134 lines
5.4 KiB
Python
134 lines
5.4 KiB
Python
import sys
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import cv2
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import numpy as np
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import os
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def find_photo_boundaries(image, background_color, tolerance=30, min_area=10000, min_contour_area=500):
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mask = cv2.inRange(image, background_color - tolerance, background_color + tolerance)
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mask = cv2.bitwise_not(mask)
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kernel = np.ones((5,5),np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=2)
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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photo_boundaries = []
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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area = w * h
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contour_area = cv2.contourArea(contour)
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if area >= min_area and contour_area >= min_contour_area:
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photo_boundaries.append((x, y, w, h))
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return photo_boundaries
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def estimate_background_color(image, sample_points=5):
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h, w, _ = image.shape
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points = [
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(0, 0),
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(w - 1, 0),
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(w - 1, h - 1),
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(0, h - 1),
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(w // 2, h // 2),
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]
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colors = []
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for x, y in points:
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colors.append(image[y, x])
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return np.median(colors, axis=0)
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def auto_rotate(image, angle_threshold=10):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours) == 0:
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return image
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largest_contour = max(contours, key=cv2.contourArea)
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mu = cv2.moments(largest_contour)
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if mu["m00"] == 0:
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return image
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x_centroid = int(mu["m10"] / mu["m00"])
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y_centroid = int(mu["m01"] / mu["m00"])
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coords = np.column_stack(np.where(binary > 0))
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u, _, vt = np.linalg.svd(coords - np.array([[y_centroid, x_centroid]]), full_matrices=False)
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angle = np.arctan2(u[1, 0], u[0, 0]) * 180 / np.pi
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if angle < -45:
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angle = -(90 + angle)
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else:
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angle = -angle
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if abs(angle) < angle_threshold:
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return image
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(h, w) = image.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, angle, 1.0)
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return cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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def crop_borders(image, border_color, tolerance=30):
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mask = cv2.inRange(image, border_color - tolerance, border_color + tolerance)
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours) == 0:
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return image
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largest_contour = max(contours, key=cv2.contourArea)
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x, y, w, h = cv2.boundingRect(largest_contour)
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return image[y:y+h, x:x+w]
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def split_photos(input_file, output_directory, tolerance=30, min_area=10000, min_contour_area=500, angle_threshold=10, border_size=0):
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image = cv2.imread(input_file)
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background_color = estimate_background_color(image)
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# Add a constant border around the image
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image = cv2.copyMakeBorder(image, border_size, border_size, border_size, border_size, cv2.BORDER_CONSTANT, value=background_color)
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photo_boundaries = find_photo_boundaries(image, background_color, tolerance)
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if not os.path.exists(output_directory):
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os.makedirs(output_directory)
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# Get the input file's base name without the extension
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input_file_basename = os.path.splitext(os.path.basename(input_file))[0]
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for idx, (x, y, w, h) in enumerate(photo_boundaries):
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cropped_image = image[y:y+h, x:x+w]
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cropped_image = auto_rotate(cropped_image, angle_threshold)
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# Remove the added border
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cropped_image = cropped_image[border_size:-border_size, border_size:-border_size]
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output_path = os.path.join(output_directory, f"{input_file_basename}_{idx+1}.png")
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cv2.imwrite(output_path, cropped_image)
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print(f"Saved {output_path}")
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python3 split_photos.py <input_file> <output_directory> [tolerance] [min_area] [min_contour_area] [angle_threshold] [border_size]")
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print("\nParameters:")
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print(" <input_file> - The input scanned image containing multiple photos.")
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print(" <output_directory> - The directory where the result images should be placed.")
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print(" [tolerance] - Optional. Determines the range of color variation around the estimated background color (default: 30).")
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print(" [min_area] - Optional. Sets the minimum area threshold for a photo (default: 10000).")
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print(" [min_contour_area] - Optional. Sets the minimum contour area threshold for a photo (default: 500).")
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print(" [angle_threshold] - Optional. Sets the minimum absolute angle required for the image to be rotated (default: 10).")
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print(" [border_size] - Optional. Sets the size of the border added and removed to prevent white borders in the output (default: 0).")
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sys.exit(1)
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input_file = sys.argv[1]
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output_directory = sys.argv[2]
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tolerance = int(sys.argv[6]) if len(sys.argv) > 3 else 20
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min_area = int(sys.argv[8]) if len(sys.argv) > 4 else 8000
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min_contour_area = int(sys.argv[10]) if len(sys.argv) > 5 else 500
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angle_threshold = int(sys.argv[4]) if len(sys.argv) > 6 else 60
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border_size = int(sys.argv[12]) if len(sys.argv) > 7 else 0
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split_photos(input_file, output_directory, tolerance=tolerance, min_area=min_area, min_contour_area=min_contour_area, angle_threshold=angle_threshold, border_size=border_size)
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