I tried to change it by deepstream-test2. Primary detector works fine. I think I have problems with tracker and secondary detector.
Edit:
It seems also tracker works fine. Only my custom model from jetson-inference Hello Ai World not working as expected. It is not perfect but was working with jetson-inference. Any ideas welcome. What do i need here to detect if person wearing safety helmet or not ?
My files:
#!/usr/bin/env python3
import sys
sys.path.append('../')
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
import pyds
import configparser
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
past_tracking_meta=[0]
def osd_sink_pad_buffer_probe(pad,info,u_data):
frame_number=0
#Intiallizing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_VEHICLE:0,
PGIE_CLASS_ID_PERSON:0,
PGIE_CLASS_ID_BICYCLE:0,
PGIE_CLASS_ID_ROADSIGN:0
}
num_rects=0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
# Retrieve batch metadata from the gst_buffer
# Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
# C address of gst_buffer as input, which is obtained with hash(gst_buffer)
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# The casting is done by pyds.NvDsFrameMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number=frame_meta.frame_num
num_rects = frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
obj_counter[obj_meta.class_id] += 1
try:
l_obj=l_obj.next
except StopIteration:
break
# Acquiring a display meta object. The memory ownership remains in
# the C code so downstream plugins can still access it. Otherwise
# the garbage collector will claim it when this probe function exits.
display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
display_meta.num_labels = 1
py_nvosd_text_params = display_meta.text_params[0]
# Setting display text to be shown on screen
# Note that the pyds module allocates a buffer for the string, and the
# memory will not be claimed by the garbage collector.
# Reading the display_text field here will return the C address of the
# allocated string. Use pyds.get_string() to get the string content.
py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], obj_counter[PGIE_CLASS_ID_PERSON])
# Now set the offsets where the string should appear
py_nvosd_text_params.x_offset = 10
py_nvosd_text_params.y_offset = 12
# Font , font-color and font-size
py_nvosd_text_params.font_params.font_name = "Serif"
py_nvosd_text_params.font_params.font_size = 10
# set(red, green, blue, alpha); set to White
py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
# Text background color
py_nvosd_text_params.set_bg_clr = 1
# set(red, green, blue, alpha); set to Black
py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
# Using pyds.get_string() to get display_text as string
print(pyds.get_string(py_nvosd_text_params.display_text))
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
try:
l_frame=l_frame.next
except StopIteration:
break
#past traking meta data
if(past_tracking_meta[0]==1):
l_user=batch_meta.batch_user_meta_list
while l_user is not None:
try:
# Note that l_user.data needs a cast to pyds.NvDsUserMeta
# The casting is done by pyds.NvDsUserMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone
user_meta=pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if(user_meta and user_meta.base_meta.meta_type==pyds.NvDsMetaType.NVDS_TRACKER_PAST_FRAME_META):
try:
# Note that user_meta.user_meta_data needs a cast to pyds.NvDsPastFrameObjBatch
# The casting is done by pyds.NvDsPastFrameObjBatch.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone
pPastFrameObjBatch = pyds.NvDsPastFrameObjBatch.cast(user_meta.user_meta_data)
except StopIteration:
break
for trackobj in pyds.NvDsPastFrameObjBatch.list(pPastFrameObjBatch):
print("streamId=",trackobj.streamID)
print("surfaceStreamID=",trackobj.surfaceStreamID)
for pastframeobj in pyds.NvDsPastFrameObjStream.list(trackobj):
print("numobj=",pastframeobj.numObj)
print("uniqueId=",pastframeobj.uniqueId)
print("classId=",pastframeobj.classId)
print("objLabel=",pastframeobj.objLabel)
for objlist in pyds.NvDsPastFrameObjList.list(pastframeobj):
print('frameNum:', objlist.frameNum)
print('tBbox.left:', objlist.tBbox.left)
print('tBbox.width:', objlist.tBbox.width)
print('tBbox.top:', objlist.tBbox.top)
print('tBbox.right:', objlist.tBbox.height)
print('confidence:', objlist.confidence)
print('age:', objlist.age)
try:
l_user=l_user.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
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
if(len(args)==3):
past_tracking_meta[0]=int(args[2])
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", "usb-cam-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")
print("Creating Video Converter \n")
# Adding videoconvert -> nvvideoconvert as not all
# raw formats are supported by nvvideoconvert;
# Say YUYV is unsupported - which is the common
# raw format for many logi usb cams
# In case we have a camera with raw format supported in
# nvvideoconvert, GStreamer plugins' capability negotiation
# shall be intelligent enough to reduce compute by
# videoconvert doing passthrough (TODO we need to confirm this)
# videoconvert to make sure a superset of raw formats are supported
vidconvsrc = Gst.ElementFactory.make("videoconvert", "convertor_src1")
if not vidconvsrc:
sys.stderr.write(" Unable to create videoconvert \n")
# nvvideoconvert to convert incoming raw buffers to NVMM Mem (NvBufSurface API)
nvvidconvsrc = Gst.ElementFactory.make("nvvideoconvert", "convertor_src2")
if not nvvidconvsrc:
sys.stderr.write(" Unable to create Nvvideoconvert \n")
caps_vidconvsrc = Gst.ElementFactory.make("capsfilter", "nvmm_caps")
if not caps_vidconvsrc:
sys.stderr.write(" Unable to create capsfilter \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")
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
if not sgie1:
sys.stderr.write(" Unable to make sgie1 \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 cam %s " %args[1])
caps_v4l2src.set_property('caps', Gst.Caps.from_string("video/x-raw, framerate=30/1"))
caps_vidconvsrc.set_property('caps', Gst.Caps.from_string("video/x-raw(memory:NVMM)"))
source.set_property('device', args[1])
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")
sgie1.set_property('config-file-path', "dstest1_sgie1_config.txt")
#Set properties of tracker
config = configparser.ConfigParser()
config.read('dstest1_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)
# Set sync = false to avoid late frame drops at the display-sink
sink.set_property('sync', False)
print("Adding elements to Pipeline \n")
pipeline.add(source)
pipeline.add(caps_v4l2src)
pipeline.add(vidconvsrc)
pipeline.add(nvvidconvsrc)
pipeline.add(caps_vidconvsrc)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(sgie1)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
pipeline.add(sink)
if is_aarch64():
pipeline.add(transform)
# we link the elements together
# v4l2src -> nvvideoconvert -> mux ->
# nvinfer -> nvvideoconvert -> nvosd -> video-renderer
print("Linking elements in the Pipeline \n")
source.link(caps_v4l2src)
caps_v4l2src.link(vidconvsrc)
vidconvsrc.link(nvvidconvsrc)
nvvidconvsrc.link(caps_vidconvsrc)
sinkpad = streammux.get_request_pad("sink_0")
if not sinkpad:
sys.stderr.write(" Unable to get the sink pad of streammux \n")
srcpad = caps_vidconvsrc.get_static_pad("src")
if not srcpad:
sys.stderr.write(" Unable to get source pad of caps_vidconvsrc \n")
srcpad.link(sinkpad)
streammux.link(pgie)
pgie.link(tracker)
tracker.link(sgie1)
sgie1.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))
dstest1_pgie_config.txt
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-file=../../../../samples/models/Primary_Detector/resnet10.caffemodel
proto-file=../../../../samples/models/Primary_Detector/resnet10.prototxt
model-engine-file=/opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
labelfile-path=../../../../samples/models/Primary_Detector/labels.txt
int8-calib-file=../../../../samples/models/Primary_Detector/cal_trt.bin
force-implicit-batch-dim=1
batch-size=1
network-mode=1
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid
#scaling-filter=0
#scaling-compute-hw=0
[class-attrs-all]
pre-cluster-threshold=0.2
eps=0.2
group-threshold=1
dstest1_sgie1_config.txt
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
onnx-file=../../../../samples/models/Helmet/ssd-mobilenet.onnx
model-engine-file=../../../../samples/models/Helmet/ssd-mobilenet.onnx_b1_gpu0_fp16.engine
labelfile-path=../../../../samples/models/Helmet/labels.txt
int8-calib-file=../../../../samples/models/Primary_Detector/cal_trt.bin
batch-size=1
network-mode=1
num-detected-classes=3
interval=0
gie-unique-id=1
#output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid
#scaling-filter=0
#scaling-compute-hw=0
is-classifier=1
#output-blob-names=predictions/Softmax
classifier-async-mode=1
classifier-threshold=0.9
parse-bbox-func-name=NvDsInferParseCustomTfSSD
custom-lib-path=/home/netcoz/libnvds_infercustomparser.so
[class-attrs-all]
pre-cluster-threshold=0.2
eps=0.2
group-threshold=1
dstest1_tracker_config.txt same as deepstream-test-2
Sorry if this is hard to follow. Thanks