1：版本说明：
qt 5.12.10
opencv 4.5.3 （yolov5模型部署要求opencv>4.5.0）
2：检测的代码
yolo.h
#pragma once
#include<iostream>
#include<cmath>
#include<vector>
#include <opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
class yolo
{
public:
yolo() {}
~yolo(){}
bool readModel(cv::dnn::Net &net,std::string &netpath, bool isCuda);
struct Output
{
int id;//结果类别id
float confidence;//结果置信度
cv::Rect box;//矩形框
int ship_id;//船的id
int truck_id;//人的id
int person_id;//人的id
int staring_id;//车的id
int forklift_id;//车的id
int unload_car_id;//船的id
int load_car_id;//人的id
int private_car_id;//车的id
};
struct Output_max_confidence
{
int id;//结果类别id
float confidence;//结果置信度
cv::Rect box;//矩形框
};
int ship_num;//总人数
int car_trucks_num;//总车数
int person_num;//总船数
int stacking_area_num;//总人数
int car_forklift_num;//总车数
int unload_car_num;//总船数
int load_car_num;//总人数
int car_private_num;//总车数
bool Detect(cv::Mat &SrcImg, cv::dnn::Net &net, std::vector<Output> &output);
void drawPred(cv::Mat &img, std::vector<Output> result, std::vector<cv::Scalar> color);
//参数为私有参数，当然也可以是设置成公开或者保护。
Output_max_confidence get_only_one_max_confidence(std::vector<Output> result);
float findMax(std::vector<float> vec);
int getPositionOfMax(std::vector<float> vec, int max);
\
void drawRect(cv::Mat &img, yolo::Output_max_confidence result);
private:
//计算归一化函数
float Sigmoid(float x) {
return static_cast<float>(1.f / (1.f + exp(-x)));
}
//anchors
const float netAnchors[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 } };
//stride
const float netStride[3] = { 8.0, 16.0, 32.0 };
const int netWidth = 640; //网络模型输入大小
const int netHeight = 640;
float nmsThreshold = 0.02;
float boxThreshold = 0.05;
float classThreshold = 0.45;
//类名
// std::vector<std::string> className = { "car", "person"};
// std::vector<std::string> className = { "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
// "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
// "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
// "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
// "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
// "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
// "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
// "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
// "hair drier", "toothbrush"};
std::vector<std::string> className = { "ship","car_trucks","person","stacking_area","car_forklift","unload_car","load_car","car_private"};//"car","person"
};
yolo.cpp
#include "yolo.h"
#include<iostream>
#include<cmath>
#include<vector>
#include <opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
using namespace std;
using namespace cv;
using namespace dnn;
#pragma execution_character_set("utf-8");
bool yolo::readModel(Net &net, string &netPath, bool isCuda = false) {
try {
net = readNetFromONNX(netPath);
cout<<"load net successfull!"<<endl;
}
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<string> outputLayerName{"345","403", "461","output" };
//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出
net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
//接上面
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
std::vector<int> ship_id;//记录下人数的id号码----补充
std::vector<int> trucks_id;//记录下车数的id号码----补充
std::vector<int> person_id;//记录下人数的id号码----补充
std::vector<int> stacking_id;//记录下车数的id号码----补充
std::vector<int> forklift_id;//记录下人数的id号码----补充
std::vector<int> unload_car_id;//记录下车数的id号码----补充
std::vector<int> load_car_id;//记录下人数的id号码----补充
std::vector<int> car_private_id;//记录下车数的id号码----补充
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 yolo5.0
float box_score = pdata[4];//获取每一行的box框中含有某个物体的概率 yolo6.0
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); //yolo 5.0
max_class_socre = (float)max_class_socre; //yolo 6.0
if (max_class_socre > classThreshold) {
//rect [x,y,w,h]
//yolov5 5.0格式
//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
//yolov5 6.0格式：
float x = pdata[0];// (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride]; //x
float y = pdata[1];// (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride]; //y
float w = pdata[2];// powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w; //w
float h = pdata[3];// 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
}
}
}
}
//接上面执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）
vector<int> nms_result;
int ship_id_ini=0;//初始化人数，
int trucks_id_ini=0;//初始化人数，
int person_id_ini=0;//初始化车辆数
int stacking_id_ini=0;//初始化车辆数
int forklift_id_ini=0;//初始化车辆数
int unload_car_id_ini=0;//初始化车辆数
int load_car_id_ini=0;//初始化车辆数
int car_private_id_ini=0;//初始化车辆数
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(result.id==0)//当类别数等于船的时候
{
result.ship_id=ship_id_ini;//当船等于
ship_id_ini=ship_id_ini+1;
}
if(result.id==1)//当类别数等于车的时候
{
result.truck_id=trucks_id_ini;//当车数等于
trucks_id_ini=trucks_id_ini+1;
}
if(result.id==2)//当类别数等于人的时候
{
result.person_id=person_id_ini;//当人数等于
person_id_ini=person_id_ini+1;
}if(result.id==3)//当类别数等于人的时候
{
result.staring_id=stacking_id_ini;//当人数等于
stacking_id_ini=stacking_id_ini+1;
}
if(result.id==4)//当类别数等于人的时候
{
result.forklift_id=forklift_id_ini;//当人数等于
forklift_id_ini=forklift_id_ini+1;
}
if(result.id==5)//当类别数等于人的时候
{
result.unload_car_id=unload_car_id_ini;//当人数等于
unload_car_id_ini=unload_car_id_ini+1;
}
if(result.id==6)//当类别数等于人的时候
{
result.load_car_id=load_car_id_ini;//当人数等于
load_car_id_ini=load_car_id_ini+1;
}
if(result.id==7)//当类别数等于人的时候
{
result.private_car_id=car_private_id_ini;//当人数等于
car_private_id_ini=car_private_id_ini+1;
}
}
ship_num=ship_id_ini;
car_trucks_num=trucks_id_ini;
person_num=person_id_ini;
stacking_area_num=stacking_id_ini;
car_forklift_num=forklift_id_ini ;//总车数
unload_car_num=unload_car_id_ini;//总船数
load_car_num=load_car_id_ini;//总人数
car_private_num=car_private_id_ini;//总车数
if (output.size())
return true;
else
return false;
}//这个括号是最后
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)+" id:"+to_string(result[i].person_id);
string label;
if(result[i].id==0)
{
label = className[result[i].id] ;
}
if(result[i].id==1)
{
label = className[result[i].id] ;
}
if(result[i].id==2)
{
label = className[result[i].id] ;
}
if(result[i].id==3)
{
label = className[result[i].id] ;
}
if(result[i].id==4)
{
label = className[result[i].id] ;
}
if(result[i].id==5)
{
label = className[result[i].id] ;
}
if(result[i].id==6)
{
label = className[result[i].id] ;
}
if(result[i].id==7)
{
label = className[result[i].id];
}
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();
}
//这个是针对悍马的画框
void yolo::drawRect(cv::Mat &img, yolo::Output_max_confidence result)
{
int left, top;
left = result.box.x;
top = result.box.y;
rectangle(img, result.box,Scalar(0,0,255) , 2, 8);
// string label = className[result[i].id] + ":" + to_string(result[i].confidence)+" id:"+to_string(result[i].person_id);
string label;
if(result.id==0)
{
label = className[result.id];
}
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, 2,Scalar(0,0,255), 2);
}
float yolo::findMax(std::vector<float> vec) {
float max = -999;
for (auto v : vec) {
if (max < v) max = v;
}
return max;
}
int yolo::getPositionOfMax(std::vector<float> vec, int max) {
auto distance = find(vec.begin(), vec.end(), max);
return distance - vec.begin();
}
//返回一个最大置信度的框
yolo::Output_max_confidence yolo::get_only_one_max_confidence(std::vector<Output> result)
{
std::vector<float>confidence;
for (int i = 0; i < result.size(); i++)
{
confidence.push_back(result.at(i).confidence);
// cout<<"result.at(i).confidence"<<result.at(i).confidence<<endl;
}
float maxConfidence=findMax(confidence);
int position = result.size()-getPositionOfMax(confidence, maxConfidence);
// cout<<"max_confidengce"<<maxConfidence<<"position:"<<position<<endl;
Output_max_confidence o_m_c;
o_m_c.confidence=maxConfidence;
o_m_c.id=position;
o_m_c.box=result.at(position).box;
return o_m_c ;
}
检测的调用代码测试案例
这段调用的例子，只要把frame 改成你们自己的图片即可
yolo test; //创建yolo类
cv::dnn::Net net; //创建yolo网络
vector< cv::Scalar> color;//Bounding Box颜色
QString Filename_onnx="quanjingbest.onnx";
cv::String filename_onnx=Filename_onnx.toStdString();
vector<yolo::Output>result_video;
test.readModel(net,filename_onnx,false);
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));
}
cv::Mat frame=cv::imread("D://1.jpg");
if(test.Detect(frame, net, result_video)) //调用YOLO模型检测
test.drawPred(frame, result_video, color);
cv::imshow("a",frame);
cv::waitKey(1);4：分割的主要代码
YoloSeg.h
#ifndef YOLO_SEG_H
#define YOLO_SEG_H
#pragma once
#include<iostream>
#include<opencv2/opencv.hpp>
#include "yolov5_seg_utils.h"
class YoloSeg {
public:
YoloSeg() {
}
~YoloSeg() {}
/** \brief Read onnx-model
* \param[out] read onnx file into cv::dnn::Net
* \param[in] modelPath:onnx-model path
* \param[in] isCuda:if true and opencv built with CUDA(cmake),use OpenCV-GPU,else run it on cpu.
*/
bool ReadModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
/** \brief detect.
* \param[in] srcImg:a 3-channels image.
* \param[out] output:detection results of input image.
*/
bool Detect(cv::Mat& srcImg, cv::dnn::Net& net, std::vector<OutputSeg>& output);
#if(defined YOLO_P6 && YOLO_P6==true)
const int _netWidth = 1280; //ONNX图片输入宽度
const int _netHeight = 1280; //ONNX图片输入高度
const int _segWidth = 320; //_segWidth=_netWidth/mask_ratio
const int _segHeight = 320;
const int _segChannels = 32;
#else
const int _netWidth = 640; //ONNX图片输入宽度
const int _netHeight = 640; //ONNX图片输入高度
const int _segWidth = 160; //_segWidth=_netWidth/mask_ratio
const int _segHeight = 160;
const int _segChannels = 32;
#endif // YOLO_P6
float _classThreshold = 0.25;
float _nmsThreshold = 0.45;
float _maskThreshold = 0.5;
public:
std::vector<std::string> _className = { "steel"};//类别名，换成自己的模型需要修改此项
};
#endif // YOLO_SEG_H
YoloSeg.cpp
#include "yolo_seg.h"
#include"yolo_seg.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
bool YoloSeg::ReadModel(Net& net, string& netPath, bool isCuda = false) {
try {
net = readNet(netPath);
#if CV_VERSION_MAJOR==4 &&CV_VERSION_MINOR==7&&CV_VERSION_REVISION==0
net.enableWinograd(false); //bug of opencv4.7.x in AVX only platform ,https://github.com/opencv/opencv/pull/23112 and https://github.com/opencv/opencv/issues/23080
//net.enableWinograd(true); //If your CPU supports AVX2, you can set it true to speed up
#endif
}
catch (const std::exception&) {
return false;
}
if (isCuda) {
//cuda
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); //or DNN_TARGET_CUDA_FP16
}
else {
//cpu
cout << "Inference device: CPU" << endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
return true;
}
bool YoloSeg::Detect(Mat& srcImg, Net& net, vector<OutputSeg>& output) {
Mat blob;
output.clear();
int col = srcImg.cols;
int row = srcImg.rows;
Mat netInputImg;
Vec4d params;
LetterBox(srcImg, netInputImg, params, cv::Size(_netWidth, _netHeight));
blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(0, 0, 0), true, false);
//**************************************************************************************************************************************************/
//如果在其他设置没有问题的情况下但是结果偏差很大，可以尝试下用下面两句语句
// If there is no problem with other settings, but results are a lot different from Python-onnx , you can try to use the following two sentences
//
//$ 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(114, 114,114), true, false);
//****************************************************************************************************************************************************/
net.setInput(blob);
std::vector<cv::Mat> net_output_img;
//*********************************************************************************************************************************
//net.forward(net_output_img, net.getUnconnectedOutLayersNames());
//opencv4.5.x和4.6.x这里输出不一致，推荐使用下面的固定名称输出
// 如果使用net.forward(net_output_img, net.getUnconnectedOutLayersNames())，需要确认下net.getUnconnectedOutLayersNames()返回值中output0在前，output1在后，否者出错
//
// The outputs of opencv4.5.x and 4.6.x are inconsistent.Please make sure "output0" is in front of "output1" if you use net.forward(net_output_img, net.getUnconnectedOutLayersNames())
//*********************************************************************************************************************************
vector<string> output_layer_names{ "output0","output1" };
net.forward(net_output_img, output_layer_names); //获取output的输出
std::vector<int> class_ids;//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
std::vector<vector<float>> picked_proposals; //output0[:,:, 5 + _className.size():net_width]===> for mask
int net_width = _className.size() + 5 + _segChannels;// 80 + 5 + 32 = 117
int out0_width= net_output_img[0].size[2];
// assert(net_width == out0_width, "Error Wrong number of _className or _segChannels"); //模型类别数目不对或者_segChannels设置错误
int net_height = net_output_img[0].size[1];// 25200
float* pdata = (float*)net_output_img[0].data;
for (int r = 0; r < net_height; r++) { //lines
float box_score = pdata[4];
if (box_score >= _classThreshold) {
cv::Mat scores(1, _className.size(), CV_32FC1, pdata + 5); // 可是 后面不只是有80个类别的概率；
Point classIdPoint;
double max_class_socre;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = (float)max_class_socre;
if (max_class_socre >= _classThreshold) {
vector<float> temp_proto(pdata + 5 + _className.size(), pdata + net_width); // Mask Coeffcients,mask的掩码系数
picked_proposals.push_back(temp_proto);
//rect [x,y,w,h]
float x = (pdata[0] - params[2]) / params[0]; //x
float y = (pdata[1] - params[3]) / params[1]; //y
float w = pdata[2] / params[0]; //w
float h = pdata[3] / params[1]; //h
int left = MAX(int(x - 0.5 * w + 0.5), 0);
int top = MAX(int(y - 0.5 * h + 0.5), 0);
class_ids.push_back(classIdPoint.x);
confidences.push_back(max_class_socre * box_score);
boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5)));
}
}
pdata += net_width;//下一行
}
//NMS
vector<int> nms_result;
cv::dnn::NMSBoxes(boxes, confidences, _classThreshold, _nmsThreshold, nms_result);
std::vector<vector<float>> temp_mask_proposals;
Rect holeImgRect(0, 0, srcImg.cols, srcImg.rows);
for (int i = 0; i < nms_result.size(); ++i) {
int idx = nms_result[i];
OutputSeg result;
result.id = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx] & holeImgRect;
temp_mask_proposals.push_back(picked_proposals[idx]);
output.push_back(result);
}
MaskParams mask_params;
mask_params.params = params;
mask_params.srcImgShape = srcImg.size();
for (int i = 0; i < temp_mask_proposals.size(); ++i) {
GetMask2(Mat(temp_mask_proposals[i]).t(), net_output_img[1], output[i], mask_params); // 注意这里是net_output_img[1]，为原型mask
}
//******************** ****************
// 老版本的方案，如果上面GetMask2出错，建议使用这个。
// If the GetMask2() still reports errors , it is recommended to use GetMask().
// Mat mask_proposals;
//for (int i = 0; i < temp_mask_proposals.size(); ++i)
// mask_proposals.push_back(Mat(temp_mask_proposals[i]).t());
//GetMask(mask_proposals, net_output_img[1], output, mask_params);
//*****************************************************/
if (output.size())
return true;
else
return false;
}
yolov5_seg_utils.h
#ifndef YOLOV5_SEG_UTILS_H
#define YOLOV5_SEG_UTILS_H
#pragma once
#include<iostream>
#include <numeric>
#include<opencv2/opencv.hpp>
#define YOLO_P6 false //是否使用P6模型
#define ORT_OLD_VISON 12 //ort1.12.0 之前的版本为旧版本API
struct OutputSeg {
int id; //结果类别id
float confidence; //结果置信度
cv::Rect box; //矩形框
cv::Mat boxMask; //矩形框内mask，节省内存空间和加快速度
};
struct MaskParams {
int segChannels = 32;
int segWidth = 160;
int segHeight = 160;
int netWidth = 640;
int netHeight = 640;
float maskThreshold = 0.5;
cv::Size srcImgShape;
cv::Vec4d params;
};
bool CheckParams(int netHeight, int netWidth, const int* netStride, int strideSize);
void DrawPred(cv::Mat& img, std::vector<OutputSeg> result, std::vector<std::string> classNames, std::vector<cv::Scalar> color);
void LetterBox(const cv::Mat& image, cv::Mat& outImage,
cv::Vec4d& params, //[ratio_x,ratio_y,dw,dh]
const cv::Size& newShape = cv::Size(640, 640),
bool autoShape = false,
bool scaleFill = false,
bool scaleUp = true,
int stride = 32,
const cv::Scalar& color = cv::Scalar(114, 114, 114));
void GetMask(const cv::Mat& maskProposals, const cv::Mat& maskProtos, std::vector<OutputSeg>& output, const MaskParams& maskParams);
void GetMask2(const cv::Mat& maskProposals, const cv::Mat& maskProtos, OutputSeg& output, const MaskParams& maskParams);
#endif // YOLOV5_SEG_UTILS_H
yolov5_seg_utils.cpp
#include "yolov5_seg_utils.h"
#pragma once
#include "yolov5_seg_utils.h"
using namespace cv;
using namespace std;
bool CheckParams(int netHeight, int netWidth, const int* netStride, int strideSize) {
if (netHeight % netStride[strideSize - 1] != 0 || netWidth % netStride[strideSize - 1] != 0)
{
cout << "Error:_netHeight and _netWidth must be multiple of max stride " << netStride[strideSize - 1] << "!" << endl;
return false;
}
return true;
}
void LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape,
bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color)
{
if (false) {
int maxLen = MAX(image.rows, image.cols);
outImage = Mat::zeros(Size(maxLen, maxLen), CV_8UC3);
image.copyTo(outImage(Rect(0, 0, image.cols, image.rows)));
params[0] = 1;
params[1] = 1;
params[3] = 0;
params[2] = 0;
}
cv::Size shape = image.size();
float r = std::min((float)newShape.height / (float)shape.height,
(float)newShape.width / (float)shape.width);
if (!scaleUp)
r = std::min(r, 1.0f);
float ratio[2]{ r, r };
int new_un_pad[2] = { (int)std::round((float)shape.width * r),(int)std::round((float)shape.height * r) };
auto dw = (float)(newShape.width - new_un_pad[0]);
auto dh = (float)(newShape.height - new_un_pad[1]);
if (autoShape)
{
dw = (float)((int)dw % stride);
dh = (float)((int)dh % stride);
}
else if (scaleFill)
{
dw = 0.0f;
dh = 0.0f;
new_un_pad[0] = newShape.width;
new_un_pad[1] = newShape.height;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1])
{
cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1]));
}
else {
outImage = image.clone();
}
int top = int(std::round(dh - 0.1f));
int bottom = int(std::round(dh + 0.1f));
int left = int(std::round(dw - 0.1f));
int right = int(std::round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}
void GetMask(const cv::Mat& maskProposals, const cv::Mat& maskProtos, std::vector<OutputSeg>& output, const MaskParams& maskParams) {
//cout << maskProtos.size << endl;
int seg_channels = maskParams.segChannels;
int net_width = maskParams.netWidth;
int seg_width = maskParams.segWidth;
int net_height = maskParams.netHeight;
int seg_height = maskParams.segHeight;
float mask_threshold = maskParams.maskThreshold;
Vec4f params = maskParams.params;
Size src_img_shape = maskParams.srcImgShape;
Mat protos = maskProtos.reshape(0, { seg_channels,seg_width * seg_height });
Mat matmul_res = (maskProposals * protos).t();
Mat masks = matmul_res.reshape(output.size(), { seg_width,seg_height });
vector<Mat> maskChannels;
split(masks, maskChannels);
for (int i = 0; i < output.size(); ++i) {
Mat dest, mask;
//sigmoid
cv::exp(-maskChannels[i], dest);
dest = 1.0 / (1.0 + dest);
Rect roi(int(params[2] / net_width * seg_width), int(params[3] / net_height * seg_height), int(seg_width - params[2] / 2), int(seg_height - params[3] / 2));
dest = dest(roi);
resize(dest, mask, src_img_shape, INTER_NEAREST);
//crop
Rect temp_rect = output[i].box;
mask = mask(temp_rect) > mask_threshold;
output[i].boxMask = mask;
}
}
void GetMask2(const Mat& maskProposals, const Mat& mask_protos, OutputSeg& output, const MaskParams& maskParams) {
int seg_channels = maskParams.segChannels;
int net_width = maskParams.netWidth;
int seg_width = maskParams.segWidth;
int net_height = maskParams.netHeight;
int seg_height = maskParams.segHeight;
float mask_threshold = maskParams.maskThreshold;
Vec4f params = maskParams.params;
Size src_img_shape = maskParams.srcImgShape;
Rect temp_rect = output.box;
// 把已经到原图的检测框坐标信息 映射到 获得mask原型分支的输入尺寸上【160, 160】
int rang_x = floor((temp_rect.x * params[0] + params[2]) / net_width * seg_width);
int rang_y = floor((temp_rect.y * params[1] + params[3]) / net_height * seg_height);
int rang_w = ceil(((temp_rect.x + temp_rect.width) * params[0] + params[2]) / net_width * seg_width) - rang_x;
int rang_h = ceil(((temp_rect.y + temp_rect.height) * params[0] + params[3]) / net_width * seg_height) - rang_y;
//
rang_w = MAX(rang_w, 1);
rang_h = MAX(rang_h, 1);
if (rang_x + rang_w > seg_width){
if (seg_width - rang_x > 0)
rang_w =seg_width -rang_x;
else
rang_x -= 1;
}
if (rang_y + rang_h > seg_height) {
if (seg_height - rang_y > 0)
rang_h = seg_height - rang_y;
else
rang_y -= 1;
}
vector<Range> roi_ranges;
roi_ranges.push_back(Range(0,1));
roi_ranges.push_back(Range::all());
roi_ranges.push_back(Range(rang_y, rang_h+rang_y));
roi_ranges.push_back(Range(rang_x, rang_w+rang_x));
// 裁剪mask原型
Mat temp_mask_protos = mask_protos(roi_ranges).clone(); // 剪裁原型，保存检测框内部的原型，其余位置清零, 以此来获得感兴趣区域(roi)
Mat protos = temp_mask_protos.reshape(0, { seg_channels, rang_w*rang_h});// 检测至检测框大小？
// mask系数与mask原型做矩阵乘法
Mat matmul_res = (maskProposals * protos).t(); // mask系数【1,32】 与 mask原型【32, h*w】进行矩阵相称
Mat masks_feature = matmul_res.reshape(1,{rang_h, rang_w}); //【1,h,w】
Mat dest, mask;
// sigmod
cv::exp(-masks_feature, dest);
dest = 1.0 / (1.0 + dest);
// 检测框坐标 映射到 原图尺寸
int left = floor((net_width / seg_width * rang_x - params[2]) / params[0]);
int top = floor((net_width / seg_height * rang_y - params[3]) / params[1]);
int width = ceil(net_width / seg_height * rang_w / params[0]);
int height = ceil(net_height / seg_height * rang_h / params[1]);
// 检测框mask缩放到原图尺寸
resize(dest, mask, Size(width, height), INTER_NEAREST);
// 阈值化
mask = mask(temp_rect - Point(left, top)) > mask_threshold;
output.boxMask = mask;
}
void DrawPred(Mat& img, vector<OutputSeg> result, std::vector<std::string> classNames, vector<Scalar> color) {
Mat mask = img.clone();
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;
// 目标画框
rectangle(img, result[i].box, color[result[i].id], 2, 8);
// 目标mask，这里非目标像素值为0
mask(result[i].box).setTo(color[result[i].id], result[i].boxMask);
string label = classNames[result[i].id] + ":" + to_string(result[i].confidence);
// 框左上角打印信息
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, color[result[i].id], 2);
}
addWeighted(img, 0.5, mask, 0.5, 0, img);
}
分割的调用代码测试案例
void frmMain::test_yoloseg()
{
string model_path = "20231001segquanjing.onnx";
YoloSeg test;
Net net;
if (test.ReadModel(net, model_path, true)) {
cout << "read net ok!" << endl;
}
else {
return ;
}
vector<OutputSeg> result;
cv::Mat img_seg_test=cv::imread("E:/data_seg/quanjingCameraPicture2/1/segdata2278.jpg");
bool find = test.Detect(img_seg_test, net, result);
if (find) {
DrawPred(img_seg_test, result, test._className, color);
}
else {
cout << "Detect Failed!"<<endl;
}
string label = "steel:" ; //ms
putText(img_seg_test, label, Point(30,30), FONT_HERSHEY_SIMPLEX,0.5, Scalar(0,0,255), 2, 8);
QPixmap img_test_xmap= my_publiction.cvMatToQPixmap(img_seg_test);
// 设置QLabel的最小尺寸
ui->lab1->setMinimumSize(600, 600);
ui->lab1->setScaledContents(true);
img_test_xmap = img_test_xmap.scaled( ui->lab1->width(), ui->lab1->height(), Qt::KeepAspectRatio,Qt::SmoothTransformation); // 将图像缩放到QLabel的大小
ui->lab1->setPixmap(img_test_xmap);
// imshow("result", img_seg_test);
}分割的效果图如下：
![[Spring] Spring5——IOC 简介（二）](https://img-blog.csdnimg.cn/acdd080d850849fcb6e456c3168fb3e6.png)
