Yolo_object_tracking.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Yolo object tracking",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbaqYUe42J0T",
        "colab_type": "code",
        "outputId": "6317fbb6-7396-482c-9809-aa433012e923",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "!git clone https://github.com/snehitvaddi/YOLOv3-Object-Tracking"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'YOLOv3-Object-Tracking'...\n",
            "remote: Enumerating objects: 9, done.\u001b[K\n",
            "remote: Counting objects:  11% (1/9)\u001b[K\rremote: Counting objects:  22% (2/9)\u001b[K\rremote: Counting objects:  33% (3/9)\u001b[K\rremote: Counting objects:  44% (4/9)\u001b[K\rremote: Counting objects:  55% (5/9)\u001b[K\rremote: Counting objects:  66% (6/9)\u001b[K\rremote: Counting objects:  77% (7/9)\u001b[K\rremote: Counting objects:  88% (8/9)\u001b[K\rremote: Counting objects: 100% (9/9)\u001b[K\rremote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 61 (delta 4), reused 0 (delta 0), pack-reused 52\u001b[K\n",
            "Unpacking objects: 100% (61/61), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0-q4kYUqP7k",
        "colab_type": "code",
        "outputId": "ad0d0694-3c7f-4367-8306-e8e0b8ed85f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd yolov3_deepsort/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/yolov3_deepsort\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUfoaS0QqQVX",
        "colab_type": "code",
        "outputId": "e4374fa9-3c7a-4b0a-bf90-8e0994b560e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conda-cpu.yml  LICENSE\t\t  README.md\t\tweights\n",
            "conda-gpu.yml  load_weights.py\t  requirements-gpu.txt\tyolov3_tf2\n",
            "data\t       model_data\t  requirements.txt\n",
            "deep_sort      object_tracker.py  tools\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wi5iMQ_ZrUPZ",
        "colab_type": "code",
        "outputId": "9aaaad26-699f-4c49-8710-d5c40d5bacfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/yolov3_deepsort\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pHY26No8aCt",
        "colab_type": "code",
        "outputId": "48afbf36-e3a4-4cdf-8f52-56485451935d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pip install -r requirements.txt"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/16/b07e3f7a4a024b47918f7018967eb984b0c542458a6141d8c48515aa81d4/tensorflow-2.0.1-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
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            "Collecting opencv-python==4.1.1.26\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5e/7e/bd5425f4dacb73367fddc71388a47c1ea570839197c2bcad86478e565186/opencv_python-4.1.1.26-cp36-cp36m-manylinux1_x86_64.whl (28.7MB)\n",
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            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n",
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            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
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            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.1->-r requirements.txt (line 1)) (1.27.2)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.1->-r requirements.txt (line 1)) (1.1.0)\n",
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            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
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            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.1->-r requirements.txt (line 1)) (1.0.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.1->-r requirements.txt (line 1)) (0.4.1)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.1->-r requirements.txt (line 1)) (2.21.0)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->seaborn->-r requirements.txt (line 6)) (2.4.6)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.22.0->seaborn->-r requirements.txt (line 6)) (2018.9)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.1->-r requirements.txt (line 1)) (2019.11.28)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.1->-r requirements.txt (line 1)) (2.8)\n",
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            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=c7e952ae7c5aceac02f16e15500c0d779396cbedf57334a181eaec64e881f7a2\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, gast, tensorflow, opencv-python\n",
            "  Found existing installation: tensorboard 2.2.0\n",
            "    Uninstalling tensorboard-2.2.0:\n",
            "      Successfully uninstalled tensorboard-2.2.0\n",
            "  Found existing installation: tensorflow-estimator 2.2.0rc0\n",
            "    Uninstalling tensorflow-estimator-2.2.0rc0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0rc0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorflow 2.2.0rc2\n",
            "    Uninstalling tensorflow-2.2.0rc2:\n",
            "      Successfully uninstalled tensorflow-2.2.0rc2\n",
            "  Found existing installation: opencv-python 4.1.2.30\n",
            "    Uninstalling opencv-python-4.1.2.30:\n",
            "      Successfully uninstalled opencv-python-4.1.2.30\n",
            "Successfully installed gast-0.2.2 opencv-python-4.1.1.26 tensorboard-2.0.2 tensorflow-2.0.1 tensorflow-estimator-2.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFxN_6eYsOki",
        "colab_type": "code",
        "outputId": "81663bea-66b5-4d25-d1c0-ad3a59a66c71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "# Run this only id weights fike does not exist in 'data/weights' folder. \n",
        "!wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-31 19:11:15--  https://pjreddie.com/media/files/yolov3.weights\n",
            "Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n",
            "Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248007048 (237M) [application/octet-stream]\n",
            "Saving to: ‘weights/yolov3.weights’\n",
            "\n",
            "weights/yolov3.weig 100%[===================>] 236.52M   263KB/s    in 13m 58s \n",
            "\n",
            "2020-03-31 19:25:14 (289 KB/s) - ‘weights/yolov3.weights’ saved [248007048/248007048]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gu09M9G5pg63",
        "colab_type": "code",
        "outputId": "12164efd-a7a2-4d63-ac6e-eb406abbb7da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        }
      },
      "source": [
        "!python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n",
            "2020-04-02 11:17:42.361791: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2020-04-02 11:17:42.365731: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2020-04-02 11:17:42.365957: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3136bc0 executing computations on platform Host. Devices:\n",
            "2020-04-02 11:17:42.365991: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version\n",
            "Model: \"yolov3_tiny\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "yolo_darknet (Model)            ((None, None, None,  6298480     input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "yolo_conv_0 (Model)             (None, None, None, 2 263168      yolo_darknet[1][1]               \n",
            "__________________________________________________________________________________________________\n",
            "yolo_conv_1 (Model)             (None, None, None, 3 33280       yolo_conv_0[1][0]                \n",
            "                                                                 yolo_darknet[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "yolo_output_0 (Model)           (None, None, None, 3 1312511     yolo_conv_0[1][0]                \n",
            "__________________________________________________________________________________________________\n",
            "yolo_output_1 (Model)           (None, None, None, 3 951295      yolo_conv_1[1][0]                \n",
            "__________________________________________________________________________________________________\n",
            "yolo_boxes_0 (Lambda)           ((None, None, None,  0           yolo_output_0[1][0]              \n",
            "__________________________________________________________________________________________________\n",
            "yolo_boxes_1 (Lambda)           ((None, None, None,  0           yolo_output_1[1][0]              \n",
            "__________________________________________________________________________________________________\n",
            "yolo_nms (Lambda)               ((None, 100, 4), (No 0           yolo_boxes_0[0][0]               \n",
            "                                                                 yolo_boxes_0[0][1]               \n",
            "                                                                 yolo_boxes_0[0][2]               \n",
            "                                                                 yolo_boxes_1[0][0]               \n",
            "                                                                 yolo_boxes_1[0][1]               \n",
            "                                                                 yolo_boxes_1[0][2]               \n",
            "==================================================================================================\n",
            "Total params: 8,858,734\n",
            "Trainable params: 8,852,366\n",
            "Non-trainable params: 6,368\n",
            "__________________________________________________________________________________________________\n",
            "I0402 11:17:43.764951 139893918091136 load_weights.py:19] model created\n",
            "I0402 11:17:43.765574 139893918091136 utils.py:47] yolo_darknet/conv2d bn\n",
            "I0402 11:17:43.766936 139893918091136 utils.py:47] yolo_darknet/conv2d_1 bn\n",
            "I0402 11:17:43.768332 139893918091136 utils.py:47] yolo_darknet/conv2d_2 bn\n",
            "I0402 11:17:43.769513 139893918091136 utils.py:47] yolo_darknet/conv2d_3 bn\n",
            "I0402 11:17:43.770890 139893918091136 utils.py:47] yolo_darknet/conv2d_4 bn\n",
            "I0402 11:17:43.773292 139893918091136 utils.py:47] yolo_darknet/conv2d_5 bn\n",
            "I0402 11:17:43.780423 139893918091136 utils.py:47] yolo_darknet/conv2d_6 bn\n",
            "I0402 11:17:43.817667 139893918091136 utils.py:47] yolo_conv_0/conv2d_7 bn\n",
            "I0402 11:17:43.820400 139893918091136 utils.py:47] yolo_output_0/conv2d_8 bn\n",
            "I0402 11:17:43.826788 139893918091136 utils.py:47] yolo_output_0/conv2d_9 bias\n",
            "I0402 11:17:43.828109 139893918091136 utils.py:47] yolo_conv_1/conv2d_10 bn\n",
            "I0402 11:17:43.829282 139893918091136 utils.py:47] yolo_output_1/conv2d_11 bn\n",
            "I0402 11:17:43.834196 139893918091136 utils.py:47] yolo_output_1/conv2d_12 bias\n",
            "I0402 11:17:43.835077 139893918091136 load_weights.py:22] weights loaded\n",
            "I0402 11:17:44.003509 139893918091136 load_weights.py:26] sanity check passed\n",
            "I0402 11:17:44.054806 139893918091136 load_weights.py:29] weights saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "962cu7OZrYZQ",
        "colab_type": "code",
        "outputId": "d15024e4-997f-4bbb-a7b5-2513e9a6838d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "!python object_tracker.py --video ./data/video/traffic.mp4 --output ./data/video/traffic-results2.avi --weights ./weights/yolov3-tiny.tf --tiny\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n",
            "2020-04-02 11:19:24.251284: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2020-04-02 11:19:24.255508: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2020-04-02 11:19:24.255711: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x244a840 executing computations on platform Host. Devices:\n",
            "2020-04-02 11:19:24.255745: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version\n",
            "I0402 11:19:25.725575 140340618041216 object_tracker.py:55] weights loaded\n",
            "I0402 11:19:25.725969 140340618041216 object_tracker.py:58] classes loaded\n",
            ": cannot connect to X server \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_AkWSpKpkTCS",
        "colab_type": "text"
      },
      "source": [
        "## <strong>Playing of result.avi file.</strong>\n",
        "* Colab cannot play .avi file so we nees to convert .avi file to .mp4.\n",
        "* you can do this by below code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaqErT2bk2Ww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ffmpeg -i ./data/video/traffic-results2.avi ./data/video/output.mp4\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oP7PUTP04cp5",
        "colab": {}
      },
      "source": [
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "mp4 = open('./data/video/output.mp4','rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"\"\"\n",
        "<video controls>\n",
        "      <source src=\"%s\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\" % data_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWaDTCvSnuh6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}