• Hardware Platform (Jetson / GPU) - DGPU
• DeepStream Version - 7
• JetPack Version (valid for Jetson only) - NA
• TensorRT Version - 8.6.1
• NVIDIA GPU Driver Version (valid for DGPU only) - 535.216.01
• Issue Type( questions, new requirements, bugs) - questions
Hello @yuweiw,
I am facing an issue while trying to save frames from the DeepStream pipeline. I am encountering the following error:
Error: “Currently we only support RGBA color format.”
To resolve this, I added a caps filter before the pgie (primary inference) element, as suggested in some previous threads. However, this leads to a segmentation fault (core dumped) issue every time I try to use the caps filter.
And also I tried adding the caps-filter at different stages.
Here is the part of the code where I am working with the caps filter:
import sys
sys.path.append('../')
import platform
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
MUXER_BATCH_TIMEOUT_USEC = 33000
def pgie_src_pad_buffer_probe(pad, info, u_data):
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer")
return
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:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
frame_copy = np.array(n_frame, copy=True, order='C')
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_RGBA2BGR)
l_obj = frame_meta.obj_meta_list
while l_obj is not None:
try:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
print(f"Detected Object: {obj_meta.obj_label}, Confidence: {obj_meta.confidence:.2f}")
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 main(args):
if len(args) < 1:
sys.stderr.write(f"usage: {args[0]} <h264_elementary_stream>\n")
sys.exit(1)
#platform_info = PlatformInfo()
Gst.init(None)
# Create Pipeline
pipeline = Gst.Pipeline()
if not pipeline:
sys.stderr.write("Unable to create Pipeline\n")
sys.exit(1)
def on_pad_added(demuxer, pad, h264parser):
# Only link if not already linked
if not h264parser.get_static_pad("sink").is_linked():
pad.link(h264parser.get_static_pad("sink"))
# Elements
source = Gst.ElementFactory.make("filesrc", "file-source")
demuxer = Gst.ElementFactory.make("qtdemux", "qt-demuxer")
h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
streammux = Gst.ElementFactory.make("nvstreammux", "stream-muxer")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "nvvideo-converter")
# capsfilter = Gst.ElementFactory.make("capsfilter", "capsfilter")
# caps = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
# capsfilter.set_property("caps", caps)
nvosd = Gst.ElementFactory.make("nvdsosd", "nv-onscreendisplay")
sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
if not all([source, h264parser, decoder, streammux, pgie, nvvidconv, nvosd, sink]):
sys.stderr.write("Unable to create one or more elements\n")
sys.exit(1)
# Set Properties
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", MUXER_BATCH_TIMEOUT_USEC)
pgie.set_property("config-file-path", "/home/dstream/Documents/Detection_Deep_Stream_App/configs/cofig_infer_yolov8_forum.txt") # Ensure this file exists
# Add Elements to Pipeline
pipeline.add(source)
pipeline.add(demuxer)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(nvvidconv)
#pipeline.add(capsfilter)
pipeline.add(nvosd)
pipeline.add(sink)
# Link Elements
source.link(demuxer)
source.link(demuxer)
#demuxer.connect("pad-added", lambda demux, pad: pad.link(h264parser.get_static_pad("sink")))
demuxer.connect("pad-added", lambda d, p: on_pad_added(d, p, h264parser))
h264parser.link(decoder)
#h264parser.link(decoder)
# Link decoder to streammux
sinkpad = streammux.get_request_pad("sink_0")
srcpad = decoder.get_static_pad("src")
srcpad.link(sinkpad)
# Link the rest
streammux.link(pgie)
pgie.link(nvvidconv)
nvvidconv.link(nvosd)
#capsfilter.link(nvosd)
nvosd.link(sink)
# Add Probe for Detection Metadata
pgie_src_pad = pgie.get_static_pad("src")
if pgie_src_pad:
pgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, pgie_src_pad_buffer_probe, 0)
# Event Loop and Bus Handling
loop = GLib.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)
# Start Pipeline
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except KeyboardInterrupt:
pass
finally:
pipeline.set_state(Gst.State.NULL)
if __name__ == "__main__":
sys.exit(main(sys.argv))
When I don’t use the caps filter, I receive the error message: “Currently we only support RGBA color format.” But if I try to use the caps filter, the pipeline crashes with a segmentation fault.
Issue:
- Without caps filter: I am receiving the error “Currently we only support RGBA color format.”
- With caps filter: The pipeline crashes with a segmentation fault (core dumped).
Request for Guidance:
- How can I solve the issue and successfully save frames from the DeepStream pipeline?
- At which specific stage of the DeepStream pipeline can I save frames?
- Is there a specific approach for saving frames in any format from the DeepStream pipeline?