Update app.py

app.py

@@ -24,28 +24,28 @@ def detect_objects(frame, confidence_threshold=0.5):
     inputs = {session.get_inputs()[0].name: image_input}
     outputs = session.run(None, inputs)
     
-    # Assuming YOLO model outputs are in the form of [boxes, confidences, class_probs]
-    boxes, confidences, class_probs = outputs
+    # # Assuming YOLO model outputs are in the form of [boxes, confidences, class_probs]
+    # boxes, confidences, class_probs = outputs
 
-    # Post-processing: Filter boxes by confidence threshold
-    detections = []
-    for i, confidence in enumerate(confidences[0]):
-        if confidence >= confidence_threshold:
-            x1, y1, x2, y2 = boxes[0][i]
-            class_id = np.argmax(class_probs[0][i])  # Get class with highest probability
-            detections.append((x1, y1, x2, y2, confidence, class_id))
+    # # Post-processing: Filter boxes by confidence threshold
+    # detections = []
+    # for i, confidence in enumerate(confidences[0]):
+    #     if confidence >= confidence_threshold:
+    #         x1, y1, x2, y2 = boxes[0][i]
+    #         class_id = np.argmax(class_probs[0][i])  # Get class with highest probability
+    #         detections.append((x1, y1, x2, y2, confidence, class_id))
     
-    # Draw bounding boxes and labels on the image
-    for (x1, y1, x2, y2, confidence, class_id) in detections:
-        color = (0, 255, 0)  # Green color for bounding boxes
-        cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
-        label = f"Class {class_id}: {confidence:.2f}"
-        cv2.putText(image, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+    # # Draw bounding boxes and labels on the image
+    # for (x1, y1, x2, y2, confidence, class_id) in detections:
+    #     color = (0, 255, 0)  # Green color for bounding boxes
+    #     cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
+    #     label = f"Class {class_id}: {confidence:.2f}"
+    #     cv2.putText(image, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
     
-    # Convert the image back to BGR for displaying in Gradio
-    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    # # Convert the image back to BGR for displaying in Gradio
+    # image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     
-    return image_bgr
+    return outputs
 
 # Gradio interface to use the webcam for real-time object detection
 # Added a slider for the confidence threshold