notebooks/nfl07d-submission-notebook.ipynb

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   },
   "source": [
    "# Submission Notebook"
   ]
  },
  {
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    {
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     "output_type": "stream",
     "text": [
      "/kaggle\n",
      "/kaggle/DCNv2\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle\n",
    "!cp -r /kaggle/input/nfldata-dcnv2/DCNv2 /kaggle/.\n",
    "%cd /kaggle/DCNv2\n",
    "! chmod a+x *\n",
    "! ./make.sh &> /dev/null"
   ]
  },
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   "source": [
    "# Install motmetrics\n",
    "! pip install -qq /kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install/py-cpuinfo-8.0.0.tar.gz\n",
    "! pip install -qq /kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install/xmltodict-0.12.0-py2.py3-none-any.whl\n",
    "! pip install -qq /kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install/pytest_benchmark-3.4.1-py2.py3-none-any.whl\n",
    "! pip install -qq /kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install/flake8_import_order-0.18.1-py2.py3-none-any.whl\n",
    "! pip install -qq /kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install/motmetrics-1.2.0-py3-none-any.whl"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle\n",
      "/kaggle/FairMOT\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle\n",
    "!cp -r /kaggle/input/nfldata-fairmot-package/FairMOT /kaggle/.\n",
    "%cd /kaggle/FairMOT\n",
    "! pip install -qq --no-deps --no-index --find-links=/kaggle/input/nfl07b-fairmot-dependencies/pre_fairmot_install -r requirements.txt #&> /dev/null"
   ]
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     "text": [
      "Found existing installation: datasets 1.12.1\r\n",
      "Uninstalling datasets-1.12.1:\r\n",
      "  Successfully uninstalled datasets-1.12.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "! pip uninstall -y datasets"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "# User\n",
    "! cp -r /kaggle/input/nfl-helmet-assignment-kaggle/nfl_helmet_assignment_kaggle /kaggle/."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import os, sys\n",
    "import subprocess\n",
    "from multiprocessing import Pool\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from IPython.display import display, Video\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# FairMOT imports\n",
    "sys.path.append('/kaggle/DCNv2')\n",
    "sys.path.append('/kaggle/FairMOT/src/lib')\n",
    "from datasets.dataset.jde import LoadVideo\n",
    "\n",
    "# nfl_helmet_assignment_kaggle imports\n",
    "sys.path.append('/kaggle/input/easydict-master/easydict-master')\n",
    "sys.path.append(\n",
    "    ('/kaggle/input/yolov5-deepsort-pytorch/Yolov5_DeepSort_Pytorch-master'\n",
    "     '/Yolov5_DeepSort_Pytorch-master/deep_sort_pytorch')\n",
    ")\n",
    "sys.path.append('/kaggle/nfl_helmet_assignment_kaggle/')\n",
    "from helmet_tracker.utils.score import check_submission\n",
    "from helmet_tracker.utils.video import ffmpeg_friendly_encode\n",
    "from helmet_tracker.utils.features import add_track_features\n",
    "from helmet_tracker.models.helmet_mapping import mapping_df_2d\n",
    "from helmet_tracker.models.fairmot_postprocess import fmot_postprocess_hmap"
   ]
  },
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   "execution_count": 7,
   "id": "ac124c25",
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   "source": [
    "# For traindebug videos\n",
    "\n",
    "# BASE_DIR = '/kaggle/input/nfl-health-and-safety-helmet-assignment'\n",
    "\n",
    "# dir_video = os.path.join(BASE_DIR, 'train')\n",
    "# tracking = pd.read_csv(f'{BASE_DIR}/train_player_tracking.csv')\n",
    "# helmets = pd.read_csv(f'{BASE_DIR}/train_baseline_helmets.csv')\n",
    "\n",
    "# # For developing, restrict to \"traindebug\" samples\n",
    "\n",
    "# pth_ss_hmap = '/kaggle/input/nfl-mydata/submission_helmtrack_debug.csv'\n",
    "# ss_hmap = pd.read_csv(pth_ss_hmap)\n",
    "# videos = ss_hmap['video_frame'].str.split('_').str[:3].str.join('_').unique()[:2]\n",
    "\n",
    "# helmets['video'] = helmets['video_frame'].str.split('_').str[:3].str.join('_')\n",
    "# helmets = helmets[helmets['video'].isin(videos)]"
   ]
  },
  {
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   "execution_count": 8,
   "id": "c044809f",
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   "outputs": [],
   "source": [
    "BASE_DIR = '/kaggle/input/nfl-health-and-safety-helmet-assignment'\n",
    "\n",
    "dir_video = os.path.join(BASE_DIR, 'test')\n",
    "tracking = pd.read_csv(f'{BASE_DIR}/test_player_tracking.csv')\n",
    "helmets = pd.read_csv(f'{BASE_DIR}/test_baseline_helmets.csv')\n",
    "\n",
    "videos = [pth.split('.')[0] for pth in os.listdir(dir_video)]\n",
    "\n",
    "if len(videos) == 6:\n",
    "    videos = videos[:1]\n",
    "\n",
    "helmets['video'] = helmets['video_frame'].str.split('_').str[:3].str.join('_')\n",
    "helmets = helmets[helmets['video'].isin(videos)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb2e0efa",
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "['57906_000718_Sideline']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5605df",
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     "start_time": "2021-11-02T15:02:25.554580",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Helmet mapping 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce2ec649",
   "metadata": {
    "execution": {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/440 [00:00<?, ?it/s]"
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   ],
   "source": [
    "\n",
    "# Add estimated video frame to NGS data\n",
    "\n",
    "tracking = add_track_features(tracking) \n",
    "\n",
    "CONF_THRE = 0.3\n",
    "\n",
    "df_list = list(helmets.groupby('video_frame'))\n",
    "df_list = [(video_frame, df, tracking, CONF_THRE) for video_frame, df in df_list]\n",
    "\n",
    "def _mapping_df(args):\n",
    "    video_frame, df, tracking, conf_thre = args\n",
    "    return mapping_df_2d(video_frame, df, tracking, conf_thre=conf_thre)\n",
    "\n",
    "p = Pool(processes=os.cpu_count())\n",
    "ss_hmap = []\n",
    "with tqdm(total=len(df_list)) as pbar:\n",
    "    for this_df in p.imap(_mapping_df, df_list):\n",
    "        ss_hmap.append(this_df)\n",
    "        pbar.update(1)\n",
    "p.close()\n",
    "\n",
    "ss_hmap = pd.concat(ss_hmap)\n"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# FairMOT"
   ]
  },
  {
   "cell_type": "code",
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   "id": "9a22979f",
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       "  0%|          | 0/1 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
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     "text": [
      "/kaggle/FairMOT/src\n",
      "Fix size testing.\r\n",
      "training chunk_sizes: [6, 6]\r\n",
      "The output will be saved to  /kaggle/FairMOT/src/lib/../../exp/mot/default\r\n",
      "heads {'hm': 1, 'wh': 4, 'id': 128, 'reg': 2}\r\n",
      "2021-11-02 15:16:43 [INFO]: Starting tracking...\r\n",
      "Lenth of the video: 440 frames\r\n",
      "Creating model...\r\n",
      "loaded /kaggle/input/nfldata-fairmot/exp/mot/nfl_ft_mix_dla34/model_last.pth, epoch 16\r\n",
      "2021-11-02 15:16:52 [INFO]: Processing frame 0 (100000.00 fps)\r\n",
      "2021-11-02 15:16:56 [INFO]: Processing frame 20 (11.98 fps)\r\n",
      "2021-11-02 15:17:00 [INFO]: Processing frame 40 (12.48 fps)\r\n",
      "2021-11-02 15:17:05 [INFO]: Processing frame 60 (12.00 fps)\r\n",
      "2021-11-02 15:17:09 [INFO]: Processing frame 80 (12.10 fps)\r\n",
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      "2021-11-02 15:17:18 [INFO]: Processing frame 120 (11.95 fps)\r\n",
      "2021-11-02 15:17:22 [INFO]: Processing frame 140 (11.90 fps)\r\n",
      "2021-11-02 15:17:27 [INFO]: Processing frame 160 (11.70 fps)\r\n",
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      "2021-11-02 15:17:59 [INFO]: Processing frame 300 (10.96 fps)\r\n",
      "2021-11-02 15:18:04 [INFO]: Processing frame 320 (10.95 fps)\r\n",
      "2021-11-02 15:18:08 [INFO]: Processing frame 340 (10.94 fps)\r\n",
      "2021-11-02 15:18:13 [INFO]: Processing frame 360 (10.85 fps)\r\n",
      "2021-11-02 15:18:17 [INFO]: Processing frame 380 (10.90 fps)\r\n",
      "2021-11-02 15:18:22 [INFO]: Processing frame 400 (10.92 fps)\r\n",
      "2021-11-02 15:18:26 [INFO]: Processing frame 420 (10.99 fps)\r\n",
      "2021-11-02 15:18:30 [INFO]: save results to /kaggle/FairMOT/demo/57906_000718_Sideline/results.txt\r\n",
      "ffmpeg version 9c33b2f Copyright (c) 2000-2021 the FFmpeg developers\r\n",
      "  built with gcc 9.3.0 (crosstool-NG 1.24.0.133_b0863d8_dirty)\r\n",
      "  configuration: --prefix=/opt/conda --cc=/home/conda/feedstock_root/build_artifacts/ffmpeg_1627813612080/_build_env/bin/x86_64-conda-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-gpl --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-libx264 --enable-pic --enable-pthreads --enable-shared --enable-static --enable-version3 --enable-zlib --enable-libmp3lame --pkg-config=/home/conda/feedstock_root/build_artifacts/ffmpeg_1627813612080/_build_env/bin/pkg-config\r\n",
      "  libavutil      56. 51.100 / 56. 51.100\r\n",
      "  libavcodec     58. 91.100 / 58. 91.100\r\n",
      "  libavformat    58. 45.100 / 58. 45.100\r\n",
      "  libavdevice    58. 10.100 / 58. 10.100\r\n",
      "  libavfilter     7. 85.100 /  7. 85.100\r\n",
      "  libavresample   4.  0.  0 /  4.  0.  0\r\n",
      "  libswscale      5.  7.100 /  5.  7.100\r\n",
      "  libswresample   3.  7.100 /  3.  7.100\r\n",
      "  libpostproc    55.  7.100 / 55.  7.100\r\n",
      "Input #0, image2, from '/kaggle/FairMOT/demo/57906_000718_Sideline/frame/%05d.jpg':\r\n",
      "  Duration: 00:00:17.60, start: 0.000000, bitrate: N/A\r\n",
      "    Stream #0:0: Video: mjpeg (Baseline), yuvj420p(pc, bt470bg/unknown/unknown), 1920x1080 [SAR 1:1 DAR 16:9], 25 fps, 25 tbr, 25 tbn, 25 tbc\r\n",
      "\u001b[0;33mPlease use -b:a or -b:v, -b is ambiguous\r\n",
      "\u001b[0mStream mapping:\r\n",
      "  Stream #0:0 -> #0:0 (mjpeg (native) -> mpeg4 (native))\r\n",
      "Press [q] to stop, [?] for help\r\n",
      "\u001b[1;34m[swscaler @ 0x564b2b35e8c0] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\r\n",
      "\u001b[0mOutput #0, mp4, to '/kaggle/FairMOT/demo/57906_000718_Sideline/MOT16-03-results.mp4':\r\n",
      "  Metadata:\r\n",
      "    encoder         : Lavf58.45.100\r\n",
      "    Stream #0:0: Video: mpeg4 (mp4v / 0x7634706D), yuv420p, 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 5000 kb/s, 25 fps, 12800 tbn, 25 tbc\r\n",
      "    Metadata:\r\n",
      "      encoder         : Lavc58.91.100 mpeg4\r\n",
      "    Side data:\r\n",
      "      cpb: bitrate max/min/avg: 0/0/5000000 buffer size: 0 vbv_delay: N/A\r\n",
      "frame=  440 fps= 40 q=9.3 Lsize=   10889kB time=00:00:17.56 bitrate=5080.1kbits/s speed=1.59x    \r\n",
      "video:10887kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.026166%\r\n"
     ]
    }
   ],
   "source": [
    "! mkdir -p /root/.cache/torch/hub/checkpoints/\n",
    "!cp /kaggle/input/models-fairmot/dla34-ba72cf86.pth /root/.cache/torch/hub/checkpoints/.\n",
    "\n",
    "load_model = '/kaggle/input/nfldata-fairmot/exp/mot/nfl_ft_mix_dla34/model_last.pth'\n",
    "conf_thres = 0.3\n",
    "\n",
    "dir_demo = '/kaggle/FairMOT/demo'\n",
    "\n",
    "for video in tqdm(videos, total=len(videos)):\n",
    "    \n",
    "    input_video = os.path.join(dir_video, f'{video}.mp4')\n",
    "    output_root = os.path.join(dir_demo, video)\n",
    "\n",
    "    ! mkdir -p {output_root}\n",
    "    %cd /kaggle/FairMOT/src\n",
    "    ! python demo.py mot --load_model {load_model} --conf_thres {conf_thres} --input-video {input_video} --output-root {output_root}\n",
    "\n",
    "\n",
    "    "
   ]
  },
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    "tags": []
   },
   "source": [
    "# Post-process helmet mapping with FairMOT"
   ]
  },
  {
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   "id": "1ab254de",
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   "source": [
    "video_frame = ss_hmap['video_frame'].str.split('_')\n",
    "ss_hmap['video'] = video_frame.str[:3].str.join('_')\n",
    "ss_hmap['frame'] = video_frame.str[3].astype(int)"
   ]
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       "  0%|          | 0/1 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.42 s, sys: 31.5 ms, total: 5.45 s\n",
      "Wall time: 5.52 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "outs = []\n",
    "for video in tqdm(videos, total=len(videos)):\n",
    "    \n",
    "    df_hmap = ss_hmap.query('video==@video')\n",
    "\n",
    "    pth_fmot = os.path.join(dir_demo, video, 'results.txt')\n",
    "    pth_video = os.path.join(dir_video, video + '.mp4')\n",
    "\n",
    "    df_tracks = fmot_postprocess_hmap(df_hmap, pth_fmot, pth_video)\n",
    "    \n",
    "    outs.append(df_tracks)\n",
    "    \n",
    "ss_fmot = pd.concat(outs, axis=0)"
   ]
  },
  {
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   "execution_count": 14,
   "id": "4c4c8263",
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   "outputs": [],
   "source": [
    "assert ss_fmot['label_fairmot'].isna().sum() == 0\n",
    "\n",
    "ss_fmot.drop('label', axis=1, inplace=True)\n",
    "ss_fmot.rename(columns={'label_fairmot': 'label'}, inplace=True)\n",
    "\n",
    "columns = pd.read_csv(\n",
    "    '/kaggle/input/nfl-health-and-safety-helmet-assignment/sample_submission.csv').columns.values\n",
    "ss_fmot = ss_fmot[columns]"
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  },
  {
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   "id": "37fa5403",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Check submission and save to disk"
   ]
  },
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   "execution_count": 15,
   "id": "6815f58f",
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    "check_submission(ss_fmot)"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f83285d1",
   "metadata": {
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     "exception": false,
     "start_time": "2021-11-02T15:18:48.749130",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "ss_fmot.to_csv('/kaggle/working/submission.csv', index=False)"
   ]
  },
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   "execution_count": null,
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