refactor: Replace local remove_border with image-panel-border-cleaner package.

app.py

@@ -30,117 +30,7 @@ import cv2 as cv # The project uses 'cv' as an alias for cv2
 # Import Kumiko's core library and its page module dependency
 import kumikolib
 import lib.page
-
-# ----------------------------------------------------------------------
-# Border Removal Function and Dependencies
-# ----------------------------------------------------------------------
-
-# Ensure the thinning function is available
-try:
-    # Attempt to import the thinning function from the contrib module
-    from cv2.ximgproc import thinning
-except ImportError:
-    # If opencv-contrib-python is not installed, print a warning and provide a dummy function
-    print("Warning: cv2.ximgproc.thinning not found. Border removal might be less effective.")
-    print("Please install 'opencv-contrib-python' via 'pip install opencv-contrib-python'")
-    def thinning(src, thinningType=None): # Dummy function to prevent crashes
-        return src
-
-
-def _find_best_border_line(roi_mask: np.ndarray, axis: int, scan_range: range) -> int:
-    """
-    A helper function to find the best border line along a single axis.
-    It scans from the inside-out and returns the index of the line with the highest score.
-    """
-    best_index, max_score = scan_range.start, -1
-    
-    total_span = abs(scan_range.stop - scan_range.start)
-    if total_span == 0:
-        return best_index
-
-    for i in scan_range:
-        if axis == 1: # Horizontal scan (for top/bottom borders)
-            continuity_score = np.count_nonzero(roi_mask[i, :])
-        else: # Vertical scan (for left/right borders)
-            continuity_score = np.count_nonzero(roi_mask[:, i])
-            
-        progress = abs(i - scan_range.start)
-        position_weight = progress / total_span
-        
-        score = continuity_score * (1 + position_weight)
-        
-        if score >= max_score:
-            max_score, best_index = score, i
-            
-    return best_index
-
-
-def remove_border(panel_image: np.ndarray, 
-                  search_zone_ratio: float = 0.25, 
-                  padding: int = 5) -> np.ndarray:
-    """
-    Removes borders using skeletonization and weighted projection analysis.
-    """
-    if panel_image is None or panel_image.shape[0] < 30 or panel_image.shape[1] < 30:
-        return panel_image
-
-    pad_size = 15
-    # Use 'cv' which is the alias for cv2 in this project
-    padded_image = cv.copyMakeBorder(
-        panel_image, pad_size, pad_size, pad_size, pad_size,
-        cv.BORDER_CONSTANT, value=[255, 255, 255]
-    )
-    
-    gray = cv.cvtColor(padded_image, cv.COLOR_BGR2GRAY)
-    _, thresh = cv.threshold(gray, 240, 255, cv.THRESH_BINARY_INV)
-    
-    contours, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
-
-    if not contours:
-        return panel_image
-
-    largest_contour = max(contours, key=cv.contourArea)
-    x, y, w, h = cv.boundingRect(largest_contour)
-
-    filled_mask = np.zeros_like(gray)
-    cv.drawContours(filled_mask, [largest_contour], -1, 255, cv.FILLED)
-    
-    erosion_iterations = 5 
-    hollow_contour = cv.subtract(filled_mask, cv.erode(filled_mask, np.ones((3,3), np.uint8), iterations=erosion_iterations))
-    
-    skeleton = thinning(hollow_contour)
-    
-    roi_mask = skeleton[y:y+h, x:x+w]
-
-    top_search_end = int(h * search_zone_ratio)
-    bottom_search_start = h - top_search_end
-    left_search_end = int(w * search_zone_ratio)
-    right_search_start = w - left_search_end
-    
-    top_range = range(top_search_end, -1, -1)
-    bottom_range = range(bottom_search_start, h)
-    left_range = range(left_search_end, -1, -1)
-    right_range = range(right_search_start, w)
-    
-    best_top_y = _find_best_border_line(roi_mask, axis=1, scan_range=top_range)
-    best_bottom_y = _find_best_border_line(roi_mask, axis=1, scan_range=bottom_range)
-    best_left_x = _find_best_border_line(roi_mask, axis=0, scan_range=left_range)
-    best_right_x = _find_best_border_line(roi_mask, axis=0, scan_range=right_range)
-
-    final_x1 = x + best_left_x + padding
-    final_y1 = y + best_top_y + padding
-    final_x2 = x + best_right_x - padding
-    final_y2 = y + best_bottom_y - padding
-    
-    if final_x1 >= final_x2 or final_y1 >= final_y2: 
-        return panel_image
-        
-    cropped = padded_image[final_y1:final_y2, final_x1:final_x2]
-    
-    if cropped.shape[0] < 10 or cropped.shape[1] < 10: 
-        return panel_image
-        
-    return cropped
+from image_panel_border_cleaner import remove_border
 
 
 # ----------------------------------------------------------------------

requirements.txt

@@ -1,3 +1,5 @@
 opencv-contrib-python
 requests
-gradio
+gradio
+
+git+https://github.com/avan06/image-panel-border-cleaner.git