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

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

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