Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 5.1
• JetPack Version (valid for Jetson only)
• TensorRT Version 7.2
• NVIDIA GPU Driver Version (valid for GPU only) 470
• Issue Type( questions, new requirements, bugs) questions
• 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)
I need to filter only a few classes from coco classes and, i want to first extract only person co-ordinates into a list so i can compare other object with every person in a frame.
i tried the following code but it is not working
i got the following error after completing person extraction loop.
#!/usr/bin/env python3
################################################################################
# Copyright (c) 2020, 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.
################################################################################
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
import torch
import torchvision.ops.boxes as bops
import cv2
import numpy as np
PGIE_CLASS_ID_TOOTHBRUSH = 79
PGIE_CLASS_ID_HAIR_DRYER = 78
PGIE_CLASS_ID_TEDDY_BEAR = 77
PGIE_CLASS_ID_SCISSORS = 76
PGIE_CLASS_ID_VASE = 75
PGIE_CLASS_ID_CLOCK = 74
PGIE_CLASS_ID_BOOK = 73
PGIE_CLASS_ID_REFRIGERATOR = 72
PGIE_CLASS_ID_SINK = 71
PGIE_CLASS_ID_TOASTER = 70
PGIE_CLASS_ID_OVEN = 69
PGIE_CLASS_ID_MICROWAVE = 68
PGIE_CLASS_ID_CELL_PHONE = 67
PGIE_CLASS_ID_KEYBOARD = 66
PGIE_CLASS_ID_REMOTE = 65
PGIE_CLASS_ID_MOUSE = 64
PGIE_CLASS_ID_LAPTOP = 63
PGIE_CLASS_ID_TVMONITOR = 62
PGIE_CLASS_ID_TOILET = 61
PGIE_CLASS_ID_DININGTABLE= 60
PGIE_CLASS_ID_BED = 59
PGIE_CLASS_ID_POTTEDPLANT = 58
PGIE_CLASS_ID_SOFA = 57
PGIE_CLASS_ID_CHAIR = 56
PGIE_CLASS_ID_CAKE = 55
PGIE_CLASS_ID_DONUT = 54
PGIE_CLASS_ID_PIZZA = 53
PGIE_CLASS_ID_HOT_DOG = 52
PGIE_CLASS_ID_CARROT = 51
PGIE_CLASS_ID_BROCCOLI = 50
PGIE_CLASS_ID_ORANGE = 49
PGIE_CLASS_ID_SANDWICH = 48
PGIE_CLASS_ID_APPLE = 47
PGIE_CLASS_ID_BANANA = 46
PGIE_CLASS_ID_BOWL = 45
PGIE_CLASS_ID_SPOON = 44
PGIE_CLASS_ID_KNIFE = 43
PGIE_CLASS_ID_FORK = 42
PGIE_CLASS_ID_CUP = 41
PGIE_CLASS_ID_WINE_GLASS = 40
PGIE_CLASS_ID_BOTTLE = 39
PGIE_CLASS_ID_TENNIS_RACKET = 38
PGIE_CLASS_ID_SURFBOARD = 37
PGIE_CLASS_ID_SKATEBOARD = 36
PGIE_CLASS_ID_BASEBALL_GLOVE = 35
PGIE_CLASS_ID_BASEBALL_BAT = 34
PGIE_CLASS_ID_KITE = 33
PGIE_CLASS_ID_SPORTS_BALL = 32
PGIE_CLASS_ID_SNOWBOARD = 31
PGIE_CLASS_ID_SKIS = 30
PGIE_CLASS_ID_FRISBEE = 29
PGIE_CLASS_ID_SUITCASE = 28
PGIE_CLASS_ID_TIE = 27
PGIE_CLASS_ID_HANDBAG = 26
PGIE_CLASS_ID_UMBRELLA = 25
PGIE_CLASS_ID_BACKPACK = 24
PGIE_CLASS_ID_GIRAFFE = 23
PGIE_CLASS_ID_ZEBRA = 22
PGIE_CLASS_ID_BEAR = 21
PGIE_CLASS_ID_ELEPHANT = 20
PGIE_CLASS_ID_COW = 19
PGIE_CLASS_ID_SHEEP = 18
PGIE_CLASS_ID_HORSE = 17
PGIE_CLASS_ID_DOG = 16
PGIE_CLASS_ID_CAT = 15
PGIE_CLASS_ID_BIRD = 14
PGIE_CLASS_ID_BENCH = 13
PGIE_CLASS_ID_PARKING_METER = 12
PGIE_CLASS_ID_STOP_SIGN = 11
PGIE_CLASS_ID_FIRE_HYDRANT = 10
PGIE_CLASS_ID_TRAFFIC_LIGHT = 9
PGIE_CLASS_ID_BOAT = 8
PGIE_CLASS_ID_TRUCK = 7
PGIE_CLASS_ID_TRAIN = 6
PGIE_CLASS_ID_BUS = 5
PGIE_CLASS_ID_AEROPLANE = 4
PGIE_CLASS_ID_MOTORBIKE = 3
PGIE_CLASS_ID_VEHICLE = 2
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 0
past_tracking_meta=[0]
def calculate_iou(persons, bag): # box2-> baggages
ious = {'output': {'bbox': [], 'box1':[], 'box2': []}}
for p_box in persons:
ious['output']['bbox'].append(bops.box_iou(p_box, bag).tolist())
ious['output']['box1'].append(p_box)
ious['output']['box2'].append(bag)
outputs = {"bbox":[], "box1": [], "box2": []}
try:
idx_list = np.nonzero(ious['output']['bbox'])
for idx in idx_list[0]:
outputs["bbox"].append(ious['output']['bbox'][idx])
outputs["box1"].append(ious['output']['box1'][idx])
outputs["box2"].append(ious['output']['box2'][idx])
return outputs["bbox"], outputs["box1"], outputs["box2"]
except Exception as e:
print(e)
outputs = None, None, None
return outputs
def torchTolist(box): #, conf, cls):
t_box = box[0][0].tolist()
# t_box.append(conf.tolist())
# t_box.append(cls.tolist())
return t_box
def osd_sink_pad_buffer_probe(pad,info,u_data):
frame_number=0
#Intiallizing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_TOOTHBRUSH:0,
PGIE_CLASS_ID_HAIR_DRYER:0,
PGIE_CLASS_ID_TEDDY_BEAR:0,
PGIE_CLASS_ID_SCISSORS:0,
PGIE_CLASS_ID_VASE:0,
PGIE_CLASS_ID_CLOCK:0,
PGIE_CLASS_ID_BOOK:0,
PGIE_CLASS_ID_REFRIGERATOR:0,
PGIE_CLASS_ID_SINK:0,
PGIE_CLASS_ID_TOASTER:0,
PGIE_CLASS_ID_OVEN:0,
PGIE_CLASS_ID_MICROWAVE:0,
PGIE_CLASS_ID_CELL_PHONE:0,
PGIE_CLASS_ID_KEYBOARD:0,
PGIE_CLASS_ID_REMOTE:0,
PGIE_CLASS_ID_MOUSE:0,
PGIE_CLASS_ID_LAPTOP:0,
PGIE_CLASS_ID_TVMONITOR:0,
PGIE_CLASS_ID_TOILET:0,
PGIE_CLASS_ID_DININGTABLE:0,
PGIE_CLASS_ID_BED:0,
PGIE_CLASS_ID_POTTEDPLANT:0,
PGIE_CLASS_ID_SOFA:0,
PGIE_CLASS_ID_CHAIR:0,
PGIE_CLASS_ID_CAKE:0,
PGIE_CLASS_ID_DONUT:0,
PGIE_CLASS_ID_PIZZA:0,
PGIE_CLASS_ID_HOT_DOG:0,
PGIE_CLASS_ID_CARROT:0,
PGIE_CLASS_ID_BROCCOLI:0,
PGIE_CLASS_ID_ORANGE:0,
PGIE_CLASS_ID_SANDWICH:0,
PGIE_CLASS_ID_APPLE:0,
PGIE_CLASS_ID_BANANA:0,
PGIE_CLASS_ID_BOWL:0,
PGIE_CLASS_ID_SPOON:0,
PGIE_CLASS_ID_KNIFE:0,
PGIE_CLASS_ID_FORK:0,
PGIE_CLASS_ID_CUP:0,
PGIE_CLASS_ID_WINE_GLASS:0,
PGIE_CLASS_ID_BOTTLE:0,
PGIE_CLASS_ID_TENNIS_RACKET:0,
PGIE_CLASS_ID_SURFBOARD:0,
PGIE_CLASS_ID_SKATEBOARD:0,
PGIE_CLASS_ID_BASEBALL_GLOVE:0,
PGIE_CLASS_ID_BASEBALL_BAT:0,
PGIE_CLASS_ID_KITE:0,
PGIE_CLASS_ID_SPORTS_BALL:0,
PGIE_CLASS_ID_SNOWBOARD:0,
PGIE_CLASS_ID_SKIS:0,
PGIE_CLASS_ID_FRISBEE:0,
PGIE_CLASS_ID_SUITCASE:0,
PGIE_CLASS_ID_TIE:0,
PGIE_CLASS_ID_HANDBAG:0,
PGIE_CLASS_ID_UMBRELLA:0,
PGIE_CLASS_ID_BACKPACK:0,
PGIE_CLASS_ID_GIRAFFE:0,
PGIE_CLASS_ID_ZEBRA:0,
PGIE_CLASS_ID_BEAR:0,
PGIE_CLASS_ID_ELEPHANT:0,
PGIE_CLASS_ID_COW:0,
PGIE_CLASS_ID_SHEEP:0,
PGIE_CLASS_ID_HORSE:0,
PGIE_CLASS_ID_DOG:0,
PGIE_CLASS_ID_CAT:0,
PGIE_CLASS_ID_BIRD:0,
PGIE_CLASS_ID_BENCH:0,
PGIE_CLASS_ID_PARKING_METER:0,
PGIE_CLASS_ID_STOP_SIGN:0,
PGIE_CLASS_ID_FIRE_HYDRANT:0,
PGIE_CLASS_ID_TRAFFIC_LIGHT:0,
PGIE_CLASS_ID_BOAT:0,
PGIE_CLASS_ID_TRUCK:0,
PGIE_CLASS_ID_TRAIN:0,
PGIE_CLASS_ID_BUS:0,
PGIE_CLASS_ID_AEROPLANE:0,
PGIE_CLASS_ID_MOTORBIKE:0,
PGIE_CLASS_ID_VEHICLE:0,
PGIE_CLASS_ID_BICYCLE:0,
PGIE_CLASS_ID_PERSON: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 # [ person-1:{rect, confi, ..}, person-2{..}, dog1:{...}]
persons = []
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
p_obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
print("inside person loop")
if p_obj_meta.class_id == 0:
bbox={'xmin':p_obj_meta.rect_params.left,
'ymin':p_obj_meta.rect_params.left+p_obj_meta.rect_params.width,
'xmax':p_obj_meta.rect_params.top+p_obj_meta.rect_params.height,
'ymax':p_obj_meta.rect_params.top}
# res['objects'].append({'label':obj_meta.obj_label,
# 'confidence':obj_meta.confidence,'bbox':bbox.copy()})
persons.append(torch.tensor([[bbox['xmin'], bbox['ymin'],
bbox['xmax'], bbox['ymax']]]))
try:
l_obj=l_obj.next
except StopIteration:
break
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
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
if obj_meta.class_id in [24, 26, 28]:
bbox={'xmin':obj_meta.rect_params.left,
'ymin':obj_meta.rect_params.left+obj_meta.rect_params.width,
'xmax':obj_meta.rect_params.top+obj_meta.rect_params.height,
'ymax':obj_meta.rect_params.top}
bag= torch.tensor([[bbox['xmin'], bbox['ymin'], bbox['xmax'], bbox['ymax']]])
# find iou over all bagges and persons
ious, box1, box2 = calculate_iou(persons, bag)
print("ious", ious)
print("box1", box1)
print("box2", box2)
if ious and len(ious)==1:
bbox = torchTolist(box1)
pbox = torchTolist(box2)
color = (0, 0, 255, 0)
n_frame = draw_bounding_boxes(n_frame, bbox, 'person', color)
n_frame = draw_bounding_boxes(n_frame, pbox, str(obj_meta.obj_label), color)
if ious and len(ious)>1:
# call pose estimation
# all person keypoints
idx= np.array(ious).argmax()
bbox = box1[idx][0].tolist()
pbox = box2[idx][0].tolist()
color = (0, 0, 255, 0)
n_frame = draw_bounding_boxes(n_frame, bbox, 'person', color)
n_frame = draw_bounding_boxes(n_frame, pbox, str(obj_meta.obj_label), color)
else:
color = (0, 0, 0, 255)
n_frame = draw_bounding_boxes(n_frame, bbox, str(obj_meta.obj_label), color)
try:
l_obj=l_obj.next
except StopIteration:
break
# 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)
# send_json_api()
cv2.imwrite(f'imgs/frame{frame_number}.png', frame_copy)
# 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={} Person_count={}".format(frame_number, num_rects, 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 draw_bounding_boxes(image, box, label, id_, color):
x1 = int(box[0])
y1 = int(box[2])
x2 = int(box[1])
y2 = int(box[3])
image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2, cv2.LINE_4)
image = cv2.putText(image, label+ f'-{id_}', (x1 - 10, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255, 0), 2)
return image
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])
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") # file_name
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") # .h264
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 sgie1:
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")
# 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)
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("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
pgie.set_property('config-file-path', "yolov4_pgie_config.txt")
sgie1.set_property('config-file-path', "dstest2_sgie1_config.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()
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(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(sink)
if is_aarch64():
pipeline.add(transform)
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)
# 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(nvvidconv)
# sgie1.link(sgie2)
# 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 = 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)# yolov4_detector / pass 2 person
# osdsinkpad2.add_probe(Gst.PadProbeType.BUFFER, pose_estimation, 0) # single person
# osdsinkpad3.add_probe(Gst.PadProbeType.BUFFER, tracker, 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))
error:
ERROR: nvdsinfer_context_impl.cpp:1573 Failed to synchronize on cuda copy-coplete-event, cuda err_no:700, err_str:cudaErrorIllegalAddress
0:00:12.580713112 575 0x47be140 WARN nvinfer gstnvinfer.cpp:2021:gst_nvinfer_output_loop: error: Failed to dequeue output from inferencing. NvDsInferContext error: NVDSINFER_CUDA_ERROR
0:00:12.580787656 575 0x47be140 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::releaseBatchOutput() <nvdsinfer_context_impl.cpp:1607> [UID = 1]: Tried to release an outputBatchID which is already with the context
Cuda failure: status=700 in CreateTextureObj at line 2902
nvbufsurftransform.cpp:2703: => Transformation Failed -2
Segmentation fault (core dumped)