yolov8-mnn C++部署

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本文链接：https://blog.csdn.net/zaibeijixing/article/details/131581809
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目录

准备工作

1、MNN编译

2、yolov8-mnn文件夹构建

3、编译

4、执行

 附：

yolov8_demo.cpp

CMakeLists.txt

---

准备工作

下载MNN，GitHub - alibaba/MNN ，并编译，生成依赖库。

 下载 https://github.com/wangzhaode/yolov8-mnn/ 代码，主要用到.cpp和CMakeLists.txt，或者直接见文末附件，该代码可放在MNN的同级目录。

1、MNN编译

在MNN编译后，再按如下步骤再次编译，生成如下3个依赖项。

cd MNN

mkdir build_s

cmake -DMNN_BUILD_OPENCV=ON -DMNN_IMGCODECS=ON ..

make

cp  libMNN.so  express/libMNN_Express.so  tools/cv/libMNNOpenCV.so  ../yolov8-mnn/cpp/libs

2、yolov8-mnn文件夹构建

主要是构建文件结构，包括依赖的MNN头文件和上述生成的.so文件，以便正确链接编译。

此处此处直接按照readMe命令会发现路径可能细节不对或偏差，需要自己联系上下文改正即可，最好直接按照下图构建，最终的目的是达到如图操作。

 其中：

build_s是编译生成所用；

include用来存放MNN的头文件；

libs用来存放MNN编译的依赖库；

s_存放执行命令所需文件，非必需，上述3个必需。

3、编译

上述文件夹构建完成，即可编译生成可执行文件

cd yolov8-mnn/cpp

mkdir build_s  &&  cd build_s

cmake ..

make

4、执行

此时把生成的yolov8_demo，下载的yolov8n.mnn和图片bus.jpg放入S_路径下。执行命令：

./yolov8_demo yolov8n.mnn bus.jpg

 输出如下：

### box: {670.723145, 375.684723, 809.897644, 873.593018}, idx: 8254, score: 0.866349

### box: {49.070282, 399.742523, 243.129242, 902.202576}, idx: 8243, score: 0.865096

### box: {219.769562, 405.623505, 345.611481, 858.568176}, idx: 8225, score: 0.829490

### box: {14.499327, 224.708481, 790.586121, 746.901184}, idx: 8188, score: 0.829328

### box: {-0.187493, 551.358398, 62.278885, 874.772766}, idx: 8280, score: 0.365177

result image write to `res.jpg`.

 附：

yolov8_demo.cpp

#include 
#include 
#include 
#include 
#include 
#include 

#include 

using namespace MNN;
using namespace MNN::Express;
using namespace MNN::CV;

int main(int argc, const char* argv[]) {
    if (argc < 3) {
        MNN_PRINT("Usage: ./yolov8_demo.out model.mnn input.jpg [forwardType] [precision] [thread]\n");
        return 0;
    }
    int thread = 4;
    int precision = 0;
    int forwardType = MNN_FORWARD_CPU;
    if (argc >= 4) {
        forwardType = atoi(argv[3]);
    }
    if (argc >= 5) {
        precision = atoi(argv[4]);
    }
    if (argc >= 6) {
        thread = atoi(argv[5]);
    }
    MNN::ScheduleConfig sConfig;
    sConfig.type = static_cast(forwardType);
    sConfig.numThread = thread;
    BackendConfig bConfig;
    bConfig.precision = static_cast(precision);
    sConfig.backendConfig = &bConfig;
    std::shared_ptr rtmgr = std::shared_ptr(Executor::RuntimeManager::createRuntimeManager(sConfig));
    if(rtmgr == nullptr) {
        MNN_ERROR("Empty RuntimeManger\n");
        return 0;
    }
    rtmgr->setCache(".cachefile");

    std::shared_ptr net(Module::load(std::vector{}, std::vector{}, argv[1], rtmgr));
    auto original_image = imread(argv[2]);
    auto dims = original_image->getInfo()->dim;
    int ih = dims[0];
    int iw = dims[1];
    int len = ih > iw ? ih : iw;
    float scale = len / 640.0;
    std::vector padvals { 0, len - ih, 0, len - iw, 0, 0 };
    auto pads = _Const(static_cast(padvals.data()), {3, 2}, NCHW, halide_type_of());
    auto image = _Pad(original_image, pads, CONSTANT);
    image = resize(image, Size(640, 640), 0, 0, INTER_LINEAR, -1, {0., 0., 0.}, {1./255., 1./255., 1./255.});
    auto input = _Unsqueeze(image, {0});
    input = _Convert(input, NC4HW4);
    auto outputs = net->onForward({input});
    auto output = _Convert(outputs[0], NCHW);
    output = _Squeeze(output);
    // output shape: [84, 8400]; 84 means: [cx, cy, w, h, prob * 80]
    auto cx = _Gather(output, _Scalar(0));
    auto cy = _Gather(output, _Scalar(1));
    auto w = _Gather(output, _Scalar(2));
    auto h = _Gather(output, _Scalar(3));
    std::vector startvals { 4, 0 };
    auto start = _Const(static_cast(startvals.data()), {2}, NCHW, halide_type_of());
    std::vector sizevals { -1, -1 };
    auto size = _Const(static_cast(sizevals.data()), {2}, NCHW, halide_type_of());
    auto probs = _Slice(output, start, size);
    // [cx, cy, w, h] -> [y0, x0, y1, x1]
    auto x0 = cx - w * _Const(0.5);
    auto y0 = cy - h * _Const(0.5);
    auto x1 = cx + w * _Const(0.5);
    auto y1 = cy + h * _Const(0.5);
    auto boxes = _Stack({x0, y0, x1, y1}, 1);
    auto scores = _ReduceMax(probs, {0});
    auto ids = _ArgMax(probs, 0);
    auto result_ids = _Nms(boxes, scores, 100, 0.45, 0.25);
    auto result_ptr = result_ids->readMap();
    auto box_ptr = boxes->readMap();
    auto ids_ptr = ids->readMap();
    auto score_ptr = scores->readMap();
    for (int i = 0; i < 100; i++) {
        auto idx = result_ptr[i];
        if (idx < 0) break;
        auto x0 = box_ptr[idx * 4 + 0] * scale;
        auto y0 = box_ptr[idx * 4 + 1] * scale;
        auto x1 = box_ptr[idx * 4 + 2] * scale;
        auto y1 = box_ptr[idx * 4 + 3] * scale;
        auto class_idx = ids_ptr[idx];
        auto score = score_ptr[idx];
        printf("### box: {%f, %f, %f, %f}, idx: %d, score: %f\n", x0, y0, x1, y1, idx, score);
        rectangle(original_image, {x0, y0}, {x1, y1}, {0, 0, 255}, 2);
    }
    if (imwrite("res.jpg", original_image)) {
        MNN_PRINT("result image write to `res.jpg`.\n");
    }
    rtmgr->updateCache();
    return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.0)
project(yolov8_demo)#yolov8-mnn

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")

# include dir
include_directories(${CMAKE_CURRENT_LIST_DIR}/include/)

# libs dir
link_directories(${CMAKE_CURRENT_LIST_DIR}/libs)

# source files
FILE(GLOB SRCS ${CMAKE_CURRENT_LIST_DIR}/yolov8_demo.cpp)

# target
add_executable(yolov8_demo ${SRCS})

# link
if (MSVC)
    target_link_libraries(yolov8_demo MNN)
else()
    target_link_libraries(yolov8_demo MNN MNN_Express MNNOpenCV)
endif()

 本文主要参考https://github.com/wangzhaode/yolov8-mnn   并对其中的疏漏和错误进行了修改。