Updated crop function to return alpha image

app.py

@@ -91,6 +91,58 @@ def build_model(hypar,device):
     return net
 
 
+def crop_signature(original_image_path, mask, padding=32):
+    """
+    Crop the signature from the original image using the provided mask and create a transparent image.
+    :param original_image_path: The file path of the original image.
+    :param mask: The binary mask of the signature.
+    :param padding: Padding to add around the bounding box of the signature.
+    :return: Cropped transparent image containing the signature.
+    """
+    # Convert the mask to a binary image
+    _, binary_mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
+
+    # Find contours from the binary mask
+    contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Open the original image
+    original_image = Image.open(original_image_path).convert("RGB")
+
+    # If contours are found, proceed to crop
+    if contours:
+        # Find the combined bounding box of all contours
+        min_x, min_y = original_image.width, original_image.height
+        max_x = max_y = 0
+        for contour in contours:
+            x, y, w, h = cv2.boundingRect(contour)
+            min_x, min_y = min(min_x, x), min(min_y, y)
+            max_x, max_y = max(max_x, x + w), max(max_y, y + h)
+
+        # Add padding to the bounding box
+        x_padded = max(min_x - padding, 0)
+        y_padded = max(min_y - padding, 0)
+        w_padded = min(max_x + padding, original_image.width) - x_padded
+        h_padded = min(max_y + padding, original_image.height) - y_padded
+
+        # Crop the original image using the combined bounding box with padding
+        cropped_image = original_image.crop((x_padded, y_padded, x_padded + w_padded, y_padded + h_padded))
+
+        # Create a new image for the mask with the same size as the cropped image and make it transparent
+        mask_image = Image.new("L", cropped_image.size, 0)
+        mask_array = np.array(mask, dtype=np.uint8)  # Convert the mask to an array
+        # Crop the mask array to the same bounding box
+        cropped_mask_array = mask_array[y_padded:y_padded+h_padded, x_padded:x_padded+w_padded]
+        mask_image.putdata(cropped_mask_array.flatten())
+
+        # Convert mask image to alpha channel and combine it with the cropped image
+        cropped_image.putalpha(mask_image)
+
+        return cropped_image
+
+    # If no contours are found, return the original image
+    return original_image.convert("RGBA")
+
+'''
 def crop_signature(original_image_path, mask, padding=32):
     """
     Crop the signature from the original image using the provided mask.
@@ -133,7 +185,7 @@ def crop_signature(original_image_path, mask, padding=32):
 
     # If no contours are found, return the original image
     return original_image
-
+'''
 
 def smooth_and_denoise(mask):
     """