refactor: rename package from image_panel_border_cleaner to manga_panel_processor

app.py

@@ -15,7 +15,7 @@ import shutil
 from tqdm import tqdm
 
 from image_processing.panel import generate_panel_blocks, generate_panel_blocks_by_ai
-from image_panel_border_cleaner import remove_border
+from manga_panel_processor import remove_border
 
 # --- UI Description ---
 DESCRIPTION = """

image_processing/panel.py

@@ -7,6 +7,7 @@ from image_processing.image import is_contour_rectangular, apply_adaptive_thresh
 from myutils.myutils import load_images, load_image
 from tqdm import tqdm
 from image_processing.model import model
+from manga_panel_processor import sort_panels_by_column_then_row
 
 class OutputMode:
     BOUNDING = 'bounding'
@@ -308,71 +309,6 @@ def get_fallback_panels(
     
     return panels
 
-def _sort_items_by_reading_order(items: list, rtl_order: bool, image_height: int) -> list:
-    """
-    Sorts contours or bounding boxes based on reading order (top-to-bottom, then LTR/RTL).
-    This function is robust against minor vertical misalignments by grouping items into rows.
-
-    Parameters:
-    - items: A list of contours (np.ndarray) or bounding boxes (tuple of x,y,w,h).
-    - rtl_order: If True, sort horizontally from right-to-left.
-    - image_height: The height of the original image, used for calculating tolerance.
-
-    Returns:
-    - A sorted list of the input items.
-    """
-    if not items:
-        return []
-
-    # Unify items into a list of (item, bbox) tuples for consistent processing
-    item_bboxes = []
-    for item in items:
-        if isinstance(item, np.ndarray): # It's a contour
-            bbox = cv2.boundingRect(item)
-        else: # It's already a bbox tuple
-            bbox = item
-        item_bboxes.append((item, bbox))
-
-    # Initial sort by top y-coordinate
-    item_bboxes.sort(key=lambda x: x[1][1])
-
-    rows = []
-    current_row = []
-    if item_bboxes:
-        # Start the first row
-        current_row.append(item_bboxes[0])
-        first_item_in_row_bbox = item_bboxes[0][1]
-        
-        # Define a dynamic tolerance based on the height of the first panel in a row.
-        # A panel can be considered in the same row if its top is not lower than 
-        # the first panel's top + 30% of its height. This is a robust heuristic.
-        # We also add a minimum tolerance for very short panels.
-        y_tolerance = max(10, int(first_item_in_row_bbox[3] * 0.3))
-
-        for item, bbox in item_bboxes[1:]:
-            # If the current panel's y is within the tolerance of the current row's start y
-            if bbox[1] < first_item_in_row_bbox[1] + y_tolerance:
-                current_row.append((item, bbox))
-            else:
-                # Finish the current row
-                # Sort the completed row horizontally
-                current_row.sort(key=lambda x: -x[1][0] if rtl_order else x[1][0])
-                rows.append(current_row)
-                
-                # Start a new row
-                current_row = [(item, bbox)]
-                first_item_in_row_bbox = bbox
-                y_tolerance = max(10, int(first_item_in_row_bbox[3] * 0.3))
-
-        # Add the last processed row
-        if current_row:
-            current_row.sort(key=lambda x: -x[1][0] if rtl_order else x[1][0])
-            rows.append(current_row)
-
-    # Flatten the rows and extract the original items in the correct order
-    sorted_items = [item for row in rows for item, bbox in row]
-    
-    return sorted_items
 
 def generate_panel_blocks(
         image: np.ndarray, 
@@ -404,7 +340,7 @@ def generate_panel_blocks(
     # For RTL, we sort by x-coordinate in descending order (by negating it).
     if contours:
         image_height = image.shape[0]
-        contours = _sort_items_by_reading_order(contours, rtl_order, image_height)
+        contours = sort_panels_by_column_then_row(contours, rtl_order)
 
     def get_panels(contours):
         panels = extract_panels(image, contours, mode=mode)
@@ -453,7 +389,7 @@ def generate_panel_blocks_by_ai(
     # Bounding boxes are already (x, y, w, h), so we access coordinates directly.
     if bounding_boxes:
         image_height = image.shape[0]
-        bounding_boxes = _sort_items_by_reading_order(bounding_boxes, rtl_order, image_height)
+        bounding_boxes = sort_panels_by_column_then_row(bounding_boxes, rtl_order)
 
     def get_panels(bounding_boxes):
         panels = []

requirements.txt

@@ -7,4 +7,4 @@ tqdm
 torch
 yolov5
 
-git+https://github.com/avan06/image-panel-border-cleaner.git
+git+https://github.com/avan06/manga-panel-processor.git