Multiprocessing library in python is not working in deepstream python apps

Hi,

I am trying to use multiprocessing library with deepstream python apps. When I use the multiprocessing code for separate process execution with any other normal python code then it is working properly. But when i am integrating with deepstream python apps, the multiprocess is calling the function only once. it is not running continuously as separate thread.

Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU)
• DeepStream Version
• JetPack Version (valid for Jetson only)
• TensorRT Version
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs)
• 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)
• The pipeline being used

I am using jetson nano production module.

Jetpack 6.1 is flashed with deepstream 6.0.1 and remaining package versions are preinstalled with jetpack. python version 3.6.

Issue type is question.

I am using deep stream python apps test 3 example. I am trying to add multiprocessing functions to it but it is calling function only once. The same multiprocessing function is work if i run it as separate code without deep stream example.

Can you provide how to reproduce your issue?

#!/usr/bin/env python3

################################################################################

SPDX-FileCopyrightText: Copyright (c) 2020-2021 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 gi
import configparser

gi.require_version(‘Gst’, ‘1.0’)
from gi.repository import GLib, Gst
from ctypes import *
import time
import sys
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import PERF_DATA
import numpy as np
import pyds
import cv2
import os
import os.path
from os import path
import multiprocessing

perf_data = None
frame_count = {}
saved_count = {}
global PGIE_CLASS_ID_VEHICLE
PGIE_CLASS_ID_VEHICLE = 0
global PGIE_CLASS_ID_PERSON
PGIE_CLASS_ID_PERSON = 2

MAX_DISPLAY_LEN = 64
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
MUXER_OUTPUT_WIDTH = 1920
MUXER_OUTPUT_HEIGHT = 1080
MUXER_BATCH_TIMEOUT_USEC = 4000000
TILED_OUTPUT_WIDTH = 1920
TILED_OUTPUT_HEIGHT = 1080
GST_CAPS_FEATURES_NVMM = “memory:NVMM”
pgie_classes_str = [“Vehicle”, “TwoWheeler”, “Person”, “RoadSign”]

MIN_CONFIDENCE = 0.3
MAX_CONFIDENCE = 0.4

def check():
print(‘check function has been called…’)

p1 = multiprocessing.Process(target=check)
p1.start()

tiler_sink_pad_buffer_probe will extract metadata received on tiler src pad

and update params for drawing rectangle, object information etc.

def tiler_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
    l_obj = frame_meta.obj_meta_list
    num_rects = frame_meta.num_obj_meta
    is_first_obj = True
    save_image = False
    obj_counter = {
        PGIE_CLASS_ID_VEHICLE: 0,
        PGIE_CLASS_ID_PERSON: 0,
        PGIE_CLASS_ID_BICYCLE: 0,
        PGIE_CLASS_ID_ROADSIGN: 0
    }
    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
        # Periodically check for objects with borderline confidence value that may be false positive detections.
        # If such detections are found, annotate the frame with bboxes and confidence value.
        # Save the annotated frame to file.
        if saved_count["stream_{}".format(frame_meta.pad_index)] % 30 == 0 and (
                MIN_CONFIDENCE < obj_meta.confidence < MAX_CONFIDENCE):
            if is_first_obj:
                is_first_obj = False
                # 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 = draw_bounding_boxes(n_frame, obj_meta, obj_meta.confidence)
                # 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)


            save_image = True

        try:
            l_obj = l_obj.next
        except StopIteration:
            break

    print("Frame Number=", frame_number, "Number of Objects=", num_rects, "Vehicle_count=",
          obj_counter[PGIE_CLASS_ID_VEHICLE], "Person_count=", obj_counter[PGIE_CLASS_ID_PERSON])
    # update frame rate through this probe
    stream_index = "stream{0}".format(frame_meta.pad_index)
    global perf_data
    perf_data.update_fps(stream_index)
    if save_image:
        img_path = "{}/stream_{}/frame_{}.jpg".format(folder_name, frame_meta.pad_index, frame_number)
        cv2.imwrite(img_path, frame_copy)
    saved_count["stream_{}".format(frame_meta.pad_index)] += 1
    try:
        l_frame = l_frame.next
    except StopIteration:
        break

return Gst.PadProbeReturn.OK

def draw_bounding_boxes(image, obj_meta, confidence):
confidence = ‘{0:.2f}’.format(confidence)
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)
obj_name = pgie_classes_str[obj_meta.class_id]
# image = cv2.rectangle(image, (left, top), (left + width, top + height), (0, 0, 255, 0), 2, cv2.LINE_4)
color = (0, 0, 255, 0)
w_percents = int(width * 0.05) if width > 100 else int(width * 0.1)
h_percents = int(height * 0.05) if height > 100 else int(height * 0.1)
linetop_c1 = (left + w_percents, top)
linetop_c2 = (left + width - w_percents, top)
image = cv2.line(image, linetop_c1, linetop_c2, color, 6)
linebot_c1 = (left + w_percents, top + height)
linebot_c2 = (left + width - w_percents, top + height)
image = cv2.line(image, linebot_c1, linebot_c2, color, 6)
lineleft_c1 = (left, top + h_percents)
lineleft_c2 = (left, top + height - h_percents)
image = cv2.line(image, lineleft_c1, lineleft_c2, color, 6)
lineright_c1 = (left + width, top + h_percents)
lineright_c2 = (left + width, top + height - h_percents)
image = cv2.line(image, lineright_c1, lineright_c2, color, 6)
# Note that on some systems cv2.putText erroneously draws horizontal lines across the image
image = cv2.putText(image, obj_name + ‘,C=’ + str(confidence), (left - 10, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255, 0), 2)
return image

def cb_newpad(decodebin, decoder_src_pad, data):
print(“In cb_newpad\n”)
caps = decoder_src_pad.get_current_caps()
gststruct = caps.get_structure(0)
gstname = gststruct.get_name()
source_bin = data
features = caps.get_features(0)

# Need to check if the pad created by the decodebin is for video and not
# audio.
if (gstname.find("video") != -1):
    # Link the decodebin pad only if decodebin has picked nvidia
    # decoder plugin nvdec_*. We do this by checking if the pad caps contain
    # NVMM memory features.
    if features.contains("memory:NVMM"):
        # Get the source bin ghost pad
        bin_ghost_pad = source_bin.get_static_pad("src")
        if not bin_ghost_pad.set_target(decoder_src_pad):
            sys.stderr.write("Failed to link decoder src pad to source bin ghost pad\n")
    else:
        sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")

def decodebin_child_added(child_proxy, Object, name, user_data):
print(“Decodebin child added:”, name, “\n”)
if name.find(“decodebin”) != -1:
Object.connect(“child-added”, decodebin_child_added, user_data)

if "source" in name:
    source_element = child_proxy.get_by_name("source")
    if source_element.find_property('drop-on-latency') != None:
        Object.set_property("drop-on-latency", True)

def create_source_bin(index, uri):
print(“Creating source bin”)

# Create a source GstBin to abstract this bin's content from the rest of the
# pipeline
bin_name = "source-bin-%02d" % index
print(bin_name)
nbin = Gst.Bin.new(bin_name)
if not nbin:
    sys.stderr.write(" Unable to create source bin \n")

# Source element for reading from the uri.
# We will use decodebin and let it figure out the container format of the
# stream and the codec and plug the appropriate demux and decode plugins.
uri_decode_bin = Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
if not uri_decode_bin:
    sys.stderr.write(" Unable to create uri decode bin \n")
# We set the input uri to the source element
uri_decode_bin.set_property("uri", uri)
# Connect to the "pad-added" signal of the decodebin which generates a
# callback once a new pad for raw data has beed created by the decodebin
uri_decode_bin.connect("pad-added", cb_newpad, nbin)
uri_decode_bin.connect("child-added", decodebin_child_added, nbin)

# We need to create a ghost pad for the source bin which will act as a proxy
# for the video decoder src pad. The ghost pad will not have a target right
# now. Once the decode bin creates the video decoder and generates the
# cb_newpad callback, we will set the ghost pad target to the video decoder
# src pad.
Gst.Bin.add(nbin, uri_decode_bin)
bin_pad = nbin.add_pad(Gst.GhostPad.new_no_target("src", Gst.PadDirection.SRC))
if not bin_pad:
    sys.stderr.write(" Failed to add ghost pad in source bin \n")
    return None
return nbin

def main(args):
# Check input arguments
if len(args) < 2:
sys.stderr.write(“usage: %s [uri2] … [uriN] \n” % args[0])
sys.exit(1)

global perf_data
perf_data = PERF_DATA(len(args) - 2)
number_sources = len(args) - 2

global folder_name
folder_name = args[-1]
if path.exists(folder_name):
    sys.stderr.write("The output folder %s already exists. Please remove it first.\n" % folder_name)
    sys.exit(1)

os.mkdir(folder_name)
print("Frames will be saved in ", folder_name)
# Standard GStreamer initialization
Gst.init(None)

# Create gstreamer elements */
# Create Pipeline element that will form a connection of other elements
print("Creating Pipeline \n ")
pipeline = Gst.Pipeline()
is_live = False

if not pipeline:
    sys.stderr.write(" Unable to create Pipeline \n")
print("Creating streamux \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")

pipeline.add(streammux)
for i in range(number_sources):
    os.mkdir(folder_name + "/stream_" + str(i))
    frame_count["stream_" + str(i)] = 0
    saved_count["stream_" + str(i)] = 0
    print("Creating source_bin ", i, " \n ")
    uri_name = args[i + 1]
    if uri_name.find("rtsp://") == 0:
        is_live = True
    source_bin = create_source_bin(i, uri_name)
    if not source_bin:
        sys.stderr.write("Unable to create source bin \n")
    pipeline.add(source_bin)
    padname = "sink_%u" % i
    sinkpad = streammux.get_request_pad(padname)
    if not sinkpad:
        sys.stderr.write("Unable to create sink pad bin \n")
    srcpad = source_bin.get_static_pad("src")
    if not srcpad:
        sys.stderr.write("Unable to create src pad bin \n")
    srcpad.link(sinkpad)
print("Creating Pgie \n ")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
    sys.stderr.write(" Unable to create pgie \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)
print("Creating tiler \n ")
tiler = Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
if not tiler:
    sys.stderr.write(" Unable to create tiler \n")
print("Creating nvvidconv \n ")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
    sys.stderr.write(" Unable to create nvvidconv \n")
print("Creating nvosd \n ")
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
    sys.stderr.write(" Unable to create nvosd \n")
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")

if is_live:
    print("Atleast one of the sources is live")
    streammux.set_property('live-source', 1)

streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batch-size', number_sources)
streammux.set_property('batched-push-timeout', 4000000)
pgie.set_property('config-file-path', "dstest_imagedata_config.txt")
pgie_batch_size = pgie.get_property("batch-size")
if (pgie_batch_size != number_sources):
    print("WARNING: Overriding infer-config batch-size", pgie_batch_size, " with number of sources ",
          number_sources, " \n")
    pgie.set_property("batch-size", number_sources)
tiler_rows = int(math.sqrt(number_sources))
tiler_columns = int(math.ceil((1.0 * number_sources) / tiler_rows))
tiler.set_property("rows", tiler_rows)
tiler.set_property("columns", tiler_columns)
tiler.set_property("width", TILED_OUTPUT_WIDTH)
tiler.set_property("height", TILED_OUTPUT_HEIGHT)

sink.set_property("sync", 0)
sink.set_property("qos", 0)

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)
    nvvidconv1.set_property("nvbuf-memory-type", mem_type)
    tiler.set_property("nvbuf-memory-type", mem_type)

print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
if is_aarch64():
    pipeline.add(transform)
pipeline.add(sink)

print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(nvvidconv1)
nvvidconv1.link(filter1)
filter1.link(tiler)
tiler.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 = GLib.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)

tiler_sink_pad = tiler.get_static_pad("sink")
if not tiler_sink_pad:
    sys.stderr.write(" Unable to get src pad \n")
else:
    tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
    # perf callback function to print fps every 5 sec
    GLib.timeout_add(5000, perf_data.perf_print_callback)

# List the sources
print("Now playing...")
for i, source in enumerate(args[:-1]):
    if i != 0:
        print(i, ": ", source)

print("Starting pipeline \n")
# start play back and listed to events		
pipeline.set_state(Gst.State.PLAYING)
try:
    loop.run()
except:
    pass
# cleanup
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)

if name == ‘main’:
sys.exit(main(sys.argv))

Above code is deepstream multistream imagedata test code with multistream

Any response pls?

Can you paste as one attachment? the format is hard to see and put in python file.

test.py (16.9 KB)

above live is reference code in this i have added multiprocessing test code.

check function running only once.

I resolved it by myself

Glad to know.
Can you share how you resolve it for others to leverage?