FaceDetection_last.ipynb

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      "/opt/conda/lib/python3.7/site-packages/facenet_pytorch/models/utils/detect_face.py:210: UserWarning: This overload of nonzero is deprecated:\n",
      "\tnonzero()\n",
      "Consider using one of the following signatures instead:\n",
      "\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:882.)\n",
      "  mask_inds = mask.nonzero()\n",
      "/opt/ml/input/data/eval/images/0001b62fb1057a1182db2c839de232dbef0d1e90.txt\n",
      "/opt/ml/input/data/eval/images/0007a12486cabfd419323a5eb75bcbb7fcd3e394.txt\n",
      "/opt/ml/input/data/eval/images/0007d32b13ba3ee17f1c9aaf0f4df70599d04e8f.txt\n",
      "/opt/ml/input/data/eval/images/0015ff2242eb049f9242db967f4530a701d3d4f7.txt\n",
      "/opt/ml/input/data/eval/images/0016162a11c94b63dc4c96c3923efed1f54fe57c.txt\n",
      "/opt/ml/input/data/eval/images/0029767238f52914849b61187349a5c18b4fb10b.txt\n",
      "/opt/ml/input/data/eval/images/0029d0cf7d329c2c7d37206b1d9bd7c653c228ea.txt\n",
      "\n"
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     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-91ab94d0f353>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mboxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmtcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mboxes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mimg_width\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m384\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/facenet_pytorch/models/mtcnn.py\u001b[0m in \u001b[0;36mdetect\u001b[0;34m(self, img, landmarks)\u001b[0m\n\u001b[1;32m    315\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0monet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthresholds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m             )\n\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/facenet_pytorch/models/utils/detect_face.py\u001b[0m in \u001b[0;36mdetect_face\u001b[0;34m(imgs, minsize, pnet, rnet, onet, threshold, factor, device)\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m                 \u001b[0mimg_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimage_inds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m                 \u001b[0mim_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m24\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m24\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0mim_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mim_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mim_data\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m127.5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.0078125\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/facenet_pytorch/models/utils/detect_face.py\u001b[0m in \u001b[0;36mimresample\u001b[0;34m(img, sz)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m     \u001b[0mim_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"area\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mim_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36minterpolate\u001b[0;34m(input, size, scale_factor, mode, align_corners, recompute_scale_factor)\u001b[0m\n\u001b[1;32m   3139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'area'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3140\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0moutput_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3141\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0madaptive_avg_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3142\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'area'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3143\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0moutput_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36madaptive_avg_pool2d\u001b[0;34m(input, output_size)\u001b[0m\n\u001b[1;32m    934\u001b[0m                 adaptive_avg_pool2d, (input,), input, output_size)\n\u001b[1;32m    935\u001b[0m     \u001b[0m_output_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list_with_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madaptive_avg_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_output_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from glob import glob\n",
    "from PIL import Image\n",
    "from facenet_pytorch import MTCNN\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "mtcnn = MTCNN()\n",
    "\n",
    "img_list = glob('/opt/ml/input/data/eval/images/*')\n",
    "img_list.sort()\n",
    "bbox_list = []\n",
    "\n",
    "for i, img in tqdm(enumerate(img_list)):\n",
    "    boxes, probs = mtcnn.detect(Image.open(img))\n",
    "    if boxes is None:\n",
    "        img_width = 384\n",
    "        img_height = 512\n",
    "        crop_width = 224\n",
    "        crop_height = 224\n",
    "        boxes = [[img_width / 2 - crop_width / 2, img_height / 2 - crop_height / 2,\n",
    "                  img_width / 2 + crop_width / 2, img_height / 2 + crop_height / 2]]\n",
    "    p = os.path.dirname(img) + '/' + os.path.basename(img).split('.')[0] + '.txt'\n",
    "    print(p)\n",
    "    with open(p, 'wb') as f:\n",
    "        pickle.dump(boxes[0], f)"
   ]
  }
 ]
}