util_align_images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import shutil\n",
    "from PIL import Image\n",
    "import dlib\n",
    "\n",
    "hyperstyle_root = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath('__file__')), 'hyperstyle'))\n",
    "sys.path.insert(0, hyperstyle_root)\n",
    "\n",
    "from hyperstyle.notebooks.notebook_utils import run_alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Processed and saved: datasets/celeba_hq_aligned\\male\\200807.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\200865.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\201176.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\201916.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\202222.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\202421.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\202453.jpg\n",
      "Processed and saved: datasets/celeba_hq_aligned\\male\\202543.jpg\n"
     ]
    }
   ],
   "source": [
    "predictor = dlib.shape_predictor('pretrained_models/shape_predictor_68_face_landmarks.dat')\n",
    "\n",
    "def process_and_save_jpg_files(input_dir, output_dir):\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    for root, _, files in os.walk(input_dir):\n",
    "        for file in files:\n",
    "            if file.lower().endswith('.jpg'):\n",
    "                try:\n",
    "                    file_path = os.path.join(root, file)\n",
    "                    processed_image = run_alignment(file_path, predictor)\n",
    "                    relative_path = os.path.relpath(root, input_dir)\n",
    "                    output_file_dir = os.path.join(output_dir, relative_path)\n",
    "                    if not os.path.exists(output_file_dir):\n",
    "                        os.makedirs(output_file_dir)\n",
    "                    output_file_path = os.path.join(output_file_dir, file)\n",
    "                    processed_image.save(output_file_path)\n",
    "                    print(f\"Processed and saved: {output_file_path}\")\n",
    "                except Exception as e:\n",
    "                    print(str(e))\n",
    "                    pass\n",
    "\n",
    "# Example usage\n",
    "input_directory = 'datasets/celeba_hq'\n",
    "output_directory = 'datasets/celeba_hq_aligned'\n",
    "process_and_save_jpg_files(input_directory, output_directory)"
   ]
  }
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