atom-predict/msunet/DataPostprocess_xj.ipynb

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   "source": [
    "import os\n",
    "import cv2\n",
    "import json\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from PIL import Image\n",
    "from skimage.measure import label\n",
    "from skimage.measure import regionprops\n",
    "from utils.labelme import save_pred_to_json"
   ]
  },
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    "# Results 2 Json"
   ]
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   "execution_count": 10
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   "source": [
    "json_path = '/home/gao/mouclear/cc/code/egnn_xj/logs/0/version_0/e2e.json'\n",
    "save_path = '/home/gao/mouclear/cc/data/xj/e2e/test_result'\n",
    "\n",
    "os.makedirs(save_path, exist_ok=True)"
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   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'img_path'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[12], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43msave_pred_to_json\u001B[49m\u001B[43m(\u001B[49m\u001B[43mjson_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msave_path\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/mouclear/cc/code/msunet/utils/labelme.py:187\u001B[0m, in \u001B[0;36msave_pred_to_json\u001B[0;34m(json_path, save_path)\u001B[0m\n\u001B[1;32m    185\u001B[0m pred \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpred\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m    186\u001B[0m lb \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m], dtype\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39muint8)\n\u001B[0;32m--> 187\u001B[0m img_path \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(\u001B[43mdata\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mimg_path\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m)\n\u001B[1;32m    188\u001B[0m nums \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(img_path)\n\u001B[1;32m    190\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(nums):\n",
      "\u001B[0;31mKeyError\u001B[0m: 'img_path'"
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    "save_pred_to_json(json_path, save_path)"
   ]
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