stats.ipynb

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   "id": "6a386f8d-10c6-4b91-a3b3-e0f5712eb36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75241f66-5f94-4539-8706-4ee50cb02e27",
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    "files = os.listdir(\"/auto/plzen1/home/mhruz/JSALT2024/YouTubeASL/clips_cropped\")\n",
    "files = [f for f in files if \".json\" in f]"
   ]
  },
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   ],
   "source": [
    "face = []\n",
    "l_hand = []\n",
    "r_hand = []\n",
    "\n",
    "for idx, file in enumerate(tqdm(files)):\n",
    "    path = os.path.join(\"/auto/plzen1/home/mhruz/JSALT2024/YouTubeASL/clips_cropped\", file)\n",
    "    with open(path, \"r\") as f:\n",
    "        keypoints = json.load(f)['joints']\n",
    "    \n",
    "    frames = len(keypoints)\n",
    "    _face = 0\n",
    "    _l_hand = 0\n",
    "    _r_hand = 0\n",
    "    for fidx in keypoints:\n",
    "        kp = keypoints[fidx]\n",
    "        if len(kp[\"face_landmarks\"]) > 0:\n",
    "            _face += 1\n",
    "        if len(kp[\"right_hand_landmarks\"]) > 0:\n",
    "            _r_hand += 1\n",
    "        if len(kp[\"left_hand_landmarks\"]) > 0:\n",
    "            _l_hand += 1\n",
    "    face.append(_face/frames)\n",
    "    l_hand.append(_l_hand/frames)\n",
    "    r_hand.append(_r_hand/frames)\n",
    "    \n",
    "    if ((idx+1) % 100) == 0:\n",
    "        print(\"face \", np.round(np.mean(face), 3), np.round(np.std(face), 3))\n",
    "        print(\"left \", np.round(np.mean(l_hand), 3), np.round(np.std(l_hand), 3))\n",
    "        print(\"right\", np.round(np.mean(r_hand), 3), np.round(np.std(r_hand), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80a62d43-314a-4405-b869-556a818462eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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