makeTestFile.ipynb

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    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import json\n",
    "import math\n",
    "import csv\n",
    "import os"
   ]
  },
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    "def denormalization(arr, angle, translation):\n",
    "\n",
    "    theta = (angle)/180*math.pi\n",
    "    c, s = np.cos(theta), np.sin(theta)\n",
    "    R = np.array(((c, -s), (s, c)))\n",
    "    \n",
    "    #rotate\n",
    "    arr = np.array(R.dot(arr.transpose())).transpose()\n",
    "    #translate\n",
    "    arr += translation\n",
    "#     arr[...,0] += (AGENT[19,0] - AGENT[0,0])\n",
    "#     arr[...,1] += (AGENT[19,1] - AGENT[0,1])\n",
    "    return arr"
   ]
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    "\n",
    "def prediction(root_dir, file):\n",
    "    json_dict = {}\n",
    "\n",
    "    with open(root_dir + file, 'r') as json_data:\n",
    "        json_dict = json.load(json_data)\n",
    "\n",
    "    preds = np.array(json_dict['preds'][0])[...,:2]\n",
    "    preds = np.array([denormalization(p, -json_dict['rotation'], -np.array(json_dict['translation'])) for p in preds])\n",
    "    seq_id = int(json_dict['csv_file'].split('.')[0])\n",
    "    return preds, seq_id\n"
   ]
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     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.cluster.dbscan_ module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.cluster. Anything that cannot be imported from sklearn.cluster is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from argoverse.evaluation.competition_util import generate_forecasting_h5\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "['ResultsJson/BEST_smoothing_smoothing/', 'ResultsJson/LAST_smoothing_nonsmoothing/', 'ResultsJson/LAST_smoothing_smoothing/', 'ResultsJson/BEST_smoothing_nonsmoothing/']\n",
      "['competition_files/BEST_smoothing_smoothing/', 'competition_files/LAST_smoothing_nonsmoothing/', 'competition_files/LAST_smoothing_smoothing/', 'competition_files/BEST_smoothing_nonsmoothing/']\n"
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   "source": [
    "\n",
    "\n",
    "\n",
    "root_dir = [\"ResultsJson/\" + d + \"/\" for d in(os.listdir(\"ResultsJson\"))]\n",
    "output_path = [\"competition_files/\" + d + \"/\" for d in(os.listdir(\"ResultsJson\"))]\n",
    "print(root_dir)\n",
    "print(output_path)\n",
    "for i in range(len(root_dir)):\n",
    "    os.makedirs(output_path[i], exist_ok = True)\n",
    "\n",
    "    file_list = os.listdir(root_dir[i])\n",
    "    output_all = {}\n",
    "    for file in tqdm(file_list):\n",
    "        p, seq_id = prediction(root_dir[i], file)\n",
    "        output_all[seq_id] = p \n",
    "    generate_forecasting_h5(output_all, output_path[i]) #this might take awhile"
   ]
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