ret, frame = cap.read()
if not ret:
break
# Resize the frame to reduce latency
frame = cv2.resize(frame, (640, 480))
# Run face detection asynchronously
faces = await detect_faces(session, frame)
# Draw face rectangles
if faces:
for face in faces:
rect = face['faceRectangle']
cv2.rectangle(frame, (rect['left'], rect['top']),
(rect['left'] + rect['width'], rect['top'] + rect['height']),
(0, 255, 0), 2)
cv2.imshow('Frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# RTSP stream URL
rtsp_url = "rtsp://your-stream-url"
# Run the stream processing
asyncio.run(process_stream(rtsp_url))
The above implementation is a kind of solution that will provide a good balance between accuracy and reduced latency for processing RTSP streams with Azure Face API using Python. Adjusting the frame size, rate, and leveraging asynchronous processing will help mitigate latency issues.
Lastly, Optimization Strategies
Deploy edge computing solutions to preprocess and filter frames before sending them to the cloud.
If the latency is still high, consider sending frames in batches to reduce the number of API calls.
Process multiple RTSP streams in parallel using multithreading or multiprocessing, depending on your hardware capabilities.
References
Source: Async IO in Python. Accessed, 7/22/2024.
Source: OpenCV VideoCapture Class.Accessed, 7/22/2024.
Source: Real-Time Video Processing with OpenCV and Python. Accessed, 7/22/2024.
Source: Reducing Video Latency. Accessed, 7/22/2024.
Accept Answer
I hope this is helpful! Do not hesitate to let me know if you have any other questions.
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Best Regards,
Sina Salam