MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import detect_faces, show_bboxes\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "from torchvision import transforms, datasets\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Path Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = '../Dataset/emotiw/'\n",
    "\n",
    "processed_dataset_path = '../Dataset/FaceCoordinates/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = sorted(os.listdir(dataset_path + 'test_shared/test/'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_filelist = [x.split('.')[0] for x in test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['test_1', 'test_10', 'test_100', 'test_1000', 'test_1001', 'test_1002', 'test_1003', 'test_1004', 'test_1005', 'test_1006']\n"
     ]
    }
   ],
   "source": [
    "print(test_filelist[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3011\n"
     ]
    }
   ],
   "source": [
    "print(len(test_filelist))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Faces from Image using MTCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_filelist)):\n",
    "    print(test_filelist[i])\n",
    "    img_name = os.path.join(dataset_path, 'test_shared/test/', test_filelist[i]+ '.jpg')\n",
    "    image = Image.open(img_name)\n",
    "    try:\n",
    "        if os.path.isfile(processed_dataset_path + 'test/' + test_filelist[i] + '.npz'):\n",
    "            print(test_filelist[i] + ' Already present')\n",
    "            continue\n",
    "        bounding_boxes, landmarks = detect_faces(image)\n",
    "        bounding_boxes = np.asarray(bounding_boxes)\n",
    "        if bounding_boxes.size == 0:\n",
    "            print('MTCNN model handling empty face condition at ' + test_filelist[i])\n",
    "        np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=bounding_boxes, b=landmarks)\n",
    "                      \n",
    "    except ValueError:\n",
    "        print('No faces detected for ' + test_filelist[i] + \". Also MTCNN failed.\")\n",
    "        np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=np.zeros(1), b=np.zeros(1))"
   ]
  }
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