is too low. This means that the prediction does overlap with a label of the same class. However, the IoU +between the prediction and the label is lower than the IoU threshold which is selected in the top bar. + - Prediction with higher confidence is already matched with the ground-truth object. +Since the mAP score chooses the prediction with the highest model confidence that has an IOU larger than the set threshold, other predictions that matched the label with a sufficiently high IOU will be considered false positives. +4. Note, that the boxed magenta object is the prediction, while the remaining objects are labels for the same +image/frame. + diff --git a/docs/docs/workflows/improve-your-models/metric-importance.md b/docs/docs/workflows/improve-your-models/metric-importance.md new file mode 100644 index 000000000..2c8a32fb2 --- /dev/null +++ b/docs/docs/workflows/improve-your-models/metric-importance.md @@ -0,0 +1,60 @@ +--- +sidebar_position: 2 +--- + +# Find Important Metrics + +**Visualise the relationship between your model performance and metrics** + + +With this workflow, you will be able to identify the most important [metrics](/category/metrics) for your model performance and prioritise further data exploration and actions. + + `Prerequisites:` Dataset, Labels, Predictions + +### Steps: +1. Navigate to the _Model Assertions_ > _Metrics_ tab. +2. Select label classes to include in the top left drop down menu. +3. Determine IoU threshold using the slider in the top bar. By default, IoU threshold is set to 0.50. +4. Next, Encord Active automatically computes mAP, mAR, [Metric Importance](/pages/model-assertions/metrics), and +[Metric Correlation](/pages/model-assertions/metrics). + + **Metric importance**: Measures the *strength* of the dependency between a metric and model + performance. A high value means that the model performance would be strongly affected by + a change in the metric. For example, a high importance in 'Brightness' implies that a change + in that quantity would strongly affect model performance. Values range from 0 (no dependency) + to 1 (perfect dependency, one can completely predict model performance simply by looking + at this metric). + + **Metric [correlation](https://en.wikipedia.org/wiki/Correlation)**: Measures the *linearity + and direction* of the dependency between a metric and model performance. + Crucially, this metric tells us whether a positive change in a metric + will lead to a positive change (positive correlation) or a negative change (negative correlation) + in model performance. Values range from -1 to 1. +5. Metrics denoted with (P) are _Prediction-level metrics_ and metrics with (F) are _Frame-level metrics_. +6. Once an important metric is identified, navigate to _Performance By Metric_ in the _Model Assertions_ tab. +7. Select the important metric you want to understand using the drop-down menu on the top bar. +8. By default, the performance chart is shown in aggregate for all classes, optionally you can choose to decompose performance by class or select individual classes to be shown in the top left drop down menu. +9. The plot shows the _True Positive Rate_ (TPR) and the _False Negative Rate_ (FNR) by metric to help you identify which metric characteristics you model have a hard time predicting. + +### Example + +![metric_importance](../../images/index_importance.png) +Metric importance plots indicate that _Object Area - Relative (P)_ is an important metric that has an important relationship +with the model performance. + +In this case, go to **Performance By Metric** page and choose "_Object Area - Relative (P)_" in the **Select metric for your predictions** drop down menu. +Here, you can understand why _Object Area - Relative (P)_ has a relationship with the model performance, and you can act based upon insights you got from here. +Let's examine _Object Area - Relative (P)_ metric: + +![metric_importance](../../images/object_area_relative_performance.png) + +As indicated in the details, this metric refers to the object area as a percentage of total image area. +Blue dashed horizontal line (around 0.17 TPR and 0.77 FNR) is the average true positive rate and false negative rate of the selected classes, respectively. +So, what we get from the above graph is that objects, whose area is less than the 0.24%, have a very low performance. +In other words, the model predictions that are small are very often incorrect. +Similarly, labelled objects, for which the area is small, has a high false negative rate. + +Based on this insight, you may improve your model with several actions, such as: +- Filtering model predictions to not include the smallest objects +- Increasing your model's input resolution +- Increasing the confidence threshold for small objects diff --git a/docs/docs/workflows/improve-your-models/understand-performance-metrics.md b/docs/docs/workflows/improve-your-models/understand-performance-metrics.md new file mode 100644 index 000000000..2cf761fdb --- /dev/null +++ b/docs/docs/workflows/improve-your-models/understand-performance-metrics.md @@ -0,0 +1,29 @@ +--- +sidebar_position: 1 +--- + +# Visualise Performance Metrics + +**Visualise performance of your model** + +Encord Active enables you to visualise the important performance metrics, such as mean Average-Precision (mAP), for your model. +Performance metrics can be visualised based on different classes and intersection-over-Union (IoU) thresholds. +Performance metrics are supported for bounding-boxes (object detection) and polygons (segmentation). + +`Prerequisites:` Dataset, Labels, Predictions + +### Steps +1. Navigate to the _Model Assertions_ > _Metrics_ tab on the left sidebar. +2. Under the **Subset selection scores**, you will see the average precision (AP) and average recall (AR) for each class in the graph to the left +and Precision-Recall curves for each class on the graph to the right. +3. You can select classes of interest and change IoU threshold on the upper sidebar to customize plots. +4. On the **Mean scores** plot, you can observe in which classes the model has difficulty and in which classes it does well. +5. According to insights you get here, you can, e.g., prioritize from which classes you need to collect more data. + +### Example +Comparing **person** and **clock** objects. + +![clock_vs_person_performance](../../images/clock_vs_person_performance.png) + +From the above figure, it is apparent that clock class degrades overall performance considerably. So, when collecting and labelling more data, prioritizing it +over person class will make more sense for overall performance. diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js new file mode 100644 index 000000000..9ffc437f0 --- /dev/null +++ b/docs/docusaurus.config.js @@ -0,0 +1,96 @@ +// @ts-check +// Note: type annotations allow type checking and IDEs autocompletion + +const lightCodeTheme = require("prism-react-renderer/themes/github"); +const darkCodeTheme = require("prism-react-renderer/themes/dracula"); +const math = require("remark-math"); +const katex = require("rehype-katex"); + +/** @type {import('@docusaurus/types').Config} */ +const config = { + title: "Encord Active Docs", + tagline: "Dinosaurs are cool", + url: "https://encord-active-docs.web.app", + baseUrl: "/", + onBrokenLinks: "throw", + onBrokenMarkdownLinks: "warn", + favicon: "img/favicon.ico", + + i18n: { + defaultLocale: "en", + locales: ["en"], + }, + + plugins: [["./src/plugins/hotjar", { applicationId: 3217620 }]], + + presets: [ + [ + "classic", + /** @type {import('@docusaurus/preset-classic').Options} */ + ({ + docs: { + routeBasePath: "/", + sidebarPath: require.resolve("./sidebars.js"), + editUrl: + "https://github.com/encord-team/encord-active/edit/main/docs", + remarkPlugins: [math], + rehypePlugins: [katex], + }, + blog: false, + theme: { + customCss: require.resolve("./src/css/custom.css"), + }, + }), + ], + ], + stylesheets: [ + { + href: "https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css", + type: "text/css", + integrity: + "sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM", + crossorigin: "anonymous", + }, + ], + + themeConfig: + /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ + ({ + navbar: { + title: "Encord Active", + logo: { + alt: "Encord Logo", + src: "img/logo.svg", + }, + items: [ + { + href: "https://github.com/encord-team/encord-active/", + label: "GitHub", + position: "right", + }, + ], + }, + footer: { + links: [], + copyright: `Copyright © ${new Date().getFullYear()} Encord`, + logo: { + src: "img/logo.svg", + }, + }, + + prism: { + theme: lightCodeTheme, + darkTheme: darkCodeTheme, + }, + + algolia: { + appId: "27UJHL6JLV", + apiKey: "fccd2c05f9d37252e81614ac46f2b8d3", + indexName: "ea_prod", + + contextualSearch: false, + }, + }), +}; + +module.exports = config; diff --git a/docs/firebase.json b/docs/firebase.json new file mode 100644 index 000000000..1699ad50f --- /dev/null +++ b/docs/firebase.json @@ -0,0 +1,16 @@ +{ + "hosting": [ + { + "target": "prod", + "public": "build", + "ignore": ["firebase.json", "**/.*", "**/node_modules/**"], + "redirects": [ + { + "source": "/docs/getting-started", + "destination": "/", + "type": 301 + } + ] + } + ] +} diff --git a/docs/package-lock.json b/docs/package-lock.json new file mode 100644 index 000000000..61524668f --- /dev/null +++ b/docs/package-lock.json @@ -0,0 +1,21474 @@ +{ + "name": "website", + "version": "0.0.0", + 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"tsc" + }, + "dependencies": { + "@docusaurus/core": "^2.2.0", + "@docusaurus/preset-classic": "^2.2.0", + "@mdx-js/react": "^1.6.22", + "clsx": "^1.2.1", + "hast-util-is-element": "^1.1.0", + "prism-react-renderer": "^1.3.5", + "react": "^17.0.2", + "react-dom": "^17.0.2", + "rehype-katex": "^5.0.0", + "remark-math": "^3.0.1" + }, + "devDependencies": { + "@docusaurus/module-type-aliases": "^2.2.0", + "@tsconfig/docusaurus": "^1.0.5", + "typescript": "^4.7.4" + }, + "browserslist": { + "production": [ + ">0.5%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "engines": { + "node": ">=16.14" + } +} diff --git a/docs/sidebars.js b/docs/sidebars.js new file mode 100644 index 000000000..8f1e3da2f --- /dev/null +++ b/docs/sidebars.js @@ -0,0 +1,33 @@ +/** + * Creating a sidebar enables you to: + - create an ordered group of docs + - render a sidebar for each doc of that group + - provide next/previous navigation + + The sidebars can be generated from the filesystem, or explicitly defined here. + + Create as many sidebars as you want. + */ + +// @ts-check + +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const sidebars = { + // By default, Docusaurus generates a sidebar from the docs folder structure + tutorialSidebar: [{ type: "autogenerated", dirName: "." }], + + // But you can create a sidebar manually + /* + tutorialSidebar: [ + 'intro', + 'hello', + { + type: 'category', + label: 'Tutorial', + items: ['tutorial-basics/create-a-document'], + }, + ], + */ +}; + +module.exports = sidebars; diff --git a/docs/src/css/custom.css b/docs/src/css/custom.css new file mode 100644 index 000000000..33e223b78 --- /dev/null +++ b/docs/src/css/custom.css @@ -0,0 +1,13 @@ +:root { + --ifm-color-primary: #514cd8; + --docusaurus-highlighted-code-line-bg: rgba(86, 88, 221, 0.1); +} + +[data-theme="dark"] { + --ifm-color-primary: #ae85ff; +} + +.text-orange { + color: #ff8a6a; +} + diff --git a/docs/src/js/toc-filter-functions.js b/docs/src/js/toc-filter-functions.js new file mode 100644 index 000000000..f00fe608a --- /dev/null +++ b/docs/src/js/toc-filter-functions.js @@ -0,0 +1,23 @@ +export default function getSubsectionNodes(toc, sectionLabel) { + console.log(sectionLabel); + let inSubsection = false; + const subtoc = []; + for (var i = 0; i < toc.length; i++) { + const node = toc[i]; + console.log(node); + if (node.level === 2) { + if (node.id === sectionLabel) { + inSubsection = true; + } else { + inSubsection = false; + } + continue; + } + if (inSubsection) { + subtoc.push(node); + } + } + console.log(subtoc); + return subtoc; +} + diff --git a/docs/src/plugins/hotjar/index.js b/docs/src/plugins/hotjar/index.js new file mode 100644 index 000000000..1f39909a9 --- /dev/null +++ b/docs/src/plugins/hotjar/index.js @@ -0,0 +1,39 @@ +module.exports = function (_, options) { + const { applicationId } = options; + + if (!applicationId) { + throw new Error( + `You need to pass 'applicationId' as options to the plugin` + ); + } + + return { + name: "docusaurus-hotjar-plugin", + injectHtmlTags: () => + !process.env.NODE_ENV === "production" + ? {} + : { + headTags: [ + { + attributes: { type: "module" }, + tagName: "script", + innerHTML: ` + (function (h, o, t, j, a, r) { + h.hj = + h.hj || + function () { + (h.hj.q = h.hj.q || []).push(arguments); + }; + h._hjSettings = { hjid: ${applicationId}, hjsv: 6 }; + a = o.getElementsByTagName("head")[0]; + r = o.createElement("script"); + r.async = 1; + r.src = t + h._hjSettings.hjid + j + h._hjSettings.hjsv; + a.appendChild(r); + })(window, window.document, "https://static.hotjar.com/c/hotjar-", ".js?sv="); + `, + }, + ], + }, + }; +}; diff --git a/docs/static/.nojekyll b/docs/static/.nojekyll new file mode 100644 index 000000000..e69de29bb diff --git a/docs/static/img/docusaurus.png b/docs/static/img/docusaurus.png new file mode 100644 index 000000000..f458149e3 Binary files 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a/docs/static/img/tutorials/label_duplicate_tag_add.png b/docs/static/img/tutorials/label_duplicate_tag_add.png new file mode 100644 index 000000000..545d412d3 Binary files /dev/null and b/docs/static/img/tutorials/label_duplicate_tag_add.png differ diff --git a/docs/static/img/tutorials/landing-page.png b/docs/static/img/tutorials/landing-page.png new file mode 100644 index 000000000..ce65103cb Binary files /dev/null and b/docs/static/img/tutorials/landing-page.png differ diff --git a/docs/static/img/tutorials/map-mar-fragment.png b/docs/static/img/tutorials/map-mar-fragment.png new file mode 100644 index 000000000..ddf3d5ed9 Binary files /dev/null and b/docs/static/img/tutorials/map-mar-fragment.png differ diff --git a/docs/static/img/tutorials/performance-by-object-area-coco.png b/docs/static/img/tutorials/performance-by-object-area-coco.png new file mode 100644 index 000000000..a34e95b18 Binary files /dev/null and b/docs/static/img/tutorials/performance-by-object-area-coco.png differ diff --git a/docs/static/img/tutorials/precision-recall-coco.png b/docs/static/img/tutorials/precision-recall-coco.png new file mode 100644 index 000000000..0a06036ed Binary files /dev/null and b/docs/static/img/tutorials/precision-recall-coco.png differ diff --git a/docs/static/img/tutorials/tag-added-for-oranges.png b/docs/static/img/tutorials/tag-added-for-oranges.png new file mode 100644 index 000000000..6eed4a775 Binary files /dev/null and b/docs/static/img/tutorials/tag-added-for-oranges.png differ diff --git a/docs/static/img/undraw_docusaurus_mountain.svg b/docs/static/img/undraw_docusaurus_mountain.svg new file mode 100644 index 000000000..af961c49a --- /dev/null +++ b/docs/static/img/undraw_docusaurus_mountain.svg @@ -0,0 +1,171 @@ + + Easy to Use + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/static/img/undraw_docusaurus_react.svg b/docs/static/img/undraw_docusaurus_react.svg new file mode 100644 index 000000000..94b5cf08f --- /dev/null +++ b/docs/static/img/undraw_docusaurus_react.svg @@ -0,0 +1,170 @@ + + Powered by React + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/static/img/undraw_docusaurus_tree.svg b/docs/static/img/undraw_docusaurus_tree.svg new file mode 100644 index 000000000..d9161d339 --- /dev/null +++ b/docs/static/img/undraw_docusaurus_tree.svg @@ -0,0 +1,40 @@ + + Focus on What Matters + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/tsconfig.json b/docs/tsconfig.json new file mode 100644 index 000000000..6f4756980 --- /dev/null +++ b/docs/tsconfig.json @@ -0,0 +1,7 @@ +{ + // This file is not used in compilation. It is here just for a nice editor experience. + "extends": "@tsconfig/docusaurus/tsconfig.json", + "compilerOptions": { + "baseUrl": "." + } +} diff --git a/frontend/.firebaserc b/frontend/.firebaserc new file mode 100644 index 000000000..9dcbb8502 --- /dev/null +++ b/frontend/.firebaserc @@ -0,0 +1,5 @@ +{ + "projects": { + "default": "encord-active-frontend" + } +} diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 000000000..a547bf36d --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/firebase.json b/frontend/firebase.json new file mode 100644 index 000000000..1a13c99a0 --- /dev/null +++ b/frontend/firebase.json @@ -0,0 +1,12 @@ +{ + "hosting": { + "public": "dist", + "ignore": [], + "rewrites": [ + { + "source": "**", + "destination": "/index.html" + } + ] + } +} diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 000000000..e0d1c8408 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Vite + React + TS + + +
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a/frontend/postcss.config.cjs b/frontend/postcss.config.cjs new file mode 100644 index 000000000..cbfea5ea2 --- /dev/null +++ b/frontend/postcss.config.cjs @@ -0,0 +1,7 @@ +module.exports = { + plugins: { + "tailwindcss/nesting": {}, + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 000000000..e7b8dfb1b --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/components/index.ts b/frontend/src/components/index.ts new file mode 100644 index 000000000..ea782c892 --- /dev/null +++ b/frontend/src/components/index.ts @@ -0,0 +1 @@ +export * from "./pages-menu"; diff --git a/frontend/src/components/pages-menu/PagesMenu.module.css b/frontend/src/components/pages-menu/PagesMenu.module.css new file mode 100644 index 000000000..85aa056c4 --- /dev/null +++ b/frontend/src/components/pages-menu/PagesMenu.module.css @@ -0,0 +1,18 @@ +.transparent-submenu { + :global(.ant-menu-sub.ant-menu-inline) { + background-color: unset; + } + + :global(.ant-menu-submenu-selected > .ant-menu-submenu-title) { + @apply text-black/[.85] font-semibold; + } + + :global(.ant-menu-item-selected) { + @apply !bg-white rounded-md font-semibold; + color: unset; + + &::after { + @apply !border-none; + } + } +} diff --git a/frontend/src/components/pages-menu/PagesMenu.tsx b/frontend/src/components/pages-menu/PagesMenu.tsx new file mode 100644 index 000000000..323caa80b --- /dev/null +++ b/frontend/src/components/pages-menu/PagesMenu.tsx @@ -0,0 +1,40 @@ +import { Menu } from "antd"; +import { ItemType } from "antd/lib/menu/hooks/useItems"; +import useResizeObserver from "use-resize-observer"; + +import { useEffect } from "react"; +import { Streamlit } from "streamlit-component-lib"; +import { useRenderData } from "../../streamlit/StreamlitProvider"; + +import classes from "./PagesMenu.module.css"; + +export const PagesMenu = () => { + const { + args: { items }, + } = useRenderData<{ items: ItemType[] }>(); + const [first] = items + ?.map((item) => item?.key?.toString()) + .filter(Boolean) as string[]; + + const { ref, height = 0 } = useResizeObserver(); + + useEffect(() => { + Streamlit.setFrameHeight(height); + }, [height]); + + if (!first || !items) throw new Error("`items` prop must be provided"); + + return ( +
+ Streamlit.setComponentValue(key)} + forceSubMenuRender={true} + /> +
+ ); +}; diff --git a/frontend/src/components/pages-menu/index.ts b/frontend/src/components/pages-menu/index.ts new file mode 100644 index 000000000..6cd8951ff --- /dev/null +++ b/frontend/src/components/pages-menu/index.ts @@ -0,0 +1 @@ +export * from "./PagesMenu"; diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 000000000..1393976c6 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,23 @@ +@import "antd/dist/antd.css"; + +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + @apply font-sans text-base font-normal text-white/[.87] bg-neutral-800; + color-scheme: light dark; + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + -webkit-text-size-adjust: 100%; +} + +#root { + @apply w-full h-full max-h-screen; +} + +body { + @apply m-0 px-4 flex place-items-center min-w-[320px] h-screen; +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 000000000..face6f49b --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,19 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import { PagesMenu } from "./components"; +import "./index.css"; + +import { BrowserRouter, Route, Routes } from "react-router-dom"; +import { StreamlitProvider } from "./streamlit/StreamlitProvider"; + +ReactDOM.createRoot(document.getElementById("root") as HTMLElement).render( + + + + + } /> + + + + +); diff --git a/frontend/src/streamlit/ErrorBoundry.tsx b/frontend/src/streamlit/ErrorBoundry.tsx new file mode 100644 index 000000000..ab85ac525 --- /dev/null +++ b/frontend/src/streamlit/ErrorBoundry.tsx @@ -0,0 +1,39 @@ +import React from "react"; + +interface ErrorBoundaryProps { + children: React.ReactNode; +} +interface ErrorBoundaryState { + error: Error | undefined; +} + +/** + * Shows errors thrown from child components. + */ +export class ErrorBoundary extends React.PureComponent< + ErrorBoundaryProps, + ErrorBoundaryState +> { + constructor(props: ErrorBoundaryProps) { + super(props); + this.state = { error: undefined }; + } + + static getDerivedStateFromError(error: Error) { + // Update state so the next render will show the fallback UI. + return { error }; + } + + render() { + if (this.state.error != null) { + return ( +
+

Component Error

+ {this.state.error.message} +
+ ); + } + + return this.props.children; + } +} diff --git a/frontend/src/streamlit/StreamlitProvider.tsx b/frontend/src/streamlit/StreamlitProvider.tsx new file mode 100644 index 000000000..ad1c04be8 --- /dev/null +++ b/frontend/src/streamlit/StreamlitProvider.tsx @@ -0,0 +1,59 @@ +import React, { useContext, useEffect } from "react"; +import { RenderData, Streamlit } from "streamlit-component-lib"; +import { ErrorBoundary } from "./ErrorBoundry"; +import { useNullableRenderData } from "./UseNullableRenderData"; + +const renderDataContext = React.createContext( + undefined +); + +// A narrower type for streamlit RederData args +type StreamlitDefaultRenderArgs = { + default: null | unknown; + key: null | unknown; +}; + +/** + * Returns `RenderData` received from Streamlit. + * Accepts a generic args type definition (NOTE: A `Partial` would be applied + * on `args` since we can't guarantee they will actually be sent) + */ +export const useRenderData = >(): Omit< + RenderData, + "args" +> & { args: Partial & StreamlitDefaultRenderArgs } => { + const contextValue = useContext(renderDataContext); + if (contextValue == null) { + throw new Error( + "useRenderData() must be used inside " + ); + } + + return contextValue; +}; + +/** + * Wrapper for React-hooks-based Streamlit components. + * + * Bootstraps the communication interface between Streamlit and the component. + */ +export const StreamlitProvider = (props: { children: React.ReactNode }) => { + const renderData = useNullableRenderData(); + + useEffect(() => { + Streamlit.setFrameHeight(); + }); + + // Don't render until we've gotten our first data from Streamlit. + if (renderData == null) { + return null; + } + + return ( + + + {props.children} + + + ); +}; diff --git a/frontend/src/streamlit/UseNullableRenderData.ts b/frontend/src/streamlit/UseNullableRenderData.ts new file mode 100644 index 000000000..912702de3 --- /dev/null +++ b/frontend/src/streamlit/UseNullableRenderData.ts @@ -0,0 +1,28 @@ +import { useEffect, useState } from "react"; +import { RenderData, Streamlit } from "streamlit-component-lib/dist/streamlit"; + +export const useNullableRenderData = (): RenderData | undefined => { + const [renderData, setRenderData] = useState(); + + useEffect(() => { + const onRenderEvent = (event: Event): void => { + const renderEvent = event as CustomEvent; + setRenderData(renderEvent.detail); + }; + + // Set up event listeners, and signal to Streamlit that we're ready. + // We won't render the component until we receive the first RENDER_EVENT. + Streamlit.events.addEventListener(Streamlit.RENDER_EVENT, onRenderEvent); + Streamlit.setComponentReady(); + + const cleanup = () => { + Streamlit.events.removeEventListener( + Streamlit.RENDER_EVENT, + onRenderEvent + ); + }; + return cleanup; + }, []); + + return renderData; +}; diff --git a/frontend/src/streamlit/index.ts b/frontend/src/streamlit/index.ts new file mode 100644 index 000000000..3eb9ea7b2 --- /dev/null +++ b/frontend/src/streamlit/index.ts @@ -0,0 +1 @@ +export * from "./StreamlitProvider"; diff --git a/frontend/src/vite-env.d.ts b/frontend/src/vite-env.d.ts new file mode 100644 index 000000000..11f02fe2a --- /dev/null +++ b/frontend/src/vite-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/frontend/tailwind.config.cjs b/frontend/tailwind.config.cjs new file mode 100644 index 000000000..ecf62b50b --- /dev/null +++ b/frontend/tailwind.config.cjs @@ -0,0 +1,8 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + extend: {}, + }, + plugins: [], +}; diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 000000000..3d0a51a86 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,21 @@ +{ + "compilerOptions": { + "target": "ESNext", + "useDefineForClassFields": true, + "lib": ["DOM", "DOM.Iterable", "ESNext"], + "allowJs": false, + "skipLibCheck": true, + "esModuleInterop": false, + "allowSyntheticDefaultImports": true, + "strict": true, + "forceConsistentCasingInFileNames": true, + "module": "ESNext", + "moduleResolution": "Node", + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx" + }, + "include": ["src"], + "references": [{ "path": "./tsconfig.node.json" }] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json new file mode 100644 index 000000000..9d31e2aed --- /dev/null +++ b/frontend/tsconfig.node.json @@ -0,0 +1,9 @@ +{ + "compilerOptions": { + "composite": true, + "module": "ESNext", + "moduleResolution": "Node", + "allowSyntheticDefaultImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 000000000..b1b5f91e5 --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vitejs.dev/config/ +export default defineConfig({ + plugins: [react()] +}) diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 000000000..78686ca0a --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "encord-active", + "lockfileVersion": 2, + "requires": true, + "packages": {} +} diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 000000000..2991d6909 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3240 @@ +[[package]] +name = "altair" +version = "4.2.0" +description = "Altair: A declarative statistical visualization library for Python." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +entrypoints = "*" +jinja2 = "*" +jsonschema = ">=3.0" +numpy = "*" +pandas = ">=0.18" +toolz = "*" + +[package.extras] +dev = ["black", "docutils", "flake8", "ipython", "m2r", "mistune (<2.0.0)", "pytest", "recommonmark", "sphinx", "vega-datasets"] + +[[package]] +name = "ansicon" +version = "1.89.0" +description = "Python wrapper for loading Jason Hood's ANSICON" +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "astroid" +version = "2.12.12" +description = "An abstract syntax tree for Python with inference support." +category = "dev" +optional = false +python-versions = ">=3.7.2" + +[package.dependencies] +lazy-object-proxy = ">=1.4.0" +typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""} +wrapt = {version = ">=1.11,<2", markers = "python_version < \"3.11\""} + +[[package]] +name = "asttokens" +version = "2.0.8" +description = "Annotate AST trees with source code positions" +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[package.extras] +test = ["astroid (<=2.5.3)", "pytest"] + +[[package]] +name = "atomicwrites" +version = "1.4.1" +description = "Atomic file 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+encord-active = "encord_active.app.cli.main:cli" + +[tool.poetry.dependencies] +python = ">=3.9,<3.9.7 || >3.9.7,<3.11" +encord = "^0.1.43" +numpy = "^1.23.1" +opencv-python = "4.5.5.64" +streamlit = "1.13.0" +natsort = "^8.1.0" +pandas = "^1.4.3" +shapely = "^1.7.0" +watchdog = "^2.1.9" +torch = "^1.12.1" +torchvision = "^0.13.1" +faiss-cpu = "^1.7.2" +matplotlib = "^3.5.3" +scikit-learn = "^1.0.1" +scipy = "1.8.1" +av = "^9.2.0" +pytz = "^2022.2.1" +types-pytz = "^2022.2.1" +plotly = "^5.10.0" +screeninfo = "^0.8.1" +termcolor = "^2.0.1" +loguru = "^0.6.0" +python-dotenv = "^0.21.0" +streamlit-nested-layout = "^0.1.1" +typer = "^0.6.1" +inquirer = "^2.10.0" +rich = "^12.6.0" +PyYAML = "^6.0" +toml = "^0.10.2" +pydantic = "^1.10.2" +pycocotools = {version = "^2.0.6", optional = true} +psutil = "^5.9.4" + + +[tool.poetry.extras] +coco = ["pycocotools"] + + +[tool.poetry.dev-dependencies] +pytest = "^5.2" +black = "^22.6.0" +isort = "^5.10.1" +pre-commit = "^2.16.0" +mypy = "^0.981" 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E0104,E0105,E0106,E0107,E0108,E0110,E0111,E0112,E0113,E0114,E0115,E0116,E0117,E0118,E0119,E0202,E0203,E0211,E0213,E0236,E0237,E0238,E0239,E0240,E0241,E0242,E0243,E0244,E0301,E0302,E0303,E0304,E0305,E0306,E0307,E0308,E0309,E0310,E0311,E0312,E0313,E0402,E0601,E0602,E0602,E0603,E0604,E0605,E0633,E0643,E0701,E0702,E0703,E0704,E0710,E0711,E0712,E1003,E1101,E1101,E1111,E1120,E1121,E1123,E1124,E1125,E1126,E1127,E1128,E1129,E1130,E1131,E1132,E1133,E1134,E1135,E1136,E1137,E1138,E1139,E1140,E1141,E1142,E1200,E1201,E1205,E1205,E1206,E1300,E1301,E1302,E1303,E1304,E1305,E1306,E1307,E1310,E1507,E1700,E1701,E2501,E2502,E2510,E2511,E2512,E2513,E2514,E2515,E4702,E4703,W0611,W0621,W1309,W1404,W1514" diff --git a/src/encord_active/__init__.py b/src/encord_active/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/__init__.py b/src/encord_active/app/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/actions_page/__init__.py b/src/encord_active/app/actions_page/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/actions_page/coco_parser/__init__.py b/src/encord_active/app/actions_page/coco_parser/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/actions_page/export_balance.py b/src/encord_active/app/actions_page/export_balance.py new file mode 100644 index 000000000..ca3b4a6e5 --- /dev/null +++ b/src/encord_active/app/actions_page/export_balance.py @@ -0,0 +1,227 @@ +import io +import json +from datetime import datetime +from encodings import utf_8 +from typing import Dict, List, Tuple +from zipfile import ZipFile + +import altair as alt +import numpy as np +import pandas as pd +import streamlit as st +from tqdm import tqdm + +import encord_active.app.common.state as state +from encord_active.app.common import metric as iutils +from encord_active.app.common.components import multiselect_with_all_option +from encord_active.app.common.metric import MetricData +from encord_active.app.common.utils import set_page_config, setup_page +from encord_active.app.data_quality.common import MetricType, load_available_metrics +from encord_active.lib.coco.encoder import generate_coco_file + + +def add_partition(): + st.session_state[state.NUMBER_OF_PARTITIONS] += 1 + + +def remove_partition(): + st.session_state[state.NUMBER_OF_PARTITIONS] -= 1 + + +def metrics_panel() -> Tuple[List[MetricData], int]: + """ + Panel for selecting the metrics to balance over. + + Returns: + selected_metrics (List[MetricData]): The metrics to balance over. + seed (int): The seed for the random sampling. + """ + # TODO - add label metrics + metrics = load_available_metrics(MetricType.DATA_QUALITY.value) # type: ignore + metric_names = [metric.name for metric in metrics] + + col1, col2 = st.columns([6, 1]) + with col1: + selected_metric_names = multiselect_with_all_option( + label="Metrics to balance", + options=metric_names, + key="balance_metrics", + default=["All"], + ) + seed = col2.number_input("Seed", value=42, step=1, key="seed") + + if "All" in selected_metric_names: + selected_metric_names = metric_names + selected_metrics = [metric for metric in metrics if metric.name in selected_metric_names] + return selected_metrics, int(seed) + + +def partitions_panel() -> Dict[str, int]: + """ + Panel for setting the partition sizes. + + Returns: + A dictionary with the partition names as keys and the partition sizes as values. + """ + partition_sizes = {} + for i in range(st.session_state[state.NUMBER_OF_PARTITIONS]): + partition_columns = st.columns((4, 12, 1)) + partition_name = partition_columns[0].text_input( + f"Name of partition {i + 1}", key=f"name_partition_{i + 1}", value=f"Partition {i + 1}" + ) + partition_sizes[partition_name] = partition_columns[1].slider( + f"Data percentage in partition {i + 1}", + key=f"size_partition_{i + 1}", + min_value=1, + max_value=100, + value=100 // st.session_state[state.NUMBER_OF_PARTITIONS], + step=1, + ) + if i > 0: + partition_columns[2].button("❌", key=f"delete_partition_{i + 1}", on_click=remove_partition) + st.button("➕ Add partition", on_click=add_partition) + + if sum(partition_sizes.values()) != 100: + st.warning( + f"The sum of the partition sizes is not 100%. " + f"{100-sum(partition_sizes.values())}% of samples will not be assigned to a partition." + ) + + partition_sizes["Unassigned"] = 100 - sum(partition_sizes.values()) + + return partition_sizes + + +def balance_dataframe(selected_metrics: List[MetricData], partition_sizes: Dict[str, int], seed: int) -> pd.DataFrame: + """ + Balances the dataset over the selected metrics and partition sizes. + Currently, it is done by random sampling. + + Args: + selected_metrics (List[MetricData]): The metrics to balance over. + partition_sizes (Dict[str,int]): The dictionary of partition names : partition sizes. + seed (int): The seed for the random sampling. + + Returns: + pd.Dataframe: A dataframe with the following columns: sample identifiers, metric values and allocated partition. + """ + # Collect metric dataframes + merged_df_list = [] + for i, m in enumerate(selected_metrics): + df = iutils.load_metric(m, normalize=False).copy() + merged_df_list.append(df[["identifier", "score"]].rename(columns={"score": m.name})) + + # Merge all dataframes by identifier + merged_df = merged_df_list.pop() + for df_tmp in merged_df_list: + merged_df = merged_df.merge(df_tmp, on="identifier", how="outer") + + # Randomly sample from each partition and add column to merged_df + n_samples = len(merged_df) + selection_df = merged_df.copy() + merged_df["partition"] = "" + for partition_name, partition_size in [(k, v) for k, v in partition_sizes.items()][:-1]: + n_partition = int(np.floor(n_samples * partition_size / 100)) + partition_df = selection_df.sample(n=n_partition, replace=False, random_state=seed) + # Remove samples from selection_df + selection_df = selection_df[~selection_df["identifier"].isin(partition_df["identifier"])] + # Add partition column to merged_df + merged_df.loc[partition_df.index, "partition"] = partition_name + + # Assign the remaining samples to the last partition + merged_df.loc[merged_df["partition"] == "", "partition"] = list(partition_sizes.keys())[-1] + return merged_df + + +def get_partitions_zip(partition_dict: Dict[str, pd.DataFrame]) -> bytes: + """ + Creates a zip file with a COCO json object for each partition. + + Args: + partition_dict (Dict[str, pd.DataFrame]): A dictionary of partition names : partition dataframes. + + Returns: + bytes: The zip file as a byte array. + """ + with st.spinner("Generating COCO files"): + zip_io = io.BytesIO() + with ZipFile(zip_io, mode="w") as zf: + partition_dict.pop("Unassigned", None) + for partition_name, partition in tqdm(partition_dict.items(), desc="Generating COCO files"): + coco_json = generate_coco_file(partition, st.session_state.project_dir, st.session_state.ontology_file) + with zf.open(partition_name.replace(" ", "_").lower() + ".json", "w") as zip_file: + writer = utf_8.StreamWriter(zip_file) + json.dump(coco_json, writer) # type: ignore + zip_io.seek(0) + partitions_zip_file = zip_io.read() + return partitions_zip_file + + +def export_balance(): + setup_page() + st.header("Balance & Export") + st.write( + "Here you can create balanced partitions of your dataset over a set of metrics and export them as a CSV file." + ) + + if not st.session_state.get(state.NUMBER_OF_PARTITIONS): + st.session_state[state.NUMBER_OF_PARTITIONS] = 1 + + selected_metrics, seed = metrics_panel() + partition_sizes = partitions_panel() + + with st.spinner("Balancing dataset..."): + balanced_df = balance_dataframe(selected_metrics=selected_metrics, partition_sizes=partition_sizes, seed=seed) + + # Resulting partition sizes + with st.expander("Resulting partition sizes"): + st.warning("Due to rounding errors, the resulting partition sizes might not be exactly as specified. ") + cols = st.columns(len(partition_sizes)) + partition_dict: Dict[str, pd.DataFrame] = {} + for col, (partition_name, partition_size) in zip(cols, partition_sizes.items()): + partition = balanced_df[balanced_df["partition"] == partition_name] + partition_dict[partition_name] = partition + n_partition_df = partition.shape[0] + col.write( + f"{partition_name} ({round(100*n_partition_df/balanced_df.shape[0], 4)}%): {n_partition_df} samples" + ) + + action_columns = st.columns((3, 3, 8)) + is_pressed = action_columns[0].button( + "🌀 Generate COCO file", + help="Generate COCO file with filtered data", + ) + + partitions_zip_file = get_partitions_zip(partition_dict) if is_pressed else "" + + action_columns[1].download_button( + "⬇ Download filtered data", + partitions_zip_file, + file_name=f"encord-active-coco-partitions-{datetime.now().strftime('%Y_%m_%d %H_%M_%S')}.zip", + disabled=not is_pressed, + help="Ensure you have generated an updated COCO file before downloading", + ) + + st.subheader("View data distributions") + # Plot distribution of partitions for each metric + for m in selected_metrics: + with st.expander(f"{m.name} - Partition distribution"): + # Get altair layered histogram of partitions + chart = ( + alt.Chart(balanced_df) + .mark_bar( + binSpacing=0, + ) + .encode( + x=alt.X(f"{m.name}:Q", bin=alt.Bin(maxbins=50)), + y="count()", + color="partition:N", + tooltip=["partition", "count()"], + ) + ) + st.altair_chart(chart, use_container_width=True) + + +if __name__ == "__main__": + set_page_config() + export_balance() diff --git a/src/encord_active/app/actions_page/export_filter.py b/src/encord_active/app/actions_page/export_filter.py new file mode 100644 index 000000000..9d10fc444 --- /dev/null +++ b/src/encord_active/app/actions_page/export_filter.py @@ -0,0 +1,195 @@ +import json +from datetime import datetime +from typing import Tuple, cast + +import pandas as pd +import streamlit as st +from pandas.api.types import ( + is_categorical_dtype, + is_datetime64_any_dtype, + is_numeric_dtype, +) + +import encord_active.app.common.state as state +from encord_active.app.common.action_utils import create_new_project_on_encord_platform +from encord_active.app.common.utils import load_merged_df, set_page_config, setup_page +from encord_active.app.db.tags import Tags +from encord_active.lib.coco.encoder import generate_coco_file + + +def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """ + Adds a UI on top of a dataframe to let apps filter columns + + Args: + df (pd.DataFrame): Original dataframe + + Returns: + pd.DataFrame: Filtered dataframe + """ + with st.container(): + columns_to_filter = df.columns.to_list() + if "url" in columns_to_filter: + columns_to_filter.remove("url") + # Move tags to first option. + tags_column = columns_to_filter.pop(columns_to_filter.index("tags")) + columns_to_filter = [tags_column] + columns_to_filter + + to_filter_columns = st.multiselect("Filter dataframe on", columns_to_filter) + if to_filter_columns: + df = df.copy() + + for column in to_filter_columns: + left, right = st.columns((1, 20)) + left.write("↳") + key = f"filter-select-{column.replace(' ', '_').lower()}" + + if column == "tags": + tag_filters = right.multiselect("Choose tags to filter", options=Tags().all(), key=key) + filtered_rows = [True if set(tag_filters) <= set(x) else False for x in df["tags"]] + df = df.loc[filtered_rows] + + # Treat columns with < 10 unique values as categorical + elif is_categorical_dtype(df[column]) or df[column].nunique() < 10: + if df[column].isnull().sum() != df.shape[0]: + user_cat_input = right.multiselect( + f"Values for {column}", + df[column].unique(), + default=list(df[column].unique()), + key=key, + ) + df = df[df[column].isin(user_cat_input)] + else: + right.markdown(f"For column *{column}*, all values are NaN") + elif is_numeric_dtype(df[column]): + _min = float(df[column].min()) + _max = float(df[column].max()) + step = (_max - _min) / 100 + user_num_input = right.slider( + f"Values for {column}", + min_value=_min, + max_value=_max, + value=(_min, _max), + step=step, + key=key, + ) + df = df[df[column].between(*user_num_input)] + elif is_datetime64_any_dtype(df[column]): + first = df[column].min() + last = df[column].max() + res = right.date_input( + f"Values for {column}", min_value=first, max_value=last, value=(first, last), key=key + ) + if isinstance(res, tuple) and len(res) == 2: + res = cast(Tuple[pd.Timestamp, pd.Timestamp], map(pd.to_datetime, res)) # type: ignore + start_date, end_date = res + df = df.loc[df[column].between(start_date, end_date)] + else: + user_text_input = right.text_input( + f"Substring or regex in {column}", + key=key, + ) + if user_text_input: + df = df[df[column].astype(str).str.contains(user_text_input)] + + return df + + +def export_filter(): + setup_page() + message_placeholder = st.empty() + + st.header("Filter & Export") + + load_merged_df() + filtered_df = filter_dataframe(st.session_state[state.MERGED_DATAFRAME].copy()) + filtered_df.reset_index(inplace=True) + st.markdown(f"**Total row:** {filtered_df.shape[0]}") + st.dataframe(filtered_df, use_container_width=True) + + action_columns = st.columns((3, 3, 2, 2, 2, 2, 2)) + is_pressed = action_columns[0].button( + "🌀 Generate COCO file", + help="Generate COCO file with filtered data", + ) + + with st.spinner(text="Generating COCO file"): + coco_json = ( + generate_coco_file(filtered_df, st.session_state.project_dir, st.session_state.ontology_file) + if is_pressed + else "" + ) + + action_columns[1].download_button( + "⬇ Download filtered data", + json.dumps(coco_json), + file_name=f"encord-active-coco-filtered-{datetime.now().strftime('%Y_%m_%d %H_%M_%S')}.json", + disabled=not is_pressed, + help="Ensure you have generated an updated COCO file before downloading", + ) + + def _clone_button_pressed(): + st.session_state[state.ACTION_PAGE_CLONE_BUTTON] = True + + action_columns[3].button( + "🏗 Clone", on_click=_clone_button_pressed, help="Clone the filtered data into a new Encord dataset and project" + ) + delete_btn = action_columns[4].button("❌ Review", help="Assign the filtered data for review on the Encord platform") + edit_btn = action_columns[5].button( + "🖋 Re-label", help="Assign the filtered data for relabelling on the Encord platform" + ) + augment_btn = action_columns[6].button("➕ Augment", help="Augment your dataset based on the filered data") + + if any([delete_btn, edit_btn, augment_btn]): + st.session_state[state.ACTION_PAGE_CLONE_BUTTON] = False + message_placeholder.markdown( + """ +
+ For more information and help on using this feature reach out to us on the + Encord Active Slack +community + or shoot us an + email🙏 +
+""", + unsafe_allow_html=True, + ) + + if ( + state.ACTION_PAGE_PREVIOUS_FILTERED_NUM not in st.session_state + or st.session_state[state.ACTION_PAGE_PREVIOUS_FILTERED_NUM] != filtered_df.shape[0] + ): + st.session_state[state.ACTION_PAGE_PREVIOUS_FILTERED_NUM] = filtered_df.shape[0] + st.session_state[state.ACTION_PAGE_CLONE_BUTTON] = False + + if st.session_state[state.ACTION_PAGE_CLONE_BUTTON]: + with st.form("new_project_form"): + st.subheader("Create a new project with the selected items") + l_column, r_column = st.columns(2) + + dataset_title = l_column.text_input( + "Dataset title", value=f"Subset: {st.session_state.project_dir.name} ({filtered_df.shape[0]})" + ) + dataset_description = l_column.text_area("Dataset description") + + project_title = r_column.text_input( + "Project title", value=f"Sub-project: {st.session_state.project_dir.name} ({filtered_df.shape[0]})" + ) + project_description = r_column.text_area("Project description") + + create_new_project = st.form_submit_button("➕ Create") + if create_new_project: + if dataset_title == "": + st.error("Dataset title cannot be empty!") + return + if project_title == "": + st.error("Project title cannot be empty!") + return + create_new_project_on_encord_platform( + dataset_title, dataset_description, project_title, project_description, filtered_df + ) + + +if __name__ == "__main__": + set_page_config() + export_filter() diff --git a/src/encord_active/app/app_config.py b/src/encord_active/app/app_config.py new file mode 100644 index 000000000..59cbf9ea2 --- /dev/null +++ b/src/encord_active/app/app_config.py @@ -0,0 +1,101 @@ +from enum import Enum +from pathlib import Path + +import rich +import toml +import typer +from rich.panel import Panel + + +class ConfigProperties(str, Enum): + PROJECTS_DIR = "projects_dir" + SSH_KEY_PATH = "ssh_key_path" + + +CONFIG_PROPERTIES = [p.value for p in ConfigProperties] + + +class AppConfig: + def __init__(self, app_name: str): + self.app_name = app_name + + app_dir = Path(typer.get_app_dir(self.app_name)) + if not app_dir.is_dir(): + app_dir.mkdir(parents=True) + + self.config_file = app_dir / "config.toml" + self.load() + + def save(self): + with open(self.config_file, "w", encoding="utf-8") as f: + toml.dump(self.contents, f) + + def load(self): + if not self.config_file.is_file(): + self.config_file.touch() + + self.contents = toml.load(self.config_file) + + def get_or_query_ssh_key(self) -> Path: + if ConfigProperties.SSH_KEY_PATH.value not in self.contents: + panel = Panel( + """ +Encord Active needs to know the path to the [blue]private ssh key[/blue] which is associated with Encord. +Don't know this? Please see our documentation on the topic to get more help. + +[blue]https://docs.encord.com/admins/settings/public-keys/#set-up-public-key-authentication[/blue] + """, + title="SSH Key Path", + expand=False, + ) + + rich.print(panel) + + ssh_key_path_str = typer.prompt("Where is your private ssh key stored?") + ssh_key_path: Path = Path(ssh_key_path_str).expanduser().absolute() + + if not ssh_key_path.exists(): + rich.print(f"[red]The provided path `{ssh_key_path}` does not seem to be correct.") + typer.Abort() + + self.contents[ConfigProperties.SSH_KEY_PATH.value] = ssh_key_path.as_posix() + self.save() + else: + ssh_key_path = Path(self.contents[ConfigProperties.SSH_KEY_PATH.value]).expanduser() + + return ssh_key_path + + def get_or_query_project_path(self): + if ConfigProperties.PROJECTS_DIR.value not in self.contents: + panel = Panel( + """ +Encord Active needs to know a directory where your data can be stored locally on your machine. +The directory that you provide will be populated with one sub-directory for each project you +import: [blue]https://encord-active-docs.web.app/cli/import-encord-project[/blue] +or +download from the sandbox repository: [blue]https://encord-active-docs.web.app/cli/download-sandbox-data[/blue] + """, + title="Project Directory", + expand=False, + ) + rich.print(panel) + + project_path_str = typer.prompt("Where should your project directory be located?") + project_path: Path = Path(project_path_str).expanduser().absolute().resolve() + + if project_path.is_file(): + rich.print(f"{project_path} already exists as a file and not a directory") + typer.Abort() + + typer.confirm(f"Are you sure you want to use {project_path}?", abort=True) + + if not project_path.exists(): + rich.print("Creating directory [blue]`{project_path}`") + project_path.mkdir(parents=True) + + self.contents[ConfigProperties.PROJECTS_DIR.value] = project_path.as_posix() + self.save() + else: + project_path = Path(self.contents[ConfigProperties.PROJECTS_DIR.value]).expanduser() + + return project_path diff --git a/src/encord_active/app/app_print_utils.py b/src/encord_active/app/app_print_utils.py new file mode 100644 index 000000000..4057cd0ff --- /dev/null +++ b/src/encord_active/app/app_print_utils.py @@ -0,0 +1,11 @@ +from json import dumps as json_dumps +from typing import Optional + +from encord import EncordUserClient + + +def get_projects_json(config, query: Optional[str] = None) -> str: + ssh_key_path = config.get_or_query_ssh_key() + client = EncordUserClient.create_with_ssh_private_key(ssh_key_path.read_text()) + projects = map(lambda x: x["project"], client.get_projects(title_like=query)) + return json_dumps({p.project_hash: p.title for p in projects}, indent=2) diff --git a/src/encord_active/app/assets/encord_2_02.png b/src/encord_active/app/assets/encord_2_02.png new file mode 100644 index 000000000..4313f5c67 Binary files /dev/null and b/src/encord_active/app/assets/encord_2_02.png differ diff --git a/src/encord_active/app/assets/favicon-32x32.png b/src/encord_active/app/assets/favicon-32x32.png new file mode 100644 index 000000000..e719c22ce Binary files /dev/null and b/src/encord_active/app/assets/favicon-32x32.png differ diff --git a/src/encord_active/app/cli/__init__.py b/src/encord_active/app/cli/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/cli/config.py b/src/encord_active/app/cli/config.py new file mode 100644 index 000000000..2a001c300 --- /dev/null +++ b/src/encord_active/app/cli/config.py @@ -0,0 +1,67 @@ +import rich +import toml +import typer + +from encord_active.app.app_config import CONFIG_PROPERTIES, AppConfig + +APP_NAME = "encord-active" + +app_config = AppConfig(APP_NAME) +config_cli = typer.Typer(rich_markup_mode="markdown") + + +@config_cli.command() +def list(): + """ + List Encord Active configuration properties. + """ + rich.print(toml.dumps(app_config.contents) or "[bold red]Nothing configured.") + + +def _check_property(property: str): + if property not in CONFIG_PROPERTIES: + rich.print(f"[bold red]`{property}` is not a valid property.") + rich.print("Valid properties are:") + rich.print(CONFIG_PROPERTIES) + exit() + + +@config_cli.command() +def get( + property: str = typer.Argument(..., help="Name of the property"), +): + """ + Print the value of an Encord Active configuration property. + """ + _check_property(property) + value = app_config.contents.get(property) + rich.print(f"{property} = {value}" or f"[bold red]Property `{property}` not configured.") + + +@config_cli.command() +def set( + property: str = typer.Argument(..., help="Name of the property"), + value: str = typer.Argument(..., help="Value to set"), +): + """ + Sets an Encord Active configuration property. + """ + _check_property(property) + app_config.contents[property] = value + app_config.save() + + rich.print(f"[bold green]Property `{property}` has been set.") + + +@config_cli.command() +def unset( + property: str = typer.Argument(..., help="Name of the property"), +): + """ + Unsets the value of an Encord Active configuration property. + """ + _check_property(property) + del app_config.contents[property] + app_config.save() + + rich.print(f"[bold green]Property `{property}` was unset.") diff --git a/src/encord_active/app/cli/imports.py b/src/encord_active/app/cli/imports.py new file mode 100644 index 000000000..d4ba16d1a --- /dev/null +++ b/src/encord_active/app/cli/imports.py @@ -0,0 +1,138 @@ +import json +import pickle +from pathlib import Path +from typing import Optional + +import typer + +from encord_active.app.cli.config import app_config + +import_cli = typer.Typer(rich_markup_mode="markdown") + + +@import_cli.command(name="predictions") +def import_predictions( + predictions_path: Path = typer.Argument( + ..., + help="Path to a predictions file.", + dir_okay=False, + ), + coco: bool = typer.Option(False, help="Import a coco result format file"), +): + """ + [bold]Imports[/bold] a predictions file. The predictions should be using the `Prediction` model and be stored in a pkl file. + If `--coco` option is specified the file should be a json following the coco results format. :brain: + """ + + from encord_active.app.common.cli_helpers import choose_local_project + + project_dir = choose_local_project(app_config) + if not project_dir: + raise typer.Abort() + + from encord_active.app.common.cli_helpers import get_local_project + + project = get_local_project(project_dir) + + if coco: + import numpy as np + + from encord_active.app.db.predictions import BoundingBox, Format, Prediction + from encord_active.lib.coco.importer import IMAGE_DATA_UNIT_FILENAME + from encord_active.lib.coco.parsers import parse_results + + image_data_unit = json.loads((Path(project_dir) / IMAGE_DATA_UNIT_FILENAME).read_text(encoding="utf-8")) + ontology = json.loads((Path(project_dir) / "ontology.json").read_text(encoding="utf-8")) + category_to_hash = {obj["id"]: obj["featureNodeHash"] for obj in ontology["objects"]} + predictions = [] + results = json.loads(Path(predictions_path).read_text(encoding="utf-8")) + + coco_results = parse_results(results) + for res in coco_results: + image = image_data_unit[str(res.image_id)] + img_h, img_w = image["height"], image["width"] + + if res.segmentation is not None: + format = Format.POLYGON + data = res.segmentation / np.array([[img_h, img_w]]) + elif res.bbox: + format = Format.BOUNDING_BOX + x, y, w, h = res.bbox + data = BoundingBox(x=x / img_w, y=y / img_h, w=w / img_w, h=h / img_h) + else: + raise Exception("Unsupported result format") + + predictions.append( + Prediction( + data_hash=image["data_hash"], + class_id=category_to_hash[str(res.category_id)], + confidence=res.score, + format=format, + data=data, + ) + ) + else: + with open(predictions_path, "rb") as f: + predictions = pickle.load(f) + + from encord_active.app.db.predictions import ( + import_predictions as app_import_predictions, + ) + + app_import_predictions(project, project_dir, predictions) + + +@import_cli.command(name="project") +def import_project( + images_dir: Optional[Path] = typer.Argument( + None, + help="Path to the directory containing the dataset images. (only required with `--cooc`)", + file_okay=False, + exists=True, + ), + annotations_file: Optional[Path] = typer.Argument( + None, + help="Path to the file containing the dataset annotations. (only required with `--cooc`)", + dir_okay=False, + exists=True, + ), + encord_project_hash: Optional[str] = typer.Option( + None, "--project_hash", help="Encord project hash of the project you wish to import." + ), + coco: bool = typer.Option(False, help="Import a project from the coco format"), +): + """ + [bold]Imports[/bold] a new project from Encord or a local coco project 📦 + """ + if coco: + from encord_active.lib.coco.importer import CocoImporter + from encord_active.lib.metrics.run_all import run_metrics + + if not annotations_file: + raise typer.BadParameter("`annotations_file` argument is missing") + elif not images_dir: + raise typer.BadParameter("`images_dir` argument is missing") + + ssh_key_path = app_config.get_or_query_ssh_key() + projects_root = app_config.get_or_query_project_path() + + coco_importer = CocoImporter( + images_dir_path=images_dir, + annotations_file_path=annotations_file, + ssh_key_path=ssh_key_path, + destination_dir=projects_root, + ) + + dataset = coco_importer.create_dataset() + ontology = coco_importer.create_ontology() + coco_importer.create_project( + dataset=dataset, + ontology=ontology, + ) + run_metrics(data_dir=coco_importer.project_dir) + else: + from encord_active.lib.metrics.import_encord_project import ( + main as import_script, + ) + + import_script(app_config, encord_project_hash=encord_project_hash) diff --git a/src/encord_active/app/cli/main.py b/src/encord_active/app/cli/main.py new file mode 100644 index 000000000..ec5d3d2bb --- /dev/null +++ b/src/encord_active/app/cli/main.py @@ -0,0 +1,96 @@ +import sys +from pathlib import Path +from typing import Optional + +import inquirer as i +import rich +import typer + +import encord_active.app.conf # pylint: disable=unused-import +from encord_active.app.cli.config import APP_NAME, app_config, config_cli +from encord_active.app.cli.imports import import_cli +from encord_active.app.cli.print import print_cli + +cli = typer.Typer( + rich_markup_mode="rich", + help=""" +All commands in this CLI have a --help option, which will guide you on the way. +If you don't find the information you need here, we recommend that you visit +our main documentation: [blue]https://encord-active-docs.web.app[/blue] +""", + epilog=""" +Made by Encord. Contact Encord here: [blue]https://encord.com/contact_us/[/blue] to learn more +about our active learning platform for computer vision. +""", +) +cli.add_typer(config_cli, name="config", help="Configure global settings 🔧") +cli.add_typer(import_cli, name="import", help="Import Projects or Predictions ⬇️") +cli.add_typer(print_cli, name="print") + + +@cli.command() +def download(): + """ + Try out Encord Active fast. [bold]Download[/bold] an existing dataset to get started. 📁 + + * Available prebuilt projects will be listed and the user can select one from the menu. + """ + project_parent_dir = app_config.get_or_query_project_path() + + from encord_active.lib.metrics.fetch_prebuilt_metrics import ( + PREBUILT_PROJECTS, + fetch_prebuilt_project_size, + ) + + rich.print("Loading prebuilt projects ...") + project_names_with_storage = [] + for project_name in PREBUILT_PROJECTS.keys(): + project_size = fetch_prebuilt_project_size(project_name) + modified_project_name = project_name + (f" ({project_size} mb)" if project_size is not None else "") + project_names_with_storage.append(modified_project_name) + + questions = [i.List("project_name", message="Choose a project", choices=project_names_with_storage)] + answers = i.prompt(questions) + if not answers or "project_name" not in answers: + rich.print("No project was selected.") + raise typer.Abort() + project_name = answers["project_name"].split(" ", maxsplit=1)[0] + + # create project folder + project_dir = project_parent_dir / project_name + project_dir.mkdir(exist_ok=True) + + from encord_active.lib.metrics.fetch_prebuilt_metrics import fetch_prebuilt_project + + fetch_prebuilt_project(project_name, project_dir) + + +@cli.command() +def visualise( + project_path: Optional[Path] = typer.Argument( + None, + help="Path of the project you would like to visualise", + file_okay=False, + ), +): + """ + Launches the application with the provided project ✨ + """ + from encord_active.app.common.cli_helpers import choose_local_project + + project_path = project_path or choose_local_project(app_config) + + if not project_path: + raise typer.Abort() + + streamlit_page = (Path(__file__).parents[2] / "streamlit_entrypoint.py").expanduser().absolute() + data_dir = project_path.expanduser().absolute().as_posix() + sys.argv = ["streamlit", "run", streamlit_page.as_posix(), data_dir] + + from streamlit.web import cli as stcli + + sys.exit(stcli.main()) # pylint: disable=no-value-for-parameter + + +if __name__ == "__main__": + cli(prog_name=APP_NAME) diff --git a/src/encord_active/app/cli/print.py b/src/encord_active/app/cli/print.py new file mode 100644 index 000000000..65a764ae5 --- /dev/null +++ b/src/encord_active/app/cli/print.py @@ -0,0 +1,146 @@ +import json +import sys +from pathlib import Path +from typing import Any, Dict, Optional + +import rich +import typer +from rich.markup import escape + +from encord_active.app.cli.config import app_config + +print_cli = typer.Typer(rich_markup_mode="markdown") + +state: Dict[str, Any] = {} + + +@print_cli.command(name="encord-projects") +def print_encord_projects( + query: Optional[str] = typer.Option(None, help="Optional fuzzy title filter; SQL syntax."), +): + """ + Print the mapping between \`project_hash\`es of your Encord projects and their titles. + + You can query projects by title with the SQL fuzzy syntax. To look for a title including "validation" you would do: + + > encord-active print encord-projects --query "%validation%" + + """ + from encord_active.app.app_print_utils import get_projects_json + + json_projects = get_projects_json(app_config, query) + if state.get("json_output"): + Path("./encord-projects.json").write_text(json_projects, encoding="utf-8") + else: + rich.print(escape(json_projects)) + + +@print_cli.command(name="ontology") +def print_ontology(): + """ + [bold]Prints[/bold] an ontology mapping between the class name to the `featureNodeHash` JSON format. + """ + from encord_active.app.common.cli_helpers import choose_local_project + + project_dir = choose_local_project(app_config) + if not project_dir: + raise typer.Abort() + + from encord_active.app.common.cli_helpers import get_local_project + + project = get_local_project(project_dir) + objects = project.ontology["objects"] + + ontology = {o["name"].lower(): o["featureNodeHash"] for o in objects} + json_ontology = json.dumps(ontology, indent=2) + + if state.get("json_output"): + Path("./ontology.json").write_text(json_ontology, encoding="utf-8") + rich.print("Stored mapping in [blue]`./ontology.json`") + else: + rich.print(escape(json_ontology)) + + +@print_cli.command(name="data-mapping") +def print_data_mapping(limit: int = typer.Option(None, help="Limit the result to the first `limit` data hashes")): + """ + [bold]Prints[/bold] a mapping between `data_hashes` and their corresponding `filename` + """ + from encord_active.app.common.cli_helpers import choose_local_project + + project_dir = choose_local_project(app_config) + if not project_dir: + raise typer.Abort() + + mapping: Dict[str, str] = {} + + for label in (project_dir / "data").iterdir(): + if not label.is_dir() and not (label / "label_row.json").is_file(): + continue + + label_row = json.loads((label / "label_row.json").read_text()) + mapping = { + **mapping, + **{data_hash: value["data_title"] for data_hash, value in label_row["data_units"].items()}, + } + if limit and len(mapping) > limit: + break + + if limit and limit < len(mapping): + mapping = {k: v for i, (k, v) in enumerate(mapping.items()) if i < limit} + + json_mapping = json.dumps(mapping, indent=2) + + if state.get("json_output"): + Path("./data_mapping.json").write_text(json_mapping, encoding="utf-8") + rich.print("Stored mapping in [blue]`./data_mapping.json`") + else: + rich.print(escape(json_mapping)) + + +@print_cli.command(name="system-info") +def print_system_info(): + """ + [bold]Prints[/bold] the information of the system for the purpose of bug reporting. + """ + import platform + + import psutil + + def get_size(bytes, suffix="B"): + """ + Scale bytes to its proper format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + factor = 1024 + for unit in ["", "K", "M", "G", "T", "P"]: + if bytes < factor: + return f"{bytes:.2f}{unit}{suffix}" + bytes /= factor + + print("System Information:") + uname = platform.uname() + print(f"\tSystem: {uname.system}") + print(f"\tRelease: {uname.release}") + print(f"\tMachine: {uname.machine}") + print(f"\tProcessor: {uname.processor}") + print(f"\tPython: {sys.version}") + print("\nCPU Info:") + print("\tPhysical cores:", psutil.cpu_count(logical=False)) + print("\tTotal cores:", psutil.cpu_count(logical=True)) + print(f"\tTotal CPU Usage: {psutil.cpu_percent()}%") + print("\nMemory Information:") + svmem = psutil.virtual_memory() + print(f"\tTotal: {get_size(svmem.total)}") + print(f"\tAvailable: {get_size(svmem.available)}") + print(f"\tUsed: {get_size(svmem.used)}") + + +@print_cli.callback() +def main(json: bool = False): # pylint: disable=redefined-outer-name + """ + [bold]Print[/bold] useful information 🖨️ + """ + state["json_output"] = json diff --git a/src/encord_active/app/common/action_utils.py b/src/encord_active/app/common/action_utils.py new file mode 100644 index 000000000..a23333474 --- /dev/null +++ b/src/encord_active/app/common/action_utils.py @@ -0,0 +1,256 @@ +import json +from pathlib import Path + +import pandas as pd +import streamlit as st +from encord import Dataset, EncordUserClient, Project +from encord.constants.enums import DataType +from encord.exceptions import AuthorisationError +from encord.orm.dataset import Image, StorageLocation +from encord.utilities.label_utilities import construct_answer_dictionaries +from tqdm import tqdm + +from encord_active.lib.common.utils import fetch_project_meta + + +def _update_mapping( + user_client: EncordUserClient, new_dataset_hash: str, label_row_hash: str, data_unit_hash: str, out_mapping: dict +): + updated_dataset = user_client.get_dataset(new_dataset_hash) + for new_data_row in updated_dataset.data_rows: + if new_data_row["data_hash"] not in out_mapping: + out_mapping[new_data_row["data_hash"]] = { + "label_row_hash": label_row_hash, + "data_unit_hash": data_unit_hash, + } + return + + +def create_a_new_dataset( + user_client: EncordUserClient, dataset_title: str, dataset_description: str, filtered_dataset: pd.DataFrame +) -> tuple[str, dict[str, dict[str, str]]]: + new_du_to_original: dict[str, dict] = {} + user_client.create_dataset( + dataset_title=dataset_title, dataset_type=StorageLocation.CORD_STORAGE, dataset_description=dataset_description + ) + dataset_hash: str = user_client.get_datasets(title_eq=dataset_title)[0]["dataset"].dataset_hash + dataset: Dataset = user_client.get_dataset(dataset_hash) + + # The following operation is for image groups (to upload them efficiently) + label_hash_to_data_units: dict[str, list] = {} + for index, item in tqdm(filtered_dataset.iterrows(), total=filtered_dataset.shape[0]): + label_row_hash, data_unit_hash, *rest = item["identifier"].split("_") + label_hash_to_data_units.setdefault(label_row_hash, []).append(data_unit_hash) + + temp_progress_bar = st.empty() + temp_progress_bar.progress(0.0) + uploaded_label_rows: set = set() + for counter, (index, item) in enumerate(filtered_dataset.iterrows()): + label_row_hash, data_unit_hash, frame_id, *rest = item["identifier"].split("_") + json_txt = (st.session_state.project_dir / "data" / label_row_hash / "label_row.json").expanduser().read_text() + label_row = json.loads(json_txt) + + if label_row_hash not in uploaded_label_rows: + if label_row["data_type"] == DataType.IMAGE.value: + image_path = list( + Path(st.session_state.project_dir / "data" / label_row_hash / "images").glob(f"{data_unit_hash}.*") + )[0] + uploaded_image: Image = dataset.upload_image( + file_path=image_path, title=label_row["data_units"][data_unit_hash]["data_title"] + ) + + new_du_to_original[uploaded_image["data_hash"]] = { + "label_row_hash": label_row_hash, + "data_unit_hash": data_unit_hash, + } + + elif label_row["data_type"] == DataType.IMG_GROUP.value: + image_paths = [] + image_names = [] + if len(label_hash_to_data_units[label_row_hash]) > 0: + for data_unit in label_hash_to_data_units[label_row_hash]: + img_path: Path = list( + Path(st.session_state.project_dir / "data" / label_row_hash / "images").glob( + f"{data_unit}.*" + ) + )[0] + image_paths.append(img_path.as_posix()) + image_names.append(img_path.name) + + # Unfortunately the following function does not return metadata related to the uploaded items + dataset.create_image_group(file_paths=image_paths, title=label_row["data_title"]) + + # Since create_image_group does not return info related to the uploaded images, we should find its + # data_hash in a hacky way + _update_mapping(user_client, dataset_hash, label_row_hash, data_unit_hash, new_du_to_original) + + elif label_row["data_type"] == DataType.VIDEO.value: + video_path = list( + Path(st.session_state.project_dir / "data" / label_row_hash / "images").glob(f"{data_unit_hash}.*") + )[0].as_posix() + + # Unfortunately the following function does not return metadata related to the uploaded items + dataset.upload_video(file_path=video_path, title=label_row["data_units"][data_unit_hash]["data_title"]) + + # Since upload_video does not return info related to the uploaded video, we should find its data_hash + # in a hacky way + _update_mapping(user_client, dataset_hash, label_row_hash, data_unit_hash, new_du_to_original) + + else: + st.error(f'Undefined data type {label_row["data_type"]} for label_row={label_row["label_hash"]}') + + uploaded_label_rows.add(label_row_hash) + + temp_progress_bar.progress(counter / filtered_dataset.shape[0]) + + temp_progress_bar.empty() + return dataset_hash, new_du_to_original + + +def create_new_project_on_encord_platform( + dataset_title: str, + dataset_description: str, + project_title: str, + project_description: str, + filtered_dataset: pd.DataFrame, +): + try: + project_meta = fetch_project_meta(st.session_state.project_dir) + except (KeyError, FileNotFoundError) as _: + st.markdown( + f""" + ❌ No `project_meta.yaml` file in the project folder. + Please create `project_meta.yaml` file in **{st.session_state.project_dir}** folder with the following content + and try again: + ``` yaml + project_hash: + ssh_key_path: /path/to/your/encord/ssh_key + ``` + """ + ) + return + + meta_file = st.session_state.project_dir / "project_meta.yaml" + ssh_key_path = project_meta.get("ssh_key_path") + if not ssh_key_path: + st.error(f"`ssh_key_path` not specified in the project meta data file `{meta_file}`.") + return + + ssh_key_path = Path(ssh_key_path).expanduser() + if not ssh_key_path.is_file(): + st.error(f"No SSH file in location:{ssh_key_path}") + return + + user_client = EncordUserClient.create_with_ssh_private_key( + Path(ssh_key_path).expanduser().read_text(encoding="utf-8"), + ) + + original_project_hash = project_meta.get("project_hash") + if not original_project_hash: + st.error(f"`project_hash` not specified in the project meta data file `{meta_file}`.") + return + + original_project: Project = user_client.get_project(original_project_hash) + try: + if original_project.project_hash == original_project_hash: + pass + except AuthorisationError: + st.error( + f'The user associated to the ssh key `{ssh_key_path}` does not have access to the project with project hash `{original_project_hash}`. Run "encord-active config set ssh_key_path /path/to/your/key_file" to set it.' + ) + return + + datasets_with_same_title = user_client.get_datasets(title_eq=dataset_title) + if len(datasets_with_same_title) > 0: + st.error( + f"Dataset title '{dataset_title}' already exists in your list of datasets at Encord. Please use a different title." + ) + return None + + label = st.empty() + label.text("Step 1/2: Uploading data...") + new_dataset_hash, new_du_to_original = create_a_new_dataset( + user_client, dataset_title, dataset_description, filtered_dataset + ) + + new_project_hash: str = user_client.create_project( + project_title=project_title, + dataset_hashes=[new_dataset_hash], + project_description=project_description, + ontology_hash=original_project.get_project().ontology_hash, + ) + + new_project: Project = user_client.get_project(new_project_hash) + + # Copy labels from old project to new project + # Three things to copy: labels, object_answers, classification_answers + + all_new_label_rows = new_project.label_rows + label.text("Step 2/2: Uploading labels...") + temp_progress_bar = st.empty() + temp_progress_bar.progress(0.0) + for counter, new_label_row in enumerate(all_new_label_rows): + initiated_label_row: dict = new_project.create_label_row(new_label_row["data_hash"]) + + with open( + ( + st.session_state.project_dir + / "data" + / new_du_to_original[new_label_row["data_hash"]]["label_row_hash"] + / "label_row.json" + ).expanduser(), + "r", + encoding="utf-8", + ) as file: + original_label_row = json.load(file) + + if initiated_label_row["data_type"] in [DataType.IMAGE.value, DataType.VIDEO.value]: + + original_labels = original_label_row["data_units"][ + new_du_to_original[new_label_row["data_hash"]]["data_unit_hash"] + ]["labels"] + initiated_label_row["data_units"][new_label_row["data_hash"]]["labels"] = original_labels + initiated_label_row["object_answers"] = original_label_row["object_answers"] + initiated_label_row["classification_answers"] = original_label_row["classification_answers"] + + if original_labels != {}: + initiated_label_row = construct_answer_dictionaries(initiated_label_row) + new_project.save_label_row(initiated_label_row["label_hash"], initiated_label_row) + + elif initiated_label_row["data_type"] == DataType.IMG_GROUP.value: + object_hashes: set = set() + classification_hashes: set = set() + + # Currently img_groups are matched using data_title, it should be fixed after SDK update + for data_unit in initiated_label_row["data_units"].values(): + for original_data_unit in original_label_row["data_units"].values(): + if original_data_unit["data_hash"] == data_unit["data_title"].split(".")[0]: + data_unit["labels"] = original_data_unit["labels"] + for obj in data_unit["labels"].get("objects", []): + object_hashes.add(obj["objectHash"]) + for classification in data_unit["labels"].get("classifications", []): + classification_hashes.add(classification["classificationHash"]) + + initiated_label_row["object_answers"] = original_label_row["object_answers"] + initiated_label_row["classification_answers"] = original_label_row["classification_answers"] + + # Remove unused object/classification answers + for object_hash in object_hashes: + initiated_label_row["object_answers"].pop(object_hash) + + for classification_hash in classification_hashes: + initiated_label_row["classification_answers"].pop(classification_hash) + + initiated_label_row = construct_answer_dictionaries(initiated_label_row) + new_project.save_label_row(initiated_label_row["label_hash"], initiated_label_row) + + # remove unused object and classification answers + + temp_progress_bar.progress(counter / len(all_new_label_rows)) + + temp_progress_bar.empty() + label.info("🎉 New project is created!") + new_project_link = f"https://app.encord.com/projects/view/{new_project_hash}/summary" + new_dataset_link = f"https://app.encord.com/datasets/view/{new_dataset_hash}" + st.markdown(f"[Go to new project]({new_project_link})") + st.markdown(f"[Go to new dataset]({new_dataset_link})") diff --git a/src/encord_active/app/common/cli_helpers.py b/src/encord_active/app/common/cli_helpers.py new file mode 100644 index 000000000..74917c06e --- /dev/null +++ b/src/encord_active/app/common/cli_helpers.py @@ -0,0 +1,38 @@ +from pathlib import Path +from typing import Optional + +import inquirer as i +import rich +from encord import EncordUserClient, Project + +from encord_active.app.app_config import AppConfig, ConfigProperties +from encord_active.lib.common.utils import fetch_project_meta + + +def choose_local_project(config: AppConfig) -> Optional[Path]: + projects_dir = config.contents.get(ConfigProperties.PROJECTS_DIR.value) + if not projects_dir: + rich.print("[bold red] Projects dir is not defined[/bold] (Probably means you haven't imported a project).") + return None + else: + projects_dir = Path(projects_dir) + + project_names = [p.resolve() for p in projects_dir.glob("*") if p.is_dir() and (p / "project_meta.yaml").is_file()] + questions = [i.List("project_name", message="Choose a project", choices=project_names)] + answers = i.prompt(questions) + if not answers or "project_name" not in answers: + return None + + selected_project = answers["project_name"] + return projects_dir / selected_project + + +def get_local_project(project_dir: Path) -> Project: + project_meta = fetch_project_meta(project_dir) + + ssh_key_path = Path(project_meta["ssh_key_path"]) + with open(ssh_key_path.expanduser(), "r", encoding="utf-8") as f: + key = f.read() + + client = EncordUserClient.create_with_ssh_private_key(key) + return client.get_project(project_meta.get("project_hash")) diff --git a/src/encord_active/app/common/colors.py b/src/encord_active/app/common/colors.py new file mode 100644 index 000000000..c79e90ad5 --- /dev/null +++ b/src/encord_active/app/common/colors.py @@ -0,0 +1,34 @@ +from enum import Enum +from typing import Tuple + +import cv2 +import numpy as np + + +class Color(Enum): + PURPLE = "#5658dd" # Encord purple + GREEN = "#46D202" + RED = "#FD0A5A" + LITE_PURPLE = "#d449c1" + PINK = "#ff5794" + ORANGE = "#ff8a6a" + LITE_ORANGE = "#ffc357" + YELLOW = "#ffde00" + BLUE = "#0075ef" + + +def hex_to_rgb(value: str, lighten=0.0) -> Tuple[int, ...]: + """ + "#000011" -> (0, 0, 255) + :param value: the hex color. + :return: the rgb values. + """ + value = value.lstrip("#") + lv = len(value) + color = np.array([int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3)], dtype=np.uint8) + + color_hsv = cv2.cvtColor(color.reshape((1, 1, 3)), cv2.COLOR_RGB2HSV) + color_hsv[..., -1] = max(0, min(255, int(color_hsv[..., -1] * (1 + lighten)))) + + color_rgb = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB) + return tuple(color_rgb.squeeze().tolist()) diff --git a/src/encord_active/app/common/components/__init__.py b/src/encord_active/app/common/components/__init__.py new file mode 100644 index 000000000..0198a2439 --- /dev/null +++ b/src/encord_active/app/common/components/__init__.py @@ -0,0 +1,3 @@ +from .data_tags import build_data_tags +from .multi_select import multiselect_with_all_option +from .sticky.sticky import sticky_header diff --git a/src/encord_active/app/common/components/bulk_tagging_form.py b/src/encord_active/app/common/components/bulk_tagging_form.py new file mode 100644 index 000000000..e468926c0 --- /dev/null +++ b/src/encord_active/app/common/components/bulk_tagging_form.py @@ -0,0 +1,61 @@ +from enum import Enum +from typing import List, NamedTuple, Optional + +import streamlit as st +from pandas import DataFrame + +import encord_active.app.common.state as state +from encord_active.app.db.merged_metrics import MergedMetrics + + +class TagAction(str, Enum): + ADD = "Add" + REMOVE = "Remove" + + +class BulkLevel(str, Enum): + PAGE = "Page" + RANGE = "Range" + + +class TaggingFormResult(NamedTuple): + submitted: bool + tags: List[str] + level: BulkLevel + action: TagAction + + +def action_bulk_tags(subset: DataFrame, selected_tags: List[str], action: TagAction): + if not selected_tags: + return + + all_df: DataFrame = st.session_state[state.MERGED_DATAFRAME].copy() + + for identifier, tags in all_df.loc[subset.identifier, "tags"].items(): + if action == TagAction.ADD: + next = list(set(tags + selected_tags)) + elif action == TagAction.REMOVE: + next = list(set(tag for tag in tags if tag not in selected_tags)) + else: + raise Exception(f"Action {action} is not supported") + + all_df.at[identifier, "tags"] = next + + st.session_state[state.MERGED_DATAFRAME] = all_df + MergedMetrics().replace_all(all_df) + + +def bulk_tagging_form() -> Optional[TaggingFormResult]: + with st.expander("Bulk Tagging"): + with st.form("bulk_tagging"): + select, level_radio, action_radio, button = st.columns([6, 2, 2, 1]) + all_tags = st.session_state.get(state.ALL_TAGS) or [] + selected_tags = select.multiselect(label="Tags", options=all_tags) + level = level_radio.radio("Level", ["Page", "Range"], horizontal=True) + action = action_radio.radio("Action", [a.value for a in TagAction], horizontal=True) + submitted = button.form_submit_button("Submit") + + if not submitted: + return None + + return TaggingFormResult(submitted, selected_tags, BulkLevel(level), TagAction(action)) diff --git a/src/encord_active/app/common/components/data_tags.py b/src/encord_active/app/common/components/data_tags.py new file mode 100644 index 000000000..eed8b0014 --- /dev/null +++ b/src/encord_active/app/common/components/data_tags.py @@ -0,0 +1,98 @@ +from dataclasses import dataclass +from typing import List + +import pandas as pd +import streamlit as st + + +@dataclass +class Property: + key: str + icon: str + icon_color: str + name: str + is_code: bool = False + is_url: bool = False + + def get_key(self, metric_name): + return self.key + + def get_name(self, metric_name): + return self.name + + def get_icon_color(self, value): + return self.icon_color + + +class MetricProperty(Property): + def get_key(self, metric_name): + return metric_name + + def get_name(self, metric_name): + return metric_name + + +class OutlierProperty(Property): + COLORS = { + "Severe": "red", + "Moderate": "orange", + "Low": "green", + } + + def get_icon_color(self, value): + return self.COLORS.get(value, self.icon_color) + + +properties: List[Property] = [ + MetricProperty("metric", "tag", "purple", ""), + Property("class_name", "c", "green", "Predicted class", is_code=True), + Property("label_class_name", "c", "purple", "Class"), + Property("iou", "i", "yellow", "IOU"), + Property("confidence", "microchip", "orange", "Model confidence"), + Property("url", "pencil", "blue", "Link to Encord editor", is_url=True), + Property("annotator", "user", "purple", "Annotator"), + OutlierProperty("outlier", "circle", "blue", "Outlier severity"), +] + +TAG_TEMPLATE = """
+ + %s + %s +
""" + + +def build_data_tags(row: pd.Series, metric_name: str): + tag_list = [] + for p in properties: + key = p.get_key(metric_name) + if not key in row: + continue + _value = row[key] + value = "" + if isinstance(_value, str): + value = _value + elif isinstance(_value, float): + value = f"{_value:.3f}" + elif isinstance(_value, int): + value = f"{_value:d}" + + if len(value) == 0 or value == "nan": + continue + + if p.is_code: + value = f"{value}" + + if p.is_url: + value = f'Editor' + + if value: + tag_list.append(TAG_TEMPLATE % (p.icon, p.get_icon_color(value), value, p.get_name(metric_name))) + + tags = "\n".join(tag_list) + + html = f""" +
+ {tags} +
+""" + st.markdown(html, unsafe_allow_html=True) diff --git a/src/encord_active/app/common/components/individual_tagging.py b/src/encord_active/app/common/components/individual_tagging.py new file mode 100644 index 000000000..9c0a5e6c0 --- /dev/null +++ b/src/encord_active/app/common/components/individual_tagging.py @@ -0,0 +1,113 @@ +from typing import List + +import streamlit as st +from pandas import Series + +import encord_active.app.common.state as state +from encord_active.app.db.merged_metrics import MergedMetrics +from encord_active.app.db.tags import Tags + + +def update_image_tags(identifier: str, key: str): + tags = st.session_state[key] + st.session_state[state.MERGED_DATAFRAME].at[identifier, "tags"] = tags + MergedMetrics().update_tags(identifier, tags) + + +def multiselect_tag(row: Series, key_prefix: str): + identifier = row["identifier"] + tag_status = st.session_state[state.MERGED_DATAFRAME].at[identifier, "tags"] + + if not isinstance(identifier, str): + st.error("Multiple rows with the same identifier were found. Please create a new issue.") + return + + key = f"{key_prefix}_multiselect_{identifier}" + + st.multiselect( + label="Tag image", + options=st.session_state.get(state.ALL_TAGS) or [], + default=tag_status if len(tag_status) else None, + key=key, + label_visibility="collapsed", + on_change=update_image_tags, + args=(identifier, key), + ) + + +def tag_display_with_counts(): + all_tags = Tags().all() + if not all_tags: + return + + tag_counts = st.session_state[state.MERGED_DATAFRAME]["tags"].value_counts() + all_counts = {tag: 0 for tag in all_tags} + for unique_list, count in tag_counts.items(): + for tag in unique_list: + all_counts[tag] = all_counts.get(tag, 0) + count + + sorted_tags = sorted(all_counts.items(), key=lambda x: x[0].lower()) + st.markdown( + f""" +
+ {' '.join((f'{tag} {cnt}' for tag, cnt in sorted_tags))} +
+ """, + unsafe_allow_html=True, + ) + + +def tag_display(tags: List[str]): + """ + Usage:: + + tags = ['list', 'of', 'tags'] + st.markdown(tag_display(all_tags), unsafe_allow_html=True) + + """ + sorted_tags = sorted(tags, key=lambda x: x.lower()) + return f""" +
+ {' '.join((f'{tag}' for tag in sorted_tags))} +
""" + + +def tag_creator(): + with st.sidebar: + st.markdown("
", unsafe_allow_html=True) + st.markdown("### Tags", unsafe_allow_html=True) + + # Show message after on change. + if "new_tag_message" in st.session_state: + st.info(st.session_state.new_tag_message) + del st.session_state.new_tag_message + + all_tags = Tags().all() + st.session_state[state.ALL_TAGS] = all_tags + + def on_tag_entered(): + tag_name = st.session_state.get("new_tag_name_input", "").strip() + if not tag_name.strip(): + msg = "Cannot add empty tag." + st.session_state.new_tag_message = msg + return + + st.session_state.new_tag_name_input = "" + + if tag_name in all_tags: + st.session_state.new_tag_message = "Tag is already in project tags" + return + + all_tags.append(tag_name) + Tags().create_tag(tag_name) + + st.session_state[state.ALL_TAGS] = all_tags + + st.text_input( + "Tag name", + label_visibility="collapsed", + on_change=on_tag_entered, + placeholder="Enter new tag", + key="new_tag_name_input", + ) + tag_display_with_counts() diff --git a/src/encord_active/app/common/components/multi_select.py b/src/encord_active/app/common/components/multi_select.py new file mode 100644 index 000000000..b6bd9adc5 --- /dev/null +++ b/src/encord_active/app/common/components/multi_select.py @@ -0,0 +1,39 @@ +import logging +from typing import List, Optional, cast + +import streamlit as st +from natsort import natsorted + +logger = logging.getLogger(__name__) + + +def multiselect_with_all_option( + label: str, + options: List[str], + key: str, + custom_all_name: str = "All", + default: Optional[List[str]] = None, + **kwargs, +): + if "on_change" in kwargs: + logger.warning("`st.multiselect.on_change` is being overwritten by custom multiselect with All option") + + default_list = [custom_all_name] if default is None else default + + # Filter classes + def on_change(prev: List[str] = default_list): + next: List[str] = (st.session_state.get(key) or default_list).copy() + + if custom_all_name not in prev and custom_all_name in next: + next = [custom_all_name] + if len(next) > 1 and custom_all_name in next: + next.remove(custom_all_name) + + st.session_state[key] = next + + sorted_options = cast(List[str], natsorted(list(options))) + options = [custom_all_name] + sorted_options + + current: List[str] = st.session_state.get(key) or default_list + + return st.multiselect(label, options, key=key, on_change=on_change, args=(current,), default=default_list, **kwargs) diff --git a/src/encord_active/app/common/components/sticky/sticky.html b/src/encord_active/app/common/components/sticky/sticky.html new file mode 100644 index 000000000..e5ef07aa5 --- /dev/null +++ b/src/encord_active/app/common/components/sticky/sticky.html @@ -0,0 +1,39 @@ + diff --git a/src/encord_active/app/common/components/sticky/sticky.py b/src/encord_active/app/common/components/sticky/sticky.py new file mode 100644 index 000000000..aa2522158 --- /dev/null +++ b/src/encord_active/app/common/components/sticky/sticky.py @@ -0,0 +1,15 @@ +from pathlib import Path + +import streamlit as st +from streamlit.components.v1 import html + + +def sticky_header(): + container = st.container() + container.markdown('
', unsafe_allow_html=True) + + with container: + with open(Path(__file__).parent / "sticky.html", encoding="utf-8") as f: + html(f.read(), height=0, width=0) + + return container diff --git a/src/encord_active/app/common/css.py b/src/encord_active/app/common/css.py new file mode 100644 index 000000000..6b9b9b07f --- /dev/null +++ b/src/encord_active/app/common/css.py @@ -0,0 +1,119 @@ +from hashlib import md5 +from pathlib import Path + +import streamlit as st +import streamlit.elements.image as st_image +from PIL import Image + +from encord_active.app.common.colors import Color + + +def write_page_css(): + # Prepare url for encord logo + logo_pth = Path(__file__).parents[1] / "assets" / "encord_2_02.png" + logo = Image.open(logo_pth) + img_url = st_image.image_to_url(logo, 500, True, "RGB", "PNG", f"logo-{md5(logo.tobytes()).hexdigest()}") + + # Write css + st.markdown( + f""" + + + """, + unsafe_allow_html=True, + ) diff --git a/src/encord_active/app/common/embedding_utils.py b/src/encord_active/app/common/embedding_utils.py new file mode 100644 index 000000000..68f732951 --- /dev/null +++ b/src/encord_active/app/common/embedding_utils.py @@ -0,0 +1,219 @@ +import os +import pickle +from typing import List, Optional, Tuple + +import faiss +import numpy as np +import streamlit as st +from faiss import IndexFlatL2 + +import encord_active.app.common.state as state +from encord_active.lib.common.utils import ( + fix_duplicate_image_orders_in_knn_graph_single_row, +) + + +@st.experimental_memo(show_spinner=False) +def get_collections(embedding_name: str) -> list[dict]: + embedding_path = st.session_state.embeddings_dir / embedding_name + collections = [] + if os.path.isfile(embedding_path): + with open(embedding_path, "rb") as f: + collections = pickle.load(f) + return collections + + +@st.experimental_memo +def get_collections_and_metadata(embedding_name: str) -> Tuple[list[dict], dict]: + try: + collections = get_collections(embedding_name) + + embedding_metadata_file_name = "embedding_classifications_metadata.pkl" + embedding_metadata_path = st.session_state.embeddings_dir / embedding_metadata_file_name + if os.path.isfile(embedding_metadata_path): + with open(embedding_metadata_path, "rb") as f: + question_hash_to_collection_indexes_local = pickle.load(f) + else: + question_hash_to_collection_indexes_local = {} + + return collections, question_hash_to_collection_indexes_local + except Exception as e: + return [], {} + + +def get_key_from_index(collection: dict, question_hash: Optional[str] = None, has_annotation=True) -> str: + label_hash = collection["label_row"] + du_hash = collection["data_unit"] + frame_idx = int(collection["frame"]) + + if not has_annotation: + key = f"{label_hash}_{du_hash}_{frame_idx:05d}" + else: + if question_hash: + key = f"{label_hash}_{du_hash}_{frame_idx:05d}_{question_hash}" + else: + object_hash = collection["objectHash"] + key = f"{label_hash}_{du_hash}_{frame_idx:05d}_{object_hash}" + + return key + + +def get_identifier_to_neighbors( + collections: list[dict], nearest_indexes: np.ndarray, has_annotation=True +) -> dict[str, list]: + nearest_neighbors = {} + n, k = nearest_indexes.shape + for i in range(n): + key = get_key_from_index(collections[i], has_annotation=has_annotation) + temp_list = [] + for j in range(1, k): + temp_list.append( + { + "key": get_key_from_index(collections[nearest_indexes[i, j]], has_annotation=has_annotation), + "name": collections[nearest_indexes[i, j]].get("name", "Does not have a label"), + } + ) + nearest_neighbors[key] = temp_list + return nearest_neighbors + + +def convert_to_indexes(collections, question_hash_to_collection_indexes): + embedding_databases, indexes = {}, {} + + for question_hash in question_hash_to_collection_indexes: + selected_collections = [collections[i] for i in question_hash_to_collection_indexes[question_hash]] + + if len(selected_collections) > 10: + embedding_database = np.stack(list(map(lambda x: x["embedding"], selected_collections))) + + index = faiss.IndexFlatL2(embedding_database.shape[1]) + index.add(embedding_database) # pylint: disable=no-value-for-parameter + + embedding_databases[question_hash] = embedding_database + indexes[question_hash] = index + + return embedding_databases, indexes + + +@st.experimental_memo +def get_faiss_index_image(_collections: list) -> dict[str, IndexFlatL2]: + indexes = {} + + for question_hash in st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES]: + selected_collections = [ + _collections[i] for i in st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES][question_hash] + ] + + if len(selected_collections) > 10: + embedding_database = np.stack(list(map(lambda x: x["embedding"], selected_collections))) + + index = faiss.IndexFlatL2(embedding_database.shape[1]) + index.add(embedding_database) # pylint: disable=no-value-for-parameter + + indexes[question_hash] = index + + return indexes + + +@st.experimental_memo +def get_faiss_index_object(_collections: list[dict], faiss_index_name: str) -> IndexFlatL2: + """ + + Args: + _collections: Underscore is used to skip hashing for this object to make this function faster. + faiss_index_name: Since we skip hashing for collections, we need another parameter for memoization. + + Returns: Faiss Index object for searching embeddings. + + """ + embeddings_list: List[list] = [x["embedding"] for x in _collections] + embeddings = np.array(embeddings_list).astype(np.float32) + + if len(embeddings.shape) != 2: + return + + db_index = faiss.IndexFlatL2(embeddings.shape[1]) + db_index.add(embeddings) # pylint: disable=no-value-for-parameter + return db_index + + +@st.experimental_memo +def get_object_keys_having_similarities(_collections: list[dict]) -> dict: + return {get_key_from_index(collection): i for i, collection in enumerate(_collections)} + + +@st.experimental_memo +def get_image_keys_having_similarities(_collections: list[dict]) -> dict: + return {get_key_from_index(collection, has_annotation=False): i for i, collection in enumerate(_collections)} + + +def add_labeled_image_neighbors_to_cache(image_identifier: str, question_feature_hash: str) -> None: + collection_id = st.session_state[state.IMAGE_KEYS_HAVING_SIMILARITIES]["_".join(image_identifier.split("_")[:3])] + collection_item_index = st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES][question_feature_hash].index( + collection_id + ) + embedding = np.array([st.session_state[state.COLLECTIONS_IMAGES][collection_id]["embedding"]]).astype(np.float32) + nearest_distances, nearest_indexes = st.session_state[state.FAISS_INDEX_IMAGE][question_feature_hash].search( + embedding, int(st.session_state[state.K_NEAREST_NUM] + 1) + ) + nearest_indexes = fix_duplicate_image_orders_in_knn_graph_single_row(collection_item_index, nearest_indexes) + + temp_list = [] + for nearest_index in nearest_indexes[0, 1:]: + collection_index = st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES][question_feature_hash][ + nearest_index + ] + temp_list.append( + { + "key": get_key_from_index( + st.session_state[state.COLLECTIONS_IMAGES][collection_index], + question_hash=question_feature_hash, + has_annotation=st.session_state[state.CURRENT_INDEX_HAS_ANNOTATION], + ), + "name": st.session_state[state.COLLECTIONS_IMAGES][collection_index]["classification_answers"][ + question_feature_hash + ]["answer_name"], + } + ) + + st.session_state[state.IMAGE_SIMILARITIES][question_feature_hash][image_identifier] = temp_list + + +def _get_nearest_items_from_nearest_indexes(nearest_indexes: np.ndarray, collection_type: str) -> list[dict]: + temp_list = [] + for nearest_index in nearest_indexes[0, 1:]: + temp_list.append( + { + "key": get_key_from_index( + st.session_state[collection_type][nearest_index], + has_annotation=st.session_state[state.CURRENT_INDEX_HAS_ANNOTATION], + ), + "name": st.session_state[collection_type][nearest_index].get("name", "No label"), + } + ) + + return temp_list + + +def add_object_neighbors_to_cache(object_identifier: str) -> None: + item_index = st.session_state[state.OBJECT_KEYS_HAVING_SIMILARITIES][object_identifier] + embedding = np.array([st.session_state[state.COLLECTIONS_OBJECTS][item_index]["embedding"]]).astype(np.float32) + nearest_distances, nearest_indexes = st.session_state[state.FAISS_INDEX_OBJECT].search( + embedding, int(st.session_state[state.K_NEAREST_NUM] + 1) + ) + + st.session_state[state.OBJECT_SIMILARITIES][object_identifier] = _get_nearest_items_from_nearest_indexes( + nearest_indexes, state.COLLECTIONS_OBJECTS + ) + + +def add_image_neighbors_to_cache(image_identifier: str): + collection_id = st.session_state[state.IMAGE_KEYS_HAVING_SIMILARITIES][image_identifier] + embedding = np.array([st.session_state[state.COLLECTIONS_IMAGES][collection_id]["embedding"]]).astype(np.float32) + nearest_distances, nearest_indexes = st.session_state[state.FAISS_INDEX_IMAGE_NO_LABEL].search( + embedding, int(st.session_state[state.K_NEAREST_NUM] + 1) + ) + + st.session_state[state.IMAGE_SIMILARITIES_NO_LABEL][image_identifier] = _get_nearest_items_from_nearest_indexes( + nearest_indexes, state.COLLECTIONS_IMAGES + ) diff --git a/src/encord_active/app/common/metric.py b/src/encord_active/app/common/metric.py new file mode 100644 index 000000000..20cd2a0a4 --- /dev/null +++ b/src/encord_active/app/common/metric.py @@ -0,0 +1,43 @@ +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict + +import pandas as pd +import streamlit as st + + +@dataclass +class MetricData: + name: str + path: Path + meta: Dict[str, Any] + level: str + + +@st.cache(allow_output_mutation=True) +def load_metric(metric: MetricData, normalize: bool, *, sorting_key="score") -> pd.DataFrame: + """ + Load and sort the selected csv file and cache it, so we don't need to perform this + heavy computation each time the slider in the UI is moved. + :param metric: The metric to load data from. + :param normalize: whether to apply normalisation to the scores or not. + :param sorting_key: key by which to sort dataframe (default: "score") + :return: a pandas data frame with all the scores. + """ + df = pd.read_csv(metric.path).sort_values([sorting_key, "identifier"], ascending=True).reset_index() + + if normalize: + min_val = metric.meta.get("min_value") + max_val = metric.meta.get("max_value") + if min_val is None: + min_val = df["score"].min() + if max_val is None: + max_val = df["score"].max() + + diff = max_val - min_val + if diff == 0: # Avoid dividing by zero + diff = 1.0 + + df["score"] = (df["score"] - min_val) / diff + + return df diff --git a/src/encord_active/app/common/page.py b/src/encord_active/app/common/page.py new file mode 100644 index 000000000..0e2d8e07f --- /dev/null +++ b/src/encord_active/app/common/page.py @@ -0,0 +1,102 @@ +from abc import ABC, abstractmethod + +import streamlit as st +from screeninfo import Monitor, ScreenInfoError, get_monitors + +import encord_active.app.common.state as state +from encord_active.lib.common.metric import EmbeddingType + + +class Page(ABC): + @property + @abstractmethod + def title(self) -> str: + pass + + @abstractmethod + def sidebar_options(self, *args, **kwargs): + """ + Used to append options to the sidebar. + """ + pass + + @abstractmethod + def build(self, *args, **kwargs): + pass + + def __call__(self, *args, **kwargs): + return self.build(*args, **kwargs) + + def __repr__(self): + return f"{type(self).__name__}()" + + @staticmethod + def row_col_settings_in_sidebar(): + m_max = Monitor(height=0, width=0, x=0, y=0) + try: + monitors = get_monitors() + except ScreenInfoError: + monitors = [] + + for m in monitors: + if m.width * m.height > m_max.width * m_max.height: + m_max = m + + col_default_max, row_default_max = 10, 4 + default_mv_column_num = min(m_max.width // 200, col_default_max) or 4 + default_mv_row_num = min(m_max.height // 200, row_default_max) or 4 + default_knn_num = 8 + + with st.expander("Settings"): + if state.MAIN_VIEW_COLUMN_NUM not in st.session_state: + st.session_state[state.MAIN_VIEW_COLUMN_NUM] = default_mv_column_num + + if state.MAIN_VIEW_ROW_NUM not in st.session_state: + st.session_state[state.MAIN_VIEW_ROW_NUM] = default_mv_row_num + + if state.K_NEAREST_NUM not in st.session_state: + st.session_state[state.K_NEAREST_NUM] = default_knn_num + + with st.form(key="settings_from"): + st.checkbox( + "Metric normalization", + key=state.NORMALIZATION_STATUS, + help="If checked, score values will be normalized between 0 and 1. Otherwise, \ + original values will be shown.", + ) + + setting_columns = st.columns(2) + + setting_columns[0].number_input( + "Columns", + min_value=2, + max_value=col_default_max, + value=st.session_state[state.MAIN_VIEW_COLUMN_NUM], + key=state.MAIN_VIEW_COLUMN_NUM, + help="Number of columns to show images in the main view", + ) + + setting_columns[1].number_input( + "Rows", + min_value=1, + max_value=row_default_max, + value=st.session_state[state.MAIN_VIEW_ROW_NUM], + key=state.MAIN_VIEW_ROW_NUM, + help="Number of rows to show images in the main view", + ) + + if state.DATA_PAGE_METRIC in st.session_state.keys(): + if st.session_state[state.DATA_PAGE_METRIC].meta.get( + "embedding_type", EmbeddingType.NONE.value + ) in [ + EmbeddingType.CLASSIFICATION.value, + EmbeddingType.OBJECT.value, + ]: + st.number_input( + "k nearest", + min_value=4, + max_value=20, + key=state.K_NEAREST_NUM, + help="Number of the most similar images to show", + ) + st.form_submit_button(label="Apply") diff --git a/src/encord_active/app/common/state.py b/src/encord_active/app/common/state.py new file mode 100644 index 000000000..2113f36ee --- /dev/null +++ b/src/encord_active/app/common/state.py @@ -0,0 +1,89 @@ +from pathlib import Path + +import streamlit as st + +# CONSTANTS +PROJECT_CACHE_FILE = Path.home() / ".encord_assertions" / "current_project_dir.txt" + +# DATABASE +DB_FILE_NAME = "sqlite.db" + +# STATE VARIABLE KEYS +CLASS_SELECTION = "class_selection" +IGNORE_FRAMES_WO_PREDICTIONS = "ignore_frames_wo_predictions" +IOU_THRESHOLD = "iou_threshold_scaled" # After normalization +IOU_THRESHOLD_ = "iou_threshold_" # Before normalization +MERGED_DATAFRAME = "merged_dataframe" +ALL_TAGS = "all_tags" + +# SIMILARITY KEYS +OBJECT_KEYS_HAVING_SIMILARITIES = "object_keys_having_similarities" +IMAGE_KEYS_HAVING_SIMILARITIES = "image_keys_having_similarity" +OBJECT_SIMILARITIES = "object_similarities" +IMAGE_SIMILARITIES = "image_similarities" +IMAGE_SIMILARITIES_NO_LABEL = "image_similarities_no_label" +FAISS_INDEX_OBJECT = "faiss_index_object" +FAISS_INDEX_IMAGE = "faiss_index_image" +FAISS_INDEX_IMAGE_NO_LABEL = "faiss_index_image_no_label" +CURRENT_INDEX_HAS_ANNOTATION = "current_index_has_annotation" +QUESTION_HASH_TO_COLLECTION_INDEXES = "question_hash_to_collection_indexes" +COLLECTIONS_IMAGES = "collections_images" +COLLECTIONS_OBJECTS = "collections_objects" + + +# DATA QUALITY PAGE +DATA_PAGE_METRIC = "data_page_metric" # metric +DATA_PAGE_METRIC_NAME = "data_page_metric_name" # metric name +DATA_PAGE_CLASS = "data_page_class_selection" # class +DATA_PAGE_ANNOTATOR = "data_page_annotator_selection" # annotator + + +# PREDICTIONS PAGE +PREDICTIONS_LABEL_METRIC = "predictions_label_metric" +PREDICTIONS_DECOMPOSE_CLASSES = "predictions_decompose_classes" +PREDICTIONS_METRIC = "predictions_metric" +PREDICTIONS_NBINS = "predictions_nbins" + +# TILING & PAGINATION +MAIN_VIEW_COLUMN_NUM = "main_view_column_num" +MAIN_VIEW_ROW_NUM = "main_view_row_num" +K_NEAREST_NUM = "k_nearest_num" + +METRIC_VIEW_PAGE_NUMBER = "metric_view_page_number" +FALSE_NEGATIVE_VIEW_PAGE_NUMBER = "false_negative_view_page_number" + +NORMALIZATION_STATUS = "normalization_status" +METRIC_METADATA_SCORE_NORMALIZATION = "score_normalization" + +# Export page +NUMBER_OF_PARTITIONS = "number_of_partitions" +ACTION_PAGE_CLONE_BUTTON = "action_page_clone_button" +ACTION_PAGE_PREVIOUS_FILTERED_NUM = "action_page_previous_filtered" + + +def set_project_dir(project_dir: str) -> bool: + _project_dir = Path(project_dir).expanduser().absolute() + + if not _project_dir.is_dir(): + return False + else: + PROJECT_CACHE_FILE.parent.mkdir(parents=True, exist_ok=True) + with PROJECT_CACHE_FILE.open("w", encoding="utf-8") as f: + f.write(_project_dir.as_posix()) + st.session_state.project_dir = _project_dir + return True + + +def populate_session_state(): + if "project_dir" not in st.session_state: + # Try using a cached one + if PROJECT_CACHE_FILE.is_file(): + with PROJECT_CACHE_FILE.open("r", encoding="utf-8") as f: + st.session_state.project_dir = Path(f.readline()) + + st.session_state.metric_dir = st.session_state.project_dir / "metrics" + st.session_state.embeddings_dir = st.session_state.project_dir / "embeddings" + st.session_state.predictions_dir = st.session_state.project_dir / "predictions" + st.session_state.data_dir = st.session_state.project_dir / "data" + st.session_state.ontology_file = st.session_state.project_dir / "ontology.json" + st.session_state.db_path = st.session_state.project_dir / DB_FILE_NAME diff --git a/src/encord_active/app/common/utils.py b/src/encord_active/app/common/utils.py new file mode 100644 index 000000000..fa3fe83bd --- /dev/null +++ b/src/encord_active/app/common/utils.py @@ -0,0 +1,229 @@ +import json +from json import JSONDecodeError +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import cv2 +import numpy as np +import pandas as pd +import streamlit as st + +import encord_active.app.common.state as state +from encord_active.app.common.colors import Color, hex_to_rgb +from encord_active.app.common.css import write_page_css +from encord_active.app.common.state import populate_session_state +from encord_active.app.db.merged_metrics import MergedMetrics +from encord_active.lib.common.utils import get_du_size + + +def set_page_config(): + project_root = Path(__file__).parents[1] + favicon_pth = project_root / "assets" / "favicon-32x32.png" + st.set_page_config( + page_title="Encord Active", + layout="wide", + page_icon=favicon_pth.as_posix(), + ) + + +def setup_page(): + populate_session_state() + write_page_css() + + +def load_json(json_file: Path) -> Optional[dict]: + if not json_file.exists(): + return None + + with json_file.open("r", encoding="utf-8") as f: + try: + return json.load(f) + except JSONDecodeError: + return None + + +def load_or_fill_image(row: Union[pd.Series, str]) -> np.ndarray: + """ + Tries to read the infered image path. If not possible, generates a white image + and indicates what the error seemd to be embedded in the image. + :param row: A csv row from either a metric, a prediction, or a label csv file. + :return: Numpy / cv2 image. + """ + + read_error = False + key = __get_key(row) + + img_pth: Optional[Path] = key_to_image_path(key) + + if img_pth and img_pth.is_file(): + try: + image = cv2.imread(img_pth.as_posix()) + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + except cv2.error: + pass + + # Read not successful, so tell the user why + error_text = "Image not found" if not img_pth else "File seems broken" + + _, du_hash, *_ = key.split("_") + lr = json.loads(key_to_lr_path(key).read_text(encoding="utf-8")) + + h, w = get_du_size(lr["data_units"].get(du_hash, {}), None) or (600, 900) + + image = np.ones((h, w, 3), dtype=np.uint8) * 255 + image[:4, :] = [255, 0, 0] + image[-4:, :] = [255, 0, 0] + image[:, :4] = [255, 0, 0] + image[:, -4:] = [255, 0, 0] + font = cv2.FONT_HERSHEY_SIMPLEX + pos = int(0.05 * min(w, h)) + cv2.putText(image, error_text, (pos, 2 * pos), font, w / 900, hex_to_rgb("#999999"), 2, cv2.LINE_AA) + + return image + + +def get_df_subset(df: pd.DataFrame, selected_metric: Optional[str]): + if selected_metric not in df: + return df + + max_val = float(df[selected_metric].max()) + np.finfo(float).eps.item() + min_val = float(df[selected_metric].min()) + + if max_val <= min_val: + return df + + step = max(0.01, (max_val - min_val) // 100) + start, end = st.slider("Choose quality", max_value=max_val, min_value=min_val, value=(min_val, max_val), step=step) + subset = df[df[selected_metric].between(start, end)] + + return subset + + +def build_pagination(subset, n_cols, n_rows, selected_metric): + n_items = n_cols * n_rows + col1, col2 = st.columns(spec=[1, 4]) + + with col1: + sorting_order = st.selectbox("Sort samples within selected interval", ["Ascending", "Descending"]) + + with col2: + last = len(subset) // n_items + 1 + page_num = st.slider("Page", 1, last) if last > 1 else 1 + + low_lim = (page_num - 1) * n_items + high_lim = page_num * n_items + + sorted_subset = subset.sort_values(by=selected_metric, ascending=sorting_order == "Ascending") + paginated_subset = sorted_subset[low_lim:high_lim] + return paginated_subset + + +def __get_key(row: Union[pd.Series, str]): + if isinstance(row, pd.Series): + if "identifier" not in row: + raise ValueError("A Series passed but the series doesn't contain 'identifier'") + return str(row["identifier"]) + elif isinstance(row, str): + return row + else: + raise Exception(f"Undefined row type {row}") + + +def __get_geometry(obj: dict, img_h: int, img_w: int) -> Optional[Tuple[str, np.ndarray]]: + """ + Convert Encord object dictionary to polygon coordinates used to draw geometries + with opencv. + + :param obj: the encord object dict + :param w: the image width + :param h: the image height + :return: The polygon coordinates + """ + + if obj["shape"] == "polygon": + p = obj["polygon"] + polygon = np.array([[p[str(i)]["x"] * img_w, p[str(i)]["y"] * img_h] for i in range(len(p))]) + elif obj["shape"] == "bounding_box": + b = obj["boundingBox"] + polygon = np.array( + [ + [b["x"] * img_w, b["y"] * img_h], + [(b["x"] + b["w"]) * img_w, b["y"] * img_h], + [(b["x"] + b["w"]) * img_w, (b["y"] + b["h"]) * img_h], + [b["x"] * img_w, (b["y"] + b["h"]) * img_h], + ] + ) + else: + return None + + polygon = polygon.reshape((-1, 1, 2)).astype(int) + return obj.get("color", Color.PURPLE.value), polygon + + +def get_geometries( + row: Union[pd.Series, str], img_h: int, img_w: int, skip_object_hash: bool = False +) -> List[Tuple[str, np.ndarray]]: + """ + Loads cached label row and computes geometries from the label row. + If the ``identifier`` in the ``row`` contains an object hash, only that object will + be drawn. If no object hash exists, all polygons / bboxes will be drawn. + :param row: the pandas row of the selected csv file. + :return: List of tuples of (hex color, polygon: [[x, y], ...]) + """ + key = __get_key(row) + _, du_hash, frame, *remainder = key.split("_") + + lr_pth = key_to_lr_path(key) + with lr_pth.open("r") as f: + label_row = json.load(f) + + du = label_row["data_units"][du_hash] + + geometries = [] + objects = ( + du["labels"].get("objects", []) + if "video" not in du["data_type"] + else du["labels"][str(int(frame))].get("objects", []) + ) + + if remainder and not skip_object_hash: + # Return specific geometries + geometry_object_hashes = set(remainder) + for obj in objects: + if obj["objectHash"] in geometry_object_hashes: + geometries.append(__get_geometry(obj, img_h=img_h, img_w=img_w)) + else: + # Get all geometries + for obj in objects: + if obj["shape"] not in {"polygon", "bounding_box"}: + continue + geometries.append(__get_geometry(obj, img_h=img_h, img_w=img_w)) + + valid_geometries = list(filter(None, geometries)) + return valid_geometries + + +def key_to_lr_path(key: str) -> Path: + label_hash, *_ = key.split("_") + return st.session_state.data_dir / label_hash / "label_row.json" + + +def key_to_image_path(key: str) -> Optional[Path]: + """ + Infer image path from the identifier stored in the csv files. + :param key: the row["identifier"] from a csv row + :return: The associated image path if it exists or a path to a placeholder otherwise + """ + label_hash, du_hash, frame, *_ = key.split("_") + img_folder = st.session_state.data_dir / label_hash / "images" + + # check if it is a video frame + frame_pth = next(img_folder.glob(f"{du_hash}_{int(frame)}.*"), None) + if frame_pth is not None: + return frame_pth + return next(img_folder.glob(f"{du_hash}.*"), None) # So this is an img_group image + + +def load_merged_df(): + if state.MERGED_DATAFRAME not in st.session_state: + st.session_state[state.MERGED_DATAFRAME] = MergedMetrics().all() diff --git a/src/encord_active/app/conf/__init__.py b/src/encord_active/app/conf/__init__.py new file mode 100644 index 000000000..40c47ae9f --- /dev/null +++ b/src/encord_active/app/conf/__init__.py @@ -0,0 +1,10 @@ +from os import environ + +from dotenv import load_dotenv + +from encord_active.app.conf.logging import setup_logging + +load_dotenv() +setup_logging() + +FRONTEND = environ.get("FRONTEND", "http://localhost:5173") diff --git a/src/encord_active/app/conf/logging.py b/src/encord_active/app/conf/logging.py new file mode 100644 index 000000000..2d1a8a0a1 --- /dev/null +++ b/src/encord_active/app/conf/logging.py @@ -0,0 +1,9 @@ +import logging + + +def setup_logging(): + disabled_loggers = ["faiss.loader"] + + for logger_name in disabled_loggers: + logger = logging.getLogger(logger_name) + logger.setLevel(logging.ERROR) diff --git a/src/encord_active/app/data_quality/__init__.py b/src/encord_active/app/data_quality/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/data_quality/common.py b/src/encord_active/app/data_quality/common.py new file mode 100644 index 000000000..202e61a9b --- /dev/null +++ b/src/encord_active/app/data_quality/common.py @@ -0,0 +1,81 @@ +from enum import Enum +from pathlib import Path +from typing import List, Union + +import cv2 +import numpy as np +import streamlit as st +from natsort import natsorted +from pandas import Series + +from encord_active.app.common.colors import hex_to_rgb +from encord_active.app.common.metric import MetricData +from encord_active.app.common.utils import get_geometries, load_json, load_or_fill_image + + +class MetricType(Enum): + DATA_QUALITY = "data_quality" + LABEL_QUALITY = "label_quality" + + +def get_metric_operation_level(pth: Path) -> str: + if not all([pth.exists(), pth.is_file(), pth.suffix == ".csv"]): + return "" + + with pth.open("r", encoding="utf-8") as f: + _ = f.readline() # Header, which we don't care about + csv_row = f.readline() # Content line + + if not csv_row: # Empty metric + return "" + + key, _ = csv_row.split(",", 1) + _, _, _, *object_hashes = key.split("_") + return "O" if object_hashes else "F" + + +@st.experimental_memo +def load_available_metrics(metric_type_selection: MetricType) -> List[MetricData]: + def criterion(x): + return x is None if metric_type_selection == MetricType.DATA_QUALITY.value else x is not None + + metric_dir = st.session_state.metric_dir + if not metric_dir.is_dir(): + return [] + + paths = natsorted([p for p in metric_dir.iterdir() if p.suffix == ".csv"], key=lambda x: x.stem.split("_", 1)[1]) + levels = list(map(get_metric_operation_level, paths)) + + make_name = lambda p: p.name.split("_", 1)[1].rsplit(".", 1)[0].replace("_", " ").title() + names = [f"{make_name(p)}" for p, l in zip(paths, levels)] + meta_data = [load_json(f.with_suffix(".meta.json")) for f in paths] + + out: List[MetricData] = [] + + if not meta_data: + return out + + for p, n, m, l in zip(paths, names, meta_data, levels): + if m is None or not l or not criterion(m.get("annotation_type")): + continue + + out.append(MetricData(name=n, path=p, meta=m, level=l)) + + out = natsorted(out, key=lambda i: (i.level, i.name)) # type: ignore + return out + + +def show_image_and_draw_polygons(row: Union[Series, str], draw_polygons: bool = True) -> np.ndarray: + # === Read and annotate the image === # + image = load_or_fill_image(row) + + # === Draw polygons / bboxes if available === # + is_closed = True + thickness = int(image.shape[1] / 150) + + img_h, img_w = image.shape[:2] + if draw_polygons: + for color, geometry in get_geometries(row, img_h=img_h, img_w=img_w): + image = cv2.polylines(image, [geometry], is_closed, hex_to_rgb(color), thickness) + + return image diff --git a/src/encord_active/app/data_quality/sub_pages/__init__.py b/src/encord_active/app/data_quality/sub_pages/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/data_quality/sub_pages/explorer.py b/src/encord_active/app/data_quality/sub_pages/explorer.py new file mode 100644 index 000000000..149cbb9dd --- /dev/null +++ b/src/encord_active/app/data_quality/sub_pages/explorer.py @@ -0,0 +1,475 @@ +import re +from typing import List + +import altair as alt +import numpy as np +import pandas as pd +import plotly.express as px +import streamlit as st +from natsort import natsorted +from pandas import Series +from streamlit.delta_generator import DeltaGenerator + +import encord_active.app.common.state as state +from encord_active.app.common import embedding_utils +from encord_active.app.common.components import ( + build_data_tags, + multiselect_with_all_option, +) +from encord_active.app.common.components.bulk_tagging_form import ( + BulkLevel, + action_bulk_tags, + bulk_tagging_form, +) +from encord_active.app.common.components.individual_tagging import ( + multiselect_tag, + tag_creator, +) +from encord_active.app.common.metric import MetricData, load_metric +from encord_active.app.common.page import Page +from encord_active.app.common.utils import ( + build_pagination, + get_df_subset, + load_merged_df, +) +from encord_active.app.data_quality.common import ( + load_or_fill_image, + show_image_and_draw_polygons, +) +from encord_active.lib.common.metric import AnnotationType, EmbeddingType + + +class ExplorerPage(Page): + title = "🔎 Explorer" + + def sidebar_options(self, available_metrics: List[MetricData]): + load_merged_df() + tag_creator() + + if not available_metrics: + st.error("Your data has not been indexed. Make sure you have imported your data correctly.") + st.stop() + + non_empty_metrics = [metric for metric in available_metrics if not load_metric(metric, normalize=False).empty] + sorted_metrics = sorted(non_empty_metrics, key=lambda i: i.name) + + metric_names = list(map(lambda i: i.name, sorted_metrics)) + + col1, col2, col3 = st.columns(3) + selected_metric_name = col1.selectbox( + "Select a metric to order your data by", + metric_names, + help="The data in the main view will be sorted by the selected metric. ", + ) + + metric_idx = metric_names.index(selected_metric_name) + st.session_state[state.DATA_PAGE_METRIC] = sorted_metrics[metric_idx] + st.session_state[state.DATA_PAGE_METRIC_NAME] = selected_metric_name + + if state.NORMALIZATION_STATUS not in st.session_state: + st.session_state[state.NORMALIZATION_STATUS] = st.session_state[state.DATA_PAGE_METRIC].meta.get( + state.METRIC_METADATA_SCORE_NORMALIZATION, True + ) # If there is no information on the meta file, just normalize (its probability is higher) + + df = load_metric( + st.session_state[state.DATA_PAGE_METRIC], normalize=st.session_state[state.NORMALIZATION_STATUS] + ) + + if df.shape[0] > 0: + class_set = sorted(list(df["object_class"].unique())) + with col2: + selected_classes = multiselect_with_all_option("Filter by class", class_set, key=state.DATA_PAGE_CLASS) + + is_class_selected = ( + df.shape[0] * [True] if "All" in selected_classes else df["object_class"].isin(selected_classes) + ) + df_class_selected = df[is_class_selected] + + annotators = get_annotator_level_info(df_class_selected) + annotator_set = sorted(annotators["annotator"]) + + with col3: + selected_annotators = multiselect_with_all_option( + "Filter by annotator", + annotator_set, + key=state.DATA_PAGE_ANNOTATOR, + ) + + annotator_selected = ( + df_class_selected.shape[0] * [True] + if "All" in selected_annotators + else df_class_selected["annotator"].isin(selected_annotators) + ) + + self.row_col_settings_in_sidebar() + # For now go the easy route and just filter the dataframe here + return df_class_selected[annotator_selected] + + def build(self, selected_df: pd.DataFrame): + st.markdown(f"# {self.title}") + meta = st.session_state[state.DATA_PAGE_METRIC].meta + st.markdown(f"## {meta['title']}") + st.markdown(meta["long_description"]) + + if selected_df.empty: + return + + fill_dataset_properties_window(selected_df) + fill_annotator_properties_window(selected_df) + fill_data_quality_window(selected_df) + + +def get_annotator_level_info(df: pd.DataFrame) -> dict[str, list]: + annotator_set = natsorted(list(df["annotator"].unique())) + annotators: dict[str, list] = {"annotator": annotator_set, "total annotation": [], "score": []} + + for annotator in annotator_set: + annotators["total annotation"].append(df[df["annotator"] == annotator].shape[0]) + annotators["score"].append(df[df["annotator"] == annotator]["score"].mean()) + + return annotators + + +def fill_dataset_properties_window(current_df: pd.DataFrame): + dataset_expander = st.expander("Dataset Properties", expanded=True) + dataset_columns = dataset_expander.columns(3) + + cls_set = natsorted(list(current_df["object_class"].unique())) + + dataset_columns[0].metric("Number of labels", current_df.shape[0]) + dataset_columns[1].metric("Number of classes", len(cls_set)) + dataset_columns[2].metric("Number of images", get_unique_data_units_size(current_df)) + + if len(cls_set) > 1: + classes = {} + for cls in cls_set: + classes[cls] = (current_df["object_class"] == cls).sum() + + source = pd.DataFrame({"class": list(classes.keys()), "count": list(classes.values())}) + + fig = px.bar(source, x="class", y="count") + fig.update_layout(title_text="Distribution of the classes", title_x=0.5, title_font_size=20) + dataset_expander.plotly_chart(fig, use_container_width=True) + + +def fill_annotator_properties_window(current_df: pd.DataFrame): + annotators = get_annotator_level_info(current_df) + if not (len(annotators["annotator"]) == 1 and (not isinstance(annotators["annotator"][0], str))): + annotator_expander = st.expander("Annotator Statistics", expanded=False) + + annotator_columns = annotator_expander.columns([2, 2]) + + # 1. Pie Chart + annotator_columns[0].markdown( + "
Distribution of the annotations
", unsafe_allow_html=True + ) + source = pd.DataFrame( + { + "annotator": annotators["annotator"], + "total": annotators["total annotation"], + "score": [f"{score:.3f}" for score in annotators["score"]], + } + ) + + fig = px.pie(source, values="total", names="annotator", hover_data=["score"]) + # fig.update_layout(title_text="Distribution of the annotations", title_x=0.5, title_font_size=20) + + annotator_columns[0].plotly_chart(fig, use_container_width=True) + + # 2. Table View + annotator_columns[1].markdown( + "
Detailed annotator statistics
", unsafe_allow_html=True + ) + + annotators["annotator"].append("all") + annotators["total annotation"].append(current_df.shape[0]) + + df_mean_score = current_df["score"].mean() + annotators["score"].append(df_mean_score) + deviations = 100 * ((np.array(annotators["score"]) - df_mean_score) / df_mean_score) + annotators["deviations"] = deviations + annotators_df = pd.DataFrame.from_dict(annotators) + + def _format_deviation(val): + return f"{val:.1f}%" + + def _format_score(val): + return f"{val:.3f}" + + def _color_red_or_green(val): + color = "red" if val < 0 else "green" + return f"color: {color}" + + def make_pretty(styler): + styler.format(_format_deviation, subset=["deviations"]) + styler.format(_format_score, subset=["score"]) + styler.applymap(_color_red_or_green, subset=["deviations"]) + return styler + + annotator_columns[1].dataframe(annotators_df.style.pipe(make_pretty), use_container_width=True) + + +def fill_data_quality_window(current_df: pd.DataFrame): + annotation_type = st.session_state[state.DATA_PAGE_METRIC].meta.get("annotation_type") + if ( + (annotation_type is None) + or (len(annotation_type) == 1 and annotation_type[0] == str(AnnotationType.CLASSIFICATION.RADIO.value)) + or ( + st.session_state[state.DATA_PAGE_METRIC].meta.get("title") + in [ + "Frame object density", + "Object Count", + ] + ) + ): # TODO find a better way to filter these later because titles can change + embedding_type = str(EmbeddingType.CLASSIFICATION.value) + else: + embedding_type = str(EmbeddingType.OBJECT.value) + + populate_embedding_information(embedding_type) + + if (embedding_type == str(EmbeddingType.CLASSIFICATION.value)) and len( + st.session_state[state.COLLECTIONS_IMAGES] + ) == 0: + st.write("Image-level embedding file is not available for this project.") + return + if (embedding_type == str(EmbeddingType.OBJECT.value)) and len(st.session_state[state.COLLECTIONS_OBJECTS]) == 0: + st.write("Object-level embedding file is not available for this project.") + return + + n_cols = int(st.session_state[state.MAIN_VIEW_COLUMN_NUM]) + n_rows = int(st.session_state[state.MAIN_VIEW_ROW_NUM]) + + chart = get_histogram(current_df) + st.altair_chart(chart, use_container_width=True) + subset = get_df_subset(current_df, "score") + + st.write(f"Interval contains {subset.shape[0]} of {current_df.shape[0]} annotations") + + paginated_subset = build_pagination(subset, n_cols, n_rows, "score") + + form = bulk_tagging_form() + + if form and form.submitted: + df = paginated_subset if form.level == BulkLevel.PAGE else subset + action_bulk_tags(df, form.tags, form.action) + + if len(paginated_subset) == 0: + st.error("No data in selected interval") + else: + cols: List = [] + similarity_expanders = [] + for i, (row_no, row) in enumerate(paginated_subset.iterrows()): + if not cols: + cols = list(st.columns(n_cols)) + similarity_expanders.append(st.expander("Similarities", expanded=True)) + + with cols.pop(0): + build_card(embedding_type, i, row, similarity_expanders) + + +def populate_embedding_information(embedding_type: str): + if embedding_type == EmbeddingType.CLASSIFICATION.value: + if st.session_state[state.DATA_PAGE_METRIC].meta.get("title") == "Image-level Annotation Quality": + collections, question_hash_to_collection_indexes = embedding_utils.get_collections_and_metadata( + "cnn_classifications.pkl" + ) + st.session_state[state.COLLECTIONS_IMAGES] = collections + st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES] = question_hash_to_collection_indexes + st.session_state[state.IMAGE_KEYS_HAVING_SIMILARITIES] = embedding_utils.get_image_keys_having_similarities( + collections + ) + st.session_state[state.FAISS_INDEX_IMAGE] = embedding_utils.get_faiss_index_image(collections) + st.session_state[state.CURRENT_INDEX_HAS_ANNOTATION] = True + + if state.IMAGE_SIMILARITIES not in st.session_state: + st.session_state[state.IMAGE_SIMILARITIES] = {} + for question_hash in st.session_state[state.QUESTION_HASH_TO_COLLECTION_INDEXES].keys(): + st.session_state[state.IMAGE_SIMILARITIES][question_hash] = {} + else: + collections = embedding_utils.get_collections("cnn_classifications.pkl") + st.session_state[state.COLLECTIONS_IMAGES] = collections + st.session_state[state.IMAGE_KEYS_HAVING_SIMILARITIES] = embedding_utils.get_image_keys_having_similarities( + collections + ) + st.session_state[state.FAISS_INDEX_IMAGE_NO_LABEL] = embedding_utils.get_faiss_index_object( + collections, state.FAISS_INDEX_IMAGE_NO_LABEL + ) + st.session_state[state.CURRENT_INDEX_HAS_ANNOTATION] = False + + if state.IMAGE_SIMILARITIES_NO_LABEL not in st.session_state: + st.session_state[state.IMAGE_SIMILARITIES_NO_LABEL] = {} + + elif embedding_type == EmbeddingType.OBJECT.value: + collections = embedding_utils.get_collections("cnn_objects.pkl") + st.session_state[state.COLLECTIONS_OBJECTS] = collections + st.session_state[state.OBJECT_KEYS_HAVING_SIMILARITIES] = embedding_utils.get_object_keys_having_similarities( + collections + ) + st.session_state[state.FAISS_INDEX_OBJECT] = embedding_utils.get_faiss_index_object( + collections, state.FAISS_INDEX_OBJECT + ) + st.session_state[state.CURRENT_INDEX_HAS_ANNOTATION] = True + + if state.OBJECT_SIMILARITIES not in st.session_state: + st.session_state[state.OBJECT_SIMILARITIES] = {} + + +def build_card(card_type: str, card_no: int, row: Series, _similarity_expanders: list[DeltaGenerator]): + """ + Builds each sub card (the content displayed for each row in a csv file). + """ + + if card_type == EmbeddingType.CLASSIFICATION.value: + + button_name = "show similar images" + if st.session_state[state.DATA_PAGE_METRIC].meta.get("title") == "Image-level Annotation Quality": + image = load_or_fill_image(row) + similarity_callback = show_similar_classification_images + else: + if st.session_state[state.DATA_PAGE_METRIC].meta.get("annotation_type") is None: + image = load_or_fill_image(row) + else: + image = show_image_and_draw_polygons(row) + similarity_callback = show_similar_images + elif card_type == EmbeddingType.OBJECT.value: + image = show_image_and_draw_polygons(row) + button_name = "show similar objects" + similarity_callback = show_similar_object_images + + else: + st.write(f"{card_type} card type is not defined in EmbeddingTypes") + return + + st.image(image) + multiselect_tag(row, "explorer") + + target_expander = _similarity_expanders[card_no // st.session_state[state.MAIN_VIEW_COLUMN_NUM]] + + st.button( + str(button_name), + key=f"similarity_button_{row['identifier']}", + on_click=similarity_callback, + args=(row, target_expander), + ) + + # === Write scores and link to editor === # + tags_row = row.copy() + metric_name = st.session_state[state.DATA_PAGE_METRIC_NAME] + if "object_class" in tags_row and not pd.isna(tags_row["object_class"]): + tags_row["label_class_name"] = tags_row["object_class"] + tags_row.drop("object_class") + tags_row[metric_name] = tags_row["score"] + build_data_tags(tags_row, metric_name) + + if not pd.isnull(row["description"]): + # Hacky way for now (with incorrect rounding) + description = re.sub(r"(\d+\.\d{0,3})\d*", r"\1", row["description"]) + st.write(f"Description: {description}") + + +def get_histogram(current_df: pd.DataFrame): + # TODO: Unify with app/model_assertions/sub_pages/__init__.py:SamplesPage.get_histogram + metric_name = st.session_state[state.DATA_PAGE_METRIC_NAME] + if metric_name: + title_suffix = f" - {metric_name}" + else: + metric_name = "Score" # Used for plotting + + bar_chart = ( + alt.Chart(current_df, title=f"Data distribution{title_suffix}") + .mark_bar() + .encode( + alt.X("score:Q", bin=alt.Bin(maxbins=100), title=metric_name), + alt.Y("count()", title="Num. samples"), + tooltip=[ + alt.Tooltip("score:Q", title=metric_name, format=",.3f", bin=True), + alt.Tooltip("count():Q", title="Num. samples", format="d"), + ], + ) + .properties(height=200) + ) + return bar_chart + + +def show_similar_classification_images(row: Series, expander: DeltaGenerator): + feature_hash = row["identifier"].split("_")[-1] + + if row["identifier"] not in st.session_state[state.IMAGE_SIMILARITIES][feature_hash].keys(): + embedding_utils.add_labeled_image_neighbors_to_cache(row["identifier"], feature_hash) + + nearest_images = st.session_state[state.IMAGE_SIMILARITIES][feature_hash][row["identifier"]] + + division = 4 + column_id = 0 + + for nearest_image in nearest_images: + if column_id == 0: + st_columns = expander.columns(division) + + image = load_or_fill_image(nearest_image["key"]) + + st_columns[column_id].image(image) + st_columns[column_id].write(f"Annotated as `{nearest_image['name']}`") + column_id += 1 + column_id = column_id % division + + +def show_similar_images(row: Series, expander: DeltaGenerator): + image_identifier = "_".join(row["identifier"].split("_")[:3]) + + if image_identifier not in st.session_state[state.IMAGE_SIMILARITIES_NO_LABEL].keys(): + embedding_utils.add_image_neighbors_to_cache(image_identifier) + + nearest_images = st.session_state[state.IMAGE_SIMILARITIES_NO_LABEL][image_identifier] + + division = 4 + column_id = 0 + + for nearest_image in nearest_images: + if column_id == 0: + st_columns = expander.columns(division) + + image = load_or_fill_image(nearest_image["key"]) + + st_columns[column_id].image(image) + st_columns[column_id].write(f"Annotated as `{nearest_image['name']}`") + column_id += 1 + column_id = column_id % division + + +def show_similar_object_images(row: Series, expander: DeltaGenerator): + object_identifier = "_".join(row["identifier"].split("_")[:4]) + + if object_identifier not in st.session_state[state.OBJECT_KEYS_HAVING_SIMILARITIES]: + expander.write("Similarity search is not available for this object.") + return + + if object_identifier not in st.session_state[state.OBJECT_SIMILARITIES].keys(): + embedding_utils.add_object_neighbors_to_cache(object_identifier) + + nearest_images = st.session_state[state.OBJECT_SIMILARITIES][object_identifier] + + division = 4 + column_id = 0 + + for nearest_image in nearest_images: + if column_id == 0: + st_columns = expander.columns(division) + + image = show_image_and_draw_polygons(nearest_image["key"]) + + st_columns[column_id].image(image) + st_columns[column_id].write(f"Annotated as `{nearest_image['name']}`") + column_id += 1 + column_id = column_id % division + + +def get_unique_data_units_size(current_df: pd.DataFrame): + data_units = set() + identifiers = current_df["identifier"] + for identifier in identifiers: + key_components = identifier.split("_") + data_units.add(key_components[0] + "_" + key_components[1]) + + return len(data_units) diff --git a/src/encord_active/app/data_quality/sub_pages/summary.py b/src/encord_active/app/data_quality/sub_pages/summary.py new file mode 100644 index 000000000..9600e8730 --- /dev/null +++ b/src/encord_active/app/data_quality/sub_pages/summary.py @@ -0,0 +1,151 @@ +from typing import List + +import pandas as pd +import streamlit as st + +import encord_active.app.common.state as state +from encord_active.app.common import metric as iutils +from encord_active.app.common.components import build_data_tags +from encord_active.app.common.components.individual_tagging import ( + multiselect_tag, + tag_creator, +) +from encord_active.app.common.page import Page +from encord_active.app.common.utils import load_merged_df +from encord_active.app.data_quality.common import ( + MetricType, + load_available_metrics, + show_image_and_draw_polygons, +) + + +class SummaryPage(Page): + title = "📑 Summary" + + def sidebar_options(self): + self.row_col_settings_in_sidebar() + load_merged_df() + tag_creator() + + def build(self, metric_type_selection: MetricType): + st.markdown(f"# {self.title}") + st.subheader("Outliers by IQR range for every metric") + + with st.expander("Description", expanded=True): + st.write( + "Box plot visualisation is a common technique in descriptive statistics to detect outliers. " + "Interquartile range ($IQR$) refers to the difference between the 25th ($Q1$) and 75th ($Q3$)" + " percentile and tells how spread the middle values are." + ) + st.write( + "Here we uses $score$ values for each metric to determine their outlier status according to how distant " + "they are to the minimum or maximum values:" + ) + + st.markdown( + '- **Severe**: ' + "used for those samples where $(score \leq Q1-2.5 \\times IQR) \lor (Q3+2.5 \\times IQR \leq " + "score)$. \n" + '- **Moderate**: ' + "used for those samples where $score$ does not fall into **Severe** status and $(score \leq Q1-1.5 " + "\\times IQR) \lor (Q3+1.5 \\times IQR \leq score)$. \n" + '- **Low**: ' + "used for those samples where $Q1-1.5 \\times IQR < score < Q3+1.5 \\times IQR$. ", + unsafe_allow_html=True, + ) + + st.warning( + "The outlier status is calculated for each metric separately; the same sample can be considered " + "an outlier for one metric and a non-outlier for another." + ) + + n_cols = int(st.session_state[state.MAIN_VIEW_COLUMN_NUM]) + n_rows = int(st.session_state[state.MAIN_VIEW_ROW_NUM]) + n_items_in_page = n_cols * n_rows + + metrics = load_available_metrics(metric_type_selection) + + for idx in metrics: + df = iutils.load_metric(idx, normalize=False) + + current_df = df.copy() + + if df.empty: + continue + + moderate_iqr_scale = 1.5 + severe_iqr_scale = 2.5 + Q1 = current_df["score"].quantile(0.25) + Q3 = current_df["score"].quantile(0.75) + IQR = Q3 - Q1 + + current_df["dist_to_iqr"] = 0 + current_df.loc[current_df["score"] > Q3, "dist_to_iqr"] = (current_df["score"] - Q3).abs() + current_df.loc[current_df["score"] < Q1, "dist_to_iqr"] = (current_df["score"] - Q1).abs() + current_df.sort_values(by="dist_to_iqr", inplace=True, ascending=False) + + moderate_lb, moderate_ub = Q1 - moderate_iqr_scale * IQR, Q3 + moderate_iqr_scale * IQR + severe_lb, severe_ub = Q1 - severe_iqr_scale * IQR, Q3 + severe_iqr_scale * IQR + + n_moderate_outliers = ( + ((severe_lb <= current_df["score"]) & (current_df["score"] < moderate_lb)) + | ((severe_ub >= current_df["score"]) & (current_df["score"] > moderate_ub)) + ).sum() + + n_severe_outliers = ((current_df["score"] < severe_lb) | (current_df["score"] > severe_ub)).sum() + + with st.expander(label=f"{idx.name} Outliers - {n_severe_outliers} severe, {n_moderate_outliers} moderate"): + st.markdown(idx.meta["long_description"]) + + if n_severe_outliers + n_moderate_outliers == 0: + st.success("No outliers found!") + continue + + st.error(f"Number of severe outliers: {n_severe_outliers}/{len(current_df)}") + st.warning(f"Number of moderate outliers: {n_moderate_outliers}/{len(current_df)}") + value = st.slider( + "distance to IQR", + min_value=float(current_df["dist_to_iqr"].min()), + max_value=float(current_df["dist_to_iqr"].max()), + step=max( + 0.1, + float( + (current_df["dist_to_iqr"].max() - current_df["dist_to_iqr"].min()) + / (len(current_df) / n_items_in_page) + ), + ), + value=float(current_df["dist_to_iqr"].max()), + key=f"dist_to_iqr{idx.name}", + ) + + selected_df = current_df[current_df["dist_to_iqr"] <= value][:n_items_in_page] + + cols: List = [] + for i, (row_no, row) in enumerate(selected_df.iterrows()): + if not cols: + cols = list(st.columns(n_cols)) + + with cols.pop(0): + image = show_image_and_draw_polygons(row) + st.image(image) + + multiselect_tag(row, f"{idx.name}_summary") + + # === Write scores and link to editor === # + + tags_row = row.copy() + if row["score"] > severe_ub or row["score"] < severe_lb: + tags_row["outlier"] = "Severe" + elif row["score"] > moderate_ub or row["score"] < moderate_lb: + tags_row["outlier"] = "Moderate" + else: + tags_row["outlier"] = "Low" + + if "object_class" in tags_row and not pd.isna(tags_row["object_class"]): + tags_row["label_class_name"] = tags_row["object_class"] + tags_row.drop("object_class") + tags_row[idx.name] = tags_row["score"] + build_data_tags(tags_row, idx.name) + + if not pd.isnull(row["description"]): + st.write(f"Description: {row['description']}. ") diff --git a/src/encord_active/app/db/connection.py b/src/encord_active/app/db/connection.py new file mode 100644 index 000000000..cc179d136 --- /dev/null +++ b/src/encord_active/app/db/connection.py @@ -0,0 +1,19 @@ +import sqlite3 +from typing import Optional + +import streamlit as st + + +class DBConnection: + def __init__(self, file: Optional[str] = None): + if file: + self.file = file + else: + self.file = st.session_state.db_path + + def __enter__(self): + self.conn = sqlite3.connect(self.file) + return self.conn + + def __exit__(self, type, value, traceback): + self.conn.__exit__(type, value, traceback) diff --git a/src/encord_active/app/db/merged_metrics.py b/src/encord_active/app/db/merged_metrics.py new file mode 100644 index 000000000..92016b877 --- /dev/null +++ b/src/encord_active/app/db/merged_metrics.py @@ -0,0 +1,116 @@ +import json +from typing import List + +import pandas as pd +import streamlit as st +from encord.project_ontology.classification_type import ClassificationType + +from encord_active.app.common.state import MERGED_DATAFRAME +from encord_active.app.db.connection import DBConnection + +TABLE_NAME = "merged_metrics" + + +def build_merged_metrics() -> pd.DataFrame: + main_df_images = pd.DataFrame(columns=["identifier"]) + main_df_objects = pd.DataFrame(columns=["identifier"]) + main_df_image_quality = pd.DataFrame() + + for index in st.session_state.metric_dir.glob("*.csv"): + meta_pth = index.with_suffix(".meta.json") + if not meta_pth.is_file(): + continue + + with meta_pth.open("r", encoding="utf-8") as f: + meta = json.load(f) + + metric_scores = pd.read_csv(index) + if metric_scores.shape[0] == 0: + continue + + if len(metric_scores["identifier"][0].split("_")) == 3: + # Image-level index + if main_df_images.shape[0] == 0: + columns_to_merge = metric_scores[["identifier", "url", "score"]] + else: + columns_to_merge = metric_scores[["identifier", "score"]] + main_df_images = pd.merge(main_df_images, columns_to_merge, how="outer", on="identifier") + main_df_images.rename(columns={"score": f"{meta['title']}"}, inplace=True) + else: + # Image-level quality index + if ( + meta["annotation_type"][0] == ClassificationType.RADIO.value + and meta["data_type"] == "image" + and meta["embedding_type"] == "classification" + ): + main_df_image_quality = metric_scores + main_df_image_quality.rename(columns={"score": f"{meta['title']}"}, inplace=True) + continue + + # Object-level index + if main_df_objects.shape[0] == 0: + columns_to_merge = metric_scores[["identifier", "url", "score"]] + else: + columns_to_merge = metric_scores[["identifier", "score"]] + main_df_objects = pd.merge(main_df_objects, columns_to_merge, how="outer", on="identifier") + main_df_objects.rename(columns={"score": f"{meta['title']}"}, inplace=True) + + main_df = pd.concat([main_df_images, main_df_objects, main_df_image_quality]) + main_df["tags"] = None + main_df.set_index("identifier", inplace=True) + return main_df + + +def marshall_tags(df: pd.DataFrame): + copy = df.copy() + copy.tags = copy.tags.apply(lambda tags: ",".join(tags or [])) + return copy + + +def unmarshall_tags(df: pd.DataFrame): + copy = df.copy() + copy.tags = copy.tags.apply(lambda tags: [] if not tags else tags.split(",")) + return copy + + +def ensure_initialised(fn): + def wrapper(*args, **kwargs): + try: + return fn(*args, **kwargs) + except: + merged_metrics = st.session_state.get(MERGED_DATAFRAME) + if merged_metrics is None: + merged_metrics = build_merged_metrics() + MergedMetrics().replace_all(merged_metrics) + return fn(*args, **kwargs) + + return wrapper + + +class MergedMetrics(object): + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(MergedMetrics, cls).__new__(cls) + return cls.instance + + @ensure_initialised + def get_row(self, id: str): + with DBConnection() as conn: + return pd.read_sql(f"SELECT * FROM {TABLE_NAME} where IDENTIFIER = '{id}'", conn) + + @ensure_initialised + def update_tags(self, id: str, tags: List[str]): + with DBConnection() as conn: + conn.execute(f"UPDATE {TABLE_NAME} SET tags = '{','.join(tags)}' WHERE IDENTIFIER = '{id}'") + + @ensure_initialised + def all(self): + with DBConnection() as conn: + merged_metrics = pd.read_sql(f"SELECT * FROM {TABLE_NAME}", conn, index_col="identifier") + return unmarshall_tags(merged_metrics) + + def replace_all(self, df: pd.DataFrame): + with DBConnection() as conn: + marshall_tags(df).to_sql( + name=TABLE_NAME, con=conn, if_exists="replace", index=True, index_label="identifier" + ) diff --git a/src/encord_active/app/db/predictions.py b/src/encord_active/app/db/predictions.py new file mode 100644 index 000000000..7afa55954 --- /dev/null +++ b/src/encord_active/app/db/predictions.py @@ -0,0 +1,95 @@ +import json +from enum import Enum +from pathlib import Path +from typing import Annotated, Any, Dict, List, NamedTuple, Optional, Union + +import numpy as np +from encord import Project +from pydantic import BaseModel, Field, validator + +from encord_active.lib.metrics.run_all import run_all_prediction_metrics +from encord_active.lib.model_predictions.iterator import PredictionIterator +from encord_active.lib.model_predictions.prediction_writer import PredictionWriter + +RelativeFloat = Annotated[float, Field(ge=0, le=1)] + + +class Format(str, Enum): + BOUNDING_BOX = "bounding-box" + MASK = "mask" + POLYGON = "polygon" + + +class BoundingBox(BaseModel): + x: RelativeFloat + y: RelativeFloat + h: RelativeFloat + w: RelativeFloat + + +class Point(NamedTuple): + x: RelativeFloat + y: RelativeFloat + + +class Prediction(BaseModel): + data_hash: str + frame: Optional[int] = 0 + class_id: str + confidence: float + format: Format + data: Union[BoundingBox, np.ndarray] + track_id: Optional[Union[str, int]] = None + + @validator("data", pre=True) + def transform(v): # pylint: disable=no-self-argument + if isinstance(v, str): + v = json.loads(v) + if isinstance(v, list): + v = np.array(v) + + return v + + @validator("data") + def format_and_data_valid(cls, v, values): # pylint: disable=no-self-argument + format = values.get("format") + if format == Format.BOUNDING_BOX: + assert v.x + v.w <= 1 and v.y + v.h <= 1, f"{v} is not a valid relative bounding box." + elif format == Format.MASK: + uni = np.unique(v) + assert uni == (0, 1) or uni == (1, 0) or uni == (1) or uni == (0) + elif format == Format.POLYGON: + if isinstance(v, np.ndarray): + assert np.all(np.logical_and(v >= 0.0, v <= 1.0)), "`data` contains an invalid point." + + else: + raise ValueError("Invalid format") + + return v + + class Config: + arbitrary_types_allowed = True + json_encoders = {np.ndarray: lambda v: json.dumps(v.tolist())} + + +def import_predictions(project: Project, data_dir: Path, predictions: List[Prediction]): + with PredictionWriter(data_dir, project) as writer: + for pred in predictions: + data: Dict[str, Any] = {} + + if isinstance(pred.data, np.ndarray): + data["polygon"] = pred.data + elif isinstance(pred.data, BoundingBox): + data["bbox"] = pred.data.dict() + else: + raise Exception("Data format not supported.") + + writer.add_prediction( + data_hash=pred.data_hash, + class_uid=pred.class_id, + confidence_score=pred.confidence, + frame=pred.frame, + **data, + ) + + run_all_prediction_metrics(data_dir=data_dir, iterator_cls=PredictionIterator) diff --git a/src/encord_active/app/db/tags.py b/src/encord_active/app/db/tags.py new file mode 100644 index 000000000..df19ff0fe --- /dev/null +++ b/src/encord_active/app/db/tags.py @@ -0,0 +1,50 @@ +from sqlite3 import OperationalError +from typing import Callable, List + +import streamlit as st + +from encord_active.app.common.state import ALL_TAGS +from encord_active.app.db.connection import DBConnection + +TABLE_NAME = "tags" + + +def ensure_existence(fn: Callable): + def wrapper(*args, **kwargs): + try: + return fn(*args, **kwargs) + except OperationalError: + with DBConnection() as conn: + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {TABLE_NAME} ( + id INTEGER PRIMARY KEY, + label TEXT NOT NULL + ) + """ + ) + all_tags = st.session_state.get(ALL_TAGS) + if all_tags: + conn.executemany(f" INSERT INTO {TABLE_NAME} (label) VALUES(?) ", [all_tags]) + + return fn(*args, **kwargs) + + return wrapper + + +class Tags(object): + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(Tags, cls).__new__(cls) + + return cls.instance + + @ensure_existence + def all(self) -> List[str]: + with DBConnection() as conn: + return [tag for tag, in conn.execute(f"SELECT label FROM {TABLE_NAME}").fetchall()] + + @ensure_existence + def create_tag(self, tag: str): + with DBConnection() as conn: + return conn.execute(f"INSERT INTO {TABLE_NAME} (label) VALUES('{tag}')") diff --git a/src/encord_active/app/frontend_components.py b/src/encord_active/app/frontend_components.py new file mode 100644 index 000000000..1daa953b4 --- /dev/null +++ b/src/encord_active/app/frontend_components.py @@ -0,0 +1,12 @@ +import streamlit.components.v1 as components + +from encord_active.app.conf import FRONTEND + +_pages_menu = components.declare_component( + "pages-menu", + url=f"{FRONTEND}/pages-menu", +) + + +def pages_menu(items) -> str: + return _pages_menu(items=items) diff --git a/src/encord_active/app/known-issues.md b/src/encord_active/app/known-issues.md new file mode 100644 index 000000000..a3d397d06 --- /dev/null +++ b/src/encord_active/app/known-issues.md @@ -0,0 +1,5 @@ +# Known Issues + +1. [x] ~~When rerunning streamlit from another page than the Getting started page, the + `st.session_state.project_dir` is not available.~~ +2. diff --git a/src/encord_active/app/model_assertions/__init__.py b/src/encord_active/app/model_assertions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/app/model_assertions/components/__init__.py b/src/encord_active/app/model_assertions/components/__init__.py new file mode 100644 index 000000000..1965cc738 --- /dev/null +++ b/src/encord_active/app/model_assertions/components/__init__.py @@ -0,0 +1,2 @@ +from .false_negative_view import false_negative_view +from .metric_view import metric_view diff --git a/src/encord_active/app/model_assertions/components/false_negative_view.py b/src/encord_active/app/model_assertions/components/false_negative_view.py new file mode 100644 index 000000000..6b82363e7 --- /dev/null +++ b/src/encord_active/app/model_assertions/components/false_negative_view.py @@ -0,0 +1,83 @@ +from typing import List + +import cv2 +import pandas as pd +import streamlit as st +from streamlit.delta_generator import DeltaGenerator + +import encord_active.app.model_assertions.components.utils as cutils +from encord_active.app.common import state +from encord_active.app.common.colors import Color, hex_to_rgb +from encord_active.app.common.components import build_data_tags +from encord_active.app.common.utils import ( + build_pagination, + get_df_subset, + load_or_fill_image, +) + + +def __show_image_and_fn( + label: pd.Series, + predictions: pd.DataFrame, + box_color: Color = Color.RED, + mask_opacity=0.5, +): + """ + :param label: The csv row of the false-negative label to display. + :param predictions: All the predictions on the same image with the samme predicted class. + :param box_color: The hex color to use when drawing the prediction. + """ + isClosed = True + thickness = 5 + + image = load_or_fill_image(label) + + # Draw predictions + for _, pred in predictions.iterrows(): + pred_color: str = st.session_state.full_class_idx[str(pred["class_id"])]["color"] + image = cutils.draw_mask(pred, image, mask_opacity=mask_opacity, color=pred_color) + + # Draw label + label_color = hex_to_rgb(box_color.value) + image = cv2.polylines(image, [cutils.get_bbox(label)], isClosed, label_color, thickness, lineType=cv2.LINE_8) + image = cutils.draw_mask(label, image, mask_opacity=mask_opacity, color=box_color) + + st.image(image) + + +def __build_card( + label: pd.Series, + predictions: pd.DataFrame, + st_col: DeltaGenerator, + box_color: Color = Color.RED, +): + with st_col: + __show_image_and_fn(label, predictions, box_color=box_color) + + cls = st.session_state.full_class_idx[str(label["class_id"])]["name"] + label = label.copy() + label["label_class_name"] = cls + # === Write scores and link to editor === # + build_data_tags(label, st.session_state[state.PREDICTIONS_LABEL_METRIC]) + + +def false_negative_view(false_negatives, model_predictions, color: Color): + if state.FALSE_NEGATIVE_VIEW_PAGE_NUMBER not in st.session_state: + st.session_state[state.FALSE_NEGATIVE_VIEW_PAGE_NUMBER] = 1 + + n_cols, n_rows = int(st.session_state[state.MAIN_VIEW_COLUMN_NUM]), int(st.session_state[state.MAIN_VIEW_ROW_NUM]) + selected_metric = st.session_state.get(state.PREDICTIONS_LABEL_METRIC) + subset = get_df_subset(false_negatives, selected_metric) + paginated_subset = build_pagination(subset, n_cols, n_rows, selected_metric) + + if len(paginated_subset) == 0: + st.error("No data in selected quality interval") + else: + # === Fill in the container === # + cols: List = [] + for i, label in paginated_subset.iterrows(): + if not cols: + cols = list(st.columns(n_cols)) + + frame_preds = model_predictions[model_predictions["img_id"] == label["img_id"]] + __build_card(label, frame_preds, cols.pop(0), box_color=color) diff --git a/src/encord_active/app/model_assertions/components/index_view.py b/src/encord_active/app/model_assertions/components/index_view.py new file mode 100644 index 000000000..34d71833e --- /dev/null +++ b/src/encord_active/app/model_assertions/components/index_view.py @@ -0,0 +1,79 @@ +from typing import List + +import cv2 +import numpy as np +import pandas as pd +import streamlit as st +from streamlit.delta_generator import DeltaGenerator + +import encord_active.app.model_assertions.components.utils as cutils +from encord_active.app.common import state +from encord_active.app.common.colors import Color, hex_to_rgb +from encord_active.app.common.components import build_data_tags +from encord_active.app.common.utils import ( + build_pagination, + get_df_subset, + get_geometries, + load_or_fill_image, +) + + +def __show_image_and_draw_polygons_plus_prediction(row: pd.Series, box_color: Color = Color.GREEN) -> np.ndarray: + isClosed = True + thickness = 5 + + # Label polygons + image = load_or_fill_image(row) + img_h, img_w = image.shape[:2] + for color, geometry in get_geometries(row, img_h=img_h, img_w=img_w, skip_object_hash=True): + image = cv2.polylines(image, [geometry], isClosed, hex_to_rgb(color), thickness) + + # Prediction polygon + box = cutils.get_bbox(row) + pred_color = hex_to_rgb(box_color.value) + image = cv2.polylines(image, [box], isClosed, pred_color, thickness // 2, lineType=cv2.LINE_8) + image = cutils.draw_mask(row, image, color=box_color) + + return image + + +def __build_card(row: pd.Series, st_col: DeltaGenerator, box_color: Color = Color.GREEN): + with st_col: + image = __show_image_and_draw_polygons_plus_prediction(row, box_color=box_color) + st.image(image) + + # === Write scores and link to editor === # + build_data_tags(row, st.session_state.predictions_metric) + + if row["fp_reason"] and not row["tps"] == 1.0: + st.write(f"Reason: {row['fp_reason']}") + + with st.expander("Details"): + tmp_df = pd.DataFrame(row) + if row["tps"] == 1.0: + tmp_df = tmp_df.drop("fp_reason", axis=0) + tmp_df = tmp_df.drop(["url", "x1", "y1", "x2", "y2", "rle", "Unnamed: 0"], axis=0) + st.dataframe(tmp_df) + + +def metric_view( + df: pd.DataFrame, + box_color: Color = Color.GREEN, +): + if state.METRIC_VIEW_PAGE_NUMBER not in st.session_state: + st.session_state[state.METRIC_VIEW_PAGE_NUMBER] = 1 + + n_cols, n_rows = int(st.session_state[state.MAIN_VIEW_COLUMN_NUM]), int(st.session_state[state.MAIN_VIEW_ROW_NUM]) + selected_metric = st.session_state.get(state.PREDICTIONS_METRIC, "") + subset = get_df_subset(df, selected_metric) + paginated_subset = build_pagination(subset, n_cols, n_rows, selected_metric) + + if len(paginated_subset) == 0: + st.error("No data in selected quality interval") + else: + # === Fill in the container === # + cols: List = [] + for i, row in paginated_subset.iterrows(): + if not cols: + cols = list(st.columns(n_cols)) + __build_card(row, cols.pop(0), box_color=box_color) diff --git a/src/encord_active/app/model_assertions/components/metric_view.py b/src/encord_active/app/model_assertions/components/metric_view.py new file mode 100644 index 000000000..1d905084b --- /dev/null +++ b/src/encord_active/app/model_assertions/components/metric_view.py @@ -0,0 +1,72 @@ +from typing import List + +import cv2 +import numpy as np +import pandas as pd +import streamlit as st +from streamlit.delta_generator import DeltaGenerator + +import encord_active.app.model_assertions.components.utils as cutils +from encord_active.app.common import state +from encord_active.app.common.colors import Color, hex_to_rgb +from encord_active.app.common.components import build_data_tags +from encord_active.app.common.utils import ( + build_pagination, + get_df_subset, + get_geometries, + load_or_fill_image, +) + + +def __show_image_and_draw_polygons_plus_prediction(row: pd.Series, box_color: Color = Color.GREEN) -> np.ndarray: + isClosed = True + thickness = 5 + + # Label polygons + image = load_or_fill_image(row) + img_h, img_w = image.shape[:2] + for color, geometry in get_geometries(row, img_h=img_h, img_w=img_w, skip_object_hash=True): + image = cv2.polylines(image, [geometry], isClosed, hex_to_rgb(color), thickness) + + # Prediction polygon + box = cutils.get_bbox(row) + pred_color = hex_to_rgb(box_color.value) + image = cv2.polylines(image, [box], isClosed, pred_color, thickness // 2, lineType=cv2.LINE_8) + image = cutils.draw_mask(row, image, color=box_color) + + return image + + +def __build_card(row: pd.Series, st_col: DeltaGenerator, box_color: Color = Color.GREEN): + with st_col: + image = __show_image_and_draw_polygons_plus_prediction(row, box_color=box_color) + st.image(image) + + # === Write scores and link to editor === # + build_data_tags(row, st.session_state.predictions_metric) + + if row["fp_reason"] and not row["tps"] == 1.0: + st.write(f"Reason: {row['fp_reason']}") + + +def metric_view( + df: pd.DataFrame, + box_color: Color = Color.GREEN, +): + if state.METRIC_VIEW_PAGE_NUMBER not in st.session_state: + st.session_state[state.METRIC_VIEW_PAGE_NUMBER] = 1 + + n_cols, n_rows = int(st.session_state[state.MAIN_VIEW_COLUMN_NUM]), int(st.session_state[state.MAIN_VIEW_ROW_NUM]) + selected_metric = st.session_state.get(state.PREDICTIONS_METRIC, "") + subset = get_df_subset(df, selected_metric) + paginated_subset = build_pagination(subset, n_cols, n_rows, selected_metric) + + if len(paginated_subset) == 0: + st.error("No data in selected quality interval") + else: + # === Fill in the container === # + cols: List = [] + for i, row in paginated_subset.iterrows(): + if not cols: + cols = list(st.columns(n_cols)) + __build_card(row, cols.pop(0), box_color=box_color) diff --git a/src/encord_active/app/model_assertions/components/utils.py b/src/encord_active/app/model_assertions/components/utils.py new file mode 100644 index 000000000..201e34a4c --- /dev/null +++ b/src/encord_active/app/model_assertions/components/utils.py @@ -0,0 +1,35 @@ +from typing import Tuple, Union + +import cv2 +import numpy as np +import pandas as pd + +from encord_active.app.common.colors import Color, hex_to_rgb +from encord_active.lib.common.utils import rle_to_binary_mask + + +def get_bbox(row: pd.Series): + x1, y1, x2, y2 = row["x1"], row["y1"], row["x2"], row["y2"] + return np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]).reshape((-1, 1, 2)).astype(int) + + +def draw_mask(row: pd.Series, image: np.ndarray, mask_opacity: float = 0.5, color: Union[Color, str] = Color.PURPLE): + isClosed = True + thickness = 2 + + hex_color = color.value if isinstance(color, Color) else color + _color: Tuple[int, ...] = hex_to_rgb(hex_color) + _color_outline: Tuple[int, ...] = hex_to_rgb(hex_color, lighten=-0.5) + if isinstance(row["rle"], str): + mask = rle_to_binary_mask(eval(row["rle"])) + + # Draw contour line + contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + image = cv2.polylines(image, contours, isClosed, _color_outline, thickness, lineType=cv2.LINE_8) + + # Fill polygon with opacity + patch = np.zeros_like(image) + mask_select = mask == 1 + patch[mask_select] = _color + image[mask_select] = cv2.addWeighted(image, (1 - mask_opacity), patch, mask_opacity, 0)[mask_select] + return image diff --git a/src/encord_active/app/model_assertions/data.py b/src/encord_active/app/model_assertions/data.py new file mode 100644 index 000000000..2bd12f578 --- /dev/null +++ b/src/encord_active/app/model_assertions/data.py @@ -0,0 +1,218 @@ +import json +from pathlib import Path +from typing import Dict, List, Optional, Tuple, cast + +import pandas as pd +import streamlit as st +from natsort import natsorted + +from encord_active.app.common.utils import load_json + + +def check_model_prediction_availability(): + predictions_path = st.session_state.predictions_dir / "predictions.csv" + return predictions_path.is_file() + + +def merge_objects_and_scores( + object_df: pd.DataFrame, metric_pth: Optional[Path] = None, ignore_object_scores=True +) -> Tuple[pd.DataFrame, List[str]]: + metric_names: List[str] = [] + object_df["identifier_no_oh"] = object_df["identifier"].str.replace(r"^(\S{73}_\d+)(.*)", r"\1", regex=True) + + if metric_pth is not None: + # Import prediction scores + for metric in metric_pth.iterdir(): + if not metric.suffix == ".csv": + continue + + meta_pth = metric.with_suffix(".meta.json") + if not meta_pth.is_file(): + continue + + with meta_pth.open("r", encoding="utf-8") as f: + meta = json.load(f) + + metric_scores = pd.read_csv(metric, index_col="identifier") + # Ignore empty data frames. + if metric_scores.shape[0] == 0: + continue + + title = f"{meta['title']} (P)" + metric_names.append(title) + + has_object_level_keys = len(metric_scores.index[0].split("_")) > 3 + metric_column = "identifier" if has_object_level_keys else "identifier_no_oh" + # Join data and rename column to metric name. + object_df = object_df.join(metric_scores["score"], on=[metric_column]) + object_df[title] = object_df["score"] + object_df.drop("score", axis=1, inplace=True) + + # Import frame level scores + for metric_file in st.session_state.metric_dir.iterdir(): + if metric_file.is_dir() or metric_file.suffix != ".csv": + continue + + # Read first row to see if metric has frame level scores + with metric_file.open("r", encoding="utf-8") as f: + f.readline() # header + key, *_ = f.readline().split(",") + + if not key: # Empty metric + continue + + label_hash, du_hash, frame, *rest = key.split("_") + type_indicator = "F" # Frame level + join_column = "identifier_no_oh" + if rest and ignore_object_scores: + # There are object hashes included in the key, so ignore. + continue + elif rest: + type_indicator = "O" + join_column = "identifier" + + meta_pth = metric_file.with_suffix(".meta.json") + if not meta_pth.is_file(): + continue + + with meta_pth.open("r", encoding="utf-8") as f: + meta = json.load(f) + + metric_scores = pd.read_csv(metric_file, index_col="identifier") + + title = f"{meta['title']} ({type_indicator})" + metric_names.append(title) + + # Join data and rename column to metric name. + object_df = object_df.join(metric_scores["score"], on=join_column) + object_df[title] = object_df["score"] + object_df.drop("score", axis=1, inplace=True) + object_df.drop("identifier_no_oh", axis=1, inplace=True) + metric_names = cast(List[str], natsorted(metric_names, key=lambda x: x[-3:] + x[:-3])) + return object_df, metric_names + + +@st.cache(allow_output_mutation=True) +def get_model_predictions() -> Optional[Tuple[pd.DataFrame, List[str]]]: + """ + Loads predictions and their associated metric scores. + :param predictions_path: + :return: + - predictions: The predictions with their metric scores + - column names: Unit test column names. + """ + predictions_path = st.session_state.predictions_dir / "predictions.csv" + if not predictions_path.is_file(): + st.error(f"Labels file `{st.session_state.predictions_dir / 'predictions.csv'} is missing.") + return None + + predictions_df = pd.read_csv(predictions_path) + + # Extract label_hash, du_hash, frame + identifiers = predictions_df["identifier"].str.split("_", expand=True) + identifiers.columns = ["label_hash", "du_hash", "frame", "object_hash"][: len(identifiers.columns)] + identifiers["frame"] = pd.to_numeric(identifiers["frame"]) + predictions_df = pd.concat([predictions_df, identifiers], axis=1) + + # Load predictions scores (metrics) + pred_idx_pth = st.session_state.predictions_dir / "metrics" + if not pred_idx_pth.exists(): + return predictions_df, [] + + predictions_df, metric_names = merge_objects_and_scores(predictions_df, metric_pth=pred_idx_pth) + + return predictions_df, metric_names + + +@st.cache(allow_output_mutation=True) +def get_labels() -> Optional[Tuple[pd.DataFrame, List[str]]]: + labels_path = st.session_state.predictions_dir / "labels.csv" + if not labels_path.is_file(): + st.error(f"Labels file `{st.session_state.predictions_dir / 'labels.csv'} is missing") + return None + + labels_df = pd.read_csv(labels_path) + + # Extract label_hash, du_hash, frame + identifiers = labels_df["identifier"].str.split("_", expand=True) + identifiers = identifiers.iloc[:, :3] + identifiers.columns = ["label_hash", "du_hash", "frame"] + identifiers["frame"] = pd.to_numeric(identifiers["frame"]) + + labels_df = pd.concat([labels_df, identifiers], axis=1) + labels_df, label_metric_names = merge_objects_and_scores(labels_df, ignore_object_scores=False) + return labels_df, label_metric_names + + +@st.cache(allow_output_mutation=True) +def get_gt_matched() -> Optional[dict]: + gt_path = st.session_state.predictions_dir / "ground_truths_matched.json" + return load_json(gt_path) + + +@st.cache() +def get_class_idx() -> Optional[dict]: + class_idx_pth = st.session_state.predictions_dir / "class_idx.json" + return load_json(class_idx_pth) + + +@st.cache() +def get_metadata_files() -> Dict[str, dict]: + out: Dict[str, dict] = {} + # Look in data metrics + data_metrics = out.setdefault("data", {}) + if st.session_state.metric_dir.is_dir(): + for f in st.session_state.metric_dir.iterdir(): + if not f.name.endswith(".meta.json"): + continue + + meta = load_json(f) + if meta is None: + continue + + data_metrics[meta["title"]] = meta + + prediction_metrics = out.setdefault("prediction", {}) + if (st.session_state.predictions_dir / "metrics").is_dir(): + for f in st.session_state.metric_dir.iterdir(): + if not f.name.endswith(".meta.json"): + continue + + meta = load_json(f) + if meta is None: + continue + + prediction_metrics[meta["title"]] = meta + + return out + + +def filter_labels_for_frames_wo_predictions(): + """ + Note: data_root is not used in the code, but utilized by `st` to determine what + to cache, so please don't remove. + """ + _predictions = st.session_state.model_predictions + pred_keys = _predictions["img_id"].unique() + _labels = st.session_state.sorted_labels + return _labels[_labels["img_id"].isin(pred_keys)] + + +def prediction_and_label_filtering(labels, metrics, model_pred, precisions): + # Filtering based on selection + # In the following a "_" prefix means the the data has been filtered according to selected classes. + # Predictions + class_idx = st.session_state.selected_class_idx + row_selection = model_pred["class_id"].isin(set(map(int, class_idx.keys()))) + _model_pred = model_pred[row_selection].copy() + # Labels + row_selection = labels["class_id"].isin(set(map(int, class_idx.keys()))) + _labels = labels[row_selection] + + chosen_name_set = set(map(lambda x: x["name"], class_idx.values())).union({"Mean"}) + _metrics = metrics[metrics["class_name"].isin(chosen_name_set)] + _precisions = precisions[precisions["class_name"].isin(chosen_name_set)] + name_map = {int(k): v["name"] for k, v in class_idx.items()} + _model_pred["class_name"] = _model_pred["class_id"].map(name_map) + _labels["class_name"] = _labels["class_id"].map(name_map) + return _labels, _metrics, _model_pred, _precisions diff --git a/src/encord_active/app/model_assertions/map_mar.py b/src/encord_active/app/model_assertions/map_mar.py new file mode 100644 index 000000000..e22e84be3 --- /dev/null +++ b/src/encord_active/app/model_assertions/map_mar.py @@ -0,0 +1,198 @@ +from typing import Dict, Optional, Tuple + +import numpy as np +import pandas as pd +import streamlit as st + + +@st.experimental_memo +def compute_mAP_and_mAR( + iou_threshold: float, + rec_thresholds: Optional[np.ndarray] = None, + ignore_unmatched_frames: bool = False, # pylint: disable=unused-argument +) -> Tuple[pd.DataFrame, pd.DataFrame, np.ndarray, pd.DataFrame, np.ndarray]: + """ + Computes a bunch of quantities used to display results in the UI. The main + purpose of this function is to filter true positives from false positives + by considering the IOU threshold and the order of the confidence scores. + The IOU threshold is precomputed and available in `_predictions`, as each + prediction can atmost match one label. As such, if an iou value is >0, it + means that the prediction's best match (highest iou) has the given value. + + + :param iou_threshold: The IOU threshold to compute scores for. + :param rec_thresholds: The recall thresholds to compute the scores for. + Default here is the same as `torchmetrics`. + :param ignore_unmatched_labels: If set to true, will not normalize by + all class labels, but only those associated with images for which + there exist predictions. + :return: + - metrics_df: A df with AP_{class_name} and AR_{class_name} for every class + name in the `_class_map`. This is used with `altair` to plot scores. + - prec_df: Precision-recall data used with `altair` as well. Again grouped + by `class_name`. + - tps: An indicator array for which `tps[i] == True` if `_predictions.iloc[i]` + was a true positive. Otherwise False. + - reasons: A string for each entry in `_predictions` giving the reason for + why this point was a false negative. + (`reasons[i]` == ""` if `tps[i] == True`). + - fns: An indicator array for which `fns[j] == True` if `_labels.iloc[j]` + was not matched by any prediction. + """ + """ + :param _predictions: A df with the predictions ordered (DESC) by the + models confidence scores. + """ + _predictions = st.session_state.model_predictions + """ + :param _labels: The df with the labels. The labels are only used in + this function to build a list of false negatives, as the matching + between labels and predictions were already done during import. + """ + _labels = st.session_state.labels + """ + :param _gt_matched: A dictionary with format:: + + _gt_matched[(img_id: int, class_id: int)] = \ + [{"lidx": lidx: int, "pidxs": [pidx1: int, pidx2, ... ]}] + + each entry in the `_gt_matched` is thus a list of all the labels of a + particular class (`class_id`) from a particular image (`img_id`). + Each entry in the list contains the index to which it belongs in the + `_labels` data frame (`lidx`) and the indices of all the predictions + that matches the given label best sorted by confidence score. + That is, if `lidx == 512` and `pidx1 == 256`, it + means that prediction 256 (`_predictions.iloc[256]`) matched label + 512 (`_labels.iloc[512]`) with the highest iou of all the prediction's + matches. Furthermore, as `pidx1` comes before `pidx2`, it means that:: + + _predictions.iloc[pidx1]["confidence"] >= _predictions.iloc[pidx2]["confidence"] + + """ + _gt_matched = st.session_state.gt_matched + """ + :param _class_map: This is a mapping between class indices and essential + metadata. The dict has the structure:: + + { + "": { # <-- Note string type here <-- + "name": "The name of the object class", + "heatureHash": "e2f0be6c", + "color": "#001122", + } + } + """ + _class_map = st.session_state.full_class_idx + rec_thresholds = rec_thresholds or np.linspace(0.0, 1.00, round(1.00 / 0.01) + 1) + + full_index_list = np.arange(_predictions.shape[0]) + pred_class_list = _predictions["class_id"].to_numpy(dtype=int) + ious = _predictions["iou"].to_numpy(dtype=float) + + # == Output containers == # + # Stores the mapping between class_idx and list_idx of the following two lists + class_idx_map = {} + precisions = [] + recalls = [] + + _tps = np.zeros((_predictions.shape[0],), dtype=bool) + reasons = [f"No overlapping label of class `{_class_map[str(i)]['name']}`." for i in pred_class_list] + _fns = np.zeros((_labels.shape[0],), dtype=bool) + + pred_img_ids = set(_predictions["img_id"]) + label_include_ids = set(_labels.index[_labels["img_id"].isin(pred_img_ids)]) + + cidx = 0 + for class_idx in _class_map: + if ignore_unmatched_frames: + # nb_labels = sum([len([c for c in l if c in pred_img_ids]) for l in _gt_matched[class_idx].values()]) + nb_labels = sum( + [len([t for t in l if t["lidx"] in label_include_ids]) for l in _gt_matched.get(class_idx, {}).values()] + ) + else: + nb_labels = sum([len(l) for l in _gt_matched.get(class_idx, {}).values()]) + + if nb_labels == 0: + continue + + class_idx_map[cidx] = class_idx # Keep track of the order of the output lists + pred_select = pred_class_list == int(class_idx) + if pred_select.sum() == 0: + precisions.append(np.zeros(rec_thresholds.shape)) + recalls.append(np.array(0.0)) + cidx += 1 + continue + + class_level_to_full_list_idx = full_index_list[pred_select] + full_list_to_class_level_idx: Dict[int, int] = {v.item(): i for i, v in enumerate(class_level_to_full_list_idx)} + + _ious = ious[pred_select] + TP_candidates = set(class_level_to_full_list_idx[_ious >= iou_threshold].astype(int).tolist()) + TP = np.zeros(_ious.shape[0]) + + for img_idx, img_label_matches in _gt_matched[class_idx].items(): + for label_match in img_label_matches: + found_one = False + for tp_idx in label_match["pidxs"]: + if found_one: + reasons[tp_idx] = "Prediction with higher confidence already matched label." + elif tp_idx in TP_candidates: + TP[full_list_to_class_level_idx[tp_idx]] = 1 + _tps[tp_idx] = True + reasons[tp_idx] = "" + found_one = True + else: + reasons[tp_idx] = "IOU too low." + + if not found_one: + lidx = label_match["lidx"] + _fns[lidx] = True + + FP = 1 - TP + + TP_cumsum = np.cumsum(TP, axis=0) + FP_cumsum = np.cumsum(FP, axis=0) + rc = TP_cumsum / (nb_labels + np.finfo(np.float64).eps) + pr = TP_cumsum / (FP_cumsum + TP_cumsum + np.finfo(np.float64).eps) + prec = np.zeros((rec_thresholds.shape[0],)) + + # Remove zigzags for AUC + diff_zero = np.zeros((1,)) + diff = np.ones((1,)) + while not np.all(diff == 0): + diff = np.concatenate([pr[1:] - pr[:-1], diff_zero], axis=0).clip(min=0) + pr += diff + + inds = np.searchsorted(rc, rec_thresholds, side="left") + num_inds = inds.argmax() if inds.max() >= TP.shape[0] else rec_thresholds.shape[0] + inds = inds[:num_inds] # type: ignore + prec[:num_inds] = pr[inds] # type: ignore + + precisions.append(prec) + recalls.append(rc[-1]) + cidx += 1 + + # Compute indices and compose data frames. + _precisions = np.array(precisions, dtype=float) + metrics = [ + ["mAP", np.mean(_precisions.mean(axis=1)).item(), "Mean"], + ] + metrics += [ + [f"AP_{_class_map[class_idx]['name']}", _precisions[cidx].mean().item(), _class_map[class_idx]["name"]] + for cidx, class_idx in class_idx_map.items() + ] + metrics += [["mAR", np.mean(recalls).item(), "Mean"]] + metrics += [ + [f"AR_{_class_map[class_idx]['name']}", recalls[cidx].item(), _class_map[class_idx]["name"]] + for cidx, class_idx in class_idx_map.items() + ] + metrics_df = pd.DataFrame(metrics, columns=["metric", "value", "class_name"]) + + prec_data = [] + columns = ["rc_threshold", "class_name", "precision"] + for cidx, class_idx in class_idx_map.items(): + for rc_idx, rc_threshold in enumerate(rec_thresholds): + prec_data.append([rc_threshold, _class_map[class_idx]["name"], _precisions[cidx, rc_idx]]) + + prec_df = pd.DataFrame(prec_data, columns=columns) + return metrics_df, prec_df, _tps, pd.DataFrame(reasons, columns=["fp_reason"]), _fns diff --git a/src/encord_active/app/model_assertions/settings.py b/src/encord_active/app/model_assertions/settings.py new file mode 100644 index 000000000..301748060 --- /dev/null +++ b/src/encord_active/app/model_assertions/settings.py @@ -0,0 +1,49 @@ +from copy import deepcopy + +import streamlit as st + +import encord_active.app.common.components as cst +import encord_active.app.common.state as state + + +def common_settings(): + class_idx = st.session_state.full_class_idx + col1, col2, col3 = st.columns([4, 4, 3]) + + with col1: + selected_classes = cst.multiselect_with_all_option( + "Select classes to include", + list(map(lambda x: x["name"], class_idx.values())), + key=state.CLASS_SELECTION, + help="With this selection, you can choose which classes to include in the main page.", + ) + + if "All" in selected_classes: + st.session_state.selected_class_idx = deepcopy(class_idx) + else: + st.session_state.selected_class_idx = {k: v for k, v in class_idx.items() if v["name"] in selected_classes} + + with col2: + # IOU + iou_threshold = st.slider( + "Select an IOU threshold", + min_value=0, + max_value=100, + value=50, + key=state.IOU_THRESHOLD_, + help="The mean average precision (mAP) score is based on true positives and false positives. " + "The IOU threshold determines how closely predictions need to match labels to be considered " + "as true positives.", + ) + st.session_state[state.IOU_THRESHOLD] = iou_threshold / 100 + + with col3: + st.write("") + st.write("") + # Ignore unmatched frames + st.checkbox( + "Ignore frames without predictions", + key=state.IGNORE_FRAMES_WO_PREDICTIONS, + help="Scores like mAP and mAR are effected negatively if there are frames in the dataset for which there " + "exist no predictions. With this flag, you can ignore those.", + ) diff --git a/src/encord_active/app/model_assertions/sub_pages/__init__.py b/src/encord_active/app/model_assertions/sub_pages/__init__.py new file mode 100644 index 000000000..2ff8455f2 --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/__init__.py @@ -0,0 +1,137 @@ +from abc import ABC, abstractmethod + +import altair as alt +import pandas as pd +import streamlit as st +from screeninfo import Monitor, ScreenInfoError, get_monitors + +import encord_active.app.common.state as state +from encord_active.app.common.page import Page + + +class ModelAssertionsPage(Page): + @property + @abstractmethod + def title(self) -> str: + pass + + @abstractmethod + def sidebar_options(self): + """ + Used to append options to the sidebar. + """ + pass + + @abstractmethod + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + pass + + def __call__( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + return self.build(model_predictions, labels, metrics, precisions) + + def __repr__(self): + return f"{type(self).__name__}()" + + @staticmethod + def prediction_metric_in_sidebar(): + """ + Note: Adding the fixed options "confidence" and "iou" works here because + confidence is required on import and IOU is computed during prediction + import. So the two columns are already available in the + `st.session_state.model_predictions` data frame. + """ + fixed_options = {"confidence": "Model Confidence", "iou": "IOU"} + st.selectbox( + "Select metric for your predictions", + st.session_state.prediction_metric_names + list(fixed_options.keys()), + key=state.PREDICTIONS_METRIC, + format_func=lambda s: fixed_options.get(s, s), + help="The data in the main view will be sorted by the selected metric. " + "(F) := frame scores, (P) := prediction scores.", + ) + + @staticmethod + def metric_details_description(metric_name: str = ""): + if not metric_name: + metric_name = st.session_state[state.PREDICTIONS_METRIC] + metric_meta = st.session_state.metric_meta["prediction"].get(metric_name[:-4], {}) # Remove " (P)" + if not metric_meta: + metric_meta = st.session_state.metric_meta["data"].get(metric_name[:-4], {}) # Remove " (P)" + if metric_meta: + st.markdown(f"### The {metric_meta['title']} metric") + st.markdown(metric_meta["long_description"]) + + @staticmethod + def row_col_settings_in_sidebar(): + with st.expander("Settings"): + m_max = Monitor(height=0, width=0, x=0, y=0) + try: + monitors = get_monitors() + except ScreenInfoError: + monitors = [] + + for m in monitors: + if m.width * m.height > m_max.width * m_max.height: + m_max = m + + default_mv_column_num = (m_max.width // 250) or 4 + default_mv_row_num = (m_max.height // 250) or 4 + + if state.MAIN_VIEW_COLUMN_NUM not in st.session_state: + st.session_state[state.MAIN_VIEW_COLUMN_NUM] = default_mv_column_num + + if state.MAIN_VIEW_ROW_NUM not in st.session_state: + st.session_state[state.MAIN_VIEW_ROW_NUM] = default_mv_row_num + + with st.form(key="settings_from"): + setting_columns = st.columns(2) + + setting_columns[0].number_input( + "Columns", + min_value=2, + value=default_mv_column_num, + key=state.MAIN_VIEW_COLUMN_NUM, + help="Number of columns to show images in the main view", + ) + + setting_columns[1].number_input( + "Rows", + min_value=1, + value=default_mv_row_num, + key=state.MAIN_VIEW_ROW_NUM, + help="Number of rows to show images in the main view", + ) + + st.form_submit_button(label="Apply") + + +class HistogramMixin: + @staticmethod + def get_histogram(data_frame: pd.DataFrame, metric_column: str): + title_suffix = f" - {metric_column}" + bar_chart = ( + alt.Chart(data_frame, title=f"Data distribution{title_suffix}") + .mark_bar() + .encode( + alt.X(f"{metric_column}:Q", bin=alt.Bin(maxbins=100), title=metric_column), + alt.Y("count()", title="Num. samples"), + tooltip=[ + alt.Tooltip(f"{metric_column}:Q", title=metric_column, format=",.3f", bin=True), + alt.Tooltip("count():Q", title="Num. samples", format="d"), + ], + ) + .properties(height=200) + ) + return bar_chart diff --git a/src/encord_active/app/model_assertions/sub_pages/false_negatives.py b/src/encord_active/app/model_assertions/sub_pages/false_negatives.py new file mode 100644 index 000000000..7e4fc4a6b --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/false_negatives.py @@ -0,0 +1,55 @@ +import pandas as pd +import streamlit as st + +import encord_active.app.common.state as state +from encord_active.app.common.colors import Color +from encord_active.app.model_assertions.components import false_negative_view + +from . import HistogramMixin, ModelAssertionsPage + + +class FalseNegativesPage(ModelAssertionsPage, HistogramMixin): + title = "🔍 False Negatives" + + def sidebar_options(self): + st.selectbox( + "Select metric for your labels", + st.session_state.label_metric_names, + key=state.PREDICTIONS_LABEL_METRIC, + help="The data in the main view will be sorted by the selected metric. " + "(F) := frame scores, (O) := object scores.", + ) + self.row_col_settings_in_sidebar() + + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + st.markdown(f"# {self.title}") + st.header("False Negatives") + metric_name = st.session_state[state.PREDICTIONS_LABEL_METRIC] + with st.expander("Details"): + color = Color.PURPLE + st.markdown( + f"""### The view +These are the labels that were not matched with any predictions. + +--- +**Color**: +The {color.name.lower()} boxes mark the false negatives. +That is, the labels that were not matched to any predictions. +The remaining objects are predictions, where colors correspond to their predicted class (identical colors to labels objects in the editor). +""", + unsafe_allow_html=True, + ) + self.metric_details_description(metric_name) + fns_df = labels[labels["fns"]].dropna(subset=[metric_name]) + if fns_df.shape[0] == 0: + st.write("No false negatives") + else: + histogram = self.get_histogram(fns_df, metric_name) + st.altair_chart(histogram, use_container_width=True) + false_negative_view(fns_df, model_predictions, color=color) diff --git a/src/encord_active/app/model_assertions/sub_pages/false_positives.py b/src/encord_active/app/model_assertions/sub_pages/false_positives.py new file mode 100644 index 000000000..a2f36900c --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/false_positives.py @@ -0,0 +1,51 @@ +import pandas as pd +import streamlit as st + +from encord_active.app.common.colors import Color +from encord_active.app.model_assertions.components import metric_view + +from . import HistogramMixin, ModelAssertionsPage + + +class FalsePositivesPage(ModelAssertionsPage, HistogramMixin): + title = "🌡 False Positives" + + def sidebar_options(self): + self.prediction_metric_in_sidebar() + self.row_col_settings_in_sidebar() + + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + st.markdown(f"# {self.title}") + color = Color.RED + with st.expander("Details"): + st.markdown( + f"""### The view +These are the predictions for which either of the following is true +1. The IOU between the prediction and the best matching label was too low +2. There was another prediction with higher model confidence which matched the label already +3. The predicted class didn't match + +--- + +**Color**: +The {color.name.lower()} boxes marks the false positive predictions. +The remaining colors correspond to the dataset labels with the colors you are used to from the label editor. +""", + unsafe_allow_html=True, + ) + self.metric_details_description() + + metric_name = st.session_state.predictions_metric + fp_df = model_predictions[model_predictions["tps"] == 0.0].dropna(subset=[metric_name]) + if fp_df.shape[0] == 0: + st.write("No false positives") + else: + histogram = self.get_histogram(fp_df, metric_name) + st.altair_chart(histogram, use_container_width=True) + metric_view(fp_df, box_color=color) diff --git a/src/encord_active/app/model_assertions/sub_pages/metrics.py b/src/encord_active/app/model_assertions/sub_pages/metrics.py new file mode 100644 index 000000000..3fba3f060 --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/metrics.py @@ -0,0 +1,212 @@ +import altair as alt +import pandas as pd +import streamlit as st +from sklearn.feature_selection import mutual_info_regression + +from . import ModelAssertionsPage + + +class MetricsPage(ModelAssertionsPage): + title = "📈 Metrics" + + @staticmethod + def create_pr_charts(metrics, precisions): + _metrics = metrics[~metrics["metric"].isin({"mAR", "mAP"})].copy() + + tmp = "m" + _metrics["metric"].str.split("_", n=1, expand=True) + tmp.columns = ["group", "_"] + _metrics["group"] = tmp["group"] + _metrics["average"] = "average" # Legend title + + class_selection = alt.selection_multi(fields=["class_name"]) + + class_bars = ( + alt.Chart(_metrics, title="Mean scores") + .mark_bar() + .encode( + alt.X("value", title="", scale=alt.Scale(domain=[0.0, 1.0])), + alt.Y("metric", title=""), + alt.Color("class_name"), + tooltip=[alt.Tooltip("metric", title="Metric"), alt.Tooltip("value", title="Value", format=",.3f")], + opacity=alt.condition(class_selection, alt.value(1), alt.value(0.1)), + ) + .properties(height=300) + ) + # Average + mean_bars = class_bars.encode( + alt.X("mean(value):Q", title="", scale=alt.Scale(domain=[0.0, 1.0])), + alt.Y("group:N", title=""), + alt.Color("average:N"), + tooltip=[ + alt.Tooltip("group:N", title="Metric"), + alt.Tooltip("mean(value):Q", title="Value", format=",.3f"), + ], + ) + bar_chart = (class_bars + mean_bars).add_selection(class_selection) + + class_precisions = ( + alt.Chart(precisions, title="Precision-Recall Curve") + .mark_line(point=True) + .encode( + alt.X("rc_threshold", title="Recall", scale=alt.Scale(domain=[0.0, 1.0])), + alt.Y("precision", scale=alt.Scale(domain=[0.0, 1.0])), + alt.Color("class_name"), + tooltip=[ + alt.Tooltip("class_name", title="Class"), + alt.Tooltip("rc_threshold", title="Recall"), + alt.Tooltip("precision", title="Precision", format=",.3f"), + ], + opacity=alt.condition(class_selection, alt.value(1.0), alt.value(0.2)), + ) + .properties(height=300) + ) + mean_precisions = ( + class_precisions.transform_calculate(average="'average'") + .mark_line(point=True) + .encode( + alt.X("rc_threshold"), + alt.Y("average(precision):Q"), + alt.Color("average:N"), + tooltip=[ + alt.Tooltip("average:N", title="Aggregate"), + alt.Tooltip("rc_threshold", title="Recall"), + alt.Tooltip("average(precision)", title="Avg. precision", format=",.3f"), + ], + ) + ) + precision_chart = (class_precisions + mean_precisions).add_selection(class_selection) + return bar_chart | precision_chart + + @staticmethod + def create_metric_importance_charts(model_predictions, num_samples): + if num_samples < model_predictions.shape[0]: + _predictions = model_predictions.sample(num_samples, axis=0, random_state=42) + else: + _predictions = model_predictions + + num_tps = (_predictions["tps"].iloc[:num_samples] != 0).sum() + if num_tps < 50: + st.warning( + f"Not enough true positives ({num_tps}) to calculate reliable metric importance. " + "Try increasing the number of samples or lower the IoU threshold in the side bar." + ) + + scores = _predictions["iou"] * _predictions["tps"] + metrics = _predictions[st.session_state.prediction_metric_names] + + correlations = metrics.fillna(0).corrwith(scores, axis=0).to_frame("correlation") + correlations["index"] = correlations.index.T + + mi = pd.DataFrame.from_dict( + {"index": metrics.columns, "importance": mutual_info_regression(metrics.fillna(0), scores)} + ) + sorted_metrics = mi.sort_values("importance", ascending=False, inplace=False)["index"].to_list() + + mutual_info_bars = ( + alt.Chart(mi, title="Metric Importance") + .mark_bar() + .encode( + alt.X("importance", title="Importance", scale=alt.Scale(domain=[0.0, 1.0])), + alt.Y("index", title="Metric", sort=sorted_metrics), + alt.Color("importance", scale=alt.Scale(scheme="blues")), + tooltip=[ + alt.Tooltip("index", title="Metric"), + alt.Tooltip("importance", title="Importance", format=",.3f"), + ], + ) + .properties(height=400) + ) + + correlation_bars = ( + alt.Chart(correlations, title="Metric Correlations") + .mark_bar() + .encode( + alt.X("correlation", title="Correlation", scale=alt.Scale(domain=[-1.0, 1.0])), + alt.Y("index", title="Metric", sort=sorted_metrics), + alt.Color("correlation", scale=alt.Scale(scheme="redyellowgreen", align=0.0)), + tooltip=[ + alt.Tooltip("index", title="Metric"), + alt.Tooltip("correlation", title="Correlation", format=",.3f"), + ], + ) + .properties(height=400) + ) + + return alt.hconcat(mutual_info_bars, correlation_bars).resolve_scale(color="independent") + + def sidebar_options(self): + pass + + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + st.markdown(f"# {self.title}") + + _map = metrics[metrics["metric"] == "mAP"]["value"].item() + _mar = metrics[metrics["metric"] == "mAR"]["value"].item() + col1, col2 = st.columns(2) + col1.metric("mAP", f"{_map:.3f}") + col2.metric("mAR", f"{_mar:.3f}") + st.markdown("""---""") + + st.subheader("Metric Importance") + with st.container(): + with st.expander("Description"): + st.write( + "The following charts show the dependency between model performance and each index. " + "In other words, these charts answer the question of how much is model " + "performance affected by each index. This relationship can be decomposed into two metrics:" + ) + st.markdown( + "- **Metric importance**: measures the *strength* of the dependency between and metric and model " + "performance. A high value means that the model performance would be strongly affected by " + "a change in the index. For example, a high importance in 'Brightness' implies that a change " + "in that quantity would strongly affect model performance. Values range from 0 (no dependency) " + "to 1 (perfect dependency, one can completely predict model performance simply by looking " + "at this index)." + ) + st.markdown( + "- **Metric [correlation](https://en.wikipedia.org/wiki/Correlation)**: measures the *linearity " + "and direction* of the dependency between an index and model performance. " + "Crucially, this metric tells us whether a positive change in an index " + "will lead to a positive change (positive correlation) or a negative change (negative correlation) " + "in model performance . Values range from -1 to 1." + ) + st.write( + "Finally, you can also select how many samples are included in the computation " + "with the slider, as well as filter by class with the dropdown in the side bar." + ) + + if model_predictions.shape[0] > 60_000: # Computation are heavy so allow computing for only a subset. + num_samples = st.slider( + "Number of samples", + min_value=1, + max_value=len(model_predictions), + step=max(1, (len(model_predictions) - 1) // 100), + value=max((len(model_predictions) - 1) // 2, 1), + help="To avoid too heavy computations, we subsample the data at random to the selected size, " + "computing importance values.", + ) + if num_samples < 100: + st.warning( + "Number of samples is too low to compute reliable index importances. " + "We recommend using at least 100 samples.", + ) + else: + num_samples = model_predictions.shape[0] + + with st.spinner("Computing index importance..."): + chart = self.create_metric_importance_charts( + model_predictions, + num_samples=num_samples, + ) + st.altair_chart(chart, use_container_width=True) + + st.subheader("Subset selection scores") + with st.container(): + chart = self.create_pr_charts(metrics, precisions) + st.altair_chart(chart, use_container_width=True) diff --git a/src/encord_active/app/model_assertions/sub_pages/performance_by_metric.py b/src/encord_active/app/model_assertions/sub_pages/performance_by_metric.py new file mode 100644 index 000000000..4aecc9003 --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/performance_by_metric.py @@ -0,0 +1,289 @@ +import re +from collections import namedtuple +from enum import Enum +from typing import Optional + +import altair as alt +import pandas as pd +import streamlit as st + +import encord_active.app.common.state as state + +from . import ModelAssertionsPage + +FLOAT_FMT = ",.4f" +PCT_FMT = ",.2f" +COUNT_FMT = ",d" +CompositeChart = namedtuple("CompositeChart", "chart xmin xmax") + +sub_plot_args = dict(width=370, height=300) +zoom_view_args = dict(height=50, width=800, title="Select to Zoom") + + +class ChartSubject(Enum): + TPR = "true positive rate" + FNR = "false negative rate" + + +class PerformanceMetric(ModelAssertionsPage): + title = "⚡️ Performance by Metric" + + def build_bar_chart( + self, + sorted_predictions: pd.DataFrame, + metric_name: str, + show_decomposition: bool, + title: str, + subject: ChartSubject, + ) -> alt.Chart: + str_type = "predictions" if subject == ChartSubject.TPR else "labels" + largest_bin_count = sorted_predictions["bin"].value_counts().max() + chart = ( + alt.Chart(sorted_predictions, title=title) + .transform_joinaggregate(total="count(*)") + .transform_calculate( + pctf=f"1 / {largest_bin_count}", + pct="100 / datum.total", + ) + .mark_bar(align="center", opacity=0.2) + ) + if show_decomposition: + # Aggregate over each class + return chart.encode( + alt.X("bin:Q", scale=self.x_scale), + alt.Y("sum(pctf):Q", stack="zero"), + alt.Color("class_name:N", scale=self.class_scale, legend=alt.Legend(symbolOpacity=1)), + tooltip=[ + alt.Tooltip("bin", title=metric_name, format=FLOAT_FMT), + alt.Tooltip("count():Q", title=f"Num. {str_type}", format=COUNT_FMT), + alt.Tooltip("sum(pct):Q", title=f"% of total {str_type}", format=PCT_FMT), + alt.Tooltip("class_name:N", title="Class name"), + ], + ) + else: + # Only use aggregate over all classes + return chart.encode( + alt.X("bin:Q", scale=self.x_scale), + alt.Y("sum(pctf):Q", stack="zero"), + tooltip=[ + alt.Tooltip("bin", title=metric_name, format=FLOAT_FMT), + alt.Tooltip("count():Q", title=f"Num. {str_type}", format=COUNT_FMT), + alt.Tooltip("sum(pct):Q", title=f"% of total {str_type}", format=PCT_FMT), + ], + ) + + def build_line_chart( + self, bar_chart: alt.Chart, metric_name: str, show_decomposition: bool, title: str + ) -> alt.Chart: + legend = alt.Legend(title="class name".title()) + title_shorthand = "".join(w[0].upper() for w in title.split()) + + line_chart = bar_chart.mark_line(point=True, opacity=0.5 if show_decomposition else 1.0).encode( + alt.X("bin:Q", scale=self.x_scale), + alt.Y("mean(indicator):Q"), + alt.Color("average:N", legend=legend, scale=self.class_scale), + tooltip=[ + alt.Tooltip("bin", title=metric_name, format=FLOAT_FMT), + alt.Tooltip("mean(indicator):Q", title=title_shorthand, format=FLOAT_FMT), + alt.Tooltip("average:N", title="Class name"), + ], + strokeDash=alt.value([5, 5]), + ) + + if show_decomposition: + line_chart += line_chart.mark_line(point=True).encode( + alt.Color("class_name:N", legend=legend, scale=self.class_scale), + tooltip=[ + alt.Tooltip("bin", title=metric_name, format=FLOAT_FMT), + alt.Tooltip("mean(indicator):Q", title=title_shorthand, format=FLOAT_FMT), + alt.Tooltip("class_name:N", title="Class name"), + ], + strokeDash=alt.value([10, 0]), + opacity=alt.condition(self.class_selection, alt.value(1), alt.value(0.1)), + ) + line_chart = line_chart.add_selection(self.class_selection) + return line_chart + + def build_average_rule(self, indicator_mean: float, title: str): + title_shorthand = "".join(w[0].upper() for w in title.split()) + return ( + alt.Chart(pd.DataFrame({"y": [indicator_mean], "average": ["Average"]})) + .mark_rule() + .encode( + alt.Y("y"), + alt.Color("average:N", scale=self.class_scale), + strokeDash=alt.value([5, 5]), + tooltip=[alt.Tooltip("y", title=f"Average {title_shorthand}", format=FLOAT_FMT)], + ) + ) + + def make_composite_chart( + self, df: pd.DataFrame, title: str, metric_name: str, subject: ChartSubject + ) -> Optional[CompositeChart]: + # Avoid over-shooting number of bins. + if metric_name not in df.columns: + return None + + num_unique_values = df[metric_name].unique().shape[0] + n_bins = min(st.session_state.get(state.PREDICTIONS_NBINS, 100), num_unique_values) + + # Avoid nans + df = df[[metric_name, "indicator", "class_name"]].copy().dropna(subset=[metric_name]) + + if df.empty: + st.info(f"{title}: No values for the selected metric: {metric_name}") + return None + + # Bin the data + df["bin_info"] = pd.qcut(df[metric_name], q=n_bins, duplicates="drop") + df["bin"] = df["bin_info"].map(lambda x: x.mid) + + if df["bin"].dropna().empty: + st.info(f"No scores for the selected metric: {metric_name}") + return None + + df["average"] = "Average" # Indicator for altair charts + show_decomposition = st.session_state[state.PREDICTIONS_DECOMPOSE_CLASSES] + + bar_chart = self.build_bar_chart(df, metric_name, show_decomposition, title=title, subject=subject) + line_chart = self.build_line_chart(bar_chart, metric_name, show_decomposition, title=title) + mean_rule = self.build_average_rule(df["indicator"].mean(), title=title) + + chart_composition = bar_chart + line_chart + mean_rule + chart_composition = chart_composition.encode( + alt.X(title=metric_name.title()), alt.Y(title=title.title()) + ).properties( + **sub_plot_args + ) # TODO Is there a better way to set size? + return CompositeChart(chart_composition, df["bin"].min(), df["bin"].max()) + + def sidebar_options(self): + c1, c2, c3 = st.columns([4, 4, 3]) + with c1: + self.prediction_metric_in_sidebar() + + with c2: + st.number_input( + "Number of buckets (n)", + min_value=5, + max_value=200, + value=50, + help="Choose the number of bins to discritize the prediction metric values into.", + key=state.PREDICTIONS_NBINS, + ) + with c3: + st.write("") # Make some spacing. + st.write("") + st.checkbox( + "Show class decomposition", + key=state.PREDICTIONS_DECOMPOSE_CLASSES, + help="When checked, every plot will have a separate component for each class.", + ) + + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + st.markdown(f"# {self.title}") + + if model_predictions.shape[0] == 0: + st.write("No predictions of the given class(es).") + elif state.PREDICTIONS_METRIC not in st.session_state: + # This shouldn't happen with the current flow. The only way a user can do this + # is if he/she write custom code to bypass running the metrics. In this case, + # I think that it is fair to not give more information than this. + st.write( + "No metrics computed for the your model predictions. " + "With `encord-active import predictions /path/to/predictions.pkl`, " + "Encord Active will automatically run compute the metrics." + ) + else: + with st.expander("Details", expanded=False): + st.markdown( + """### The View + +On this page, your model scores are displayed as a function of the metric that you selected in the top bar. +Samples are discritized into $n$ equally sized buckets and the middle point of each bucket is displayed as the x-value in the plots. +Bars indicate the number of samples in each bucket, while lines indicate the true positive and false negative rates of each bucket. + + +Metrics marked with (P) are metrics computed on your predictions. +Metrics marked with (F) are frame level metrics, which depends on the frame that each prediction is associated +with. In the "False Negative Rate" plot, (O) means metrics compoted on Object labels. + +For metrics that are computed on predictions (P) in the "True Positive Rate" plot, the corresponding "label metrics" (O/F) computed +on your labels are used for the "False Negative Rate" plot. +""", + unsafe_allow_html=True, + ) + self.metric_details_description() + + metric_name = st.session_state[state.PREDICTIONS_METRIC] + + label_metric_name = metric_name + if metric_name[-3:] == "(P)": # Replace the P with O: "Metric (P)" -> "Metric (O)" + label_metric_name = re.sub(r"(.*?\()P(\))", r"\1O\2", metric_name) + + if not label_metric_name in labels.columns: + label_metric_name = re.sub( + r"(.*?\()O(\))", r"\1F\2", label_metric_name + ) # Look for it in frame label metrics. + + classes_for_coloring = ["Average"] + if st.session_state.get(state.PREDICTIONS_DECOMPOSE_CLASSES, False): + unique_classes = set(model_predictions["class_name"].unique()).union(labels["class_name"].unique()) + classes_for_coloring += sorted(list(unique_classes)) + + # Ensure same colors between plots + self.class_scale = alt.Scale( + domain=classes_for_coloring, + ) # Used to sync colors between plots. + self.class_selection = alt.selection_multi(fields=["class_name"]) # Used to sync selections between plots. + + # Allow zooming via a "zoom_chart" + self.x_scale_interval = alt.selection_interval(encodings=["x"]) + self.x_scale = alt.Scale(domain=self.x_scale_interval.ref()) + + # TPR + predictions = model_predictions.rename(columns={"tps": "indicator"}) + tpr = self.make_composite_chart(predictions, "True Positive Rate", metric_name, subject=ChartSubject.TPR) + if tpr is None: + st.stop() + + # FNR + fnr = self.make_composite_chart( + labels.rename(columns={"fns": "indicator"}), + "False Negative Rate", + label_metric_name, + subject=ChartSubject.FNR, + ) + + if fnr is None: # Label metric couldn't be matched to + zoom_chart = ( + tpr.chart.encode( + alt.X("bin:Q"), alt.Y(title="TPR") # Avoid zooming the actual zoom selection view. + ) + .properties(**zoom_view_args) + .add_selection(self.x_scale_interval) + ) + st.altair_chart(zoom_chart & tpr.chart.properties(width=800)) + st.stop() + + # Zoom chart - get entire range of boths tprs and fnrs + xmin = min(tpr.xmin, fnr.xmin) + xmax = max(tpr.xmax, fnr.xmax) + zoom_chart = ( + tpr.chart.encode( + alt.X( + "bin:Q", scale=alt.Scale(domain=[xmin, xmax]) + ), # Avoid zooming the actual zoom selection view. + alt.Y(title="TPR"), + ) + .properties(**zoom_view_args) + .add_selection(self.x_scale_interval) + ) + + st.altair_chart(zoom_chart & (tpr.chart | fnr.chart)) diff --git a/src/encord_active/app/model_assertions/sub_pages/true_positives.py b/src/encord_active/app/model_assertions/sub_pages/true_positives.py new file mode 100644 index 000000000..99606b9ee --- /dev/null +++ b/src/encord_active/app/model_assertions/sub_pages/true_positives.py @@ -0,0 +1,49 @@ +import pandas as pd +import streamlit as st + +from encord_active.app.common.colors import Color +from encord_active.app.model_assertions.components import metric_view + +from . import HistogramMixin, ModelAssertionsPage + + +class TruePositivesPage(ModelAssertionsPage, HistogramMixin): + title = "✅ True Positives" + + def sidebar_options(self): + self.prediction_metric_in_sidebar() + self.row_col_settings_in_sidebar() + + def build( + self, + model_predictions: pd.DataFrame, + labels: pd.DataFrame, + metrics: pd.DataFrame, + precisions: pd.DataFrame, + ): + st.markdown(f"# {self.title}") + + with st.expander("Details"): + color = Color.PURPLE + st.markdown( + f"""### The view +These are the predictions for which the IOU was sufficiently high and the confidence score was +the highest amongst predictions that overlap with the label. + +--- + +**Color**: +The {color.name.lower()} boxes marks the true positive predictions. +The remaining colors correspond to the dataset labels with the colors you are used to from the label editor. + """, + unsafe_allow_html=True, + ) + self.metric_details_description() + metric_name = st.session_state.predictions_metric + tp_df = model_predictions[model_predictions["tps"] == 1.0].dropna(subset=[metric_name]) + if tp_df.shape[0] == 0: + st.write("No true positives") + else: + histogram = self.get_histogram(tp_df, metric_name) + st.altair_chart(histogram, use_container_width=True) + metric_view(tp_df, box_color=color) diff --git a/src/encord_active/app/static/landing-page-documentation.md b/src/encord_active/app/static/landing-page-documentation.md new file mode 100644 index 000000000..357f6211c --- /dev/null +++ b/src/encord_active/app/static/landing-page-documentation.md @@ -0,0 +1,11 @@ +## Documentation +This page is not meant as a complete documentation, but merely a place for you to get started. +We recommend that you visit our [Documentation](https://encord-active-docs.web.app/) to find in-depth descriptions of topics like: + +- 🏆 [Common workflows](https://encord-active-docs.web.app/category/workflows) to improve your model performance +- 🦮 [Hands-on Tutorials](https://encord-active-docs.web.app/category/tutorials) +- 💫 Importing [data](https://encord-active-docs.web.app/workflows/import-data) and [predictions](https://encord-active-docs.web.app/workflows/import-predictions) +- 💻 [Writing your own custom metrics](https://encord-active-docs.web.app/metrics/write-your-own) +- 📄 [What is displayed in each page](https://encord-active-docs.web.app/category/pages) +- ⌨️ [How to use the CLI](https://encord-active-docs.web.app/category/command-line-interface) + diff --git a/src/encord_active/app/static/landing-page-intro.md b/src/encord_active/app/static/landing-page-intro.md new file mode 100644 index 000000000..ddfa76ab0 --- /dev/null +++ b/src/encord_active/app/static/landing-page-intro.md @@ -0,0 +1,10 @@ +## Encord Active +**Encord Active** is an open-source Python library that enables you to get act upon insights you get from your data, labels, and models. +With Encord Active, you can: + +* Build high quality datasets +* Find label errors and improve them +* Build more robust models + +This page provides general information on how to use Encord Active. + diff --git a/src/encord_active/app/streamlit_entrypoint.py b/src/encord_active/app/streamlit_entrypoint.py new file mode 100644 index 000000000..22af258fc --- /dev/null +++ b/src/encord_active/app/streamlit_entrypoint.py @@ -0,0 +1,82 @@ +import argparse +from functools import reduce +from typing import Callable, Dict, Optional, Union + +import streamlit as st + +from encord_active.app.actions_page.export_balance import export_balance +from encord_active.app.actions_page.export_filter import export_filter +from encord_active.app.common import state +from encord_active.app.common.utils import set_page_config +from encord_active.app.data_quality.common import MetricType +from encord_active.app.frontend_components import pages_menu +from encord_active.app.model_assertions.sub_pages.false_negatives import ( + FalseNegativesPage, +) +from encord_active.app.model_assertions.sub_pages.false_positives import ( + FalsePositivesPage, +) +from encord_active.app.model_assertions.sub_pages.metrics import MetricsPage +from encord_active.app.model_assertions.sub_pages.performance_by_metric import ( + PerformanceMetric, +) +from encord_active.app.model_assertions.sub_pages.true_positives import ( + TruePositivesPage, +) +from encord_active.app.views.landing_page import landing_page +from encord_active.app.views.metrics import explorer, summary +from encord_active.app.views.model_assertions import model_assertions + +Pages = Dict[str, Union[Callable, "Pages"]] # type: ignore + +pages: Pages = { + "Encord Active": landing_page, + "Data Quality": {"Summary": summary(MetricType.DATA_QUALITY), "Explorer": explorer(MetricType.DATA_QUALITY)}, + "Label Quality": {"Summary": summary(MetricType.LABEL_QUALITY), "Explorer": explorer(MetricType.LABEL_QUALITY)}, + "Model Assertions": { + "Metrics": model_assertions(MetricsPage()), + "Performance By Metric": model_assertions(PerformanceMetric()), + "True Positives": model_assertions(TruePositivesPage()), + "False Positives": model_assertions(FalsePositivesPage()), + "False Negatives": model_assertions(FalseNegativesPage()), + }, + "Actions": {"Filter & Export": export_filter, "Balance & Export": export_balance}, +} + +SEPARATOR = "#" + + +def to_item(k, v, parent_key: Optional[str] = None): + # NOTE: keys must be unique for the menu to render properly + composite_key = SEPARATOR.join(filter(None, [parent_key, k])) + item = {"key": composite_key, "label": k} + if isinstance(v, dict): + item["children"] = to_items(v, parent_key=composite_key) + return item + + +def to_items(d: dict, parent_key: Optional[str] = None): + return [to_item(k, v, parent_key) for k, v in d.items()] + + +def main(project_path: str): + set_page_config() + + if not state.set_project_dir(project_path): + st.error(f"Project not found for directory `{project_path}`.") + + with st.sidebar: + items = to_items(pages) + key = pages_menu(items=items) + path = key.split(SEPARATOR) if key else [] + + render = reduce(dict.__getitem__, path, pages) if path else pages["Encord Active"] # type: ignore + if callable(render): + render() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("project_dir", type=str, help="Directory in which your project data is stored") + args = parser.parse_args() + main(args.project_dir) diff --git a/src/encord_active/app/views/landing_page.py b/src/encord_active/app/views/landing_page.py new file mode 100644 index 000000000..af7286dcb --- /dev/null +++ b/src/encord_active/app/views/landing_page.py @@ -0,0 +1,30 @@ +from pathlib import Path + +import streamlit as st + +from encord_active.app.common.utils import set_page_config, setup_page + + +def landing_page(): + setup_page() + + logo_pth = Path(__file__).parents[1] / "assets" / "encord_2_02.png" + _, col, _ = st.columns([4, 2, 4]) # Hack to center and scale content + with col: + st.image(logo_pth.as_posix()) + + _, col, _ = st.columns([2, 6, 2]) + + with col: + st.video("https://youtu.be/C0h_HqX1WxU") + + left_col, right_col = st.columns(2) + with left_col: + st.markdown((Path(__file__).parent.parent / "static" / "landing-page-intro.md").read_text()) + with right_col: + st.markdown((Path(__file__).parent.parent / "static" / "landing-page-documentation.md").read_text()) + + +if __name__ == "__main__": + set_page_config() + landing_page() diff --git a/src/encord_active/app/views/metrics.py b/src/encord_active/app/views/metrics.py new file mode 100644 index 000000000..b15ef0d7e --- /dev/null +++ b/src/encord_active/app/views/metrics.py @@ -0,0 +1,35 @@ +from encord_active.app.common.components import sticky_header +from encord_active.app.common.utils import setup_page +from encord_active.app.data_quality.common import MetricType, load_available_metrics +from encord_active.app.data_quality.sub_pages.explorer import ExplorerPage +from encord_active.app.data_quality.sub_pages.summary import SummaryPage + + +def summary(metric_type: MetricType): + def render(): + setup_page() + page = SummaryPage() + + with sticky_header(): + page.sidebar_options() + + page.build(metric_type.value) + + return render + + +def explorer(metric_type: MetricType): + def render(): + setup_page() + page = ExplorerPage() + available_metrics = load_available_metrics(metric_type.value) + + with sticky_header(): + selected_df = page.sidebar_options(available_metrics) + + if selected_df is None: + return + + page.build(selected_df) + + return render diff --git a/src/encord_active/app/views/model_assertions.py b/src/encord_active/app/views/model_assertions.py new file mode 100644 index 000000000..8bb963793 --- /dev/null +++ b/src/encord_active/app/views/model_assertions.py @@ -0,0 +1,76 @@ +from copy import deepcopy + +import streamlit as st + +import encord_active.app.common.state as state +import encord_active.app.model_assertions.data as pred_data +from encord_active.app.common.components import sticky_header +from encord_active.app.common.utils import setup_page +from encord_active.app.model_assertions.map_mar import compute_mAP_and_mAR +from encord_active.app.model_assertions.settings import common_settings +from encord_active.app.model_assertions.sub_pages import Page + + +def model_assertions(page: Page): + def render(): + setup_page() + + if not pred_data.check_model_prediction_availability(): + st.markdown( + "# Missing Model Predictions\n" + "This project does not have any imported predictions. " + "Please refer to the " + "[Importing Model Predictions](https://encord-active-docs.web.app/sdk/importing-model-predictions) " + "section of the documentation to learn how to import your predictions." + ) + return + + st.session_state.selected_class_idx = pred_data.get_class_idx() + st.session_state.full_class_idx = deepcopy(pred_data.get_class_idx()) + ( + st.session_state.model_predictions, + st.session_state.prediction_metric_names, + ) = pred_data.get_model_predictions() or (None, None) + if st.session_state.selected_class_idx is None: + st.error("Couldn't load model predictions") + return + + st.session_state.labels, st.session_state.label_metric_names = pred_data.get_labels() or (None, None) + if st.session_state.labels is None: + return + + st.session_state.gt_matched = pred_data.get_gt_matched() + st.session_state.metric_meta = pred_data.get_metadata_files() + + with sticky_header(): + common_settings() + page.sidebar_options() + + metrics, precisions, tps, fp_reasons, fns = compute_mAP_and_mAR( + iou_threshold=st.session_state.get(state.IOU_THRESHOLD), + ignore_unmatched_frames=st.session_state[state.IGNORE_FRAMES_WO_PREDICTIONS], + ) + st.session_state.model_predictions["tps"] = tps.astype(float) + st.session_state.model_predictions["fp_reason"] = fp_reasons["fp_reason"] + st.session_state.labels["fns"] = fns + + # Sort predictions and labels according to selected metrics. + pred_sort_column = st.session_state.get(state.PREDICTIONS_METRIC, st.session_state.prediction_metric_names[0]) + st.session_state.sorted_model_predictions = st.session_state.model_predictions.sort_values( + [pred_sort_column], axis=0 + ) + + label_sort_column = st.session_state.get(state.PREDICTIONS_LABEL_METRIC, st.session_state.label_metric_names[0]) + st.session_state.sorted_labels = st.session_state.labels.sort_values([label_sort_column], axis=0) + + if st.session_state[state.IGNORE_FRAMES_WO_PREDICTIONS]: + labels = pred_data.filter_labels_for_frames_wo_predictions() + else: + labels = st.session_state.sorted_labels + + _labels, _metrics, _model_pred, _precisions = pred_data.prediction_and_label_filtering( + labels, metrics, st.session_state.sorted_model_predictions, precisions + ) + page.build(model_predictions=_model_pred, labels=_labels, metrics=_metrics, precisions=_precisions) + + return render diff --git a/src/encord_active/lib/__init__.py b/src/encord_active/lib/__init__.py new file mode 100644 index 000000000..5bd7b5c6b --- /dev/null +++ b/src/encord_active/lib/__init__.py @@ -0,0 +1,6 @@ +import sys + +from loguru import logger + +logger.remove() +logger.add(sys.stderr, level="INFO") diff --git a/src/encord_active/lib/coco/__init__.py b/src/encord_active/lib/coco/__init__.py new file mode 100644 index 000000000..08942209a --- /dev/null +++ b/src/encord_active/lib/coco/__init__.py @@ -0,0 +1,10 @@ +try: + """Trying to import the coco encoder to see if dependencies are installed.""" + from encord_active.lib.coco.encoder import CocoEncoder as _CocoEncoder +except ModuleNotFoundError: + raise SystemExit( + """ + Some dependencies were not found. Please ensure to install encord-active[coco] when using the COCO parsers/importerself. + Addional information can be found here: https://encord-active-docs.web.app/installation#coco-extras + """ + ) diff --git a/src/encord_active/lib/coco/datastructure.py b/src/encord_active/lib/coco/datastructure.py new file mode 100644 index 000000000..962fd98f0 --- /dev/null +++ b/src/encord_active/lib/coco/datastructure.py @@ -0,0 +1,115 @@ +from dataclasses import dataclass +from datetime import date +from typing import List, Optional, Tuple + +COCO_PERMANENT_ANNOTATIONS = [ + "id_", + "image_id", + "category_id", + "segmentation", + "area", + "bbox", + "iscrowd", + "keypoints", + "num_keypoints", +] + + +class SuperClass: + def asdict(self) -> dict: + pass + + +@dataclass +class CocoInfo: + # DENIS: extensibility?? + contributor: Optional[str] + date_created: date + url: str + version: str # DENIS: figure this out + year: str + description: str + + +@dataclass +class CocoCategory: + supercategory: str + id_: int + name: str + + +@dataclass +class CocoImage: + id_: int + height: int + width: int + file_name: str + coco_url: str + flickr_url: str + + +CocoBbox = Tuple[float, float, float, float] +"""x, y, w, h""" + + +@dataclass +class CocoAnnotation(SuperClass): + # DENIS: does this depend on the format? Can this be extended? + area: float + bbox: CocoBbox + category_id: int + id_: int # DENIS: how is this translated to the json. => maybe a wrapper around asdict + image_id: int + iscrowd: int + segmentation: List # DENIS: this is actually some union + keypoints: Optional[List[int]] = None + num_keypoints: Optional[int] = None + track_id: Optional[int] = None + encord_track_uuid: Optional[str] = None + rotation: Optional[float] = None + + +@dataclass +class CocoResult(SuperClass): + image_id: int + category_id: int + score: float + bbox: Optional[CocoBbox] = None + segmentation: Optional[List] = None # DENIS: this is actually some union] + keypoints: Optional[List[int]] = None + + +@dataclass +class Coco: + info: CocoInfo + licenses: List[dict] # TODO: + categories: List[CocoCategory] + images: List[CocoImage] + annotations: List[CocoAnnotation] + + +@dataclass +class EncordCocoMetadata: + image_id: int + image_url: str + image_path: str + image_title: str + label_hash: str + data_hash: str + + +def to_attributes_field(res: dict, include_null_annotations: bool = False) -> dict: + res_tmp = {} + for key, value in res.items(): + if value is None and not include_null_annotations: + continue + + if key in COCO_PERMANENT_ANNOTATIONS: + if key == "id_": + res_tmp["id"] = value + else: + res_tmp[key] = value + else: + attr = res_tmp.setdefault("attributes", {}) + attr[key] = value + return res_tmp diff --git a/src/encord_active/lib/coco/encoder.py b/src/encord_active/lib/coco/encoder.py new file mode 100644 index 000000000..b4ad38514 --- /dev/null +++ b/src/encord_active/lib/coco/encoder.py @@ -0,0 +1,803 @@ +"""DENIS: think properly about the structure, the transcoders and how we want to subclass or extend this so people +can plug into the different parts easily. +ideas +* a class where the individual parts can be overwritten +* a class where the individual transformers can be re-assigned +* +DENIS: how are we going to document this in Sphinx if this class is independent? +DENIS: +* parallel downloads with a specific flag +* saving the annotation file with a specific flag +* labels class for better type support. +""" +import copy +import datetime +import json +import logging +import subprocess +import tempfile +from dataclasses import asdict, dataclass +from itertools import chain +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import pandas as pd +import requests +from encord.objects.common import Shape +from encord.objects.ontology_object import Object +from encord.objects.ontology_structure import OntologyStructure +from shapely.geometry import Polygon +from tqdm import tqdm + +from encord_active.lib.coco.datastructure import ( + CocoAnnotation, + CocoBbox, + SuperClass, + to_attributes_field, +) + +logger = logging.getLogger(__name__) + +DOWNLOAD_FILES_DEFAULT = False +FORCE_DOWNLOAD_DEFAULT = False +DOWNLOAD_FILE_PATH_DEFAULT = Path(".") +INCLUDE_VIDEOS_DEFAULT = True +INCLUDE_UNANNOTATED_VIDEOS_DEFAULT = False +INCLUDE_NULL_ANNOTATIONS = True + + +@dataclass +class Size: + width: int + height: int + + +@dataclass +class ImageLocation: + data_hash: str + file_name: str + + +@dataclass +class VideoLocation: + data_hash: str + file_name: str + frame_num: int + + +DataLocation = Union[ImageLocation, VideoLocation] + + +class EncodingError(Exception): + def __init__(self, message: str): + super().__init__(message) + + +def get_size(*args, **kwargs) -> Size: + # DENIS: this belongs in a utils folder. + return Size(1, 2) # A stub for now + + +def get_polygon_from_dict(polygon_dict, W, H): + return [(polygon_dict[str(i)]["x"] * W, polygon_dict[str(i)]["y"] * H) for i in range(len(polygon_dict))] + + +# DENIS: TODO: focus on doing the parser for now for segmentations for images as it was intended. Seems like +# for other formats I can still add stuff or have the clients extend what we have. + +# DENIS: should these labels be the data structure that I've invented for them instead of the encord dict? +class CocoEncoder: + """This class has been purposefully built in a modular fashion for extensibility in mind. You are encouraged to + subclass this encoder and change any of the custom functions. The return types are given for convenience, but these + can also be subclassed for your needs. + All functions which could be static are deliberately not made static so you have the option to access the `self` + object at any time. + """ + + def __init__(self, labels_list: List[dict], metrics: dict, ontology: OntologyStructure): + self._labels_list = labels_list + self._metrics = metrics + self._ontology = ontology + self._coco_json: dict = dict() + self._current_annotation_id: int = 0 + self._object_hash_to_track_id_map: dict = dict() + self._coco_categories_id_to_ontology_object_map: dict = dict() # DENIS: do we need this? + self._feature_hash_to_coco_category_id_map: dict = dict() + self._data_hash_to_image_id_map: dict = dict() + """Map of (data_hash, frame_offset) to the image id""" + + # self._data_location_to_image_id_map = dict() + + self._download_files = DOWNLOAD_FILES_DEFAULT + self._force_download = FORCE_DOWNLOAD_DEFAULT + self._download_file_path = DOWNLOAD_FILE_PATH_DEFAULT + self._include_videos = INCLUDE_VIDEOS_DEFAULT + self._include_unannotated_videos = INCLUDE_UNANNOTATED_VIDEOS_DEFAULT + self._include_null_annotations = INCLUDE_NULL_ANNOTATIONS + + def encode( + self, + *, + download_files: bool = DOWNLOAD_FILES_DEFAULT, + force_download: bool = FORCE_DOWNLOAD_DEFAULT, + download_file_path: Path = DOWNLOAD_FILE_PATH_DEFAULT, + include_videos: bool = INCLUDE_VIDEOS_DEFAULT, + include_unannotated_videos: bool = INCLUDE_UNANNOTATED_VIDEOS_DEFAULT, + include_null_annotation: bool = INCLUDE_NULL_ANNOTATIONS, + ) -> dict: + """ + Args: + download_files: If set to true, the images are downloaded into a local directory and the `coco_url` of the + images will point to the location of the local directory. DENIS: can also maybe have a Path obj here. + force_download: If set to true, the images are downloaded even if they already exist in the local directory. + download_file_path: + Root path to where the images and videos are downloaded or where downloaded images are looked up from. + For example, if `include_unannotated_videos = True` then this is the root path of the + `videos/` directory. + include_videos: If set to true, the videos are included in the output. + include_unannotated_videos: + This will be ignored if the files are not downloaded (whether they are being downloaded now or they + were already there) in which case it will default to False. The code will assume that the video is + downloaded and expanded correctly in the same way that would happen if the video was downloaded via + the `download_files = True` argument. + include_null_annotation: + If set to true, then annotations with null values will still be included in the json file.""" + self._download_files = download_files + self._force_download = force_download + self._download_file_path = download_file_path + self._include_videos = include_videos + self._include_unannotated_videos = include_unannotated_videos + self._include_null_annotations = include_null_annotation + + self._coco_json["info"] = self.get_info() + self._coco_json["categories"] = self.get_categories() + self._coco_json["images"] = self.get_images() + self._coco_json["annotations"] = self.get_annotations() + + return self._coco_json + + def get_info(self) -> dict: + return { + "description": self.get_description(), + "contributor": None, # TODO: these fields also need a response + "date_created": str(datetime.datetime.now().isoformat()), + "url": None, + "version": None, + "year": None, + } + + def get_description(self) -> Optional[str]: + if len(self._labels_list) == 0: + return None + else: + return self._download_file_path.as_posix().split("/")[-1] + + def get_categories(self) -> List[dict]: + """This does not translate classifications as they are not part of the Coco spec.""" + categories = [] + for object_ in self._ontology.objects: + categories.append(self.get_category(object_)) + + return categories + + def get_category(self, object_: Object) -> dict: + super_category = self.get_super_category(object_) + ret = { + "supercategory": super_category, + "id": self.add_to_object_map_and_get_next_id(object_), + "name": object_.name, + "object_hash": object_.feature_node_hash, + } + if object_.shape.value == "point": + # TODO: we will have to do something similar for skeletons. + ret["keypoints"] = "keypoint" + ret["skeleton"] = [] + return ret + + def get_super_category(self, object_: Object) -> str: + return object_.shape.value + + def add_to_object_map_and_get_next_id(self, object_: Object) -> int: + # 0 id is reserved for the background class + id_ = len(self._coco_categories_id_to_ontology_object_map) + 1 + self._coco_categories_id_to_ontology_object_map[id_] = object_ + self._feature_hash_to_coco_category_id_map[object_.feature_node_hash] = id_ + return id_ + + def get_category_name(self, object_: Object) -> str: + return object_.name + + def get_images(self) -> list: + """All the data is in the specific label_row""" + images = [] + for labels in tqdm(self._labels_list, desc="Downloading data units", position=0, leave=True): + label_hash = labels["label_hash"] + for data_unit in labels["data_units"].values(): + data_type = data_unit["data_type"] + if "application/dicom" in data_type: + images.extend(self.get_dicom(label_hash, data_unit)) + elif "video" not in data_type: + images.append(self.get_image(label_hash, data_unit)) + else: + images.extend(self.get_video_images(label_hash, data_unit)) + return images + + def get_dicom(self, label_hash: str, data_unit: dict) -> list: + # NOTE: could give an option whether to include dicoms, but this is inferred by which labels we request. + + data_hash = data_unit["data_hash"] + + images = [] + + height = data_unit["height"] + width = data_unit["width"] + + for frame_num in data_unit["labels"].keys(): + dicom_image = self.get_dicom_image(label_hash, data_hash, height, width, int(frame_num)) + images.append(dicom_image) + + return images + + def get_image(self, label_hash: str, data_unit: dict) -> dict: + # DENIS: we probably want a map of this image id to image hash in our DB, including the image_group hash. + + """ + DENIS: next up: here we need to branch off and create the videos + * coco_url, height, width will be the same + * id will be continuous + * file_name will be also continuous according to all the images that are being extracted from the video. + Do all the frames, and the ones without annotations will just have no corresponding annotations. We can + still later have an option to exclude them and delete the produced images. + """ + image_id = len(self._data_hash_to_image_id_map) + data_hash = data_unit["data_hash"] + self._data_hash_to_image_id_map[(data_hash, 0)] = image_id + return { + "coco_url": data_unit["data_link"], + "id": image_id, + "image_title": data_unit["data_title"], + "file_name": self.get_file_name_and_download_image(label_hash, data_unit), + "height": data_unit["height"], + "width": data_unit["width"], + "label_hash": label_hash, + "data_hash": data_hash, + "frame_num": 0, + } + + def get_file_name_and_download_image(self, label_hash: str, data_unit: dict) -> str: + data_hash = data_unit["data_hash"] + url = data_unit["data_link"] + image_extension = data_unit["data_type"].split("/")[-1] + + relative_destination_path = Path("data").joinpath( + Path(label_hash), Path("images"), Path(f"{data_hash}.{image_extension}") + ) + absolute_destination_path = self._download_file_path.joinpath(relative_destination_path) + download_condition = self._download_files and not absolute_destination_path.exists() + + if download_condition or self._force_download: + self.download_image(url, absolute_destination_path) + + return str(relative_destination_path) + + def get_video_images(self, label_hash: str, data_unit: dict) -> List[dict]: + if not self._include_videos: + return [] + + video_title = data_unit["data_title"] + url = data_unit["data_link"] + data_hash = data_unit["data_hash"] + + destination_path = self._download_file_path.joinpath(Path("data"), Path(label_hash), Path("images")) + download_condition = self._download_files and len(list(destination_path.glob("*.png"))) == 0 + if download_condition or self._force_download: + self.download_video_images(url, label_hash, data_hash) + + images = [] + coco_url = data_unit["data_link"] + height = data_unit["height"] + width = data_unit["width"] + + path_to_video_dir = self._download_file_path.joinpath(Path("videos"), Path(data_hash)) + if self._include_unannotated_videos and path_to_video_dir.is_dir(): + # DENIS: log something for transparency? + for frame_num in range(len(list(path_to_video_dir.iterdir()))): + images.append( + self.get_video_image(label_hash, data_hash, video_title, coco_url, height, width, int(frame_num)) + ) + else: + for frame_num in data_unit["labels"].keys(): + images.append( + self.get_video_image(label_hash, data_hash, video_title, coco_url, height, width, int(frame_num)) + ) + + return images + + # def get_frame_numbers(self, data_unit: dict) -> Iterator: # DENIS: use this to remove the above if/else. + + def get_dicom_image(self, label_hash: str, data_hash: str, height: int, width: int, frame_num: int) -> dict: + image_id = len(self._data_hash_to_image_id_map) + self._data_hash_to_image_id_map[(data_hash, frame_num)] = image_id + + return { + # DICOM does not have a one to one mapping between a frame and a DICOM series file. + "coco_url": "", + "id": image_id, + "file_name": self.get_dicom_file_path(data_hash, frame_num), + "height": height, + "width": width, + "label_hash": label_hash, + "data_hash": data_hash, + "frame_num": frame_num, + } + + def get_video_image( + self, label_hash: str, data_hash: str, video_title: str, coco_url: str, height: int, width: int, frame_num: int + ): + image_id = len(self._data_hash_to_image_id_map) + self._data_hash_to_image_id_map[(data_hash, frame_num)] = image_id + + return { + "coco_url": coco_url, + "id": image_id, + "video_title": video_title, + "file_name": self.get_video_file_path(data_hash, frame_num), + "height": height, + "width": width, + "label_hash": label_hash, + "data_hash": data_hash, + "frame_num": frame_num, + } + + def get_dicom_file_path(self, data_hash: str, frame_num: int) -> str: + path = Path("dicom") / data_hash / str(frame_num) + return str(path) + + def get_video_file_path(self, data_hash: str, frame_num: int) -> str: + frame_file_name = Path(f"{frame_num}.jpg") + video_file_path = Path("videos").joinpath(Path(data_hash), frame_file_name) + return str(video_file_path) + + def download_video_images(self, url: str, data_hash: str, destination_path: Path) -> None: + with tempfile.TemporaryDirectory(str(Path("."))) as temporary_directory: + video_location = Path(temporary_directory).joinpath(Path(data_hash)) + download_file( + url, + video_location, + ) + + extract_frames(video_location, destination_path, data_hash) + + def get_annotations(self): + annotations = [] + + # DENIS: need to make sure at least one image + for labels in tqdm(self._labels_list, desc="Processing annotations"): + label_hash = labels["label_hash"] + for data_unit in labels["data_units"].values(): + data_hash = data_unit["data_hash"] + data_unit_metrics = self._metrics[label_hash][data_hash] + + if data_unit["data_type"] in ["video", "application/dicom"]: + if not self._include_videos: + continue + for frame_num, frame_item in data_unit["labels"].items(): + image_id = self.get_image_id(data_hash, int(frame_num)) + objects = frame_item["objects"] + object_metrics = copy.deepcopy(data_unit_metrics[f"{frame_num:05d}"]) + annotations.extend(self.get_annotation(objects, object_metrics, image_id)) + + else: + image_id = self.get_image_id(data_hash) + objects = data_unit["labels"]["objects"] + object_metrics = copy.deepcopy(data_unit_metrics["00000"]) + annotations.extend(self.get_annotation(objects, object_metrics, image_id)) + + return annotations + + # DENIS: naming with plural/singular + def get_annotation(self, objects: List[dict], metrics: dict, image_id: int) -> List[dict]: + annotations = [] + frame_level = metrics.pop("frame-level") + for object_ in objects: + shape = object_["shape"] + + # DENIS: abstract this + for image_data in self._coco_json["images"]: + if image_data["id"] == image_id: + size = Size(width=image_data["width"], height=image_data["height"]) + + # DENIS: would be nice if this shape was an enum => with the Json support. + if shape == Shape.BOUNDING_BOX.value: + # DENIS: how can I make sure this can be extended properly? At what point do I transform this to a JSON? + # maybe I can have an `asdict` if this is a dataclass, else just keep the json and have the return type + # be a union?! + res = self.get_bounding_box(object_, image_id, size) + elif shape == Shape.ROTATABLE_BOUNDING_BOX.value: + res = self.get_rotatable_bounding_box(object_, image_id, size) + elif shape == Shape.POLYGON.value: + res = self.get_polygon(object_, image_id, size) + elif shape == Shape.POLYLINE.value: + res = self.get_polyline(object_, image_id, size) + elif shape == Shape.POINT.value: + res = self.get_point(object_, image_id, size) + elif shape == Shape.SKELETON.value: + res = self.get_skeleton(object_, image_id, size) + else: + raise ValueError(f"Unsupported shape: {shape}") + + res_dict = asdict(res) + res_dict["frame_metrics"] = frame_level + res_dict["object_metrics"] = metrics.get(object_["objectHash"], {}) + annotations.append(to_attributes_field(res_dict, include_null_annotations=self._include_null_annotations)) + return annotations + + def get_bounding_box(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + x, y = ( + object_["boundingBox"]["x"] * size.width, + object_["boundingBox"]["y"] * size.height, + ) + w, h = ( + object_["boundingBox"]["w"] * size.width, + object_["boundingBox"]["h"] * size.height, + ) + area = w * h + segmentation = [[x, y, x + w, y, x + w, y + h, x, y + h]] + bbox = (x, y, w, h) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + ) + + def get_rotatable_bounding_box(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + x, y = ( + object_["rotatableBoundingBox"]["x"] * size.width, + object_["rotatableBoundingBox"]["y"] * size.height, + ) + w, h = ( + object_["rotatableBoundingBox"]["w"] * size.width, + object_["rotatableBoundingBox"]["h"] * size.height, + ) + area = w * h + segmentation = [[x, y, x + w, y, x + w, y + h, x, y + h]] + bbox = (x, y, w, h) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + rotation = object_["rotatableBoundingBox"]["theta"] + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + rotation=rotation, + ) + + def get_polygon(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + polygon = get_polygon_from_dict(object_["polygon"], size.width, size.height) + segmentation = [list(chain(*polygon))] + polygon = Polygon(polygon) + area = polygon.area + x, y, x_max, y_max = polygon.bounds + w, h = x_max - x, y_max - y + + bbox = (x, y, w, h) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + ) + + def get_polyline(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + """Polylines are technically not supported in COCO, but here we use a trick to allow a representation.""" + polygon = get_polygon_from_dict(object_["polyline"], size.width, size.height) + polyline_coordinate = self.join_polyline_from_polygon(list(chain(*polygon))) + segmentation = [polyline_coordinate] + area = 0 + bbox = self.get_bbox_for_polyline(polygon) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + ) + + def get_bbox_for_polyline(self, polygon: list) -> CocoBbox: + if len(polygon) == 2: + # We have the edge case of a single edge polygon. + first_point = polygon[0] + second_point = polygon[1] + x = min(first_point[0], second_point[0]) + y = min(first_point[1], second_point[1]) + w = abs(first_point[0] - second_point[0]) + h = abs(first_point[1] - second_point[1]) + else: + polygon_shapely = Polygon(polygon) + x, y, x_max, y_max = polygon_shapely.bounds + w, h = x_max - x, y_max - y + return (x, y, w, h) + + @staticmethod + def join_polyline_from_polygon(polygon: List[float]) -> List[float]: + """ + Essentially a trick to represent a polyline in coco. We pretend for this to be a polygon and join every + coordinate from the end back to the beginning, so it will essentially be an area-less polygon. + This function technically changes the input polygon in place. + """ + if len(polygon) % 2 != 0: + raise RuntimeError("The polygon has an unaccepted shape.") + + idx = len(polygon) - 2 + while idx >= 0: + y_coordinate = polygon[idx] + x_coordinate = polygon[idx + 1] + polygon.append(y_coordinate) + polygon.append(x_coordinate) + idx -= 2 + + return polygon + + def get_point(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + x, y = ( + object_["point"]["0"]["x"] * size.width, + object_["point"]["0"]["y"] * size.height, + ) + w, h = 0, 0 + area = 0 + segmentation = [[x, y]] + keypoints = [x, y, 2] + num_keypoints = 1 + + bbox = (x, y, w, h) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + keypoints, + num_keypoints, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + ) + + def get_skeleton(self, object_: dict, image_id: int, size: Size) -> Union[CocoAnnotation, SuperClass]: + # DENIS: next up: check how this is visualised. + area = 0 + segmentation: List = [] + keypoints: List = [] + for point in object_["skeleton"].values(): + keypoints += [ + point["x"] * size.width, + point["y"] * size.height, + 2, + ] + num_keypoints = len(keypoints) // 3 + xs, ys = ( + keypoints[::3], + keypoints[1::3], + ) + x, y, x_max, y_max = min(xs), min(ys), max(xs), max(ys) + w, h = x_max - x, y_max - y + + # DENIS: think if the next two lines should be in `get_coco_annotation_default_fields` + bbox = (x, y, w, h) + category_id = self.get_category_id(object_) + id_, iscrowd, track_id, encord_track_uuid = self.get_coco_annotation_default_fields(object_) + + return CocoAnnotation( + area, + bbox, + category_id, + id_, + image_id, + iscrowd, + segmentation, + keypoints, + num_keypoints, + track_id=track_id, + encord_track_uuid=encord_track_uuid, + ) + + def get_category_id(self, object_: dict) -> int: + feature_hash = object_["featureHash"] + try: + return self._feature_hash_to_coco_category_id_map[feature_hash] + except KeyError: + raise EncodingError( + f"The feature_hash `{feature_hash}` was not found in the provided ontology. Please " + f"ensure that the ontology matches the labels provided." + ) + + def get_coco_annotation_default_fields(self, object_: dict) -> Tuple[int, int, Optional[int], Optional[str]]: + id_ = self.next_annotation_id() + iscrowd = 0 + track_id = self.get_and_set_track_id(object_hash=object_["objectHash"]) + encord_track_uuid = object_["objectHash"] + return id_, iscrowd, track_id, encord_track_uuid + + def next_annotation_id(self) -> int: + next_ = self._current_annotation_id + self._current_annotation_id += 1 + return next_ + + def get_and_set_track_id(self, object_hash: str) -> int: + if object_hash in self._object_hash_to_track_id_map: + return self._object_hash_to_track_id_map[object_hash] + else: + next_track_id = len(self._object_hash_to_track_id_map) + self._object_hash_to_track_id_map[object_hash] = next_track_id + return next_track_id + + def download_image(self, url: str, path: Path): + """Check if directory exists, create the directory if needed, download the file, store it into the path.""" + path.parent.mkdir(parents=True, exist_ok=True) + download_file(url, path) + + def get_image_id(self, data_hash: str, frame_num: int = 0) -> int: + return self._data_hash_to_image_id_map[(data_hash, frame_num)] + + +def download_file( + url: str, + destination: Path, +): + + r = requests.get(url, stream=True) + with open(destination, "wb") as f: + for chunk in r.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + f.write(chunk) + f.flush() + return destination + + +def extract_frames(video_file_name: Path, img_dir: Path, data_hash: str): + logger.info(f"Extracting frames from video: {video_file_name}") + + # DENIS: for the rest to work, I will need to throw if the current directory exists and give a nice user warning. + img_dir.mkdir(parents=True, exist_ok=True) + command = f"ffmpeg -i {video_file_name} -start_number 0 {img_dir}/{data_hash}_%d.png -hide_banner" + if subprocess.run(command, shell=True, capture_output=True, stdout=None, check=True).returncode != 0: + raise RuntimeError( + "Splitting videos into multiple image files failed. Please ensure that you have FFMPEG " + f"installed on your machine: https://ffmpeg.org/download.html The comamand that failed was `{command}`." + ) + + +def generate_coco_file(df: pd.DataFrame, project_dir: Path, ontology_file: Path) -> dict: + """ + Generate coco JSON file given dataframe. + + Args: + df: dataframe with selected samples + + Returns: + dict: Dictionary object of COCO annotations + """ + # Load label rows and get metrics dict + label_rows = load_label_rows(df, project_dir / "data") + metrics = df_to_nested_dict(df) + + # Load ontology json to dict + with open(ontology_file, "r", encoding="utf-8") as f: + ontology_dict = json.load(f) + + # Generate COCO annotations file + encoder = CocoEncoder( + labels_list=list(label_rows.values()), + metrics=metrics, + ontology=OntologyStructure.from_dict(ontology_dict), + ) + coco_json = encoder.encode( + download_files=True, + download_file_path=project_dir, + ) + + return coco_json + + +def df_to_nested_dict(df: pd.DataFrame) -> dict: + """ + Convert dataframe of metrics to nested dictionary with the following structure: + "": { + "": { + "": { + "frame-level": {frame level metrics}, + "": {object level metrics}, + "": {object level metrics}, + ... + } + } + } + Args: + df: dataframe of metrics + + Returns: + dict: nested dictionary of metrics + """ + metrics: Dict[str, dict] = {} + for row in tqdm(df.iterrows(), desc="Parsing metrics", total=len(df)): + row = row[1] + out = row[0].split("_") + if len(out) == 3: + object_hashes = None + label_hash, data_hash, frame_number = out + elif len(out) > 3: + label_hash, data_hash, frame_number, *object_hashes = out + else: + raise ValueError(f"Invalid identifier `{row[0]}`") + + # Create metrics dict + label_dict = metrics.setdefault(label_hash, {}) + data_dict = label_dict.setdefault(data_hash, {}) + frame_dict = data_dict.setdefault(frame_number, {}) + + if object_hashes is None: # Frame level metric + frame_dict.setdefault("frame-level", {}).update( + {k: v for k, v in row.items() if k not in ["identifier", "url"] and not pd.isnull(v)} + ) + else: # Object level metric + for object_hash in object_hashes: + frame_dict.setdefault(object_hash, {}).update( + {k: v for k, v in row.items() if k not in ["identifier", "url"] and not pd.isnull(v)} + ) + return metrics + + +def load_label_rows(df: pd.DataFrame, data_dir: Path) -> dict[str, dict]: + """ + Given a dataframe with selected samples, load the label row jsons into a dictionary. + + Args: + df: dataframe with selected samples + + Returns: + dict: Dictionary of label rows + """ + label_rows: Dict[str, dict] = {} + for id_tmp in tqdm(df.identifier, desc="Loading labels"): + label_hash = id_tmp.split("_")[0] + if label_hash not in label_rows.keys(): + # Read label json and add to dict + label_json = data_dir.joinpath(label_hash, "label_row.json") + with open(label_json, "r", encoding="utf-8") as f: + label_rows[label_hash] = json.load(f) + return label_rows diff --git a/src/encord_active/lib/coco/importer.py b/src/encord_active/lib/coco/importer.py new file mode 100644 index 000000000..0ad1abe2f --- /dev/null +++ b/src/encord_active/lib/coco/importer.py @@ -0,0 +1,203 @@ +import json +from pathlib import Path +from shutil import copyfile +from typing import Dict, List, Optional + +import requests +import typer +import yaml +from encord import Dataset, EncordUserClient, Project +from encord.objects.common import Shape +from encord.objects.ontology_object import Object +from encord.objects.ontology_structure import OntologyStructure +from encord.ontology import Ontology +from encord.orm.dataset import CreateDatasetResponse, DataRow, Image, StorageLocation +from encord.utilities import label_utilities +from tqdm import tqdm + +from encord_active.lib.coco.datastructure import CocoAnnotation, CocoImage +from encord_active.lib.coco.parsers import ( + parse_annotations, + parse_categories, + parse_images, + parse_info, +) +from encord_active.lib.coco.utils import make_object_dict + +IMAGE_DATA_UNIT_FILENAME = "image_data_unit.json" + + +def upload_img( + dataset_tmp: Dataset, + coco_image: CocoImage, + image_path: Path, +) -> Optional[Image]: + file_path = image_path / coco_image.file_name + + if not file_path.is_file(): + url = coco_image.coco_url or coco_image.flickr_url + if url: + img_data = requests.get(url).content + file_path.write_bytes(img_data) + else: + print(f"Image {coco_image.file_name} not found") + return None + + return dataset_tmp.upload_image( + title=str(coco_image.id_), + file_path=file_path, + ) + + +def upload_annotation( + project: Project, + annotations: Dict[int, List[CocoAnnotation]], + data_unit: DataRow, + id_to_obj: Dict[int, Object], +): + data_hash = data_unit["data_hash"] + lr_dict = project.create_label_row(data_hash) + label_hash = lr_dict["label_hash"] + lr = project.get_label_row(label_hash, get_signed_url=False) + try: + annotation_list = annotations[int(data_unit["data_title"])] + except KeyError: + print(f"No annotations for {data_unit['data_title']}") + annotation_list = [] + data_unit = lr["data_units"][data_hash] + img_w, img_h = data_unit["width"], data_unit["height"] + + objects = [] + for annot in annotation_list: + obj = id_to_obj[annot.category_id] + polygon = annot.segmentation + polygon_points = [(polygon[i] / img_w, polygon[i + 1] / img_h) for i in range(0, len(polygon), 2)] + objects.append(make_object_dict(ontology_object=obj.to_dict(), object_data=polygon_points)) + + lr["data_units"][data_hash]["labels"] = {"objects": objects, "classifications": []} + updated_lr = label_utilities.construct_answer_dictionaries(lr) + project.save_label_row(uid=label_hash, label=updated_lr) + + return data_hash, updated_lr + + +def build_local_lr( + image: CocoImage, + image_path: Path, + data_hash: str, + lr: label_utilities.LabelRow, + lr_path: Path, +): + lr_path.parent.mkdir(parents=True, exist_ok=True) + lr_path.write_text(json.dumps(lr, indent=2), encoding="utf-8") + + dst_dir_path = lr_path.parent / "images" + dst_dir_path.mkdir(exist_ok=True) + img_file = Path(image.file_name) + + data_type = lr["data_units"][data_hash]["data_type"] + new_suffix = f".{data_type.split('/')[1]}" + copyfile( + image_path / img_file, + dst_dir_path / f"{data_hash}{new_suffix}", + ) + + +class CocoImporter: + def __init__(self, images_dir_path: Path, annotations_file_path: Path, ssh_key_path: Path, destination_dir: Path): + self.ssh_key_path = ssh_key_path + self.images_dir = images_dir_path + self.annotations_file_path = annotations_file_path + + if not self.images_dir.is_dir(): + raise NotADirectoryError(f"Images directory '{self.images_dir}' doesn't exist") + if not self.annotations_file_path.is_file(): + raise FileNotFoundError(f"Annotation file '{self.annotations_file_path}' doesn't exist") + + self.user_client = EncordUserClient.create_with_ssh_private_key(ssh_key_path.read_text()) + + annotations_file = json.loads(self.annotations_file_path.read_text(encoding="utf-8")) + self.info = parse_info(annotations_file["info"]) + self.categories = parse_categories(annotations_file["categories"]) + self.images = parse_images(annotations_file["images"]) + self.annotations = parse_annotations(annotations_file["annotations"]) + + title = self.info.description.replace(" ", "-") + with_prefix = f"[EA]{title}" + existing_projects = {o["project"].title: o["project"] for o in self.user_client.get_projects()} + + while with_prefix in existing_projects: + title = typer.prompt( + f"We found a project named '{title}', please provide a new name for this project", type=str + ) + with_prefix = f"[EA]{title}" + + self.title = with_prefix + self.project_dir = destination_dir / self.title + + def create_dataset(self) -> Dataset: + print(f"Creating a new dataset: {self.title}") + dataset_response: CreateDatasetResponse = self.user_client.create_dataset( + self.title, StorageLocation.CORD_STORAGE + ) + dataset: Dataset = self.user_client.get_dataset(dataset_response["dataset_hash"]) + + for _, coco_image in tqdm(self.images.items(), desc="Uploading images"): + upload_img(dataset, coco_image, self.images_dir) + + return dataset + + def create_ontology(self) -> Ontology: + print(f"Creating a new dataset: {self.title}") + ontology_structure = OntologyStructure() + for cat in self.categories: + ontology_structure.add_object(name=cat.name, shape=Shape.POLYGON, uid=cat.id_) + + ontology: Ontology = self.user_client.create_ontology( + self.title, + description=self.info.description, + structure=ontology_structure, + ) + + return ontology + + def create_project(self, dataset: Dataset, ontology: Ontology) -> Project: + print(f"Creating a new project: {self.title}") + project_hash = self.user_client.create_project( + project_title=self.title, + dataset_hashes=[dataset.dataset_hash], + ontology_hash=ontology.ontology_hash, + ) + + project = self.user_client.get_project(project_hash) + + self.project_dir.mkdir(exist_ok=True) + + ontology_file = self.project_dir / "ontology.json" + ontology_file.write_text(json.dumps(project.ontology)) + + project_meta = { + "project_title": project.title, + "project_description": project.description, + "project_hash": project.project_hash, + "ssh_key_path": self.ssh_key_path.as_posix(), + } + meta_file_path = self.project_dir / "project_meta.yaml" + meta_file_path.write_text(yaml.dump(project_meta), encoding="utf-8") + + id_to_obj = {obj.uid: obj for obj in ontology.structure.objects} + + image_to_du = {} + + for data_unit in tqdm(dataset.data_rows, desc="Uploading annotations"): + data_hash, lr = upload_annotation(project, self.annotations, data_unit, id_to_obj) + image_id = lr["data_title"] + image = self.images[image_id] + image_to_du[image_id] = {"data_hash": data_hash, "height": image.height, "width": image.width} + + lr_path = self.project_dir / "data" / lr["label_hash"] / "label_row.json" + build_local_lr(image, self.images_dir, data_hash, lr, lr_path) + + (Path(self.project_dir) / IMAGE_DATA_UNIT_FILENAME).write_text(json.dumps(image_to_du)) + + return project diff --git a/src/encord_active/lib/coco/parsers.py b/src/encord_active/lib/coco/parsers.py new file mode 100644 index 000000000..ab025ed9f --- /dev/null +++ b/src/encord_active/lib/coco/parsers.py @@ -0,0 +1,118 @@ +from typing import Dict, List + +from tqdm import tqdm + +from encord_active.lib.coco.datastructure import ( + CocoAnnotation, + CocoCategory, + CocoImage, + CocoInfo, + CocoResult, +) +from encord_active.lib.coco.utils import annToMask +from encord_active.lib.common.utils import mask_to_polygon + + +def parse_info(info: Dict) -> CocoInfo: + return CocoInfo( + contributor=info["contributor"], + date_created=info["date_created"], + url=info["url"], + version=info["version"], + year=info["year"], + description=info["description"], + ) + + +def parse_categories(categories: Dict) -> List[CocoCategory]: + return [ + CocoCategory( + supercategory=category["supercategory"] if "supercategory" in category.keys() else "", + id_=category["id"], + name=category["name"], + ) + for category in tqdm(categories, desc="Parsing categories") + ] + + +def parse_images(images: Dict) -> Dict[str, CocoImage]: + return { + str(image["id"]): CocoImage( + id_=image["id"], + height=image["height"], + width=image["width"], + file_name=image["file_name"], + coco_url=image["coco_url"], + flickr_url=image["flickr_url"], + ) + for image in tqdm(images, desc="Parsing images") + } + + +def parse_annotations(annotations: List[Dict]) -> Dict[int, List[CocoAnnotation]]: + annot_dict: Dict[int, List[CocoAnnotation]] = {} + for annotation in tqdm(annotations, desc="Parsing annotations"): + if annotation["iscrowd"] == 1: + continue + + segmentations = annotation["segmentation"] + + if isinstance(segmentations, list) and not isinstance(segmentations[0], list): + segmentations = [segmentations] + elif isinstance(segmentations, dict): + h, w = segmentations["size"] + mask = annToMask(annotation, h=h, w=w) + poly, inferred_bbox = mask_to_polygon(mask) + if poly is None or inferred_bbox != annotation["bbox"]: + print(f"Annotation '{annotation['id']}', contains an invalid polygon. Skipping ...") + continue + segmentations = [poly] + + annot_dict.setdefault(annotation["image_id"], []) + + for segment in segmentations: + annot_dict[annotation["image_id"]].append( + CocoAnnotation( + area=annotation["area"], + bbox=annotation["bbox"], + category_id=annotation["category_id"], + id_=annotation["id"], + image_id=annotation["image_id"], + iscrowd=annotation["iscrowd"], + segmentation=segment, + ) + ) + + return annot_dict + + +def parse_results(results: List[Dict]): + coco_results: List[CocoResult] = [] + for result in tqdm(results, desc="Parsing results"): + segmentations = result["segmentation"] + bbox = result.get("bbox") + + if isinstance(segmentations, list) and not isinstance(segmentations[0], list): + segmentations = [segmentations] + elif isinstance(segmentations, dict): + h, w = segmentations["size"] + mask = annToMask(result, h=h, w=w) + poly, inferred_bbox = mask_to_polygon(mask) + if poly is None or (bbox is not None and inferred_bbox != bbox): + print(f"Annotation '{result['id']}', contains an invalid polygon. Skipping ...") + continue + bbox = inferred_bbox + segmentations = [poly] + + for segment in segmentations: + coco_results.append( + CocoResult( + bbox=bbox, + category_id=result["category_id"], + image_id=result["image_id"], + segmentation=segment, + score=result["score"], + ) + ) + + return coco_results diff --git a/src/encord_active/lib/coco/utils.py b/src/encord_active/lib/coco/utils.py new file mode 100644 index 000000000..064cafde2 --- /dev/null +++ b/src/encord_active/lib/coco/utils.py @@ -0,0 +1,138 @@ +import uuid +from datetime import datetime +from typing import Dict, List, Optional, Tuple, Union + +import pytz +from pycocotools.mask import decode, frPyObjects, merge + +GMT_TIMEZONE = pytz.timezone("GMT") +DATETIME_STRING_FORMAT = "%a, %d %b %Y %H:%M:%S %Z" +Point = Tuple[float, float] +BBOX_KEYS = {"x", "y", "h", "w"} + + +def __get_timestamp(): + now = datetime.now() + new_timezone_timestamp = now.astimezone(GMT_TIMEZONE) + return new_timezone_timestamp.strftime(DATETIME_STRING_FORMAT) + + +def __lower_snake_case(s: str): + return "_".join(s.lower().split()) + + +def make_object_dict( + ontology_object: dict, + object_data: Union[Point, List[Point], Dict[str, float], None] = None, + object_hash: Optional[str] = None, +) -> dict: + """ + :type ontology_object: The ontology object to associate with the ``object_data``. + :type object_data: The data to put in the object dictionary. This has to conform + with the ``shape`` parameter defined in + ``ontology_object["shape"]``. + - ``shape == "point"``: For key-points, the object data should be + a tuple with x, y coordinates as floats. + - ``shape == "bounding_box"``: For bounding boxes, the + ``object_data`` needs to be a dict with info: + {"x": float, "y": float, "h": float, "w": float} specifying the + top right corner of the box (x, y) and the height and width of + the bounding box. + - ``shape in ("polygon", "polyline")``: For polygons and + polylines, the format is an iterable of points: + [(x, y), ...] specifying (ordered) points in the + polygon/polyline. + If ``object_hash`` is none, a new hash will be generated. + :type object_hash: If you want the object to have the same id across frames (for + videos only), you can specify the object hash, which need to be + an eight-character hex string (e.g., use + ``str(uuid.uuid4())[:8]`` or the ``objectHash`` from an + associated object. + :returns: An object dictionary conforming with the Encord label row data format. + """ + if object_hash is None: + object_hash = str(uuid.uuid4())[:8] + + timestamp: str = __get_timestamp() + shape: str = ontology_object["shape"] + + object_dict = { + "name": ontology_object["name"], + "color": ontology_object["color"], + "value": __lower_snake_case(ontology_object["name"]), + "createdAt": timestamp, + "createdBy": "robot@cord.tech", + "confidence": 1, + "objectHash": object_hash, + "featureHash": ontology_object["featureNodeHash"], + "lastEditedAt": timestamp, + "lastEditedBy": "robot@encord.com", + "shape": shape, + "manualAnnotation": False, + "reviews": [], + } + + if shape in ["polygon", "polyline"] and object_data: + if not isinstance(object_data, list): + raise ValueError(f"The `object_data` for {shape} should be a list of points.") + + object_dict[shape] = {str(i): {"x": round(x, 4), "y": round(y, 4)} for i, (x, y) in enumerate(object_data)} + + elif shape == "point": + if not isinstance(object_data, tuple): + raise ValueError(f"The `object_data` for {shape} should be a tuple.") + if len(object_data) != 2: + raise ValueError(f"The `object_data` for {shape} should have two coordinates.") + if not isinstance(object_data[0], float): + raise ValueError(f"The `object_data` for {shape} should contain floats.") + + object_dict[shape] = {"0": {"x": round(object_data[0], 4), "y": round(object_data[1], 4)}} + + elif shape == "bounding_box": + if not isinstance(object_data, dict): + raise ValueError(f"The `object_data` for {shape} should be a dictionary.") + if len(BBOX_KEYS.union(set(object_data.keys()))) != 4: + raise ValueError(f"The `object_data` for {shape} should have keys {BBOX_KEYS}.") + if not isinstance(object_data["x"], float): + raise ValueError(f"The `object_data` for {shape} should float values.") + + object_dict["boundingBox"] = {k: round(v, 4) for k, v in object_data.items()} + + return object_dict + + +def find_ontology_object(ontology: dict, encord_name: str): + try: + obj = next((o for o in ontology["objects"] if o["name"].lower() == encord_name.lower())) + except StopIteration: + raise ValueError(f"Couldn't match Encord ontology name `{encord_name}` to objects in the " f"Encord ontology.") + return obj + + +def annToRLE(ann, h, w): + """ + Convert annotation which can be polygons, uncompressed RLE to RLE. + :return: binary mask (numpy 2D array) + """ + segm = ann["segmentation"] + if isinstance(segm, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = frPyObjects(segm, h, w) + rle = merge(rles) + elif isinstance(segm["counts"], list): + # uncompressed RLE + rle = frPyObjects(segm, h, w) + else: + # rle + rle = ann["segmentation"] + return rle + + +def annToMask(ann, h, w): + """ + Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. + :return: binary mask (numpy 2D array) + """ + rle = annToRLE(ann, h, w) + return decode(rle) diff --git a/src/encord_active/lib/common/__init__.py b/src/encord_active/lib/common/__init__.py new file mode 100644 index 000000000..3dc1f76bc --- /dev/null +++ b/src/encord_active/lib/common/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/src/encord_active/lib/common/iterator.py b/src/encord_active/lib/common/iterator.py new file mode 100644 index 000000000..7193d6197 --- /dev/null +++ b/src/encord_active/lib/common/iterator.py @@ -0,0 +1,194 @@ +import json +from abc import abstractmethod +from collections.abc import Sized +from copy import deepcopy +from dataclasses import asdict +from functools import reduce +from itertools import chain +from json import JSONDecodeError +from pathlib import Path +from typing import Any, Dict, Generator, List, Optional, Tuple, Union + +from encord.exceptions import EncordException +from encord.orm.label_log import LabelLog +from loguru import logger +from tqdm import tqdm + +from encord_active.lib.common.prepare import EAProject, prepare_data + + +class Iterator(Sized): + def __init__(self, cache_dir: Path, subset_size: Optional[int] = None, **kwargs): + self.cache_dir = cache_dir + self.subset_size = subset_size + self.dataset_title = "" + self.label_hash = "" + self.du_hash = "" + self.frame = -1 + self.num_frames = -1 + self.project: EAProject = prepare_data(cache_dir, subset_size=subset_size, **kwargs) + self.label_rows = self.project.label_rows + + @abstractmethod + def iterate(self, desc: str = "") -> Generator[Tuple[dict, Optional[Path]], None, None]: + pass + + @abstractmethod + def get_identifier(self, object: Union[dict, list[dict], None] = None, frame: Optional[int] = None) -> Any: + pass + + @abstractmethod + def get_data_url(self) -> str: + pass + + @abstractmethod + def get_label_logs(self, object_hash: Optional[str] = None, refresh: bool = False) -> List[dict]: + pass + + @staticmethod + @abstractmethod + def update_cache_dir(cache_dir: Path) -> Path: + pass + + +class DatasetIterator(Iterator): + def __init__(self, cache_dir: Path, subset_size: Optional[int] = None, **kwargs): + super().__init__(cache_dir, subset_size, **kwargs) + self.key = "" + self.length = reduce( + lambda s, lr: s + + sum(map(lambda du: 1 if "objects" in du["labels"] else len(du["labels"]), lr["data_units"].values())), + self.label_rows.values(), + 0, + ) + + def iterate(self, desc: str = "") -> Generator[Tuple[dict, Optional[Path]], None, None]: + pbar = tqdm(total=self.length, desc=desc, leave=False) + for label_hash, label_row in self.label_rows.items(): + self.dataset_title = label_row["dataset_title"] + self.label_hash = label_hash + if label_row.data_type in {"img_group", "image"}: + self.num_frames = len(label_row.data_units) + image_paths = self.project.image_paths[label_hash] + + data_units = sorted(label_row.data_units.values(), key=lambda du: int(du["data_sequence"])) + for data_unit, image_path in zip(data_units, image_paths): + self.du_hash = data_unit["data_hash"] + self.frame = int(data_unit["data_sequence"]) + yield data_unit, image_path + pbar.update(1) + elif label_row.data_type == "video": + data_unit, *_ = label_row["data_units"].values() + self.du_hash = data_unit["data_hash"] + + fake_data_unit = deepcopy(data_unit) + for frame_sequence, frame_annotations in data_unit["labels"].items(): + self.frame = int(frame_sequence) + fake_data_unit["labels"] = frame_annotations + + image_path = None + if self.project.image_paths: + image_folder = self.cache_dir / "data" / self.label_hash / "images" + image_path = next(image_folder.glob(f"{self.du_hash}_{frame_sequence}.*"), None) + + yield fake_data_unit, image_path + pbar.update(1) + else: + logger.error(f"Label row '{label_hash}' with data type '{label_row.data_type}' is not recognized") + + def __len__(self): + return self.length + + def get_identifier(self, object: Union[dict, list[dict], None] = None, frame: Optional[int] = None): + frame_idx = frame if frame is not None else self.frame + key = f"{self.label_hash}_{self.du_hash}_{frame_idx:05d}" + + if object is not None: + if isinstance(object, dict): + objects = [object] + else: + objects = object # object is expected to be a list[dict] + hashes = [obj["objectHash"] if "objectHash" in obj else obj["featureHash"] for obj in objects] + return "_".join(chain([key], hashes)) + return key + + def get_data_url(self) -> str: + base_url = "https://app.encord.com/label_editor/" + data_url = f"{base_url}{self.project.label_row_meta[self.label_hash]['data_hash']}&{self.project.project_hash}" + if isinstance(self.frame, int): + data_url += f"/{self.frame}" + return data_url + + @staticmethod + def __filter_logs_on_object_hash(logs: List[dict], object_hash: Optional[str] = None) -> List[dict]: + if object_hash is None: + return logs + + return list(filter(lambda x: x["annotation_hash"] == object_hash, logs)) + + def get_label_logs(self, object_hash: Optional[str] = None, refresh: bool = False) -> List[dict]: + """ + Fetches label logs from the sdk if they are not cached locally. The `refresh=True` will fetch a new version + of the label logs. + + :param object_hash: If specified, only label logs associated with the given object will be returned. + :param refresh: flag to fetch the most recent label logs instead of using the cached version. + :return: A list of label logs for the + """ + if not hasattr(self, "du_hash"): + raise ValueError( + "Label logs are only available when you are iterating data.\n" + "Consider using `iterator.project.get_label_logs()` if you want all (non-cached) label logs for the " + "project." + ) + + no_logs_file = self.cache_dir / "label_logs" / self.label_hash / "no_logs.txt" + if (no_logs_file).exists(): + return [] + + label_logs_file = self.cache_dir / "label_logs" / self.label_hash / f"{self.frame:05d}.json" + if label_logs_file.parent.exists() and not refresh: + if label_logs_file.exists(): + try: + with label_logs_file.open("r", encoding="utf-8") as f: + json_logs: List[dict] = json.load(f)["logs"] + return self.__filter_logs_on_object_hash(json_logs, object_hash=object_hash) + except JSONDecodeError: + pass + return [] + + # Load label logs and store them according to frames. + data_hash = self.project.label_row_meta[self.label_hash]["data_hash"] + try: + # logs = self.project.get_label_logs(data_hash=data_hash) + # TODO commented while we figure out how to address this from cache only + logs: List[LabelLog] = [] + return logs + except EncordException as e: + no_logs_file.parent.mkdir(parents=True, exist_ok=True) + no_logs_file.touch(exist_ok=True) + return [] + + frame_logs: Dict[int, List[LabelLog]] = {} + for log in logs: + frame_logs.setdefault(log.frame, []).append(log) + + out_logs: List[dict] = [] + for frame, tmp_logs in frame_logs.items(): + frame_name = f"{frame:05d}.json" if frame is not None else "No-frame.json" + tmp_log_file = self.cache_dir / "label_logs" / self.label_hash / frame_name + tmp_log_file.parent.mkdir(parents=True, exist_ok=True) + + json_logs = list(map(asdict, tmp_logs)) + with tmp_log_file.open("w") as f: + json.dump({"logs": json_logs}, f) + + if frame == self.frame: + out_logs = json_logs + + return self.__filter_logs_on_object_hash(out_logs, object_hash=object_hash) + + @staticmethod + def update_cache_dir(cache_dir: Path) -> Path: + # Just use the root + return cache_dir diff --git a/src/encord_active/lib/common/metric.py b/src/encord_active/lib/common/metric.py new file mode 100644 index 000000000..6c257b9c8 --- /dev/null +++ b/src/encord_active/lib/common/metric.py @@ -0,0 +1,185 @@ +from abc import ABC, abstractmethod +from enum import Enum +from hashlib import md5 +from typing import List, Optional, Union + +from encord.project_ontology.classification_type import ClassificationType +from encord.project_ontology.object_type import ObjectShape + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.writer import CSVMetricWriter + + +class MetricType(Enum): + SEMANTIC = "semantic" + GEOMETRIC = "geometric" + HEURISTIC = "heuristic" + + +class DataType(Enum): + IMAGE = "image" + SEQUENCE = "sequence" + + +class AnnotationType: + NONE = None + OBJECT = ObjectShape + CLASSIFICATION = ClassificationType + ALL = [OBJECT, CLASSIFICATION] + + +class EmbeddingType(Enum): + CLASSIFICATION = "classification" + OBJECT = "object" + HU_MOMENTS = "hu_moments" + NONE = "none" + + +class Metric(ABC): + @abstractmethod + def test(self, iterator: Iterator, writer: CSVMetricWriter): + """ + This is where you should perform your data indexing. + + :param iterator: The iterator with which you can iterate through the dataset as + many times you like. The iterator scans through one label rows at a time, + continuously indexing each frame of a video. + + Use:: + + for data_unit, img_pth in iterator.iterate(desc="Progress bar description"): + pass + + + :param writer: The writer where you should store your scores. + Use:: + + writer.write(score, objects) + + + :return: + """ + pass + + @property + @abstractmethod + def TITLE(self) -> str: + """ + Set the title of your metric. Aim for no more than 80 characters. + The title will be used, e.g., when displaying results in the app. + + The property can, e.g., be set as a class property:: + + class MyClass(Metric): + TITLE = "This is the title" + # ... + + """ + pass + + @property + @abstractmethod + def METRIC_TYPE(self) -> MetricType: + """ + Type of the metric. Choose one of the following: + - Geometric: metrics related to the geometric properties of annotations. + This type includes size, shape, location, etc. + - Semantic: metrics based on the *contents* of annotations, images or videos. + This type includes ResNet embedding distances, image uncertainties, etc. + - Heuristic: any other metric. For example, brightness, sharpness, object counts, etc. + + + The property can, e.g., be set as a class property:: + + class MyClass(Metric): + METRIC_TYPE = MetricType.GEOMETRIC + # ... + + """ + pass + + @property + @abstractmethod + def DATA_TYPE(self) -> Union[List[DataType], DataType]: + """ + Type of data with which the metric operates. Choose one of the following: + - Image: metric requires individual images. E.g. bounding box location + - Sequence: metric requires sequences of images to harness temporal information + (videos, e.g. bounding box IoU across video frames) or volumetric information + (DICOM, e.g. bounding box IoU across slices). + + The property can, e.g., be set as a class property:: + + class MyClass(Metric): + DATA_TYPE = DataType.IMAGE + # ... + + """ + pass + + @property + @abstractmethod + def ANNOTATION_TYPE(self) -> Optional[Union[List[AnnotationType], AnnotationType]]: + """ + Type of annotations the metric operates needs. Choose one of the following: + - Object: includes bounding box, polygon, polyline, keypoint and skeleton + - Classification: includes text, radio button and checklist + + The property can, e.g., be set as a class property:: + + class MyClass(Metric): + ANNOTATION_TYPE = AnnotationType.OBJECT.POLYGON + # ... + + """ + pass + + @property + @abstractmethod + def SHORT_DESCRIPTION(self) -> str: + """ + Set a short description of what the metric does. Aim for at most 120 + characters. As for the title, this can be set as a class property. + + The short description will be used in lists and other places, where space is + limited. + """ + pass + + @property + @abstractmethod + def LONG_DESCRIPTION(self) -> str: + """ + Set a verbose description of what the metric does. If it is based on ideas + from papers, etc., this is where such links can be included. If you write + markdown, this will be rendered appropriately in the app. + + The long description will be used when much space is available and when + providing full details makes sense. + """ + pass + + @property + def SCORE_NORMALIZATION(self) -> bool: + """ + If the score normalization will be applied to metrics from this metric as default in the app. + :return: True or False + """ + + return False + + @property + def NEEDS_IMAGES(self): + """ + If the metric need to look at image content. This is automatically inferred from the metric type: + - MetricType.GEOMETRIC: False + - MetricType.SEMANTIC: True + - MetricType.HEURISTIC: True + + """ + return False if self.METRIC_TYPE == MetricType.GEOMETRIC else True + + def get_unique_name(self): + name_hash = md5((self.TITLE + self.SHORT_DESCRIPTION + self.LONG_DESCRIPTION).encode()).hexdigest() + + return f"{name_hash[:8]}_{self.TITLE.lower().replace(' ', '_')}" diff --git a/src/encord_active/lib/common/prepare.py b/src/encord_active/lib/common/prepare.py new file mode 100644 index 000000000..bbc3cbf47 --- /dev/null +++ b/src/encord_active/lib/common/prepare.py @@ -0,0 +1,242 @@ +import itertools +import json +import logging +import os +from concurrent.futures import ThreadPoolExecutor as Executor +from concurrent.futures import as_completed +from dataclasses import dataclass, field +from functools import partial +from pathlib import Path +from typing import Dict, List, Optional, Union + +import encord.exceptions +import requests +from encord import Project +from encord.orm.label_row import LabelRow +from encord.project import LabelRowMetadata +from tqdm import tqdm + +from encord_active.lib.common.utils import fetch_project_meta, slice_video_into_frames + +logger = logging.getLogger(__name__) +encord_logger = logging.getLogger("encord") +encord_logger.setLevel(logging.ERROR) + + +@dataclass +class EAProject: + label_rows: Dict[str, LabelRow] + label_row_meta: Dict[str, LabelRowMetadata] + image_paths: Dict[str, List[Path]] + project_meta: Dict[str, str] + ontology: Dict[str, dict] + project_hash: str = field(init=False) + + def __post_init__(self): + self.project_hash = self.project_meta["project_hash"] + + +def collect_async(fn, job_args, key_fn, max_workers=min(10, (os.cpu_count() or 1) + 4), **kwargs): + """ + Distribute work across multiple workers. Good for, e.g., downloading data. + Will return results in dictionary. + :param fn: The function to be applied + :param job_args: Arguments to `fn`. + :param key_fn: Function to determine dictionary key for the result (given the same input as `fn`). + :param max_workers: Number of workers to distribute work over. + :param kwargs: Arguments passed on to tqdm. + :return: Dictionary {key_fn(*job_args): fn(*job_args)} + """ + job_args = list(job_args) + if not isinstance(job_args[0], tuple): + job_args = [(j,) for j in job_args] + + results = {} + with tqdm(total=len(job_args), **kwargs) as pbar: + with Executor(max_workers=max_workers) as exe: + jobs = {exe.submit(fn, *args): key_fn(*args) for args in job_args} + for job in as_completed(jobs): + key = jobs[job] + + result = job.result() + if result: + results[key] = result + + pbar.update(1) + return results + + +def download_file( + url: str, + destination: Path, + byte_size=1024, +): + if destination.is_file(): + return destination + + r = requests.get(url, stream=True) + with destination.open("wb") as f: + for chunk in r.iter_content(chunk_size=byte_size): + if chunk: # filter out keep-alive new chunks + f.write(chunk) + f.flush() + + return destination + + +def get_label_row(lr, client, cache_dir, refresh=False) -> Optional[LabelRow]: + if isinstance(cache_dir, str): + cache_dir = Path(cache_dir) + + if not lr["label_hash"]: + return None + + cache_pth = cache_dir / "data" / lr["label_hash"] / "label_row.json" + + if not refresh and cache_pth.is_file(): + # Load cached version + try: + with cache_pth.open("r") as f: + return LabelRow(json.load(f)) + except json.decoder.JSONDecodeError: + pass + + try: + lr = client.get_label_row(lr["label_hash"]) + except encord.exceptions.UnknownException as e: + logger.warning( + f"Failed to download label row with label_hash {lr['label_hash'][:8]}... and data_title {lr['data_title']}" + ) + return None + + # Cache label row. + cache_pth.parent.mkdir(parents=True, exist_ok=True) + with cache_pth.open("w") as f: + json.dump(lr, f, indent=2) + + return lr + + +def download_all_label_rows(client, subset_size: Optional[int] = None, **kwargs) -> Dict[str, LabelRow]: + label_rows = list(itertools.islice(filter(lambda x: x["label_hash"], client.label_rows), subset_size)) + + return collect_async( + partial(get_label_row, client=client, **kwargs), + label_rows, + lambda lr: lr["label_hash"], + desc="Collecting label rows from Encord SDK.", + ) + + +def download_images_from_data_unit(lr, cache_dir, **kwargs) -> Optional[List[Path]]: + if isinstance(cache_dir, str): + cache_dir = Path(cache_dir) + + label_hash = lr.label_hash + + if label_hash is None: + return None + + label_pth = cache_dir / "data" / label_hash + label_pth.mkdir(parents=True, exist_ok=True) + + lr_path = label_pth / "label_row.json" + if not lr_path.exists(): + with (label_pth / "label_row.json").open("w") as f: + json.dump(lr, f, indent=2) + + frame_pth = label_pth / "images" + + frame_pth.mkdir(parents=True, exist_ok=True) + frame_pths: List[Path] = [] + data_units = sorted(lr.data_units.values(), key=lambda du: int(du["data_sequence"])) + for du in data_units: + suffix = f".{du['data_type'].split('/')[1]}" + out_pth = (frame_pth / du["data_hash"]).with_suffix(suffix) + out_pth = download_file(du["data_link"], out_pth) + frame_pths.append(out_pth) + + # if label row's data type is video then extract frames + if lr.data_type == "video": + video_path = frame_pths[0] + frame_pths.clear() + for out_pth in slice_video_into_frames(video_path)[0].values(): + frame_pths.append(out_pth) + + return frame_pths + + +def download_all_images(label_rows, cache_dir: Union[str, Path], **kwargs) -> Dict[str, List[Path]]: + return collect_async( + partial(download_images_from_data_unit, cache_dir=cache_dir, **kwargs), + label_rows.values(), + lambda lr: lr.label_hash, + desc="Collecting frames from label rows.", + ) + + +def read_project_data_from_cache(cache_dir: Path, subset_size: Optional[int]) -> EAProject: + label_rows = dict( + itertools.islice( + ( + (p.name, LabelRow(json.loads((p / "label_row.json").read_text()))) + for p in (cache_dir / "data").iterdir() + if (p / "label_row.json").is_file() + ), + subset_size, + ) + ) + + project_meta = fetch_project_meta(cache_dir) + label_row_meta = json.loads((cache_dir / "label_row_meta.json").read_text()) + ontology = json.loads((cache_dir / "ontology.json").read_text()) + image_paths = download_all_images(label_rows, cache_dir) + + return EAProject( + label_rows=label_rows, + label_row_meta=label_row_meta, + image_paths=image_paths, + ontology=ontology, + project_meta=project_meta, + ) + + +def prepare_data( + cache_dir: Path, subset_size: Optional[int] = None, project: Optional[Project] = None, **kwargs +) -> EAProject: + """ + Fetches data either from cache or from the Encord project if it's available. + + :param cache_dir: The root directory of the project. + :param subset_size: The number of label rows to fetch. None means all. + :param project: An encord project for fetching data. If not provided, will only use cached files. + + :returns: all the raw project data. + """ + if project is None: + return read_project_data_from_cache(cache_dir, subset_size=subset_size) + + label_rows = download_all_label_rows(project, subset_size=subset_size, cache_dir=cache_dir, **kwargs) + image_paths = download_all_images(label_rows, cache_dir=cache_dir, **kwargs) + + project_meta = fetch_project_meta(cache_dir) + # Cache ontology object + ontology_file = cache_dir / "ontology.json" + if not ontology_file.exists(): + with ontology_file.open("w") as f: + json.dump(project.ontology, f) + + label_row_meta = {lr["label_hash"]: lr for lr in project.label_rows if lr["label_hash"] is not None} + # Cache label rows metadata + label_row_metadata_file = cache_dir / "label_row_meta.json" + if not label_row_metadata_file.exists(): + with label_row_metadata_file.open("w") as f: + json.dump(label_row_meta, f) + + return EAProject( + label_row_meta=label_row_meta, + label_rows=label_rows, + project_meta=project_meta, + image_paths=image_paths, + ontology=project.ontology, + ) diff --git a/src/encord_active/lib/common/tester.py b/src/encord_active/lib/common/tester.py new file mode 100644 index 000000000..65cbdda93 --- /dev/null +++ b/src/encord_active/lib/common/tester.py @@ -0,0 +1,71 @@ +import json +import logging +from enum import Enum +from pathlib import Path +from typing import Any, List, Type, Union + +from loguru import logger + +from encord_active.lib.common.iterator import DatasetIterator, Iterator +from encord_active.lib.common.metric import Metric +from encord_active.lib.common.utils import fetch_project_info +from encord_active.lib.common.writer import CSVMetricWriter, StatisticsObserver + +logger = logger.opt(colors=True) + + +def __get_value(o): + if isinstance(o, (float, int, str)): + return o + if isinstance(o, Enum): + return __get_value(o.value) + if isinstance(o, (list, tuple)): + return [__get_value(v) for v in o] + return None + + +def __get_object_attributes(obj: Any): + metric_properties = {v.lower(): __get_value(getattr(obj, v)) for v in dir(obj)} + metric_properties = {k: v for k, v in metric_properties.items() if (v is not None or k == "annotation_type")} + return metric_properties + + +@logger.catch() +def perform_test( + metrics: Union[Metric, List[Metric]], + data_dir: Path, + iterator_cls: Type[Iterator] = DatasetIterator, + use_cache_only: bool = False, + **kwargs, +): + all_tests: List[Metric] = metrics if isinstance(metrics, list) else [metrics] + + project = None if use_cache_only else fetch_project_info(data_dir) + iterator = iterator_cls(data_dir, project=project, **kwargs) + cache_dir = iterator.update_cache_dir(data_dir) + + for metric in all_tests: + logger.info(f"Running Metric {metric.TITLE.title()}") + unique_metric_name = metric.get_unique_name() + + stats = StatisticsObserver() + with CSVMetricWriter(cache_dir, iterator, prefix=unique_metric_name) as writer: + writer.attach(stats) + + try: + metric.test(iterator, writer) + except Exception as e: + logging.critical(e, exc_info=True) + + # Store meta-data about the scores. + meta_file = (cache_dir / "metrics" / f"{unique_metric_name}.meta.json").expanduser() + + with meta_file.open("w") as f: + json.dump( + { + **__get_object_attributes(metric), + **__get_object_attributes(stats), + }, + f, + indent=2, + ) diff --git a/src/encord_active/lib/common/utils.py b/src/encord_active/lib/common/utils.py new file mode 100644 index 000000000..d1aa5e3b4 --- /dev/null +++ b/src/encord_active/lib/common/utils.py @@ -0,0 +1,340 @@ +import os +import shutil +import warnings +from concurrent.futures import ThreadPoolExecutor as Executor +from itertools import product +from pathlib import Path +from typing import Any, Collection, Dict, List, Optional, Tuple, Union + +import av +import cv2 +import numpy as np +import yaml +from encord import EncordUserClient, Project +from loguru import logger +from shapely.errors import ShapelyDeprecationWarning +from shapely.geometry import Polygon +from tqdm import tqdm + +# Silence shapely deprecation warnings from v1.* to v2.0 +warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning) +from encord_active.lib.coco.datastructure import CocoBbox + + +def fetch_project_meta(data_dir: Path) -> dict: + meta_file = data_dir / "project_meta.yaml" + if not meta_file.is_file(): + raise FileNotFoundError(f"Couldn't find meta file for project in {meta_file}") + + with meta_file.open("r", encoding="utf-8") as f: + return yaml.safe_load(f) + + +def fetch_project_info(data_dir: Path) -> Project: + project_meta = fetch_project_meta(data_dir) + # == Key file == # + if "ssh_key_path" not in project_meta: + raise ValueError("SSH Key path missing in project metadata.") + + private_key_file = Path(project_meta["ssh_key_path"]).expanduser().absolute() + with private_key_file.open("r", encoding="utf-8") as f: + private_key = f.read() + client = EncordUserClient.create_with_ssh_private_key(private_key) + + # == Project hash == # + if "project_hash" not in project_meta: + raise ValueError("`project_hash` is missing in project metadata.") + project_hash = project_meta["project_hash"] + project = client.get_project(project_hash=project_hash) + return project + + +def get_du_size(data_unit: dict, img_pth: Optional[Path] = None) -> Optional[Tuple[int, int]]: + if "width" in data_unit and "height" in data_unit: + return int(data_unit["height"]), int(data_unit["width"]) + + image_corrupted = False + if img_pth is not None and img_pth.is_file(): + try: + image = cv2.imread(img_pth.as_posix()) + return image.shape[:2] + except cv2.error: + image_corrupted = True + + return None + + +def get_object_coordinates(o: dict) -> Optional[list[tuple[Any, Any]]]: + """ + Convert Encord object dict into list of coordinates. + :param o: the Encord object dict. + :return: the list of coordinates. + """ + if o["shape"] in ["polygon", "polyline", "point", "skeleton"]: + points_dict = o[o["shape"]] + points = [(points_dict[str(i)]["x"], points_dict[str(i)]["y"]) for i in range(len(points_dict))] + elif o["shape"] == "bounding_box": + bbox = o["boundingBox"] + points = [ + (bbox["x"], bbox["y"]), + (bbox["x"] + bbox["w"], bbox["y"]), + (bbox["x"] + bbox["w"], bbox["y"] + bbox["h"]), + (bbox["x"], bbox["y"] + bbox["h"]), + ] + else: + logger.warning(f"Unknown shape {o['shape']} in get_object_coordinates function") + return None + + return points + + +def get_polygon(o: dict) -> Optional[Polygon]: + """ + Convert object dict into shapely polygon. + :param o: the Encord object dict. + :return: The polygon object. + """ + if o["shape"] in ["bounding_box", "polygon"]: + points = get_object_coordinates(o) + else: + logger.warning(f"Unknown shape {o['shape']} in get_polygon function") + return None + + if points is None or len(points) < 3: + logger.debug("Less than 3 points") + return None + + polygon = Polygon(points) + if not polygon.is_simple: + logger.debug("Not simple") + return None + + return polygon + + +def get_geometry_from_encord_object(obj: dict, w: int, h: int) -> Optional[np.ndarray]: + """ + Convert Encord object dictionary to polygon coordinates used to draw geometries + with opencv. + :param obj: the encord object dict + :param w: the image width + :param h: the image height + :return: The polygon coordinates + """ + + polygon = get_polygon(obj) + if polygon: + img_size = np.array([[w, h]]) + return (np.array(polygon.exterior.xy).T * img_size).astype(int) + else: + return None + + +def get_iou(p1: Polygon, p2: Polygon): + """ + Compute IOU between two polygons. If polygons are invalid, 0 will be returned. + :param p1: polygon 1 + :param p2: polygon 2 + :return: the IOU. + """ + try: + intersect = p1.intersection(p2).area + union = p1.union(p2).area + return intersect / union + except: + return 0 + + +def slice_video_into_frames( + video_path: Path, out_dir: Path = None, wanted_frames: Collection[int] = None +) -> Tuple[Dict[int, Path], List[int]]: + frames_dir = out_dir if out_dir else video_path.parent + frames_dir_existed = frames_dir.exists() + + sliced_frames: Dict[int, Path] = {} + dropped_frames: List[int] = [] + + if frames_dir_existed: + frames = { + int(p.stem.rsplit("_", 1)[-1].split(".", 1)[0]): p for p in frames_dir.iterdir() if p.suffix == ".png" + } + if frames: + return frames, [] + + try: + frames_dir.mkdir(parents=True, exist_ok=True) + + video_name = video_path.stem + + with av.open(str(video_path), mode="r") as container: + for frame in tqdm( + container.decode(video=0), + desc="Extracting frames from video", + leave=True, + total=container.streams.video[0].frames, + ): + frame_num = frame.index + + if wanted_frames is None or frame_num in wanted_frames: + + if not frame.is_corrupt: + frame_name = f"{video_name}_{frame_num}.png" + + frame_path = Path(frames_dir, frame_name) + frame.to_image().save(frame_path) + + sliced_frames[frame_num] = frame_path + else: + dropped_frames.append(frame_num) + + return sliced_frames, dropped_frames + except Exception: + if not frames_dir_existed: + shutil.rmtree(frames_dir) + else: + for _, frame_path in sliced_frames.items(): + delete_locally_cached_file(frame_path) + raise + + +def delete_locally_cached_file(file_path) -> None: + """ + Deleted cached file from local storage + :param file_path: str with local file path + """ + try: + os.remove(file_path) + except Exception: + logger.exception(f"Failed to remove local file [{file_path}]") + + +def get_bbox_from_encord_label_object(obj: dict, w: int, h: int) -> Optional[tuple]: + transformed_obj = get_geometry_from_encord_object(obj, w, h) + if transformed_obj is not None: + return cv2.boundingRect(transformed_obj) + else: + logger.debug("Detected invalid polygon (self-crossing or less than 3 vertices).") + return None + + +def fix_duplicate_image_orders_in_knn_graph_all_rows(nearest_items: np.ndarray) -> np.ndarray: + """ + Duplicate images create problem in nearest neighbor order, for example for index 6 its closest + neighbors can be [5,6,1,9,3] if 5 and 6 is duplicate, it should be [6,5,1,9,3]. This function ensures that + the first item is the queried index. + + :param nearest_items: nearest metrics obtained from search method of faiss index + :return: fixed nearest metrics + """ + for i, row in enumerate(nearest_items): + if i != row[0]: + target_index = np.where(row == i) + if len(target_index[0]) == 0: + row[0] = i + else: + row[0], row[target_index[0][0]] = row[target_index[0][0]], row[0] + + return nearest_items + + +def fix_duplicate_image_orders_in_knn_graph_single_row(row_no: int, nearest_items: np.ndarray) -> np.ndarray: + """ + Duplicate images create problem in nearest neighbor order, for example for index 6 its closest + neighbors can be [5,6,1,9,3] if 5 and 6 is duplicate, it should be [6,5,1,9,3]. This function ensures that + the first item is the queried index. + + :param nearest_items: nearest metrics obtained from search method of faiss index + :return: fixed nearest metrics + """ + if nearest_items[0, 0] == row_no: + return nearest_items + else: + target_index = np.where(nearest_items[0] == row_no) + nearest_items[0, 0], nearest_items[0, target_index[0]] = nearest_items[0, target_index[0]], nearest_items[0, 0] + return nearest_items + + +def binary_mask_to_rle(mask: np.ndarray) -> Dict[str, list]: + """ + Converts a binary mask into a Run-length Encoding (RLE). + + :param binary_mask: A [h, w] binary mask. + :return: A dict `{"size": [h, w], "counts": [0, 3, 21, 3, ... ]}`. + """ + flat_mask = mask.flatten() + fix_first = flat_mask[0] == 1 + flat_mask = np.pad(flat_mask, (1, 1), "constant", constant_values=(1 - flat_mask[0], 1 - flat_mask[-1])) + + switch_indices = np.where(flat_mask[1:] != flat_mask[:-1])[0] + rle = switch_indices[1:] - switch_indices[:-1] + + if fix_first: + rle = np.pad(rle, (1, 0), "constant", constant_values=(0, 0)) + + return {"size": list(mask.shape), "counts": rle.tolist()} + + +def rle_to_binary_mask(rle: Dict[str, list]) -> np.ndarray: + """ + RLE to binary mask. + + :param rle: The run-length encoding dictinary. + :return: a binary mask of shape `rle["size"]`. + """ + counts = rle["counts"] + size = rle["size"] + mask = np.zeros(np.prod(size), dtype=np.uint8) + + val = 0 + idx = 0 + for count in counts: + if val == 1: + mask[idx : idx + count] = val + val = 1 - val + idx += count + + return mask.reshape(*size) + + +def mask_iou(m1: np.ndarray, m2: np.ndarray) -> float: + intersection = (m1 & m2).sum() # type: ignore + union = (m1 | m2).sum() # type: ignore + if union == 0: + return 0.0 + return intersection / union + + +def __rle_iou(rle1: dict, rle2: dict) -> float: + """ + Compute intersection over union for rle dictionaries. + """ + m1 = rle_to_binary_mask(rle1) + m2 = rle_to_binary_mask(rle2) + return mask_iou(m1, m2) + + +def rle_iou(rle1: Union[dict, list[dict]], rle2: Union[dict, list[dict]]) -> np.ndarray: + """ + Compute intersection over union for rle dictionaries. + """ + if not isinstance(rle1, list): + rle1 = [rle1] + if not isinstance(rle2, list): + rle2 = [rle2] + + with Executor(max_workers=os.cpu_count()) as exe: + jobs = [exe.submit(__rle_iou, r1, r2) for r1, r2 in product(rle1, rle2)] + ious = [job.result() for job in jobs] + return np.array(ious).reshape(len(rle1), len(rle2)) + + +def mask_to_polygon(mask: np.ndarray) -> Tuple[Optional[List[Any]], CocoBbox]: + [x, y, w, h] = cv2.boundingRect(mask) + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + for contour in contours: + # Valid polygons have >= 6 coordinates (3 points) + if contour.size >= 6: + return contour.squeeze(1).tolist(), (x, y, w, h) + + return None, (x, y, w, h) diff --git a/src/encord_active/lib/common/writer.py b/src/encord_active/lib/common/writer.py new file mode 100644 index 000000000..cea3ec71e --- /dev/null +++ b/src/encord_active/lib/common/writer.py @@ -0,0 +1,220 @@ +import csv +import math +from abc import ABC, abstractmethod +from itertools import chain +from pathlib import Path +from typing import List, Optional, Union + +import numpy as np + +from encord_active.lib.common.iterator import Iterator + +SCORE_CSV_FIELDS = ["identifier", "score", "description", "object_class", "annotator", "frame", "url"] +EMBEDDING_CSV_FIELDS = ["identifier", "embedding", "description", "object_class", "frame", "url"] + + +class MetricObserver(ABC): + @abstractmethod + def on_value_insert(self, value: Union[float, int, list]): + pass + + @abstractmethod + def on_metric_close(self): + pass + + +class StatisticsObserver(MetricObserver): + def __init__(self): + self.min_value = math.inf + self.max_value = -math.inf + self.num_rows = 0 + self.mean_value = 0 + + def on_value_insert(self, value: Union[float, int, list]): + if isinstance(value, list): + value = float(np.linalg.norm(np.array(value))) + elif not isinstance(value, (int, float)): + raise TypeError(f"Expected float, int, or list, got {type(value)}") + + self.min_value = min(self.min_value, value) + self.max_value = max(self.max_value, value) + self.mean_value = (self.mean_value * self.num_rows + value) / (self.num_rows + 1) + self.num_rows += 1 + + def on_metric_close(self): + pass + + +class Writer(ABC): + _observers: List[MetricObserver] = [] + + def attach(self, observer: MetricObserver): + self._observers.append(observer) + + def remove_listener(self, observer: MetricObserver): + self._observers.remove(observer) + + @abstractmethod + def __enter__(self): + pass + + @abstractmethod + def __exit__(self, exc_type, exc_val, exc_tb): + for observer in self._observers: + observer.on_metric_close() + + def write(self, value): + for observer in self._observers: + observer.on_value_insert(value) + + +class CSVWriter(Writer): + def __init__(self, filename: Path, iterator: Iterator): + super(CSVWriter, self).__init__() + + self.iterator = iterator + + self.filename = filename + self.filename.parent.mkdir(parents=True, exist_ok=True) + self.csv_file = self.filename.open("w", newline="", encoding="utf-8") + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.csv_file.close() # Notify observers + super(CSVWriter, self).__exit__(exc_type, exc_val, exc_tb) + + def get_identifier( + self, + labels: Union[list[dict], dict, None] = None, + label_hash: Optional[str] = None, + du_hash: Optional[str] = None, + frame: Optional[int] = None, + ): + label_hash = self.iterator.label_hash if label_hash is None else label_hash + du_hash = self.iterator.du_hash if du_hash is None else du_hash + frame = self.iterator.frame if frame is None else frame + + identifier = f"{label_hash}_{du_hash}_{frame:05d}" + + if labels is not None: + if isinstance(labels, dict): + labels = [labels] + hashes = [lbl["objectHash"] if "objectHash" in lbl else lbl["featureHash"] for lbl in labels] + return "_".join(chain([identifier], hashes)) + return identifier + + +class CSVMetricWriter(CSVWriter): + def __init__(self, data_path: Path, iterator: Iterator, prefix: str): + filename = (data_path / "metrics" / f"{prefix}.csv").expanduser() + super(CSVMetricWriter, self).__init__(filename=filename, iterator=iterator) + + self.writer = csv.DictWriter(self.csv_file, fieldnames=SCORE_CSV_FIELDS) + self.writer.writeheader() + + def write( + self, + score: Union[float, int], + labels: Union[list[dict], dict, None] = None, + description: str = "", + label_class: Optional[str] = None, + label_hash: Optional[str] = None, + du_hash: Optional[str] = None, + frame: Optional[int] = None, + url: Optional[str] = None, + annotator: Optional[str] = None, + key: Optional[str] = None, # TODO obsolete parameter, remove from metrics first + ): + if not isinstance(score, (float, int)): + raise TypeError("score must be a float or int") + if isinstance(labels, list) and len(labels) == 0: + labels = None + elif isinstance(labels, dict): + labels = [labels] + + label_hash = self.iterator.label_hash if label_hash is None else label_hash + du_hash = self.iterator.du_hash if du_hash is None else du_hash + frame = self.iterator.frame if frame is None else frame + url = self.iterator.get_data_url() if url is None else url + + if labels is None: + label_class = "" if label_class is None else label_class + annotator = "" if annotator is None else annotator + else: + label_class = labels[0]["name"] if label_class is None else label_class + annotator = labels[0]["lastEditedBy"] if "lastEditedBy" in labels[0] else labels[0]["createdBy"] + + # remember to remove if clause (not its content) when writer's format (obj, score) is enforced on all metrics + # start hack + if key is None: + key = self.get_identifier(labels, label_hash, du_hash, frame) + # end hack + + row = { + "identifier": key, + "score": score, + "description": description, + "object_class": label_class, + "frame": frame, + "url": url, + "annotator": annotator, + } + + self.writer.writerow(row) + self.csv_file.flush() + + super().write(score) + + +class CSVEmbeddingWriter(CSVWriter): + def __init__(self, data_path: Path, iterator: Iterator, prefix: str): + filename = (data_path / "embeddings" / f"{prefix}.csv").expanduser() + super(CSVEmbeddingWriter, self).__init__(filename=filename, iterator=iterator) + + self.writer = csv.DictWriter(self.csv_file, fieldnames=EMBEDDING_CSV_FIELDS) + self.writer.writeheader() + + def write( + self, + value: Union[float, list], + labels: Union[list[dict], dict, None] = None, + description: str = "", + label_class: Optional[str] = None, + label_hash: Optional[str] = None, + du_hash: Optional[str] = None, + frame: Optional[int] = None, + url: Optional[str] = None, + ): + if not isinstance(value, (float, list)): + raise TypeError("value must be a float or list") + + if isinstance(labels, list) and len(labels) == 0: + labels = None + elif isinstance(labels, dict): + labels = [labels] + + label_hash = self.iterator.label_hash if label_hash is None else label_hash + du_hash = self.iterator.du_hash if du_hash is None else du_hash + frame = self.iterator.frame if frame is None else frame + url = self.iterator.get_data_url() if url is None else url + + if labels is None: + label_class = "" if label_class is None else label_class + else: + label_class = labels[0]["name"] if label_class is None else label_class + + row = { + "identifier": self.get_identifier(labels, label_hash, du_hash, frame), + "embedding": value, + "description": description, + "object_class": label_class, + "frame": frame, + "url": url, + } + + self.writer.writerow(row) + self.csv_file.flush() + + super().write(value) diff --git a/src/encord_active/lib/embeddings/__init__.py b/src/encord_active/lib/embeddings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/lib/embeddings/cnn_embed.py b/src/encord_active/lib/embeddings/cnn_embed.py new file mode 100644 index 000000000..7c418369f --- /dev/null +++ b/src/encord_active/lib/embeddings/cnn_embed.py @@ -0,0 +1,226 @@ +import logging +import os +import pickle +import time +from pathlib import Path +from typing import List, Optional, Tuple + +import torch +import torchvision.transforms as torch_transforms +from encord.project_ontology.classification_type import ClassificationType +from encord.project_ontology.object_type import ObjectShape +from PIL import Image +from torch import nn +from torchvision.models import EfficientNet_V2_S_Weights, efficientnet_v2_s +from torchvision.models.feature_extraction import create_feature_extractor + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.utils import get_bbox_from_encord_label_object + +logger = logging.getLogger(__name__) +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def get_model_and_transforms() -> Tuple[nn.Module, nn.Module]: + weights = EfficientNet_V2_S_Weights.DEFAULT + model = efficientnet_v2_s(weights=weights).to(DEVICE) + embedding_extractor = create_feature_extractor(model, return_nodes={"avgpool": "my_avgpool"}) + for p in embedding_extractor.parameters(): + p.requires_grad = False + embedding_extractor.eval() + return embedding_extractor, weights.transforms() + + +def adjust_image_channels(image: torch.Tensor) -> torch.Tensor: + if image.shape[0] == 4: + image = image[:3] + elif image.shape[0] < 3: + image = image.repeat(3, 1, 1) + + return image + + +def image_path_to_tensor(image_path: Path) -> torch.Tensor: + image = Image.open(image_path.as_posix()) + transform = torch_transforms.ToTensor() + image = transform(image) + + image = adjust_image_channels(image) + + return image + + +def assemble_object_batch(data_unit: dict, img_path: Path, transforms: Optional[nn.Module]): + if transforms is None: + transforms = torch.nn.Sequential() + + image = image_path_to_tensor(img_path) + img_batch: List[torch.Tensor] = [] + + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] in [ObjectShape.POLYGON.value, ObjectShape.BOUNDING_BOX.value]: + try: + out = get_bbox_from_encord_label_object( + obj, + image.shape[2], + image.shape[1], + ) + + if out is None: + continue + x, y, w, h = out + + img_patch = image[:, y : y + h, x : x + w] + img_batch.append(transforms(img_patch)) + except Exception as e: + logger.warning(f"Error with object {obj['objectHash']}: {e}") + continue + return torch.stack(img_batch).to(DEVICE) if len(img_batch) > 0 else None + + +@torch.inference_mode() +def generate_cnn_embeddings(iterator: Iterator, filepath: str) -> None: + start = time.perf_counter() + feature_extractor, transforms = get_model_and_transforms() + + collections = [] + for data_unit, img_pth in iterator.iterate(desc="Embedding object data."): + if img_pth is None: + continue + + batches = assemble_object_batch(data_unit, img_pth, transforms=transforms) + if batches is None: + continue + + embeddings = feature_extractor(batches)["my_avgpool"] + embeddings_torch = torch.flatten(embeddings, start_dim=1).cpu().detach().numpy() + + for obj, emb in zip(data_unit["labels"]["objects"], embeddings_torch): + if obj["shape"] in [ObjectShape.POLYGON.value, ObjectShape.BOUNDING_BOX.value]: + + last_edited_by = obj["lastEditedBy"] if "lastEditedBy" in obj.keys() else obj["createdBy"] + + entry = { + "label_row": iterator.label_hash, + "data_unit": data_unit["data_hash"], + "frame": iterator.frame, + "objectHash": obj["objectHash"], + "lastEditedBy": last_edited_by, + "featureHash": obj["featureHash"], + "name": obj["name"], + "dataset_title": iterator.dataset_title, + "embedding": emb, + } + + collections.append(entry) + + with open(filepath, "wb") as f: + pickle.dump(collections, f) + + logger.info( + f"Generating {len(iterator)} embeddings took {str(time.perf_counter() - start)} seconds", + ) + + +@torch.inference_mode() +def generate_cnn_classification_embeddings(iterator: Iterator, filepath: str) -> None: + ontology_class_hash_to_index: dict[str, dict] = {} + ontology_class_hash_to_question_hash: dict[str, str] = {} + + # TODO: This only evaluates immediate classes, not nested ones + # TODO: Look into other types of classifications apart from radio buttons + for class_label in iterator.project.ontology["classifications"]: + class_question = class_label["attributes"][0] + if class_question["type"] == ClassificationType.RADIO.value: + ontology_class_hash = class_label["featureNodeHash"] + ontology_class_hash_to_index[ontology_class_hash] = {} + ontology_class_hash_to_question_hash[ontology_class_hash] = class_question["featureNodeHash"] + for index, option in enumerate(class_question["options"]): + ontology_class_hash_to_index[ontology_class_hash][option["featureNodeHash"]] = index + + start = time.perf_counter() + feature_extractor, transforms = get_model_and_transforms() + + collections = [] + for data_unit, img_pth in iterator.iterate(desc="Embedding image data."): + if not img_pth: + continue + temp_entry = {} + temp_entry["label_row"] = iterator.label_hash + temp_entry["data_unit"] = data_unit["data_hash"] + temp_entry["frame"] = str(iterator.frame) + temp_entry["url"] = data_unit["data_link"] + + temp_classification_hash = {} + classification_answers = iterator.label_rows[iterator.label_hash]["classification_answers"] + for classification in data_unit["labels"].get("classifications", []): + + classification_hash = classification["classificationHash"] + ontology_class_hash = classification["featureHash"] + if ontology_class_hash in ontology_class_hash_to_index.keys(): + for classification_answer in classification_answers[classification_hash]["classifications"]: + if ( + classification_answer["featureHash"] + == ontology_class_hash_to_question_hash[ontology_class_hash] + ): + temp_classification_hash[ontology_class_hash] = { + "answer_featureHash": classification_answer["answers"][0]["featureHash"], + "answer_name": classification_answer["answers"][0]["name"], + "annotator": classification["createdBy"], + } + + temp_entry["classification_answers"] = temp_classification_hash # type: ignore[assignment] + + image = image_path_to_tensor(img_pth) + transformed_image = transforms(image).unsqueeze(0) + embedding = feature_extractor(transformed_image.to(DEVICE))["my_avgpool"] + embedding = torch.flatten(embedding).cpu().detach().numpy() + + temp_entry["embedding"] = embedding + + collections.append(temp_entry) + + with open(filepath, "wb") as f: + pickle.dump(collections, f) + + logger.info( + f"Generating {len(iterator)} embeddings took {str(time.perf_counter() - start)} seconds", + ) + + +def get_cnn_embeddings(iterator: Iterator, embedding_type: str = "objects", *, force: bool = False) -> list: + target_folder = os.path.join(iterator.cache_dir, "embeddings") + if embedding_type == "objects": + embedding_file = "cnn_objects" + elif embedding_type == "classifications": + embedding_file = "cnn_classifications" + else: + raise Exception(f"Undefined embedding type '{embedding_type}' for get_cnn_embeddings method") + + embedding_path = os.path.join(target_folder, f"{embedding_file}.pkl") + + if force: + logger.info("Regenerating CNN embeddings...") + if embedding_type == "objects": + generate_cnn_embeddings(iterator, filepath=embedding_path) + elif embedding_type == "classifications": + generate_cnn_classification_embeddings(iterator, filepath=embedding_path) + + with open(embedding_path, "rb") as f: + cnn_embeddings = pickle.load(f) + logger.info("Done!") + else: + try: + with open(embedding_path, "rb") as f: + cnn_embeddings = pickle.load(f) + except FileNotFoundError: + logger.info(f"{embedding_path} not found. Generating embeddings...") + if embedding_type == "objects": + generate_cnn_embeddings(iterator, filepath=embedding_path) + elif embedding_type == "classifications": + generate_cnn_classification_embeddings(iterator, filepath=embedding_path) + with open(embedding_path, "rb") as f: + cnn_embeddings = pickle.load(f) + logger.info("Done!") + + return cnn_embeddings diff --git a/src/encord_active/lib/embeddings/hu_embed.py b/src/encord_active/lib/embeddings/hu_embed.py new file mode 100644 index 000000000..4a7e29a13 --- /dev/null +++ b/src/encord_active/lib/embeddings/hu_embed.py @@ -0,0 +1,57 @@ +import logging + +import cv2 +import numpy as np +import pandas as pd + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.utils import get_du_size, get_object_coordinates +from encord_active.lib.common.writer import CSVEmbeddingWriter + +logger = logging.getLogger(__name__) +HU_FILENAME = "hu_moments-embeddings" + + +def generate_hu_embeddings(iterator: Iterator): + with CSVEmbeddingWriter(iterator.cache_dir, iterator, prefix=HU_FILENAME) as writer: + for data_unit, img_pth in iterator.iterate(desc="Generating HU embeddings"): + size = get_du_size(data_unit, img_pth) + if not size: + continue + height, width = size + + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] != "polygon": + continue + + points = get_object_coordinates(obj) + if not points: # avoid corrupted objects without vertices (empty list - []) + continue + + polygon = (np.array(points) * (width, height)).astype(np.int32) + mask = np.zeros((height, width), dtype="uint8") + mask = cv2.fillPoly(mask, [polygon], 255) + moments = cv2.HuMoments(cv2.moments(mask)).flatten() + + writer.write(moments.tolist(), obj) + + +def get_hu_embeddings(iterator: Iterator, *, force: bool = False) -> pd.DataFrame: + hu_moments_path = iterator.cache_dir / f"embeddings/{HU_FILENAME}.csv" + + if force: + logger.info("Regenerating embeddings...") + generate_hu_embeddings(iterator) + hu_moments_df = pd.read_csv(hu_moments_path) + logger.info("Done!") + else: + try: + hu_moments_df = pd.read_csv(hu_moments_path) + except FileNotFoundError: + logger.info(f"{hu_moments_path} not found. Generating embeddings...") + generate_hu_embeddings(iterator) + hu_moments_df = pd.read_csv(hu_moments_path) + logger.info("Done!") + + hu_moments_df = hu_moments_df.sort_values(["identifier"], ascending=True).reset_index() + return hu_moments_df diff --git a/src/encord_active/lib/metrics/example.py b/src/encord_active/lib/metrics/example.py new file mode 100644 index 000000000..7f2d33683 --- /dev/null +++ b/src/encord_active/lib/metrics/example.py @@ -0,0 +1,65 @@ +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class ExampleMetric(Metric): + TITLE = "Example Title" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "Assigns same value and description to all objects." + LONG_DESCRIPTION = r"""For long descriptions, I can use Markdown to _format_ the text. + +I can, e.g., make a +[hyperlink](https://memegenerator.net/instance/74454868/europe-its-the-final-markdown) +to the awesome paper that proposed the method. + +Or use math to better explain the method: +$$h_{\lambda}(x) = \frac{1}{x^\intercal x}$$ +""" + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + + logger.info("My custom logging") + + # Preprocessing happens here. + # You can build/load databases of embeddings, compute statistics etc, + # ... + + for data_unit, img_pth in iterator.iterate(desc="Progress bar description"): + # Frame level score (data quality) + writer.write(1337, description="Your description of the score [can be omitted]") + + for obj in data_unit["labels"].get("objects", []): + # Object level score (label / model prediction quality) + if not obj["shape"] in valid_annotation_types: + continue + + # This is where you do the actual inference. + + # Some convenient properties associated with the current data. + # ``iterator.label_hash`` the label hash of the current data unit + # ``iterator.du_hash`` the data unit hash of + # ``iterator.frame`` the frame of the current data unit hash of + # ``iterator.num_frames`` the total number of frames in the label row. + + # Do your thing (inference) + # ... + # Then + writer.write(42, labels=obj, description="Your description of the score [can be omitted]") + + +if __name__ == "__main__": + import sys + from pathlib import Path + + from encord_active.lib.common.tester import perform_test + + path = sys.argv[1] + perform_test(ExampleMetric(), data_dir=Path(path)) diff --git a/src/encord_active/lib/metrics/fetch_prebuilt_metrics.py b/src/encord_active/lib/metrics/fetch_prebuilt_metrics.py new file mode 100644 index 000000000..bf51e7d1d --- /dev/null +++ b/src/encord_active/lib/metrics/fetch_prebuilt_metrics.py @@ -0,0 +1,72 @@ +import os +import shutil +from pathlib import Path +from typing import Optional + +import requests +import rich +from rich.markup import escape +from rich.panel import Panel +from tqdm import tqdm + +# GCP bucket links will be added here +PREBUILT_PROJECTS = { + "[open-source][validation]-coco-2017-dataset": "https://storage.googleapis.com/encord-active-sandbox-data/%5Bopen-source%5D%5Bvalidation%5D-coco-2017-dataset.zip", + "[open-source][test]-limuc-ulcerative-colitis-classification": "https://storage.googleapis.com/encord-active-sandbox-data/%5Bopen-source%5D%5Btest%5D-limuc-ulcerative-colitis-classification.zip", + "[open-source]-covid-19-segmentations": "https://storage.googleapis.com/encord-active-sandbox-data/%5Bopen-source%5D-covid-19-segmentations.zip", + "[open-source][validation]-bdd-dataset": "https://storage.googleapis.com/encord-active-sandbox-data/%5Bopen-source%5D%5Bvalidation%5D-bdd-dataset.zip", +} + + +def fetch_prebuilt_project_size(project_name: str) -> Optional[float]: + """ + Returns the storage size (mb) of a prebuilt project stored in the cloud + + :param project_name: Prebuilt project name + :return: Prebuilt project's storage size (mb). Can return None if the information is not available. + """ + if project_name not in PREBUILT_PROJECTS.keys(): + return None + url = PREBUILT_PROJECTS[project_name] + with requests.head(url) as r: + total_length = fetch_response_content_length(r) + size = round(total_length / 1000000.0, 1) if total_length is not None else None + return size + + +def fetch_response_content_length(r: requests.Response) -> Optional[int]: + return int(r.headers["content-length"]) if "content-length" in r.headers.keys() else None + + +def fetch_prebuilt_project(project_name: str, out_dir: Path): + url = PREBUILT_PROJECTS[project_name] + output_file_name = "prebuilt_project.zip" + output_file_path = out_dir / output_file_name + rich.print(f"Output destination: {escape(out_dir.as_posix())}") + if not out_dir.is_dir(): + exit() + + r = requests.get(url, stream=True) + total_length = fetch_response_content_length(r) + with open(output_file_path.as_posix(), "wb") as f: + with tqdm(total=total_length, unit="B", unit_scale=True, desc="Downloading sandbox project", ascii=True) as bar: + for chunk in tqdm(r.iter_content(chunk_size=1024 * 1024)): + f.write(chunk) + bar.update(len(chunk)) + + rich.print("Unpacking zip file. May take a bit.") + shutil.unpack_archive(output_file_path, out_dir) + os.remove(output_file_path) + + rich.print( + Panel( + f""" +Successfully downloaded sandbox dataset. To view the data, run: + +[cyan blink]encord-active visualise "{escape(out_dir.as_posix())}" + """, + title="🌟 Success 🌟", + style="green", + expand=False, + ) + ) diff --git a/src/encord_active/lib/metrics/geometric/__init__.py b/src/encord_active/lib/metrics/geometric/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/lib/metrics/geometric/annotation_duplicates.py b/src/encord_active/lib/metrics/geometric/annotation_duplicates.py new file mode 100644 index 000000000..d233c34bb --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/annotation_duplicates.py @@ -0,0 +1,67 @@ +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import get_iou, get_polygon +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class AnnotationDuplicates(Metric): + TITLE = "Annotation Duplicates" + SHORT_DESCRIPTION = "Ranks annotations by how likely they are to represent the same object" + LONG_DESCRIPTION = r"""Ranks annotations by how likely they are to represent the same object. +> [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) +is used to measure closeness of two annotations.""" + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + ] + + def __init__(self, threshold: float = 0.6): + # todo expose additional parameters along with their meaning + # threshold: used along with Jaccard similarity coefficient to calculate likelihood of duplicates in annotations + super().__init__() + self.threshold = threshold + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, _ in iterator.iterate(desc="Looking for duplicates"): + objects = [obj for obj in data_unit["labels"].get("objects", []) if obj["shape"] in valid_annotation_types] + polygons = [get_polygon(obj) for obj in objects] + + duplicated_annotations = set() + for i, obj1 in enumerate(objects): + score = 0.0 + match = None + for j, obj2 in enumerate(objects): + if j == i or obj1["featureHash"] != obj2["featureHash"]: + continue + cur_score = get_iou(polygons[i], polygons[j]) + if cur_score > score: + score = cur_score + match = obj2 + + identifiers = [obj1] + if match is None or score < self.threshold: + description = "Possible non duplicated object" + elif (obj1["objectHash"], match["objectHash"]) in duplicated_annotations: + # avoid reporting twice same annotation pair (in case (obj2, obj1) was previously analysed) + continue + else: + identifiers.append(match) + description = "Possible duplicates" + duplicated_annotations.add((match["objectHash"], obj1["objectHash"])) + + writer.write(score, labels=identifiers, description=description) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) diff --git a/src/encord_active/lib/metrics/geometric/hu_static.py b/src/encord_active/lib/metrics/geometric/hu_static.py new file mode 100644 index 000000000..c2ab74b49 --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/hu_static.py @@ -0,0 +1,98 @@ +from typing import List + +import numpy as np +from loguru import logger +from sklearn.decomposition import PCA + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import get_object_coordinates +from encord_active.lib.common.writer import CSVEmbeddingWriter, CSVMetricWriter +from encord_active.lib.embeddings.hu_embed import get_hu_embeddings + +logger = logger.opt(colors=True) + + +def compute_cls_distances(embeddings: np.ndarray, labels: np.ndarray) -> np.ndarray: + """Compute distance between class samples and the class centroid.""" + cls_distance_vector = np.zeros_like(labels, dtype=np.float32) + for cls in set(labels): + cls_mask = labels == cls + cls_embeddings = embeddings[cls_mask] + centroid = cls_embeddings.mean(axis=0) + cls_distances = np.linalg.norm(cls_embeddings - centroid, axis=1) + cls_distance_vector[cls_mask] = cls_distances + cls_distance_vector[cls_distance_vector == 0] = cls_distance_vector[cls_distance_vector != 0].min() + return cls_distance_vector + + +class HuMomentsStatic(Metric): + TITLE = "Shape outlier detection" + SHORT_DESCRIPTION = "Calculates potential outliers by polygon shape." + LONG_DESCRIPTION = r"""Computes the Euclidean distance between the polygons' + [Hu moments](https://en.wikipedia.org/wiki/Image_moment) for each class and + the prototypical class moments.""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.POLYGON] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + hu_moments_df = get_hu_embeddings(iterator, force=True) + + moments_list: List[np.ndarray] = [] + obj_list: List[dict] = [] + obj_hashes: List[str] = [] + cls_list: List[str] = [] + for data_unit, img_pth in iterator.iterate(desc="Computing HU moments"): + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + points = get_object_coordinates(obj) + if not points: # avoid corrupted objects without vertices ([]) + continue + + key = writer.get_identifier(obj) + moments = np.array(eval(hu_moments_df.loc[hu_moments_df["identifier"] == key, "embedding"].values[0])) + + obj_list.append(obj) + obj_hashes.append(obj["objectHash"]) + cls_list.append(obj["name"]) + moments_list.append(moments) + + cls_dict = {cls: i for i, cls in enumerate(set(cls_list))} + cls_ary = np.array([cls_dict[cls] for cls in cls_list]) + + if not moments_list: + logger.info("[Skipping] No polygons to evaluate.") + return + + X = np.stack(moments_list, axis=0) + X = (X - X.mean(0)) / X.std(0) + + distance_vector = 1 / compute_cls_distances(X, cls_ary) + n = len(obj_list) + for data_unit, img_pth in iterator.iterate(desc="writing scores"): + for obj in data_unit["labels"].get("objects", []): + try: + index = obj_hashes.index(obj["objectHash"]) + except: + continue + + writer.write(float(distance_vector[index]), obj, label_class=cls_list[index]) + + pca_coordinates = PCA(n_components=2).fit_transform(X) + with CSVEmbeddingWriter(iterator.cache_dir, iterator, prefix="hu_2d-embedding") as coords_writer: + for data_unit, img_pth in iterator.iterate(desc="writing scores"): + if "objects" not in data_unit["labels"]: + continue + + for obj in data_unit["labels"]["objects"]: + try: + index = obj_hashes.index(obj["objectHash"]) + except: + continue + + coords_writer.write(pca_coordinates[index].tolist(), obj, label_class=cls_list[index]) diff --git a/src/encord_active/lib/metrics/geometric/hu_temporal.py b/src/encord_active/lib/metrics/geometric/hu_temporal.py new file mode 100644 index 000000000..138ba3b91 --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/hu_temporal.py @@ -0,0 +1,79 @@ +from typing import Optional, Union + +import numpy as np +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter +from encord_active.lib.embeddings.hu_embed import get_hu_embeddings + +logger = logger.opt(colors=True) + + +def cosine_similarity(a, b): + return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) + + +class MomentDecayingAverage: + def __init__(self, decay: Union[float, int] = 0.5): + decay = max(min(1, decay), 0) # force 0 <= decay <= 1 + self.decay = decay + self.vector: Optional[np.ndarray] = None + + def compare(self, vector: np.ndarray): + if self.vector is None: + self.vector = vector + return 1 + + val = cosine_similarity(self.vector, vector) + self.vector = self.decay * self.vector + (1 - self.decay) * vector + return val + + +class MomentStore: + def __init__(self): + self.moment_averages: dict[str, MomentDecayingAverage] = {} + + def score(self, key: str, moments: np.ndarray): + decaying_average = self.moment_averages.setdefault(key, MomentDecayingAverage()) + return decaying_average.compare(moments) + + +class HuMomentsTemporalMetric(Metric): + TITLE = "Polygon Shape Similarity" + SHORT_DESCRIPTION = "Ranks objects by how similar they are to their instances in previous frames." + LONG_DESCRIPTION = r"""Ranks objects by how similar they are to their instances in previous frames +based on [Hu moments](https://en.wikipedia.org/wiki/Image_moment). The more an object's shape changes, +the lower its score will be.""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.SEQUENCE + ANNOTATION_TYPE = [AnnotationType.OBJECT.POLYGON] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + moment_store = MomentStore() + hu_moments_df = get_hu_embeddings(iterator) + hu_moments_identifiers = set(hu_moments_df["identifier"]) + + for data_unit, img_pth in iterator.iterate(desc="Computing moment similarity"): + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + key = writer.get_identifier(obj) + if key not in hu_moments_identifiers: # check if identifier was discarded by get_hu_embeddings + continue + + moments = np.array(eval(hu_moments_df.loc[hu_moments_df["identifier"] == key, "embedding"].values[0])) + score = moment_store.score(obj["objectHash"], moments) + writer.write(score, obj) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) diff --git a/src/encord_active/lib/metrics/geometric/image_border_closeness.py b/src/encord_active/lib/metrics/geometric/image_border_closeness.py new file mode 100644 index 000000000..e36b4de30 --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/image_border_closeness.py @@ -0,0 +1,53 @@ +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import get_object_coordinates +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class ImageBorderCloseness(Metric): + TITLE = "Annotation closeness to image borders" + SHORT_DESCRIPTION = "Ranks annotations by how close they are to image borders." + LONG_DESCRIPTION = r"""This metric ranks annotations by how close they are to image borders.""" + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + AnnotationType.OBJECT.POLYLINE, + AnnotationType.OBJECT.KEY_POINT, + AnnotationType.OBJECT.SKELETON, + ] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, _ in iterator.iterate(desc="Computing closeness to border"): + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + coordinates = get_object_coordinates(obj) + if not coordinates: # avoid corrupted objects without vertices ([]) and unknown objects' shape (None) + continue + + x_coordinates = [x for x, _ in coordinates] + min_x = min(x_coordinates) + max_x = max(x_coordinates) + + y_coordinates = [y for _, y in coordinates] + min_y = min(y_coordinates) + max_y = max(y_coordinates) + + score = max(1 - min_x, 1 - min_y, max_x, max_y) + writer.write(score, obj) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) diff --git a/src/encord_active/lib/metrics/geometric/object_size.py b/src/encord_active/lib/metrics/geometric/object_size.py new file mode 100644 index 000000000..6eff7b8ba --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/object_size.py @@ -0,0 +1,169 @@ +from loguru import logger +from shapely.ops import unary_union + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import ( + get_bbox_from_encord_label_object, + get_du_size, + get_polygon, +) +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +def get_area(obj: dict) -> float: + if obj["shape"] in {"bounding_box", "polygon"}: + polygon = get_polygon(obj) + area = 0.0 if polygon is None else polygon.area + else: + logger.warning(f"Unknown shape {obj['shape']} in get_area function") + area = 0.0 + return area + + +class RelativeObjectAreaMetric(Metric): + TITLE = "Object Area - Relative" + SHORT_DESCRIPTION = "Computes object area as a percentage of total image area" + LONG_DESCRIPTION = r"""Computes object area as a percentage of total image area.""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + ] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, _ in iterator.iterate(desc="Computing object area"): + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + obj_area = get_area(obj) + writer.write(100 * obj_area, obj) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + + +class OccupiedTotalAreaMetric(Metric): + TITLE = "Frame object density" + SHORT_DESCRIPTION = "Computes the percentage of image area that's occupied by objects" + LONG_DESCRIPTION = r"""Computes the percentage of image area that's occupied by objects.""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + ] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, _ in iterator.iterate(desc="Computing total object area"): + polygons = [] + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + poly = get_polygon(obj) + if not poly: # avoid corrupted objects without vertices ([]) and polygons with less than 3 vertices + continue + polygons.append(poly) + + occupied_area = unary_union(polygons).area + writer.write(100 * occupied_area) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + + +class AbsoluteObjectAreaMetric(Metric): + TITLE = "Object Area - Absolute" + SHORT_DESCRIPTION = "Computes object area in amount of pixels" + LONG_DESCRIPTION = r"""Computes object area in amount of pixels.""" + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + ] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, img_pth in iterator.iterate(desc="Computing pixel area"): + size = get_du_size(data_unit, img_pth) + if not size: + continue + img_h, img_w = size + img_area = img_h * img_w + + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + obj_area = get_area(obj) + pixel_area = int(img_area * obj_area) + writer.write(pixel_area, obj) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + + +class ObjectAspectRatioMetric(Metric): + TITLE = "Object Aspect Ratio" + SHORT_DESCRIPTION = "Computes aspect ratios of objects" + LONG_DESCRIPTION = r"""Computes aspect ratios ($width/height$) of objects.""" + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [ + AnnotationType.OBJECT.BOUNDING_BOX, + AnnotationType.OBJECT.POLYGON, + ] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + + for data_unit, img_pth in iterator.iterate(desc="Computing aspect ratio"): + size = get_du_size(data_unit, img_pth) + if not size: + continue + img_h, img_w = size + + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + bbox = get_bbox_from_encord_label_object(obj, w=img_w, h=img_h) + if bbox is None: + continue + + x, y, w, h = bbox + if h == 0: + continue + ar = w / h + + writer.write(ar, obj) + found_any = True + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) diff --git a/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py b/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py new file mode 100644 index 000000000..361cbdf95 --- /dev/null +++ b/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py @@ -0,0 +1,127 @@ +import numpy as np +from loguru import logger +from sklearn import preprocessing +from tqdm import tqdm + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class OcclusionDetectionOnVideo(Metric): + TITLE = "Detect Occlusion in Video" + SHORT_DESCRIPTION = "Tracks objects and detect outliers" + LONG_DESCRIPTION = r"""This metric collects information related to object size and aspect ratio for each track + and find outliers among them.""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.GEOMETRIC + DATA_TYPE = DataType.SEQUENCE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX] + + def __init__( + self, + low_threshold: float = 0.3, + medium_threshold: float = 0.4, + high_threshold: float = 0.7, + min_samples: int = 5, + ): + super().__init__() + self.low_threshold = low_threshold + self.medium_threshold = medium_threshold + self.high_threshold = high_threshold + self.min_samples = min_samples + self.min_max_scaler = preprocessing.MinMaxScaler() + + def get_description_from_occlusion(self, distance: float) -> str: + if distance > self.high_threshold: + return "There is a high probability of occlusion" + elif distance > self.medium_threshold: + return "There is a moderate probability of occlusion" + elif distance > self.low_threshold: + return "There is a low probability of occlusion" + else: + return "There is no occlusion" + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + + videos: dict[str, dict[str, dict]] = {} + for label_row_hash, label_row in tqdm(iterator.label_rows.items(), desc="Looking for occlusions", leave=False): + if label_row["data_type"] == "video": + videos[label_row_hash] = {} + + data_unit = list(label_row["data_units"].values())[0] + for label in data_unit["labels"]: + for obj in data_unit["labels"][label]["objects"]: + if obj["shape"] not in valid_annotation_types: + continue + + object_hash = obj["objectHash"] + if object_hash not in videos[label_row_hash].keys(): + videos[label_row_hash][object_hash] = {"area": [], "aspect_ratio": [], "frame": []} + + h = obj["boundingBox"]["h"] + w = obj["boundingBox"]["w"] + if h == 0: + continue + videos[label_row_hash][object_hash]["area"].append(h * w) + videos[label_row_hash][object_hash]["aspect_ratio"].append(w / h) + videos[label_row_hash][object_hash]["frame"].append(label) + + for object_hash in videos[label_row_hash].keys(): + if len(videos[label_row_hash][object_hash]["frame"]) >= self.min_samples: + data_points = np.stack( + ( + videos[label_row_hash][object_hash]["area"], + videos[label_row_hash][object_hash]["aspect_ratio"], + ), + axis=1, + ) + data_points_scaled = self.min_max_scaler.fit_transform(data_points) + + data_points_mean = np.mean(data_points_scaled, axis=0) + distances = np.linalg.norm(data_points_scaled - data_points_mean, axis=1) + + # We want both distant and low area samples, so filter according to that + occlusions = np.logical_and( + (distances > self.low_threshold), (data_points_scaled[:, 0] < data_points_mean[0]) + ) + + distances_adjusted = np.full(data_points.shape[0], 0.0) + distances_adjusted[occlusions] = distances[occlusions] + + descriptions = list(map(self.get_description_from_occlusion, distances_adjusted)) + + scores = np.full(data_points.shape[0], 100.0) + scores_full = 1 / distances + scores[occlusions] = scores_full[occlusions] + + videos[label_row_hash][object_hash]["scores"] = scores + videos[label_row_hash][object_hash]["descriptions"] = descriptions + + if not videos: + logger.info("[Skipping] No videos in dataset. ") + + for data_unit, img_pth in iterator.iterate(desc="Storing occlusion index"): + label_row_hash = iterator.label_hash + if label_row_hash not in videos.keys(): + continue + + for obj in data_unit["labels"].get("objects", []): + object_hash = obj["objectHash"] + if ( + obj["shape"] not in valid_annotation_types + or "scores" not in videos[label_row_hash][object_hash].keys() + ): + continue + + # todo check if speedup is needed in case a lot of same type's objects are in a label row + score_index = videos[label_row_hash][object_hash]["frame"].index(str(iterator.frame)) + + writer.write( + videos[label_row_hash][obj["objectHash"]]["scores"][score_index], + obj, + description=videos[iterator.label_hash][obj["objectHash"]]["descriptions"][score_index], + ) diff --git a/src/encord_active/lib/metrics/heuristic/__init__.py b/src/encord_active/lib/metrics/heuristic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/lib/metrics/heuristic/_annotation_time.py b/src/encord_active/lib/metrics/heuristic/_annotation_time.py new file mode 100644 index 000000000..f13aaaf6e --- /dev/null +++ b/src/encord_active/lib/metrics/heuristic/_annotation_time.py @@ -0,0 +1,37 @@ +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class AnnotationTimeMetric(Metric): + TITLE = "Annotation Time" + SHORT_DESCRIPTION = "Ranks annotations by the time it took to make them." + LONG_DESCRIPTION = r"""Ranks annotations by the time it took to make them. + +If no logs are available for a particular object, it will get score 0.""" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.ALL + SCORE_NORMALIZATION = True + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + found_any = False + + for data_unit, img_pth in iterator.iterate(desc="Computing annotation times"): + for obj in data_unit["labels"]["objects"]: + object_label_logs = iterator.get_label_logs(object_hash=obj["objectHash"]) + + if not object_label_logs: + writer.write(0.0, obj, description="No logs available.") + continue + + times = map(lambda x: x.get("time_taken", None) or 0.0, object_label_logs) + writer.write(sum(times) / 1000, obj, description="Annotation time (seconds).") + found_any = True + + if not found_any: + logger.info("[Note] Couldn't get any label logs. All objects will have score 0.") diff --git a/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py b/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py new file mode 100644 index 000000000..b0d369d70 --- /dev/null +++ b/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py @@ -0,0 +1,137 @@ +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import get_iou, get_polygon +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class HighIOUChangingClasses(Metric): + TITLE = "Inconsistent Object Classification and Track IDs" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.SEQUENCE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "Looks for overlapping objects with different classes (across frames)." + LONG_DESCRIPTION = r"""This algorithm looks for overlapping objects in consecutive +frames that have different classes. Furthermore, if classes are the same for overlapping objects but have different +track-ids, they will be flagged as potential inconsistencies in tracks. + + +**Example 1:** +``` + Frame 1 Frame 2 +┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ DOG:1 │ │ +│ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ +│ │ │ │ +└───────────────────┘ └───────────────────┘ +``` +`Dog:1` will be flagged as potentially wrong class, because it overlaps with `CAT:1`. + +**Example 2:** +``` + Frame 1 Frame 2 +┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ CAT:2 │ │ +│ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ +│ │ │ │ +└───────────────────┘ └───────────────────┘ +``` +`Cat:2` will be flagged as potentially having a broken track, because track ids `1` and `2` doesn't match. + +""" + + def __init__(self, threshold: float = 0.8): + super(HighIOUChangingClasses, self).__init__() + self.threshold = threshold + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + found_valid = False + + label_hash = "" + previous_objects = None + previous_polygons = None + for data_unit, img_pth in iterator.iterate(desc="Looking for overlapping objects"): + label_row = iterator.label_rows[iterator.label_hash] + data_type = label_row["data_type"] + if not (data_type == "video" or (data_type == "img_group" and len(label_row["data_units"]) > 1)): + # Not a sequence + continue + + objects = [o for o in data_unit["labels"]["objects"] if o["shape"] in valid_annotation_types] + polygons = list(map(get_polygon, objects)) + found_any |= len(polygons) > 0 + + # Remove invalid polygons and flag them as such. + for i in range(len(objects) - 1, -1, -1): + if polygons[i] is None: + writer.write(0.0, objects[i], description="Invalid polygon") + objects.pop(i) + polygons.pop(i) + + if label_hash != iterator.label_hash or previous_objects is None: + label_hash = iterator.label_hash + previous_objects = objects + previous_polygons = polygons + continue + + for obj, polygon in zip(objects, polygons): + best_idx = -1 + best_iou = 0.0 + + for i, old_polygon in enumerate(previous_polygons): + iou = get_iou(polygon, old_polygon) + if iou > best_iou: + best_idx = i + best_iou = iou + + if best_iou == 0 or best_idx == -1: + writer.write(1.0, obj) + continue + + prev_object = previous_objects[best_idx] + if prev_object["objectHash"] == obj["objectHash"]: + writer.write(1.0, obj) + elif best_iou > self.threshold and prev_object["featureHash"] != obj["featureHash"]: + # Overlapping objects with different classes + writer.write( + 1 - best_iou, + obj, + description=f"`{obj['name']}` in frame {iterator.frame} overlaps with " + f"`{prev_object['name']}` in frame {iterator.frame - 1}", + ) + elif best_iou > self.threshold and prev_object["featureHash"] == obj["featureHash"]: + writer.write( + 1 - best_iou, + obj, + description=f"`{obj['name']}` in frame {iterator.frame - 1} and {iterator.frame} have " + f"different track ids.", + ) + else: + # IOU < threshold so probably not an issue + writer.write(1.0, obj) + found_valid = True + + previous_polygons = polygons + previous_objects = objects + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + elif not found_valid: + logger.info( + f"[Skipping] No valid object labels of types {{{', '.join(valid_annotation_types)}}}." + ) diff --git a/src/encord_active/lib/metrics/heuristic/img_features.py b/src/encord_active/lib/metrics/heuristic/img_features.py new file mode 100644 index 000000000..e15b01ca2 --- /dev/null +++ b/src/encord_active/lib/metrics/heuristic/img_features.py @@ -0,0 +1,265 @@ +from typing import Callable, Union + +import cv2 +import numpy as np + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter + + +def iterate_with_rank_fn( + iterator: Iterator, + writer: CSVMetricWriter, + rank_fn: Callable, + name: str, + color_space: int = cv2.COLOR_BGR2RGB, +): + for data_unit, img_pth in iterator.iterate(desc=f"Looking for {name}"): + if img_pth is None: + continue + try: + image = cv2.imread(img_pth.as_posix()) + image = cv2.cvtColor(image, color_space) + except cv2.error: + continue + writer.write(rank_fn(image)) + + +class ContrastMetric(Metric): + TITLE = "Contrast" + SHORT_DESCRIPTION = "Ranks images by their contrast." + LONG_DESCRIPTION = r"""Ranks images by their contrast. + +Contrast is computed as the standard deviation of the pixel values. +""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_contrast(image): + return image.std() / 255 + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_contrast, self.TITLE) + + +class Wrapper: # we can't have a non-default-constructible Metric implementation at module level + class ColorMetric(Metric): + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + def __init__( + self, + color_name: str, + hue_filters: Union[list, list[list]], + saturation_filters=[50, 255], + value_filters=[20, 255], + ): + self.color_name = color_name + self._title = f"{color_name} Values".title() + self._short_description = f"Ranks images by how {color_name.lower()} the average value of the image is." + self._long_description = f"""Ranks images by how {color_name.lower()} the average value of the + image is.""" + self.hue_filters = hue_filters + self.saturation_filters = saturation_filters + self.value_filters = value_filters + + hsv_test = all(0 <= item <= 179 for item in self.__flatten_nested_lists(hue_filters)) + if not hsv_test: + raise ValueError("Hue parameter should be in [0, 179]") + + saturation_test = all(0 <= item <= 255 for item in saturation_filters) + if not saturation_test: + raise ValueError("Saturation parameter should be in [0, 255]") + + value_test = all(0 <= item <= 255 for item in value_filters) + if not value_test: + raise ValueError("Value parameter should be in [0, 255]") + + @property + def TITLE(self) -> str: + return self._title + + @property + def SHORT_DESCRIPTION(self) -> str: + return self._short_description + + @property + def LONG_DESCRIPTION(self) -> str: + return self._long_description + + def __flatten_nested_lists(self, nested_list): + out = [] + + for item in nested_list: + if isinstance(item, list): + out.extend(self.__flatten_nested_lists(item)) + + else: + out.append(item) + return out + + def rank_by_hsv_filtering(self, image): + if self.color_name.lower() != "red": + mask = cv2.inRange( + image, + np.array([self.hue_filters[0], self.saturation_filters[0], self.value_filters[0]]), + np.array([self.hue_filters[1], self.saturation_filters[1], self.value_filters[1]]), + ) + ratio = np.sum(mask > 0) / (image.shape[0] * image.shape[1]) + + else: + lower_spectrum = [ + np.array([self.hue_filters[0][0], self.saturation_filters[0], self.value_filters[0]]), + np.array([self.hue_filters[0][1], self.saturation_filters[1], self.value_filters[1]]), + ] + upper_spectrum = [ + np.array([self.hue_filters[1][0], self.saturation_filters[0], self.value_filters[0]]), + np.array([self.hue_filters[1][1], self.saturation_filters[1], self.value_filters[1]]), + ] + + lower_mask = cv2.inRange(image, lower_spectrum[0], lower_spectrum[1]) + upper_mask = cv2.inRange(image, upper_spectrum[0], upper_spectrum[1]) + mask = lower_mask + upper_mask + ratio = np.sum(mask > 0) / (image.shape[0] * image.shape[1]) + + return ratio + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn( + iterator, writer, self.rank_by_hsv_filtering, self.TITLE, color_space=cv2.COLOR_BGR2HSV + ) + + +# Inputs for new color algorithm +class RedMetric(Wrapper.ColorMetric): + def __init__(self): + super(RedMetric, self).__init__( + "Red", + hue_filters=[[0, 10], [170, 179]], + ) + + +class GreenMetric(Wrapper.ColorMetric): + def __init__(self): + super(GreenMetric, self).__init__("Green", hue_filters=[35, 75]) + + +class BlueMetric(Wrapper.ColorMetric): + def __init__(self): + super(BlueMetric, self).__init__("Blue", hue_filters=[90, 130]) + + +class BrightnessMetric(Metric): + TITLE = "Brightness" + SHORT_DESCRIPTION = "Ranks images by their brightness." + LONG_DESCRIPTION = r"""Ranks images their brightness. + +Brightness is computed as the average (normalized) pixel value across each image. +""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_brightness(image): + return image.mean() / 255 + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_brightness, self.TITLE) + + +class SharpnessMetric(Metric): + TITLE = "Sharpness" + SHORT_DESCRIPTION = "Ranks images by their sharpness." + LONG_DESCRIPTION = r"""Ranks images by their sharpness. + +Sharpness is computed by applying a Laplacian filter to each image and computing the +variance of the output. In short, the score computes "the amount of edges" in each +image. + +```python +score = cv2.Laplacian(image, cv2.CV_64F).var() +``` +""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_sharpness(image): + return cv2.Laplacian(image, cv2.CV_64F).var() + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_sharpness, self.TITLE) + + +class BlurMetric(Metric): + TITLE = "Blur" + SHORT_DESCRIPTION = "Ranks images by their blurriness." + LONG_DESCRIPTION = r"""Ranks images by their blurriness. + +Blurriness is computed by applying a Laplacian filter to each image and computing the +variance of the output. In short, the score computes "the amount of edges" in each +image. Note that this is $1 - \text{sharpness}$. + +```python +score = 1 - cv2.Laplacian(image, cv2.CV_64F).var() +``` +""" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_blur(image): + return 1 - cv2.Laplacian(image, cv2.CV_64F).var() + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_blur, self.TITLE) + + +class AspectRatioMetric(Metric): + TITLE = "Aspect Ratio" + SHORT_DESCRIPTION = "Ranks images by their aspect ratio (width/height)." + LONG_DESCRIPTION = r"""Ranks images by their aspect ratio (width/height). + +Aspect ratio is computed as the ratio of image width to image height. +""" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_aspect_ratio(image): + return image.shape[1] / image.shape[0] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_aspect_ratio, self.TITLE) + + +class AreaMetric(Metric): + TITLE = "Area" + SHORT_DESCRIPTION = "Ranks images by their area (width*height)." + LONG_DESCRIPTION = r"""Ranks images by their area (width*height). + +Area is computed as the product of image width and image height. +""" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.NONE + + @staticmethod + def rank_by_area(image): + return image.shape[0] * image.shape[1] + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + return iterate_with_rank_fn(iterator, writer, self.rank_by_area, self.TITLE) diff --git a/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py b/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py new file mode 100644 index 000000000..15c25b3ef --- /dev/null +++ b/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py @@ -0,0 +1,221 @@ +""" +Detects when objects with sufficient iou are missing in "in-between" frames. +""" + +from typing import List, Tuple, Union + +from loguru import logger +from shapely.geometry import Polygon + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.utils import get_iou, get_polygon +from encord_active.lib.common.writer import CSVMetricWriter + +logger = logger.opt(colors=True) + + +class ErrorStore: + """ + "Dict" that stores the lowest value if value exists already. + """ + + def __init__(self): + self.errors = {} + + def add(self, object: dict, frame: int, score: Union[int, float], description=""): + key = (object["objectHash"], frame) + if key not in self.errors or self.errors[key][1] > score: + self.errors[key] = (object, score, description) + + +class MissingObjectsMetric(Metric): + TITLE = "Missing Objects and Broken Tracks" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.SEQUENCE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "Identifies missing objects and broken tracks based on object overlaps." + LONG_DESCRIPTION = r"""Identifies missing objects by comparing object overlaps based +on a running window. + +**Case 1:** +If an intermediate frame (frame $i$) doesn't include an object in the same +region, as the two surrounding framge ($i-1$ and $i+1$), it is flagged. + +``` + Frame i-1 Frame i Frame i+1 +┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ │ │ +│ ┌───────┐ │ │ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ │ │ CAT:1 │ │ +│ │ │ │ │ │ │ │ │ │ +│ └───────┘ │ │ │ │ └───────┘ │ +│ │ │ │ │ │ +│ │ │ │ │ │ +└───────────────────┘ └───────────────────┘ └───────────────────┘ +``` +Frame $i$ will be flagged as potentially missing an object. + +**Case 2:** +If objects of the same class overlap in three consecutive frames ($i-1$, $i$, and $i+1$) but do not share object +hash, they will be flagged as a potentially broken track. + +``` + Frame i-1 Frame i Frame i+1 +┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐ +│ │ │ │ │ │ +│ ┌───────┐ │ │ ┌───────┐ │ │ ┌───────┐ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ │ CAT:1 │ │ │ │ CAT:2 │ │ │ │ CAT:1 │ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ └───────┘ │ │ └───────┘ │ │ └───────┘ │ +│ │ │ │ │ │ +│ │ │ │ │ │ +└───────────────────┘ └───────────────────┘ └───────────────────┘ +``` +`CAT:2` will be marked as potentially having a wrong track id. +""" + + def __init__(self, threshold: float = 0.5): + super().__init__() + self.threshold = threshold + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + found_any = False + found_valid = False + found_sequential = False + error_store = ErrorStore() + # Prepare sliding window of previous two frames to compare polygons over time + window: List[List[Tuple[dict, Polygon]]] = [] + + for data_unit, img_pth in iterator.iterate(desc="Looking for broken tracks"): + label_row = iterator.label_rows[iterator.label_hash] + frame = iterator.frame + + data_type = label_row["data_type"] + if not (data_type == "video" or (data_type == "img_group" and len(label_row["data_units"]) > 1)): + # Not a sequence + continue + found_sequential = True + + objects = [o for o in data_unit["labels"]["objects"] if o["shape"] in valid_annotation_types] + polygons = list(map(get_polygon, objects)) + + if len(polygons) == 0: + continue + found_any = True + + # Remove invalid polygons and flag them as such in the csv file. + for i in range(len(objects) - 1, -1, -1): + if polygons[i] is None: + error_store.add(object=objects[i], frame=frame, score=0.0) + objects.pop(i) + polygons.pop(i) + + if len(objects) == 0: + continue + found_valid = True + + # First two iterations just populates the window. + if len(window) < 2: + window.append(list(zip(objects, polygons))) + continue + + # Idea: + # 1. Find best IOU match from two frames back (of same class). + # 2. If there is a sufficiently good match, check that there is also a good + # match in the in-between frame. + # 3. If not, flag it as potentially missing. + for obj, poly in zip(objects, polygons): + best_iou = 0 + best_object: dict = {} + + # 1. Look for highest IOU two frames back + for old_obj, old_poly in window[0]: + if old_obj["value"] != obj["value"]: + continue + + iou = get_iou(poly, old_poly) + if iou > best_iou and old_obj["value"] == obj["value"]: + best_iou = iou + best_object = old_obj + + if best_iou < self.threshold / 2: # Skip objects that are not similar enough. + error_store.add(object=obj, frame=frame, score=1.0) + continue + + # 2. Look for the in-between match + best_mid_iou = 0 + best_mid_hash = "" + for mid_obj, mid_poly in window[1]: + if mid_obj["featureHash"] != obj["featureHash"]: + continue + + iou = get_iou(poly, mid_poly) + if iou > best_mid_iou: + best_mid_iou = iou + best_mid_hash = mid_obj["objectHash"] + + if best_mid_iou > self.threshold: + # We'll only execute this when best_object is actually an object due to `continue` in line 144 + object_hashes = { + best_object.get("objectHash"), + best_mid_hash, + obj["objectHash"], + } + if len(object_hashes) > 1: + error_store.add( + object=obj, + frame=frame, + score=0.5, + description=f"Track may be broken between frames [{iterator.frame - 2}:{iterator.frame}]", + ) + else: + error_store.add(object=obj, frame=frame, score=1.0) + continue + + # 3. If missing, flag. + error_store.add( + object=best_object, + frame=frame - 2, + score=0.0, + description=f"`{obj['value']}` could be missing on next frame ({iterator.frame-1}).", + ) + + window.pop(0) + window.append(list(zip(objects, polygons))) + + if not found_sequential: + logger.info("[Skipping] Sequential data found.") + return + + if not found_any: + logger.info( + f"[Skipping] No object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + return + elif not found_valid: + logger.info( + f"[Skipping] No valid object labels of types {{{', '.join(valid_annotation_types)}}}." + ) + return + + # Collect the results in the CSV file. + # Everything not found above with get score "1" meaning "no issues". + annotated = {k: False for k in error_store.errors} + for data_unit, _ in iterator.iterate(desc="Storing results"): + for obj in data_unit["labels"].get("objects", []): + key = (obj["objectHash"], iterator.frame) + if key in error_store.errors: + annotated[key] = True + _obj, score, desc = error_store.errors[key] + writer.write(score, _obj, description=desc) + + if not all(annotated.values()): + logger.warning("Found errors that were not logged. This should not have happened.") + for k, v in annotated.items(): + if not v: + logger.info(k) diff --git a/src/encord_active/lib/metrics/heuristic/object_counting.py b/src/encord_active/lib/metrics/heuristic/object_counting.py new file mode 100644 index 000000000..6c71eff7d --- /dev/null +++ b/src/encord_active/lib/metrics/heuristic/object_counting.py @@ -0,0 +1,17 @@ +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter + + +class ObjectsCountMetric(Metric): + TITLE = "Object Count" + SHORT_DESCRIPTION = "Counts number of objects in the image" + LONG_DESCRIPTION = r"""Counts number of objects in the image.""" + METRIC_TYPE = MetricType.HEURISTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.ALL + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + for data_unit, img_pth in iterator.iterate(desc="Counting objects"): + score = len(data_unit["labels"]["objects"]) if "objects" in data_unit["labels"] else 0 + writer.write(score) diff --git a/src/encord_active/lib/metrics/import_encord_project.py b/src/encord_active/lib/metrics/import_encord_project.py new file mode 100644 index 000000000..07a644524 --- /dev/null +++ b/src/encord_active/lib/metrics/import_encord_project.py @@ -0,0 +1,114 @@ +import re +import sys +from pathlib import Path +from typing import Optional + +import encord.exceptions +import rich +import typer +import yaml +from encord import EncordUserClient, Project +from rich.markup import escape +from rich.panel import Panel + +from encord_active.app.app_config import AppConfig +from encord_active.app.app_print_utils import get_projects_json + +project_root = Path(__file__).parents[1] +sys.path.append(project_root.as_posix()) + +from encord_active.lib.metrics.run_all import run_metrics + +PROJECT_HASH_REGEX = r"([0-9a-f]{8})-([0-9a-f]{4})-([0-9a-f]{4})-([0-9a-f]{4})-([0-9a-f]{12})" + + +def main(config: AppConfig, encord_project_hash: Optional[str] = None): + ssh_key_path = config.get_or_query_ssh_key() + projects_root = config.get_or_query_project_path() + + client = EncordUserClient.create_with_ssh_private_key(ssh_key_path.read_text(encoding="utf-8")) + + if not encord_project_hash: + # == Get project hash === # + rich.print( + Panel( + """ + Encord Active will need to know the project hash associated with the project you + wish to import. If you don't know this hash already, [bold green]you can enter "?"[/bold green] in the following + prompt to get all your `project_hash`es and associated project titles listed. + """, + title="The Encord Project", + expand=False, + ) + ) + + project: Optional[Project] = None + if encord_project_hash: + try: + project = client.get_project(encord_project_hash) + _ = project.title + except encord.exceptions.AuthorisationError: + rich.print("⚡️ [red]You don't have access to the project, sorry[/red] 😫") + exit() + else: + while not encord_project_hash: + _encord_project_hash = typer.prompt("Specify project hash").strip().lower() + + if _encord_project_hash == "?": + rich.print(escape(get_projects_json(config))) + + if not re.match(PROJECT_HASH_REGEX, _encord_project_hash): + rich.print("🙈 [orange]Project hash does not have the correct format.") + rich.print("The format is a (hex) uuid: aaaaaaaa-bbbb-bbbb-bbbb-000000000000") + rich.print("💡 Hint: You can type [green]?[/green] to list all your projects") + continue + + project = client.get_project(_encord_project_hash) + try: + _ = project.title + except encord.exceptions.AuthorisationError: + rich.print("⚡️ [red]You don't have access to the project, sorry[/red] 😫") + continue + + encord_project_hash = _encord_project_hash + + if project is None: + exit() + + project_path = projects_root / project.title.replace(" ", "-") + project_path.mkdir(exist_ok=True, parents=True) + + meta_data = { + "project_title": project.title, + "project_description": project.description, + "project_hash": project.project_hash, + "ssh_key_path": ssh_key_path.as_posix(), + } + meta_file_path = project_path / "project_meta.yaml" + yaml_str = yaml.dump(meta_data) + with meta_file_path.open("w", encoding="utf-8") as f: + f.write(yaml_str) + + rich.print("Stored the following data:") + rich.print(f"[magenta]{yaml_str}") + rich.print(f"In file: [blue]`{escape(meta_file_path.as_posix())}`") + rich.print() + rich.print("Now downloading data and running metrics") + + run_metrics(data_dir=project_path) + + panel = Panel( + """ +The data is downloaded and the metrics are complete. + +Now run + +[cyan]encord-active visualise[/cyan] + +to open Encord Active and see your project. +""", + title="🌟 Success 🌟", + style="green", + expand=False, + ) + rich.print(panel) diff --git a/src/encord_active/lib/metrics/run_all.py b/src/encord_active/lib/metrics/run_all.py new file mode 100644 index 000000000..b1166c7e3 --- /dev/null +++ b/src/encord_active/lib/metrics/run_all.py @@ -0,0 +1,77 @@ +import inspect +import logging +import os +from importlib import import_module +from typing import Callable, List, Optional, Union + +from encord.project_ontology.object_type import ObjectShape + +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.tester import perform_test + + +def get_metrics(module: Optional[Union[str, list[str]]] = None, filter_func=lambda x: True): + if module is None: + module = ["geometric", "heuristic", "semantic"] + elif isinstance(module, str): + module = [module] + + return [i for m in module for i in get_module_metrics(m, filter_func)] + + +def get_module_metrics(module_name: str, filter_func: Callable) -> List: + if __name__ == "__main__": + base_module_name = "" + else: + base_module_name = __name__[: __name__.rindex(".")] + "." # Remove "run_all" + + metrics = [] + path = os.path.join(os.path.dirname(__file__), *module_name.split(".")) + for file in os.listdir(path): + if file.endswith(".py") and not file.startswith("_") and not file.split(".")[0].endswith("_"): + logging.debug("Importing %s", file) + clsmembers = inspect.getmembers( + import_module(f"{base_module_name}{module_name}.{file.split('.')[0]}"), inspect.isclass + ) + for cls in clsmembers: + if issubclass(cls[1], Metric) and cls[1] != Metric and filter_func(cls[1]): + metrics.append((f"{base_module_name}{module_name}.{file.split('.')[0]}", f"{cls[0]}")) + + return metrics + + +def run_all_heuristic_metrics(): + run_metrics(filter_func=lambda x: x.METRIC_TYPE == MetricType.HEURISTIC) + + +def run_all_image_metrics(): + run_metrics(filter_func=lambda x: x.DATA_TYPE == DataType.IMAGE) + + +def run_all_polygon_metrics(): + run_metrics(filter_func=lambda x: x.ANNOTATION_TYPE in [AnnotationType.OBJECT.POLYGON, AnnotationType.ALL]) + + +def run_all_prediction_metrics(**kwargs): + # Return all metrics that apply to objects. + def filter(m: Metric): + at = m.ANNOTATION_TYPE + if isinstance(at, list): + for t in at: + if isinstance(t, ObjectShape): + return True + return False + else: + return isinstance(at, ObjectShape) + + run_metrics(filter_func=filter, **kwargs) + + +def run_metrics(filter_func: Callable = lambda x: True, **kwargs): + metrics: List[Metric] = list( + map( + lambda mod_cls: import_module(mod_cls[0]).__getattribute__(mod_cls[1])(), + get_metrics(filter_func=filter_func), + ) + ) + perform_test(metrics, **kwargs) diff --git a/src/encord_active/lib/metrics/semantic/__init__.py b/src/encord_active/lib/metrics/semantic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/lib/metrics/semantic/_class_uncertainty.py b/src/encord_active/lib/metrics/semantic/_class_uncertainty.py new file mode 100644 index 000000000..d1713c7f7 --- /dev/null +++ b/src/encord_active/lib/metrics/semantic/_class_uncertainty.py @@ -0,0 +1,240 @@ +import logging +import os +from contextlib import contextmanager +from pathlib import Path + +import numpy as np +import torch +import tqdm +from torch import nn +from torch.nn import LeakyReLU + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter +from encord_active.lib.embeddings.cnn_embed import get_cnn_embeddings + +logger = logging.getLogger(__name__) +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +class DenseBlock(nn.Module): + def __init__( + self, + input_dim, + out_dim, + layers=3, + residual=0, + dropout=None, + activation=nn.LeakyReLU, + ): + super().__init__() + if residual > 0: + assert input_dim == out_dim, "Input and output dimensions must match for residual connections" + + self.residual = residual + step = int(round((out_dim - input_dim) / layers)) + + modules = [] + for _ in range(layers - 1): + modules.append(nn.Linear(input_dim, input_dim + step)) + modules.append(activation(inplace=True)) + if dropout: + modules.append(nn.Dropout(dropout)) + input_dim = input_dim + step + modules.append(nn.Linear(input_dim, out_dim)) + self.module = nn.Sequential(*modules) + + def forward(self, x): + output = self.module(x) + return self.residual * x + (1 - self.residual) * output if self.residual > 0 else output + + +class BayesianClassifier(nn.Module): + def __init__(self, n_classes, n_train_samples, n_test_samples): + super().__init__() + self.n_samples = n_train_samples + self.n_train_samples = n_train_samples + self.n_test_samples = n_test_samples + + self.classifier = nn.Sequential( + DenseBlock(512, n_classes, dropout=0.1, activation=LeakyReLU), + nn.Softmax(dim=-1), + ) + + def sample(self, embedding): + outputs = [] + for _ in range(self.n_samples): + outputs.append(self.classifier(embedding)) + return torch.stack(outputs, dim=-1) + + def forward(self, embedding): + outputs = self.sample(embedding) + return outputs + + @contextmanager + def mc_eval(self): + """Switch to evaluation mode with MC Dropout active.""" + istrain_classifier = self.classifier.training + try: + self.n_samples = self.n_test_samples + self.classifier.eval() + # Keep dropout active + for m in self.classifier.modules(): + if m.__class__.__name__.startswith("Dropout"): + m.train() + yield self.classifier + finally: + self.n_samples = self.n_train_samples + if istrain_classifier: + self.classifier.train() + + +def get_prediction_statistics(outputs): + if len(outputs.shape) < 3: + outputs = outputs.unsqueeze(-1) + mean_probs = torch.mean(outputs, dim=-1) + entropy = -torch.sum(torch.log(mean_probs) * mean_probs, dim=-1) + model_preds = torch.argmax(mean_probs, dim=1) + return model_preds, mean_probs, entropy + + +def train_model(classifier, model_path, batches, idx_to_counts, name_to_idx): + train_batches, val_batches = train_test_split(batches) + optimizer = torch.optim.Adam(classifier.parameters(), lr=1e-3) + criterion = nn.CrossEntropyLoss(weight=1 / torch.tensor(list(idx_to_counts.values()))).to(DEVICE) + count = 0 + min_loss = torch.inf + for epoch in range(50): + classifier.train() + + train_loss_epoch = 0 + train_accuracy_epoch = 0 + for batch in train_batches: + optimizer.zero_grad() + labels, mean_probs, model_preds, entropy = base_predict(classifier, batch, name_to_idx) + + loss = criterion(mean_probs, labels) + loss.backward() + optimizer.step() + train_loss_epoch += loss.item() / len(train_batches) + train_accuracy_epoch += (model_preds == labels).sum().item() / (len(train_batches) * len(labels)) + + with classifier.mc_eval(): + val_loss_epoch = 0 + val_accuracy_epoch = 0 + for batch in val_batches: + labels, mean_probs, model_preds, entropy = base_predict(classifier, batch, name_to_idx) + + loss = criterion(mean_probs, labels) + val_loss_epoch += loss.item() / len(val_batches) + val_accuracy_epoch += (model_preds == labels).sum().item() / (len(val_batches) * len(labels)) + + if val_loss_epoch < min_loss: + count = 0 + min_loss = val_loss_epoch + torch.save(classifier.state_dict(), model_path) + else: + count += 1 + if count == 10: + break + + logger.debug(f"---------------------------Epoch {epoch} ---------------------------") + logger.debug(f"Train loss {train_loss_epoch} || Val loss {val_loss_epoch}") + logger.debug(f"Train accuracy {train_accuracy_epoch} || Val accuracy {val_accuracy_epoch}") + + +def base_predict(classifier, batch, name_to_idx): + embeddings = torch.Tensor([eval(x) for x in batch["embedding"]]).to(DEVICE) + labels = torch.LongTensor([name_to_idx[x] for x in batch["object_class"]]).to(DEVICE) + outputs = classifier(embeddings) + model_preds, mean_probs, entropy = get_prediction_statistics(outputs) + return labels, mean_probs, model_preds, entropy + + +def train_test_split(batches): + sizes = [int(0.75 * len(batches)), int(0.25 * len(batches))] + sizes = [max(1, x) for x in sizes] + cumsum = np.cumsum(sizes) + cumsum = np.insert(cumsum, 0, 0) + train_batches, val_batches = [batches[cumsum[i] : cumsum[i + 1]] for i in range(len(sizes))] + return train_batches, val_batches + + +def get_batches_and_model(resnet_embeddings_df): + cls_set = set(resnet_embeddings_df["object_class"]) + name_to_idx = {name: idx for idx, name in enumerate(cls_set)} + idx_to_counts = { + name_to_idx[cls_name]: (resnet_embeddings_df["object_class"] == cls_name).sum() for cls_name in cls_set + } + classifier = BayesianClassifier(n_classes=len(cls_set), n_train_samples=3, n_test_samples=20).to(DEVICE) + batches = np.array_split(resnet_embeddings_df, resnet_embeddings_df.shape[0] // 64) + return batches, classifier, idx_to_counts, name_to_idx + + +def preliminaries(iterator): + model_path = os.path.join(iterator.cache_dir, "models", f"{Path(__file__).stem}_classifier.pt") + os.makedirs(os.path.dirname(model_path), exist_ok=True) + resnet_embeddings_df = get_cnn_embeddings(iterator, force=False) + batches, classifier, idx_to_counts, name_to_idx = get_batches_and_model(resnet_embeddings_df) + if not os.path.isfile(model_path): + train_model(classifier, model_path, batches, idx_to_counts, name_to_idx) + classifier.load_state_dict(torch.load(model_path)) + return batches, classifier, name_to_idx, resnet_embeddings_df + + +class EntropyMetric(Metric): + TITLE = "Class Entropy" + METRIC_TYPE = MetricType.SEMANTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "Estimates the uncertainty of the assigned label through the distribution entropy" + LONG_DESCRIPTION = r"""Uses the entropy of the distribution over labels from a lightweight classifier neural +network and Monte-Carlo Dropout to estimate the uncertainty of the label. """ + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + batches, classifier, name_to_idx, resnet_embeddings_df = preliminaries(iterator) + with classifier.mc_eval() and torch.inference_mode(): + pbar = tqdm.tqdm(total=len(resnet_embeddings_df), desc="Predicting uncertainty") + for batch in batches: + labels, mean_probs, model_preds, entropy = base_predict(classifier, batch, name_to_idx) + + for i, ent in enumerate(entropy): + writer.write( + round(ent.item(), 4), + key=batch["identifier"].iloc[i], + description=f"Assigned class is {batch['object_class'].iloc[i]}.", + label_class=batch["object_class"].iloc[i], + url=batch["url"].iloc[i], + frame=batch["frame"].iloc[i], + ) + pbar.update(1) + + +class ConfidenceScoreMetric(Metric): + TITLE = "Class Confidence Score" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.SEMANTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + SHORT_DESCRIPTION = "Estimates the confidence of the assigned label." + LONG_DESCRIPTION = r"""Estimates the confidence of the assigned label as the probability of the assigned label.""" + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + batches, classifier, name_to_idx, resnet_embeddings_df = preliminaries(iterator) + + with classifier.mc_eval() and torch.inference_mode(): + pbar = tqdm.tqdm(total=len(resnet_embeddings_df), desc="Predicting uncertainty") + for batch in batches: + labels, mean_probs, model_preds, entropy = base_predict(classifier, batch, name_to_idx) + model_confidence = torch.gather(mean_probs, dim=-1, index=labels.unsqueeze(-1)) + for i, conf in enumerate(model_confidence): + writer.write( + round(conf.item(), 6), + key=batch["identifier"].iloc[i], + description=f"Assigned class is {batch['object_class'].iloc[i]} with confidence {round(conf.item(), 6)}.", + label_class=batch["object_class"].iloc[i], + url=batch["url"].iloc[i], + frame=batch["frame"].iloc[i], + ) + pbar.update(1) diff --git a/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py b/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py new file mode 100644 index 000000000..795dde682 --- /dev/null +++ b/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py @@ -0,0 +1,230 @@ +import os +from contextlib import contextmanager +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torchvision +import tqdm +from torch import nn +from torchvision.io import read_image +from torchvision.models.segmentation import DeepLabV3_MobileNet_V3_Large_Weights + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import AnnotationType, DataType, Metric, MetricType +from encord_active.lib.common.writer import CSVMetricWriter +from encord_active.lib.metrics.semantic._class_uncertainty import train_test_split + +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +class SegmentationHead(nn.Module): + def __init__(self, n_classes: int, n_train_samples: int, n_test_samples: int): + super().__init__() + self.n_samples = n_train_samples + self.n_train_samples = n_train_samples + self.n_test_samples = n_test_samples + + self.backbone = torchvision.models.segmentation.deeplabv3_mobilenet_v3_large( + weights=DeepLabV3_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1 + ) + + for param in self.backbone.parameters(): + param.requires_grad = False + + self.heads = nn.Sequential( + nn.Conv2d(21, 21 + (n_classes - 21) // 2, kernel_size=(1, 1), stride=(1, 1)), + nn.LeakyReLU(), + nn.Dropout2d(0.1), + nn.Conv2d(21 + (n_classes - 21) // 2, n_classes, kernel_size=(1, 1), stride=(1, 1)), + ) + + def forward(self, img): + with torch.no_grad(): + embedding = self.backbone(img)["out"] + x = self.sample(embedding) + return x + + def sample(self, embedding): + outputs = [] + for _ in range(self.n_samples): + outputs.append(self.heads(embedding)) + return torch.stack(outputs, dim=-1) + + @contextmanager + def mc_eval(self): + """Switch to evaluation mode with MC Dropout active.""" + istrain_head = self.heads.training + try: + self.n_samples = self.n_test_samples + self.eval() + + # Keep dropout active + for m in self.heads.modules(): + if m.__class__.__name__.startswith("Dropout"): + m.train() + yield self + finally: + self.n_samples = self.n_train_samples + if istrain_head: + self.heads.train() + self.backbone.eval() + + +def get_batches(iterator, batch_size=6): + width = height = 224 + transforms = DeepLabV3_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.transforms() + transforms.resize_size = [width, height] + name_to_idx = {"background": 0} + idx_to_counts = {0: 0} + image_list, mask_list, key_list = [], [], [] + for du, img_pth in iterator.iterate(desc="Loading data"): + img_mask = np.zeros((height, width), dtype=np.uint8) + + for obj in du["labels"]["objects"]: + if obj["shape"] != "polygon": + continue + + cls_name = obj["name"] + if cls_name not in name_to_idx: + name_to_idx[cls_name] = len(name_to_idx) + cls_idx: int = name_to_idx[cls_name] + + p = obj["polygon"] + polygon = np.array( + [(p[str(i)]["x"] * width, p[str(i)]["y"] * height) for i in range(len(p))], + dtype=np.int32, + ) + + img_mask = cv2.fillPoly(img_mask, [polygon], cls_idx) + + if cls_idx not in idx_to_counts: + idx_to_counts[cls_idx] = (img_mask == cls_idx).sum() + else: + idx_to_counts[cls_idx] += (img_mask == cls_idx).sum() + + idx_to_counts[0] += (img_mask == 0).sum() + + img_tensor = read_image(img_pth.as_posix()) / 255 + + if img_tensor.shape[0] == 4: + img_tensor = img_tensor[:3] + + if img_tensor.shape[0] < 3: + for i in range(3 - img_tensor.shape[0]): + img_tensor = torch.cat([img_tensor, img_tensor[-1:]], dim=0) + + image_list.append(transforms(img_tensor)) + mask_list.append(torch.LongTensor(img_mask)) + key_list.append(iterator.get_identifier()) + + img_batches = [torch.stack(image_list[i : i + batch_size], dim=0) for i in range(0, len(image_list), batch_size)] + mask_batches = [torch.stack(mask_list[i : i + batch_size], dim=0) for i in range(0, len(mask_list), batch_size)] + key_batches = [key_list[i : i + batch_size] for i in range(0, len(key_list), batch_size)] + return list(zip(img_batches, mask_batches, key_batches)), idx_to_counts, name_to_idx + + +def get_prediction_statistics(logits): + if len(logits.shape) < 5: + logits = logits.unsqueeze(-1) + + mean_logits = logits.mean(dim=-1) + mean_probs = torch.softmax(mean_logits, dim=1) + entropy_map = -(torch.log(mean_probs) * mean_probs).sum(1) + model_preds = mean_probs.argmax(dim=1) + return model_preds, mean_logits, entropy_map + + +def train_model(model, model_path, batches): + train_batches, val_batches = train_test_split(batches) + optimizer = torch.optim.Adam(model.heads.parameters(), lr=1e-3) + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.95, last_epoch=-1) + + criterion = nn.CrossEntropyLoss().to(DEVICE) + + count = 0 + min_loss = torch.inf + for epoch in range(50): + model.backbone.eval() + model.heads.train() + + train_loss_epoch = 0 + train_accuracy_epoch = 0 + for imgs, masks, keys in train_batches: + optimizer.zero_grad() + labels = masks.to(DEVICE) + logits = model(imgs.to(DEVICE)).squeeze() + model_preds, mean_logits, entropy_map = get_prediction_statistics(logits) + loss = criterion(mean_logits, labels) + loss.backward() + optimizer.step() + train_loss_epoch += loss.item() / len(train_batches) + train_accuracy_epoch += (model_preds == labels).sum().item() / (len(train_batches) * labels.nelement()) + + with torch.inference_mode(): + with model.mc_eval(): + val_loss_epoch = 0 + val_accuracy_epoch = 0 + for imgs, masks, keys in val_batches: + labels = masks.to(DEVICE) + logits = model(imgs.to(DEVICE)).squeeze() + model_preds, mean_logits, entropy_map = get_prediction_statistics(logits) + loss = criterion(mean_logits, labels) + val_loss_epoch += loss.item() / len(val_batches) + val_accuracy_epoch += (model_preds == labels).sum().item() / (len(val_batches) * labels.nelement()) + + scheduler.step() + + if val_loss_epoch < min_loss: + count = 0 + min_loss = val_loss_epoch + torch.save(model.heads.state_dict(), model_path) + else: + count += 1 + if count == 10: + break + + print(f"---------------------------Epoch {epoch} ---------------------------") + print(f"Train loss {train_loss_epoch} || Val loss {val_loss_epoch}") + print(f"Train accuracy {train_accuracy_epoch} || Val accuracy {val_accuracy_epoch}") + + +class EntropyHeatmapMetric(Metric): + TITLE = "Heatmap uncertainty" + SCORE_NORMALIZATION = True + METRIC_TYPE = MetricType.SEMANTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = AnnotationType.ALL + SHORT_DESCRIPTION = ( + "Estimates the uncertainty of the segmentation through the entropy of the pixelwise class distribution" + ) + LONG_DESCRIPTION = r"""""" + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + model_path = os.path.join(iterator.cache_dir, "models", f"{Path(__file__).stem}_model.pt") + os.makedirs(os.path.dirname(model_path), exist_ok=True) + + batches, idx_to_counts, name_to_idx = get_batches(iterator) + + model = SegmentationHead(n_classes=len(idx_to_counts), n_train_samples=1, n_test_samples=10).to(DEVICE) + + if not os.path.isfile(model_path): + train_model(model, model_path, batches) + model.heads.load_state_dict(torch.load(model_path)) + + with torch.inference_mode(): + with model.mc_eval(): + pbar = tqdm.tqdm(total=len(batches), desc="Predicting uncertainty") + for imgs, masks, keys in batches: + logits = model(imgs.to(DEVICE)).squeeze() + model_preds, mean_logits, entropy_map = get_prediction_statistics(logits) + + for i, (k, ent) in enumerate(zip(keys, entropy_map)): + writer.write( + round(ent.mean().item(), 4), + key=k, # remove key somehow + description=str(ent.type(torch.HalfTensor).cpu().tolist()), + url="", + ) + pbar.update(1) diff --git a/src/encord_active/lib/metrics/semantic/img_classification_quality.py b/src/encord_active/lib/metrics/semantic/img_classification_quality.py new file mode 100644 index 000000000..171ec90e9 --- /dev/null +++ b/src/encord_active/lib/metrics/semantic/img_classification_quality.py @@ -0,0 +1,281 @@ +import os +import pickle +from collections import Counter +from pathlib import Path + +import faiss +import numpy as np +import torch +from encord.project_ontology.classification_type import ClassificationType +from loguru import logger + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import ( + AnnotationType, + DataType, + EmbeddingType, + Metric, + MetricType, +) +from encord_active.lib.common.writer import CSVMetricWriter +from encord_active.lib.embeddings.cnn_embed import get_cnn_embeddings + +logger = logger.opt(colors=True) +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +class ImageLevelQualityTest(Metric): + TITLE = "Image-level Annotation Quality" + SHORT_DESCRIPTION = "Compares image classifications against similar images" + LONG_DESCRIPTION = r"""This metric creates embeddings from images. Then, these embeddings are used to build + nearest neighbor graph. Similar embeddings' classifications are compared against each other. + """ + NEEDS_IMAGES = True + EMBEDDING_TYPE = EmbeddingType.CLASSIFICATION + METRIC_TYPE = MetricType.SEMANTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.CLASSIFICATION.RADIO] + + def __init__(self, num_nearest_neighbors: int = 10, certainty_ratio: float = 0.60): + """ + + :param num_nearest_neighbors: determines how many nearest neighbors' labels should be checked for the quality. + This parameter should be +1 than the actual intended number because in the nearest neighbor graph queried + embedding already exists + :param certainty_ratio: ratio to determine if the annotation is mislabeled or there is a different problem in + the annotation + """ + super(ImageLevelQualityTest, self).__init__() + self.collections: list[dict] = [] + self.featureNodeHash_to_index: dict[str, dict] = {} + self.featureNodeHash_to_name: dict[str, dict] = {} + self.featureNodeHash_to_question_name: dict[str, str] = {} + self.index_to_answer_name: dict[str, dict] = {} + self.identifier_to_embedding: dict[str, np.ndarray] = {} + self.question_hash_to_collection_indexes: dict[str, list] = {} + self.cache_dir: Path = Path() + self.num_nearest_neighbors = num_nearest_neighbors + self.certainty_ratio = certainty_ratio + + def extract_description_info(self, question_hash: str, nearest_labels: dict[str, np.ndarray], index: int): + gt_label = nearest_labels["gt_label"][index] + neighbor_labels = nearest_labels["neighbor_labels"][index].tolist() + + threshold = int(len(neighbor_labels) * self.certainty_ratio) + counter = Counter(neighbor_labels) + target_label, target_label_frequency = counter.most_common(1)[0] + + if gt_label == target_label and target_label_frequency > threshold: + description = f":heavy_check_mark: For the question `{self.featureNodeHash_to_question_name[question_hash]}`, the image is correctly annotated as `{self.index_to_answer_name[question_hash][gt_label]}`" + + elif gt_label != target_label and target_label_frequency > threshold: + description = f":x: For the question `{self.featureNodeHash_to_question_name[question_hash]}`, the image is annotated as `{self.index_to_answer_name[question_hash][gt_label]}`. Similar \ + images were annotated as `{self.index_to_answer_name[question_hash][target_label]}`." + + else: # covers cases for target_label_frequency <= threshold: + description = f":question: For the question `{self.featureNodeHash_to_question_name[question_hash]}`, the image is annotated as `{self.index_to_answer_name[question_hash][gt_label]}`. \ + The annotated class may be wrong, as the most similar objects have different classes." + + return description + + def convert_to_indexes(self): + embedding_databases, indexes = {}, {} + + for question_hash in self.featureNodeHash_to_name.keys(): + selected_collections = [ + self.collections[i] for i in self.question_hash_to_collection_indexes[question_hash] + ] + if len(selected_collections) > self.num_nearest_neighbors: + embedding_database = np.stack(list(map(lambda x: x["embedding"], selected_collections))) + + index = faiss.IndexFlatL2(embedding_database.shape[1]) + index.add(embedding_database) # pylint: disable=no-value-for-parameter + + embedding_databases[question_hash] = embedding_database + indexes[question_hash] = index + + return embedding_databases, indexes + + def get_nearest_indexes(self): + # from collections build faiss index + embedding_databases, indexes = self.convert_to_indexes() + + nearest_metrics = {} + for question_hash in indexes: + nearest_distance, nearest_index = indexes[question_hash].search( # pylint: disable=no-value-for-parameter + embedding_databases[question_hash], self.num_nearest_neighbors + ) + nearest_metrics[question_hash] = nearest_index + + return nearest_metrics + + def transform_neighbors_to_labels_for_all_questions(self, nearest_indexes: dict[str, np.ndarray]): + nearest_labels_all_questions = {} + + for question in nearest_indexes: + noisy_labels_list = [] + for i in range(nearest_indexes[question].shape[0]): + collection_index = self.question_hash_to_collection_indexes[question][i] + answer_featureHash = self.collections[collection_index]["classification_answers"][question][ + "answer_featureHash" + ] + gt_label = self.featureNodeHash_to_index[question][answer_featureHash] + noisy_labels_list.append(gt_label) + + noisy_labels = np.array(noisy_labels_list).astype(np.int32) + nearest_labels = np.take(noisy_labels, nearest_indexes[question]) + noisy_labels_tmp, nearest_labels_except_self = np.split(nearest_labels, [1], axis=-1) + assert np.all(noisy_labels == noisy_labels_tmp.squeeze()), "Failed class index extraction" + + nearest_labels_all_questions[question] = { + "gt_label": noisy_labels, + "neighbor_labels": nearest_labels_except_self, + } + + return nearest_labels_all_questions + + def convert_nearest_labels_to_scores(self, nearest_labels: dict[str, np.ndarray]) -> list[float]: + """ + Higher scores mean more reliable label + :param nearest_labels: output of the index.search() + :return: score for each row + """ + + label_matches = np.equal(nearest_labels["neighbor_labels"], np.expand_dims(nearest_labels["gt_label"], axis=-1)) + collections_scores = label_matches.mean(axis=-1) + + return collections_scores + + def convert_nearest_labels_to_scores_for_all_questions(self, nearest_labels_all_questions: dict[str, dict]): + collections_scores_all_questions = {} + for question in nearest_labels_all_questions: + scores = self.convert_nearest_labels_to_scores(nearest_labels_all_questions[question]) + collections_scores_all_questions[question] = scores + return collections_scores_all_questions + + def create_key_score_pairs(self, nearest_indexes: dict[str, np.ndarray]) -> dict[str, dict]: + + nearest_labels_all_questions = self.transform_neighbors_to_labels_for_all_questions(nearest_indexes) + collections_scores_all_questions = self.convert_nearest_labels_to_scores_for_all_questions( + nearest_labels_all_questions + ) + + key_score_pairs = {} + for i, collection in enumerate(self.collections): + label_hash = collection["label_row"] + du_hash = collection["data_unit"] + frame_idx = int(collection["frame"]) + + key = f"{label_hash}_{du_hash}_{frame_idx:05d}" + + temp_entry = {} + for question in collection["classification_answers"]: + if question in collections_scores_all_questions: + sub_collection_index = self.question_hash_to_collection_indexes[question].index(i) + score = collections_scores_all_questions[question][sub_collection_index] + + temp_entry[question] = { + "score": score, + "description": self.extract_description_info( + question, nearest_labels_all_questions[question], sub_collection_index + ), + "class_name": self.featureNodeHash_to_question_name[question], + "annotator": collection["classification_answers"][question]["annotator"], + } + + key_score_pairs[key] = temp_entry + return key_score_pairs + + def build_question_hash_to_collection_index(self): + target_folder = os.path.join(self.cache_dir, "embeddings") + embedding_metadata_path = os.path.join(target_folder, "embedding_classifications_metadata.pkl") + if os.path.isfile(embedding_metadata_path): + with open(embedding_metadata_path, "rb") as f: + self.question_hash_to_collection_indexes = pickle.load(f) + else: + for question_hash in self.featureNodeHash_to_name.keys(): + self.question_hash_to_collection_indexes[question_hash] = [] + + for i, collection in enumerate(self.collections): + for question_hash in self.featureNodeHash_to_name.keys(): + if question_hash in collection["classification_answers"]: + self.question_hash_to_collection_indexes[question_hash].append(i) + + if not os.path.isdir(target_folder): + os.makedirs(target_folder) + with open(embedding_metadata_path, "wb") as f: + pickle.dump(self.question_hash_to_collection_indexes, f) + + def fix_nearest_indexes(self, nearest_indexes: dict[str, np.ndarray]) -> None: + """ + In the presence of duplicate images, ordering of the nearest metrics might be confused. This function ensures + that first column value is always equal to row number. + :param nearest_indexes: dictionary to keep the nearest metrics for different classification questions + :return: None + """ + for question in nearest_indexes: + question_nearest_metrics = nearest_indexes[question] + for i in range(question_nearest_metrics.shape[0]): + if i != question_nearest_metrics[i, 0]: + item_index = np.where(question_nearest_metrics[i] == i)[0][0] + question_nearest_metrics[i, 0], question_nearest_metrics[i, item_index] = ( + question_nearest_metrics[i, item_index], + question_nearest_metrics[i, 0], + ) + + def setup(self, iterator: Iterator) -> bool: + """ + Looks for classes to use. + :param iterator: + :return: True if there is anything to do based on the ontology. Otherwise, False. + """ + self.cache_dir = iterator.cache_dir + # TODO: This only evaluates immediate classes, not nested ones + # TODO: Look into other types of classifications apart from radio buttons + found_any = False + for class_label in iterator.project.ontology["classifications"]: + class_question = class_label["attributes"][0] + if class_question["type"] == ClassificationType.RADIO.value: + found_any = True + self.featureNodeHash_to_index[class_label["featureNodeHash"]] = {} + self.featureNodeHash_to_name[class_label["featureNodeHash"]] = {} + self.featureNodeHash_to_question_name[class_label["featureNodeHash"]] = class_label["attributes"][0][ + "name" + ] + self.index_to_answer_name[class_label["featureNodeHash"]] = {} + + for counter, option in enumerate(class_question["options"]): + self.featureNodeHash_to_index[class_label["featureNodeHash"]][option["featureNodeHash"]] = counter + self.featureNodeHash_to_name[class_label["featureNodeHash"]][option["featureNodeHash"]] = option[ + "label" + ] + self.index_to_answer_name[class_label["featureNodeHash"]][counter] = option["label"] + + return found_any + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + project_has_classifications = self.setup(iterator) + if not project_has_classifications: + logger.info("[Skipping] No frame level classifications in the project ontology.") + + self.collections = get_cnn_embeddings(iterator, embedding_type="classifications") + if len(self.collections) > 0: + self.build_question_hash_to_collection_index() + nearest_indexes = self.get_nearest_indexes() + self.fix_nearest_indexes(nearest_indexes) + key_score_pairs = self.create_key_score_pairs(nearest_indexes) + + for data_unit, img_pth in iterator.iterate(desc="Storing index"): + key = iterator.get_identifier() + if key in key_score_pairs: + for classification in data_unit["labels"].get("classifications", []): + question_featureHash = classification["featureHash"] + if question_featureHash in key_score_pairs[key]: + writer.write( + key_score_pairs[key][question_featureHash]["score"], + key=key + "_" + question_featureHash, + description=key_score_pairs[key][question_featureHash]["description"], + labels=classification, + ) + else: + logger.info("[Skipping] The embedding file is empty.") diff --git a/src/encord_active/lib/metrics/semantic/img_object_quality.py b/src/encord_active/lib/metrics/semantic/img_object_quality.py new file mode 100644 index 000000000..0286d4fa8 --- /dev/null +++ b/src/encord_active/lib/metrics/semantic/img_object_quality.py @@ -0,0 +1,143 @@ +import logging +from collections import Counter +from typing import List + +import faiss +import numpy as np +from tqdm import tqdm + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.metric import ( + AnnotationType, + DataType, + EmbeddingType, + Metric, + MetricType, +) +from encord_active.lib.common.utils import ( + fix_duplicate_image_orders_in_knn_graph_all_rows, +) +from encord_active.lib.common.writer import CSVMetricWriter +from encord_active.lib.embeddings.cnn_embed import get_cnn_embeddings + +logger = logging.getLogger(__name__) + + +class ObjectEmbeddingSimilarityTest(Metric): + TITLE = "Object Annotation Quality" + SHORT_DESCRIPTION = "Compares object annotations against similar image crops" + LONG_DESCRIPTION = r"""This metric transforms polygons into bounding boxes + and an embedding for each bounding box is extracted. Then, these embeddings are compared + with their neighbors. If the neighbors are annotated differently, a low score is given to it. + """ + NEEDS_IMAGES = True + EMBEDDING_TYPE = EmbeddingType.OBJECT + METRIC_TYPE = MetricType.SEMANTIC + DATA_TYPE = DataType.IMAGE + ANNOTATION_TYPE = [AnnotationType.OBJECT.BOUNDING_BOX, AnnotationType.OBJECT.POLYGON] + + def __init__(self, num_nearest_neighbors: int = 10, certainty_ratio: float = 0.6, project_dataset_name: str = ""): + """ + :param num_nearest_neighbors: determines how many nearest neighbors' labels should be checked for the quality. + This parameter should be +1 than the actual intended number because in the nearest neighbor graph queried + embedding already exists + :param project_dataset_name: if QM is wanted to be run on specific dataset, name should be given here. If it is + empty, it means evaluate all datasets in the project + """ + super(ObjectEmbeddingSimilarityTest, self).__init__() + self.collections: dict[str, dict] = {} + self.featureNodeHash_to_index: dict[str, int] = {} + self.index_to_object_name: dict[int, str] = {} + self.object_name_to_index: dict[str, int] = {} + self.num_nearest_neighbors = num_nearest_neighbors + self.certainty_ratio = certainty_ratio + self.project_dataset_name = project_dataset_name + + def setup(self, iterator) -> bool: + found_any = False + for i, object_label in enumerate(iterator.project.ontology["objects"]): + found_any = True + self.featureNodeHash_to_index[object_label["featureNodeHash"]] = i + self.index_to_object_name[i] = object_label["name"] + self.object_name_to_index[object_label["name"]] = i + return found_any + + def convert_to_index(self): + embeddings_list: List[list] = [] + noisy_labels_list: List[int] = [] + for x in self.collections.values(): + embeddings_list.append(x["embedding"]) + noisy_labels_list.append(self.object_name_to_index[x["name"]]) + + embeddings = np.array(embeddings_list).astype(np.float32) + noisy_labels = np.array(noisy_labels_list).astype(np.int32) + + db_index = faiss.IndexFlatL2(embeddings.shape[1]) + db_index.add(embeddings) # pylint: disable=no-value-for-parameter + return embeddings, db_index, noisy_labels + + def get_description_info(self, nearest_labels: np.ndarray, noisy_label: int): + threshold = int(len(nearest_labels) * self.certainty_ratio) + counter = Counter(nearest_labels) + target_label, target_label_frequency = counter.most_common(1)[0] + + if noisy_label == target_label and target_label_frequency > threshold: + description = ( + f":heavy_check_mark: The object is correctly annotated as `{self.index_to_object_name[noisy_label]}`" + ) + elif noisy_label != target_label and target_label_frequency > threshold: + description = f":x: The object is annotated as `{self.index_to_object_name[noisy_label]}`. Similar \ + objects were annotated as `{self.index_to_object_name[target_label]}`." + else: # covers cases for target_label_frequency <= threshold: + description = f":question: The object is annotated as `{self.index_to_object_name[noisy_label]}`. \ + The annotated class may be wrong, as the most similar objects have different classes." + return description + + def unpack_collections(self, collections: list) -> None: + for item in tqdm(collections, desc="Unpacking embeddings"): + identifier = self.get_identifier_from_collection_item(item) + if item["dataset_title"] == self.project_dataset_name or self.project_dataset_name == "": + self.collections[identifier] = item + + def get_identifier_from_collection_item(self, item): + return f'{item["label_row"]}_{item["data_unit"]}_{item["frame"]:05d}_{item["objectHash"]}' + + def test(self, iterator: Iterator, writer: CSVMetricWriter): + ontology_contains_objects = self.setup(iterator) + if not ontology_contains_objects: + logger.info("[Skipping] No objects in the project ontology.") + return + + collections = get_cnn_embeddings(iterator, embedding_type="objects") + if len(collections) > 0: + embedding_identifiers = [self.get_identifier_from_collection_item(item) for item in collections] + + self.unpack_collections(collections) + + embedding_database, index, noisy_labels = self.convert_to_index() + nearest_distances, nearest_metrics = index.search( # pylint: disable=no-value-for-parameter + embedding_database, self.num_nearest_neighbors + ) + nearest_metrics = fix_duplicate_image_orders_in_knn_graph_all_rows(nearest_metrics) + + nearest_labels = np.take(noisy_labels, nearest_metrics) + noisy_labels_tmp, nearest_labels_except_self = np.split(nearest_labels, [1], axis=-1) + assert np.all(noisy_labels == noisy_labels_tmp.squeeze()), "Failed class index extraction" + + label_matches = np.equal(nearest_labels_except_self, np.expand_dims(noisy_labels, axis=-1)) + collections_scores = label_matches.mean(axis=-1) + + valid_annotation_types = {annotation_type.value for annotation_type in self.ANNOTATION_TYPE} + for data_unit, img_pth in iterator.iterate(desc="Storing index"): + for obj in data_unit["labels"].get("objects", []): + if obj["shape"] not in valid_annotation_types: + continue + + key = iterator.get_identifier(object=obj) + if key in self.collections: + assert obj["name"] == self.collections[key]["name"], "Indexing inconsistencies" + idx = embedding_identifiers.index(key) + description = self.get_description_info(nearest_labels_except_self[idx], noisy_labels[idx]) + writer.write(collections_scores[idx], obj, description=description) + else: + logger.info("[Skipping] The object embedding file is empty.") diff --git a/src/encord_active/lib/model_predictions/__init__.py b/src/encord_active/lib/model_predictions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/encord_active/lib/model_predictions/importers.py b/src/encord_active/lib/model_predictions/importers.py new file mode 100644 index 000000000..17d3b2ae3 --- /dev/null +++ b/src/encord_active/lib/model_predictions/importers.py @@ -0,0 +1,215 @@ +# KITTI Reference: https://docs.nvidia.com/tao/archive/tlt-20/tlt-user-guide/text/preparing_data_input.html + +import json +import logging +import re +from pathlib import Path +from typing import Callable, Dict, List, Optional, Tuple, cast + +import cv2 +import numpy as np +import pandas as pd +from encord import Project +from encord.objects.common import Shape +from encord.objects.ontology_object import Object +from encord.objects.ontology_structure import OntologyStructure +from pandas.errors import EmptyDataError +from PIL import Image +from tqdm import tqdm + +from encord_active.lib.common.prepare import download_all_label_rows +from encord_active.lib.model_predictions.prediction_writer import PredictionWriter + +logger = logging.getLogger(__name__) +KITTI_COLUMNS = [ + ("class_name", str), + ("truncation", float), # [0, 1] + ("occlusion", int), # [0, 3], [ 0 = fully visible, 1 = partly visible, 2 = largely occluded, 3 = unknown] + ("alpha", float), # [-pi, pi] + # bbox + ("xmin", float), # [0, img_width] + ("ymin", float), # [0, img_height] + ("xmax", float), # [0, img_width] + ("ymax", float), # [0, img_height] + # 3-D dimensions (in meters + ("height", float), + ("width", float), + ("length", float), + # 3-D object location x, y, z in camera coordinates (in meters) + ("location_x", float), + ("location_y", float), + ("location_z", float), + # Rotation ry around the Y-axis in camera coordinates + ("rotation_y", float), # [-pi, pi] +] + +KITTI_FILE_NAME_REGEX = r"^(?P[a-f0-9\-]*)__(?P.*)$" +PNG_FILE_NAME_REGEX = r"^(?P.*?)\.png$" + + +def import_mask_predictions( + project: Project, + data_root: Path, + cache_dir: Path, + prediction_writer: PredictionWriter, + class_map: Optional[Dict[str, int]] = None, + file_name_regex: str = PNG_FILE_NAME_REGEX, + du_hash_name_lookup: Callable[[Path], Tuple[str, int]] = None, +): + """ + Will look for mask + :param project: The project for which to which the predictions are associated + :param data_root: The root folder of the segmentation masks + :param cache_dir: The cache directory of the (potentially) cached label rows, images, etc. + :param prediction_writer: The writer to store the predictions with. + :param class_map: A Dictionary which contains ``(featureNodeHash, class_idx)`` pairs, such that if + a pixel in a mask has value 1 and corresponds to ``featureNodeHash == "abcdef01``, then + ``class_map["abcdef01"] == 1``. + If the ``class_map`` is not specified, it will be generated by:: + + polygon_classes = sorted({ + o for o in OntologyStructure.from_dict(project.ontology).objects if o.shape == Shape.POLYGON + }, + key=lambda x: int(x.id) + ) + {o.feature_node_hash: i+1 for o in enumerate(polygon_classes) if o.shape == "polygon"} + + :param file_name_regex: A regex pattern to match annotation masks. As default, all pngs in the `data_root` + will be considered as predictions. Example strings that are matched are:: + + "/path/to/data_root/masks/some_name.png" + "/path/to/data_root/some_name.png" + + :param du_hash_name_lookup: A transform ``f(file_path) -> (data_unit_hash: str, frame: int)`` to translate between + data units and their associated mask file names. You can omit this argument if you + + #. Store masks with identical filenames to the uploaded files. + #. (only required for videos) append a frame number for video predictions with two under scores. + + For example, if you have predictions for the video ``my_video.mp4``, then you should store prediction masks as + ``my_video__0.png``, ``my_video__1.png``, etc. + + If you, on the other hand, store files with other naming conventions, we need to be able to match files with + specific data units (and frames if videos). For example, you may store predictions in folder structures like:: + + predictions + ├── 00115807-3206-4301-b513-026de3f2015e # data unit hash + │ ├── 0.png # Frame number. + │ └── 1.png + └── 001722b8-1624-4169-bb11-5977127dcea9 + ├── 0.png + └── 1.png + + You will then need to specify a lambda function like the following:: + + du_hash_name_lookup = lambda file_pth: (file_pth.parent.name, int(file_pth.name)) + + + """ + label_rows = download_all_label_rows(project, cache_dir=cache_dir) + du_hash_lookup: Dict[str, Tuple[str, int]] = {} + + if not class_map: + ontology = OntologyStructure.from_dict(project.ontology) + ontology_polygons: List[Object] = [o for o in ontology.objects if o.shape == Shape.POLYGON] + class_map = {o.feature_node_hash: i + 1 for i, o in enumerate(ontology_polygons)} + + ontology_hash_lookup: Dict[int, str] = {v: k for k, v in class_map.items()} + + if du_hash_name_lookup is None: + du_hash_lookup = {} + for label_row in label_rows.values(): + for du in label_row["data_units"].values(): + fname = du["data_title"].rsplit(".", 1)[0] # File name without the extension + if isinstance(du["labels"], list): # Videos + for frame_num in du["labels"]: + du_hash_lookup[f"{fname}__{frame_num:05d}"] = du["data_hash"], int(frame_num) + else: # Images + du_hash_lookup[fname] = du["data_hash"], 0 + + queue = [data_root] + with tqdm(total=None, desc="Importing masks", leave=False) as pbar: + while queue: + pth = queue.pop() + if pth.is_dir(): + queue += list(pth.iterdir()) + continue + elif pth.is_file(): + match = re.match(file_name_regex, pth.as_posix()) + if not match: + continue + + if du_hash_name_lookup: + du_hash, frame = du_hash_name_lookup(pth) + else: + du_hash, frame = du_hash_lookup.get(pth.stem, ("", -1)) + + if not du_hash: + logger.warning(f"Couldn't match file {pth} to any data unit.") + continue + + input_mask = np.array(Image.open(pth)) + for cls in np.unique(input_mask): + if cls == 0: # background + continue + + class_hash = ontology_hash_lookup[cls] + + mask = np.zeros_like(input_mask) + mask[input_mask == cls] = 1 + + contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + + for contour in contours: + if len(contour) < 3 or cv2.contourArea(contour) < 4: + continue + _mask = np.zeros_like(mask) + _mask = cv2.fillPoly(_mask, [contour], 1) + + prediction_writer.add_prediction(du_hash, class_hash, confidence_score=1.0, polygon=_mask) + + pbar.update(1) + + +def import_KITTI_labels( + project: Project, data_root: Path, prediction_writer: PredictionWriter, file_name_regex: str = KITTI_FILE_NAME_REGEX +): + + label_files = [f for f in (data_root / "labels").iterdir() if f.suffix == ".txt"] + + # === Prepare ontology lookup === # + with (data_root / "ontology_label_map.json").open("r", encoding="utf-8") as f: + label_name_map = json.load(f) + + object_name_to_hash = { + o["name"]: o["featureNodeHash"] for o in project.ontology["objects"] if o["shape"] == "bounding_box" + } + hash_lookup: Dict[str, Optional[str]] = {v: object_name_to_hash.get(k) for k, v in label_name_map.items()} + + for file in tqdm(label_files, desc="Importing label files"): + match = re.match(file_name_regex, file.name) + if not match: + logger.info(f"Couldn't match file {file.name} to specified regex") + continue + + data_hash, image_name = match.groups() + try: + df = pd.read_csv(file, sep=" ", header=None) + except EmptyDataError: + continue + + headers = list(map(lambda x: x[0], KITTI_COLUMNS)) + # Hack to account for additional "custom" columns + headers += [f"undefined{i}" for i in range(df.shape[1] - len(headers))] + df.columns = headers + + for _, row in df.iterrows(): + class_name = cast(str, hash_lookup[row["class_name"]]) + bbox = { + "x": row["xmin"], + "y": row["ymin"], + "w": row["xmax"] - row["xmin"], + "h": row["ymax"] - row["ymin"], + } + conf = row["undefined0"] + prediction_writer.add_prediction(data_hash, class_name, confidence_score=conf, bbox=bbox) diff --git a/src/encord_active/lib/model_predictions/iterator.py b/src/encord_active/lib/model_predictions/iterator.py new file mode 100644 index 000000000..3c6f5efe6 --- /dev/null +++ b/src/encord_active/lib/model_predictions/iterator.py @@ -0,0 +1,197 @@ +import json +import logging +from copy import deepcopy +from datetime import datetime +from functools import partial +from pathlib import Path +from typing import Any, Dict, Generator, List, Optional, Tuple, Union + +import cv2 +import numpy as np +import pandas as pd +import pytz +from pandas import Series +from tqdm import tqdm + +from encord_active.lib.common.iterator import Iterator +from encord_active.lib.common.utils import rle_to_binary_mask + +logger = logging.getLogger(__name__) +GMT_TIMEZONE = pytz.timezone("GMT") +DATETIME_STRING_FORMAT = "%a, %d %b %Y %H:%M:%S %Z" +BBOX_KEYS = {"x", "y", "h", "w"} + + +# === UTILITIES === # +def get_timestamp(): + now = datetime.now() + new_timezone_timestamp = now.astimezone(GMT_TIMEZONE) + return new_timezone_timestamp.strftime(DATETIME_STRING_FORMAT) + + +def lower_snake_case(s: str): + return "_".join(s.lower().split()) + + +class PredictionIterator(Iterator): + def __init__(self, cache_dir: Path, subset_size: Optional[int] = None, **kwargs): + super().__init__(cache_dir, subset_size, **kwargs) + label_hashes = set(self.label_rows.keys()) + + # Predictions + predictions_file = cache_dir / "predictions" / "predictions.csv" + predictions = pd.read_csv(predictions_file, index_col=0) + self.length = predictions["img_id"].unique().shape[0] + + identifiers = predictions["identifier"].str.split("_", expand=True) + identifiers.columns = ["label_hash", "du_hash", "frame", "object_hash"][: len(identifiers.columns)] + identifiers["frame"] = pd.to_numeric(identifiers["frame"]) + + predictions = pd.concat([predictions, identifiers], axis=1) + predictions["pidx"] = predictions.index + + self.predictions = predictions[predictions["label_hash"].isin(label_hashes)] + + # Class index + class_idx_file = cache_dir / "predictions" / "class_idx.json" + with class_idx_file.open("r", encoding="utf-8") as f: + class_idx = json.load(f) + + self.ontology_objects = { + int(k): next(o for o in self.project.ontology["objects"] if v["featureHash"] == o["featureNodeHash"]) + for k, v in class_idx.items() + } + self.row_cache: List[Tuple[str, str, int, Dict[Any, Any], Optional[Path]]] = [] + + def get_image_path(self, pred: Series) -> Optional[Path]: + img_folder = self.cache_dir / "data" / pred["label_hash"] / "images" + du_hash = pred["du_hash"] + image_options = list(img_folder.glob(f"{du_hash}.*")) + if len(image_options) == 1: + return image_options[0] + elif len(image_options) > 1: + re_matches = [frame_file for frame_file in image_options if frame_file.stem == f"{du_hash}_{pred['frame']}"] + if re_matches: + return re_matches[0] + return None + + def get_encord_object(self, pred: Tuple[Any, Series], width: int, height: int): + _pred = pred[1] + ontology_object: dict = self.ontology_objects[_pred["class_id"]] + if ontology_object["shape"] == "bounding_box": + x1, y1, x2, y2 = _pred["x1"], _pred["y1"], _pred["x2"], _pred["y2"] + object_data = { + "x": x1 / width, + "y": y1 / height, + "w": (x2 - x1) / width, + "h": (y2 - y1) / height, + } + else: + mask = rle_to_binary_mask(eval(_pred["rle"])) + # Note: This approach may generate invalid polygons (self crossing) :-( + contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if len(contours) > 1: + max_idx = np.argmax(list(map(cv2.contourArea, contours))) + contour = contours[max_idx].reshape((-1, 2)) + else: + contour = contours[0].reshape((-1, 2)) + + if contour.shape[0] < 3: + pass + # logger.warning("Skipping contour with less than 3 vertices.") + object_data = { + str(i): {"x": round(c[0] / width, 4), "y": round(c[1] / height, 4)} for i, c in enumerate(contour) + } + + object_hash = _pred["identifier"].rsplit("_", 1)[1] + timestamp: str = get_timestamp() + shape: str = ontology_object["shape"] + + object_dict = { + "name": ontology_object["name"], + "color": ontology_object["color"], + "value": lower_snake_case(ontology_object["name"]), + "createdAt": timestamp, + "createdBy": "model_predictions@encord.com", + "confidence": _pred["confidence"], + "objectHash": object_hash, + "featureHash": ontology_object["featureNodeHash"], + "lastEditedAt": timestamp, + "lastEditedBy": "model_predictions@encord.com", + "shape": shape, + "manualAnnotation": False, + "reviews": [], + } + + if shape == "bounding_box": + object_dict["boundingBox"] = {k: round(v, 4) for k, v in object_data.items()} + elif shape == "polygon": + object_dict["polygon"] = object_data + + return object_dict + + def iterate(self, desc: str = "") -> Generator[Tuple[dict, Optional[Path]], None, None]: + pbar = tqdm(total=self.length, desc=desc, leave=False) + if self.row_cache: + for self.label_hash, self.du_hash, self.frame, du, pth in self.row_cache: + yield du, pth + pbar.update(1) + return + + for label_hash, lh_group in self.predictions.groupby("label_hash"): + if label_hash not in self.label_rows: + continue + + self.label_hash = label_hash + label_row = self.label_rows[label_hash] + self.dataset_title = label_row["dataset_title"] + + for frame, fr_preds in lh_group.groupby("frame"): + self.du_hash = fr_preds.iloc[0]["du_hash"] + self.frame = frame + + du = deepcopy(label_row["data_units"][self.du_hash]) + width = int(du["width"]) + height = int(du["height"]) + + du["labels"] = { + "objects": list( + map(partial(self.get_encord_object, width=width, height=height), fr_preds.iterrows()) + ), + "classifications": [], + } + pth = self.get_image_path(fr_preds.iloc[0]) + yield du, pth + self.row_cache.append((self.label_hash, self.du_hash, self.frame, du, pth)) + pbar.update(1) + + def __len__(self): + return self.length + + def get_identifier(self, object: Union[dict, list[dict], None] = None, frame: Optional[int] = None) -> Any: + """ + Note that this only makes sense for scoring each object individually. + """ + label_hash = self.label_hash + du_hash = self.du_hash + frame = self.frame if frame is None else frame + + key = f"{label_hash}_{du_hash}_{frame:05d}" + if object: + objects = [object] if isinstance(object, dict) else object + hashes = [obj["objectHash"] if "objectHash" in obj else obj["featureHash"] for obj in objects] + return "_".join([key] + hashes) + return key + + def get_data_url(self): + # No need for the url as it is in the predictions.csv file already + return "" + + def get_label_logs(self, object_hash: Optional[str] = None, refresh: bool = False) -> List[dict]: + # Fail safe + raise NotImplementedError("Label logs are not available for predictions.") + + @staticmethod + def update_cache_dir(cache_dir: Path) -> Path: + # Store prediction specific scores in the predictions subdirectory + return cache_dir / "predictions" diff --git a/src/encord_active/lib/model_predictions/prediction_writer.py b/src/encord_active/lib/model_predictions/prediction_writer.py new file mode 100644 index 000000000..ca9fbd55f --- /dev/null +++ b/src/encord_active/lib/model_predictions/prediction_writer.py @@ -0,0 +1,476 @@ +import json +import logging +from base64 import b64encode +from enum import Enum +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple, Union +from uuid import uuid4 + +import cv2 +import numpy as np +import pandas as pd +import torch +from encord import Project +from torchvision.ops import box_iou +from tqdm import tqdm + +from encord_active.lib.common.prepare import prepare_data +from encord_active.lib.common.utils import binary_mask_to_rle, rle_iou + +logger = logging.getLogger(__name__) +BBOX_KEYS = {"x", "y", "w", "h"} +BASE_URL = "https://app.encord.com/label_editor/" + + +class PredictionType(Enum): + FRAME = "frame" + BBOX = "bounding_box" + POLYGON = "polygon" + + +PREDICTION_CSV_COLUMNS = ["identifier", "url", "img_id", "class_id", "confidence", "x1", "y1", "x2", "y2", "rle", "iou"] +LABEL_CSV_COLUMNS = ["identifier", "url", "img_id", "class_id", "object_hash", "x1", "y1", "x2", "y2", "rle"] + + +class LKey(Enum): + ID = 0 + URL = 1 + IMG_ID = 2 + CLASS = 3 + OBJ_ID = 4 + X1 = 5 + Y1 = 6 + X2 = 7 + Y2 = 8 + RLE = 9 + + +class PKey(Enum): + ID = 0 + URL = 1 + IMG_ID = 2 + CLASS = 3 + CONF = 4 + X1 = 5 + Y1 = 6 + X2 = 7 + Y2 = 8 + RLE = 9 + + +def polyobj_to_nparray(o: dict, width: int, height: int) -> np.ndarray: + return np.array( + [(o["polygon"][str(i)]["x"] * width, o["polygon"][str(i)]["y"] * height) for i in range(len(o["polygon"]))] + ) + + +def points_to_mask(points: np.ndarray, width: int, height: int): + mask = np.zeros((height, width), dtype=np.uint8) + mask = cv2.fillPoly(mask, [points.astype(int)], 1) # type: ignore + return mask + + +def get_img_ious(detections, ground_truth_img): + # Note: we assume that all labels and predictions are of same type, i.e., either segmentations or bboxes. + if ground_truth_img[0][1][LKey.RLE.value] is not None: # Segmentation + det_coco_format = [i[1][PKey.RLE.value] for i in detections] + gt_coco_format = [i[1][LKey.RLE.value] for i in ground_truth_img] + + return torch.from_numpy(rle_iou(det_coco_format, gt_coco_format)) + else: + _pred_boxes = torch.tensor([d[1][PKey.X1.value : PKey.Y2.value + 1] for d in detections]) # type: ignore + _label_boxes = torch.tensor([l[LKey.X1.value : LKey.Y2.value + 1] for _, l in ground_truth_img]) + return box_iou(_pred_boxes, _label_boxes) + + +def precompute_MAP_features( + pred_boxes: List[List[Any]], + true_boxes: List[List[Any]], +) -> Tuple[torch.Tensor, Dict[int, Dict[int, List[Dict[str, Any]]]]]: + """ + Calculates mean average precision for given iou threshold and rec_thresholds. + Parameters: + pred_boxes: list of lists containing all bboxes with each bboxes + specified as:: + + [ + train_idx (int), + class_prediction(int), + prob_score (float), + x1 (float[0, img_width]), + y1 (float[0, img_height]), + x2 (float[0, img_width]), + y2 (float[0, img_height]), + ] + + true_boxes: Similar as pred_boxes except all the correct ones. + I.e., the prob_score doesn't matter and will be ignored. + + Returns: + ious: used as input for the next call to the function. + ground_truths_matched: used as input for the next call to the function. + """ + # Do all the heavy lifting of computing ious and matching predictions to objects. + ground_truths: Dict[Tuple[int, int], List[Any]] = {} + predictions: Dict[Tuple[int, int], Tuple[int, List[Any]]] = {} + pred_boxes.sort(key=lambda x: x[PKey.CONF.value], reverse=True) + + for lidx, true_box in enumerate(true_boxes): + ground_truths.setdefault((true_box[LKey.IMG_ID.value], true_box[LKey.CLASS.value]), []).append((lidx, true_box)) + + ground_truths_matched: Dict[int, Dict[int, List[Dict[str, Any]]]] = {} + for (img_id, class_idx), img_cls_labels in ground_truths.items(): + ground_truths_matched.setdefault(class_idx, {})[img_id] = [ + {"lidx": lidx, "pidxs": []} for lidx, _ in img_cls_labels + ] + + for pidx, pred_box in enumerate(pred_boxes): + predictions.setdefault((pred_box[PKey.IMG_ID.value], pred_box[PKey.CLASS.value]), []).append((pidx, pred_box)) # type: ignore + + ious = torch.zeros(len(pred_boxes), dtype=float) # type: ignore + for (img_idx, pred_cls), detections in tqdm(predictions.items(), desc="Matching predictions to labels", leave=True): + ground_truth_img = ground_truths.get((img_idx, pred_cls)) + label_matches = ground_truths_matched.get(pred_cls, {}).get(img_idx, []) + + if not ground_truth_img: + continue + + img_ious = get_img_ious(detections, ground_truth_img) # type: ignore + best_gt_idxs = torch.argmax(img_ious, dim=1, keepdim=False) + best_ious = torch.amax(img_ious, dim=1, keepdim=False) + + for i, (best_gt_idx, best_iou) in enumerate(zip(best_gt_idxs, best_ious)): + if best_iou > 0: + pidx = detections[i][0] # type: ignore + label_matches[best_gt_idx]["pidxs"].append(pidx) + ious[pidx] = best_iou + + return ious, ground_truths_matched + + +class PredictionWriter: + def __init__( + self, + cache_dir: Path, + project: Project, + prefix: str = "", + custom_object_map: Optional[Dict[str, int]] = None, + **kwargs, + ): + self.cache_dir = (cache_dir / "predictions").expanduser().absolute() + self.cache_dir.mkdir(parents=True, exist_ok=True) + + file_name = "predictions.csv" + if prefix: + file_name = f"{prefix}_{file_name}" + self.file_path = self.cache_dir / file_name + logger.info(f"Storing predictions at {self.file_path}") + + self.predictions_file = None + self.object_predictions: List[List[Any]] = [] # [[key, img_id, class_id, confidence, x1, y1, x2, y2], ...] + + self.project = project + self.object_lookup = {o["featureNodeHash"]: o for o in self.project.ontology["objects"]} + + logger.info("Fetching project label rows to be able to match predictions.") + self.label_rows = prepare_data(cache_dir, project=project, **kwargs).label_rows + self.label_row_meta = {lr["label_hash"]: lr for lr in self.project.label_rows if lr["label_hash"] is not None} + + self.uuids: Set[str] = set() + + self.__prepare_lr_lookup() + self.__prepare_image_id_lookup() + self.__prepare_class_id_lookups(custom_object_map) + self.__prepare_label_list() + + def __prepare_lr_lookup(self): + logger.debug("Preparing label row lookup") + # Top level data hashes + self.lr_lookup: Dict[str, str] = {d["data_hash"]: d["label_hash"] for d in self.project.label_rows} + # Nested data hashes for every data unit in the project + for lr in self.label_rows.values(): + self.lr_lookup.update({du_hash: lr["label_hash"] for du_hash in lr["data_units"]}) + + def __prepare_image_id_lookup(self): + logger.debug("Preparing image id lookup") + self.image_id_lookup: Dict[Tuple[str, Optional[int]], int] = {} # d[(du_hash, frame)]: int + for lr in self.label_rows.values(): + data_type = lr["data_type"] + for du in lr["data_units"].values(): + if "im" in data_type: # img_group or image + frame = int(du["data_sequence"]) + self.image_id_lookup[(du["data_hash"], frame)] = len(self.image_id_lookup) + else: # Video + for fr in du["labels"]: + frame = int(fr) + self.image_id_lookup[(du["data_hash"], frame)] = len(self.image_id_lookup) + + def __prepare_class_id_lookups(self, custom_map: Optional[Dict[str, int]]): + logger.debug("Preparing class id lookup") + self.custom_map = False + if custom_map: + feature_hashes = {o["featureNodeHash"] for o in self.project.ontology["objects"]} + custom_hashes = set(custom_map.keys()) + if not all([c in feature_hashes for c in custom_hashes]): + raise ValueError("custom map keys should correspond to `featureNodeHashes` from the project ontology.") + + self.object_class_id_lookup = custom_map + self.custom_map = True + else: + self.object_class_id_lookup = {} + for obj in self.project.ontology["objects"]: + self.object_class_id_lookup[obj["featureNodeHash"]] = len(self.object_class_id_lookup) + + self.classification_class_id_lookup: Dict[str, int] = {} + for obj in self.project.ontology["classifications"]: + self.classification_class_id_lookup[obj["featureNodeHash"]] = len(self.classification_class_id_lookup) + + def __prepare_label_list(self): + logger.debug("Preparing label list") + self.object_labels: List[List[Any]] = [] + + def append_object_label(du_hash: str, frame: int, o: dict, width: int, height: int): + class_id = self.get_class_id(o) + if class_id is None: # Ignore unwanted classes (defined by what is in `self.object_class_id_lookup`) + return + + label_hash = self.lr_lookup[du_hash] + row = [ + f"{label_hash}_{du_hash}_{frame:05d}_{o['objectHash']}", + f"{BASE_URL}{self.label_row_meta[label_hash]['data_hash']}&{self.project.project_hash}/{frame}", + self.get_image_id(du_hash, frame), # Image id + class_id, # Class id + o["objectHash"], # Object hash + None, # bbox.x1 + None, # bbox.y1 + None, # bbox.x2 + None, # bbox.y2 + None, # RLE encoding of mask + ] + + if o["shape"] == "bounding_box": + bbox = o.get("boundingBox") + if not (bbox and self.__check_bbox(bbox)): + return # Invalid bounding box object + + x, y, w, h = [bbox[k] for k in ["x", "y", "w", "h"]] + row[LKey.X1.value] = round(x * width, 2) # bbox.x1 + row[LKey.Y1.value] = round(y * height, 2) # bbox.y1 + row[LKey.X2.value] = round((x + w) * width, 2) # bbox.x2 + row[LKey.Y2.value] = round((y + h) * height, 2) # bbox.y2 + elif o["shape"] == "polygon": + points = polyobj_to_nparray(o, width=width, height=height) + if points.size == 0: + return + x1, y1 = points.min(axis=0) + x2, y2 = points.max(axis=0) + row[LKey.X1.value] = x1 # bbox.x1 + row[LKey.Y1.value] = y1 # bbox.y1 + row[LKey.X2.value] = x2 # bbox.x2 + row[LKey.Y2.value] = y2 # bbox.y2 + + mask = points_to_mask(points, width=width, height=height) + row[LKey.RLE.value] = binary_mask_to_rle(mask) + else: + # Only supporting polygons and bounding boxes. + return + self.object_labels.append(row) + + for label_hash, lr in tqdm(self.label_rows.items(), desc="Preparing labels", leave=True): + data_type = lr["data_type"] + for du_hash, du in lr["data_units"].items(): + height = int(du["height"]) + width = int(du["width"]) + + if "im" in data_type: # img_group or image + frame = int(du["data_sequence"]) + for obj in du["labels"]["objects"]: + append_object_label(du_hash, frame, obj, width, height) + else: # Video + for fr, labels in du["labels"].items(): + frame = int(fr) + for obj in labels["objects"]: + append_object_label(du_hash, frame, obj, width, height) + + def get_image_id(self, data_hash: str, frame: Optional[int]): + return self.image_id_lookup.setdefault((data_hash, frame), len(self.image_id_lookup)) + + def get_class_id(self, obj_dict): + fh = obj_dict["featureHash"] + if "objectHash" in obj_dict and fh in self.object_class_id_lookup: + return self.object_class_id_lookup[fh] + elif "classificationHash" in obj_dict and fh in self.classification_class_id_lookup: + return self.classification_class_id_lookup[fh] + return None + + def __enter__(self): + self.predictions_file = self.file_path.open("w") + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + # Do the matching computations + ious, ground_truths_matched = precompute_MAP_features(self.object_predictions, self.object_labels) + + # 0. The predictions + logger.info("Saving predictions") + pred_df = pd.DataFrame(self.object_predictions, columns=PREDICTION_CSV_COLUMNS[:-1]) + pred_df["iou"] = ious + pred_df.to_csv(self.file_path) + + # 1. The labels + logger.info("Saving labels") + df = pd.DataFrame(self.object_labels, columns=LABEL_CSV_COLUMNS) + df["rle"] = df["rle"].map( + lambda x: " ".join(x.reshape(-1).astype(str).tolist()) if isinstance(x, np.ndarray) else x + ) + df.to_csv(self.file_path.with_name("labels.csv")) + + # 2. The GT matches + logger.info("Saving GTs matched") + with (self.cache_dir / "ground_truths_matched.json").open("w") as f: + json.dump(ground_truths_matched, f) + + # 3. The class idx map + logger.info("Saving class index") + class_index = {} + for k, v in self.object_class_id_lookup.items(): + if k[0] == "_" or v in class_index: + continue + + class_index[v] = { + "featureHash": k, + "name": self.object_lookup[k]["name"], + "color": self.object_lookup[k]["color"], + } + + with (self.cache_dir / "class_idx.json").open("w") as f: + json.dump(class_index, f) + + @staticmethod + def __generate_hash() -> str: + return b64encode(uuid4().bytes[:6]).decode("utf-8") + + def __get_unique_object_hash(self) -> str: + object_hash = self.__generate_hash() + while object_hash in self.uuids: + object_hash = self.__generate_hash() + self.uuids.add(object_hash) + return object_hash + + @staticmethod + def __check_bbox(bbox): + bbox_keys = set(bbox.keys()) + if not len(bbox_keys.intersection(BBOX_KEYS)) == 4: + raise ValueError(f"Bbox dict keys were {bbox_keys} but should be {BBOX_KEYS}") + if not all([isinstance(v, (int, float)) for v in bbox.values()]): + raise ValueError("Bbox coordinates should be floats") + return True + + def add_prediction( + self, + data_hash: str, + class_uid: str, + confidence_score: float, + bbox: Optional[Dict[str, Union[float, int]]] = None, + polygon: Optional[Union[np.ndarray, List[Tuple[int, int]]]] = None, + frame: Optional[int] = None, + ) -> None: + """ + Add a prediction to en encord-active project. + Note that only one bounding box or polygon can be specified in any given call to this function. + + :param data_hash: The ``data_hash`` of the data unit that the prediction belongs to. + :param class_uid: The ``featureNodeHash`` of the ontology object corresponding to the class of the prediction. + :param confidence_score: The model confidence score. + :param bbox: A bounding box prediction. This should be a dict with the format:: + + { + 'x': 0.1 # normalized x-coordinate of the top-left corner of the bounding box. + 'y': 0.2 # normalized y-coordinate of the top-left corner of the bounding box. + 'w': 0.3 # normalized width of the bounding box. + 'h': 0.1 # normalized height of the bounding box. + } + + :param polygon: A polygon represented either as a list of points or a mask of size [h, w]. + :param frame: If predictions are associated with a video, then the frame number should be provided. + """ + mask = None + label_hash = self.lr_lookup.get(data_hash) + if not label_hash: + logger.warning(f"Couldn't match data hash `{data_hash}` to any label row") + return + + du = self.label_rows[label_hash]["data_units"][data_hash] + + width = int(du["width"]) + height = int(du["height"]) + + ptype: PredictionType + if bbox is None and polygon is None: + raise NotImplementedError("Frame level classifications are not supported at the moment.") + # ptype = PredictionType.FRAME + elif bbox is None and isinstance(polygon, (np.ndarray, list)): + ptype = PredictionType.POLYGON + if isinstance(polygon, list): + polygon = np.array(polygon) + + if polygon.ndim != 2: + raise ValueError("Polygon argument should have just 2 dimensions: [h, w] or [N, 2]") + + if polygon.shape[1] != 2: # Polygon is mask + np_mask = polygon + x1, y1, w, h = cv2.boundingRect(polygon) # type: ignore + else: # Polygon is points + # Read image size from label row + if np.all(np.logical_and(polygon >= 0.0, polygon <= 1.0)): + polygon = polygon * np.array([[width, height]]) + + np_mask = points_to_mask(polygon, width=width, height=height) # type: ignore + x1, y1, w, h = cv2.boundingRect(polygon.reshape(-1, 1, 2).astype(int)) # type: ignore + x2, y2 = x1 + w, y1 + h + points = [x1, y1, x2, y2] + mask = binary_mask_to_rle(np_mask) + + elif isinstance(bbox, dict) and polygon is None: + ptype = PredictionType.BBOX + self.__check_bbox(bbox) + points = [ + bbox["x"] * width, + bbox["y"] * height, + (bbox["x"] + bbox["w"]) * width, + (bbox["y"] + bbox["h"]) * height, + ] + else: + raise ValueError( + "Something seems wrong. Did you use the wrong types or did you parse both a bbox and polygon?" + ) + + _frame = 0 + if not frame: # Try to infer frame number from data hash. + label_row = self.label_rows[label_hash] + data_unit = label_row["data_units"][data_hash] + if "data_sequence" in data_unit: + _frame = int(data_unit["data_sequence"]) + + object_hash = self.__get_unique_object_hash() + + # === Write key similar to the index writer === # + key = f"{label_hash}_{data_hash}_{_frame:05d}_{object_hash}" + + class_id = self.object_class_id_lookup.get(class_uid) + if class_id is None: + raise ValueError( + f"`class_uid` didn't match any key in the " + f"{'`custom_object_map`' if self.custom_map else 'project ontology'}.\n" + f"Options are: [{', '.join(self.object_class_id_lookup.keys())}]" + ) + + ontology_object = self.object_lookup[class_uid] + if ontology_object["shape"] != ptype.value: + raise ValueError( + f"You've passed a {ptype.value} but the provided class id is of type " f"{ontology_object['shape']}" + ) + + image_id = self.get_image_id(data_hash, _frame) + url = f"{BASE_URL}{self.label_row_meta[self.lr_lookup[data_hash]]['data_hash']}&{self.project.project_hash}/{_frame}" + self.object_predictions.append([key, url, image_id, class_id, confidence_score] + points + [mask])