add patch evluation

src/orisys/Stitcher.py

@@ -446,7 +446,7 @@ class Stitcher:
         n_boxes = 0
         bk_thresh = 0  # initial black threshold
         bk_thresh_step = 5
-        bk_thresh_max = 100
+        bk_thresh_max = 125
 
         # Also include a low global V threshold to capture colored but dark tiles
         hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
@@ -503,7 +503,24 @@ class Stitcher:
             cropped_boxes.append((nx0, ny0, nx1, ny1))
 
         # sort boxes left-to-right, top-to-bottom (useful for grid)
-        cropped_boxes = sorted(cropped_boxes, key=lambda b: (b[1], b[0]))
+        # Use quantized y-coordinates to group boxes into rows, making sorting robust to noise
+        if len(cropped_boxes) > 0:
+            # Calculate row tolerance based on typical box height (use median height)
+            heights = [b[3] - b[1] for b in cropped_boxes]
+            median_height = np.median(heights) if heights else target_h
+            # Use a fraction of the box height as tolerance (e.g., 20% of box height)
+            row_tolerance = max(1, int(median_height * 0.2))
+            
+            # Quantize y-coordinates to group boxes into rows
+            def get_row_key(box):
+                y = box[1]  # y0 coordinate
+                # Round y to nearest multiple of row_tolerance to group into rows
+                quantized_y = (y // row_tolerance) * row_tolerance
+                return (quantized_y, box[0])  # Sort by quantized row, then by x
+            
+            cropped_boxes = sorted(cropped_boxes, key=get_row_key)
+        else:
+            cropped_boxes = sorted(cropped_boxes, key=lambda b: (b[1], b[0]))
         
         self.boxes = cropped_boxes
         img_with_boxes = self.draw_boxes(img= img)