Update app.py

app.py

@@ -101,21 +101,31 @@ result_queue = queue.Queue()
 
 def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
     img = frame.to_ndarray(format="bgr24")
-    hands, img = detector.findHands(img, flipType=False)
-
-    # Render hand detection results
+    
+    # Ensure dimensions are provided or preprocess image
+    height, width, _ = img.shape
+    
+    # Try passing image dimensions explicitly
+    hands, img = detector.findHands(img, flipType=False, imgDim=(width, height))
+    
     if hands:
         hand = hands[0]
         bbox = hand["bbox"]
-        cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), (255, 0, 0), 2)
-
-        cv2.putText(img, 'OpenCV', (50,50), font, 
-                   fontScale, color, thickness, cv2.LINE_AA)
-        cv2.putText(img, 'OpenCV', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)
-
-        result_queue.put(hands)
-        
-
+        cv2.rectangle(img, 
+                      (bbox[0], bbox[1]), 
+                      (bbox[0] + bbox[2], bbox[1] + bbox[3]), 
+                      (255, 0, 0), 2)
+
+        # Render text once
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        fontScale = 2
+        color = (255, 255, 255)
+        thickness = 2
+        cv2.putText(img, 'OpenCV', (50, 50), font, fontScale, color, thickness, cv2.LINE_AA)
+
+        # Pass simplified results to the queue
+        result_queue.put({"bbox": bbox, "landmarks": hand["landmarks"]})
+    
     return av.VideoFrame.from_ndarray(img, format="bgr24")