完整指南：CNStream流处理多路并发框架适配到NVIDIA Jetson Orin (一) 依赖库编译、第三方库编译安装

陈 洪 伟2024-09-04 21:28

目录

[1 jetson-ffmpeg的编译安装与配置--用来做视频编码、视频解码](#1 jetson-ffmpeg的编译安装与配置--用来做视频编码、视频解码)

[2 CV-CUDA库的编译安装与配置--用来做图像缩放、裁剪、色域转换](#2 CV-CUDA库的编译安装与配置--用来做图像缩放、裁剪、色域转换)

[3 cuda cudnn TensorRT相关库的拷贝与配置](#3 cuda cudnn TensorRT相关库的拷贝与配置)

[3.1将cuda cudnn TensorRT相关的头文件拷贝到工程中](#3.1将cuda cudnn TensorRT相关的头文件拷贝到工程中)

[3.2 将cuda cudnn TensorRT相关的库拷贝到/data/chw/compute_lib/lib/jetson_lib](#3.2 将cuda cudnn TensorRT相关的库拷贝到/data/chw/compute_lib/lib/jetson_lib)

[4 cuda_utils库编译](#4 cuda_utils库编译)

[5 算法推理库trteng_exp编译安装和配置](#5 算法推理库trteng_exp编译安装和配置)

[5.1 报错 trtNet_v2.cpp:1:10: fatal error: spdlog/fmt/fmt.h: No such file or directory](#5.1 报错 trtNet_v2.cpp:1:10: fatal error: spdlog/fmt/fmt.h: No such file or directory)

[6 算法模型转换库model2trt_v2编译安装和配置](#6 算法模型转换库model2trt_v2编译安装和配置)

[6.1 prot_parse_convert.cpp:7:10: fatal error: caffe/caffe.pb.h: No such file or directory](#6.1 prot_parse_convert.cpp:7:10: fatal error: caffe/caffe.pb.h: No such file or directory)

[6.2 error: invalid conversion from 'const char*' to 'const uint8_t*' {aka 'const unsigned char*'}](#6.2 error: invalid conversion from ‘const char*’ to ‘const uint8_t*’ {aka ‘const unsigned char*’})

[6.3 error: no matching function for call to 'google::protobuf::internal::InternalMetadata::unknown_fields() const](#6.3 error: no matching function for call to ‘google::protobuf::internal::InternalMetadata::unknown_fields() const)

[6.4 源码编译安装protobuf](#6.4 源码编译安装protobuf)

[6.5 caffe/caffe.pb.h:13:2: error: #error "This file was generated by a newer version of protoc which i](#error "This file was generated by a newer version of protoc which i)

[7 build_all.sh一键编译cuda_utils trteng_exp model2trt_v2](#7 build_all.sh一键编译cuda_utils trteng_exp model2trt_v2)

[8 测试model2trt_v2转模型](#8 测试model2trt_v2转模型)

[9 拷贝所有的计算库到./nvstream/3rdparty/jetson/compute/lib/aarch64](#9 拷贝所有的计算库到./nvstream/3rdparty/jetson/compute/lib/aarch64)

参考文献：

记录下将CNStream流处理多路并发Pipeline框架适配到NVIDIA Jetson AGX Orin的过程，以及过程中遇到的问题，我的jetson盒子是用jetpack5.1.3重新刷机之后的，这是系列博客的第一篇。

1 jetson-ffmpeg的编译安装与配置--用来做视频编码、视频解码

在jetson AGX Orin上会选择用jetson-ffmpeg做视频编解码以及图像处理工作，其中jetson-ffmpeg的编译安装见如下博客

在NVIDIA Jetson AGX Orin中使用jetson-ffmpeg调用硬件编解码加速处理-CSDN博客

编译安装完之后，相应的库文件在/usr/local/lib/，相应的头文件在/usr/local/include/

使用如下命令将编译号的jetson-ffmpeg拷贝到我的工程的./nvstream/3rdparty/ffmpeg中

bash 复制代码 
cp -rf  /usr/local/lib/lib*  /data/chw/nvstream/3rdparty/ffmpeg/lib/aarch64/
cp -rf  /usr/local/include/*   /data/chw/nvstream/3rdparty/ffmpeg/include/

这样之后，工程里面的3rdparty/config_lib_aarch64.sh脚本内容需要修改，把脚本中ffmpeg相关的内容修改如下

bash 复制代码 
#----------------------------------
# ffmpeg
#----------------------------------
cd ${root}/ffmpeg/lib/linux_lib
\cp ../${arch}/* .

ln -snf libavcodec.so.58.134.100 libavcodec.so.58
ln -snf libavcodec.so.58 libavcodec.so

ln -snf libavdevice.so.58.13.100 libavdevice.so.58
ln -snf libavdevice.so.58 libavdevice.so

ln -snf libavfilter.so.7.110.100 libavfilter.so.7
ln -snf libavfilter.so.7 libavfilter.so

ln -snf libavformat.so.58.76.100 libavformat.so.58
ln -snf libavformat.so.58 libavformat.so

ln -snf libavutil.so.56.70.100 libavutil.so.56
ln -snf libavutil.so.56 libavutil.so

ln -snf libswresample.so.3.9.100 libswresample.so.3
ln -snf libswresample.so.3 libswresample.so

ln -snf libswscale.so.5.9.100 libswscale.so.5
ln -snf libswscale.so.5 libswscale.so

ln -snf libnvmpi.so.1.0.0 libnvmpi.so.1
ln -snf libnvmpi.so.1 libnvmpi.so

2 CV-CUDA库的编译安装与配置--用来做图像缩放、裁剪、色域转换

CV-CUDA库的编译和安装见如下博客：NVIDIA Jetson AGX Orin源码编译安装CV-CUDA-CSDN博客

编译安装完之后，用如下命令将cv-cuda相关的库和头文件拷贝到工程中

bash 复制代码 
cp -rf  /opt/nvidia/cvcuda0/include/*  /data/chw/nvstream/3rdparty/jetson/cvcuda/include/
cp -rf  /opt/nvidia/cvcuda0/lib/aarch64-linux-gnu/lib* /data/chw/nvstream/3rdparty/jetson/cvcuda/lib/aarch64/

同样修改库的配置脚本3rdparty/config_lib_aarch64.sh，修改的部分内容如下

bash 复制代码 
#----------------------------------
# jetson: cv-cuda
#----------------------------------
cd ${root}/jetson/cvcuda/lib/linux_lib
\cp ../${arch}/* .
ln -snf libcvcuda.so.0.10.1 libcvcuda.so.0
ln -snf libcvcuda.so.0 libavcodec.so

ln -snf libnvcv_types.so.0.10.1 libnvcv_types.so.0
ln -snf libnvcv_types.so.0 libavdevice.so

3 cuda cudnn TensorRT相关库的拷贝与配置

由于用jetpack5.1.3刷机之后，cuda cudnn TensorRT相关的库版本和之前相比有更新，所以要把cuda cudnn TensorRT相关的库拷贝到我的工程下面。为什么不直接用/usr/local下面的库，这是为了以后如果工程迁移到别的盒子上，那么可以把这些库直接考过去，这样系统路径下的库版本不一样也可以用。

3.1将cuda cudnn TensorRT相关的头文件拷贝到工程中

这个省点事，不拷贝了，到时候makefile直接去/usr/local/cuda/include这种默认路径下找吧，反正我最终的工程代码不需要cuda这些的头文件。

3.2 将cuda cudnn TensorRT相关的库拷贝到/data/chw/compute_lib/lib/jetson_lib

用如下命令拷贝cuda相关库

bash 复制代码 
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libcublasLt.so.11.6.6.84  /data/chw/compute_lib/lib/jetson_lib/
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libcublas.so.11.6.6.84  /data/chw/compute_lib/lib/jetson_lib/
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libcudart.so.11.4.298 /data/chw/compute_lib/lib/jetson_lib/
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libcurand.so.10.2.5.297  /data/chw/compute_lib/lib/jetson_lib/
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libnvrtc-builtins.so.11.4.300   /data/chw/compute_lib/lib/jetson_lib/
cp /usr/local/cuda-11.4/targets/aarch64-linux/lib/libnvrtc.so.11.4.300   /data/chw/compute_lib/lib/jetson_lib/

用下面的命令拷贝cudnn相关库

bash 复制代码 
cp /usr/lib/aarch64-linux-gnu/libcudnn_cnn_infer.so.8.6.0  /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/libcudnn.so.8.6.0   /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/libcudnn_ops_infer.so.8.6.0   /data/chw/compute_lib/lib/jetson_lib/

用下面的命令拷贝TensorRT相关的库

bash 复制代码 
cp /usr/lib/aarch64-linux-gnu/libnvinfer.so.8.5.2   /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.5.2   /data/chw/compute_lib/lib/jetson_lib/

其他相关库的拷贝，下面的库在后面也会用到，也拷贝过去

bash 复制代码 
cp /usr/lib/aarch64-linux-gnu/tegra/libnvdla_compiler.so  /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvos.so            /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvdla_runtime.so   /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libcuda.so.1          /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvrm_host1x.so     /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvrm_mem.so        /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvsocsys.so        /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvrm_gpu.so        /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvrm_sync.so       /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvrm_chip.so       /data/chw/compute_lib/lib/jetson_lib/
cp /usr/lib/aarch64-linux-gnu/tegra/libnvsciipc.so        /data/chw/compute_lib/lib/jetson_lib/

在lib/config_lib_jetson.sh里面之前并没有增加cuda相关库的软链接命令，因为之前jetson上都是直接去的/usr/local/cuda这种默认路径下找的库，但是这次我是把这些库拷贝到工程中了，所以lib/config_lib_jetson.sh需要增加这些东西，增加内容如下：

bash 复制代码 
#----------------------------------
#Specific lib for GPU
#----------------------------------
#1> cuda
ln -snf libcublas.so.11.6.6.84 libcublas.so.11
ln -snf libcublas.so.11 libcublas.so

ln -snf libcublasLt.so.11.6.6.84 libcublasLt.so.11
ln -snf libcublasLt.so.11 libcublasLt.so

ln -snf libcudart.so.11.4.298 libcudart.so.11.0
ln -snf libcudart.so.11.0 libcudart.so.11
ln -snf libcudart.so.11 libcudart.so

ln -snf libcurand.so.10.2.5.297 libcurand.so.10
ln -snf libcurand.so.10 libcurand.so

ln -snf libnvrtc.so.11.4.300 libnvrtc.so.11
ln -snf libnvrtc.so.11 libnvrtc.so

ln -snf libnvrtc-builtins.so.11.4.300 libnvrtc-builtins.so.11.4
ln -snf libnvrtc-builtins.so.11.4 libnvrtc-builtins.so.11
ln -snf libnvrtc-builtins.so.11 libnvrtc-builtins.so

ln -snf libcudnn_ops_infer.so.8.6.0 libcudnn_ops_infer.so.8
ln -snf libcudnn_ops_infer.so.8 libcudnn_ops_infer.so

ln -snf libcudnn_cnn_infer.so.8.6.0 libcudnn_cnn_infer.so.8
ln -snf libcudnn_cnn_infer.so.8 libcudnn_cnn_infer.so

ln -snf libcudnn.so.8.6.0 libcudnn.so.8
ln -snf libcudnn.so.8 libcudnn.so

#2> TensorRT
ln -snf libnvinfer.so.8.5.2 libnvinfer.so.8
ln -snf libnvinfer.so.8 libnvinfer.so

ln -snf libnvinfer_plugin.so.8.5.2 libnvinfer_plugin.so.8
ln -snf libnvinfer_plugin.so.8 libnvinfer_plugin.so

然后执行以下这个脚本去/data/chw/compute_lib/lib目录下

bash 复制代码 
 sh config_lib_jetson.sh

4 cuda_utils库编译

然后由于我把cuda cudnn这些库拷贝过来了，那么comp_nvidia/cuda_utils/Makefile_jetson中cuda cudnn这些库的路径我要修改一下，如下所示，删除默认路径

bash 复制代码 
#LIBRARY_PATH := /usr/local/cuda/lib64
LIBRARY_PATH := ../../lib/linux_lib

这样他就直接去我自己的路径下找库了。然后直接用下面的命令编译

bash 复制代码 
make -f Makefile_jetson clean; make -f Makefile_jetson

然后ldd看一下，确实是找的我自己路径下的了，不是/usr/local/cuda-11.4/targets/aarch64-linux/lib下的了。

5 算法推理库trteng_exp编译安装和配置

和上面一样，由于我把cuda cudnn这些库拷贝过来了，那么comp_nvidia/trteng_exp/Makefile_jetson中cuda cudnn这些库的路径我要修改一下，如下所示，删除默认路径

bash 复制代码 
#LIBRARY_PATH := ../../lib/linux_lib /usr/local/cuda/lib64 /usr/lib/aarch64-linux-gnu/tegra
LIBRARY_PATH := ../../lib/linux_lib

这样他就直接去我自己的路径下找库了。然后直接用下面的命令编译。

bash 复制代码 
make -f Makefile_jetson clean; make -f Makefile_jetson

用上面的命令进行编译，

5.1 报错 trtNet_v2.cpp:1:10: fatal error: spdlog/fmt/fmt.h: No such file or directory

这是因为compute_lib/include这个头文件文件夹没有放到/data/chw/compute_lib里面，上传到盒子上，

然后再次编译

6 算法模型转换库model2trt_v2编译安装和配置

model2trt_v2除了依赖前面的cuda cudnn TensorRT那一堆库之外，在comp_nvidia/model2trt_v2/lib路径下的libnvonnxparser.so.8.0.1和libnvparsers.so.8.0.1要替换成新版本，这里cp的时候加上了-d，软链接也一并拷贝过去了，

bash 复制代码 
cp -drf /usr/lib/aarch64-linux-gnu/libnvparsers* /data/chw/compute_lib/comp_nvidia/model2trt_v2/lib/
cp -drf /usr/lib/aarch64-linux-gnu/libnvonnxparser*  /data/chw/compute_lib/comp_nvidia/model2trt_v2/lib/

修改makeifle

bash 复制代码 
## used include librarys file path  
#LIBRARY_PATH := ../../lib/linux_lib /usr/lib/aarch64-linux-gnu /usr/lib/aarch64-linux-gnu/tegra /usr/local/cuda/targets/aarch64-linux/lib
LIBRARY_PATH := ../../lib/linux_lib ./lib

然后用下面的命令编译

bash 复制代码 
make -f Makefile_jetson clean; make -f Makefile_jetson -j8

然后报错

6.1 prot_parse_convert.cpp:7:10: fatal error: caffe/caffe.pb.h: No such file or directory

bash 复制代码 
prot_parse_convert.cpp:7:10: fatal error: caffe/caffe.pb.h: No such file or directory
    7 | #include "caffe/caffe.pb.h"

解决方法

bash 复制代码 
sudo apt install -y protobuf-compiler
protoc caffe.proto --proto_path=./ --cpp_out=./caffe

6.2 error: invalid conversion from 'const char*' to 'const uint8_t*' {aka 'const unsigned char*'}

bash 复制代码 
error: invalid conversion from 'const char*' to 'const uint8_t*' {aka 'const unsigned char*'} [-fpermissive]
   78 |         const IBlobNameToTensor* blobNameToTensor = parser->parseBuffers(cleaned_proto.data(),cleaned_proto.size(),

继续编译报上面的错误，这种错误看着就像是版本更新导致的错误，我直接修改代码强制类型转换

cpp 复制代码 
	const IBlobNameToTensor* blobNameToTensor = parser->parseBuffers(reinterpret_cast<const uint8_t*>(cleaned_proto.data()),cleaned_proto.size(),
		reinterpret_cast<const uint8_t*>(model_data.data()),model_data.size(),
		*network,
		DataType::kFLOAT);

6.3 error: no matching function for call to 'google::protobuf::internal::InternalMetadata::unknown_fields() const

再次编译刷屏报类似上面两个这样的错误。

这些错误看着都是protobuf的错误，问题原因是我前面用sudo apt install -y protobuf-compiler安装的protobug太老了，需要20版本以上的。

所以先卸载掉前面安装的，然后改为源码编译安装protobuf。

bash 复制代码 
sudo apt remove protobuf-compiler

6.4 源码编译安装protobuf

首先安装依赖

bash 复制代码 
sudo apt-get install autoconf automake libtool curl make g++ unzip

然后下载、编译、安装

bash 复制代码 
git clone --branch v23.0 https://github.com/protocolbuffers/protobuf.git
cd protobuf
git submodule update --init --recursive
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local/protobuf ..
make -j$(nproc)
sudo make install

配置环境变量，编辑/etc/profile，vim /etc/profile在里面增加如下内容

bash 复制代码 
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/protobuf/lib/
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/protobuf/lib/
export PATH=$PATH:/usr/local/protobuf/bin/
export C_INCLUDE_PATH=$C_INCLUDE_PATH:/usr/local/protobuf/include/
export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/protobuf/include/
export PKG_CONFIG_PATH=/usr/local/protobuf/lib/pkgconfig/

然后

bash 复制代码 
source /etc/profile

然后

6.5 caffe/caffe.pb.h:13:2: error: #error "This file was generated by a newer version of protoc which i

继续用下面命令编译model2trt_v2

bash 复制代码 
protoc caffe.proto --proto_path=./ --cpp_out=./caffe
make -f Makefile_jetson clean; make -f Makefile_jetson -j8

报上面的错误，这错误还是protoc的版本问题，其实上面不需要源码编译安装protoc，在./compute_lib/bin/jetson目录下是有这个工具和库的，只是我没有把他传到盒子上，现在把这个上传到盒子上，

然后把上面vim /etc/profile的内容删掉。

然后不这样单独编译了，用build_all.sh一键编译脚本进行编译。

7 build_all.sh一键编译cuda_utils trteng_exp model2trt_v2

使用build_all.sh前，要把某些内容注释掉，因为这次在jetson盒子上并不是所有东西都需要编译。

另外库的版本也需要修改。

完整版的build_all.sh备份如下

bash 复制代码 
#!/bin/bash

#出现错误即退出执行
set -e 

root_dir=$(dirname "$PWD")

cd ${root_dir}

function enable_release_type() {
	device=$1
	if [ "$device" = "mlu" ]; then
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile_mlu
	elif [ "$device" = "mlu_arm" ]; then
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile_mlu_arm
	elif [ "$device" = "jetson" ]; then
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile_jetson
	elif [ "$device" = "acl_arm" ]; then
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile_acl_arm
	elif [ "$device" = "bm" ]; then
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile_bm
	else 
		sed -i "s/.*\(DBG_ENABLE\s*:=\s*\).*\$/\10/" Makefile
	fi
}

function build() {
	device=$1
	
	#设置为release版本
	enable_release_type $device
	
	if [ "$device" = "mlu" ]; then
		make -f Makefile_mlu clean
		make -f Makefile_mlu -j8
	elif [ "$device" = "mlu_arm" ]; then
		make -f Makefile_mlu_arm clean
		make -f Makefile_mlu_arm -j8
	elif [ "$device" = "jetson" ]; then
		make -f Makefile_jetson clean
		make -f Makefile_jetson -j8
	elif [ "$device" = "acl_arm" ]; then
		make -f Makefile_acl_arm clean
		make -f Makefile_acl_arm -j8
	elif [ "$device" = "bm" ]; then
		make -f Makefile_bm clean
		make -f Makefile_bm -j8
	elif [ "$device" = "gpu_arm" ]; then
		make -f Makefile_gpu_arm clean
		make -f Makefile_gpu_arm -j8
	else 
		make clean
		make -j8
	fi
}

function build_caffe() {
	device=$1
	
	if [ "$device" = "mlu" ]; then	
		#设置NEUWARE
		export NEUWARE_HOME=${root_dir}/comp_cambricon/include/neuware
		chmod +x -R ${root_dir}/comp_cambricon/include/neuware/bin
	
		#设置protoc目录
		export PATH=$PATH:${root_dir}/bin/x64
		chmod +x -R ${root_dir}/bin/x64
		export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${root_dir}/bin/x64:${root_dir}/lib/linux_lib
		ln -snf ${root_dir}/lib/linux_lib ${root_dir}/comp_cambricon/include/neuware/lib64

		cd ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/scripts
		
		chmod +x ./*.sh
		#./build_caffe_mlu270_cambricon_release.sh
		
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/lib/*.a ${root_dir}/comp_cambricon/cafl_sdk/lib
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/tools/caffe ${root_dir}/distribute/bin/mlu
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/tools/generate_quantized_pt ${root_dir}/distribute/bin/mlu
	elif [ "$device" = "mlu_arm" ]; then	
		#设置NEUWARE
		echo "1..."
		export NEUWARE_HOME=${root_dir}/comp_cambricon/include/neuware
		chmod +x -R ${root_dir}/comp_cambricon/include/neuware/bin
	    echo "2..."
		#设置protoc目录
		export PATH=$PATH:${root_dir}/bin/x64
		chmod +x -R ${root_dir}/bin/x64
		export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${root_dir}/bin/x64:${root_dir}/lib/linux_lib
		ln -snf ${root_dir}/lib/linux_lib ${root_dir}/comp_cambricon/include/neuware/lib64

		cd ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/scripts
		echo "3..."
		chmod +x ./*.sh
		#./build_caffe_mlu270_cambricon_release.sh
		#echo "4..."
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/lib/*.a ${root_dir}/comp_cambricon/cafl_sdk/lib
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/tools/caffe ${root_dir}/distribute/bin/mlu_arm
		#\cp ${root_dir}/comp_cambricon/caffe_mlu/caffe/src/caffe/build/tools/generate_quantized_pt ${root_dir}/distribute/bin/mlu_arm
	else 
		#设置protoc目录
		if [ "$device" = "jetson" ]; then
			export PATH=$PATH:${root_dir}/bin/jetson
			chmod +x -R ${root_dir}/bin/jetson
			export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${root_dir}/bin/jetson:${root_dir}/lib/linux_lib
			export CUDA_DIR=/usr/local/cuda
			
			#protoc ${root_dir}/comp_nvidia/caffe_gcs/src/caffe/proto/caffe.proto --proto_path=${root_dir}/comp_nvidia/caffe_gcs/src/caffe/proto --cpp_out=${root_dir}/caffe_gcs/src/caffe/proto
			
			#cd ${root_dir}/comp_nvidia/caffe_gcs
			#make -f Makefile_jetson clean
			#make -f Makefile_jetson -j8
		elif [ "$device" = "gpu_arm" ]; then
			export PATH=$PATH:${root_dir}/bin/gpu_arm
			chmod +x -R ${root_dir}/bin/gpu_arm
			export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${root_dir}/bin/gpu_arm:${root_dir}/lib/linux_lib
			export CUDA_DIR=/usr/local/cuda
		else
			export PATH=$PATH:${root_dir}/bin/x64
			export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${root_dir}/bin/x64:${root_dir}/lib/linux_lib
			export CUDA_DIR=${root_dir}/comp_nvidia/include/cuda
			
			chmod +x -R ${root_dir}/bin/x64
			chmod +x -R ${root_dir}/comp_nvidia/include/cuda/bin
			chmod +x -R ${root_dir}/comp_nvidia/include/cuda/nvvm/bin
			
			#设置cudnn包含文件，只针对gpu，jetson不使用此配置
			cd ${root_dir}/comp_nvidia/include/cudnn
			chmod +x config_cudnn.sh
			./config_cudnn.sh
			
			#protoc ${root_dir}/comp_nvidia/caffe_gcs/src/caffe/proto/caffe.proto --proto_path=${root_dir}/comp_nvidia/caffe_gcs/src/caffe/proto --cpp_out=${root_dir}/comp_nvidia/caffe_gcs/src/caffe/proto
			
			#cd ${root_dir}/comp_nvidia/caffe_gcs
			#make clean
			#make -j
		fi			
		
		#\cp ${root_dir}/comp_nvidia/caffe_gcs/.build_release/lib/*.a ${root_dir}/comp_nvidia/cafl_sdk/lib
	fi
}

function config_lib() {
	device=$1
	
	if [ "$device" = "mlu" ]; then
		chmod +x config_lib_mlu.sh
		./config_lib_mlu.sh 	
	elif [ "$device" = "mlu_arm" ]; then
		chmod +x config_lib_mlu_arm.sh
		./config_lib_mlu_arm.sh 
	elif [ "$device" = "jetson" ]; then
		chmod +x config_lib_jetson.sh
		./config_lib_jetson.sh
	elif [ "$device" = "acl_arm" ]; then
		chmod +x config_lib_acl_arm.sh
		./config_lib_acl_arm.sh
	elif [ "$device" = "bm" ]; then
		chmod +x config_lib_bm.sh
		./config_lib_bm.sh
	elif [ "$device" = "gpu_arm" ]; then
		chmod +x config_lib_gpu_arm.sh
		./config_lib_gpu_arm.sh
	else 
		chmod +x config_lib_gpu.sh
		./config_lib_gpu.sh
	fi
}

#build all services and libraries
function build_all(){
	device=$1
	
	echo "开始配置依赖库..."
	cd ${root_dir}/lib
	config_lib $device
	echo "**********完成依赖库配置**********"
	echo
	
	: '
	#不再使用caffe引擎
	#if [ "$device" != "mlu_arm" ] && [ "$device" != "acl_arm" ]; then
		echo "1)开始编译caffe..."
		build_caffe $device
		echo "**********完成编译caffe**********"
		echo
		
		#echo "2)开始编译cafl_sdk..."
		#cd ${root_dir}/cafl_sdk
		#build $device
		#echo "**********完成编译cafl_sdk**********"
		#echo					
	else
		echo "1)检测到device非需要caffe框架，caffe编译跳过..."
		echo "---------------------------------------------"
		echo
	fi
	'
	
	if [ "$device" == "mlu" ] || [ "$device" == "mlu_arm" ]; then
		echo "3)开始编译cnrteng_exp..."
		cd ${root_dir}/comp_cambricon/cnrteng_exp
		build $device
		echo "**********完成编译cnrteng_exp**********"
		echo
	elif [ "$device" == "acl_arm" ]; then
		echo "3)开始编译acleng_exp..."
		cd ${root_dir}/comp_ascend/acleng_exp
		build $device
		echo "**********完成编译acleng_exp**********"
		echo
		
		echo "4)开始编译acllite..."
		cd ${root_dir}/comp_ascend/acllite
		build $device
		echo "**********完成编译acllite**********"
		echo
	elif [ "$device" == "bm" ]; then
		echo "3)开始编译bmrteng_exp..."
		cd ${root_dir}/comp_bitmain/bmrteng_exp
		build $device
		echo "**********完成编译bmrteng_exp**********"
		echo
	elif [ "$device" == "gpu_arm" ]; then
		build_caffe $device
		
		echo "3)开始编译cuda_utils..."
		cd ${root_dir}/comp_nvidia/cuda_utils
		build $device
		echo "**********完成编译cuda_utils**********"
		echo
	
		echo "4)开始编译trteng_exp..."
		cd ${root_dir}/comp_nvidia/trteng_exp
		build $device
		echo "**********完成编译trteng_exp**********"
		echo
		
		echo "5)开始编译model2trt_v2..."
		cd ${root_dir}/comp_nvidia/model2trt_v2
		
		ln -snf $(pwd)/lib_gpu_arm/libnvparsers.so.8.0.1 $(pwd)/lib_gpu_arm/libnvparsers.so.8
		ln -snf $(pwd)/lib_gpu_arm/libnvparsers.so.8 $(pwd)/lib_gpu_arm/libnvparsers.so
		
		ln -snf $(pwd)/lib_gpu_arm/libnvonnxparser.so.8.0.1 $(pwd)/lib_gpu_arm/libnvonnxparser.so.8
		ln -snf $(pwd)/lib_gpu_arm/libnvonnxparser.so.8 $(pwd)/lib_gpu_arm/libnvonnxparser.so

		protoc caffe.proto --proto_path=./ --cpp_out=./caffe
		build $device
		echo "**********完成编译model2trt_v2**********"
		echo
	else
		build_caffe $device
		
		echo "3)开始编译cuda_utils..."
		cd ${root_dir}/comp_nvidia/cuda_utils
		build $device
		echo "**********完成编译cuda_utils**********"
		echo
	
		echo "4)开始编译trteng_exp..."
		cd ${root_dir}/comp_nvidia/trteng_exp
		build $device
		echo "**********完成编译trteng_exp**********"
		echo
		
		echo "5)开始编译model2trt_v2..."
		cd ${root_dir}/comp_nvidia/model2trt_v2
		
		ln -snf $(pwd)/lib/libnvparsers.so.8.5.2 $(pwd)/lib/libnvparsers.so.8
		ln -snf $(pwd)/lib/libnvparsers.so.8 $(pwd)/lib/libnvparsers.so
		
		ln -snf $(pwd)/lib/libnvonnxparser.so.8.5.2 $(pwd)/lib/libnvonnxparser.so.8
		ln -snf $(pwd)/lib/libnvonnxparser.so.8 $(pwd)/lib/libnvonnxparser.so

		protoc caffe.proto --proto_path=./ --cpp_out=./caffe
		build $device
		echo "**********完成编译model2trt_v2**********"
		echo
	fi
	
	echo "10)开始编译cfldwp2..."
	#cd ${root_dir}/cfldwp2
	#build $device
	echo "**********完成编译cfldwp2**********"
	echo
	
	echo "<----------完成编译[${device}]设备上的[计算]库---------->"
}

case "$1" in
	-h|--help)
		echo "Usage: $0 [option...]"
		echo "-h, --help           for help information"
		echo "-g, --gpu            build for gpu device"
		echo "-j, --jetson         build for jetson arch"
		echo "-m, --mlu            build for mlu device"
		echo "-ma, --mlu_arm       build for mlu_arm device"
		echo "-aa, --acl_arm       build for acl_arm device"
		echo "-b, --bm             build for bm device"
		echo "-ga, --gpu_arm       build for gpu_arm device"
		;;
	-g|--gpu)
		echo
		echo
		echo "<----------start building for [gpu] device---------->"
		echo
		build_all
		;;
	-j|--jetson)
		echo
		echo
		echo "<----------start building for [jetson] device---------->"
		echo
		build_all jetson
		;;
	-m|--mlu)
		echo
		echo
		echo "<----------start building for [mlu] device---------->"
		echo
		build_all mlu
		;;
	-ma|--mlu_arm)
		echo
		echo
		echo "<----------start building for [mlu_arm] device---------->"
		echo
		build_all mlu_arm
		;;
	-aa|--acl_arm)
		echo
		echo
		echo "<----------start building for [acl_arm] device---------->"
		echo
		build_all acl_arm
		;;
	-b|--bm)
		echo
		echo
		echo "<----------start building for [bm] device---------->"
		echo
		build_all bm
		;;
	-ga|--gpu_arm)
		echo
		echo
		echo "<----------start building for [gpu_arm] device---------->"
		echo
		build_all gpu_arm
		;;
	*)
		echo "Please use $0 -h|--help for more information"
esac

exit 0

8 测试model2trt_v2转模型

bash 复制代码 
export LD_LIBRARY_PATH=/data/chw/compute_lib/lib/linux_lib/:$LD_LIBRARY_PATH
./model2trt_v2  1 ./model/yolov5_pcb_dynamic

测试下转模型，转成功了。

9 拷贝所有的计算库到./nvstream/3rdparty/jetson/compute/lib/aarch64

bash 复制代码 
cp /data/chw/compute_lib/distribute/lib/jetson_lib/libcuda_utils.so  /data/chw/nvstream/3rdparty/jetson/compute/lib/aarch64/
cp /data/chw/compute_lib/distribute/lib/jetson_lib/libtrteng_exp.so  /data/chw/nvstream/3rdparty/jetson/compute/lib/aarch64/

下一篇博客开始写代码；

参考文献：

在NVIDIA Jetson AGX Orin中使用jetson-ffmpeg调用硬件编解码加速处理-CSDN博客

NVIDIA Jetson AGX Orin源码编译安装CV-CUDA-CSDN博客

https://github.com/Cambricon/CNStream

https://github.com/Cambricon/easydk/blob/master/samples/simple_demo/common/video_decoder.cpp

aclStream流处理多路并发Pipeline框架中 视频解码 代码调用流程整理、类的层次关系整理、回调函数赋值和调用流程整理-CSDN博客

aclStream流处理多路并发Pipeline框架中VEncode Module代码调用流程整理、类的层次关系整理、回调函数赋值和调用流程整理-CSDN博客

FFmpeg/doc/examples at master · FFmpeg/FFmpeg · GitHub

https://github.com/CVCUDA/CV-CUDA

如何使用FFmpeg的解码器---FFmpeg API教程 · FFmpeg原理

C++ API --- CV-CUDA Beta documentation (cvcuda.github.io)

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