ValueError: Could not find a backend to open '10.jpeg` with iomode `ri`

Question

I am new to MaskRCNN. I have added 15 images to the dataset/train folder however, while running the code I am getting error for the line

image = skimage.io.imread(image_path)

Error is :

ValueError: Could not find a backend to open /Users/deepali/Documents/CV Projects/wastedata-Mask_RCNN-multiple-classes/main/dataset/train/10.jpeg with iomode ri

Whole Code is as follows:

    import os
    import sys
    import json
    import datetime
    import numpy as np
    import skimage.draw
    import cv2
    from skimage import io
    from mrcnn.visualize import display_instances
    import matplotlib.pyplot as plt
    
    # Root directory of the project
    ROOT_DIR = os.path.abspath("/Users/deepali/Documents/Mask_RCNN-multiple-classes/")
    
    # 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 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 = "object"
    
        # 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 + 4  # Background + toy
        NUM_CLASSES = 1 + 2  # Background + smoke, vehicle
    
        # Number of training steps per epoch
        STEPS_PER_EPOCH = 100
    
        # Skip detections with < 90% confidence
        DETECTION_MIN_CONFIDENCE = 0.9
    
    
    ############################################################
    #  Dataset
    ############################################################
    
    class CustomDataset(utils.Dataset):
    
        def load_custom(self, dataset_dir, subset):
            """Load a subset of the bottle dataset.
            dataset_dir: Root directory of the dataset.
            subset: Subset to load: train or val
            """
            dataset_dir = '/Users/deepali/Documents/Mask_RCNN-multiple-classes/main/dataset/'
            # Add classes. We have only one class to add.
            # self.add_class("object", 1, "bottle")
            # self.add_class("object", 2, "glass")
            # self.add_class("object", 3, "paper")
            # self.add_class("object", 4, "trash")
            self.add_class("object", 1, "vehicle")
            self.add_class("object", 2, "smoke")
    
            # 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("/Users/deepali/Documents/CV Projects/wastedata-Mask_RCNN-multiple-classes/main/dataset/train/vehicle_smoke_train.json")))
            # /Users/deepali/Documents/CV Projects/wastedata-Mask_RCNN-multiple-classes/main/dataset/train/via_reg_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']] 
                objects = [s['region_attributes'] for s in a['regions']]
                # print("objects:",objects)
                class_name = objects[0]['name']
                file_name = a['filename'].split("/")
                file_name = file_name[len(file_name)-1]
                #getting class_ids with file_name
                class_ids = class_name+"_"+file_name
                # name_dict = {"bottle": 1,"glass": 2,"paper": 3,"trash": 4}
                # name_dict = {"vehicle": 1,"smoke": 2}
                # key = tuple(name_dict)
                # num_ids = [name_dict[a] for a in objects]
         
                # num_ids = [int(n['Event']) for n in objects]
                # 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.
                # print("numids",num_ids)
                image_path = os.path.join(dataset_dir, file_name)
                image = skimage.io.imread(image_path)
                height, width = image.shape[:2]
    
                self.add_image(
                    "object",  ## for a single class just add the name here
                    image_id=a['filename'],  # use file name as a unique image id
                    path=image_path,
                    width=width, height=height,
                    polygons=polygons,
                    class_ids=class_ids)
    
        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 bottle dataset image, delegate to parent class.
            image_info = self.image_info[image_id]
            if image_info["source"] != "object":
                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]
            if info["source"] != "object":
                return super(self.__class__, self).load_mask(image_id)
            num_ids = info['num_ids']
            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
            # Map class names to class IDs.
            num_ids = np.array(num_ids, dtype=np.int32)
            return mask, num_ids
    
        def image_reference(self, image_id):
            """Return the path of the image."""
            info = self.image_info[image_id]
            if info["source"] == "object":
                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=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 = 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))

Traceback (most recent call last):   
File "final.py", line 362, in <module>
      train(model)   
File "final.py", line 183, in train
     dataset_train.load_custom(args.dataset, "train")   
File "final.py", line 127, in load_custom
      image = skimage.io.imread(image_path)   
File "/Users/deepali/Documents/Mask_RCNN-multiple-classes/venv/lib/python3.7/site-packages/skimage/io/_io.py", line 53, in imread
      img = call_plugin('imread', fname, plugin=plugin, **plugin_args)   
File "/Users/deepali/Documents/Mask_RCNN-multiple-classes/venv/lib/python3.7/site-packages/skimage/io/manage_plugins.py",line 207, in call_plugin
      return func(*args, **kwargs)   
File "/Users/deepali/Documents/Mask_RCNN-multiple-classes/venv/lib/python3.7/site-packages/skimage/io/_plugins/imageio_plugin.py",line 10, in imread
      return np.asarray(imageio_imread(*args, **kwargs))   
File "/Users/deepali/Documents/Mask_RCNN-multiple-classes/venv/lib/python3.7/site-packages/imageio/core/functions.py",line 159, in imread
      with imopen(uri, "ri", plugin=format) as file:   
File "/Users/deepali/Documents/Mask_RCNN-multiple-classes/venv/lib/python3.7/site-packages/imageio/core/imopen.py",line 277, in imopen
      raise err_type(err_msg) 
ValueError: Could not find a backend to open `/Users/deepali/Documents/Mask_RCNN-multiple-classes/main/dataset/train/10.jpg with iomode ri