I am getting segmentation fault after loading first frames of both videos sources and i am using jetson orin nano
deepstream-imagedata-multistream.py:
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.platform_info import PlatformInfo
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
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 = 33000
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
# 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)
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.
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
print(saved_count)
try:
l_frame = l_frame.next
print("l_frame: ",l_frame)
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 not platform_info.is_integrated_gpu() and name.find("nvv4l2decoder") != -1:
# Use CUDA unified memory in the pipeline so frames can be easily accessed on CPU in Python.
# 0: NVBUF_MEM_CUDA_DEVICE, 1: NVBUF_MEM_CUDA_PINNED, 2: NVBUF_MEM_CUDA_UNIFIED
# Dont use direct macro here like NVBUF_MEM_CUDA_UNIFIED since nvv4l2decoder uses a
# different enum internally
Object.set_property("cudadec-memtype", 2)
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 <uri1> [uri2] ... [uriN] <folder to save frames>\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)
global platform_info
platform_info = PlatformInfo()
# 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.request_pad_simple(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 True:
sink = Gst.ElementFactory.make("fakesink","fakesink")
elif platform_info.is_integrated_gpu():
print("Creating nv3dsink \n")
sink = Gst.ElementFactory.make("fakesink", "fakesink")
# 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("fakesink", "fakesink")
# sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
else:
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', MUXER_BATCH_TIMEOUT_USEC)
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 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)
nvvidconv1.set_property("nvbuf-memory-type", vc_mem_type)
else:
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)
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)
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))
Terminal:
velens@ubuntu:/opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/deepstream-imagedata-multistream$ sudo python3 deepstream_imagedata-multistream.py file:///opt/nvidia/deepstream/deepstream-6.4/samples/streams/yoga.mp4 file:///opt/nvidia/deepstream/deepstream-6.4/samples/streams/fisheye_dist.mp4 new_frames
Frames will be saved in new_frames
Creating Pipeline
Creating streamux
Creating source_bin 0
Creating source bin
source-bin-00
Creating source_bin 1
Creating source bin
source-bin-01
Creating Pgie
Creating nvvidconv1
Creating filter1
Creating tiler
Creating nvvidconv
Creating nvosd
Is it Integrated GPU? : 1
Adding elements to Pipeline
Linking elements in the Pipeline
Now playing...
1 : file:///opt/nvidia/deepstream/deepstream-6.4/samples/streams/yoga.mp4
2 : file:///opt/nvidia/deepstream/deepstream-6.4/samples/streams/fisheye_dist.mp4
Starting pipeline
0:00:06.389595934 7804 0xaaab56512310 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:2092> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-6.4/samples/models/Primary_Detector/resnet18_trafficcamnet.etlt_b2_gpu0_int8.engine
INFO: [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x544x960
1 OUTPUT kFLOAT output_bbox/BiasAdd 16x34x60
2 OUTPUT kFLOAT output_cov/Sigmoid 4x34x60
0:00:06.784423949 7804 0xaaab56512310 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2195> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.4/samples/models/Primary_Detector/resnet18_trafficcamnet.etlt_b2_gpu0_int8.engine
0:00:06.800524542 7804 0xaaab56512310 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest_imagedata_config.txt sucessfully
Decodebin child added: source
Decodebin child added: decodebin0
Decodebin child added: source
Decodebin child added: decodebin1
**PERF: {'stream0': 0.0, 'stream1': 0.0}
Decodebin child added: qtdemux0
Decodebin child added: qtdemux1
Decodebin child added: multiqueue1
Decodebin child added: multiqueue0
Decodebin child added: h264parse0
Decodebin child added: h264parse1
Decodebin child added: capsfilter1
Decodebin child added: capsfilter0
Decodebin child added: aacparse0
Decodebin child added: nvv4l2decoder0
Decodebin child added: avdec_aac0
Opening in BLOCKING MODE
Decodebin child added: nvv4l2decoder1
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
In cb_newpad
In cb_newpad
In cb_newpad
Frame Number= 0 Number of Objects= 4 Vehicle_count= 4 Person_count= 0
{'stream_0': 0, 'stream_1': 1}
l_frame: None
Segmentation fault