<jupyter_start><jupyter_text>Mask R-CNN - Train on Shapes DatasetThis notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be too slow to train on a CPU. On a GPU, you can start to get okay-ish results in a few minutes, and good results in less than an hour.The code of the *Shapes* dataset is included below. It generates images on the fly, so it doesn't require downloading any data. And it can generate images of any size, so we pick a small image size to train faster.<jupyter_code>import os
import sys
import json
import datetime
import numpy as np
import skimage.draw
import cv2
from mrcnn.visualize import display_instances
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("D:\damage_detection")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils

# Path to trained weights file
COCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")<jupyter_output><empty_output><jupyter_text>Configurations<jupyter_code>class CustomConfig(Config):
    """Configuration for training on the toy  dataset.
    Derives from the base Config class and overrides some values.
    """
    # Give the configuration a recognizable name
    NAME = "damage"

    # We use a GPU with 12GB memory, which can fit two images.
    # Adjust down if you use a smaller GPU.
    IMAGES_PER_GPU = 2

    # Number of classes (including background)
    NUM_CLASSES = 1 + 3  # Background + toy

    # Number of training steps per epoch
    STEPS_PER_EPOCH = 100

    # Skip detections with < 90% confidence
    DETECTION_MIN_CONFIDENCE = 0.9<jupyter_output><empty_output><jupyter_text>Notebook Preferences  DatasetCreate a synthetic datasetExtend the Dataset class and add a method to load the shapes dataset, `load_shapes()`, and override the following methods:* load_image()* load_mask()* image_reference()<jupyter_code>class CustomDataset(utils.Dataset):

    def load_custom(self, dataset_dir, subset):
        """Load a subset of the Balloon dataset.
        dataset_dir: Root directory of the dataset.
        subset: Subset to load: train or val
        """
        # Add classes. We have only one class to add.
        self.add_class("damage", 1, "win")
        self.add_class("damage", 2, "scra")
        self.add_class("damage", 3, "dist")
        # Train or validation dataset?
        assert subset in ["train", "val"]
        dataset_dir = os.path.join(dataset_dir, subset)

        # Load annotations
        # VGG Image Annotator saves each image in the form:
        # { 'filename': '28503151_5b5b7ec140_b.jpg',
        #   'regions': {
        #       '0': {
        #           'region_attributes': {},
        #           'shape_attributes': {
        #               'all_points_x': [...],
        #               'all_points_y': [...],
        #               'name': 'polygon'}},
        #       ... more regions ...
        #   },
        #   'size': 100202
        # }
        # We mostly care about the x and y coordinates of each region
        annotations1 = json.load(open(os.path.join(dataset_dir, "via_region_data.json")))
        # print(annotations1)
        annotations = list(annotations1.values())  # don't need the dict keys

        # The VIA tool saves images in the JSON even if they don't have any
        # annotations. Skip unannotated images.
        annotations = [a for a in annotations if a['regions']]

        # Add images
        for a in annotations:
            # print(a)
            # Get the x, y coordinaets of points of the polygons that make up
            # the outline of each object instance. There are stores in the
            # shape_attributes (see json format above)
            polygons = [r['shape_attributes'] for r in a['regions']]
            name = [r['region_attributes'].values() for r in a['regions']]
            name_dict = {"win":1,"scra":2,"dist":3}
            name_id = [name_dict[a] for a in name]

            # load_mask() needs the image size to convert polygons to masks.
            # Unfortunately, VIA doesn't include it in JSON, so we must read
            # the image. This is only managable since the dataset is tiny.
            image_path = os.path.join(dataset_dir, a['filename'])
            image = skimage.io.imread(image_path)
            height, width = image.shape[:2]

            self.add_image(
                "damage",## for a single class just add the name here
                image_id=a['filename'],  # use file name as a unique image id
                path=image_path,
                class_id=name_id,
                width=width, height=height,
                polygons=polygons)

    def load_mask(self, image_id):
        """Generate instance masks for an image.
       Returns:
        masks: A bool array of shape [height, width, instance count] with
            one mask per instance.
        class_ids: a 1D array of class IDs of the instance masks.
        """
        # If not a balloon dataset image, delegate to parent class.
        image_info = self.image_info[image_id]
        if image_info["source"] != "damage":
            return super(self.__class__, self).load_mask(image_id)
        name_id = image_info["class_id"]

        # Convert polygons to a bitmap mask of shape
        # [height, width, instance_count]
        info = self.image_info[image_id]
        mask = np.zeros([info["height"], info["width"], len(info["polygons"])],
                        dtype=np.uint8)
        class_ids = np.array(name_id, dtype=np.int32)
        for i, p in enumerate(info["polygons"]):
            # Get indexes of pixels inside the polygon and set them to 1
            rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])
            mask[rr, cc, i] = 1

        # Return mask, and array of class IDs of each instance. Since we have
        # one class ID only, we return an array of 1s
        return (mask.astype(np.bool), class_ids)
    def image_reference(self, image_id):
        """Return the path of the image."""
        info = self.image_info[image_id]
        if info["source"] == "damage":
            return info["path"]
        else:
            super(self.__class__, self).image_reference(image_id)


def train(model):
    """Train the model."""
    # Training dataset.
    dataset_train = CustomDataset()
    dataset_train.load_custom(args.dataset, "train")
    dataset_train.prepare()

    # Validation dataset
    dataset_val = CustomDataset()
    dataset_val.load_custom(args.dataset, "val")
    dataset_val.prepare()

    # *** This training schedule is an example. Update to your needs ***
    # Since we're using a very small dataset, and starting from
    # COCO trained weights, we don't need to train too long. Also,
    # no need to train all layers, just the heads should do it.
    print("Training network heads")
    model.train(dataset_train, dataset_val,
                learning_rate=config.LEARNING_RATE,
                epochs=10,
                layers='heads')


def color_splash(image, mask):
    """Apply color splash effect.
    image: RGB image [height, width, 3]
    mask: instance segmentation mask [height, width, instance count]
    Returns result image.
    """
    # Make a grayscale copy of the image. The grayscale copy still
    # has 3 RGB channels, though.
    gray = skimage.color.gray2rgb(skimage.color.rgb2gray(image)) * 255
    # We're treating all instances as one, so collapse the mask into one layer
    mask = (np.sum(mask, -1, keepdims=True) >= 1)
    # Copy color pixels from the original color image where mask is set
    if mask.shape[0] > 0:
        splash = np.where(mask, image, gray).astype(np.uint8)
    else:
        splash = gray
    return splash


def detect_and_color_splash(model, image_path=None, video_path=None):
    assert image_path or video_path

    # Image or video?
    if image_path:
        # Run model detection and generate the color splash effect
        print("Running on {}".format(args.image))
        # Read image
        image = skimage.io.imread(args.image)
        # Detect objects
        r = model.detect([image], verbose=1)[0]
        # Color splash
        splash = color_splash(image, r['masks'])
        # Save output
        file_name = "splash_{:%Y%m%dT%H%M%S}.png".format(datetime.datetime.now())
        skimage.io.imsave(file_name, splash)
    elif video_path:
        import cv2
        # Video capture
        vcapture = cv2.VideoCapture(video_path)
        width = int(vcapture.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(vcapture.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = vcapture.get(cv2.CAP_PROP_FPS)

        # Define codec and create video writer
        file_name = "splash_{:%Y%m%dT%H%M%S}.avi".format(datetime.datetime.now())
        vwriter = cv2.VideoWriter(file_name,
                                  cv2.VideoWriter_fourcc(*'MJPG'),
                                  fps, (width, height))

        count = 0
        success = True
        while success:
            print("frame: ", count)
            # Read next image
            success, image = vcapture.read()
            if success:
                # OpenCV returns images as BGR, convert to RGB
                image = image[..., ::-1]
                # Detect objects
                r = model.detect([image], verbose=0)[0]
                # Color splash
                splash = color_splash(image, r['masks'])
                # RGB -> BGR to save image to video
                splash = splash[..., ::-1]
                # Add image to video writer
                vwriter.write(splash)
                count += 1
        vwriter.release()
    print("Saved to ", file_name)

############################################################
#  Training
############################################################

if __name__ == '__main__':
    import argparse

    # Parse command line arguments
    parser = argparse.ArgumentParser(
        description='Train Mask R-CNN to detect custom class.')
    parser.add_argument("command",
                        metavar="<command>",
                        help="'train' or 'splash'")
    parser.add_argument('--dataset', required=False,
                        metavar="/path/to/custom/dataset/",
                        help='Directory of the custom dataset')
    parser.add_argument('--weights', required=True,
                        metavar="/path/to/weights.h5",
                        help="Path to weights .h5 file or 'coco'")
    parser.add_argument('--logs', required=False,
                        default="d:\damage_detection\logs",
                        metavar="/path/to/logs/",
                        help='Logs and checkpoints directory (default=logs/)')
    parser.add_argument('--image', required=False,
                        metavar="path or URL to image",
                        help='Image to apply the color splash effect on')
    parser.add_argument('--video', required=False,
                        metavar="path or URL to video",
                        help='Video to apply the color splash effect on')
    args = parser.parse_args()

    # Validate arguments
    if args.command == "train":
        assert args.dataset, "Argument --dataset is required for training"
    elif args.command == "splash":
        assert args.image or args.video,\
               "Provide --image or --video to apply color splash"

    print("Weights: ", args.weights)
    print("Dataset: ", args.dataset)
    print("Logs: ", args.logs)

    # Configurations
    if args.command == "train":
        config = CustomConfig()
    else:
        class InferenceConfig(CustomConfig):
            # Set batch size to 1 since we'll be running inference on
            # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
            GPU_COUNT = 1
            IMAGES_PER_GPU = 1
        config = InferenceConfig()
    config.display()

    # Create model
    if args.command == "train":
        model = modellib.MaskRCNN(mode="training", config=config,
                                  model_dir=args.logs)
    else:
        model = modellib.MaskRCNN(mode="inference", config=config,
                                  model_dir=args.logs)

    # Select weights file to load
    if args.weights.lower() == "coco":
        weights_path = COCO_WEIGHTS_PATH
        # Download weights file
        if not os.path.exists(weights_path):
            utils.download_trained_weights(weights_path)
    elif args.weights.lower() == "last":
        # Find last trained weights
        weights_path = model.find_last()[1]
    elif args.weights.lower() == "imagenet":
        # Start from ImageNet trained weights
        weights_path = model.get_imagenet_weights()
    else:
        weights_path = args.weights

    # Load weights
    print("Loading weights ", weights_path)
    if args.weights.lower() == "coco":
        # Exclude the last layers because they require a matching
        # number of classes
        model.load_weights(weights_path, by_name=True, exclude=[
            "mrcnn_class_logits", "mrcnn_bbox_fc",
            "mrcnn_bbox", "mrcnn_mask"])
    else:
        model.load_weights(weights_path, by_name=True)

    # Train or evaluate
    if args.command == "train":
        train(model)
    elif args.command == "splash":
        detect_and_color_splash(model, image_path=args.image,
                                video_path=args.video)
    else:
        print("'{}' is not recognized. "
              "Use 'train' or 'splash'".format(args.command))
# Load and display random samples
image_ids = np.random.choice(dataset_train.image_ids, 4)
for image_id in image_ids:
    image = dataset_train.load_image(image_id)
    mask, class_ids = dataset_train.load_mask(image_id)
    visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)<jupyter_output><empty_output><jupyter_text>Ceate Model<jupyter_code># Create model in training mode
model = modellib.MaskRCNN(mode="training", config=config,
                          model_dir=MODEL_DIR)
# Which weights to start with?
init_with = "coco"  # imagenet, coco, or last

if init_with == "imagenet":
    model.load_weights(model.get_imagenet_weights(), by_name=True)
elif init_with == "coco":
    # Load weights trained on MS COCO, but skip layers that
    # are different due to the different number of classes
    # See README for instructions to download the COCO weights
    model.load_weights(COCO_MODEL_PATH, by_name=True,
                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", 
                                "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
    # Load the last model you trained and continue training
    model.load_weights(model.find_last()[1], by_name=True)<jupyter_output><empty_output><jupyter_text>TrainingTrain in two stages:1. 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.2. 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.<jupyter_code># Train the head branches
# Passing layers="heads" freezes all layers except the head
# layers. You can also pass a regular expression to select
# which layers to train by name pattern.
model.train(dataset_train, dataset_val, 
            learning_rate=config.LEARNING_RATE, 
            epochs=1, 
            layers='heads')
# Fine tune all layers
# Passing layers="all" trains all layers. You can also 
# pass a regular expression to select which layers to
# train by name pattern.
model.train(dataset_train, dataset_val, 
            learning_rate=config.LEARNING_RATE / 10,
            epochs=2, 
            layers="all")
# Save weights
# Typically not needed because callbacks save after every epoch
# Uncomment to save manually
# model_path = os.path.join(MODEL_DIR, "mask_rcnn_shapes.h5")
# model.keras_model.save_weights(model_path)<jupyter_output><empty_output><jupyter_text>Detection<jupyter_code>class InferenceConfig(CustomConfig):
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

inference_config = InferenceConfig()

# Recreate the model in inference mode
model = modellib.MaskRCNN(mode="inference", 
                          config=inference_config,
                          model_dir=MODEL_DIR)

# Get path to saved weights
# Either set a specific path or find last trained weights
# model_path = os.path.join(ROOT_DIR, ".h5 file name here")
model_path = model.find_last()[1]

# Load trained weights (fill in path to trained weights here)
assert model_path != "", "Provide path to trained weights"
print("Loading weights from ", model_path)
model.load_weights(model_path, by_name=True)
# Test on a random image
image_id = random.choice(dataset_val.image_ids)
original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\
    modellib.load_image_gt(dataset_val, inference_config, 
                           image_id, use_mini_mask=False)

log("original_image", original_image)
log("image_meta", image_meta)
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
log("gt_mask", gt_mask)

visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, 
                            dataset_train.class_names, figsize=(8, 8))
results = model.detect([original_image], verbose=1)

r = results[0]
visualize.display_instances(original_image, r['rois'], r['masks'], r['class_ids'], 
                            dataset_val.class_names, r['scores'], ax=get_ax())<jupyter_output><empty_output><jupyter_text>Evaluation<jupyter_code># Compute VOC-Style mAP @ IoU=0.5
# Running on 10 images. Increase for better accuracy.
image_ids = np.random.choice(dataset_val.image_ids, 10)
APs = []
for image_id in image_ids:
    # Load image and ground truth data
    image, image_meta, gt_class_id, gt_bbox, gt_mask =\
        modellib.load_image_gt(dataset_val, inference_config,
                               image_id, use_mini_mask=False)
    molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0)
    # Run object detection
    results = model.detect([image], verbose=0)
    r = results[0]
    # Compute AP
    AP, precisions, recalls, overlaps =\
        utils.compute_ap(gt_bbox, gt_class_id, gt_mask,
                         r["rois"], r["class_ids"], r["scores"], r['masks'])
    APs.append(AP)
    
print("mAP: ", np.mean(APs))<jupyter_output><empty_output>
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