Refactor load_image method to accept a path parameter directly

model/lunar_crater.py

@@ -0,0 +1,349 @@
+import os
+import sys
+import json
+import datetime
+import numpy as np
+import skimage.draw
+
+# Root directory of the project
+ROOT_DIR = os.path.abspath("../../")
+
+# 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")
+
+############################################################
+#  Configurations
+############################################################
+
+
+class CraterConfig(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 = "crater"
+
+    # We use a GPU with 12GB memory, which can fit two images.
+    # Adjust down if you use a smaller GPU.
+    IMAGES_PER_GPU = 1
+
+    # Number of classes (including background)
+    NUM_CLASSES = 1 + 1  # Background + crater
+
+    # Number of training steps per epoch
+    STEPS_PER_EPOCH = 100
+
+    # Skip detections with < 90% confidence
+    DETECTION_MIN_CONFIDENCE = 0.9
+
+
+############################################################
+#  Dataset
+############################################################
+
+class CraterDataset(utils.Dataset):
+
+    def load_crater(self,dataset_dir):
+        """Load a subset of the cratern 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("crater", 1, "crater")
+
+        # Train or validation dataset?
+        # assert subset in ["train", "val"]
+        # dataset_dir = os.path.join(dataset_dir, subset)
+
+
+        # Load annotations
+        # VGG Image Annotator (up to version 1.6) 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
+        # Note: In VIA 2.0, regions was changed from a dict to a list.
+
+
+
+
+        # dataset_dir = os.path.abspath("../../datasets/train")
+
+        annotations = json.load(open(os.path.join(dataset_dir, "via_region_data.json")))
+        annotations = list(annotations.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:
+            # Get the x, y coordinaets of points of the polygons that make up
+            # the outline of each object instance. These are stores in the
+            # shape_attributes (see json format above)
+            # The if condition is needed to support VIA versions 1.x and 2.x.
+            if type(a['regions']) is dict:
+                polygons = [r['shape_attributes'] for r in a['regions'].values()]
+            else:
+                polygons = [r['shape_attributes'] for r in a['regions']] 
+
+            # 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(
+                "crater",
+                image_id=a['filename'],  # use file name as a unique image id
+                path=image_path,
+                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 crater dataset image, delegate to parent class.
+        image_info = self.image_info[image_id]
+        if image_info["source"] != "crater":
+            return super(self.__class__, self).load_mask(image_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)
+        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(bool), np.ones([mask.shape[-1]], dtype=np.int32)
+
+
+    def image_reference(self, image_id):
+        """Return the path of the image."""
+        info = self.image_info[image_id]
+        if info["source"] == "crater":
+            return info["path"]
+        else:
+            super(self.__class__, self).image_reference(image_id)
+
+
+def train(model):
+    """Train the model."""
+    # Training dataset.
+    dataset_train = CraterDataset()
+    dataset_train.load_crater(args.dataset, "train")
+    dataset_train.prepare()
+
+    # Validation dataset
+    dataset_val = CraterDataset()
+    dataset_val.load_crater(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=30,
+                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
+    # Copy color pixels from the original color image where mask is set
+    if mask.shape[-1] > 0:
+        # We're treating all instances as one, so collapse the mask into one layer
+        mask = (np.sum(mask, -1, keepdims=True) >= 1)
+        splash = np.where(mask, image, gray).astype(np.uint8)
+    else:
+        splash = gray.astype(np.uint8)
+    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 craters.')
+    parser.add_argument("command",
+                        metavar="<command>",
+                        help="'train' or 'splash'")
+    parser.add_argument('--dataset', required=False,
+                        metavar="/path/to/crater/dataset/",
+                        help='Directory of the crater 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=DEFAULT_LOGS_DIR,
+                        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 = CraterConfig()
+    else:
+        class InferenceConfig(CraterConfig):
+            # 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 = 2
+        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()
+    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))

mrcnn/utils.py

@@ -352,11 +352,11 @@ def source_image_link(self, image_id):
         """
         return self.image_info[image_id]["path"]
 
-    def load_image(self, image_id):
+    def load_image(self, path):
         """Load the specified image and return a [H,W,3] Numpy array.
         """
         # Load image
-        image = skimage.io.imread(self.image_info[image_id]['path'])
+        image = skimage.io.imread(path)
         # If grayscale. Convert to RGB for consistency.
         if image.ndim != 3:
             image = skimage.color.gray2rgb(image)