Hardware Platform: Jetson Nano
DeepStream Version: 5.0
JetPack Version: 4.3
TensorRT Version: Not sure
Hi, I’m trying to run deepstream inference pipeline on a MJPG input stream. The camera info is as follows:
Driver Info (not using libv4l2):
Driver name : uvcvideo
Card type : USB3.0 HD Video Capture
Bus info : usb-70090000.xusb-2.1
Driver version: 4.9.140
Capabilities : 0x84200001
Video Capture
Streaming
Extended Pix Format
Device Capabilities
Device Caps : 0x04200001
Video Capture
Streaming
Extended Pix Format
ioctl: VIDIOC_ENUM_FMT
Index : 0
Type : Video Capture
Pixel Format: 'MJPG' (compressed)
Name : Motion-JPEG
Size: Discrete 1920x1080
Interval: Discrete 0.017s (60.000 fps)
Interval: Discrete 0.033s (30.000 fps)
Size: Discrete 1280x720
Interval: Discrete 0.017s (60.000 fps)
Interval: Discrete 0.033s (30.000 fps)
I’m able to get the video streamed using the following commands but I’m not sure how to convert it into a pyhton written code:
gst-launch-1.0 v4l2src device=/dev/video0 io-mode=2 ! "image/jpeg,framerate=30/1,width=1920,height=1080" ! jpegparse ! nvjpegdec ! video/x-raw ! nvvidconv ! 'video/x-raw(memory:NVMM)' ! nvoverlaysink
I’ve written a python code similar to deepstream-test1 app for this. But I’m not able to get the pipeline running. The python code is as follows:
def main(args):
# Check input arguments
if len(args) != 2:
sys.stderr.write("usage: %s <v4l2-device-path>\n" % args[0])
sys.exit(1)
# Standard GStreamer initialization
GObject.threads_init()
Gst.init(None)
# Create gstreamer elements
# Create Pipeline element that will form a connection of other elements
print("Creating Pipeline \n ")
pipeline = Gst.Pipeline()
if not pipeline:
sys.stderr.write(" Unable to create Pipeline \n")
# Source element for reading from the file
print("Creating Source \n ")
source = Gst.ElementFactory.make("v4l2src", "v4l2-source")
if not source:
sys.stderr.write(" Unable to create Source \n")
caps_v4l2src = Gst.ElementFactory.make("capsfilter", "v4l2src_caps")
if not caps_v4l2src:
sys.stderr.write(" Unable to create v4l2src capsfilter \n")
# Since the data format in the input file is elementary mjpg stream,
# we need a jpegparser
print("Creating JPEGParser \n")
jpegparser = Gst.ElementFactory.make("jpegparse", "jpeg-parser")
if not jpegparser:
sys.stderr.write(" Unable to create jpeg parser \n")
# Use nvjpegdec for hardware accelerated decode on GPU
print("Creating Decoder \n")
decoder = Gst.ElementFactory.make("nvjpegdec", "jpeg-decoder")
if not decoder:
sys.stderr.write(" Unable to create NvJPEG Decoder \n")
# Create nvstreammux instance to form batches from one or more sources.
streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
if not streammux:
sys.stderr.write(" Unable to create NvStreamMux \n")
# Use nvinfer to run inferencing on camera's output,
# behaviour of inferencing is set through config file
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
# Use convertor to convert from NV12 to RGBA as required by nvosd
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
sys.stderr.write(" Unable to create nvvidconv \n")
# Create OSD to draw on the converted RGBA buffer
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
sys.stderr.write(" Unable to create nvosd \n")
# Finally render the osd output
if is_aarch64():
transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
print("Creating EGLSink \n")
sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
if not sink:
sys.stderr.write(" Unable to create egl sink \n")
print("Playing file %s " %args[1])
source.set_property('device', args[1])
source.set_property('io-mode', 2)
caps_v4l2src.set_property('caps', Gst.Caps.from_string("image/x-raw, framerate=30/1, width=1920, height=1080"))
streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batch-size', 1)
streammux.set_property('batched-push-timeout', 4000000)
pgie.set_property('config-file-path', "dstest1_pgie_config.txt")
print("Adding elements to Pipeline \n")
pipeline.add(source)
pipeline.add(caps_v4l2src)
pipeline.add(jpegparser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
pipeline.add(sink)
if is_aarch64():
pipeline.add(transform)
# we link the elements together
# v4l-source -> caps -> jpeg-parser -> jpeg-decoder ->
# nvinfer -> nvvidconv -> nvosd -> video-renderer
print("Linking elements in the Pipeline \n")
source.link(caps_v4l2src)
caps_v4l2src.link(jpegparser)
jpegparser.link(decoder)
sinkpad = streammux.get_request_pad("sink_0")
if not sinkpad:
sys.stderr.write(" Unable to get the sink pad of streammux \n")
srcpad = decoder.get_static_pad("src")
if not srcpad:
sys.stderr.write(" Unable to get source pad of decoder \n")
srcpad.link(sinkpad)
streammux.link(pgie)
pgie.link(nvvidconv)
nvvidconv.link(nvosd)
if is_aarch64():
nvosd.link(transform)
transform.link(sink)
else:
nvosd.link(sink)
# create an event loop and feed gstreamer bus mesages to it
loop = GObject.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect ("message", bus_call, loop)
# Lets add probe to get informed of the meta data generated, we add probe to
# the sink pad of the osd element, since by that time, the buffer would have
# had got all the metadata.
osdsinkpad = nvosd.get_static_pad("sink")
if not osdsinkpad:
sys.stderr.write(" Unable to get sink pad of nvosd \n")
osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)
# start play back and listen to events
print("Starting pipeline \n")
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# cleanup
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
sys.exit(main(sys.argv))
I’m getting the following errors on running this:
Creating Pipeline
Creating Source
Creating JPEGParser
Creating Decoder
Creating EGLSink
Playing file /dev/video0
Warn: 'threshold' parameter has been deprecated. Use 'pre-cluster-threshold' instead.
Adding elements to Pipeline
Linking elements in the Pipeline
Starting pipeline
Using winsys: x11
0:00:03.932547254 17231 0x3611dc70 INFO nvinfer gstnvinfer.cpp:602:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1577> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-5.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
INFO: [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
0:00:03.932726120 17231 0x3611dc70 INFO nvinfer gstnvinfer.cpp:602:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1681> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-5.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
0:00:03.958844176 17231 0x3611dc70 INFO nvinfer gstnvinfer_impl.cpp:311:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Error: gst-stream-error-quark: Internal data stream error. (1): gstbasesrc.c(3055): gst_base_src_loop (): /GstPipeline:pipeline0/GstV4l2Src:v4l2-source:
streaming stopped, reason not-linked (-1)
