Update app.py

app.py

@@ -331,10 +331,13 @@ def validate_single_object(mask: np.ndarray, paper_contour: np.ndarray) -> None:
     # Find contours of objects within paper bounds
     contours, _ = cv2.findContours(masked_objects, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
-    # Filter out very small contours (noise)
+   
+    # Filter out very small contours (noise) and paper-sized contours
+    image_area = mask.shape[0] * mask.shape[1]
     min_area = 1000  # Minimum area threshold
-    significant_contours = [c for c in contours if cv2.contourArea(c) > min_area]
-    
+    max_area = image_area * 0.5  # Maximum 50% of image area (to exclude paper detection)
+    significant_contours = [c for c in contours if min_area < cv2.contourArea(c) < max_area]
+        
     if len(significant_contours) == 0:
         raise NoObjectDetectedError()
     elif len(significant_contours) > 1:
@@ -398,7 +401,7 @@ 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 = 1.1) -> np.ndarray:
+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
     """
@@ -777,7 +780,7 @@ def make_square(img: np.ndarray):
     
     return padded
 
-def predict_with_paper(image, paper_size, offset, offset_unit, edge_radius, finger_clearance=False):
+def predict_with_paper(image, paper_size, offset, finger_clearance=False):
     """Main prediction function using paper as reference"""
     
     logger.info(f"Starting prediction with image shape: {image.shape}")
@@ -832,10 +835,7 @@ def predict_with_paper(image, paper_size, offset, offset_unit, edge_radius, fing
         raise gr.Error(f"Error in object detection: {str(e)}")
 
     # Apply edge rounding if specified
-    if edge_radius > 0:
-        rounded_mask = round_edges(objects_mask, edge_radius, scaling_factor)
-    else:
-        rounded_mask = objects_mask.copy()
+    rounded_mask = objects_mask.copy()
     
     # Apply dilation for offset
     if offset > 0:
@@ -902,21 +902,14 @@ def predict_with_paper(image, paper_size, offset, offset_unit, edge_radius, fing
         f"Scale: {scaling_factor:.4f} mm/px | Paper: {paper_size}"
     )
 
-def predict_full_paper(image, paper_size, enable_fillet, fillet_value_mm, enable_finger_cut, selected_outputs):
-    """
-    Full prediction function with paper reference and flexible outputs
-    Returns DXF + conditionally selected additional outputs
-    """
-    radius = fillet_value_mm if enable_fillet == "On" else 0
+def predict_full_paper(image, paper_size, offset_value_mm, enable_finger_cut, selected_outputs):
     finger_flag = "On" if enable_finger_cut == "On" else "Off"
     
     # Always get all outputs from predict_with_paper
     ann, outlines, dxf_path, mask, scale_info = predict_with_paper(
         image,
         paper_size,
-        offset=0,  # No offset for now, can be added as parameter later
-        offset_unit="mm",
-        edge_radius=radius,
+        offset=offset_value_mm,
         finger_clearance=finger_flag,
     )
     
@@ -963,22 +956,14 @@ if __name__ == "__main__":
                 )
                 
                 with gr.Group():
-                    gr.Markdown("### Edge Rounding")
-                    enable_fillet = gr.Radio(
-                        choices=["On", "Off"],
-                        value="Off",
-                        label="Enable Edge Rounding",
-                        interactive=True
-                    )
-                    
-                    fillet_value_mm = gr.Slider(
+                    gr.Markdown("### Contour Offset")
+                    offset_value_mm = gr.Slider(
                         minimum=0,
-                        maximum=20,
+                        maximum=50,
                         step=1,
-                        value=5,
-                        label="Edge Radius (mm)",
-                        visible=False,
-                        interactive=True
+                        value=0,
+                        label="Offset (mm)",
+                        info="Expand contours outward by this amount"
                     )
 
                 with gr.Group():
@@ -1011,10 +996,6 @@ if __name__ == "__main__":
                     outlines_image = gr.Image(label="Outlines", visible=False) 
                     mask_image = gr.Image(label="Mask", visible=False)
         
-        # Dynamic visibility updates
-        def toggle_fillet(choice):
-            return gr.update(visible=(choice == "On"))
-
         def update_outputs_visibility(selected):
             return [
                 gr.update(visible="Annotated Image" in selected),
@@ -1022,12 +1003,6 @@ if __name__ == "__main__":
                 gr.update(visible="Mask" in selected)
             ]
         
-        # Event handlers
-        enable_fillet.change(
-            fn=toggle_fillet,
-            inputs=enable_fillet,
-            outputs=fillet_value_mm
-        )
         
         output_options.change(
             fn=update_outputs_visibility,
@@ -1040,8 +1015,7 @@ if __name__ == "__main__":
             inputs=[
                 input_image, 
                 paper_size, 
-                enable_fillet, 
-                fillet_value_mm, 
+                offset_value_mm, 
                 enable_finger_cut, 
                 output_options
             ],