Use x264enc to encode the video stream and then push the pipeline through rtmp to get stuck in the ready state without reporting an error. Just replacing x264enc with nvv4l2h264enc pipeline works fine. But in our project, we found that the rtmp stream pushed by nvv4l2h264enc cannot be played in SRS, so I want to use x264enc to encode the stream.The video stream encoded by x264enc has been verified to be able to be played in SRS. Test pipeline: gst-launch-1.0 -v v4l2src device=/dev/video0 ! videoconvert ! video/x-raw,format=I420 ! x264enc ! h264parse ! flvmux !rtmpsink location=“rtmp://192.168.1.120/test/hongyan9”
Here is my pipe graph which i using the x264enc element and stuck in the ready state .
multi_src_bin->queue->nvvideoconvert->nvinfer->nvstreamDemux->queue->nvvideoconvert->queue->nvdsosd->queue->tee->queue->nvvideoconvert->“video/x-raw, format=I420”->x264enc->h264parse->flvmux->rtmpsink
Here is my pipe graph which i using the nvv4l2h264enc element and looks works fine .
multi_src_bin->queue->nvvideoconvert->nvinfer->nvstreamDemux->queue->nvvideoconvert->queue->nvdsosd->queue->tee->queue->nvvideoconvert->“video/x-raw, format=I420”->nvv4l2h264enc->h264parse->flvmux->rtmpsink
The program is modified from deepstream-app，here is my configfile
config_hongyan_pipeline.txt (4.9 KB)
when I use x264enc to run my app :
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
gstnvtracker: Batch processing is ON
gstnvtracker: Past frame output is ON
[NvMultiObjectTracker] Initialized
WARNING: [TRT]: CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See CUDA_MODULE_LOADING in CUDA C++ Programming Guide
WARNING: [TRT]: CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See CUDA_MODULE_LOADING in CUDA C++ Programming Guide
0:00:01.476606577 406774 0x7f7b7c00fc70 INFO nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1909> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-6.2/samples/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine
INFO: …/nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x368x640
1 OUTPUT kFLOAT conv2d_bbox 16x23x40
2 OUTPUT kFLOAT conv2d_cov/Sigmoid 4x23x40
0:00:01.519927492 406774 0x7f7b7c00fc70 INFO nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2012> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.2/samples/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine
0:00:01.522756566 406774 0x7f7b7c00fc70 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary_gie> [UID 1]: Load new model:/opt/nvidia/deepstream/deepstream-6.2/workspace/Deepstream-hongyan-app/config/config_infer_primary.txt sucessfully
**PERF: FPS 0 (Avg)
PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
.PERF(0): 0.00 (0.00)
RTX4080
Ubuntu 22.04
DeepStreamSDK 6.2.0
CUDA Driver Version: 12.0
CUDA Runtime Version: 11.8
TensorRT Version: 8.5
cuDNN Version: 8.7
libNVWarp360 Version: 2.0.1d3