tracknet_vs_tako.ipynb

{
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  "metadata": {
    "colab": {
      "name": "tracknet_vs_tako.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nyck33/tennisball-tracker/blob/master/tracknet_vs_tako.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glaO5esS8kAH",
        "colab_type": "text"
      },
      "source": [
        "# TrackNet vs Tako\n",
        "\n",
        "Tried Tracknet on fedNadal.mp4 and notice that it picks up some balls on players, lines and other non-balls.  Also, it loses the ball a lot on serves and other high speed shots so want to test it against Tako ball tracker that uses the blob detection and try to combine the two methods to not lose the ball.\n",
        "Furthermore, try to use techniques in other papers to take away lines, players and audience areas.  \n",
        "Finally, need to somehow detect bounces still.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FotVUyHsr9i",
        "colab_type": "code",
        "outputId": "5c36f292-cc27-4a63-b024-88a42bc1bff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        }
      },
      "source": [
        "!pip install numpy\n",
        "!pip install matplotlib\n",
        "!pip install pillow\n",
        "!pip install keras\n",
        "!pip install opencv-python\n",
        "!pip install pydot\n",
        "!pip install h5py\n",
        "!apt-get install graphviz"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (1.16.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python2.7/dist-packages (2.2.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.16.4)\n",
            "Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.5)\n",
            "Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (3.5.4)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.1.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2018.9)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.12.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.5.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.4.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (41.0.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python2.7/dist-packages (4.3.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python2.7/dist-packages (from pillow) (0.46)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python2.7/dist-packages (2.2.4)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python2.7/dist-packages (from keras) (1.0.8)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python2.7/dist-packages (from keras) (1.16.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python2.7/dist-packages (from keras) (1.1.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python2.7/dist-packages (from keras) (2.8.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python2.7/dist-packages (from keras) (1.2.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python2.7/dist-packages (from keras) (3.13)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python2.7/dist-packages (from keras) (1.12.0)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python2.7/dist-packages (3.4.5.20)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python2.7/dist-packages (from opencv-python) (1.16.4)\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python2.7/dist-packages (1.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python2.7/dist-packages (from pydot) (2.4.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python2.7/dist-packages (2.8.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python2.7/dist-packages (from h5py) (1.16.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python2.7/dist-packages (from h5py) (1.12.0)\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.40.1-2).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_gr2UtHIfL9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "d7daa8ea-e41b-4dee-ac4b-63cf6d422dac"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WV9bXQdYO9y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4518a959-e8f6-4c1a-9572-fc04fd2a3ca2"
      },
      "source": [
        "drive.mount(\"/content/drive\", force_remount=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRfm-O22S7Tj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e940e314-ae23-40f2-e678-2d74435c8777"
      },
      "source": [
        "\n",
        "%cd /content/drive/\"My Drive\"/TrackNet-master/Code/TrackNet_Three_Frames_Input \n",
        "!pwd"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/TrackNet-master/Code/TrackNet_Three_Frames_Input\n",
            "/content/drive/My Drive/TrackNet-master/Code/TrackNet_Three_Frames_Input\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "un5074h3scyY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afbc5fc0-eeae-4751-94eb-ff9194c66481"
      },
      "source": [
        "!python predict_video.py  --save_weights_path=weights/model.3 --input_video_path=\"fedNadal.mp4\" --output_video_path=\"fedNadal_TrackNet.mp4\" --n_classes=256 "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0625 11:35:48.574276 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0625 11:35:48.586265 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0625 11:35:48.588393 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0625 11:35:48.600610 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "W0625 11:35:48.600805 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "2019-06-25 11:35:48.605988: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2019-06-25 11:35:48.606271: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f2c79aef40 executing computations on platform Host. Devices:\n",
            "2019-06-25 11:35:48.606310: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2019-06-25 11:35:48.608439: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
            "2019-06-25 11:35:48.706897: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.707469: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f2c79af2c0 executing computations on platform CUDA. Devices:\n",
            "2019-06-25 11:35:48.707523: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2019-06-25 11:35:48.707861: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.708327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-25 11:35:48.708702: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-25 11:35:48.710484: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-25 11:35:48.711948: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-25 11:35:48.712315: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-25 11:35:48.714311: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-25 11:35:48.715691: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-25 11:35:48.719965: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-25 11:35:48.720097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.720542: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.720928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-25 11:35:48.721014: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-25 11:35:48.722115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-25 11:35:48.722152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-25 11:35:48.722163: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-25 11:35:48.722463: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.722918: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-25 11:35:48.723295: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:40] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-06-25 11:35:48.723350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10802 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "W0625 11:35:49.194751 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "W0625 11:35:49.349134 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0625 11:35:50.135451 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "layer24 output shape: 256 360 640\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 9, 360, 640)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 64, 360, 640)      5248      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 64, 360, 640)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 64, 360, 640)      2560      \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 64, 360, 640)      36928     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 64, 360, 640)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 64, 360, 640)      2560      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 64, 180, 320)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 128, 180, 320)     73856     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 128, 180, 320)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 128, 180, 320)     1280      \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 128, 180, 320)     147584    \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 128, 180, 320)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 128, 180, 320)     1280      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 128, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 256, 90, 160)      295168    \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 256, 90, 160)      590080    \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 256, 90, 160)      590080    \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 256, 45, 80)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 512, 45, 80)       1180160   \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 512, 45, 80)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 512, 45, 80)       320       \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 512, 45, 80)       2359808   \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 512, 45, 80)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 512, 45, 80)       320       \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 512, 45, 80)       2359808   \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 512, 45, 80)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 512, 45, 80)       320       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 512, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 256, 90, 160)      1179904   \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 256, 90, 160)      590080    \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 256, 90, 160)      590080    \n",
            "_________________________________________________________________\n",
            "activation_13 (Activation)   (None, 256, 90, 160)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 256, 90, 160)      640       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 256, 180, 320)     0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 128, 180, 320)     295040    \n",
            "_________________________________________________________________\n",
            "activation_14 (Activation)   (None, 128, 180, 320)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 128, 180, 320)     1280      \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 128, 180, 320)     147584    \n",
            "_________________________________________________________________\n",
            "activation_15 (Activation)   (None, 128, 180, 320)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 128, 180, 320)     1280      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_3 (UpSampling2 (None, 128, 360, 640)     0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 64, 360, 640)      73792     \n",
            "_________________________________________________________________\n",
            "activation_16 (Activation)   (None, 64, 360, 640)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_16 (Batc (None, 64, 360, 640)      2560      \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 64, 360, 640)      36928     \n",
            "_________________________________________________________________\n",
            "activation_17 (Activation)   (None, 64, 360, 640)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 64, 360, 640)      2560      \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 256, 360, 640)     147712    \n",
            "_________________________________________________________________\n",
            "activation_18 (Activation)   (None, 256, 360, 640)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_18 (Batc (None, 256, 360, 640)     2560      \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 256, 230400)       0         \n",
            "_________________________________________________________________\n",
            "permute_1 (Permute)          (None, 230400, 256)       0         \n",
            "_________________________________________________________________\n",
            "activation_19 (Activation)   (None, 230400, 256)       0         \n",
            "=================================================================\n",
            "Total params: 10,722,560\n",
            "Trainable params: 10,711,200\n",
            "Non-trainable params: 11,360\n",
            "_________________________________________________________________\n",
            "W0625 11:35:50.897650 139799186335616 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "OpenCV: FFMPEG: tag 0x44495658/'XVID' is not supported with codec id 12 and format 'mp4 / MP4 (MPEG-4 Part 14)'\n",
            "OpenCV: FFMPEG: fallback to use tag 0x7634706d/'mp4v'\n",
            "2019-06-25 11:35:52.352996: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1xyfua39RCj",
        "colab_type": "text"
      },
      "source": [
        "# Tako Ball Tracker "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIJH1isJ8gLH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#tennis ball tracker from https://qiita.com/otakoma/items/04216c60fa31eae60947\n",
        "#right now overdetects lines, nets, the line for the first row of the audience, net, player, other people,\n",
        "\n",
        "import cv2\n",
        "import sys\n",
        "import numpy as np\n",
        "\n",
        "#dilate useful in joining broken parts of an object after erasing noise with erosion\n",
        "def dilation(dilationSize, kernelSize, img):  # 膨張した画像にして返す, return as an expanded image\n",
        "    kernel = np.ones((kernelSize, kernelSize), np.uint8)\n",
        "    #get a rectangulr kernel for use in dilation\n",
        "    element = cv2.getStructuringElement( \n",
        "        cv2.MORPH_RECT, (2 * dilationSize + 1, 2 * dilationSize + 1), (dilationSize, dilationSize))\n",
        "    #dilate: if all b, get b, else w, ie. bleed white into non-white areas\n",
        "    dilation_img = cv2.dilate(img, kernel, element)\n",
        "    #print(img, dilation_img)\n",
        "    return dilation_img\n",
        "\n",
        "#find ball in blob, returns a dilated img and array of ball pos\n",
        "#how is it discerning ball from players and other objects?\n",
        "def detect(gray_diff, thresh_diff=30, dilationSize=9, kernelSize=20):  # 一定面積以上の物体を検出\n",
        "    #change all pixels in grayscale image to white or black based on threshold, ie. dark grey is black, light grey is \n",
        "    #white \n",
        "    retval, black_diff = cv2.threshold(\n",
        "        gray_diff, thresh_diff, 255, cv2.THRESH_BINARY)  # 2値化\n",
        "    #bleed white into non-white\n",
        "    dilation_img = dilation(dilationSize, kernelSize, black_diff)  # 膨張処理 dilation, dilates ball\n",
        "    #print(\"28 dilation_img {}\".format(dilation_img))\n",
        "    img = dilation_img.copy()\n",
        "    #findContours finds, boundaries of image, \n",
        "    #outputs modified image, contours and hierarchy(each contour is array of coordinates of boundary pts)\n",
        "    #RETR_EXTERNAL gets the outer most contour of the shape\n",
        "    #CHAIN_APPROX_SIMPLE compresses contour to save memory, ie. 4 only saves 4 corners of rectangle\n",
        "    contours, hierarchy = cv2.findContours(\n",
        "        dilation_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 境界線検出 \n",
        "    #print(\"36 findContours: {} \\n {} \\n {}\".format(image, contours, hierarchy))\n",
        "    #array of ball pos, x, y \n",
        "    ball_pos = []\n",
        "\n",
        "    for i in range(len(contours)):  # 重心位置を計算, calculate the center of the object ball\n",
        "        count = len(contours[i])\n",
        "        area = cv2.contourArea(contours[i])  # 面積計算\n",
        "        #print(\"43 area\".format(area))\n",
        "        x, y = 0.0, 0.0\n",
        "        for j in range(count):\n",
        "            x += contours[i][j][0][0]\n",
        "            y += contours[i][j][0][1]\n",
        "\n",
        "        x /= count\n",
        "        y /= count\n",
        "        x = int(x)\n",
        "        y = int(y)\n",
        "        ball_pos.append([x, y]) #adding the x,y of contour of the ball?\n",
        "\n",
        "    return ball_pos, img\n",
        "\n",
        "#show the circle bounding box\n",
        "def displayCircle(image, ballList, thickness=5):\n",
        "    for i in range(len(ballList)):\n",
        "        x = int(ballList[i][0])\n",
        "        y = int(ballList[i][1])\n",
        "        cv2.circle(image, (x, y), 10, (0, 0, 255), thickness)\n",
        "        #print(\"63 image {}\".format(image))\n",
        "    return image\n",
        "\n",
        "#resize images\n",
        "def resizeImage(image, w=2, h=2):\n",
        "    height = image.shape[0]\n",
        "    width = image.shape[1]\n",
        "    resizedImage = cv2.resize(image, (int(width / w), int(height / h)))\n",
        "    return resizedImage\n",
        "\n",
        "#resizing blob image to same as color image to concat\n",
        "#Make the binarized image the same as the color array size\n",
        "def blackToColor(bImage):\n",
        "    #print(\"76 bImage {}\".format(bImage))\n",
        "    colorImage = np.array((bImage, bImage, bImage))\n",
        "    colorImage = colorImage.transpose(1, 2, 0)\n",
        "    #print(\"79 colorImage {}\".format(colorImage))\n",
        "    return colorImage\n",
        "\n",
        "\n",
        "def run(input_video_path, output_video_path):\n",
        "    video = cv2.VideoCapture(input_video_path)  # read video file\n",
        "    #fourcc = cv2.VideoWriter_fourcc(*'MJPG')\n",
        "    fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
        "\n",
        "    if not video.isOpened():  # ファイルがオープンできない場合の処理.\n",
        "        print(\"Could not open video\")\n",
        "        sys.exit()\n",
        "\n",
        "    vidw = video.get(cv2.CAP_PROP_FRAME_WIDTH)\n",
        "    vidh = video.get(cv2.CAP_PROP_FRAME_HEIGHT)\n",
        "    out = cv2.VideoWriter(output_video_path, fourcc, 20.0, #fps\n",
        "                          (int(vidw), int(vidh)))  # 出力先のファイルを開く\n",
        "\n",
        "    ok, frame = video.read()  # 最初のフレームを読み込む\n",
        "    if not ok:\n",
        "        print('Cannot read video file')\n",
        "        sys.exit()\n",
        "    #resize * 2##############################\n",
        "    height, width, layers = frame.shape\n",
        "    new_h = height * 2\n",
        "    new_w = width * 2\n",
        "    frame = cv2.resize(frame, (new_w, new_h))\n",
        "\n",
        "    frame_pre = frame.copy()\n",
        "\n",
        "    while True:\n",
        "        ok, frame = video.read()  # フレームを読み込む\n",
        "        if not ok:\n",
        "            break\n",
        "        #resize * 2##############################\n",
        "        height, width, layers = frame.shape\n",
        "        new_h = height * 2\n",
        "        new_w = width * 2\n",
        "        frame = cv2.resize(frame, (new_w, new_h))\n",
        "        frame_next = frame.copy()\n",
        "\n",
        "        #difference frames\n",
        "        color_diff = cv2.absdiff(frame_next, frame_pre)  # フレーム間の差分計算\n",
        "        #print(\"color_diff {}\".format(color_diff))\n",
        "        gray_diff = cv2.cvtColor(color_diff, cv2.COLOR_BGR2GRAY)  # グレースケール変換\n",
        "        #print(\"gray_diff {}\".format(gray_diff))\n",
        "\n",
        "        #turn the grayscale diff frame into b and w\n",
        "        retval, black_diff = cv2.threshold(\n",
        "            gray_diff, 30, 255, cv2.THRESH_BINARY)#ThreshBinary is black if higher, else white \n",
        "        #print(\"black_diff {}\".format(black_diff))\n",
        "        #detect ball\n",
        "        ball, dilation_img = detect(gray_diff)\n",
        "        #print(\"ball {} \\n\\n dilation_img {}\".format(ball, dilation_img))\n",
        "        #takes one frame and puts circles around ball\n",
        "        frame = displayCircle(frame, ball, 2)  # 丸で加工\n",
        "        #print(\"frame with circle {}\".format(frame))\n",
        "\n",
        "        #Make the binarized image the same as the color array size\n",
        "        cImage = blackToColor(dilation_img)  # 2値化画像をカラーの配列サイズと同じにする\n",
        "        #print(\"141 dilation_img {} \\n after: cImage {}\".format(dilation_img, cImage))\n",
        "        im1 = resizeImage(frame, 2, 2) \n",
        "        im2 = resizeImage(cImage, 2, 2)\n",
        "        im_h = cv2.hconcat([im1, im2])  # 画像を横方向に連結\n",
        "\n",
        "        #cv2.imshow(\"Tracking\", im_h)  # フレームを画面表示\n",
        "        out.write(im_h)\n",
        "\n",
        "        frame_pre = frame_next.copy()  # 次のフレームの読み込み\n",
        "\n",
        "        k = cv2.waitKey(1) & 0xff  # ESCを押したら中止\n",
        "        if k == 27:\n",
        "            break\n",
        "\n",
        "    video.release()\n",
        "    out.release()\n",
        "    cv2.destroyAllWindows()\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    inputFile=\"fedNadal.mp4\"\n",
        "    outputFile=\"fedNadal-tako.avi\"\n",
        "    run(inputFile, outputFile)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}