There is no problem here.
I modified this sample and was able to get the value of obj_meta.parent.class_id correctly.
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2022 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 configparser
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.platform_info import PlatformInfo
from common.bus_call import bus_call
import numpy as np
import pyds
import cv2
from os import path
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
PRIMARY_DETECTOR_ID = 1
SECONDARY_DETECTOR_ID = 2
face_count = 0
def sgie_src_pad_buffer_probe(pad,info,u_data):
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
l_obj=frame_meta.obj_meta_list
while l_obj is not None:
try:
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
if obj_meta.unique_component_id == SECONDARY_DETECTOR_ID:
print(f"parent cls id = {obj_meta.parent.class_id} ===> {obj_meta.parent.class_id == PGIE_CLASS_ID_PERSON}")
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def osd_sink_pad_buffer_probe(pad,info,u_data):
frame_number=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
vehicle_count = 0
person_count = 0
global face_count
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
if obj_meta.unique_component_id == PRIMARY_DETECTOR_ID:
# print("meta from primary detector ")
if obj_meta.class_id == PGIE_CLASS_ID_VEHICLE:
vehicle_count += 1
elif obj_meta.class_id == PGIE_CLASS_ID_PERSON:
person_count += 1
elif obj_meta.unique_component_id == SECONDARY_DETECTOR_ID:
# print("meta from secondary detector ")
face_count += 1
if face_count % 30 == 0:
# Getting Image data using nvbufsurface
# the input should be address of buffer and batch_id
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
n_frame = crop_object(n_frame, obj_meta)
# convert python array into numpy array format in the copy mode.
frame_copy = np.array(n_frame, copy=True, order='C')
# convert the array into cv2 default color format
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_RGBA2BGRA)
if platform_info.is_integrated_gpu(): # If Jetson, since the buffer is mapped to CPU for retrieval, it must also be unmapped
pyds.unmap_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id) # The unmap call should be made after operations with the original array are complete.
# The original array cannot be accessed after this call.
img_path = f"face_{face_count}.jpg"
cv2.imwrite(img_path, frame_copy)
#print(f"parent cls id = {obj_meta.parent.class_id} ===> {obj_meta.parent.class_id == PGIE_CLASS_ID_PERSON}")
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
print(f"Frame Number={frame_number} Number of Objects={num_rects} Vehicle_count={vehicle_count} Person_count={person_count}")
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def crop_object(image, obj_meta):
rect_params = obj_meta.rect_params
top = int(rect_params.top)
left = int(rect_params.left)
width = int(rect_params.width)
height = int(rect_params.height)
crop_img = image[top:top+height, left:left+width]
return crop_img
def main(args):
# Check input arguments
if (len(args)<2):
sys.stderr.write("usage: %s <h264_elementary_stream>\n" % args[0])
sys.exit(1)
global platform_info
platform_info = PlatformInfo()
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
primary_detector = Gst.ElementFactory.make("nvinfer", "primary-inference-engine1")
if not primary_detector:
sys.stderr.write(" Unable to create primary_detector \n")
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
secondary_detector = Gst.ElementFactory.make("nvinfer", "primary-nvinference-engine2")
if not secondary_detector:
sys.stderr.write(" Unable to make secondary_detector \n")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
sys.stderr.write(" Unable to create nvvidconv \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)
# 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 platform_info.is_integrated_gpu():
print("Creating nv3dsink \n")
sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
if not sink:
sys.stderr.write(" Unable to create nv3dsink \n")
else:
if platform_info.is_platform_aarch64():
print("Creating nv3dsink \n")
sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
else:
print("Creating EGLSink \n")
sink = Gst.ElementFactory.make("fakesink", "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
primary_detector.set_property('config-file-path', "primary_detector_config.txt")
secondary_detector.set_property('config-file-path', "secondary_detector_config.txt")
#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 not platform_info.is_integrated_gpu():
# 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)
if platform_info.is_wsl():
#opencv functions like cv2.line and cv2.putText is not able to access NVBUF_MEM_CUDA_UNIFIED memory
#in WSL systems due to some reason and gives SEGFAULT. Use NVBUF_MEM_CUDA_PINNED memory for such
#usecases in WSL. Here, nvvidconv1's buffer is used in tiler sink pad probe and cv2 operations are
#done on that.
print("using nvbuf_mem_cuda_pinned memory for nvvidconv1\n")
vc_mem_type = int(pyds.NVBUF_MEM_CUDA_PINNED)
nvvidconv.set_property("nvbuf-memory-type", vc_mem_type)
else:
nvvidconv.set_property("nvbuf-memory-type", mem_type)
print("Adding elements to Pipeline \n")
pipeline.add(source)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(primary_detector)
pipeline.add(tracker)
pipeline.add(secondary_detector)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvosd)
pipeline.add(sink)
# we link the elements together
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(primary_detector)
primary_detector.link(tracker)
tracker.link(secondary_detector)
secondary_detector.link(nvvidconv)
nvvidconv.link(filter1)
filter1.link(nvosd)
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)
sgiesrcpad = secondary_detector.get_static_pad("src")
if not sgiesrcpad:
sys.stderr.write(" Unable to get src pad of secondary_detector \n")
sgiesrcpad.add_probe(Gst.PadProbeType.BUFFER, sgie_src_pad_buffer_probe, 0)
# 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))