deepstream python yolov5使用记录
deepstream_python_apps
1、下载nvidia官方发布的 deepstream_python_apps到 /opt/nvidia/deepstream/deepstream/sour ces 目录下，根据deepstrema版本下载对应版本代码，我使用得deepstream6.0，所以我克隆v1.1.0版本代码
2、根据HOWTO.md文件安装依赖，主要是安装使用得Gst Python和pyds模块
3、运行官方例子，检查环境是否安装成功
deepstream_python_yolov5
1、下载yolov5得yolov5-deepstream-python代码，
2、编译，CUDA_VER版本根据自己版本设置
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo/3、根据tensorrt-yolov5将模型转换成engine模型
首先根据需要修改参数，主要是input w、h，batct_size：
yololayer.h
static constexpr int CLASS_NUM = 10;
static constexpr int INPUT_H = 1088; // yolov5's input height and width must be divisible by 32.
static constexpr int INPUT_W = 1088;yolov5.cpp
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.4
#define CONF_THRESH 0.5
#define BATCH_SIZE 20
#define MAX_IMAGE_INPUT_SIZE_THRESH 3000 * 3000 // ensure it exceed the maximum size in the input images !// clone code according to above #Different versions of yolov5
// download https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt
cp {tensorrtx}/yolov5/gen_wts.py {ultralytics}/yolov5
cd {ultralytics}/yolov5
python gen_wts.py -w yolov5s.pt -o yolov5s.wts
// a file 'yolov5s.wts' will be generated.cd {tensorrtx}/yolov5/
// update CLASS_NUM in yololayer.h if your model is trained on custom dataset
mkdir build
cd build
cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/build
cmake ..
make
sudo ./yolov5 -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file
sudo ./yolov5 -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov5s
sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
sudo ./yolov5 -d yolov5s.engine ../samples
// For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
sudo ./yolov5 -s yolov5_custom.wts yolov5.engine c 0.17 0.25
sudo ./yolov5 -d yolov5.engine ../samples4、检查 deepstream_yolov5_config.txt and main.py中得路径，步骤3会生成libmyplugins.so插件，因为我这边导入import ctypes报错，所以将其注销
#import ctypes
import pyds
#ctypes.cdll.LoadLibrary('/home/nvidia/lefugang/tensorrtx/yolov5/build/libmyplugins.so')5、 修改main.py中sink插件，使得可以在终端输出结果，不需要再显示器上显示画面，主要是去掉nvegltransform插件，因为该插件时跟随nveglglessink使用的。
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2021 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
# import keyboard
sys.path.append('../')
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 ctypes
import pyds
#ctypes.cdll.LoadLibrary('/home/nvidia/lefugang/tensorrtx/yolov5/build/libmyplugins.so')
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
def osd_sink_pad_buffer_probe(pad,info,u_data):
frame_number=0
#Intiallizing object counter with 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.glist_get_nvds_frame_meta()
# 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.glist_get_nvds_frame_meta(l_frame.data)
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
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.glist_get_nvds_object_meta(l_obj.data)
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
# set bbox color in rgba
print(obj_meta.class_id, obj_meta.obj_label, obj_meta.confidence)
# set the border width in pixel
obj_meta.rect_params.border_width=0
obj_meta.rect_params.has_bg_color=1
obj_meta.rect_params.bg_color.set(0.0, 0.5, 0.3, 0.4)
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.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={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], 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
return Gst.PadProbeReturn.OK
def main(args):
# Check input arguments
if len(args) != 2:
sys.stderr.write("usage: %s \n" % args[0])
sys.exit(1)
# Standard GStreamer initialization
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")
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")
# 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 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)
pgie.set_property('config-file-path', "config/deepstream_yolov5_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(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)
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(nvvidconv)
nvvidconv.link(nvosd)
nvosd.link(sink)
# create an event loop and feed gstreamer bus mesages to it
#GObject.timeout_add_seconds(5, pipeline_pause(pipeline))
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)
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))
6、运行
LD_PRELOAD=/home/nvidia/lfg/tensorrtx/yolov5/build/libmyplugins.so python main.py /opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.h264Creating Pipeline
Creating streamux
Creating source_bin 0
Creating source bin
source-bin-00
Creating Pgie
Creating tiler
Creating nvvidconv
Creating nvosd
Creating transform
Creating EGLSink
Atleast one of the sources is live
Adding elements to Pipeline
Linking elements in the Pipeline
Now playing...
1 : rtsp://admin:[email protected]:554
Starting pipeline
0:00:04.399294673 9779 0x5561021e30 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/apps/yolov5-deepstream-python/best.engine
INFO: [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT data 3x1088x1088
1 OUTPUT kFLOAT prob 6001x1x1
0:00:04.399614801 9779 0x5561021e30 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/apps/yolov5-deepstream-python/best.engine
0:00:04.429867023 9779 0x5561021e30 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus: [UID 1]: Load new model:config/deepstream_yolov5_config.txt sucessfully
Decodebin child added: source
Decodebin child added: decodebin0
Decodebin child added: rtph264depay0
Decodebin child added: h264parse0
Decodebin child added: capsfilter0
Decodebin child added: nvv4l2decoder0
Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
In cb_newpad
gstname= video/x-raw
features=
Frame Number= 0 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 1 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 2 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 3 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 4 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 5 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 6 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 7 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 8 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 9 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 10 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 11 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 12 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 13 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 14 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 15 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 16 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 17 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 18 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Frame Number= 19 Number of Objects= 0 Vehicle_count= 0 Person_count= 0 7、成功