I got this error when I run face recognition and detection models. It work fine if I disable tracker.
main function:
def main(args):
# Check input arguments
# if len(args) < 2:
# sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN] <folder to save frames>\n" % args[0])
# sys.exit(1)
for i in range(0, len(args) - 1):
fps_streams["stream{0}".format(i)] = GETFPS(i)
number_sources = len(args) - 1
print("number_sources ",number_sources)
# global folder_name
# folder_name = args[-1]
# if path.exists(folder_name):
# sys.stderr.write("The output folder %s already exists. Please remove it first.\n" % folder_name)
# sys.exit(1)
# os.mkdir(folder_name)
# print("Frames will be saved in ", folder_name)
# 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()
is_live = False
if not pipeline:
sys.stderr.write(" Unable to create Pipeline \n")
print("Creating streamux \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")
pipeline.add(streammux)
for i in range(number_sources):
# os.mkdir(folder_name + "/stream_" + str(i))
# frame_count["stream_" + str(i)] = 0
# saved_count["stream_" + str(i)] = 0
print("Creating source_bin ", i, " \n ")
uri_name = args[i + 1]
if uri_name.find("rtsp://") == 0:
is_live = True
source_bin = create_source_bin(i, uri_name)
if not source_bin:
sys.stderr.write("Unable to create source bin \n")
pipeline.add(source_bin)
padname = "sink_%u" % i
sinkpad = streammux.get_request_pad(padname)
if not sinkpad:
sys.stderr.write("Unable to create sink pad bin \n")
srcpad = source_bin.get_static_pad("src")
if not srcpad:
sys.stderr.write("Unable to create src pad bin \n")
srcpad.link(sinkpad)
print("Creating Pgie \n ")
face_detector = Gst.ElementFactory.make("nvinfer", "face-detector-inference")
if not face_detector:
sys.stderr.write(" Unable to create face_detector \n")
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
face_recogniser = Gst.ElementFactory.make("nvinfer", "face-recogniser-inference")
if not face_recogniser:
sys.stderr.write(" Unable to create face_recogniser \n")
# Add nvvidconv1 and filter1 to convert the frames to RGBA
# which is easier to work with in Python.
print("Creating nvvidconv1 \n ")
nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
if not nvvidconv1:
sys.stderr.write(" Unable to create nvvidconv1 \n")
print("Creating filter1 \n ")
caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
if not filter1:
sys.stderr.write(" Unable to get the caps filter1 \n")
filter1.set_property("caps", caps1)
print("Creating tiler \n ")
tiler = Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
if not tiler:
sys.stderr.write(" Unable to create tiler \n")
print("Creating nvvidconv \n ")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
sys.stderr.write(" Unable to create nvvidconv \n")
print("Creating nvosd \n ")
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
sys.stderr.write(" Unable to create nvosd \n")
queue1=Gst.ElementFactory.make("queue", "nvtee-que1")
if not queue1:
sys.stderr.write(" Unable to create queue1 \n")
if (is_aarch64()):
print("Creating transform \n ")
# transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
transform = Gst.ElementFactory.make("queue", "queue")
if not transform:
sys.stderr.write(" Unable to create transform \n")
print("Creating EGLSink \n")
# sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
sink = Gst.ElementFactory.make("nvoverlaysink", "nvvideo-renderer")
if not sink:
sys.stderr.write(" Unable to create egl sink \n")
if is_live:
print("Atleast one of the sources is live")
streammux.set_property('live-source', 1)
streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batch-size', number_sources)
streammux.set_property('batched-push-timeout', 400000)
face_recogniser.set_property('config-file-path', "face_recogniser_config.txt")
face_detector.set_property('config-file-path', "face_detector_config.txt")
pgie_batch_size = face_detector.get_property("batch-size")
if (pgie_batch_size != number_sources):
print("WARNING: Overriding infer-config batch-size", pgie_batch_size, " with number of sources ",
number_sources, " \n")
face_detector.set_property("batch-size", number_sources)
tiler_rows = int(math.sqrt(number_sources))
tiler_columns = int(math.ceil((1.0 * number_sources) / tiler_rows))
tiler.set_property("rows", tiler_rows)
tiler.set_property("columns", tiler_columns)
tiler.set_property("width", TILED_OUTPUT_WIDTH)
tiler.set_property("height", TILED_OUTPUT_HEIGHT)
sink.set_property("sync", 0)
if not is_aarch64():
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
streammux.set_property("nvbuf-memory-type", mem_type)
nvvidconv.set_property("nvbuf-memory-type", mem_type)
nvvidconv1.set_property("nvbuf-memory-type", mem_type)
tiler.set_property("nvbuf-memory-type", mem_type)
#Set properties of tracker
config = configparser.ConfigParser()
config.read('dstest2_tracker_config.txt')
config.sections()
for key in config['tracker']:
if key == 'tracker-width' :
tracker_width = config.getint('tracker', key)
tracker.set_property('tracker-width', tracker_width)
if key == 'tracker-height' :
tracker_height = config.getint('tracker', key)
tracker.set_property('tracker-height', tracker_height)
if key == 'gpu-id' :
tracker_gpu_id = config.getint('tracker', key)
tracker.set_property('gpu_id', tracker_gpu_id)
if key == 'll-lib-file' :
tracker_ll_lib_file = config.get('tracker', key)
tracker.set_property('ll-lib-file', tracker_ll_lib_file)
if key == 'll-config-file' :
tracker_ll_config_file = config.get('tracker', key)
tracker.set_property('ll-config-file', tracker_ll_config_file)
if key == 'enable-batch-process' :
tracker_enable_batch_process = config.getint('tracker', key)
tracker.set_property('enable_batch_process', tracker_enable_batch_process)
if key == 'enable-past-frame' :
tracker_enable_past_frame = config.getint('tracker', key)
tracker.set_property('enable_past_frame', tracker_enable_past_frame)
print("Adding elements to Pipeline \n")
pipeline.add(face_detector)
pipeline.add(tracker)
pipeline.add(face_recogniser)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(queue1)
if is_aarch64():
pipeline.add(transform)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(face_detector)
face_detector.link(tracker)
tracker.link(nvvidconv1)
nvvidconv1.link(filter1)
filter1.link(tiler)
tiler.link(face_recogniser)
# queue1.link(face_recogniser)
face_recogniser.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)
tiler_sink_pad = tiler.get_static_pad("sink")
if not tiler_sink_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
vidconvsinkpad = nvvidconv.get_static_pad("sink")
if not vidconvsinkpad:
sys.stderr.write(" Unable to get sink pad of nvvidconv \n")
vidconvsinkpad.add_probe(Gst.PadProbeType.BUFFER, sgie_sink_pad_buffer_probe, 0)
# List the sources
print("Now playing...")
for i, source in enumerate(args[:-1]):
if i != 0:
print(i, ": ", source)
print("Starting pipeline \n")
# start play back and listed to events
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# cleanup
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
sys.exit(main(sys.argv))
face_detector config:
[property]
gpu-id=0
process-mode=1
net-scale-factor=0.0039215697906911373
#onnx-file=/home/jetson-nx/codes/models/MobileSSD_face_detection_model/MobileSSD_face_detection.onnx
model-engine-file=/opt/nvidia/deepstream/deepstream-5.1/samples/models/Secondary_FaceDetect/fd_lpd.caffemodel_b1_gpu0_fp32.engine
labelfile-path=/opt/nvidia/deepstream/deepstream-5.1/samples/models/Secondary_FaceDetect/labels.txt
model-file=/opt/nvidia/deepstream/deepstream-5.1/samples/models/Secondary_FaceDetect/fd_lpd.caffemodel
proto-file=/opt/nvidia/deepstream/deepstream-5.1/samples/models/Secondary_FaceDetect/fd_lpd.prototxt
#force-implicit-batch-dim=1
batch-size=1
network-mode=0
num-detected-classes=3
interval=2
gie-unique-id=2
#operate-on-gie-id=1
#operate-on-class-ids=2
output-blob-names=output_bbox;output_cov
input-object-min-width=64
input-object-min-height=64
maintain-aspect-ratio=1
[class-attrs-all]
pre-cluster-threshold=0.7
#Post-cluster-threshold =0.7
#threshold= 0.7
eps=0.2
group-threshold=1
```
face_recogniser config:
```
gpu-id=0
process-mode=2
#net-scale-factor=0.00329215686274
net-scale-factor=0.0189601459307
offsets=112.86182266638355;112.86182266638355;112.86182266638355
#onnx-file=/home/jetson-nx/codes/models/facenet/agx_facenet_dynamic_model.onnx
model-engine-file=/home/jetson-nx/codes/models/facenet/agx_facenet_dynamic_model.onnx_b16_gpu0_fp16.engine
#force-implicit-batch-dim=1
batch-size=16
# 0=FP32 and 1=INT8 2=FP16 mode
network-mode=2
gie-unique-id=3
operate-on-gie-id=2
operate-on-class-ids=0
is-classifier=1
classifier-async-mode=0
#infer-dims=3;160;160
#input-object-min-width=30
#input-object-min-height=30
model-color-format=1
output-tensor-meta=1
