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49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
import cv2
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import numpy as np
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import sys
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import argparse
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def is_blank_image(image_path, threshold=10, white_percent=99, white_value=255, blur_size=5):
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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if image is None:
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print(f"Error: Unable to read the image file: {image_path}")
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return False
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# Apply Gaussian blur to reduce noise
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blurred_image = cv2.GaussianBlur(image, (blur_size, blur_size), 0)
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_, thresholded_image = cv2.threshold(blurred_image, white_value - threshold, white_value, cv2.THRESH_BINARY)
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# Calculate the percentage of white pixels in the thresholded image
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white_pixels = 0
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total_pixels = thresholded_image.size
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for i in range(0, thresholded_image.shape[0], 2):
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for j in range(0, thresholded_image.shape[1], 2):
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if thresholded_image[i, j] == white_value:
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white_pixels += 1
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white_pixel_percentage = (white_pixels / (i * thresholded_image.shape[1] + j + 1)) * 100
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if white_pixel_percentage < white_percent:
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return False
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print(f"Page has white pixel percent of {white_pixel_percentage}")
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return True
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Detect if an image is considered blank or not.')
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parser.add_argument('image_path', help='The path to the image file.')
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parser.add_argument('-t', '--threshold', type=int, default=10, help='Threshold for determining white pixels. The default value is 10.')
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parser.add_argument('-w', '--white_percent', type=float, default=99, help='The percentage of white pixels for an image to be considered blank. The default value is 99.')
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args = parser.parse_args()
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blank = is_blank_image(args.image_path, args.threshold, args.white_percent)
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if blank:
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# Return code 1: The image is considered blank.
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sys.exit(1)
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else:
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# Return code 0: The image is not considered blank.
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sys.exit(0)
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