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import cv2
< /code>
Importieren Sie Numpy als np < /p>
Def crop_empty_spaces_refined (Image_Path, Threshold_percentage = 0.01):
Image = Cv2.imread (Image_path, CV2. Imread_unchanged) < /p>
if image is None:
print(f"Error: Could not read image at {image_path}")
return None

if image.shape[2] == 4:  # RGBA image
gray = image[:, :, 3]  # Use alpha channel
else:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

_, thresh = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)

kernel = np.ones((3, 3), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)

contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

if contours:
image_area = image.shape[0] * image.shape[1]
min_contour_area = image_area * threshold_percentage

valid_contours = [cnt for cnt in contours if cv2.contourArea(cnt) >= min_contour_area]

if valid_contours:
# ***Corrected Bounding Box Calculation***
x_coords = []
y_coords = []

for cnt in valid_contours:
x, y, w, h = cv2.boundingRect(cnt)
x_coords.extend([x, x + w])  # Add both start and end x
y_coords.extend([y, y + h])  # Add both start and end y

x_min = min(x_coords)
y_min = min(y_coords)
x_max = max(x_coords)
y_max = max(y_coords)

cropped_image = image[y_min:y_max, x_min:x_max]

return cropped_image
else:
print("No valid contours found after filtering. Returning original image.")
return image
else:
return image