deepsort_track.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1Cmg3mpfeVE-Z877-TAYNcftT-H5agUUP",
      "authorship_tag": "ABX9TyNT3gqAcRWKpmZtjeKiqd6i",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hebrotem/Soccer-Tracking-with-DeepSORT/blob/main/deepsort_track.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dT8nOX6Ak7oF",
        "outputId": "d4638317-5e7e-4b14-c85d-221fa0fe2032"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 15005, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 15005 (delta 0), reused 0 (delta 0), pack-reused 15002\u001b[K\n",
            "Receiving objects: 100% (15005/15005), 14.09 MiB | 31.63 MiB/s, done.\n",
            "Resolving deltas: 100% (10291/10291), done.\n"
          ]
        }
      ],
      "source": [
        "!mkdir ./yolov5_deepsort\n",
        "!git clone https://github.com/ultralytics/yolov5.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ./yolov5\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CK9M37IOlMeh",
        "outputId": "8ea6b356-3c04-4be0-d669-9e391f2b57a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n",
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            "Installing collected packages: smmap, jedi, thop, gitdb, gitpython\n",
            "Successfully installed gitdb-4.0.10 gitpython-3.1.30 jedi-0.18.2 smmap-5.0.0 thop-0.1.1.post2209072238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/nwojke/deep_sort.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8S7Hg0zlTW5",
        "outputId": "94fd2a3b-e2e0-4d59-86c4-858b6d10afae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'deep_sort'...\n",
            "remote: Enumerating objects: 141, done.\u001b[K\n",
            "Receiving objects:   0% (1/141)\rReceiving objects:   1% (2/141)\rReceiving objects:   2% (3/141)\rReceiving objects:   3% (5/141)\rReceiving objects:   4% (6/141)\rReceiving objects:   5% (8/141)\rReceiving objects:   6% (9/141)\rReceiving objects:   7% (10/141)\rReceiving objects:   8% (12/141)\rReceiving objects:   9% (13/141)\rReceiving objects:  10% (15/141)\rReceiving objects:  11% (16/141)\rReceiving objects:  12% (17/141)\rReceiving objects:  13% (19/141)\rReceiving objects:  14% (20/141)\rReceiving objects:  15% (22/141)\rReceiving objects:  16% (23/141)\rReceiving objects:  17% (24/141)\rReceiving objects:  18% (26/141)\rReceiving objects:  19% (27/141)\rReceiving objects:  20% (29/141)\rReceiving objects:  21% (30/141)\rReceiving objects:  22% (32/141)\rReceiving objects:  23% (33/141)\rReceiving objects:  24% (34/141)\rReceiving objects:  25% (36/141)\rReceiving objects:  26% (37/141)\rReceiving objects:  27% (39/141)\rReceiving objects:  28% (40/141)\rReceiving objects:  29% (41/141)\rReceiving objects:  30% (43/141)\rReceiving objects:  31% (44/141)\rReceiving objects:  32% (46/141)\rReceiving objects:  33% (47/141)\rReceiving objects:  34% (48/141)\rReceiving objects:  35% (50/141)\rReceiving objects:  36% (51/141)\rReceiving objects:  37% (53/141)\rReceiving objects:  38% (54/141)\rReceiving objects:  39% (55/141)\rremote: Total 141 (delta 0), reused 0 (delta 0), pack-reused 141\u001b[K\n",
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            "Resolving deltas:   0% (0/78)\rResolving deltas:   3% (3/78)\rResolving deltas:   5% (4/78)\rResolving deltas:   8% (7/78)\rResolving deltas:  11% (9/78)\rResolving deltas:  23% (18/78)\rResolving deltas:  25% (20/78)\rResolving deltas:  39% (31/78)\rResolving deltas:  41% (32/78)\rResolving deltas:  43% (34/78)\rResolving deltas:  51% (40/78)\rResolving deltas:  53% (42/78)\rResolving deltas:  56% (44/78)\rResolving deltas:  61% (48/78)\rResolving deltas:  62% (49/78)\rResolving deltas: 100% (78/78)\rResolving deltas: 100% (78/78), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5/\n",
        "!python detect_track.py --weights yolov5m.pt  --img 640  --source '/content/football-video.mp4' --classes 0 32 --line-thickness 1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEd8g8RToW35",
        "outputId": "75005bce-c8cc-4479-8848-10e5d338d30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n",
            "python3: can't open file 'detect_track.py': [Errno 2] No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "9dam7bRZ12vL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Another repo"
      ],
      "metadata": {
        "id": "aKDQXpTs1zGK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ./yolov5_deepsort\n",
        "%cd /content/yolov5_deepsort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOJrl_Ie8K5Q",
        "outputId": "b73b455e-80ba-49c1-abc6-cba1a86c47c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5_deepsort\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CGJbpts_tWM",
        "outputId": "7901d072-e72c-4427-95bd-9cc507f237e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Yolov5_DeepSort_Pytorch'...\n",
            "remote: Enumerating objects: 4408, done.\u001b[K\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5_deepsort/Yolov5_DeepSort_Pytorch\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qeSXqfi_19v",
        "outputId": "4e2a4487-0658-4452-833a-f9f270788b5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: lap, filterpy\n",
            "  Building wheel for lap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lap: filename=lap-0.4.0-cp38-cp38-linux_x86_64.whl size=1679736 sha256=b37b4902e8ee3d4e468eee502d9032f1ac21c4f7c268e0eb2e398ea5d086d30c\n",
            "  Stored in directory: /root/.cache/pip/wheels/86/ec/8a/2baa6fb3ea3a3bdc40a9c2c4b8957b5b544f207531f7a78a89\n",
            "  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=b8df048e52ff2f23cb3c4eb80cdcc7ada5f9363e8c37c569d37ec5cbc3649ccf\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/f6/cb/40331472edf4fd399b8cad02973c6acbdf26898342928327fe\n",
            "Successfully built lap filterpy\n",
            "Installing collected packages: lap, urllib3, sentry-sdk, filterpy, ultralytics\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed filterpy-1.4.5 lap-0.4.0 sentry-sdk-1.14.0 ultralytics-8.0.20 urllib3-1.26.14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 track.py --source '/content/vid_001 (2).mp4' --save-vid --yolo-weights yolov5m.pt --img 1280"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJGKw5skAK0R",
        "outputId": "e05643ce-cf7e-4344-c807-6605180941cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrack: \u001b[0myolo_weights=[PosixPath('yolov5m.pt')], reid_weights=/content/yolov5_deepsort/Yolov5_DeepSort_Pytorch/weights/osnet_x0_25_msmt17.pt, tracking_method=bytetrack, tracking_config=trackers/bytetrack/configs/bytetrack.yaml, source=/content/vid_001 (2).mp4, imgsz=[1280, 1280], conf_thres=0.5, iou_thres=0.5, max_det=1000, device=, show_vid=False, save_txt=False, save_conf=False, save_crop=False, save_trajectories=False, save_vid=True, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/track, name=exp, exist_ok=False, line_thickness=2, hide_labels=False, hide_conf=False, hide_class=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
            "Ultralytics YOLOv8.0.20 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Traceback (most recent call last):\n",
            "  File \"track.py\", line 365, in <module>\n",
            "    main(opt)\n",
            "  File \"track.py\", line 360, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"track.py\", line 104, in run\n",
            "    model = AutoBackend(yolo_weights, device=device, dnn=dnn, fp16=half)\n",
            "  File \"/content/yolov5_deepsort/Yolov5_DeepSort_Pytorch/yolov8/ultralytics/nn/autobackend.py\", line 74, in __init__\n",
            "    model = attempt_load_weights(weights if isinstance(weights, list) else w,\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/ultralytics/nn/tasks.py\", line 348, in attempt_load_weights\n",
            "    ckpt = torch_safe_load(w)  # load ckpt\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/ultralytics/nn/tasks.py\", line 332, in torch_safe_load\n",
            "    return torch.load(file, map_location='cpu')  # load\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 771, in load\n",
            "    with _open_file_like(f, 'rb') as opened_file:\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 270, in _open_file_like\n",
            "    return _open_file(name_or_buffer, mode)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 251, in __init__\n",
            "    super(_open_file, self).__init__(open(name, mode))\n",
            "FileNotFoundError: [Errno 2] No such file or directory: 'yolov5m.pt'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 track.py --source 0 --show-vid "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-c0tv3VaeIU",
        "outputId": "1eb4f89f-9aed-43ca-8386-f251f534e8b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrack: \u001b[0myolo_weights=/content/yolov5_deepsort/Yolov5_DeepSort_Pytorch/weights/yolov5s-seg.pt, reid_weights=/content/yolov5_deepsort/Yolov5_DeepSort_Pytorch/weights/osnet_x0_25_msmt17.pt, tracking_method=strongsort, source=0, imgsz=[640, 640], conf_thres=0.5, iou_thres=0.5, max_det=1000, device=, show_vid=True, save_txt=False, save_conf=False, save_crop=False, save_trajectories=False, save_vid=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/track, name=exp, exist_ok=False, line_thickness=2, hide_labels=False, hide_conf=False, hide_class=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
            "YOLOv5 🚀 2022-12-21 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
            "[ WARN:0@5.664] global /io/opencv/modules/videoio/src/cap_v4l.cpp (902) open VIDEOIO(V4L2:/dev/video0): can't open camera by index\n",
            "Traceback (most recent call last):\n",
            "  File \"track.py\", line 342, in <module>\n",
            "    main(opt)\n",
            "  File \"track.py\", line 337, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"track.py\", line 107, in run\n",
            "    dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n",
            "  File \"/content/yolov5_deepsort/Yolov5_DeepSort_Pytorch/yolov5/utils/dataloaders.py\", line 366, in __init__\n",
            "    assert cap.isOpened(), f'{st}Failed to open {s}'\n",
            "AssertionError: 1/1: 0... Failed to open 0\n"
          ]
        }
      ]
    }
  ]
}