create_kitti_tf_record.py

# https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_kitti_tf_record.py

# ========================================================================
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========================================================================


"""Convert KITTI detection dataset to TFRecord for object detection.

Example usage:

    python3 create_kitti_tf_record.py \
            --data_dir=egohands_kitti_formatted \
            --output_path=data/egohands \
            --classes_to_use=hand \
            --label_map_path=data/egohands_label_map.pbtxt \
            --validation_set_size=500
"""


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


import hashlib
import io
import os
import random

import numpy as np
import PIL.Image as pil
import tensorflow as tf

from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
from object_detection.utils.np_box_ops import iou


tf.app.flags.DEFINE_string('data_dir', '', 'Location for the data.  '
                           'All jpg and txt files are assumed to be '
                           'present at this location.')
tf.app.flags.DEFINE_string('output_path', '', 'Path to which TFRecord '
                           'files will be written. The TFRecord with '
                           'the training set will be located at: '
                           '<output_path>_train.tfrecord.  And the '
                           'TFRecord with the validation set will be '
                           'located at: <output_path>_val.tfrecord.')
tf.app.flags.DEFINE_string('classes_to_use', 'hand,dontcare',
                           'Comma separated list of class names that '
                           'will be used.  Adding the dontcare class '
                           'will remove all bboxs in the dontcare '
                           'regions.')
tf.app.flags.DEFINE_string('label_map_path',
                           'data/egohands_label_map.pbtxt',
                           'Path to label map proto.')
tf.app.flags.DEFINE_integer('validation_set_size', '500',
                            'Number of images to be used as a '
                            'validation set.')
FLAGS = tf.app.flags.FLAGS


def convert_kitti_to_tfrecords(data_dir, output_path, classes_to_use,
                               label_map_path, validation_set_size):
  """Convert the KITTI detection dataset to TFRecords.

  Args:
    data_dir: The full path containing KITTI formatted data, with all
      jpg files located in <data_dir>/images/ and all txt files in
      <data_dir>/labels/.
    output_path: The path to which TFRecord files will be written.
      The TFRecord with the training set will be located at:
      <output_path>_train.tfrecord
      And the TFRecord with the validation set will be located at:
      <output_path>_val.tfrecord
    classes_to_use: List of strings naming the classes for which
      data should be converted.  Use the same names as presented in
      the KIITI README file.  Adding dontcare class will remove all
      other bounding boxes that overlap with areas marked as dontcare
       regions.
    label_map_path: Path to label map proto
    validation_set_size: How many images should be left as the
       validation set.  (First `validation_set_size` examples are
       selected to be in the validation set).
  """
  label_map_dict = label_map_util.get_label_map_dict(label_map_path)
  train_count = 0
  val_count = 0

  annotation_dir = os.path.join(data_dir, 'labels')
  image_dir = os.path.join(data_dir, 'images')

  train_writer = tf.python_io.TFRecordWriter('%s_train.tfrecord'%
                                             output_path)
  val_writer = tf.python_io.TFRecordWriter('%s_val.tfrecord'%
                                           output_path)

  images = sorted(tf.gfile.ListDirectory(image_dir))
  images = [f for f in images if f.endswith('jpg')]  # only keep jpg files
  assert len(images) > 0
  random.shuffle(images)
  for idx, img_name in enumerate(images):
    img_num = img_name.split('.')[0]
    is_validation_img = idx < validation_set_size
    img_anno = read_annotation_file(os.path.join(annotation_dir,
                                                 img_num+'.txt'))

    image_path = os.path.join(image_dir, img_name)

    # Filter all bounding boxes of this frame that are of a legal class,
    # and don't overlap with a dontcare region.
    # TODO(talremez) filter out targets that are truncated or heavily
    # occluded.
    annotation_for_image = filter_annotations(img_anno, classes_to_use)

    example = prepare_example(image_path, annotation_for_image, label_map_dict)
    if is_validation_img:
      val_writer.write(example.SerializeToString())
      val_count += 1
    else:
      train_writer.write(example.SerializeToString())
      train_count += 1

  train_writer.close()
  val_writer.close()


def prepare_example(image_path, annotations, label_map_dict):
  """Converts a dictionary with annotations for an image to tf.Example proto.

  Args:
    image_path: The complete path to image.
    annotations: A dictionary representing the annotation of a single object
      that appears in the image.
    label_map_dict: A map from string label names to integer ids.

  Returns:
    example: The converted tf.Example.
  """
  with tf.gfile.GFile(image_path, 'rb') as fid:
    encoded_png = fid.read()
  encoded_png_io = io.BytesIO(encoded_png)
  image = pil.open(encoded_png_io)
  image = np.asarray(image)

  key = hashlib.sha256(encoded_png).hexdigest()

  width = int(image.shape[1])
  height = int(image.shape[0])

  xmin_norm = annotations['2d_bbox_left'] / float(width)
  ymin_norm = annotations['2d_bbox_top'] / float(height)
  xmax_norm = annotations['2d_bbox_right'] / float(width)
  ymax_norm = annotations['2d_bbox_bottom'] / float(height)

  difficult_obj = [0]*len(xmin_norm)

  example = tf.train.Example(features=tf.train.Features(feature={
      'image/height': dataset_util.int64_feature(height),
      'image/width': dataset_util.int64_feature(width),
      'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')),
      'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')),
      'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
      'image/encoded': dataset_util.bytes_feature(encoded_png),
      'image/format': dataset_util.bytes_feature('png'.encode('utf8')),
      'image/object/bbox/xmin': dataset_util.float_list_feature(xmin_norm),
      'image/object/bbox/xmax': dataset_util.float_list_feature(xmax_norm),
      'image/object/bbox/ymin': dataset_util.float_list_feature(ymin_norm),
      'image/object/bbox/ymax': dataset_util.float_list_feature(ymax_norm),
      'image/object/class/text': dataset_util.bytes_list_feature(
          [x.encode('utf8') for x in annotations['type']]),
      'image/object/class/label': dataset_util.int64_list_feature(
          [label_map_dict[x] for x in annotations['type']]),
      'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
      'image/object/truncated': dataset_util.float_list_feature(
          annotations['truncated']),
      'image/object/alpha': dataset_util.float_list_feature(
          annotations['alpha']),
      'image/object/3d_bbox/height': dataset_util.float_list_feature(
          annotations['3d_bbox_height']),
      'image/object/3d_bbox/width': dataset_util.float_list_feature(
          annotations['3d_bbox_width']),
      'image/object/3d_bbox/length': dataset_util.float_list_feature(
          annotations['3d_bbox_length']),
      'image/object/3d_bbox/x': dataset_util.float_list_feature(
          annotations['3d_bbox_x']),
      'image/object/3d_bbox/y': dataset_util.float_list_feature(
          annotations['3d_bbox_y']),
      'image/object/3d_bbox/z': dataset_util.float_list_feature(
          annotations['3d_bbox_z']),
      'image/object/3d_bbox/rot_y': dataset_util.float_list_feature(
          annotations['3d_bbox_rot_y']),
  }))

  return example


def filter_annotations(img_all_annotations, used_classes):
  """Filters out annotations from the unused classes and dontcare regions.

  Filters out the annotations that belong to classes we do now wish to use and
  (optionally) also removes all boxes that overlap with dontcare regions.

  Args:
    img_all_annotations: A list of annotation dictionaries. See documentation of
      read_annotation_file for more details about the format of the annotations.
    used_classes: A list of strings listing the classes we want to keep, if the
    list contains "dontcare", all bounding boxes with overlapping with dont
    care regions will also be filtered out.

  Returns:
    img_filtered_annotations: A list of annotation dictionaries that have passed
      the filtering.
  """

  img_filtered_annotations = {}

  # Filter the type of the objects.
  relevant_annotation_indices = [
      i for i, x in enumerate(img_all_annotations['type']) if x in used_classes
  ]

  for key in img_all_annotations.keys():
    img_filtered_annotations[key] = (
        img_all_annotations[key][relevant_annotation_indices])

  if 'dontcare' in used_classes:
    dont_care_indices = [i for i,
                         x in enumerate(img_filtered_annotations['type'])
                         if x == 'dontcare']

    # bounding box format [y_min, x_min, y_max, x_max]
    all_boxes = np.stack([img_filtered_annotations['2d_bbox_top'],
                          img_filtered_annotations['2d_bbox_left'],
                          img_filtered_annotations['2d_bbox_bottom'],
                          img_filtered_annotations['2d_bbox_right']],
                         axis=1)

    ious = iou(boxes1=all_boxes,
               boxes2=all_boxes[dont_care_indices])

    # Remove all bounding boxes that overlap with a dontcare region.
    if ious.size > 0:
      boxes_to_remove = np.amax(ious, axis=1) > 0.0
      for key in img_all_annotations.keys():
        img_filtered_annotations[key] = (
            img_filtered_annotations[key][np.logical_not(boxes_to_remove)])

  return img_filtered_annotations


def read_annotation_file(filename):
  """Reads a KITTI annotation file.

  Converts a KITTI annotation file into a dictionary containing all the
  relevant information.

  Args:
    filename: the path to the annotataion text file.

  Returns:
    anno: A dictionary with the converted annotation information. See annotation
    README file for details on the different fields.
  """
  with open(filename) as f:
    content = f.readlines()
  content = [x.strip().split(' ') for x in content]

  anno = {}
  anno['type'] = np.array([x[0].lower() for x in content])
  anno['truncated'] = np.array([float(x[1]) for x in content])
  anno['occluded'] = np.array([int(x[2]) for x in content])
  anno['alpha'] = np.array([float(x[3]) for x in content])

  anno['2d_bbox_left'] = np.array([float(x[4]) for x in content])
  anno['2d_bbox_top'] = np.array([float(x[5]) for x in content])
  anno['2d_bbox_right'] = np.array([float(x[6]) for x in content])
  anno['2d_bbox_bottom'] = np.array([float(x[7]) for x in content])

  anno['3d_bbox_height'] = np.array([float(x[8]) for x in content])
  anno['3d_bbox_width'] = np.array([float(x[9]) for x in content])
  anno['3d_bbox_length'] = np.array([float(x[10]) for x in content])
  anno['3d_bbox_x'] = np.array([float(x[11]) for x in content])
  anno['3d_bbox_y'] = np.array([float(x[12]) for x in content])
  anno['3d_bbox_z'] = np.array([float(x[13]) for x in content])
  anno['3d_bbox_rot_y'] = np.array([float(x[14]) for x in content])

  return anno


def main(_):
  convert_kitti_to_tfrecords(
      data_dir=FLAGS.data_dir,
      output_path=FLAGS.output_path,
      classes_to_use=FLAGS.classes_to_use.split(','),
      label_map_path=FLAGS.label_map_path,
      validation_set_size=FLAGS.validation_set_size)

if __name__ == '__main__':
  tf.app.run()