Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU)
Jetson NX
• DeepStream Version
6.1
• JetPack Version (valid for Jetson only)
Version: 5.0.2-b231
• TensorRT Version
8.4.1.5-1+cuda11.4
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs)
can not run tracker alone
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
hi,
we want to test the tracker by itself providing bbox manually. I tried to use meta data to pass a fake bbox at the beginning of stream . the box appears and disappears right away. I tried to tune the tracker increasing maxShadowTrackingAge and others or testing with other trackers like deepsort but the bbox will just disappear. below is the sample I use to pass object meta data to tracker. I use nvds_add_obj_meta_to_frame. note that we initiate this bbox once with track_is_init and let the tracker continue the work but the box just disappear right away within the next frame.
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
import sys
sys.path.append('../')
import platform
import configparser
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
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
UNTRACKED_OBJECT_ID = 0xffffffffffffffff
track_is_init = 0
past_tracking_meta=[0]
past_tracking_meta[0]=0 # ::os:: cause screen to be black and freeze, too much data?
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.glist_get_nvds_frame_meta()
# 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.glist_get_nvds_frame_meta(l_frame.data)
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.glist_get_nvds_object_meta(l_obj.data)
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
print("object found")
print(obj_meta.obj_label)
obj_counter[obj_meta.class_id] += 1
obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.0)
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)
return Gst.PadProbeReturn.OK
def streammux_src_pad_buffer_probe(pad,info,u_data):
global track_is_init
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)
print("streammux_src_pad_buffer_probe")
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.glist_get_nvds_frame_meta()
# 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.glist_get_nvds_frame_meta(l_frame.data)
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
new_object = pyds.nvds_acquire_obj_meta_from_pool(batch_meta)
#new_object.object_id = 0 #UNTRACKED_OBJECT_ID ::os:: cause to disapear (not tracked?)
new_object.unique_component_id = 1
new_object.class_id = 2
new_object.confidence = 1.0
new_object.obj_label = 'Test'
new_object.rect_params.top = 500.0
new_object.rect_params.left = 338.0
new_object.rect_params.width = 151.0
new_object.rect_params.height = 382.0
new_object.rect_params.border_width = 3;
try:
l_frame=l_frame.next
except StopIteration:
break
if (track_is_init<15):
pyds.nvds_add_obj_meta_to_frame(frame_meta, new_object, None)
track_is_init+=1
return Gst.PadProbeReturn.OK
def main(args):
# Check input arguments
if len(args) != 2:
sys.stderr.write("usage: %s <media file or uri>\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("filesrc", "file-source")
if not source:
sys.stderr.write(" Unable to create Source \n")
# Since the data format in the input file is elementary h264 stream,
# we need a h264parser
print("Creating H264Parser \n")
h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
if not h264parser:
sys.stderr.write(" Unable to create h264 parser \n")
# Use nvdec_h264 for hardware accelerated decode on GPU
print("Creating Decoder \n")
decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
if not decoder:
sys.stderr.write(" Unable to create Nvv4l2 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")
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")
# Use nvinfer to run inferencing on decoder's output,
# behaviour of inferencing is set through config file
# 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('location', 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)
########## 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(source)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(nvvidconv)
pipeline.add(tracker)
pipeline.add(nvosd)
pipeline.add(sink)
if is_aarch64():
pipeline.add(transform)
# we link the elements together
# file-source -> h264-parser -> nvh264-decoder ->
# nvinfer -> nvvidconv -> nvosd -> video-renderer
print("Linking elements in the Pipeline \n")
source.link(h264parser)
h264parser.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(tracker)
tracker.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)
muxsrcpad = streammux.get_static_pad("src")
if not muxsrcpad:
sys.stderr.write(" Unable to get sink pad of streammux \n")
muxsrcpad.add_probe(Gst.PadProbeType.BUFFER, streammux_src_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))
below is my config_tracker_NvDCF_accuracy.yml file. I alos set
tracker-width=960
tracker-height=544
to increase features. I also try with the defaults of Deepsort with ReID engine setup and built
%YAML:1.0
################################################################################
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
################################################################################
BaseConfig:
minDetectorConfidence: 0 # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking
TargetManagement:
enableBboxUnClipping: 1 # In case the bbox is likely to be clipped by image border, unclip bbox
preserveStreamUpdateOrder: 0 # When assigning new target ids, preserve input streams' order to keep target ids in a deterministic order over multuple runs
maxTargetsPerStream: 150 # Max number of targets to track per stream. Recommended to set >10. Note: this value should account for the targets being tracked in shadow mode as well. Max value depends on the GPU memory capacity
# [Creation & Termination Policy]
minIouDiff4NewTarget: 0.5 # If the IOU between the newly detected object and any of the existing targets is higher than this threshold, this newly detected object will be discarded.
minTrackerConfidence: 0.2 # If the confidence of an object tracker is lower than this on the fly, then it will be tracked in shadow mode. Valid Range: [0.0, 1.0]
probationAge: 3 # If the target's age exceeds this, the target will be considered to be valid.
maxShadowTrackingAge: 190 # Max length of shadow tracking. If the shadowTrackingAge exceeds this limit, the tracker will be terminated.
earlyTerminationAge: 1 # If the shadowTrackingAge reaches this threshold while in TENTATIVE period, the target will be terminated prematurely.
TrajectoryManagement:
useUniqueID: 0 # Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
enableReAssoc: 1 # Enable Re-Assoc
# [Re-Assoc: Motion-based]
minTrajectoryLength4Projection: 20 # min trajectory length required to make projected trajectory
prepLength4TrajectoryProjection: 10 # the length of the trajectory during which the state estimator is updated to make projections
trajectoryProjectionLength: 90 # the length of the projected trajectory
# [Re-Assoc: Trajectory Similarity]
minTrackletMatchingScore: 0.5 # min tracklet similarity score for matching
maxAngle4TrackletMatching: 30 # max angle difference for tracklet matching [degree]
minSpeedSimilarity4TrackletMatching: 0.2 # min speed similarity for tracklet matching
minBboxSizeSimilarity4TrackletMatching: 0.6 # min bbox size similarity for tracklet matching
maxTrackletMatchingTimeSearchRange: 20 # the search space in time for max tracklet similarity
DataAssociator:
dataAssociatorType: 0 # the type of data associator among { DEFAULT= 0 }
associationMatcherType: 0 # the type of matching algorithm among { GREEDY=0, GLOBAL=1 }
checkClassMatch: 1 # If checked, only the same-class objects are associated with each other. Default: true
# [Association Metric: Thresholds for valid candidates]
minMatchingScore4Overall: 0.0 # Min total score
minMatchingScore4SizeSimilarity: 0.5 # Min bbox size similarity score
minMatchingScore4Iou: 0.3 # Min IOU score
minMatchingScore4VisualSimilarity: 0.6 # Min visual similarity score
# [Association Metric: Weights]
matchingScoreWeight4VisualSimilarity: 0.5 # Weight for the visual similarity (in terms of correlation response ratio)
matchingScoreWeight4SizeSimilarity: 0.0 # Weight for the Size-similarity score
matchingScoreWeight4Iou: 0.1 # Weight for the IOU score
StateEstimator:
stateEstimatorType: 1 # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
# [Dynamics Modeling]
processNoiseVar4Loc: 3.0 # Process noise variance for bbox center
processNoiseVar4Size: 1.0 # Process noise variance for bbox size
#processNoiseVar4Vel: 0.1 # ::os:: Original:: Process noise variance for velocity
processNoiseVar4Vel: 0.2 # Process noise variance for velocity
measurementNoiseVar4Detector: 2.0 # Measurement noise variance for detector's detection
measurementNoiseVar4Tracker: 10.0 # Measurement noise variance for tracker's localization
VisualTracker:
visualTrackerType: 1 # the type of visual tracker among { DUMMY=0, NvDCF=1 }
# [NvDCF: Feature Extraction]
useColorNames: 1 # Use ColorNames feature
useHog: 1 # Use Histogram-of-Oriented-Gradient (HOG) feature
featureImgSizeLevel: 3 # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest
featureFocusOffsetFactor_y: -0.2 # The offset for the center of hanning window relative to the feature height. The center of hanning window would move by (featureFocusOffsetFactor_y*featureMatSize.height) in vertical direction
# [NvDCF: Correlation Filter]
filterLr: 0.075 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
filterChannelWeightsLr: 0.1 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
gaussianSigma: 0.75 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
here is how I run the demo
/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test4$ sudo python3 ./test_tracker_only_add_obj2.py /opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.h264
please advise as I got stuck in this for days
thank you