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