I am using deepstream_test2.py the model I used is yolov5 from the github repo() and a custom trained classifier using Nvidia Tao on 5 labels.
python script.py
#!/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 os
no_display = True
print(os.getcwd())
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, 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
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.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
print(l_obj)
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)
# print(obj_meta.detector_bbox_info)
if (obj_meta.class_id == PGIE_CLASS_ID_VEHICLE):
cls_obj = obj_meta.classifier_meta_list
print('cls obj is',cls_obj)
while cls_obj is not None:
print('The cls is not none')
try:
cls_meta=pyds.NvDsClassifierMeta.cast(cls_obj.data)
print('the labels info', cls_meta.label_info_list)
print('component id',cls_meta.unique_component_id)
if cls_meta.unique_component_id==2:
print()
cls_meta_lbl = cls_meta.label_info_list
while cls_meta_lbl is not None:
try:
cls_meta_lbl_info=pyds.NvDsLabelInfo.cast(cls_meta_lbl.data)
result_str = str(cls_meta_lbl_info.result_label)#.tobytes().decode('iso-8859-1'))
print("result_decode", result_str)
print("result_strip:", result_str.split('\x00'))
print("result_one:", result_str.split('\x00')[0])
print("-----------------------------------------")
print("result_label:", cls_meta_lbl_info.result_label)
except StopIteration:
break
except StopIteration:
break
except StopIteration:
break
if obj_meta.class_id in obj_counter:
pass
else:
obj_counter[obj_meta.class_id] = 0
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
return Gst.PadProbeReturn.OK
def main(args):
# Check input arguments
if(len(args)<2):
sys.stderr.write("usage: %s <h264_elementary_stream> [0/1]\n" % args[0])
sys.exit(1)
# Standard GStreamer initialization
if(len(args)==3):
past_tracking_meta[0]=int(args[2])
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")
# Use nvinfer to run inferencing on decoder'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")
# sgie2 = Gst.ElementFactory.make("nvinfer", "secondary2-nvinference-engine")
# if not sgie2:
# sys.stderr.write(" Unable to make sgie2 \n")
# sgie3 = Gst.ElementFactory.make("nvinfer", "secondary3-nvinference-engine")
# if not sgie3:
# sys.stderr.write(" Unable to make sgie3 \n")
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")
if no_display:
print("Creating Fakesink \n")
sink = Gst.ElementFactory.make("fakesink", "fakesink")
sink.set_property('enable-last-sample', 0)
sink.set_property('sync', 0)
else:
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")
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 pgie and sgie
# pgie.set_property('config-file-path', "dstest2_pgie_config.txt")
pgie.set_property('config-file-path', "primary_pgie_config_yolo.txt")
sgie1.set_property('config-file-path', "sgie1_custom.txt")
#sgie2.set_property('config-file-path', "dstest2_sgie2_config.txt")
#sgie3.set_property('config-file-path', "dstest2_sgie3_config.txt")
#Set properties of tracker
config = configparser.ConfigParser()
config.read('dstest2_tracker_config.txt')
config.sections()
print('config', config.keys())
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(pgie)
pipeline.add(tracker)
pipeline.add(sgie1)
#pipeline.add(sgie2)
#pipeline.add(sgie3)
pipeline.add(nvvidconv)
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(pgie)
pgie.link(tracker)
tracker.link(sgie1)
sgie1.link(nvvidconv)
#sgie2.link(sgie3)
#sgie3.link(nvvidconv)
nvvidconv.link(nvosd)
if is_aarch64():
nvosd.link(transform)
transform.link(sink)
else:
nvosd.link(sink)
# create and event loop and feed gstreamer bus mesages to it
loop = GLib.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)
print("Starting pipeline \n")
# start play back and listed to events
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 am using one pgie(detector) and sgie(classifier)
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-color-format=0
custom-network-config=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new/model_detection_yolov5m/DeepStream-Yolo/Vehicle-CCTV_v1.0_yolov5m_acc96.cfg
model-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new/model_detection_yolov5m/DeepStream-Yolo/Vehicle-CCTV_v1.0_yolov5m_acc96.wts
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
labelfile-path=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new/model_detection_yolov5m/labels.txt
batch-size=1
network-mode=0
num-detected-classes=1
interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseYolo
custom-lib-path=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new/model_detection_yolov5m/DeepStream-Yolo/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
[class-attrs-all]
nms-iou-threshold=0.10
pre-cluster-threshold=0.25
topk=300
SGIE
[property]
gpu-id=0
net-scale-factor=1.0
offsets=103.939;116.779;123.68
#model-engine-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new1/deploy/model.engine
tlt-encoded-model=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new1/deploy/model.etlt
tlt-model-key=password
labelfile-path=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new1/deploy/labels.txt
int8-calib-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test2-new1/deploy/calib.txt
force-implicit-batch-dim=1
uff-input-blob-name=input_1
output-blob-names=predictions/Softmax
infer-dims=3;224;224
batch-size=64
network-mode=2
uff-input-order=0
input-object-min-width=64
input-object-min-height=64
network-type = 1
model-color-format=1
num-detected-classes=5
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=0
is-classifier=1
classifier-async-mode=1
classifier-threshold=0.01
maintain-aspect-ratio=0
output-tensor-meta=0
process-mode=2
#scaling-filter=0
#scaling-compute-hw=0
In almost all the frames:
the classifier_meta_list is none, but the detector is giving a good box around the image. I am not sure why classifier is giving this result. I am not sure if the classifier is not performing good.
@yuweiw @yingliu