Documentation Index
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PyTrickle data channels extend real-time sessions with non-media output. A pipeline can produce video on the media channel and structured data (transcriptions, labels, metrics) on a data channel simultaneously.
Reading data channel output
from pytrickle import TrickleSubscriber
import json
# Subscribe to the data output channel
data_sub = TrickleSubscriber(url=job.events_url)
async for segment in data_sub:
payload = json.loads(segment.data.decode('utf-8'))
if payload.get('type') == 'transcription':
print(f"[{payload['timestamp']:.1f}s] {payload['text']}")
elif payload.get('type') == 'detection':
for box in payload['boxes']:
print(f" {box['label']}: ({box['x']}, {box['y']}, {box['w']}, {box['h']})")
events_url is available on LiveVideoToVideo when the pipeline declares data output support. If the pipeline does not produce data output, events_url is None.
Writing data from a FrameProcessor
Inside a BYOC container, write data channel output through the StreamServer’s data publish interface:
from pytrickle import FrameProcessor, VideoFrame
class TranscriptionPipeline(FrameProcessor):
async def process_frame(self, frame: VideoFrame) -> VideoFrame:
# Process video as normal
result = self.model(frame.data)
# Write transcription to data channel
await self.publish_data({
'type': 'transcription',
'text': result.text,
'timestamp': frame.pts_time,
'confidence': result.confidence,
})
return VideoFrame(data=result.video, pts=frame.pts)
