Update app.py

app.py

@@ -149,6 +149,7 @@ def process_image(job_id, image_path, object_type, multiplier):
             # We assume the printed marker is 5 cm x 5 cm.
             marker_real_width_cm = 5.0
             conversion_factor = None
+            marker_bbox = None
             try:
                 # Convert to grayscale
                 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -198,12 +199,30 @@ def process_image(job_id, image_path, object_type, multiplier):
                     print("DEBUG: Detected marker with ID", ids[selected_index][0],
                           "Average side length (px):", avg_side_length,
                           "Conversion factor:", conversion_factor)
+                    # Compute marker bounding box from the detected corners
+                    marker_bbox = (int(np.min(marker_corners[:,0])),
+                                   int(np.min(marker_corners[:,1])),
+                                   int(np.max(marker_corners[:,0])),
+                                   int(np.max(marker_corners[:,1])))
+                    print("DEBUG: Marker bounding box:", marker_bbox)
                 else:
                     print("DEBUG: No markers detected in ArUco detection.")
             except Exception as e:
                 print("DEBUG: Exception during ArUco detection:", e)
                 conversion_factor = None
 
+            # --- Filter out the marker from box detections ---
+            if marker_bbox is not None:
+                print("DEBUG: Filtering out aruco marker detection from box detections.")
+                filtered_box_detections = []
+                for detection in box_detections:
+                    iou_val = compute_iou(detection["box"], marker_bbox)
+                    print("DEBUG: IoU with marker:", iou_val)
+                    if iou_val < 0.5:
+                        filtered_box_detections.append(detection)
+                box_detections = filtered_box_detections
+
+            # --- Draw remaining box detections and compute measurements ---
             for pred in box_detections:
                 x1, y1, x2, y2 = pred["box"]
                 label = pred["class"]