Update app.py

app.py

@@ -171,76 +171,6 @@ def get_birefnet():
         logger.info("BiRefNet model loaded successfully")
     return birefnet
 
-# def detect_paper_contour(image: np.ndarray) -> Tuple[np.ndarray, float]:
-#     """
-#     Detect paper in the image using contour detection as fallback
-#     Returns the paper contour and estimated scaling factor
-#     """
-#     logger.info("Using contour-based paper detection")
-    
-#     # Convert to grayscale
-#     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image
-    
-#     # Apply bilateral filter to reduce noise while preserving edges
-#     filtered = cv2.bilateralFilter(gray, 9, 75, 75)
-    
-#     # Apply adaptive threshold
-#     thresh = cv2.adaptiveThreshold(filtered, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
-#                                    cv2.THRESH_BINARY, 11, 2)
-    
-#     # Edge detection with multiple thresholds
-#     edges1 = cv2.Canny(filtered, 50, 150)
-#     edges2 = cv2.Canny(filtered, 30, 100)
-#     edges = cv2.bitwise_or(edges1, edges2)
-    
-#     # Morphological operations to connect broken edges
-#     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
-#     edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
-    
-#     # Find contours
-#     contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    
-#     # Filter contours by area and aspect ratio to find paper-like rectangles
-#     paper_contours = []
-#     image_area = image.shape[0] * image.shape[1]
-#     min_area = image_area * 0.20  # At least 15% of image
-#     max_area = image_area * 0.85  # At most 95% of image
-    
-#     for contour in contours:
-#         area = cv2.contourArea(contour)
-#         if min_area < area < max_area:
-#             # Approximate contour to polygon
-#             epsilon = 0.015 * cv2.arcLength(contour, True)
-#             approx = cv2.approxPolyDP(contour, epsilon, True)
-            
-#             # Check if it's roughly rectangular (4 corners) or close to it
-#             if len(approx) >= 4:
-#                 # Calculate bounding rectangle
-#                 rect = cv2.boundingRect(approx)
-#                 w, h = rect[2], rect[3]
-#                 aspect_ratio = w / h if h > 0 else 0
-                
-#                 # Check if aspect ratio matches common paper ratios
-#                 # A4: 1.414, A3: 1.414, US Letter: 1.294
-#                 if 1.3 < aspect_ratio < 1.5:  # More lenient tolerance
-#                     # Check if contour area is close to bounding rect area (rectangularity)
-#                     rect_area = w * h
-#                     if rect_area > 0:
-#                         extent = area / rect_area
-#                         if extent > 0.85:  # At least 70% rectangular
-#                             paper_contours.append((contour, area, aspect_ratio, extent))
-    
-#     if not paper_contours:
-#         logger.error("No paper-like contours found")
-#         raise ReferenceBoxNotDetectedError("Could not detect paper in the image using contour detection")
-    
-#     # Select the best paper contour based on area and rectangularity
-#     paper_contours.sort(key=lambda x: (x[1] * x[3]), reverse=True)  # Sort by area * extent
-#     best_contour = paper_contours[0][0]
-    
-#     logger.info(f"Paper detected using contours: area={paper_contours[0][1]}, aspect_ratio={paper_contours[0][2]:.2f}")
-    
-#     return best_contour, 0.0  # Return 0.0 as placeholder scaling factor
 def detect_paper_contour(image: np.ndarray, output_unit: str = "mm") -> Tuple[np.ndarray, float]:
     """
     Detect paper in the image using contour detection as fallback
@@ -492,38 +422,6 @@ def remove_bg(image: np.ndarray) -> np.ndarray:
         logger.error(f"Error in BiRefNet background removal: {e}")
         raise
 
-# 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
-#     """
-#     # Create paper mask with slight expansion to ensure complete removal
-#     paper_mask = np.zeros(mask.shape[:2], dtype=np.uint8)
-    
-#     # # Expand paper contour slightly
-#     # epsilon = expansion_factor * cv2.arcLength(paper_contour, True)
-#     # expanded_contour = cv2.approxPolyDP(paper_contour, epsilon, True)
-    
-#     # cv2.fillPoly(paper_mask, [expanded_contour], 255)
-#     # Create a more aggressive inward shrinking of paper bounds
-#     rect = cv2.boundingRect(paper_contour)
-#     shrink_pixels = int(min(rect[2], rect[3]) * 0.05)  # Shrink by 5% of smaller dimension
-    
-#     # Create shrunken rectangle
-#     x, y, w, h = rect
-#     shrunken_contour = np.array([
-#         [[x + shrink_pixels, y + shrink_pixels]],
-#         [[x + w - shrink_pixels, y + shrink_pixels]],
-#         [[x + w - shrink_pixels, y + h - shrink_pixels]],
-#         [[x + shrink_pixels, y + h - shrink_pixels]]
-#     ])
-    
-#     cv2.fillPoly(paper_mask, [shrunken_contour], 255)
-    
-#     # Invert paper mask and apply to object mask
-#     paper_mask_inv = cv2.bitwise_not(paper_mask)
-#     result_mask = cv2.bitwise_and(mask, paper_mask_inv)
-    
-#     return result_mask
 def mask_paper_area_in_image(image: np.ndarray, paper_contour: np.ndarray) -> np.ndarray:
     """Less aggressive masking to preserve corner objects"""
     masked_image = image.copy()
@@ -1233,9 +1131,9 @@ if __name__ == "__main__":
             inputs=[
                 input_image, 
                 paper_size, 
-                # offset_value_mm,
-                # offset_unit,
-                # enable_finger_cut, 
+                0.02,
+                'mm',
+                'Off', 
                 output_options
             ],
             outputs=[dxf_file, output_image, outlines_image, mask_image, scale_info]