成都网站建设成功案例单招网,新手学做网站pdf下载,网站头部设计优化,网站维护常识1、前言
之前分享了利用FastDettensorrt多线程推理的代码#xff0c;本想着继续学习yolotensorrt多线程的代码#xff0c;但是现在shouxieai直接开源的该项目#xff0c;而且还包含yolov8实例分割的代码。因此本文主要是对项目代码进行梳理#xff0c;一方面加深…1、前言
之前分享了利用FastDettensorrt多线程推理的代码本想着继续学习yolotensorrt多线程的代码但是现在shouxieai直接开源的该项目而且还包含yolov8实例分割的代码。因此本文主要是对项目代码进行梳理一方面加深自己对多线程、cuda编程的理解,另一方面希望给有需要的同学提供帮助。本文主要对项目整体的框架进行说明后续的文章将会对代码细节更加详细的注释。 yolotensorrt项目:https://github.com/shouxieai/infer fastdetncnn 项目:https://blog.csdn.net/weixin_42108183/article/details/129331588
2、运行该项目
首先下载项目 CMakeLists.txt
cmake_minimum_required(VERSION 2.6)
# set (CMAKE_C_COMPILER /usr/bin/gcc)
# set (CMAKE_CXX_COMPILER /usr/bin/g)
project(demo_trt)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_BUILD_TYPE Debug)
set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace)
set(HAS_PYTHON OFF)
# 修改为本机地址
set(TENSORRT_DIR /home/rex/TensorRT-8.4.0.6)
find_package(CUDA REQUIRED)
find_package(OpenCV)
include_directories(${PROJECT_SOURCE_DIR}/src${PROJECT_SOURCE_DIR}/src/trt${PROJECT_SOURCE_DIR}/src/trt/common${OpenCV_INCLUDE_DIRS}${CUDA_TOOLKIT_ROOT_DIR}/include${TENSORRT_DIR}/include${CUDNN_DIR}/include
)
link_directories(${TENSORRT_DIR}/lib${CUDA_TOOLKIT_ROOT_DIR}/lib64${CUDNN_DIR}/lib
)set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -stdc11 -Wall -O0 -Wfatal-errors -pthread -w -g)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -stdc11 -O0 -Xcompiler -fPIC -g -w ${CUDA_GEN_CODE})
file(GLOB_RECURSE cpp_srcs ${PROJECT_SOURCE_DIR}/src/*.cpp)
file(GLOB_RECURSE cuda_srcs ${PROJECT_SOURCE_DIR}/src/*.cu)
cuda_add_library(plugin_list SHARED ${cuda_srcs})
target_link_libraries(plugin_list nvinfer nvinfer_plugin)
target_link_libraries(plugin_list cuda cublas cudart cudnn)
target_link_libraries(plugin_list pthread)
target_link_libraries(plugin_list ${OpenCV_LIBS})
add_executable(demo_infer ${cpp_srcs})
target_link_libraries(demo_infer plugin_list)# 生成engine
sh /workspace/build.sh
# 修改文件中的路径为绝对路径
mkdirt build cd build
cmake ..
make -j
./../workspace/demo_infer3、文件结构
src
cpm.hpp 基于生产者消费者的多线程推理infer.cu 推理结果实现infer.hpp 推理接口封装yolo.cu 函数实现yolo.hpp 任务类型、返回结果定义、推理函数封装main.cpp 主函数
4、main.cpp
// 单batch推理
void single_inference()
{ // 推荐写绝对路径cv::Mat image cv::imread(/home/rex/Desktop/infer-lastest/workspace/inference/car.jpg);auto yolo yolo::load(/home/rex/Desktop/infer-lastest/workspace/yolov8n-seg.b1.transd.engine, yolo::Type::V8Seg);if (yolo nullptr)return;auto objs yolo-forward(cvimg(image));int i 0;cv::Mat image_mask image.clone();int img_h image_mask.rows;int img_w image_mask.cols;std::vectorcv::Scalar color;srand(time(0));for (int i 0; i 80; i){int b rand() % 256;int g rand() % 256;int r rand() % 256;color.push_back(cv::Scalar(b, g, r));}for (auto obj : objs){ // 实例分割if (obj.seg){cv::Mat mask cv::Mat(obj.seg-height, obj.seg-width, CV_8UC1, obj.seg-data);cv::resize(mask, mask, cv::Size((obj.right - obj.left), (obj.bottom - obj.top)), cv::INTER_NEAREST);inRange(mask, 127, 255, mask);cv::imwrite(cv::format(%d_mask.jpg, i), mask);i;cv::Rect rect(obj.left, obj.top, (obj.right - obj.left), (obj.bottom - obj.top));cv::Mat c image_mask(rect);c.setTo(color[obj.class_label], mask);}uint8_t b, g, r;tie(b, g, r) yolo::random_color(obj.class_label);cv::rectangle(image, cv::Point(obj.left, obj.top),cv::Point(obj.right, obj.bottom), cv::Scalar(b, g, r), 5);auto name cocolabels[obj.class_label];auto caption cv::format(%s %.2f, name, obj.confidence);int width cv::getTextSize(caption, 0, 1, 2, nullptr).width 10;cv::rectangle(image, cv::Point(obj.left - 3, obj.top - 33),cv::Point(obj.left width, obj.top), cv::Scalar(b, g, r),-1);cv::putText(image, caption, cv::Point(obj.left, obj.top - 5), 0, 1,cv::Scalar::all(0), 2, 16);}// 实例分割addWeighted(image, 0.5, image_mask, 0.5, 0, image);printf(Save result to Result.jpg, %d objects\n, objs.size());cv::imwrite(Result.jpg, image);
}5、总结
本文将简单的将infer项目运行起来了初步理解该项目的运行方式后面的文章将会对代码细节进行更加详细的学习
