Update app.py

app.py

@@ -92,45 +92,45 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
     image = frame.to_ndarray(format="bgr24")
 
     # Run inference
-    blob = cv2.dnn.blobFromImage(
-        cv2.resize(image, (128, 128)), 0.007843, (128, 128), 127.5
-    )
-    net.setInput(blob)
-    output = net.forward()
-
-    h, w = image.shape[:2]
-
-    # Convert the output array into a structured form.
-    output = output.squeeze()  # (1, 1, N, 7) -> (N, 7)
-    output = output[output[:, 2] >= score_threshold]
-    detections = [
-        Detection(
-            class_id=int(detection[1]),
-            label=CLASSES[int(detection[1])],
-            score=float(detection[2]),
-            box=(detection[3:7] * np.array([w, h, w, h])),
-        )
-        for detection in output
-    ]
-
-    # Render bounding boxes and captions
-    for detection in detections:
-        caption = f"{detection.label}: {round(detection.score * 100, 2)}%"
-        color = COLORS[detection.class_id]
-        xmin, ymin, xmax, ymax = detection.box.astype("int")
-
-        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
-        cv2.putText(
-            image,
-            caption,
-            (xmin, ymin - 15 if ymin - 15 > 15 else ymin + 15),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
-            color,
-            2,
-        )
-
-    result_queue.put(detections)
+    # blob = cv2.dnn.blobFromImage(
+    #     cv2.resize(image, (128, 128)), 0.007843, (128, 128), 127.5
+    # )
+    # net.setInput(blob)
+    # output = net.forward()
+
+    # h, w = image.shape[:2]
+
+    # # Convert the output array into a structured form.
+    # output = output.squeeze()  # (1, 1, N, 7) -> (N, 7)
+    # output = output[output[:, 2] >= score_threshold]
+    # detections = [
+    #     Detection(
+    #         class_id=int(detection[1]),
+    #         label=CLASSES[int(detection[1])],
+    #         score=float(detection[2]),
+    #         box=(detection[3:7] * np.array([w, h, w, h])),
+    #     )
+    #     for detection in output
+    # ]
+
+    # # Render bounding boxes and captions
+    # for detection in detections:
+    #     caption = f"{detection.label}: {round(detection.score * 100, 2)}%"
+    #     color = COLORS[detection.class_id]
+    #     xmin, ymin, xmax, ymax = detection.box.astype("int")
+
+    #     cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
+    #     cv2.putText(
+    #         image,
+    #         caption,
+    #         (xmin, ymin - 15 if ymin - 15 > 15 else ymin + 15),
+    #         cv2.FONT_HERSHEY_SIMPLEX,
+    #         0.5,
+    #         color,
+    #         2,
+    #     )
+
+    result_queue.put([]) #detections)
 
     return av.VideoFrame.from_ndarray(image, format="bgr24")