mirror of
https://github.com/Frooodle/Stirling-PDF.git
synced 2025-11-01 01:21:18 +01:00
117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
import argparse
|
|
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=1):
|
|
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
|
|
lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
|
|
|
|
if lines is None:
|
|
return image
|
|
|
|
# compute the median angle of the lines
|
|
angles = []
|
|
for rho, theta in lines[:, 0]:
|
|
angles.append((theta * 180) / np.pi - 90)
|
|
|
|
angle = np.median(angles)
|
|
|
|
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__":
|
|
parser = argparse.ArgumentParser(description="Split photos in an image")
|
|
parser.add_argument("input_file", help="The input scanned image containing multiple photos.")
|
|
parser.add_argument("output_directory", help="The directory where the result images should be placed.")
|
|
parser.add_argument("--tolerance", type=int, default=30, help="Determines the range of color variation around the estimated background color (default: 30).")
|
|
parser.add_argument("--min_area", type=int, default=10000, help="Sets the minimum area threshold for a photo (default: 10000).")
|
|
parser.add_argument("--min_contour_area", type=int, default=500, help="Sets the minimum contour area threshold for a photo (default: 500).")
|
|
parser.add_argument("--angle_threshold", type=int, default=10, help="Sets the minimum absolute angle required for the image to be rotated (default: 10).")
|
|
parser.add_argument("--border_size", type=int, default=0, help="Sets the size of the border added and removed to prevent white borders in the output (default: 0).")
|
|
|
|
args = parser.parse_args()
|
|
|
|
split_photos(args.input_file, args.output_directory, tolerance=args.tolerance, min_area=args.min_area, min_contour_area=args.min_contour_area, angle_threshold=args.angle_threshold, border_size=args.border_size)
|