Update app.py

app.py

@@ -57,6 +57,50 @@ def start_training():
     import src.faceRecognize.facerec.train_v2
     st.success("Training completed successfully.")
 
+def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
+    image = frame.to_ndarray(format="bgr24")
+    face_encoder = model_selector("Facenet")
+    encodings_path = './src/faceRecognize/facerec/encodings/encodings.pkl'
+    encoding_dict = load_pickle(self.encodings_path)
+    # Run inference
+    blob = cv2.dnn.blobFromImage(
+        cv2.resize(image, (300, 300)), 0.007843, (300, 300), 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 = [
+       detect(output, face_detector, face_encoder, encoding_dict)
+        for detection in output
+    ]
+
+    # Render bounding boxes and captions
+    for detection in detections:
+        # image, pred = detect(frame_resized, face_detector, self.face_encoder, self.encoding_dict)
+        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")
 
 class FaceRecognitionProcessor(VideoProcessorBase):
     def __init__(self):
@@ -118,7 +162,8 @@ def main():
             key="face-detection",
             mode=WebRtcMode.SENDRECV,
             rtc_configuration=RTC_CONFIGURATION,
-            video_frame_callback=FaceRecognitionProcessor().recv,
+            #video_frame_callback=FaceRecognitionProcessor().recv,
+            video_frame_callback = video_frame_callback
             media_stream_constraints={"video": True, "audio": False},async_processing=True
         )
         if webrtc_ctx.state.playing: