Upload ui_element_api_server.py with huggingface_hub

ui_element_api_server.py

@@ -140,65 +140,27 @@ def visualize_matches(
     original_image_array: np.ndarray,
     matches: list
 ) -> np.ndarray:
-    """Create clean visualization with smart label positioning."""
+    """Create visualization with bounding boxes."""
     img = original_image_array.copy()
     
-    font = cv2.FONT_HERSHEY_DUPLEX
-    font_scale = 0.7
-    font_thickness = 1
-    
-    # Sort by confidence to prioritize top detections for labeling
-    sorted_matches = sorted(matches, key=lambda x: x['confidence'], reverse=True)
-    
-    # Draw boxes for all matches
     for match in matches:
         bbox = match['bbox']
         center = match['center']
-        
-        # Draw bounding box - thicker and more visible
-        color = (0, 255, 0)  # Green
-        cv2.rectangle(img, (bbox['x1'], bbox['y1']), (bbox['x2'], bbox['y2']), color, 2)
-        
-        # Draw center point with larger radius
-        cv2.circle(img, (center['x'], center['y']), 4, (0, 0, 255), -1)  # Red dot
-        cv2.circle(img, (center['x'], center['y']), 5, (255, 255, 255), 1)  # White outline
-    
-    # Draw labels only for top matches to reduce clutter
-    num_labels = min(30, len(sorted_matches))  # Label only top 30
-    
-    for idx in range(num_labels):
-        match = sorted_matches[idx]
-        bbox = match['bbox']
         confidence = match['confidence']
+        template_id = match['template_id']
         
-        # Use simple index labels for top matches
-        label = f"{idx + 1}"
-        
-        # Position label at top-right corner of box
-        label_x = bbox['x2'] + 5
-        label_y = bbox['y1'] + 15
-        
-        # Clamp coordinate to image bounds
-        h, w = img.shape[:2]
-        label_x = min(label_x, w - 30)
-        label_y = min(label_y, h - 10)
-        
-        # Get text size
-        text_size = cv2.getTextSize(label, font, font_scale, font_thickness)[0]
-        
-        # Background rectangle
-        bg_pt1 = (label_x - 3, label_y - text_size[1] - 4)
-        bg_pt2 = (label_x + text_size[0] + 3, label_y + 2)
+        # Draw bounding box
+        color = (0, 255, 0)  # Green
+        thickness = 2
+        cv2.rectangle(img, (bbox['x1'], bbox['y1']), (bbox['x2'], bbox['y2']), color, thickness)
         
-        cv2.rectangle(img, bg_pt1, bg_pt2, (0, 0, 0), -1)  # Black bg
-        cv2.rectangle(img, bg_pt1, bg_pt2, (255, 255, 0), 2)  # Cyan border
+        # Draw center point
+        cv2.circle(img, (center['x'], center['y']), 3, (0, 0, 255), -1)  # Red
         
-        # Draw label text
-        cv2.putText(img, label, (label_x, label_y), font, font_scale, (255, 255, 0), font_thickness)
-    
-    # Add info text to image
-    info_text = f"Total Elements: {len(matches)} | Top {num_labels} labeled"
-    cv2.putText(img, info_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
+        # Draw label
+        label = f"ID:{template_id} ({confidence:.2f})"
+        cv2.putText(img, label, (bbox['x1'], bbox['y1'] - 5), 
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1)
     
     return img