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Video Server for EdgeRealtimeVideoAnalytics #9
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Hello @Akhtar303 This project uses a simple video server implemented in Python - it simply reads and sends the JPGs from a Redis stream: https://github.com/RedisGears/EdgeRealtimeVideoAnalytics/blob/master/app/server.py |
@itamarhaber Thanks for Immediate reply |
Yes, this pipeline can be used for multiple models and input sources (cameras). That said, the server's resources will determine the impact on performance and it is definitely a possibility that loading it too much will result in increased latencies and dropped frames. |
@itamarhaber Thanks for Immediate reply |
It should be fairly straightforward from what I remember - look for instances of the 'camera:0' literal and parametrize them. Feel free to hit us with questions if you run into any issues. |
Hello @itamarhaber |
Hello @milanlanlan
Indeed - AFAIK every DL model has its own quirks so the input needs to be prepared accordingly and the outputs post-processed as needed.
RedisAI has a pythonic client if you want to use it directly from a script (https://github.com/RedisAI/redisai-py). This demo uses RedisGears, which has built-in integration with RedisAI, so every RedisAI command is exposed as a Gears method. The RedisAI documentation is at https://oss.redislabs.com/redisai/.
I don't see how that can be circumvented - this isn't because of a RedisAI limitation but rather due I hope this makes sense - let me know if not :) |
Hello @itamarhaber EdgeRealtimeVideoAnalytics/app/gear.py Line 208 in adb94b1
in top.py this line p.execute_command('TS.RANGE', f'{args.video}:{m}', now - 2, now - 1) |
Thanks for your great implementation of EdgeRealtimeVideoAnalytics.
I have a question Which video server you used for This project Pipe Line .
i.e
video server like WebRTC and FFMPEG.
Thanks
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