examples/Tracking_Datasets.ipynb

{
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "<!-- Autogenerated by `scripts/make_examples.py` -->\n",
    "<table align=\"left\">\n",
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    "            Try in Google Colab\n",
    "        </a>\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bf7ef9-74c4-43f9-b2ad-300608bb4264",
   "metadata": {},
   "source": [
    "# Working With Video and Tracking Datasets in FiftyOne"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d16d64d-fd49-49b9-b8b4-56275b9c8988",
   "metadata": {},
   "source": [
    "In this tutorial, we’ll work with a video dataset and tackle a pedestrian tracking problem within FiftyOne. Video computer vision datasets require specialized workflows and can present specific challenges for machine learning and analysis. Fortunately, FiftyOne provides native support for video in both the UI and SDK. Let’s dive in and take a look!\n",
    "\n",
    "We will consider the problem of tracking multiple pedestrians in a street scene. For our dataset, we’ll explore the [Multiple Object Tracking (MOT17) dataset](https://motchallenge.net/) . When it comes time to do some of\n",
    "our own tracking, we’ll perform two-stage object tracking combining [Yolov8](https://github.com/ultralytics/ultralytics) with [BoT-SORT](https://github.com/NirAharon/BoT-SORT). FiftyOne’s built-in evaluation methods and embeddings view will give us insight into our tracking quality and help us search for identity swaps.\n",
    "\n",
    "The objectives of this tutorial are Encoding video data in image stacks into FiftyOne-friendly movies\n",
    "- Loading a video dataset with detections and trajectories into FiftyOne\n",
    "- Computing, storing, and analyzing per-frame trajectory statistics\n",
    "- Visualizing interesting object trajectories\n",
    "- Running a pedestrian tracker on the dataset and loading the results\n",
    "- Assessing our tracking using FiftyOne’s evaluation methods and embeddings visualization\n",
    "\n",
    "**So, what’s the takeaway?** As video-enabled devices proliferate, video data will continue to comprise a significant portion of computer vision, with its own workflows and challenges. FiftyOne’s large toolkit provides built-in support, so load up those video datasets!"
   ]
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  {
   "cell_type": "markdown",
   "id": "7ac69f36-b79a-48ff-afbe-3038ef89886b",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4505b4fc-874e-4095-89f3-9c3e9401a107",
   "metadata": {},
   "source": [
    "To get started, you need to install [FiftyOne](https://voxel51.com/docs/FiftyOne/getting_started/install.html) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7909a767-77fa-4009-b31f-dbe669f0851c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install FiftyOne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f01528c2-3a43-4d45-9749-237ccd008a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your FO version is: 0.23.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import fiftyone as fo\n",
    "import fiftyone.utils.video as fouv\n",
    "import fiftyone.zoo as foz\n",
    "import fiftyone.brain as fob\n",
    "from fiftyone import ViewField as F\n",
    "print(f'Your FO version is: {fo.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a758413-c9a1-403f-b11e-2b61b0e114a8",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1561fd4c-e1c8-49ac-8480-5875455fad7e",
   "metadata": {},
   "source": [
    "Download the [data (5.5GB)](https://motchallenge.net/data/MOT17.zip) for the [MOT17 dataset](https://motchallenge.net/data/MOT17/) . This data comprises seven videos with object detections from three different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adca5d48-c989-4ea3-be24-3ba92bfb6ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the data to a data directory\n",
    "DATA_DIR = os.path.join(os.getcwd(),'mot17data')\n",
    "!wget -P {DATA_DIR} https://motchallenge.net/data/MOT17.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bdd86bf-f4f9-4d61-b659-3c2b235816d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzip the data\n",
    "!unzip {os.path.join(DATA_DIR,'MOT17.zip')}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d59fe0-0c64-4997-9349-845eca44f3fa",
   "metadata": {},
   "source": [
    "## Encode the Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecad9300-2827-40da-9c3d-f42d65833716",
   "metadata": {},
   "source": [
    "This dataset comes as image stacks. We will encode these image stacks into movies for use with FiftyOne. (In the future, FiftyOne will support image-stacks-as-video natively.)\n",
    "\n",
    "How movies are encoded is an important consideration when working with video datasets. This is especially true in computer vision, where frame-level consistency in playback and alignment with annotations is crucial. Some encodings may work well on some platforms or with certain software, but not others. In general, it is a good idea to experiment with and test your encodings across all\n",
    "platforms and software packages used by your team and in your organization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ba2400f-e753-4ab7-a267-253544f7ebde",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"./MOT17\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0222a509-c371-414d-9052-4f99cf0e3f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a directory to hold encoded movies\n",
    "mp4_dir = os.path.join(DATA_DIR,'movies')\n",
    "!mkdir -p {mp4_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200ec702-e7b7-4071-8710-9fe12f8b6ffc",
   "metadata": {},
   "source": [
    "FiftyOne provides utilities to [reencode videos](https://docs.voxel51.com/api/FiftyOne.utils.video.html?highlight=reencode#FiftyOne.utils.video.reencode_video) for compatibility with FiftyOne. By default, this re-encoding uses the [ffmpeg](https://ffmpeg.org/) library, which accepts image stacks as input.\n",
    "\n",
    "\n",
    "In this tutorial, we will encode and work with two of the videos in the MOTS17 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "039b1e5d-95f3-4c9e-a724-a32615fa6b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "MOV_NUMS = [2,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04ad38ca-6dfd-4239-b555-7f99af043061",
   "metadata": {},
   "outputs": [],
   "source": [
    "for movi in MOV_NUMS:\n",
    "    mov_in = os.path.join(DATA_DIR,'train',f'MOT17-{movi:02}-FRCNN/img1/%06d.jpg')\n",
    "    mov_out = os.path.join(mp4_dir,f'MOT17-{movi:02}.mp4')\n",
    "    fouv.reencode_video(mov_in,mov_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c09167-ec2b-4261-a948-c0e69af29f95",
   "metadata": {},
   "source": [
    "## Create a FiftyOne Video Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9bfdc0-7955-4fb2-b277-397a05eab8ff",
   "metadata": {},
   "source": [
    "With our video data encoded into movies, it’s a snap to create a video dataset in FiftyOne using the `Dataset.from_dir` function.\n",
    "\n",
    "Calling `ensure_frames` pre-populates the `.frames` property for every video frame in each sample in the dataset. This is not mandatory, but is useful for being able to easily see how many frames\n",
    "are contained in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "981d3967-5443-4319-aaab-580e1acf68ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████████| 2/2 [26.4ms elapsed, 0s remaining, 75.8 samples/s]     \n",
      "Computing metadata...\n",
      " 100% |█████████████████████| 2/2 [243.2ms elapsed, 0s remaining, 8.2 samples/s] \n"
     ]
    }
   ],
   "source": [
    "dataset = fo.Dataset.from_dir(dataset_dir=mp4_dir,dataset_type=fo.types.VideoDirectory)\n",
    "dataset.ensure_frames()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95fa866-b5b3-44c5-bc7e-8284a50740e9",
   "metadata": {},
   "source": [
    "Let’s name our dataset and persist it so we can load it next time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7120d72-6a71-4291-adb5-d156d3d759f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.name = 'mots17'\n",
    "dataset.persistent = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea681377-54aa-403b-9f81-c1426f19fc52",
   "metadata": {},
   "source": [
    "Printing the first sample in our dataset shows us metadata for the first video. The frames attribute is an ordered dictionary containing Frame objects keyed by frame number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a50bddae-414a-4840-ac4d-08f25cfd0a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Sample: {\n",
      "    'id': '65450ab38d92e9f07dc6ec1f',\n",
      "    'media_type': 'video',\n",
      "    'filepath': '/home/dan/Documents/track/MOT17/movies/MOT17-02.mp4',\n",
      "    'tags': [],\n",
      "    'metadata': <VideoMetadata: {\n",
      "        'size_bytes': 19911561,\n",
      "        'mime_type': 'video/mp4',\n",
      "        'frame_width': 1920,\n",
      "        'frame_height': 1080,\n",
      "        'frame_rate': 25.0,\n",
      "        'total_frame_count': 600,\n",
      "        'duration': 24.0,\n",
      "        'encoding_str': 'avc1',\n",
      "    }>,\n",
      "    'frames': <Frames: 600>,\n",
      "}>\n"
     ]
    }
   ],
   "source": [
    "sample = dataset.first()\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1c61bd-adc4-4de5-be0d-5f2cfbf5434a",
   "metadata": {},
   "source": [
    "When indexing frames , make sure to use 1-based frame numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50af7021-d8b5-4f4f-8e8f-e29498c78869",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Frame: {'id': '65450ab3f3833718570f940b', 'frame_number': 1}>\n"
     ]
    }
   ],
   "source": [
    "print(sample.frames[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe19064d-356a-48bc-8550-188b7eef2458",
   "metadata": {},
   "source": [
    "## Loading Detections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d89a96-8de4-469c-93fd-5355c8bca90f",
   "metadata": {},
   "source": [
    "Let’s load some detections! The MOTS17 dataset contains ground truth annotations in csv files for each movie. These annotations include bounding box detections, classes, and identity IDs.\n",
    "\n",
    "The identity IDs make this problem a tracking problem and not just an object detection problem. Detections must be associated across frames as belonging to a particular individual or trajectory.\n",
    "\n",
    "The columns in the provided csv files, as well as the list of classes used, are as described in this [paper](https://arxiv.org/abs/1603.00831). We are primarily interested in people, so we set some of the other class labels to “na”. We also replace the “Static person” class with simply “person” as we will not distinguish the two classes here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0764802f-7e96-4ef9-a39d-61b1a3934eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading gt detections for /home/dan/Documents/track/MOT17/movies/MOT17-02.mp4\n",
      "frame 100\n",
      "frame 200\n",
      "frame 300\n",
      "frame 400\n",
      "frame 500\n",
      "frame 600\n",
      "Loading gt detections for /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4\n",
      "frame 100\n",
      "frame 200\n",
      "frame 300\n",
      "frame 400\n",
      "frame 500\n"
     ]
    }
   ],
   "source": [
    "GT_COLUMNS = ['frame','id','x0','y0','w','h','flag','class','visibility']\n",
    "GT_CLASSES = ['na','person','na','na','na','na','vehicle','person', \\\n",
    "'na','na','na','occluded','na','crowd']\n",
    "\n",
    "def read_detections(df, frame):\n",
    "    '''\n",
    "    Parse detections from MOTS17 csv/dataframe\n",
    "    df: dataframe containing GT detections\n",
    "    frame: frame (1-based) of interest\n",
    "    '''\n",
    "    \n",
    "    df_frame = df[df.frame==frame]\n",
    "    bbox_arr = df_frame[ ['x0','y0','w','h'] ].to_numpy().astype(np.float64)\n",
    "    bboxes = bbox_arr.tolist()\n",
    "    \n",
    "    frame_rel = range(0,len(bboxes))\n",
    "    ids = df_frame['id'].to_list()\n",
    "    vis = df_frame['visibility'].to_list()\n",
    "    classes = df_frame['class'].to_list()\n",
    "    classes = [GT_CLASSES[i] for i in classes]\n",
    "    return zip(bboxes, ids, vis, classes, frame_rel)\n",
    "\n",
    "for movi, samp in zip(MOV_NUMS,dataset):\n",
    "    print(f'Loading gt detections for {samp.filepath}')\n",
    "    # The ground-truth csv files are located under a particular detection method (like FRCNN),\n",
    "    # but are identical across the different detection types. We won't actually use the FRCNN detections.\n",
    "    gt_csv = os.path.join(DATA_DIR,'train',f'MOT17-{movi:02}-FRCNN/gt/gt.txt')\n",
    "    df_gt = pd.read_csv(gt_csv,names=GT_COLUMNS)\n",
    "    \n",
    "    imw = samp.metadata.frame_width\n",
    "    imh = samp.metadata.frame_height\n",
    "    df_gt[['x0','w']] /= imw\n",
    "    df_gt[['y0','h']] /= imh\n",
    "    \n",
    "    for frame_no, frame in samp.frames.items():\n",
    "        dets_gt = []\n",
    "        for bb, id, vis, cls, frm_rel in read_detections(df_gt, frame_no):\n",
    "            det = fo.Detection(bounding_box=bb, index=str(id), label=cls, visibility=vis)\n",
    "            dets_gt.append(det)\n",
    "            \n",
    "        frame['gt'] = fo.Detections(detections=dets_gt)\n",
    "        \n",
    "        if frame_no % 100 == 0:\n",
    "            print(f'frame {frame_no}')\n",
    "            \n",
    "    samp.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce2ef3d5-752a-48be-b45a-c0844020e97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiftyone as fo\n",
    "\n",
    "dataset = fo.load_dataset(\"mots17\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59a8dfc9-1d0c-4a38-ae47-ff7234d99e88",
   "metadata": {},
   "outputs": [
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AEpKKKACmU+mUAFFFSCPI60AR0VL5P8AtfpS+T/tfpQBDRUvlf7X6Unl+/6UAR0U8oB1YUmF/vfpQA2ipFhdwNiSNnphetTRafdSnCQt+PH86AKtFX/7Gvv+eP8A48v+NNfS7qJC8yrEvqzD+maAKVFSGL5NwYHnBFKkaGQBnKoR97bnt6UARUVZ+y5Lqhcsq7sMuOPz96db2LzXCxOTGWG7le3P9RQBUop7ptYjnHYkYyPWm4OcDk0AJRUphK7d+VySCMdMUscKuSu5i3OAq5z+tAENFOwMnmnpFuGScAdTigCKipWhKN8wYDGQdvX0pFjBIDNj8M0AR0VpWelfa1YiVlA7lOp/Okm0oxLuMuFHUsAPy55oAzqKlkjVH2q+/wB8U4WzbQScZpXAgoqf7OfX9KT7OfWi6HYhoqbyG9aTyT601roFiKirf2LjPmfpTVtC2ctj8KBFairf2L/pp+lJ9j5wHz+FAFWippYkjyBJuYdgOn40RQeYud2KAIaKklj8tgM5pmKHoUot7CUUuKlaOIQqyzFnPVNmMfjmldD5JdiGilxSZouLkYUUZrQ/smcIhd40eT7sbH5jRcOVmfRT3jaNyjghgcEHtSBfU0XQ/Zy7DaKnSKJusxH1X/69TCxjIyLgH/gP/wBei5PKylRV/wDs5cH9/wD+O/8A16rNBtON36UXQWZDRUnl+/6UeV/tfpRdBysjoqXyv9r9KXyP9r9KLoLMhoqf7P8A7f6Uv2b/AG/0ouhWK9FWfsv+3+lBtcfx/pRdAVqKn+z/AO3+lH2f/a/Si4EFFT/Z/wDb/Sl+zf7X6UXAr0VY+zf7f6U4Wmf4/wBKLgVaKufYuP8AWf8AjtMNrj+P9KLoCtRVyGzidsSTmP32Z/rWgnh9XUMl4GB7iP8A+vTAw6K07zS47R0D3Qw3U7On4ZqhIsYP7t2b6rj+tAEdFSxW802PLjZgeMgcfnVyHR7h8GRljHfnJoAzqKtT2Zt5gkzhQejAZFTppiv0uOvQ7f8A69FwM6ipLiLyZ3jznacZqOgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigB9FFFAFj/AJZj6VHR5h24xTd3tUxVi5yTtYtC8mAAypx6ij7bL/s/981V3e1G72qiCWWZ5mBc9OlLbTeRcJLt3bDnGcZqHd7UbvagCwJ8W8sW3/WMGznpipRffOuYwU8oRMpP3gO/tVLd7UbvagC0twkVxHLBFs2HOC2c0ouYgJE8g+U5DbQ/II98VU3e1G72oAvNfBpnZoQY3QIybvTpzR9uCvAYoQiw5wC2c5qju9qN3tQBbe6TyFiii2BZPMyWySac16hmlfySFlXDgP1Oc5BxxVLd7UbvagC3NdRyqq+RgJHsX5zwc9aVrtBbPDFCUD4zlyR+Aqnu9qN3tQBfW/T7RLM0BLOoUEPgrxg9qjjuY4ncJCfKddrIzZz+OKqbvajd7UAWfMtzID5DBMYwJOc+ucU+O5hhdjHA210KMGfPXHtVPd7UbvagC1JN5wihhQRIp4Bbue5NTX93vu0ZGVhFjkdC3Un86z93tRu9qALpvF86RxD8ko/eIW6nOcg9qiMkHmqwgIQDlS5yfxqvu9qN3tQBZurn7R5YCFVjGBltx/OoKbu9qN3tQA6im7vajd7UAOopu72o3e1ADqKbu9qN3tQA6im7vajd7UAOopu72o3e1ADqKbu9qN3tQA6kPSk3e1G6gBtFFFABTKfTKACrKxzFRtQY9SarVqRf6pfpSYFVo5lXcdlJFE8rlS4XHoM1Zn/1JHvTbQZlc+go6B1HJYKR88rE+3FLDbWxLI6sWBx8zVZHSqkhP2jaQCrHA9RSGOWCJJSvlpsz1P4etWZIUCloQNmBlV/mKq3ImVspIw9B61Hb3kqy/O3XAOeKaAtb/Lkkm52sQwxz7VqwEFlOQScVmYWeMunKtyR/Ue9WEdxf2wBwCOfegRr1Dd2y3UBibAz3xnH0qalpgc3Lp7WwYSRNIx4Rl6fjUV1auV3bRGgOF3E5IHf/APV6966jGaytXukiT7PsBJXgnt2/xpgYBJBGGJwMVM9yxSJQSvlkkAE/40ySP5TIqbUY8DP3fr+VRE0gJLh0kcui7e20nJH402TCOdm8EMevYdvxqyimO0IeONg2SoZuQcYzj/P6VUJJPzE/WmwG55qaOeSJi6Owdv4geaiAGGJPPYUZ9qQAQc1JHI0Z+U45z0zzTM+ncc0d6QFxpZZrjfJETgZCrkDp1/Si6jiEqGMsoc7unIBpkN5PCpCSYzj34qKSZpH3uSzdyT1p3A3JNVit0WOEeZtGCc1mXd9JcgA/Ko7Duap5zUrSKsZQYOe4FK4CwsqMMKWJq5kmMFhtPpVO1JEwIGakuJ28zAOB7dqTQJjnG45yQKcoyvNVPNbuaUSEsD19qLDuWitVpSRJgcAVaQkoCaqzYMpwadP4kMuD7tGcUmRt9B7VAJyzYQbR3J5JqiSxUcnKnnAp9Ml+4aAKQ4yKtW/+rFVD1NWrf/VCgCO4GZlHrSGJe2RSz/69KVmweRUz3KTa2Imj2jOajJ7CrOVYYPSlW2jYZDEVBop6alSjGat/Ysn7/H0qQIYwVCKR9etMOdFIDFdAwt9WijfzhHMq4ZT/AIVlkIQC0JH0/wDrUhjhz94qT0zx/OkVzrpoXLprWbVdrEMjLsZ8/wAXr/KpG0m2kRvIlcsMjdxtBHrWd9myMq4NN8mZDlT+R4NMLq3us0JdGIjTyZQxyQ7P8oznAAqtc6bPbIjAM5K7mCg/J9aRb6+j+9IxH+0Mj9anTWZdxMsav6Y45xjP60aDXOyh5ksZwSwPoaYZGPWtiPVreQk3MJOScZAbA44rHbG446Uikr7qwm40oakoouNwHBqcGqPFHNMhwZMGp4aq4Y0oeghwLQag9KgD1KpzHmghqwmaSkzS0yRaUU3NKKAHZpyGmU9Qwx8p59qQEw5WoW61Mg3cFgn1qO6g2ReYJTj25zQAwZJwBk+lXLaG9iIePMYP94/0rMEzBVA6jqfWrCS3ciDY77QeMNinsK5f1CJZ2ia5cKcbSAcA1CZNOtVBWLfnoduf1NUGDBjuzmrMU0MNoDNCJfnIAP0pgO/taZgEghGfpk/pTFXUrrksyDpydv6DmpVub2VdttahEP3SF7fU8U1re7b/AI+bpIVbkqX5/IcUAN/s2OPm5ulU9SoPP+fwqNpYrVwLaZpUPUMOlKI7CMZknklOf4FwP1p0c0RDC208OR3YF/8A9VAFCdxJMzjoTmo6luWZrhy6hWJ5A7VFTAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAfRRRQBdI/0Uf7oqtVo/wDHsP8AdFVR1qIFSCirkzQwPs8kNgDmmefB/wA+6/nVklairF2iARui7d4ziq9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUh6UtIelADKKKKACmU+mUAFacR/dL9KzK0IWwgHtSY0SScxN7D+tMtPvS/7o/mKft3o6qRkjgHvzTIEeN5NykcAc/WjoHUtA1Xf5buMnoSDU46VDc/66H6f1NAMS4GLhCD14PuKieJZGZVXDCprgfPG3vQg/0p/pSDqV7dp4pVChjxwFrSkZjNG6/LIiFgDVaNiGkdiQUbgj34qxPMYnViu5yuCB0680wJrLUZjKq3I+VjgNx1rWrDVQ42nlcNx+VXra4dIvLkUllOM56ihMGi8TWNr8C7FnDANnBH96rt5cMEVoGGQcn3HpWXqSPOhkiQFFbqDk07iMo/d5pACxO0Egc9KmigldC4ChemW/OoMnNAEgAbYBIfm6jsvoKSR1MhCZRCfrxmmBc88hc4zSUAOXbuTOMd896JCjSMY12qTwuc4FJwNwwD6HPSjA2k5FAABk9cCjp6Ugpc5HAoAPpS5FKgU/eJA9h1oYLuO3OM96BDTyc05DsYHAOOx5pDxQcYz3pAOJySQcZpCD60zNKDkYoAXHtmpBG6LnPXsDUaY3cnAq6PujHShjRBGXwd2cemaa2N9TkdsdagZQrdeacH7yHYt5+T8Kqx/wCs/GrJPyH6VWj/ANZ+NMRbzTJPuGlzTXPGD3/CgCkepq3BxED2NQ+WgPzPn2WrEYAUYGBQBFJg3MeemRmln2lyVHFNm/16VPHbtM21cfU9BUz3KRVBb069q1rOwmeIOVQ4/hLVOlhDDANrq8hPLHHH0q7ZABXAOef6VNncTM2SynDE+TtHopzVG5JimKsdpwODXUnpWBrW4XqDaXBUEDGefpQ0NblISn2NO871WopGDMBJEqleoCbf5Ux3XdlV2DHQEn+dKwyctETkrye+KTbDn5ZCp9c1VMlMLU0hGinC4SQNjru5p2CzfMiEeprLBJ6VKnmAY3lR6A0MEXDFGQcxlcd6YYIsZ3lR/tVH5so6P+Yp32iTPKqRSL5pLqN+zvjIwfxppicdVNW9+ACUOCAeKfHLHu+fOPSkNVpIzyMHBorUYRuflHyn1pj20Rx8o/CgtVu6M2jFXWtUJ4JFMNo2OGB/CgtVYsq4qeNiIHBHOeKDbOp7ce9WoLXNlMWBDA8CrVkrsipytaFEE08EetPWMdxVWQFS/BHPFWuVnOW0jLoWBXjsTyaNpxjOD9KoiRx/EalW6cDB5HpUWEWkeWIEbVfIxnHPPWrCX6rG0boynbtRiM7Rj1+tU0vFP3hUqzRP3+goAmaS3LgHJjCjDKecmq0ikwblOQeTk9OakMSMMoRkdCPWo8yRW0kTr9CaQmQ7MJuJwD096kZZWhXcmUXqobFSyJ5s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style=\"width: 100%; max-width: 1317px;\"/>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-847f7bed-959e-4d0f-8d3b-fa582a4b0b7c\");\n",
       "     var overlay = document.getElementById(\"fooverlay-847f7bed-959e-4d0f-8d3b-fa582a4b0b7c\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"847f7bed-959e-4d0f-8d3b-fa582a4b0b7c\" },\n",
       "              subscription: \"847f7bed-959e-4d0f-8d3b-fa582a4b0b7c\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session = fo.launch_app(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b2a6b2-4646-4df4-9417-2194db2ab377",
   "metadata": {},
   "source": [
    "Let’s do some basic exploration of our trajectories. We’ll focus on the first movie by creating a view. The `index` field in our detections captures the trajectory identity; using the `count_values` aggregation gives us a count of the number of frames for each trajectory index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c95b1746-c268-4f9e-a9ea-78268a0a9eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 83 trajectories with min/max lengths of 1/600\n",
      "{39: 376, 50: 528, 69: 280, 65: 580, 58: 303, 26: 600, 36: 516, 25: 600, 49: 218, 74: 68, 63: 111, 81: 1, 64: 111, 28: 448, 57: 467, 22: 39, 21: 80, 45: 491, 37: 279, 29: 537, 76: 155, 31: 600, 33: 266, 11: 530, 34: 248, 46: 600, 2: 56, 18: 600, 62: 304, 79: 52, 3: 575, 5: 600, 77: 12, 44: 370, 67: 349, 60: 402, 30: 600, 23: 586, 7: 581, 83: 1, 47: 600, 24: 600, 59: 323, 66: 600, 61: 550, 6: 600, 38: 351, 35: 230, 71: 60, 16: 62, 43: 479, 32: 298, 73: 121, 75: 62, 56: 278, 1: 600, 20: 322, 55: 43, 40: 529, 78: 25, 15: 600, 9: 600, 41: 373, 82: 1, 13: 526, 10: 600, 53: 498, 54: 369, 72: 152, 8: 43, 27: 570, 48: 251, 14: 306, 80: 4, 19: 549, 4: 600, 70: 206, 17: 600, 51: 600, 12: 493, 52: 533, 42: 361, 68: 315}\n"
     ]
    }
   ],
   "source": [
    "view = dataset.limit(1)\n",
    "traj_counts = view.count_values('frames.gt.detections.index')\n",
    "num_trajs = len(traj_counts)\n",
    "traj_lens = traj_counts.values()\n",
    "print(f'There are {num_trajs} trajectories with min/max lengths of {min(traj_lens)}/{max(traj_lens)}')\n",
    "print(traj_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98561b7-9e96-4566-818c-c5513ab9a01d",
   "metadata": {},
   "source": [
    "Let’s compute a basic statistic like the speed of a trajectory and add it to our dataset. We’ll compute this speed using the centroid of our detection boxes. We can also add this centroid to our dataset as a keypoint label so that these centroids can be visualized.\n",
    "\n",
    "We’ll filter our dataset first so that we only perform this computation for the `person` label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e57866b-4a26-4fb5-b9f9-aa31e63cf177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on /home/dan/Documents/track/MOT17/movies/MOT17-02.mp4\n",
      " frame 100\n",
      " frame 200\n",
      " frame 300\n",
      " frame 400\n",
      " frame 500\n",
      " frame 600\n",
      "Working on /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4\n",
      " frame 100\n",
      " frame 200\n",
      " frame 300\n",
      " frame 400\n",
      " frame 500\n"
     ]
    }
   ],
   "source": [
    "def centroid(bb):\n",
    "    # Compute the centroid of a FiftyOne-style bounding box (in normalized image coordinates)\n",
    "    x = bb[0] + bb[2]/2.0\n",
    "    y = bb[1] + bb[3]/2.0\n",
    "    return x,y\n",
    "    \n",
    "for samp in dataset.filter_labels('frames.gt',F('label')=='person').iter_samples(autosave=True):\n",
    "    print(f'Working on {samp.filepath}')\n",
    "    imw = samp.metadata.frame_width\n",
    "    imh = samp.metadata.frame_height\n",
    "    last_seen = {}\n",
    "    for fno,frame in samp.frames.items():\n",
    "        dets = frame.gt.detections\n",
    "        kps = [fo.Keypoint(points=[centroid(x.bounding_box)],label=x.label,index=x.index) for x in dets]\n",
    "        for kp in kps:\n",
    "            if fno>1 and kp.index in last_seen:\n",
    "                pts0 = last_seen[kp.index]\n",
    "                pts1 = kp.points[0]\n",
    "                dx_px = imw * (pts1[0]-pts0[0])\n",
    "                dy_px = imh * (pts1[1]-pts0[1])\n",
    "                vel = np.hypot( dx_px, dy_px )\n",
    "                kp['speed'] = vel\n",
    "            else:\n",
    "                kp['speed'] = None\n",
    "                \n",
    "        last_seen = {x.index: x.points[0] for x in kps}\n",
    "        frame['gt_kps'] = fo.Keypoints(keypoints=kps)\n",
    "        frame.save()\n",
    "        \n",
    "        if fno % 100 ==0:\n",
    "            print(f' frame {fno}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b994265-b3ad-4dda-8730-4fe7d8a9d866",
   "metadata": {},
   "source": [
    "To visualize our new frame-level field `speed` in the FiftyOne App, let’s call `add_dynamic_frame_fields` on our dataset. This adds `speed` to our dataset’s schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4ce8784-5c2b-4273-9f4f-6ab8bbb48aa2",
   "metadata": {},
   "outputs": [
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style=\"width: 100%; max-width: 1317px;\"/>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-6cb68171-f562-4e5d-a45f-5637012121c6\");\n",
       "     var overlay = document.getElementById(\"fooverlay-6cb68171-f562-4e5d-a45f-5637012121c6\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"6cb68171-f562-4e5d-a45f-5637012121c6\" },\n",
       "              subscription: \"6cb68171-f562-4e5d-a45f-5637012121c6\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.add_dynamic_frame_fields()\n",
    "session.dataset = dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c3efae-3b7b-4ac1-b33a-717046b9604d",
   "metadata": {},
   "source": [
    "We can now filter by `speed` in the sidebar for visualization of detection centroids. We can also view a histogram of speeds across all frames."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b3752c-a4e9-4ed3-b91a-fae7f83bd118",
   "metadata": {},
   "source": [
    "With the per-frame speeds saved on our detections, we can compute statistics like the average or maximum speed for each individual. To this, we’ll filter for each trajectory and use the `values` aggregation to obtain a list of speeds over all frames in its lifetime.\n",
    "\n",
    "Who’s moving quickly and who’s moving slowly? Let’s use our view to take a look at the first movie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fda3ec4a-2265-4c52-9a14-1510bd8c4e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(min av speed is 0.00, achieved by traj 1\n",
      "(max speed achieved is 59.54, achieved by traj 16\n"
     ]
    }
   ],
   "source": [
    "traj_indices = view.distinct('frames.gt.detections.index')\n",
    "traj_avspeeds = []\n",
    "traj_maxspeeds = []\n",
    "for idx in traj_indices:\n",
    "    speeds = view.filter_labels('frames.gt_kps',F('index')==idx).values('frames[].gt_kps.keypoints[].speed')\n",
    "    speeds = speeds[1:] # clip off leading None\n",
    "    if len(speeds)==0:\n",
    "        speeds = [np.nan]\n",
    "    traj_avspeeds.append(np.mean(speeds))\n",
    "    traj_maxspeeds.append(np.max(speeds))\n",
    "    \n",
    "idx = np.nanargmin(traj_avspeeds)\n",
    "print(f'(min av speed is {traj_avspeeds[idx]:.2f}, achieved by traj {traj_indices[idx]}')\n",
    "index_minavspeed = traj_indices[idx]\n",
    "\n",
    "idx = np.nanargmax(traj_maxspeeds)\n",
    "print(f'(max speed achieved is {traj_maxspeeds[idx]:.2f}, achieved by traj {traj_indices[idx]}')\n",
    "index_maxspeed = traj_indices[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0638a42-ca6d-465b-8fd8-0be783da4cd0",
   "metadata": {},
   "source": [
    "Let’s take a look at these two trajectories in the video! To do this, we can use `to_trajectories` to generate a [clip view](https://docs.voxel51.com/user_guide/using_views.html?highlight=clips%20view#clip-views) focusing on single trajectories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24d53691-7f78-4abf-b56f-ae3c58a524ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:    mots17\n",
      "Media type: video\n",
      "Num clips:  2\n",
      "Clip fields:\n",
      "    id:        fiftyone.core.fields.ObjectIdField\n",
      "    sample_id: fiftyone.core.fields.ObjectIdField\n",
      "    filepath:  fiftyone.core.fields.StringField\n",
      "    support:   fiftyone.core.fields.FrameSupportField\n",
      "    tags:      fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:  fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.VideoMetadata)\n",
      "    gt:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.odm.embedded_document.DynamicEmbeddedDocument)\n",
      "Frame fields:\n",
      "    id:           fiftyone.core.fields.ObjectIdField\n",
      "    frame_number: fiftyone.core.fields.FrameNumberField\n",
      "    gt:           fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "View stages:\n",
      "    1. Limit(limit=1)\n",
      "    2. FilterLabels(field='frames.gt', filter={'$in': ['$$this.index', [...]]}, only_matches=True, trajectories=False)\n",
      "    3. ToTrajectories(field='frames.gt', config=None)\n"
     ]
    }
   ],
   "source": [
    "# Create a trajectories view for the vehicles in the dataset\n",
    "trajectories = (\n",
    "view\n",
    ".filter_labels(\"frames.gt\", F(\"index\").is_in([index_minavspeed, index_maxspeed]) )\n",
    ".to_trajectories(\"frames.gt\")\n",
    ")\n",
    "print(trajectories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cbd9e31d-7a74-4d7e-b58e-322b6bded7be",
   "metadata": {},
   "outputs": [
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style=\"width: 100%; max-width: 1317px;\"/>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-e99896d9-6544-46dc-9715-417cbc504308\");\n",
       "     var overlay = document.getElementById(\"fooverlay-e99896d9-6544-46dc-9715-417cbc504308\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"e99896d9-6544-46dc-9715-417cbc504308\" },\n",
       "              subscription: \"e99896d9-6544-46dc-9715-417cbc504308\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session.view = trajectories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a469dfa-2be5-49da-8519-2e199ae19f5b",
   "metadata": {},
   "source": [
    "The slowest trajectory is a person who is sitting down! In retrospect, this is not so surprising. In the original ground-truth labels, this person was probably labeled as ‘Person static’.\n",
    "The fastest pedestrian speeds by the camera in the foreground."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09c4ef5-0039-4a70-9367-9c4739ccd3e5",
   "metadata": {},
   "source": [
    "## Tracking with YOLOv8 + BoT-SORT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d43de3-33ff-4da0-92b7-a1a249c7f6a0",
   "metadata": {},
   "source": [
    "Let’s do some tracking! We’ll use [Ultralytics tracking](https://docs.ultralytics.com/modes/track) to generate some object tracking predictions. This combines object detection using YOLOv8 with a second stage where detections are associated into trajectories.\n",
    "\n",
    "First, we’ll install the ultralytics package. Note that you may need to install `python3.x-dev` on your system in order to complete this installation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e24eae5f-d784-412d-b999-d12da8f4dc99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ultralytics in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (8.0.203)\n",
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      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from requests>=2.23.0->ultralytics) (2023.7.22)\n",
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      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (2.18.1)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (12.1.105)\n",
      "Requirement already satisfied: triton==2.1.0 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from torch>=1.8.0->ultralytics) (2.1.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics) (12.3.52)\n",
      "Requirement already satisfied: six>=1.5 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/dan/anaconda3/envs/dev/lib/python3.9/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5dd0c1a1-1dec-4f31-910e-fbf5a3eb33b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "737caa9d-0140-4e4b-9c0c-03e9137d54e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracking movie /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "video 1/1 (1/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 89.8ms\n",
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      "video 1/1 (390/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 1 handbag, 5.7ms\n",
      "video 1/1 (391/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.2ms\n",
      "video 1/1 (392/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.8ms\n",
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      "video 1/1 (395/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (396/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.2ms\n",
      "video 1/1 (397/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.9ms\n",
      "video 1/1 (398/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.4ms\n",
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      "video 1/1 (400/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.2ms\n",
      "video 1/1 (401/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (402/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.4ms\n",
      "video 1/1 (403/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 1 handbag, 4.6ms\n",
      "video 1/1 (404/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.8ms\n",
      "video 1/1 (405/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.2ms\n",
      "video 1/1 (406/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 1 handbag, 4.4ms\n",
      "video 1/1 (407/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.0ms\n",
      "video 1/1 (408/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 2 handbags, 4.7ms\n",
      "video 1/1 (409/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 2 handbags, 4.2ms\n",
      "video 1/1 (410/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.0ms\n",
      "video 1/1 (411/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.2ms\n",
      "video 1/1 (412/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (413/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.5ms\n",
      "video 1/1 (414/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.1ms\n",
      "video 1/1 (415/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 5.1ms\n",
      "video 1/1 (416/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.3ms\n",
      "video 1/1 (417/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 5.0ms\n",
      "video 1/1 (418/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 5.5ms\n",
      "video 1/1 (419/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.5ms\n",
      "video 1/1 (420/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 1 handbag, 6.3ms\n",
      "video 1/1 (421/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.4ms\n",
      "video 1/1 (422/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 6.2ms\n",
      "video 1/1 (423/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.2ms\n",
      "video 1/1 (424/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (425/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.8ms\n",
      "video 1/1 (426/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.1ms\n",
      "video 1/1 (427/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (428/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.3ms\n",
      "video 1/1 (429/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.6ms\n",
      "video 1/1 (430/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.3ms\n",
      "video 1/1 (431/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (432/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (433/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 1 handbag, 4.8ms\n",
      "video 1/1 (434/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.6ms\n",
      "video 1/1 (435/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.2ms\n",
      "video 1/1 (436/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.0ms\n",
      "video 1/1 (437/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 1 handbag, 5.0ms\n",
      "video 1/1 (438/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (439/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.6ms\n",
      "video 1/1 (440/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.1ms\n",
      "video 1/1 (441/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (442/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (443/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 1 handbag, 5.3ms\n",
      "video 1/1 (444/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 2 handbags, 4.9ms\n",
      "video 1/1 (445/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.4ms\n",
      "video 1/1 (446/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.7ms\n",
      "video 1/1 (447/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.3ms\n",
      "video 1/1 (448/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.5ms\n",
      "video 1/1 (449/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.7ms\n",
      "video 1/1 (450/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.3ms\n",
      "video 1/1 (451/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.8ms\n",
      "video 1/1 (452/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.1ms\n",
      "video 1/1 (453/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.4ms\n",
      "video 1/1 (454/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.8ms\n",
      "video 1/1 (455/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 5.6ms\n",
      "video 1/1 (456/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.5ms\n",
      "video 1/1 (457/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.3ms\n",
      "video 1/1 (458/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.9ms\n",
      "video 1/1 (459/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.7ms\n",
      "video 1/1 (460/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.3ms\n",
      "video 1/1 (461/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.0ms\n",
      "video 1/1 (462/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.8ms\n",
      "video 1/1 (463/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.4ms\n",
      "video 1/1 (464/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.4ms\n",
      "video 1/1 (465/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.3ms\n",
      "video 1/1 (466/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (467/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.7ms\n",
      "video 1/1 (468/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.9ms\n",
      "video 1/1 (469/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.9ms\n",
      "video 1/1 (470/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.4ms\n",
      "video 1/1 (471/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.7ms\n",
      "video 1/1 (472/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 9 persons, 5.2ms\n",
      "video 1/1 (473/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.9ms\n",
      "video 1/1 (474/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.4ms\n",
      "video 1/1 (475/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.6ms\n",
      "video 1/1 (476/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.4ms\n",
      "video 1/1 (477/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.6ms\n",
      "video 1/1 (478/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.0ms\n",
      "video 1/1 (479/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.8ms\n",
      "video 1/1 (480/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.7ms\n",
      "video 1/1 (481/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 6.2ms\n",
      "video 1/1 (482/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.4ms\n",
      "video 1/1 (483/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.3ms\n",
      "video 1/1 (484/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.6ms\n",
      "video 1/1 (485/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 5.6ms\n",
      "video 1/1 (486/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.5ms\n",
      "video 1/1 (487/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.5ms\n",
      "video 1/1 (488/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.9ms\n",
      "video 1/1 (489/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.5ms\n",
      "video 1/1 (490/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (491/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.2ms\n",
      "video 1/1 (492/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.0ms\n",
      "video 1/1 (493/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.9ms\n",
      "video 1/1 (494/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.1ms\n",
      "video 1/1 (495/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 4.5ms\n",
      "video 1/1 (496/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.1ms\n",
      "video 1/1 (497/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.1ms\n",
      "video 1/1 (498/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.6ms\n",
      "video 1/1 (499/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.2ms\n",
      "video 1/1 (500/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.7ms\n",
      "video 1/1 (501/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.6ms\n",
      "video 1/1 (502/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.2ms\n",
      "video 1/1 (503/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 4.4ms\n",
      "video 1/1 (504/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 5.3ms\n",
      "video 1/1 (505/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (506/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.8ms\n",
      "video 1/1 (507/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.1ms\n",
      "video 1/1 (508/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.4ms\n",
      "video 1/1 (509/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (510/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.7ms\n",
      "video 1/1 (511/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.8ms\n",
      "video 1/1 (512/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.3ms\n",
      "video 1/1 (513/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.7ms\n",
      "video 1/1 (514/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 5.5ms\n",
      "video 1/1 (515/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (516/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (517/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 4 persons, 4.9ms\n",
      "video 1/1 (518/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 8 persons, 5.0ms\n",
      "video 1/1 (519/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.2ms\n",
      "video 1/1 (520/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 7 persons, 4.2ms\n",
      "video 1/1 (521/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 5 persons, 5.4ms\n",
      "video 1/1 (522/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.2ms\n",
      "video 1/1 (523/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.3ms\n",
      "video 1/1 (524/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 5.4ms\n",
      "video 1/1 (525/525) /home/dan/Documents/track/MOT17/movies/MOT17-09.mp4: 384x640 6 persons, 4.7ms\n",
      "Speed: 1.5ms preprocess, 5.0ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "view = dataset.skip(1)\n",
    "mov_track = view.first().filepath\n",
    "print(f'Tracking movie {mov_track}')\n",
    "model = ultralytics.YOLO('yolov8n.pt')\n",
    "results = model.track(mov_track,show=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf6ffd4-1710-44e2-8fdc-42b91709e129",
   "metadata": {},
   "source": [
    "Let’s load our tracking results! The tracker has output detections in a csv and we will load them as we did with the groundtruth trajectories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5dd50598-a739-4853-8ecc-5ca38c551d8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frame 0, 5 detections\n",
      "frame 50, 6 detections\n",
      "frame 100, 5 detections\n",
      "frame 150, 5 detections\n",
      "frame 200, 8 detections\n",
      "frame 250, 7 detections\n",
      "frame 300, 6 detections\n",
      "frame 350, 4 detections\n",
      "frame 400, 5 detections\n",
      "frame 450, 6 detections\n",
      "frame 500, 7 detections\n"
     ]
    }
   ],
   "source": [
    "imw = view.first().metadata.frame_width\n",
    "imh = view.first().metadata.frame_height\n",
    "\n",
    "for frm,resobj in enumerate(results):\n",
    "    f = view.first().frames[frm+1]\n",
    "    try:\n",
    "        if f.yolo8 is None:\n",
    "            f.yolo8 = fo.Detections()\n",
    "    except AttributeError:\n",
    "            f['yolo8'] = fo.Detections()\n",
    "        \n",
    "    boxes = resobj.boxes\n",
    "    is_person = boxes.cls.numpy() == 0\n",
    "    bxs = boxes.data.numpy()[is_person]\n",
    "    for b in bxs:\n",
    "        bb = b[:4].tolist()\n",
    "        bb = [bb[0]/imw, bb[1]/imh, (bb[2]-bb[0])/imw, (bb[3]-bb[1])/imh]\n",
    "        idx = int(b[4])\n",
    "        conf = float(b[5])\n",
    "        det = fo.Detection(label='person',bounding_box=bb,index=idx,confidence=conf)\n",
    "        f.yolo8.detections.append(det)\n",
    "        f.save()\n",
    "        \n",
    "    if frm % 50==0:\n",
    "        print(f'frame {frm}, {len(bxs)} detections')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e1c0ff4c-28c7-4fa5-8391-7d489f1464e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
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       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
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       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f1027824ca0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session.view = view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abd7417-475a-43aa-8ca4-d978a65723a2",
   "metadata": {},
   "source": [
    "## Evaluate Our Tracking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6a7566-cd5f-4b4e-b93f-bc4bcaa12215",
   "metadata": {},
   "source": [
    "The tracker has generated detections and also stitched them into trajectories, typically by utilizing some combination of a motion and appearance/reID model. How can we gain insight into the quality of the tracking?\n",
    "\n",
    "One step is to evaluate false positive and false negative detections. This can be done using FiftyOne’s built-in evaluate_detections method, which supports video datasets and will compute evaluation statistics on frame-level detections.\n",
    "\n",
    "For multiple object tracking in video, false positive and false negative detections do not tell the whole story. Various metrics (see eg [TrackEval](https://github.com/JonathonLuiten/TrackEval) can be defined to assess different aspects of this problem. We’ll leave a deep dive into this type of evaluation for another time. Here, we focus on finding identity swaps in the tracking. One type of identity swap occurs when a predicted trajectory jumps from one individual to another. This can occur when two trajectories cross paths and occlude each other, for example. (We use the term “identity swap” informally; more careful definitions can be made, eg in [this article](https://link.springer.com/article/10.1007/s11263-020-01375-2)).\n",
    "\n",
    "One of FiftyOne’s strengths when working with video is that it is easy to switch between data modalities. We can convert full videos to individual clips, for instance, or from videos to images. In this next analysis, we’ll convert our tracked video to individual frames and work with image samples.\n",
    "\n",
    "Since we only included people during tracking, we’ll filter our view to include only the `person` class from the ground_truth.\n",
    "\n",
    "Then, we’ll call `to_frames` which will convert our view to an image/frame-based dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "455eeaa4-9f24-4b75-b502-494368427b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fiftyone import ViewField as F\n",
    "\n",
    "view_frames = view.to_frames(sample_frames=True) \\\n",
    "    .filter_labels('gt',F('label').is_in(['person','person_static']))\n",
    "ds_frames = view_frames.clone()\n",
    "ds_frames.name = view_frames.dataset_name + '_frames_yt'\n",
    "ds_frames.persistent = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb6529fb-e266-48f0-a03b-da312a417616",
   "metadata": {},
   "source": [
    "As a sanity check, how many trajectories do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70cd0fb9-a38c-45ab-8458-b68b75d40685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print(len(ds_frames.distinct('gt.detections.index')))\n",
    "print(len(ds_frames.distinct('yolo8.detections.index')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5d4f98-8f0b-4d6f-b4e6-b7da378de0f3",
   "metadata": {},
   "source": [
    "To investigate identity swaps, predicted and ground truth trajectories must be associated with each other, as their trajectory indices have been generated independently. We’ll do a very simple greedy matching process for illustrative purposes. A more [rigorous approach](https://link.springer.com/article/10.1007/s11263-020-01375-2)  might use the [Hungarian algorithm](https://en.wikipedia.org/wiki/Hungarian_algorithm) to match detections, or even entire trajectories.\n",
    "\n",
    "We’ll take the midpoint of each predicted trajectory and look for the ground truth bounding box that minimizes an L2 distance with the predicted bounding box in that frame. We assign that ground truth index to the predicted trajectory for all of its frames. We use the midpoint of the trajectory rather than its endpoints, as trajectory endpoints may involve more noise due to occlusions\n",
    "or people entering/exiting the scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11a3eb04-5775-423e-a86b-330c4eab8180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame 263: Yolo index 1 -> GT index 29, dist is 0.01\n",
      "Frame 263: Yolo index 2 -> GT index 13, dist is 0.01\n",
      "Frame 263: Yolo index 3 -> GT index 8, dist is 0.02\n",
      "Frame 269: Yolo index 4 -> GT index 13, dist is 0.02\n",
      "Frame 263: Yolo index 5 -> GT index 12, dist is 0.01\n",
      "Frame 281: Yolo index 6 -> GT index 18, dist is 0.01\n",
      "Frame 266: Yolo index 7 -> GT index 21, dist is 0.01\n",
      "Frame 249: Yolo index 8 -> GT index 22, dist is 0.01\n",
      "Frame 243: Yolo index 9 -> GT index 22, dist is 0.01\n",
      "Frame 232: Yolo index 10 -> GT index 62, dist is 0.11\n",
      "Frame 234: Yolo index 11 -> GT index 62, dist is 0.18\n"
     ]
    }
   ],
   "source": [
    "yolo_to_gt = {}\n",
    "idxs = ds_frames.distinct('yolo8.detections.index')\n",
    "for idx in idxs:\n",
    "    frms = ds_frames.filter_labels('yolo8',F('index')==idx,only_matches=True) \\\n",
    "        .values('frame_number')\n",
    "    mid_idx = len(frms) // 2\n",
    "    frm_mid = frms[mid_idx]\n",
    "    \n",
    "    samp_mid = ds_frames.match(F('frame_number')==frm_mid)\n",
    "    assert len(samp_mid)==1\n",
    "    \n",
    "    gt_bbs,gt_idxs = samp_mid.values(['gt.detections.bounding_box', 'gt.detections.index'])\n",
    "    gt_bbs = gt_bbs[0]\n",
    "    gt_idxs = gt_idxs[0]\n",
    "    bbs_gt = np.array(gt_bbs)\n",
    "    bbs_gt[:,2] += bbs_gt[:,0]\n",
    "    bbs_gt[:,3] += bbs_gt[:,1]\n",
    "    \n",
    "    bb_pred = samp_mid.filter_labels('yolo8',F('index')==idx).values('yolo8.detections.bounding_box')\n",
    "    assert len(bb_pred)==1\n",
    "    bb = np.array(bb_pred[0])\n",
    "    bb[:,2] += bb[:,0]\n",
    "    bb[:,3] += bb[:,1]\n",
    "    \n",
    "    dists = np.linalg.norm(bbs_gt-bb,axis=1)\n",
    "    gt_idx_argmin = np.argmin(dists)\n",
    "    gt_idx = gt_idxs[gt_idx_argmin]\n",
    "    yolo_to_gt[idx] = gt_idx\n",
    "    print(f'Frame {frm_mid}: Yolo index {idx} -> GT index {gt_idx}, dist is {dists[gt_idx_argmin]:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73a02e3-6513-4aea-a07a-f999e0f3278d",
   "metadata": {},
   "source": [
    "We now update our labels to account for the trajectory index. In effect, we create classes like `person00` , `person01` , etc based on the tracked individuals. With the classes updated in this way, we can run FiftyOne’s object detection evaluation out of the box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e71a17bd-2fcf-4b84-bbc4-7c61161f8829",
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in ds_frames:\n",
    "    for det in s.gt.detections:\n",
    "        det.label = f'{det.label}{det.index:02}'\n",
    "    for det in s.yolo8.detections:\n",
    "        new_index = yolo_to_gt[det.index]\n",
    "        det.label = f'{det.label}{new_index:02}'\n",
    "    s.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3447414-36b8-472a-bc93-5b8915a1f979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating detections...\n",
      " 100% |█████████████████| 525/525 [6.4s elapsed, 0s remaining, 80.2 samples/s]       \n"
     ]
    }
   ],
   "source": [
    "eval_res = ds_frames.evaluate_detections('yolo8',gt_field='gt',eval_key='eval')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfe7b1d-79f9-4935-bb38-abc860ab4f10",
   "metadata": {},
   "source": [
    "In the [evaluation patches view](https://docs.voxel51.com/user_guide/app.html#viewing-evaluation-patches), false positives often represent an identity swap where the assigned and ground truth individuals (classes) do not match.\n",
    "\n",
    "Let’s examine evaluation patches that are false positives. For simplicity we focus on large detections where the bounding box exceeds a certain size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5798baf9-31af-4580-81dc-db716447e61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "view_eval = ds_frames.to_evaluation_patches(eval_key='eval',config=None)\n",
    "bbox_area = F('bounding_box')[2] * F('bounding_box')[3]\n",
    "large_boxes = F('yolo8.detections').filter(bbox_area>0.05)\n",
    "view_eval_fps_large = view_eval.match(F('type')=='fp').match(large_boxes.length()>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bbc08c9d-1d8e-41ab-9d73-437e333a8c37",
   "metadata": {},
   "outputs": [
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' style=\"width: 100%; max-width: 1317px;\"/>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-086b1fab-6826-4fbd-a4ba-79fa283b4002\");\n",
       "     var overlay = document.getElementById(\"fooverlay-086b1fab-6826-4fbd-a4ba-79fa283b4002\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"086b1fab-6826-4fbd-a4ba-79fa283b4002\" },\n",
       "              subscription: \"086b1fab-6826-4fbd-a4ba-79fa283b4002\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session.view = view_eval_fps_large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cee43540-8798-46ab-883b-92b632325403",
   "metadata": {},
   "outputs": [],
   "source": [
    "SUSP_YOLO_IDXS = [1,29,69] # Indices identified browsing in the FiftyOne App. Tracking results may be stochastic to some extent\n",
    "trajectories = view.filter_labels(\"frames.yolo8\", F(\"index\").is_in(SUSP_YOLO_IDXS)).to_trajectories(\"frames.yolo8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "094c32d4-9cdb-46f5-8612-9ee76b900868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800\"\n",
       "            src=\"http://localhost:5151/?notebook=True&subscription=f1315c63-38cb-43e6-b518-5096304c0df2\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fa6f978efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session.view = trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b8c9f65-34a0-4a5c-9b6e-1e6fde348c45",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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