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Opencv C++实现yolov5部署onnx模型完成目标检测

前端开发和安卓开发人工智能 2024-05-02 8 0

代码分析：

头文件

#include //文件

#include //流

#include

#include //深度学习模块-仅提供推理功能

#include //图像处理模块

#include //媒体的输入输出/视频捕捉/图像和视频的编码解码/图形界面的接口

命名空间

using namespace cv;

using namespace dnn;

using namespace std;

结构体 Net_config

struct Net_config{

float confThreshold; // 置信度阈值

float nmsThreshold; // 非最大抑制阈值

float objThreshold; // 对象置信度阈值

string modelpath;

};

里面存了三个阈值和模型地址，其中置信度，顾名思义，看检测出来的物体的精准度。以测量值为中心，在一定范围内，真值出现在该范围内的几率。

endsWith()函数判断sub是不是s的子串

int endsWith(string s, string sub) {

return s.rfind(sub) == (s.length() - sub.length()) ? 1 : 0;

}

anchors_640图像接收数组

const float anchors_640[3][6] = { {10.0, 13.0, 16.0, 30.0, 33.0, 23.0},

{30.0, 61.0, 62.0, 45.0, 59.0, 119.0},

{116.0, 90.0, 156.0, 198.0, 373.0, 326.0} };

根据图像大小，选择相应长度的二维数组。深度为3，每层6个位置。

YOLO类

class YOLO{

public:

YOLO(Net_config config); //构造函数

void detect(Mat& frame); //通过图像参数，进行目标检测

private:

float* anchors;

int num_stride;

int inpWidth;

int inpHeight;

vector class_names;

int num_class;

float confThreshold;

float nmsThreshold;

float objThreshold;

const bool keep_ratio = true;

Net net;

void drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid);

Mat resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left);

};

YOLO类构造函数的重载

YOLO::YOLO(Net_config config){

this->confThreshold = config.confThreshold;

this->nmsThreshold = config.nmsThreshold;

this->objThreshold = config.objThreshold;

this->net = readNet(config.modelpath);

ifstream ifs("class.names"); //class.name中写入标签内容，当前只有'person'，位置与当前.cpp文件同级

string line;

while (getline(ifs, line)) this->class_names.push_back(line);

this->num_class = class_names.size();

if (endsWith(config.modelpath, "6.onnx")){ //根据onnx的输入图像格式选择分辨率当前为1280x1280 可灵活调整

anchors = (float*)anchors_1280;

this->num_stride = 4; //深度

this->inpHeight = 1280; //高

this->inpWidth = 1280; //宽

}

else{ //当前为640x640 可以resize满足onnx需求也可以调整数组或if中的onnx判断

anchors = (float*)anchors_640;

this->num_stride = 3;

this->inpHeight = 640;

this->inpWidth = 640;

}

重新规定图像大小函数 resize_image()

Mat YOLO::resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left){//传入图像以及需要改变的参数

int srch = srcimg.rows, srcw = srcimg.cols;

*newh = this->inpHeight;

*neww = this->inpWidth;

Mat dstimg;

if (this->keep_ratio && srch != srcw) {

float hw_scale = (float)srch / srcw;

if (hw_scale > 1) {

*newh = this->inpHeight;

*neww = int(this->inpWidth / hw_scale);

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

*left = int((this->inpWidth - *neww) * 0.5);

copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 114);

}else {

*newh = (int)this->inpHeight * hw_scale;

*neww = this->inpWidth;

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

*top = (int)(this->inpHeight - *newh) * 0.5;

copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 114);

}

}else {

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

}

return dstimg;

}

绘制预测的边界框函数 drawPred()

void YOLO::drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid){

//绘制一个显示边界框的矩形

rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 2);

//获取类名的标签及其置信度

string label = format("%.2f", conf);

label = this->class_names[classid] + ":" + label;

//在边界框顶部显示标签

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(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);

}

【核心代码】检测函数 detect()

void YOLO::detect(Mat& frame){

int newh = 0, neww = 0, padh = 0, padw = 0;

Mat dstimg = this->resize_image(frame, &newh, &neww, &padh, &padw);

Mat blob = blobFromImage(dstimg, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);

this->net.setInput(blob);

vector outs;

this->net.forward(outs, this->net.getUnconnectedOutLayersNames());

int num_proposal = outs[0].size[1];

int nout = outs[0].size[2];

if (outs[0].dims > 2){

outs[0] = outs[0].reshape(0, num_proposal);

}

vector confidences;

vector boxes;

vector classIds;

float ratioh = (float)frame.rows / newh, ratiow = (float)frame.cols / neww;

int n = 0, q = 0, i = 0, j = 0, row_ind = 0; //xmin,ymin,xamx,ymax,box_score,class_score

float* pdata = (float*)outs[0].data;

for (n = 0; n < this->num_stride; n++){ //特征图尺度

const float stride = pow(2, n + 3);

int num_grid_x = (int)ceil((this->inpWidth / stride));

int num_grid_y = (int)ceil((this->inpHeight / stride));

for (q = 0; q < 3; q++){

const float anchor_w = this->anchors[n * 6 + q * 2];

const float anchor_h = this->anchors[n * 6 + q * 2 + 1];

for (i = 0; i < num_grid_y; i++){

for (j = 0; j < num_grid_x; j++){

float box_score = pdata[4];

if (box_score > this->objThreshold){

Mat scores = outs[0].row(row_ind).colRange(5, nout);

Point classIdPoint;

double max_class_socre;

//获取最高分的值和位置

minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);

max_class_socre *= box_score;

if (max_class_socre > this->confThreshold){

const int class_idx = classIdPoint.x;

float cx = pdata[0]; //cx

float cy = pdata[1]; //cy

float w = pdata[2]; //w

float h = pdata[3]; //h

int left = int((cx - padw - 0.5 * w) * ratiow);

int top = int((cy - padh - 0.5 * h) * ratioh);

confidences.push_back((float)max_class_socre);

boxes.push_back(Rect(left, top, (int)(w * ratiow), (int)(h * ratioh)));

classIds.push_back(class_idx);

}

row_ind++;

pdata += nout;

}

// 执行非最大抑制以消除冗余重叠框

// 置信度较低

vector indices;

dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);

for (size_t i = 0; i < indices.size(); ++i){

int idx = indices[i];

Rect box = boxes[idx];

this->drawPred(confidences[idx], box.x, box.y,box.x + box.width, box.y + box.height, frame, classIds[idx]);

}

主函数

int main(){

//加载onnx模型

Net_config yolo_nets = { 0.3, 0.5, 0.3, "yolov5n_person_320.onnx" };

YOLO yolo_model(yolo_nets);

//加载单张图片

string imgpath = "112.png";

Mat srcimg = imread(imgpath);

//开始检测

yolo_model.detect(srcimg);

static const string kWinName = "Deep learning object detection in OpenCV";

namedWindow(kWinName, WINDOW_NORMAL);

imshow(kWinName, srcimg); //显示图片

waitKey(0); //保持停留

destroyAllWindows(); //关闭窗口并取消分配任何相关的内存使用

}

完整代码

#include

using namespace cv;

using namespace dnn;

using namespace std;

struct Net_config

{

float confThreshold; // Confidence threshold

float nmsThreshold; // Non-maximum suppression threshold

float objThreshold; //Object Confidence threshold

string modelpath;

};

int endsWith(string s, string sub) {

return s.rfind(sub) == (s.length() - sub.length()) ? 1 : 0;

}

const float anchors_640[3][6] = { {10.0, 13.0, 16.0, 30.0, 33.0, 23.0},

{30.0, 61.0, 62.0, 45.0, 59.0, 119.0},

{116.0, 90.0, 156.0, 198.0, 373.0, 326.0} };

const float anchors_1280[4][6] = { {19, 27, 44, 40, 38, 94},

{96, 68, 86, 152, 180, 137},

{140, 301, 303, 264, 238, 542},

{436, 615, 739, 380, 925, 792} };

class YOLO

{

public:

YOLO(Net_config config);

void detect(Mat& frame);

private:

float* anchors;

int num_stride;

int inpWidth;

int inpHeight;

vector class_names;

int num_class;

float confThreshold;

float nmsThreshold;

float objThreshold;

const bool keep_ratio = true;

Net net;

void drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid);

Mat resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left);

};

YOLO::YOLO(Net_config config)

{

this->confThreshold = config.confThreshold;

this->nmsThreshold = config.nmsThreshold;

this->objThreshold = config.objThreshold;

this->net = readNet(config.modelpath);

ifstream ifs("class.names");

string line;

while (getline(ifs, line)) this->class_names.push_back(line);

this->num_class = class_names.size();

if (endsWith(config.modelpath, "6.onnx"))

{

anchors = (float*)anchors_1280;

this->num_stride = 4;

this->inpHeight = 1280;

this->inpWidth = 1280;

}

else

{

anchors = (float*)anchors_640;

this->num_stride = 3;

this->inpHeight = 640;

this->inpWidth = 640;

}

Mat YOLO::resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left)

{

int srch = srcimg.rows, srcw = srcimg.cols;

*newh = this->inpHeight;

*neww = this->inpWidth;

Mat dstimg;

if (this->keep_ratio && srch != srcw) {

float hw_scale = (float)srch / srcw;

if (hw_scale > 1) {

*newh = this->inpHeight;

*neww = int(this->inpWidth / hw_scale);

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

*left = int((this->inpWidth - *neww) * 0.5);

copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 114);

}

else {

*newh = (int)this->inpHeight * hw_scale;

*neww = this->inpWidth;

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

*top = (int)(this->inpHeight - *newh) * 0.5;

copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 114);

}

else {

resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);

}

return dstimg;

}

void YOLO::drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid) // Draw the predicted bounding box

{

//Draw a rectangle displaying the bounding box

rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 2);

//Get the label for the class name and its confidence

string label = format("%.2f", conf);

label = this->class_names[classid] + ":" + label;

//Display the label at the top of the bounding box

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(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);

}

void YOLO::detect(Mat& frame)

{

int newh = 0, neww = 0, padh = 0, padw = 0;

Mat dstimg = this->resize_image(frame, &newh, &neww, &padh, &padw);

Mat blob = blobFromImage(dstimg, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);

this->net.setInput(blob);

vector outs;

this->net.forward(outs, this->net.getUnconnectedOutLayersNames());

int num_proposal = outs[0].size[1];

int nout = outs[0].size[2];

if (outs[0].dims > 2)

{

outs[0] = outs[0].reshape(0, num_proposal);

}

/generate proposals

vector confidences;

vector boxes;

vector classIds;

float ratioh = (float)frame.rows / newh, ratiow = (float)frame.cols / neww;

int n = 0, q = 0, i = 0, j = 0, row_ind = 0; ///xmin,ymin,xamx,ymax,box_score,class_score

float* pdata = (float*)outs[0].data;

for (n = 0; n < this->num_stride; n++) ///特征图尺度

{

const float stride = pow(2, n + 3);

int num_grid_x = (int)ceil((this->inpWidth / stride));

int num_grid_y = (int)ceil((this->inpHeight / stride));

for (q = 0; q < 3; q++) ///anchor

{

const float anchor_w = this->anchors[n * 6 + q * 2];

const float anchor_h = this->anchors[n * 6 + q * 2 + 1];

for (i = 0; i < num_grid_y; i++)

{

for (j = 0; j < num_grid_x; j++)

{

float box_score = pdata[4];

if (box_score > this->objThreshold)

{

Mat scores = outs[0].row(row_ind).colRange(5, nout);

Point classIdPoint;

double max_class_socre;

// Get the value and location of the maximum score

minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);

max_class_socre *= box_score;

if (max_class_socre > this->confThreshold)

{

const int class_idx = classIdPoint.x;

//float cx = (pdata[0] * 2.f - 0.5f + j) * stride; ///cx

//float cy = (pdata[1] * 2.f - 0.5f + i) * stride; ///cy

//float w = powf(pdata[2] * 2.f, 2.f) * anchor_w; ///w

//float h = powf(pdata[3] * 2.f, 2.f) * anchor_h; ///h

float cx = pdata[0]; ///cx

float cy = pdata[1]; ///cy

float w = pdata[2]; ///w

float h = pdata[3]; ///h

int left = int((cx - padw - 0.5 * w) * ratiow);

int top = int((cy - padh - 0.5 * h) * ratioh);

confidences.push_back((float)max_class_socre);

boxes.push_back(Rect(left, top, (int)(w * ratiow), (int)(h * ratioh)));

classIds.push_back(class_idx);

}

row_ind++;

pdata += nout;

}

// Perform non maximum suppression to eliminate redundant overlapping boxes with

// lower confidences

vector indices;

dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);

for (size_t i = 0; i < indices.size(); ++i)

{

int idx = indices[i];

Rect box = boxes[idx];

this->drawPred(confidences[idx], box.x, box.y,

box.x + box.width, box.y + box.height, frame, classIds[idx]);

}

int main()

{

Net_config yolo_nets = { 0.3, 0.5, 0.3, "yolov5n_person_320.onnx" };

YOLO yolo_model(yolo_nets);

//string imgpath = "112.png";

//Mat srcimg = imread(imgpath);

//yolo_model.detect(srcimg);

int n = 588;

for (int i = 1; i <= n; i++) {

string s = to_string(i) + ".png";

string imgpath = "F://test//p1//yanfa2//bh//cc//" + s;

cout << imgpath << endl;

Mat srcimg = imread(imgpath);

yolo_model.detect(srcimg);

imwrite("F://test//p2//yanfa2//bh//cc//" + s, srcimg);

}

//static const string kWinName = "Deep learning object detection in OpenCV";

//namedWindow(kWinName, WINDOW_NORMAL);

//imshow(kWinName, srcimg);

//waitKey(0);

//destroyAllWindows();

}

参考链接

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opencv c++YOLO 部署onnx模型目标检测

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Opencv C++实现yolov5部署onnx模型完成目标检测

毕业设计 大数据 毕设 python+opencv+机器学习车牌识别

人工智能 计算机视觉 python OpenCV 4基础篇| OpenCV像素的编辑

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