Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU)
rtx 4060
• DeepStream Version
ds7.1
• JetPack Version (valid for Jetson only)
• TensorRT Version
default
• NVIDIA GPU Driver Version (valid for GPU only)
default
• Issue Type( questions, new requirements, bugs)
unable to print the embedding using sgie, pgie model using peoplenet
• 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 will like to have a guide to implement resnet model into deepstream, I have successfully loaded the face-embeddings model as sgie.
The code below provided
import sys
sys.path.append('../')
import os
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 pyds
PGIE_CLASS_ID_PERSON = 0
PGIE_CLASS_ID_BAG = 1
PGIE_CLASS_ID_FACE = 2
MUXER_BATCH_TIMEOUT_USEC = 33000
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
#Intiallizing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_PERSON:0,
PGIE_CLASS_ID_BAG:0,
PGIE_CLASS_ID_FACE:0,
}
frame_number=frame_meta.frame_num
num_rects = frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
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
obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.8) #0.8 is alpha (opacity)
try:
l_obj=l_obj.next
except StopIteration:
break
# 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={} Bag_count={} Face_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_BAG], obj_counter[PGIE_CLASS_ID_FACE])
# 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
return Gst.PadProbeReturn.OK
def pgie_src_pad_buffer_probe(pad, info, u_data):
gst_buffer = info.get_buffer()
if not gst_buffer:
return Gst.PadProbeReturn.OK
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
l_obj = frame_meta.obj_meta_list
face_class_detected = False
while l_obj is not None:
try:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
if obj_meta.class_id == PGIE_CLASS_ID_FACE:
face_class_detected = True
break
try:
l_obj = l_obj.next
except StopIteration:
break
# if not face_class_detected:
# # No class 1 detected, clear objects so sgie does nothing
# frame_meta.obj_meta_list = None
# frame_meta.num_obj_meta = 0
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def sgie_src_pad_buffer_probe(pad, info, u_data):
# Get buffer and batch metadata
buffer = info.get_buffer()
if not buffer:
print("Unable to get buffer")
return Gst.PadProbeReturn.OK
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(buffer))
if not batch_meta:
print("No batch metadata available")
return Gst.PadProbeReturn.OK
# Iterate through frames in the batch
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
# Iterate through objects in the frame
l_obj = frame_meta.obj_meta_list
while l_obj is not None:
try:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
if obj_meta.unique_component_id == 1:
if obj_meta.class_id == 2:
l_user = obj_meta.obj_user_meta_list
# print(obj_meta.unique_component_id)
# print(obj_meta.class_id)
# print(obj_meta.object_id)
# print(obj_meta.confidence)
# print(obj_meta.detector_bbox_info)
# print(obj_meta.tracker_bbox_info)
# print(obj_meta.tracker_confidence)
# print(obj_meta.rect_params)
# print(obj_meta.mask_params)
# print(obj_meta.text_params)
# print(obj_meta.obj_label)
# print(obj_meta.classifier_meta_list)
# print(obj_meta.obj_user_meta_list)
# print(obj_meta.misc_obj_info)
# print(obj_meta.reserved)
# Filter for class ID 2 (faces)
if obj_meta.class_id == 2:
# Iterate through user metadata for tensor data
l_user = obj_meta.obj_user_meta_list
while l_user is not None:
try:
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
# Check for tensor metadata from sgie (gie-unique-id=2)
if (user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.TENSOR_OUTPUT_USER_META") and
user_meta.base_meta.component_id == 2):
tensor_meta = pyds.NvDsTensorMeta.cast(user_meta.user_meta_data)
# Extract and print embeddings (assuming FLOAT data type)
for layer in tensor_meta.tensor_layer:
if layer.data_type == 0: # FLOAT
embedding = np.array(pyds.get_tensor_data_float(layer), dtype=np.float32)
print(f"Frame {frame_meta.frame_num}, Object {obj_meta.object_id}, Face Embedding: {embedding.tolist()}")
except StopIteration:
break
try:
l_user = l_user.next
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):
# Check input arguments
if len(args) != 2:
sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
sys.exit(1)
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()
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
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
sgie = Gst.ElementFactory.make("nvinfer", "secondary-inference")
if not sgie:
sys.stderr.write(" Unable to create sgie \n")
# Use convertor to convert from NV12 to RGBA as required by nvosd
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 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("nveglglessink", "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])
if os.environ.get('USE_NEW_NVSTREAMMUX') != 'yes': # Only set these properties if not using new gst-nvstreammux
streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
streammux.set_property('batch-size', 1)
pgie.set_property('config-file-path', "dstest1_pgie_config.txt")
sgie.set_property('config-file-path', "dstest1_sgie_config.txt")
print("Adding elements to Pipeline \n")
pipeline.add(source)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(sgie)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
pipeline.add(sink)
# 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)
# Get the source pad of decoder and link it to streammux's sink pad
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")
# Link the decoder's src pad to streammux's sink pad
if srcpad.link(sinkpad) != Gst.PadLinkReturn.OK:
sys.stderr.write(" Failed to link decoder to streammux\n")
# Link the remaining elements in the pipeline
if not streammux.link(pgie):
sys.stderr.write(" Failed to link streammux to pgie\n")
if not pgie.link(sgie):
sys.stderr.write(" Failed to link pgie to sgie\n")
if not sgie.link(nvvidconv):
sys.stderr.write(" Failed to link sgie to nvvidconv\n")
if not nvvidconv.link(nvosd):
sys.stderr.write(" Failed to link nvvidconv to nvosd\n")
if not nvosd.link(sink):
sys.stderr.write(" Failed to link nvosd to sink\n")
sgie_srcpad = sgie.get_static_pad("src")
if not sgie_srcpad:
sys.stderr.write(" Unable to get src pad of sgie\n")
else:
sgie_srcpad.add_probe(Gst.PadProbeType.BUFFER, sgie_src_pad_buffer_probe, 0)
# 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)
# 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)
# start play back and listen to events
print("Starting pipeline \n")
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))
This is my sgie config file
[property]
gpu-id=0
onnx-file=./recognition_resnet27.onnx
model-engine-file=/home/wesenkhoo2/Documents/deepstream_python_apps/apps/deepstream-test1/recognition_resnet27.onnx_b1_gpu0_fp16.engine
batch-size=1
network-mode=3
gie-unique-id=2
interval=0
net-scale-factor=0.00392156862745098
operate-on-class-ids=2
# Feature extraction mode (no bbox or class parsing)
network-type=100
output-tensor-meta=1