MNN学习笔记（六）：配置visual studio项目

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这个其实很简单，原因是MNN项目组已经提供了编译好的库：

1.下载编译好的MNN库

下载地址为：https://github.com/alibaba/MNN/releases

下载两个文件：Source.code和MNN-WindowsX64-0.2.1.7.zip

2.在visual studio上进行配置

注意visual studio版本为2017，我习惯把这些库都跟opencv放一起，具体来讲就是：

首先，把从Source code中解压的include文件如图1所示，复制出来；

然后，在自己的opencv的include文件夹下面新建一个MNN子文件夹，将上面的头文件复制过去：

其次，将解压好的MNN-WindowsX64-0.2.1.7.zip文件中MNN.dll和MNN.lib放到opencv对应位置：

最后，跟配置opencv一样，新建一个项目，配置项目：

3.测试代码

总共三个文件：

第一个文件：mobilenetssd.h

#ifndef _MOBILENET_SSD_H_
#define _MOBILENET_SSD_H_#include <vector>#include "MNN/Interpreter.hpp"
#include "MNN/MNNDefine.h"
#include "MNN/Tensor.hpp"
#include "MNN/ImageProcess.hpp"#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"namespace mirror {
struct ObjectInfo {std::string name_;cv::Rect location_;float score_;
};class MobilenetSSD {
public:MobilenetSSD();~MobilenetSSD();int Init(const char* root_path);int Detect(const cv::Mat& img_src, std::vector<ObjectInfo>* objects);
private:uint8_t* GetImage(const cv::Mat& img_src) {uchar* data_ptr = new uchar[img_src.total() * 4];cv::Mat img_tmp(img_src.size(), CV_8UC4, data_ptr);cv::cvtColor(img_src, img_tmp, CV_BGR2RGBA, 4);return (uint8_t*)img_tmp.data;}private:bool initialized_;const cv::Size inputSize_ = { 300, 300 };std::vector<int> dims_ = { 1, 3, 300, 300 };const float meanVals_[3] = { 0.5f, 0.5f, 0.5f };const float normVals_[3] = { 0.007843f, 0.007843f, 0.007843f };std::vector<std::string> class_names = {"background", "aeroplane", "bicycle", "bird", "boat","bottle", "bus", "car", "cat", "chair","cow", "diningtable", "dog", "horse","motorbike", "person", "pottedplant","sheep", "sofa", "train", "tvmonitor"};std::shared_ptr<MNN::Interpreter> mobilenetssd_interpreter_;MNN::Session* mobilenetssd_sess_ = nullptr;MNN::Tensor* input_tensor_ = nullptr;std::shared_ptr<MNN::CV::ImageProcess> pretreat_data_ = nullptr;};}#endif // !_MOBILENET_SSD_H_

第二个文件：mobilenetssd.cpp

#include "mobilenetssd.h"
#include <iostream>
#include <string>#include "opencv2/imgproc.hpp"namespace mirror {MobilenetSSD::MobilenetSSD() {initialized_ = false;
}MobilenetSSD::~MobilenetSSD() {mobilenetssd_interpreter_->releaseModel();mobilenetssd_interpreter_->releaseSession(mobilenetssd_sess_);
}int MobilenetSSD::Init(const char * root_path) {std::cout << "start Init." << std::endl;std::string model_file = std::string(root_path) + "/mobilenetssd.mnn";mobilenetssd_interpreter_ = std::unique_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_file.c_str()));if (nullptr == mobilenetssd_interpreter_) {std::cout << "load model failed." << std::endl;return 10000;}MNN::ScheduleConfig schedule_config;schedule_config.type = MNN_FORWARD_CPU;schedule_config.numThread = 4;MNN::BackendConfig backend_config;backend_config.precision = MNN::BackendConfig::Precision_High;backend_config.power = MNN::BackendConfig::Power_High;schedule_config.backendConfig = &backend_config;mobilenetssd_sess_ = mobilenetssd_interpreter_->createSession(schedule_config);// image processerMNN::CV::Matrix trans;trans.setScale(1.0f, 1.0f);MNN::CV::ImageProcess::Config img_config;img_config.filterType = MNN::CV::BICUBIC;::memcpy(img_config.mean, meanVals_, sizeof(meanVals_));::memcpy(img_config.normal, normVals_, sizeof(normVals_));img_config.sourceFormat = MNN::CV::RGBA;img_config.destFormat = MNN::CV::RGB;pretreat_data_ = std::shared_ptr<MNN::CV::ImageProcess>(MNN::CV::ImageProcess::create(img_config));pretreat_data_->setMatrix(trans);std::string input_name = "data";input_tensor_ = mobilenetssd_interpreter_->getSessionInput(mobilenetssd_sess_, input_name.c_str());mobilenetssd_interpreter_->resizeTensor(input_tensor_, dims_);mobilenetssd_interpreter_->resizeSession(mobilenetssd_sess_);initialized_ = true;std::cout << "end Init." << std::endl;return 0;
}int MobilenetSSD::Detect(const cv::Mat & img_src, std::vector<ObjectInfo>* objects) {std::cout << "start detect." << std::endl;if (!initialized_) {std::cout << "model uninitialized." << std::endl;return 10000;}if (img_src.empty()) {std::cout << "input empty." << std::endl;return 10001;}int width = img_src.cols;int height = img_src.rows;// preprocesscv::Mat img_resized;cv::resize(img_src, img_resized, inputSize_);uint8_t* data_ptr = GetImage(img_resized);pretreat_data_->convert(data_ptr, inputSize_.width, inputSize_.height, 0, input_tensor_);mobilenetssd_interpreter_->runSession(mobilenetssd_sess_);std::string output_name = "detection_out";MNN::Tensor* output_tensor = mobilenetssd_interpreter_->getSessionOutput(mobilenetssd_sess_, output_name.c_str());// copy to hostMNN::Tensor output_host(output_tensor, output_tensor->getDimensionType());output_tensor->copyToHostTensor(&output_host);auto output_ptr = output_host.host<float>();for (int i = 0; i < output_host.height(); ++i) {int index = i * output_host.width();ObjectInfo object;object.name_ = class_names[int(output_ptr[index + 0])];object.score_ = output_ptr[index + 1];object.location_.x = output_ptr[index + 2] * width;object.location_.y = output_ptr[index + 3] * height;object.location_.width = output_ptr[index + 4] * width - object.location_.x;object.location_.height = output_ptr[index + 5] * height - object.location_.y;objects->push_back(object);}std::cout << "end detect." << std::endl;return 0;
}}

第三个文件：main.cpp

#include "mobilenetssd.h"
#include "opencv2/opencv.hpp"int main(int argc, char* argv[]){const char* img_path = "./data/images/test.jpg";cv::Mat img_src = cv::imread(img_path);mirror::MobilenetSSD* mobilenetssd = new mirror::MobilenetSSD();const char* root_path = "./data/models";mobilenetssd->Init(root_path);std::vector<mirror::ObjectInfo> objects;mobilenetssd->Detect(img_src, &objects);int num_objects = static_cast<int>(objects.size());for (int i = 0; i < num_objects; ++i) {std::cout << "location: " << objects[i].location_ << std::endl;cv::rectangle(img_src, objects[i].location_, cv::Scalar(255, 0, 255), 2);char text[256];sprintf_s(text, "%s %.1f%%", objects[i].name_.c_str(), objects[i].score_ * 100);int baseLine = 0;cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);cv::putText(img_src, text, cv::Point(objects[i].location_.x,objects[i].location_.y + label_size.height),cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));}cv::imwrite("./data/images/cat.jpg", img_src);cv::imshow("result", img_src);cv::waitKey(0);delete mobilenetssd;system("pause");return 0;
}

最后结果：