Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb

      "[(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]\n",
      "elephant 98.6307144165039\n",
      "elephant 77.56589651107788\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load yolov3 model and perform object detection\n",
    "# based on https://github.com/experiencor/keras-yolo3\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from numpy import expand_dims\n",
    "from keras.models import load_model\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    " \n",
    "class BoundBox:\n",
    "    def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n",
    "        self.xmin = xmin\n",
    "        self.ymin = ymin\n",
    "        self.xmax = xmax\n",
    "        self.ymax = ymax\n",
    "        self.objness = objness\n",
    "        self.classes = classes\n",
    "        self.label = -1\n",
    "        self.score = -1\n",
    " \n",
    "    def get_label(self):\n",
    "        if self.label == -1:\n",
    "            self.label = np.argmax(self.classes)\n",
    " \n",
    "        return self.label\n",
    " \n",
    "    def get_score(self):\n",
    "        if self.score == -1:\n",
    "            self.score = self.classes[self.get_label()]\n",
    " \n",
    "        return self.score\n",
    " \n",
    "def _sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    " \n",
    "def decode_netout(netout, anchors, obj_thresh, net_h, net_w):\n",
    "    grid_h, grid_w = netout.shape[:2]\n",
    "    nb_box = 3\n",
    "    netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n",
    "    nb_class = netout.shape[-1] - 5\n",
    "    boxes = []\n",
    "    netout[..., :2]  = _sigmoid(netout[..., :2])\n",
    "    netout[..., 4:]  = _sigmoid(netout[..., 4:])\n",
    "    netout[..., 5:]  = netout[..., 4][..., np.newaxis] * netout[..., 5:]\n",
    "    netout[..., 5:] *= netout[..., 5:] > obj_thresh\n",
    " \n",
    "    for i in range(grid_h*grid_w):\n",
    "        row = i / grid_w\n",
    "        col = i % grid_w\n",
    "        for b in range(nb_box):\n",
    "            # 4th element is objectness score\n",
    "            objectness = netout[int(row)][int(col)][b][4]\n",
    "            if(objectness.all() <= obj_thresh): continue\n",
    "            # first 4 elements are x, y, w, and h\n",
    "            x, y, w, h = netout[int(row)][int(col)][b][:4]\n",
    "            x = (col + x) / grid_w # center position, unit: image width\n",
    "            y = (row + y) / grid_h # center position, unit: image height\n",
    "            w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n",
    "            h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height\n",
    "            # last elements are class probabilities\n",
    "            classes = netout[int(row)][col][b][5:]\n",
    "            box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)\n",
    "            boxes.append(box)\n",
    "    return boxes\n",
    " \n",
    "def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n",
    "    new_w, new_h = net_w, net_h\n",
    "    for i in range(len(boxes)):\n",
    "        x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n",
    "        y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n",
    "        boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n",
    "        boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n",
    "        boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n",
    "        boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)\n",
    " \n",
    "def _interval_overlap(interval_a, interval_b):\n",
    "    x1, x2 = interval_a\n",
    "    x3, x4 = interval_b\n",
    "    if x3 < x1:\n",
    "        if x4 < x1:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x1\n",
    "    else:\n",
    "        if x2 < x3:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x3\n",
    " \n",
    "def bbox_iou(box1, box2):\n",
    "    intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n",
    "    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n",
    "    intersect = intersect_w * intersect_h\n",
    "    w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n",
    "    w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n",
    "    union = w1*h1 + w2*h2 - intersect\n",
    "    return float(intersect) / union\n",
    " \n",
    "def do_nms(boxes, nms_thresh):\n",
    "    if len(boxes) > 0:\n",
    "        nb_class = len(boxes[0].classes)\n",
    "    else:\n",
    "        return\n",
    "    for c in range(nb_class):\n",
    "        sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n",
    "        for i in range(len(sorted_indices)):\n",
    "            index_i = sorted_indices[i]\n",
    "            if boxes[index_i].classes[c] == 0: continue\n",
    "            for j in range(i+1, len(sorted_indices)):\n",
    "                index_j = sorted_indices[j]\n",
    "                if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n",
    "                    boxes[index_j].classes[c] = 0\n",
    " \n",
    "# load and prepare an image\n",
    "def load_image_pixels(filename, shape):\n",
    "    # load the image to get its shape\n",
    "    image = load_img(filename)\n",
    "    width, height = image.size\n",
    "    # load the image with the required size\n",
    "    image = load_img(filename, target_size=shape)\n",
    "    # convert to numpy array\n",
    "    image = img_to_array(image)\n",
    "    # scale pixel values to [0, 1]\n",
    "    image = image.astype('float32')\n",
    "    image /= 255.0\n",
    "    # add a dimension so that we have one sample\n",
    "    image = expand_dims(image, 0)\n",
    "    return image, width, height\n",
    " \n",
    "# get all of the results above a threshold\n",
    "def get_boxes(boxes, labels, thresh):\n",
    "    v_boxes, v_labels, v_scores = list(), list(), list()\n",
    "    # enumerate all boxes\n",
    "    for box in boxes:\n",
    "        # enumerate all possible labels\n",
    "        for i in range(len(labels)):\n",
    "            # check if the threshold for this label is high enough\n",
    "            if box.classes[i] > thresh:\n",
    "                v_boxes.append(box)\n",
    "                v_labels.append(labels[i])\n",
    "                v_scores.append(box.classes[i]*100)\n",
    "                # don't break, many labels may trigger for one box\n",
    "    return v_boxes, v_labels, v_scores\n",
    " \n",
    "# draw all results\n",
    "def draw_boxes(filename, v_boxes, v_labels, v_scores):\n",
    "    # load the image\n",
    "    data = pyplot.imread(filename)\n",
    "    # plot the image\n",
    "    pyplot.imshow(data)\n",
    "    # get the context for drawing boxes\n",
    "    ax = pyplot.gca()\n",
    "    # plot each box\n",
    "    for i in range(len(v_boxes)):\n",
    "        box = v_boxes[i]\n",
    "        # get coordinates\n",
    "        y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "        # calculate width and height of the box\n",
    "        width, height = x2 - x1, y2 - y1\n",
    "        # create the shape\n",
    "        rect = Rectangle((x1, y1), width, height, fill=False, color='white')\n",
    "        # draw the box\n",
    "        ax.add_patch(rect)\n",
    "        # draw text and score in top left corner\n",
    "        label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "        pyplot.text(x1, y1, label, color='white')\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    " \n",
    "# load yolov3 model\n",
    "model = load_model('model.h5')\n",
    "# define the expected input shape for the model\n",
    "input_w, input_h = 416, 416\n",
    "# define our new photo\n",
    "photo_filename = './Bilder/african-elephant.jpg'\n",
    "# load and prepare image\n",
    "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))\n",
    "# make prediction\n",
    "yhat = model.predict(image)\n",
    "# summarize the shape of the list of arrays\n",
    "print([a.shape for a in yhat])\n",
    "# define the anchors\n",
    "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n",
    "# define the probability threshold for detected objects\n",
    "class_threshold = 0.6\n",
    "boxes = list()\n",
    "for i in range(len(yhat)):\n",
    "    # decode the output of the network\n",
    "    boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)\n",
    "# correct the sizes of the bounding boxes for the shape of the image\n",
    "correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)\n",
    "# suppress non-maximal boxes\n",
    "do_nms(boxes, 0.5)\n",
    "# define the labels\n",
    "labels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"bus\", \"train\", \"truck\",\n",
    "    \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\",\n",
    "    \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\",\n",
    "    \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n",
    "    \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\",\n",
    "    \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\",\n",
    "    \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\",\n",
    "    \"chair\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\",\n",
    "    \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\",\n",
    "    \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]\n",
    "# get the details of the detected objects\n",
    "v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n",
    "# summarize what we found\n",
    "for i in range(len(v_boxes)):\n",
    "    print(v_labels[i], v_scores[i])\n",
    "# draw what we found\n",
    "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0dfpdDOGhM2"
   },
   "source": [
    "# Part V : Instance Segmentation with Mask R-CNN\n",
    "\n",
    "### Please run this section on Colab !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vOAEQt-pGhM3"
   },
   "source": [
    "Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.\n",
    "\n",
    "It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).\n",
    "\n",
    "In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.\n",
    "\n",
    "In this section, we will discover how to use the __Mask R-CNN__ model to detect objects in new photographs.\n",
    "\n",
    "After completing this tutorial, you will know:\n",
    "\n",
    "- The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.\n",
    "\n",
    "- The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.\n",
    "    \n",
    "- How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ra-bXlWXGhM4"
   },
   "source": [
    "## Mask R-CNN for Object Detection\n",
    "\n",
    "Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.\n",
    "\n",
    "It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.\n",
    "\n",
    "An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation.\n",
    "\n",
    "The __Region-Based__ Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.\n",
    "\n",
    "There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:\n",
    "\n",
    "- __R-CNN__: Bounding boxes are proposed by the “selective search” algorithm, each of which is stretched and features are extracted via a deep convolutional neural network, such as AlexNet, before a final set of object classifications are made with linear SVMs.\n",
    "\n",
    "- __Fast R-CNN__: Simplified design with a single model, bounding boxes are still specified as input, but a region-of-interest pooling layer is used after the deep CNN to consolidate regions and the model predicts both class labels and regions of interest directly.\n",
    "    \n",
    "- __Faster R-CNN__: Addition of a Region Proposal Network that interprets features extracted from the deep CNN and learns to propose regions-of-interest directly.\n",
    "    \n",
    "- __Mask R-CNN__: Extension of Faster R-CNN that adds an output model for predicting a mask for each detected object.\n",
    "\n",
    "The Mask R-CNN model introduced in the 2018 paper titled [Mask R-CNN](https://arxiv.org/abs/1703.06870) is the most recent variation of the family models and supports both object detection and object segmentation. The paper provides a nice summary of the model linage to that point:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GlXwuVoOGhM7"
   },
   "source": [
    "### Matterport Mask R-CNN Project\n",
    "\n",
    "Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.\n",
    "\n",
    "Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:\n",
    "\n",
    "- R-CNN: [Regions with Convolutional Neural Network Features, GitHub](https://github.com/rbgirshick/rcnn)\n",
    "\n",
    "- Fast R-CNN, [GitHub](https://github.com/rbgirshick/fast-rcnn)\n",
    "\n",
    "- Faster R-CNN Python Code, [GitHub](https://github.com/rbgirshick/py-faster-rcnn)\n",
    "\n",
    "- Detectron, Facebook AI, [GitHub](https://github.com/facebookresearch/Detectron)\n",
    "\n",
    "Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.\n",
    "\n",
    "The best of breed third-party implementations of Mask R-CNN is the [Mask R-CNN](https://github.com/matterport/Mask_RCNN) Project developed by Matterport. The project is open source released under a permissive license (i.e. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.\n",
    "\n",
    "Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project available, just in case there are major changes to the API in the future.\n",
    "\n",
    "The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:\n",
    "\n",
    "- Mask R-CNN Demo, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb)\n",
    "\n",
    "- Mask R-CNN – Inspect Trained Model, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/coco/inspect_model.ipynb)\n",
    "\n",
    "There are perhaps three main use cases for using the Mask R-CNN model with the Matterport library; they are:\n",
    "\n",
    "- __Object Detection Application__: Use a pre-trained model for object detection on new images.\n",
    "\n",
    "- __New Model via Transfer Learning__: Use a pre-trained model as a starting point in developing a model for a new object detection dataset.\n",
    "    \n",
    "- __New Model from Scratch__: Develop a new model from scratch for an object detection dataset.\n",
    "\n",
    "In order to get familiar with the model and the library, we will look at the first example in the next section.\n",
    "\n",
    "#### Object Detection With Mask R-CNN\n",
    "\n",
    "In this section, we will use the Matterport Mask R-CNN library to perform object detection on arbitrary photographs.\n",
    "\n",
    "Much like using a pre-trained deep CNN for image classification, e.g. such as VGG-16 trained on an ImageNet dataset, we can use a pre-trained Mask R-CNN model to detect objects in new photographs. In this case, we will use a Mask R-CNN trained on the [MS COCO object detection problem](http://cocodataset.org/#home).\n",
    "\n",
    "#### Mask R-CNN Installation\n",
    "\n",
    "The first step is to install the library.\n",
    "\n",
    "At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.\n",
    "\n",
    "Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the [installation instructions](https://github.com/matterport/Mask_RCNN#installation) buried in the library’s readme file.\n",
    "\n",
    "#### Step 0. Open Colab and Upload this Notebook\n",
    "\n",
    "#### Step 1. Clone the Mask R-CNN GitHub Repository\n",
    "\n",
    "This is as simple as running the following command from your command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "HGiDmuejGhM8",
    "outputId": "ce5ca013-96e5-4766-d2ed-b4cde9b3ca94"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/matterport/Mask_RCNN.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S7uXyFVPGhNA"
   },
   "source": [
    "This will create a new local directory with the name Mask_RCNN that looks as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "DhKn5ytcGhNA"
   },
   "source": [
    "Mask_RCNN\n",
    "├── assets\n",
    "├── build\n",
    "│   ├── bdist.macosx-10.13-x86_64\n",
    "│   └── lib\n",
    "│       └── mrcnn\n",
    "├── dist\n",
    "├── images\n",
    "├── mask_rcnn.egg-info\n",
    "├── mrcnn\n",
    "└── samples\n",
    "    ├── balloon\n",
    "    ├── coco\n",
    "    ├── nucleus\n",
    "    └── shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WvFlDgvJGhNB"
   },
   "source": [
    "#### Step 2. Install the Mask R-CNN Library\n",
    "\n",
    "The library can be installed directly via pip.\n",
    "\n",
    "Change directory into the _Mask_RCNN_ directory and run the installation script.\n",
    "\n",
    "From the command line, type the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "aEUeZhX5GhNB",
    "outputId": "be5de5a1-e821-477c-ce28-91bb9f8c3194"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('./Mask_RCNN')\n",
    "!pip3 install -r requirements.txt\n",
    "!python3 setup.py install "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DlySPeHPGhNE"
   },
   "source": [
    "The library will then install directly and you will see a lot of successful installation messages ending with the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "nAww1LboGhNF"
   },
   "source": [
    "...\n",
    "Finished processing dependencies for mask-rcnn==2.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "55X0zSm7GhNG"
   },
   "source": [
    "#### Step 3: Confirm the Library Was Installed\n",
    "\n",
    "It is always a good idea to confirm that the library was installed correctly.\n",
    "\n",
    "You can confirm that the library was installed correctly by querying it via the pip command; for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "kKXRZ1vTGhNG",
    "outputId": "9f0df55c-755f-4e11-a6c3-e8b7418eefcb"
   },
   "outputs": [],
   "source": [
    "!pip3 show mask-rcnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f0vwUrMcGhNJ"
   },
   "source": [
    "### Example of Object Localization\n",
    "\n",
    "We are going to use a pre-trained Mask R-CNN model to detect objects on a new photograph.\n",
    "\n",
    "#### Step 1. Download Model Weights\n",
    "\n",
    "First, download the weights for the pre-trained model, specifically a Mask R-CNN trained on the MS Coco dataset.\n",
    "\n",
    "The weights are available from the project GitHub project and the file is about 250 megabytes. Download the model weights to a file with the name ‘mask_rcnn_coco.h5‘ in your current working directory.\n",
    "\n",
    "[Download Weights (mask_rcnn_coco.h5)](https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5) (246 megabytes)\n",
    "\n",
    "#### Step 2. Download Sample Photograph\n",
    "\n",
    "We also need a photograph in which to detect objects.\n",
    "\n",
    "Download from Ilias the photograph to your current working directory with the filename ‘african-elephant.jpg‘\n",
    "\n",
    "\n",
    "african-elephant.jpg![grafik.png](attachment:grafik.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n8ccmDSvGhNK"
   },
   "source": [
    "#### Step 3. Load Model and Make Prediction\n",
    "\n",
    "First, the model must be defined via an instance MaskRCNN class.\n",
    "\n",
    "This class requires a configuration object as a parameter. The configuration object defines how the model might be used during training or inference.\n",
    "\n",
    "In this case, the configuration will only specify the number of images per batch, which will be one, and the number of classes to predict.\n",
    "\n",
    "You can see the full extent of the configuration object and the properties that you can override in the [config.py](https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qAfMaOOzGhNL"
   },
   "outputs": [],
   "source": [
    "%tensorflow_version 1.x\n",
    "from mrcnn.config import Config\n",
    "from mrcnn.model import MaskRCNN\n",
    "# define the test configuration\n",
    "class TestConfig(Config):\n",
    "     NAME = \"test\"\n",
    "     GPU_COUNT = 1\n",
    "     IMAGES_PER_GPU = 1\n",
    "     NUM_CLASSES = 1 + 80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1CmHYT4RGhNN"
   },
   "source": [
    "We can now define the MaskRCNN instance.\n",
    "\n",
    "We will define the model as type “inference” indicating that we are interested in making predictions and not training. We must also specify a directory where any log messages could be written, which in this case will be the current working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sg482-mcGhNO"
   },
   "outputs": [],
   "source": [
    "# define the model\n",
    "rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install 'h5py==2.10.0' --force-reinstall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9BtI50MlGhNR"
   },
   "source": [
    "The next step is to load the weights that we downloaded. You should save it on google drive and then load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_TWgehzsNOSV",
    "outputId": "73225d99-e9df-4d1c-c733-a092c97e336c"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "colab_type": "code",
    "id": "46t9gwLdGhNR",
    "outputId": "842b58f4-2678-4ad9-bbcf-aac4656392b7"
   },
   "outputs": [],
   "source": [
    "# load coco model weights\n",
    "rcnn.load_weights('/content/drive/My Drive/mask_rcnn_coco.h5', by_name=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bTBwZPvBGhNU"
   },
   "source": [
    "Now we can make a prediction for our image. First, we can load the image and convert it to a NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6k8CgLmCGhNW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "# load photograph\n",
    "img = image.load_img('/content/drive/My Drive/african-elephant.jpg')\n",
    "img = image.img_to_array(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h2hsqN-5GhNZ"
   },
   "source": [
    "We can then make a prediction with the model. Instead of calling `predict()` as we would on a normal Keras model, will call the `detect()` function and pass it the single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ubUzpG2lGhNZ"
   },
   "outputs": [],
   "source": [
    "# make prediction\n",
    "results = rcnn.detect([img], verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UfgnKPgSGhNc"
   },
   "source": [
    "The result contains a dictionary for each image that we passed into the `detect()` function, in this case, a list of a single dictionary for the one image.\n",
    "\n",
    "The dictionary has keys for the bounding boxes, masks, and so on, and each key points to a list for multiple possible objects detected in the image.\n",
    "\n",
    "The keys of the dictionary of note are as follows:\n",
    "\n",
    "- __‘rois‘__: The bound boxes or regions-of-interest (ROI) for detected objects.\n",
    "- __‘masks‘__: The masks for the detected objects.\n",
    "- __‘class_ids‘__: The class integers for the detected objects.\n",
    "- __‘scores‘__: The probability or confidence for each predicted class.\n",
    "\n",
    "We can draw each box detected in the image by first getting the dictionary for the first image (e.g. results[0]), and then retrieving the list of bounding boxes (e.g. [‘rois’])."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gb2Q5QgLGhNc"
   },
   "outputs": [],
   "source": [
    "boxes = results[0]['rois']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qUxs3u4qGhNf"
   },
   "source": [
    "Each bounding box is defined in terms of the bottom left and top right coordinates of the bounding box in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wKPg5GodGhNg"
   },
   "outputs": [],
   "source": [
    "y1, x1, y2, x2 = boxes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mp9EfU8vGhNj"
   },
   "source": [
    "We can use these coordinates to create a `Rectangle()` from the matplotlib API and draw each rectangle over the top of our image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "VbLvAtkvGhNk",
    "outputId": "1db15efd-d2a8-4a0c-fcac-e00ab09e24c7"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    "ax = pyplot.gca()\n",
    "# get coordinates\n",
    "y1, x1, y2, x2 = boxes[0]\n",
    "# calculate width and height of the box\n",
    "width, height = x2 - x1, y2 - y1\n",
    "# create the shape\n",
    "rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "# draw the box\n",
    "ax.add_patch(rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6pekthaaGhNm"
   },
   "source": [
    "To keep things neat, we can create a function to do this that will take the filename of the photograph and the list of bounding boxes to draw and will show the photo with the boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MPA85WZZGhNn"
   },
   "outputs": [],
   "source": [
    "# draw an image with detected objects\n",
    "def draw_image_with_boxes(filename, boxes_list):\n",
    "     # load the image\n",
    "     data = pyplot.imread(filename)\n",
    "     # plot the image\n",
    "     pyplot.imshow(data)\n",
    "     # get the context for drawing boxes\n",
    "     ax = pyplot.gca()\n",
    "     # plot each box\n",
    "     for box in boxes_list:\n",
    "          # get coordinates\n",
    "          y1, x1, y2, x2 = box\n",
    "          # calculate width and height of the box\n",
    "          width, height = x2 - x1, y2 - y1\n",
    "          # create the shape\n",
    "          rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "          # draw the box\n",
    "          ax.add_patch(rect)\n",
    "     # show the plot\n",
    "     pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TKjNOnR5GhNq"
   },
   "source": [
    "We can now tie all of this together and load the pre-trained model and use it to detect objects in our photograph of an elephant, then draw the photograph with all detected objects.\n",
    "\n",
    "The complete example is listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "XscAeWiLGhNq",
    "outputId": "8c0f20a6-1ff0-4162-f7a0-d2ed64370872"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from mrcnn.config import Config\n",
    "from mrcnn.model import MaskRCNN\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    " \n",
    "# draw an image with detected objects\n",
    "def draw_image_with_boxes(filename, boxes_list):\n",
    "     # load the image\n",
    "     data = pyplot.imread(filename)\n",
    "     # plot the image\n",
    "     pyplot.imshow(data)\n",
    "     # get the context for drawing boxes\n",
    "     ax = pyplot.gca()\n",
    "     # plot each box\n",
    "     for box in boxes_list:\n",
    "          # get coordinates\n",
    "          y1, x1, y2, x2 = box\n",
    "          # calculate width and height of the box\n",
    "          width, height = x2 - x1, y2 - y1\n",
    "          # create the shape\n",
    "          rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "          # draw the box\n",
    "          ax.add_patch(rect)\n",
    "     # show the plot\n",
    "     pyplot.show()\n",
    " \n",
    "# define the test configuration\n",
    "class TestConfig(Config):\n",
    "     NAME = \"test\"\n",
    "     GPU_COUNT = 1\n",
    "     IMAGES_PER_GPU = 1\n",
    "     NUM_CLASSES = 1 + 80\n",
    " \n",
    "# define the model\n",
    "rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())\n",
    "# load coco model weights\n",
    "rcnn.load_weights('/content/drive/My Drive/mask_rcnn_coco.h5', by_name=True)\n",
    "# load photograph\n",
    "img = load_img('/content/drive/My Drive/african-elephant.jpg')\n",
    "img = img_to_array(img)\n",
    "# make prediction\n",
    "results = rcnn.detect([img], verbose=0)\n",
    "# visualize the results\n",
    "draw_image_with_boxes('/content/drive/My Drive/african-elephant.jpg', results[0]['rois'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gl69hYXeGhNt"
   },
   "source": [
    "Running the example loads the model and performs object detection. More accurately, we have performed object localization, only drawing bounding boxes around detected objects.\n",
    "\n",
    "In this case, we can see that the model has correctly located the single object in the photo, the elephant, and drawn a red box around it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2JHZGM-gGhNt"
   },
   "source": [
    "## Example of Object Detection\n",
    "\n",
    "Now that we know how to load the model and use it to make a prediction, let’s update the example to perform real object detection.\n",
    "\n",
    "That is, in addition to localizing objects, we want to know what they are.\n",
    "\n",
    "The `Mask_RCNN API` provides a function called `display_instances()` that will take the array of pixel values for the loaded image and the aspects of the prediction dictionary, such as the bounding boxes, scores, and class labels, and will plot the photo with all of these annotations.\n",
    "\n",
    "One of the arguments is the list of predicted class identifiers available in the `class_id` key of the dictionary. The function also needs a mapping of ids to class labels. The pre-trained model was fit with a dataset that had 80 (81 including background) class labels, helpfully provided as a list in the [Mask R-CNN Demo, Notebook Tutorial](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb), listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TLdQQg8gGhNv"
   },
   "outputs": [],
   "source": [
    "# define 81 classes that the coco model knowns about\n",
    "class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\n",
    "               'bus', 'train', 'truck', 'boat', 'traffic light',\n",
    "               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',\n",
    "               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',\n",
    "               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',\n",
    "               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',\n",
    "               'kite', 'baseball bat', 'baseball glove', 'skateboard',\n",
    "               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',\n",
    "               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\n",
    "               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',\n",
    "               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',\n",
    "               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',\n",
    "               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',\n",
    "               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',\n",
    "               'teddy bear', 'hair drier', 'toothbrush']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "llndXml9GhNz"
   },
   "source": [
    "We can then provide the details of the prediction for the elephant photo to the display_instances() function; for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 632
    },
    "colab_type": "code",
    "id": "mIlhDj57GhNz",
    "outputId": "9e57f9b3-97af-4cb5-c389-6d6f2435ddc7"
   },
   "outputs": [],
   "source": [
    "from mrcnn.visualize import display_instances\n",
    "# get dictionary for first prediction\n",
    "r = results[0]\n",