Update app.py

app.py

@@ -465,33 +465,23 @@ def mask_paper_area_in_image(image: np.ndarray, paper_contour: np.ndarray) -> np
     
 def exclude_paper_area(mask: np.ndarray, paper_contour: np.ndarray, expansion_factor: float = 1.2) -> np.ndarray:
     """
-    Remove paper area from the mask to focus only on objects
+    Remove paper area from the mask to focus only on objects using soft boundaries
     """
-    # Get paper bounding rectangle
-    rect = cv2.boundingRect(paper_contour)
-    x, y, w, h = rect
-    
-    # Create much more aggressive inward shrinking
-    shrink_x = int(w * 0.15)  # Shrink 15% from each side
-    shrink_y = int(h * 0.15)  # Shrink 15% from top/bottom
-    
-    # Create inner object area (much smaller than paper)
-    inner_x = x + shrink_x
-    inner_y = y + shrink_y
-    inner_w = w - (2 * shrink_x)
-    inner_h = h - (2 * shrink_y)
+    # Create paper mask
+    paper_mask = np.zeros(mask.shape[:2], dtype=np.uint8)
+    cv2.fillPoly(paper_mask, [paper_contour], 255)
     
-    # Create mask for object area only
-    object_area_mask = np.zeros(mask.shape[:2], dtype=np.uint8)
-    cv2.rectangle(object_area_mask, (inner_x, inner_y), (inner_x + inner_w, inner_y + inner_h), 255, -1)
+    # Create eroded version of paper mask (soft inward boundary)
+    erosion_size = int(min(mask.shape[0], mask.shape[1]) * 0.03)  # 3% of image size
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (erosion_size, erosion_size))
+    eroded_paper_mask = cv2.erode(paper_mask, kernel, iterations=2)
     
-    # Apply mask: keep only pixels in the inner object area
-    result_mask = cv2.bitwise_and(mask, object_area_mask)
+    # Apply soft mask: keep pixels in original mask AND in eroded paper area
+    result_mask = cv2.bitwise_and(mask, eroded_paper_mask)
     
-    # Additional cleanup: remove small noise and fill gaps
-    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
-    result_mask = cv2.morphologyEx(result_mask, cv2.MORPH_OPEN, kernel, iterations=1)
-    result_mask = cv2.morphologyEx(result_mask, cv2.MORPH_CLOSE, kernel, iterations=2)
+    # Gentle cleanup without hard cuts
+    small_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+    result_mask = cv2.morphologyEx(result_mask, cv2.MORPH_CLOSE, small_kernel, iterations=1)
     
     return result_mask
 
@@ -765,13 +755,34 @@ def place_finger_cut_adjusted(
         logger.error(f"Error in place_finger_cut_adjusted: {e}")
         return fallback_solution()
 
+# def extract_outlines(binary_image: np.ndarray) -> Tuple[np.ndarray, list]:
+#     """Extract outlines from binary image"""
+#     contours, _ = cv2.findContours(
+#         binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+#     )
+#     outline_image = np.full_like(binary_image, 255)
+#     return outline_image, contours
+
 def extract_outlines(binary_image: np.ndarray) -> Tuple[np.ndarray, list]:
     """Extract outlines from binary image"""
+    # Check if contours are being cut at image boundaries
+    h, w = binary_image.shape
+    
+    # Add small border to prevent boundary cutting
+    bordered_image = cv2.copyMakeBorder(binary_image, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=0)
+    
     contours, _ = cv2.findContours(
-        binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+        bordered_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
     )
+    
+    # Adjust contour coordinates back to original image space
+    adjusted_contours = []
+    for contour in contours:
+        adjusted_contour = contour - [5, 5]  # Subtract border offset
+        adjusted_contours.append(adjusted_contour)
+    
     outline_image = np.full_like(binary_image, 255)
-    return outline_image, contours
+    return outline_image, adjusted_contours
 
 def round_edges(mask: np.ndarray, radius_mm: float, scaling_factor: float) -> np.ndarray:
     """Round mask edges using contour smoothing"""