VS2019+opencv调用训练好的YOLOv5模型（3）
前言
在生成好ONNX的文件后，需要在vs2019上编写代码调用opencv库，opencv配置详见B站贾志刚老师视频第一讲：
opencv配置转B站
一、代码实现
具体见这位老哥的博客：
代码实现
以下是我整理好的代码，实测可用：
yolo.h
#pragma once
#include yolo.cpp
//yolo.cpp中实现readModel函数
//在yolo.cpp中使用命名空间
#include "yolo.h"
using namespace std;
using namespace cv;
using namespace dnn;
bool Yolo::readModel(Net& net, string& netPath, bool isCuda = false) {
try {
net = readNetFromONNX(netPath);
}
catch (const std::exception&) {
return false;
}
//cuda
if (isCuda) {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
}
//cpu
else {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
return true;
}
bool Yolo::Detect(Mat& SrcImg, Net& net, vector<Output>& output) {
Mat blob;
int col = SrcImg.cols;
int row = SrcImg.rows;
int maxLen = MAX(col, row);
Mat netInputImg = SrcImg.clone();
if (maxLen > 1.2 * col || maxLen > 1.2 * row) {
Mat resizeImg = Mat::zeros(maxLen, maxLen, CV_8UC3);
SrcImg.copyTo(resizeImg(Rect(0, 0, col, row)));
netInputImg = resizeImg;
}
blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(104, 117, 123), true, false);
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0,0), true, false);//如果训练集未对图片进行减去均值操作，则需要设置为这句
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(114, 114,114), true, false);
net.setInput(blob);
std::vector<cv::Mat> netOutputImg;
//vector outputLayerName{"345","403", "461","output" };
//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出
net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
std::vector<int> classIds;//结果id数组
std::vector<float> confidences;//结果每个id对应置信度数组
std::vector<cv::Rect> boxes;//每个id矩形框
float ratio_h = (float)netInputImg.rows / netHeight;
float ratio_w = (float)netInputImg.cols / netWidth;
int net_width = className.size() + 5; //输出的网络宽度是类别数+5
float* pdata = (float*)netOutputImg[0].data;
for (int stride = 0; stride < 3; stride++) { //stride
int grid_x = (int)(netWidth / netStride[stride]);
int grid_y = (int)(netHeight / netStride[stride]);
for (int anchor = 0; anchor < 3; anchor++) { //anchors
const float anchor_w = netAnchors[stride][anchor * 2];
const float anchor_h = netAnchors[stride][anchor * 2 + 1];
for (int i = 0; i < grid_y; i++) {
for (int j = 0; j < grid_y; j++) {
float box_score = Sigmoid(pdata[4]);//获取每一行的box框中含有某个物体的概率
if (box_score > boxThreshold) {
//为了使用minMaxLoc(),将85长度数组变成Mat对象
cv::Mat scores(1, className.size(), CV_32FC1, pdata + 5);
Point classIdPoint;
double max_class_socre;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = Sigmoid((float)max_class_socre);
if (max_class_socre > classThreshold) {
//rect [x,y,w,h]
float x = (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride]; //x
float y = (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride]; //y
float w = powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w; //w
float h = powf(Sigmoid(pdata[3]) * 2.f, 2.f) * anchor_h; //h
int left = (x - 0.5 * w) * ratio_w;
int top = (y - 0.5 * h) * ratio_h;
classIds.push_back(classIdPoint.x);
confidences.push_back(max_class_socre * box_score);
boxes.push_back(Rect(left, top, int(w * ratio_w), int(h * ratio_h)));
}
}
pdata += net_width;//指针移到下一行
}
}
}
}
vector<int> nms_result;
NMSBoxes(boxes, confidences, classThreshold, nmsThreshold, nms_result);
for (int i = 0; i < nms_result.size(); i++) {
int idx = nms_result[i];
Output result;
result.id = classIds[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
output.push_back(result);
}
if (output.size())
return true;
else
return false;
}
void Yolo::drawPred(Mat& img, vector<Output> result, vector<Scalar> color) {
for (int i = 0; i < result.size(); i++) {
int left, top;
left = result[i].box.x;
top = result[i].box.y;
int color_num = i;
rectangle(img, result[i].box, color[result[i].id], 2, 8);
string label = className[result[i].id] + ":" + to_string(result[i].confidence);
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);
}
imshow("res", img);
//imwrite("./result.jpg", img);
waitKey();
//destroyAllWindows();
}
int main()
{
cout << "Hello World" << endl;
string img_path = "E:/OpenCV_image/test1.jpg";
string model_path = "D:/YOLOv5/yolov5-5.0/models/yolov5s.onnx";
Yolo test;
Net net;
if (test.readModel(net, model_path, true)) {
cout << "read net ok!" << endl;
}
else {
return -1;
}
//生成随机颜色
vector<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(Scalar(b, g, r));
}
vector<Output> result;
Mat img = imread(img_path);
if (test.Detect(img, net, result)) {
test.drawPred(img, result, color);
}
else {
cout << "Detect Failed!" << endl;
}
system("pause");
return 0;
}
二、运行结果
