Stirling-PDF/scripts/split_photos.py
2023-05-01 21:57:48 +01:00

135 lines
5.4 KiB
Python

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)