Is this a normal speed? Also after running for a while will get RTP: PT=60: bad cseq 47d2 expected=49d2te=N/A speed=0. There are several options for H.265 encoding in MFormats SDK and MPlatform SDK - both CPU-based and GPU-powered. How can I pass the same options to it I googled and came to following command: ffmpeg -i input.mp4 -vcodec h264nvenc -rc constqp -qp 23 -acodec copy h264nvenc.mp4 But the file I get is much larger. I am pushing the rgb frame to pipe and pushing to the server by this cmd ffmpeg -hwaccel cuvid -y -f rawvideo -pix_fmt bgr24 -s wxh -i -c:v h264_nvenc -pix_fmt yuv420p -f rtsp -rtsp_transport tcp rtsp://xxxxxxĪnd get 20 fps speed= 0.8x for pushing pipe. 4 Encoding on CPU can be done via: ffmpeg -i input.mp4 -vcodec h264 -crf 23 -acodec copy h264.mp4 GPU's alternative for the codec is h264nvenc. Choose the 'H264 (Nvidia® NVENC)' or 'HEVC (Nvidia® NVENC)' option. Image=cv2.cvtColor(image,cv2.COLOR_BGRA2BGR) Today, we settle the debate between Nvidia NVENC vs. Yeah it is like a remuxing, but i will modify the frame, so i must decode and encode.Ĭap = (url) I've received a lot of questions from my 5600X vs 3700X streaming review about settings.

I have a video stream which is 2560x1920.And i write a pipeline like decoding it and then encoding it and pushing it to another server.

After i use cudacodec to get the gpu_mat, is there anyway to use this gpu_mat to inference by pytorch or i must download to cpu and make it a torch.tensor and then inference.

In my scene, i am using a Tesla V100 to decode from rtsp and encode to rtsp.

H264_nvenc options