tennisball_tracker.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tennisball-tracker.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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/tennisball_tracker.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umoHAo6HHr70",
        "colab_type": "text"
      },
      "source": [
        "# Transfer Learning using balloon and shapes\n",
        "\n",
        "upload sample2.zip and mrcnn.zip and unzip here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOJ7L_O4KH5Q",
        "colab_type": "code",
        "outputId": "0b98df5a-7c53-4b4e-fc79-557eed3fce60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get all files from git or Drive\n",
        "#!git clone https://github.com/nyck33/tennisball-tracker.git\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly9PEfCakNdK",
        "colab_type": "code",
        "outputId": "e0db6edb-55f3-4397-9fe6-d22165111d34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Remount drive if necessary\n",
        "\n",
        "drive.mount(\"/content/drive\", force_remount=True)\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/gdrive')\n",
        "\n",
        "#!unzip mrcnn.zip"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXC4cb6ZvBkY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Weights are in Drive\n",
        "#!wget \"https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9v8s8o_fWx0n",
        "colab_type": "code",
        "outputId": "cc3be601-7196-4b46-affb-cff8b30b8352",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "'''\n",
        "#cheap little test code\n",
        "f = open('balloon.py', 'r')\n",
        "contents = f.read()\n",
        "contents = str(contents)\n",
        "print(contents[:10])\n",
        "'''\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n#cheap little test code\\nf = open('balloon.py', 'r')\\ncontents = f.read()\\ncontents = str(contents)\\nprint(contents[:10])\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXMUDjg7Hr76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import itertools\n",
        "import math\n",
        "import logging\n",
        "import json\n",
        "import re\n",
        "import random\n",
        "from collections import OrderedDict\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import matplotlib.lines as lines\n",
        "from matplotlib.patches import Polygon\n",
        "\n",
        "# Root directory of the project\n",
        "#ROOT_DIR = os.path.abspath(\"../../\")\n",
        "ROOT_DIR = os.path.abspath(\"drive/My Drive/matterport-balltracker/\")\n",
        "\n",
        "# Import Mask RCNN\n",
        "sys.path.append(ROOT_DIR)  # To find local version of the library\n",
        "from mrcnn import utils\n",
        "from mrcnn import visualize\n",
        "from mrcnn.visualize import display_images\n",
        "import mrcnn.model as modellib\n",
        "from mrcnn.model import log\n",
        "\n",
        "from balloon import BalloonConfig, BalloonDataset\n",
        "\n",
        "%matplotlib inline \n",
        "\n",
        "# Directory to save logs and trained model\n",
        "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
        "\n",
        "# Local path to trained weights file\n",
        "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
        "# Download COCO trained weights from Releases if needed\n",
        "if not os.path.exists(COCO_MODEL_PATH):\n",
        "    utils.download_trained_weights(COCO_MODEL_PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DljXKrYxHr8j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "config = BalloonConfig()\n",
        "#BALLOON_DIR = os.path.join(ROOT_DIR, \"datasets/balloon\")\n",
        "BALLOON_DIR = os.path.join(ROOT_DIR, \"sample2\") #/sample2/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzWxJRaw2uvy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!unzip sample2.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swP6IafnHr8w",
        "colab_type": "code",
        "outputId": "96716c4b-bded-4474-da09-588069fc1703",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "# Load dataset\n",
        "# Get the dataset from the releases page\n",
        "# https://github.com/matterport/Mask_RCNN/releases\n",
        "dataset_train = BalloonDataset()\n",
        "dataset_train.load_balloon(BALLOON_DIR, \"train\")\n",
        "\n",
        "# Must call before using the dataset\n",
        "dataset_train.prepare()\n",
        "\n",
        "#############val set\n",
        "dataset_val = BalloonDataset()\n",
        "dataset_val.load_balloon(BALLOON_DIR, \"val\")\n",
        "\n",
        "dataset_val.prepare()\n",
        "\n",
        "print(\"Image Count: {}\".format(len(dataset_train.image_ids)))\n",
        "print(\"Class Count: {}\".format(dataset_train.num_classes))\n",
        "for i, info in enumerate(dataset_train.class_info):\n",
        "    print(\"{:3}. {:50}\".format(i, info['name']))\n",
        "    \n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image Count: 195\n",
            "Class Count: 2\n",
            "  0. BG                                                \n",
            "  1. tennisball                                        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGcFfhAeHr8-",
        "colab_type": "code",
        "outputId": "172e02af-bba1-4605-ea3c-f9e18ac52431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# Load and display random samples\n",
        "#image_ids = np.random.choice(dataset.image_ids, 4)\n",
        "image_ids = dataset_train.image_ids[5:8]\n",
        "for image_id in image_ids:\n",
        "    image = dataset_train.load_image(image_id)\n",
        "    mask, class_ids = dataset_train.load_mask(image_id)\n",
        "    visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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8g+rIlM+ecjxCDBaiKCI1QiqCIyIirc1CgUqa8NzzLzK91MaauWtpGlNgYblE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\nQmCC\n",
            "text/plain": [
              "<Figure size 1008x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PA3bl5ltuZu36FYgCkex11lrKroeZURsvP7+WSqHElJYG+nQHY0pDueqma/y3Q97rzmds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\nMfK/Pn85R7f/nCve9kvk0xqwBW/7/V/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mNs+0PvJi1XJZNNPRoelWfPTPb8vLfuHZ3PEu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\nzj/f+90AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CLqW6fDWFVZbK0g2UO6qc/MnTUGLQ1pLLFTBWk5C1WM4Btl62ZWtVZv5lNjt3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\n45zy/H+OdEf54nDD2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpOZAYZ-Hr9S",
        "colab_type": "code",
        "outputId": "272adce2-f8b8-43c5-fae7-d9da5679f0c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#for bboxes, need mask.shape\n",
        "import sys\n",
        "import numpy\n",
        "numpy.set_printoptions(threshold=sys.maxsize)\n",
        "\n",
        "print(mask.shape, mask.shape[-1])\n",
        "with open(\"mask.txt\", 'w') as f:\n",
        "    f.write(str(mask[:,:,0]))\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(360, 640, 1) 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzriZ1OPHr9d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create model in training mode\n",
        "model = modellib.MaskRCNN(mode=\"training\", config=config,\n",
        "                          model_dir=MODEL_DIR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ard3REIPHr9q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Which weights to start with?\n",
        "init_with = \"coco\"  # imagenet, coco, or last\n",
        "\n",
        "if init_with == \"imagenet\":\n",
        "    model.load_weights(model.get_imagenet_weights(), by_name=True)\n",
        "elif init_with == \"coco\":\n",
        "    # Load weights trained on MS COCO, but skip layers that\n",
        "    # are different due to the different number of classes\n",
        "    # See README for instructions to download the COCO weights\n",
        "    model.load_weights(COCO_MODEL_PATH, by_name=True,\n",
        "                       exclude=[\"mrcnn_class_logits\", \"mrcnn_bbox_fc\", \n",
        "                                \"mrcnn_bbox\", \"mrcnn_mask\"])\n",
        "elif init_with == \"last\":\n",
        "    # Load the last model you trained and continue training\n",
        "    model.load_weights(model.find_last(), by_name=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCkuXh4HO7Gc",
        "colab_type": "text"
      },
      "source": [
        "Train in two stages:\n",
        "\n",
        "Only the heads. Here we're freezing all the backbone layers and training only the randomly initialized layers (i.e. the ones that we didn't use pre-trained weights from MS COCO). To train only the head layers, pass layers='heads' to the train() function.\n",
        "\n",
        "Fine-tune all layers. For this simple example it's not necessary, but we're including it to show the process. Simply pass layers=\"all to train all layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHX18GMpHr-I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#####################\n",
        "# Directory to save logs and trained model\n",
        "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
        "\n",
        "# Local path to trained weights file\n",
        "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
        "# Download COCO trained weights from Releases if needed\n",
        "if not os.path.exists(COCO_MODEL_PATH):\n",
        "    utils.download_trained_weights(COCO_MODEL_PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnKcrG2JHr-X",
        "colab_type": "code",
        "outputId": "154bc8f5-3921-431c-9a02-454a8144c4c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3379
        }
      },
      "source": [
        "# Train the head branches\n",
        "# Passing layers=\"heads\" freezes all layers except the head\n",
        "# layers. You can also pass a regular expression to select\n",
        "# which layers to train by name pattern.\n",
        "'''\n",
        "model.train(dataset_train, dataset_val, \n",
        "            learning_rate=config.LEARNING_RATE, \n",
        "            epochs=1, \n",
        "            layers='heads')\n",
        "'''\n",
        "model.train(dataset_train, dataset_val, \n",
        "            learning_rate=config.LEARNING_RATE, \n",
        "            epochs=1, \n",
        "            layers='heads')\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Starting at epoch 0. LR=0.001\n",
            "\n",
            "Checkpoint Path: /content/drive/My Drive/matterport-balltracker/logs/tennisball20190603T1928/mask_rcnn_tennisball_{epoch:04d}.h5\n",
            "Selecting layers to train\n",
            "fpn_c5p5               (Conv2D)\n",
            "fpn_c4p4               (Conv2D)\n",
            "fpn_c3p3               (Conv2D)\n",
            "fpn_c2p2               (Conv2D)\n",
            "fpn_p5                 (Conv2D)\n",
            "fpn_p2                 (Conv2D)\n",
            "fpn_p3                 (Conv2D)\n",
            "fpn_p4                 (Conv2D)\n",
            "In model:  rpn_model\n",
            "    rpn_conv_shared        (Conv2D)\n",
            "    rpn_class_raw          (Conv2D)\n",
            "    rpn_bbox_pred          (Conv2D)\n",
            "mrcnn_mask_conv1       (TimeDistributed)\n",
            "mrcnn_mask_bn1         (TimeDistributed)\n",
            "mrcnn_mask_conv2       (TimeDistributed)\n",
            "mrcnn_mask_bn2         (TimeDistributed)\n",
            "mrcnn_class_conv1      (TimeDistributed)\n",
            "mrcnn_class_bn1        (TimeDistributed)\n",
            "mrcnn_mask_conv3       (TimeDistributed)\n",
            "mrcnn_mask_bn3         (TimeDistributed)\n",
            "mrcnn_class_conv2      (TimeDistributed)\n",
            "mrcnn_class_bn2        (TimeDistributed)\n",
            "mrcnn_mask_conv4       (TimeDistributed)\n",
            "mrcnn_mask_bn4         (TimeDistributed)\n",
            "mrcnn_bbox_fc          (TimeDistributed)\n",
            "mrcnn_mask_deconv      (TimeDistributed)\n",
            "mrcnn_class_logits     (TimeDistributed)\n",
            "mrcnn_mask             (TimeDistributed)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gradients_impl.py:110: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py:47: UserWarning: Using a generator with `use_multiprocessing=True` and multiple workers may duplicate your data. Please consider using the`keras.utils.Sequence class.\n",
            "  UserWarning('Using a generator with `use_multiprocessing=True`'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "ERROR:root:Error processing image {'id': 'frame51.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame51.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 413, 'cy': 87, 'r': 12}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 402 is out of bounds for axis 0 with size 360ERROR:root:Error processing image {'id': 'frame51.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame51.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 413, 'cy': 87, 'r': 12}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 402 is out of bounds for axis 0 with size 360\n",
            "\n",
            "ERROR:root:Error processing image {'id': 'frame62.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame62.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 380, 'cy': 103, 'r': 9}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 372 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame62.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame62.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 380, 'cy': 103, 'r': 9}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 372 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame192.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame192.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 449, 'cy': 245, 'r': 9}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 441 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame192.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame192.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 449, 'cy': 245, 'r': 9}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 441 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame181.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame181.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 388, 'cy': 145, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 382 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame54.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame54.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 424, 'cy': 79, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 418 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame181.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame181.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 388, 'cy': 145, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 382 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame54.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame54.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 424, 'cy': 79, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 418 is out of bounds for axis 0 with size 360\n",
            "ERROR:root:Error processing image {'id': 'frame178.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame178.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 373, 'cy': 114, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 367 is out of bounds for axis 0 with size 360\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  2/100 [..............................] - ETA: 27:16 - loss: 2.1364 - rpn_class_loss: 0.2070 - rpn_bbox_loss: 0.4413 - mrcnn_class_loss: 1.4881 - mrcnn_bbox_loss: 0.0000e+00 - mrcnn_mask_loss: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "ERROR:root:Error processing image {'id': 'frame178.jpg', 'source': 'tennisball', 'path': '/content/drive/My Drive/matterport-balltracker/sample2/train/frame178.jpg', 'width': 640, 'height': 360, 'circles': [{'name': 'circle', 'cx': 373, 'cy': 114, 'r': 7}]}\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n",
            "    use_mini_mask=config.USE_MINI_MASK)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n",
            "    mask, class_ids = dataset.load_mask(image_id)\n",
            "  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n",
            "    ## Note that this modifies the existing array arr, instead of creating a result array\n",
            "IndexError: index 367 is out of bounds for axis 0 with size 360\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 18/100 [====>.........................] - ETA: 3:49 - loss: 1.9955 - rpn_class_loss: 0.4828 - rpn_bbox_loss: 1.3381 - mrcnn_class_loss: 0.1746 - mrcnn_bbox_loss: 0.0000e+00 - mrcnn_mask_loss: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRemoteTraceback\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/usr/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n    result = (True, func(*args, **kwds))\n  File \"/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py\", line 626, in next_sample\n    return six.next(_SHARED_SEQUENCES[uid])\n  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1709, in data_generator\n    use_mini_mask=config.USE_MINI_MASK)\n  File \"/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\", line 1212, in load_image_gt\n    mask, class_ids = dataset.load_mask(image_id)\n  File \"/content/drive/My Drive/matterport-balltracker/balloon.py\", line 193, in load_mask\n    ## Note that this modifies the existing array arr, instead of creating a result array\nIndexError: index 367 is out of bounds for axis 0 with size 360\n\"\"\"",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    708\u001b[0m                     \"For more information see issue #1638.\")\n\u001b[0;32m--> 709\u001b[0;31m             \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    684\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index 367 is out of bounds for axis 0 with size 360",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-728e69912f84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLEARNING_RATE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             layers='heads')\n\u001b[0m",
            "\u001b[0;32m/content/drive/My Drive/matterport-balltracker/mrcnn/model.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation, custom_callbacks, no_augmentation_sources)\u001b[0m\n\u001b[1;32m   2372\u001b[0m             \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2373\u001b[0m             \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2374\u001b[0;31m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2375\u001b[0m         )\n\u001b[1;32m   2376\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mval_enqueuer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 \u001b[0mval_enqueuer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mstop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    494\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munfinished_tasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m         \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJvxU4OdHr-o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fine tune all layers\n",
        "# Passing layers=\"all\" trains all layers. You can also \n",
        "# pass a regular expression to select which layers to\n",
        "# train by name pattern.\n",
        "model.train(dataset_train, dataset_val, \n",
        "            learning_rate=config.LEARNING_RATE / 10,\n",
        "            epochs=2, \n",
        "            layers=\"all\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqDfekbHHr-t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save weights\n",
        "# Typically not needed because callbacks save after every epoch\n",
        "# Uncomment to save manually\n",
        "model_path = os.path.join(MODEL_DIR, \"mask_rcnn_tennisball.h5\")\n",
        "model.keras_model.save_weights(model_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6l4eP2LjDWW4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load weights\n",
        "model.load_weights(\"drive/My Drive/matterport-balltracker/logs/mask_rcnn_tennisball.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIGOX824Hr-2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class InferenceConfig(BalloonConfig):\n",
        "    GPU_COUNT = 1\n",
        "    IMAGES_PER_GPU = 1\n",
        "\n",
        "inference_config = InferenceConfig()\n",
        "\n",
        "# Recreate the model in inference mode\n",
        "model = modellib.MaskRCNN(mode=\"inference\", \n",
        "                          config=inference_config,\n",
        "                          model_dir=MODEL_DIR)\n",
        "\n",
        "# Get path to saved weights\n",
        "# Either set a specific path or find last trained weights\n",
        "# model_path = os.path.join(ROOT_DIR, \".h5 file name here\")\n",
        "model_path = model.find_last()\n",
        "\n",
        "# Load trained weights\n",
        "print(\"Loading weights from \", model_path)\n",
        "model.load_weights(model_path, by_name=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1P5S3F6AHr_C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test on a random image\n",
        "for i in range(5):\n",
        "    image_id = random.choice(dataset_val.image_ids)\n",
        "    original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
        "        modellib.load_image_gt(dataset_val, inference_config, image_id, use_mini_mask=False)\n",
        "\n",
        "    log(\"original_image\", original_image)\n",
        "    log(\"image_meta\", image_meta)\n",
        "    log(\"gt_class_id\", gt_class_id)\n",
        "    log(\"gt_bbox\", gt_bbox)\n",
        "    log(\"gt_mask\", gt_mask)\n",
        "\n",
        "    visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, dataset_train.class_names, figsize=(8, 8))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-AJ0G08Hr_I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaiaP6YOHr_V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}