I have a (python) pipeline that has one detector followed by some logic that gets applied each frame
this pipeline runs fine if the confidence for the detector is set around 0.6 but fails at the same frame each run if the confidence is set to a lower value and gives this error
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
the only lead I have is the values of the confidence that show that it might be related to some low conf faulty detection
but since it’s hard to debug deepstream I can’t really pinpoint the issue
it does this with two different test videos, each of them has a different frame that causes the crash
but it’s the same frame each run
(ex vid 1 always crashes at frame number x each run, while any run that uses vid 2 crashes at frame number y)
The detector is YoloV4 trained with COCO dataset, and this is its config file
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
#0=RGB, 1=BGR
model-color-format=0
model-engine-file=../models/yolov4_original_30_streams_dynamic/yolov4-dynamic_30.engine
labelfile-path=../models/yolov4_original_30_streams_dynamic/labels.txt
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=80
gie-unique-id=1
network-type=0
is-classifier=0
filter-out-class-ids=2;3;5;7 ##filter out person and vehicle classes
## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloV4
custom-lib-path=libnvdsinfer_custom_impl_YoloV4_8classees_PersonCarMotorbikeBusTruckBackpackHandbagSuitcase.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
[class-attrs-all]
nms-iou-threshold=0.6
pre-cluster-threshold=0.3
and here’s the pipeline creation in python code
def main(args):
"""
makes elements, adds them to pipeline, links them together, runs the main loop
:param args: python command arguments, including file name
"""
# Make sure no arguments are sent through terminal
if len(args) != 1:
sys.stderr.write(f"please input all your arguments in {prim_config_path}\n")
sys.exit(1)
is_live = bool(int(configFile['streammux']['live-source']))
number_sources = int(configFile['streammux']['batch-size'])
sources_in_configfile = len([s for s in configFile.sections() if "source" in s])
# 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")
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):
try:
frame_count[f"stream_{i}"] = 0
saved_count[f"stream_{i}"] = 0
fps_streams[f"stream_{i}"] = GETFPS(i)
print("Creating source_bin ", i, " \n ")
uri_name = configFile[f'source{i}']['uri']
if uri_name.find("http://") == 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)
pad_name = f"sink_{i}"
sink_pad = streammux.get_request_pad(pad_name)
if not sink_pad:
sys.stderr.write("Unable to create sink pad bin \n")
src_pad = source_bin.get_static_pad("src")
if not src_pad:
sys.stderr.write("Unable to create src pad bin \n")
src_pad.link(sink_pad)
fps = int(configFile[f"source{i}"]["fps"])
except:
print("\n\n Number of input sources doesn't match the batch-size \
specified in the config file!. \n System will start up with available streams\n\n" )
break
print("Creating Pgie \n ")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \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")
if is_aarch64():
print("Creating transform \n ")
transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
if not transform:
sys.stderr.write(" Unable to create transform \n")
print("Creating EGLSink \n")
display = int(configFile['streammux']['allow-display'])
if display:
sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
else:
sink = Gst.ElementFactory.make("fakesink", "fakesink")
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', 4000000)
pgie.set_property('config-file-path', prim_config_path)
pgie_batch_size = pgie.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")
pgie.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)
if is_live:
sink.set_property("sync", 1)
else:
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)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
if is_aarch64():
pipeline.add(transform)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(nvvidconv1)
nvvidconv1.link(filter1)
filter1.link(tiler)
tiler.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 messages 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)
# 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)
• Hardware Platform: GPU
• DeepStream Version: 5.0
• NVIDIA GPU Driver Version (valid for GPU only): 460
• Issue Type: question
