Update app.py

app.py

@@ -24,49 +24,64 @@ def segment_card(image):
     # Convert to OpenCV format
     annotated_image = image.copy()
 
+    # Dictionary to track the highest confidence detection per class
+    best_detections = {}
+
     # Extract bounding boxes and labels
     for result in results:
         for box in result.boxes:
             x1, y1, x2, y2 = map(int, box.xyxy[0])  # Bounding box coordinates
             class_id = int(box.cls[0])  # Class index
-            label = CLASS_NAMES[class_id]  # Get class label
             confidence = box.conf[0].item()  # Confidence score
+            
+            # Check if this is the highest confidence detection for the class
+            if class_id not in best_detections or confidence > best_detections[class_id]["confidence"]:
+                best_detections[class_id] = {
+                    "bbox": (x1, y1, x2, y2),
+                    "confidence": confidence
+                }
+
+    # Draw the highest confidence detections
+    for class_id, detection in best_detections.items():
+        x1, y1, x2, y2 = detection["bbox"]
+        label = CLASS_NAMES[class_id]
+        confidence = detection["confidence"]
+
+        # Draw bounding box **BELOW** text elements
+        cv2.rectangle(annotated_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+        # Set text properties
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 0.8  # Increased font size for better readability
+        font_thickness = 2
+        label_text = f"{label} ({confidence:.2f})"
+
+        # Get text size for proper background padding
+        text_size = cv2.getTextSize(label_text, font, font_scale, font_thickness)[0]
+        text_x, text_y = x1, y1 - 10
+
+        # Ensure text doesn't go out of bounds
+        text_y = max(text_y, text_size[1] + 10)
+
+        # Draw **filled rectangle background** for the text (above bounding box)
+        cv2.rectangle(
+            annotated_image, 
+            (text_x, text_y - text_size[1] - 5), 
+            (text_x + text_size[0] + 5, text_y + 5), 
+            (0, 255, 0),  # Background color (Green)
+            -1
+        )
 
-            # Draw bounding box **BELOW** text elements
-            cv2.rectangle(annotated_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-
-            # Set text properties
-            font = cv2.FONT_HERSHEY_SIMPLEX
-            font_scale = 0.7  # Increased font size and thickness for better readability
-            font_thickness = 1
-            label_text = f"{label} ({confidence:.2f})"
-
-            # Get text size for proper background padding
-            text_size = cv2.getTextSize(label_text, font, font_scale, font_thickness)[0]
-            text_x, text_y = x1, y1 - 10
-
-            # Ensure text doesn't go out of bounds
-            text_y = max(text_y, text_size[1] + 10)
-
-            # Draw **filled rectangle background** for the text (above bounding box)
-            cv2.rectangle(
-                annotated_image, 
-                (text_x, text_y - text_size[1] - 5), 
-                (text_x + text_size[0] + 5, text_y + 5), 
-                (0, 255, 0),  # Background color (Green)
-                -1
-            )
-
-            # Draw the **text label above the rectangle**
-            cv2.putText(
-                annotated_image, 
-                label_text, 
-                (text_x, text_y), 
-                font, 
-                font_scale, 
-                (0, 0, 0),  # Text color (Black for contrast)
-                font_thickness
-            )
+        # Draw the **text label above the rectangle**
+        cv2.putText(
+            annotated_image, 
+            label_text, 
+            (text_x, text_y), 
+            font, 
+            font_scale, 
+            (0, 0, 0),  # Text color (Black for contrast)
+            font_thickness
+        )
 
     return Image.fromarray(annotated_image)  # Convert back to PIL Image