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Bigvision code
2022-07-03 03:13:00 【AphilGuo】
classifier
#ifndef CLASSIFIER_H
#define CLASSIFIER_H
#include<opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
#include<iostream>
#include<fstream>
#include<vector>
#include<opencv2/core/utils/logger.hpp>
#include <QString>
#include <QByteArray>
using namespace cv;
using namespace std;
class ONNXClassifier{
public:
ONNXClassifier(const string& model_path, Size input_size);
void Classifier(const Mat& input_image, string& out_name, double& confidence);
private:
void preprocess_input(Mat& image);
bool read_labels(const string& label_path);
private:
Size input_size;
cv::dnn::Net net;
cv::Scalar default_mean;
cv::Scalar default_std;
string m_labels[2]= {
"ant", "bee"};
};
#endif // CLASSIFIER_H
mianwindow.h
#ifndef MAINWINDOW_H
#define MAINWINDOW_H
#include <QMainWindow>
#include <QLabel>
#include <opencv2/opencv.hpp>
using namespace cv;
namespace Ui {
class MainWindow;
}
class MainWindow : public QMainWindow
{
Q_OBJECT
public:
explicit MainWindow(QWidget *parent = 0);
QImage MatToImage(Mat &m);
~MainWindow();
private slots:
void on_OpenImageButton_clicked();
void on_ResizeImageButton_clicked();
void on_ViewOriImageButton_clicked();
void on_GrayImaageButton_clicked();
void on_GaussionFiltterButton_clicked();
void on_BilateralFiltterButton_clicked();
void on_ErodeButton_clicked();
void on_DilateButton_clicked();
void on_ThreshButton_clicked();
void on_CannyDetectButton_clicked();
void on_ClassifierButton_clicked();
void on_ModelButton_clicked();
void on_ObjPushButton_clicked();
private:
Ui::MainWindow *ui;
Mat m_mat;
Mat* temp;
QLabel* pic;
Mat mid_mat;
QString modelPath;
String m_model;
};
#endif // MAINWINDOW_H
yolov3.h
#ifndef YOLOV3_H
#define YOLOV3_H
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
class YOLOV3
{
public:
YOLOV3(){
}
void InitModel(string weightFile);
void detectImg(Mat& img);
void drawImg(int classId, float confidence, int left, int top, int right, int bottom, Mat& img);
private:
string classes[80] = {
"person", "bicycle", "car", "motorbike", "aeroplane", "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", "sofa", "pottedplant",
"bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster",
"sink", "refrigerator", "book", "clock", "vase", "scissors",
"teddy bear", "hair drier", "toothbrush"
};
int inWidth = 416;
int inHeight = 416;
float confThreshold = 0.5;
float nmsThreshold = 0.5;
float objThreshold = 0.5;
vector<int> blob_size{
1, 3, 416, 416};
Mat blob = Mat(blob_size, CV_32FC1, Scalar(0, 0, 0));
vector<int> classids;
vector<int> indexes;
vector<float> confidences;
vector<Rect> boxes;
vector<Mat> outs;
dnn::Net net;
};
#endif // YOLOV3_H
main.cpp
#include "mainwindow.h"
#include <QApplication>
int main(int argc, char *argv[])
{
QApplication a(argc, argv);
MainWindow w;
w.show();
return a.exec();
}
main.window.cpp
#include "mainwindow.h"
#include "ui_mainwindow.h"
#include <QFileDialog>
#include <QDir>
#include<QDebug>
#include <QPixmap>
#include <QByteArray>
#include <QMessageBox>
#include <classifier.h>
#include "yolov3.h"
MainWindow::MainWindow(QWidget *parent) :
QMainWindow(parent),
ui(new Ui::MainWindow)
{
ui->setupUi(this);
}
QImage MainWindow::MatToImage(Mat &m)
{
switch(m.type())
{
case CV_8UC1:
{
QImage img((uchar* )m.data, m.cols, m.rows, m.cols*1, QImage::Format_Grayscale8);
return img;
break;
}
case CV_8UC3:
{
QImage img((uchar* )m.data, m.cols, m.rows, m.cols*3, QImage::Format_RGB888);
return img.rgbSwapped();
break;
}
case CV_8UC4:
{
QImage img((uchar* )m.data, m.cols, m.rows, m.cols*4, QImage::Format_ARGB32);
return img;
break;
}
default:
{
QImage img;
return img;
}
}
}
MainWindow::~MainWindow()
{
delete ui;
delete pic;
delete temp;
}
void MainWindow::on_OpenImageButton_clicked()
{
auto strPath = QFileDialog::getOpenFileName(nullptr, " Select Picture ", QDir::homePath(),
"Images (*.png *.xpm *.jpg)");
pic = new QLabel;
pic->setPixmap(QPixmap(strPath));
ui->ViewImageScrollArea->setWidget(pic);
QByteArray ba;
ba.append(strPath);
m_mat = imread(ba.data());
mid_mat = m_mat.clone();
}
void MainWindow::on_ViewOriImageButton_clicked()
{
temp = &m_mat;
if(temp->empty()){
QMessageBox::warning(nullptr, " Warning ！", " Please choose a picture ！！！");
return ;
}
QImage temp1 = MatToImage(*temp);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
// While displaying the original image
mid_mat = m_mat.clone();
}
void MainWindow::on_ResizeImageButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ！", " Please choose a picture ！！！");
return ;
}
Mat dst;
// Add mouse wheel adjustment parameters
cv::resize(*temp2,dst, Size(400, 400));
QImage temp1 = MatToImage(dst);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = dst;
}
void MainWindow::on_GrayImaageButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ！", " Please choose a picture ！！！");
return ;
}
Mat gray_img;
if((*temp2).type()==CV_8UC1){
QMessageBox::warning(nullptr, " Wrong operation ", " At this time, the picture is grayscale ");
return;
}
cvtColor(*temp2, gray_img, COLOR_BGR2GRAY);
// qDebug()<<gray_img.type();
QImage temp1 = MatToImage(gray_img);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = gray_img;
}
void MainWindow::on_GaussionFiltterButton_clicked()
{
/*
Weighted average the whole image , Each pixel is determined by itself and others in the field
The pixel value is obtained after weighted average
*/
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
Mat gaussianImg;
cv::GaussianBlur(*temp2, gaussianImg, cv::Size(5, 5), 0, 0);
QImage temp1 = MatToImage(gaussianImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = gaussianImg;
}
void MainWindow::on_BilateralFiltterButton_clicked()
{
/*
* Airspace core 、 Range core
*/
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
Mat bilateralImg, dst;
cv::bilateralFilter(*temp2, bilateralImg, 15, 100, 3);
Mat kernel = (Mat_<int>(3, 3)<<0, -1, 0, -1, 5, -1, 0, -1, 0);
filter2D(bilateralImg, dst, -1, kernel, Point(-1, -1), 0);
QImage temp1 = MatToImage(dst);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = dst;
}
void MainWindow::on_ErodeButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
// Get structure elements
Mat erodeImg;
Mat structureElement = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 5), Point(-1, -1));
cv::erode(*temp2, erodeImg, structureElement);
QImage temp1 = MatToImage(erodeImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = erodeImg;
}
void MainWindow::on_DilateButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return;
}
Mat dilateImg;
Mat structureElement = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 5), Point(-1, -1));
cv::dilate(*temp2, dilateImg, structureElement);
QImage temp1 = MatToImage(dilateImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = dilateImg;
}
void MainWindow::on_ThreshButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
if(!((*temp2).type()==CV_8UC1)){
QMessageBox::warning(nullptr, " Warning ", " Please gray the picture first ");
return ;
}
Mat bin_Img;
cv::adaptiveThreshold(*temp2, bin_Img, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 5, 2);
QImage temp1 = MatToImage(bin_Img);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = bin_Img;
}
void MainWindow::on_CannyDetectButton_clicked()
{
Mat* temp2 = &mid_mat;
if((*temp2).empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
if(!((*temp2).type()==CV_8UC1)){
QMessageBox::warning(nullptr, " Warning ", " Please gray the picture first ");
return ;
}
Mat cannyImg;
cv::Canny(*temp2, cannyImg, 100, 200, 3, false);
QImage temp1 = MatToImage(cannyImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = cannyImg;
}
ONNXClassifier::ONNXClassifier(const std::string& model_path, cv::Size _input_size) :default_mean(0.485, 0.456, 0.406),
default_std(0.229, 0.224, 0.225), input_size(_input_size)
{
net = cv::dnn::readNet(model_path);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
void ONNXClassifier::preprocess_input(Mat& image){
image.convertTo(image, CV_32F, 1.0/255.0);
cv::subtract(image, default_mean, image);
cv::divide(image, default_std, image);
}
void ONNXClassifier::Classifier(const cv::Mat& input_image, std::string& out_name, double& confidence){
out_name.clear();
cv::Mat image = input_image.clone();
preprocess_input(image);
cv::Mat input_blob = cv::dnn::blobFromImage(image, 1.0, input_size, cv::Scalar(0,0,0), true);
net.setInput(input_blob);
const std::vector<cv::String>& out_names = net.getUnconnectedOutLayersNames();
cv::Mat out_tensor = net.forward(out_names[0]);
cv::Point classNumber;
double classProb;
cv::minMaxLoc(out_tensor, NULL, &classProb, NULL, &classNumber);
confidence = classProb;
out_name = m_labels[classNumber.x];
}
void MainWindow::on_ClassifierButton_clicked()
{
Mat drawImg = mid_mat.clone();
String* using_model = &m_model;
if(drawImg.empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
if(using_model->empty()){
QMessageBox::warning(nullptr, " Warning ", " Please select a model ！！！");
return ;
}
Size input_size(300, 300);
ONNXClassifier classifier(*using_model, input_size);
string result;
double confidence = 0;
classifier.Classifier(drawImg, result, confidence);
putText(drawImg, result, Point(20, 20), FONT_HERSHEY_SIMPLEX, 1.0, Scalar(0, 0, 255), 2, 8);
QImage temp1 = MatToImage(drawImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = drawImg;
}
void MainWindow::on_ModelButton_clicked()
{
modelPath = QFileDialog::getOpenFileName(nullptr, " Select File ", QDir::homePath(), "*.onnx *.weights");
QByteArray ba;
ba.append(modelPath);
m_model = modelPath.toStdString();
QMessageBox::information(nullptr, " Tips ", " Model loaded successfully ");
}
void MainWindow::on_ObjPushButton_clicked()
{
String* yoloModel = &m_model;
Mat detectImg = mid_mat.clone();
if(detectImg.empty()){
QMessageBox::warning(nullptr, " Warning ", " Please choose a picture ！！！");
return ;
}
if(yoloModel->empty()){
QMessageBox::warning(nullptr, " Warning ", " Please select a model ！！！");
return ;
}
YOLOV3 yolo;
yolo.InitModel(*yoloModel);
yolo.detectImg(detectImg);
// Convert picture type
QImage temp1 = MatToImage(detectImg);
pic->setPixmap(QPixmap::fromImage(temp1));
ui->ViewImageScrollArea->setWidget(pic);
mid_mat = detectImg;
}
yolov3.cpp
#include "yolov3.h"
RNG rng(12345);
void YOLOV3::InitModel(string weightFile)
{
string configPath = "D:\\Projects\\QtProjects\\GithubProject\\models\\yolov3\\yolov3.cfg";
this->net = cv::dnn::readNetFromDarknet(configPath, weightFile);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
void YOLOV3::detectImg(Mat &img)
{
cv::dnn::blobFromImage(img, blob, 1/255.0, Size(this->inWidth, this->inHeight), Scalar::all(0), true, false);
net.setInput(blob);
net.forward(outs, net.getUnconnectedOutLayersNames());
for(size_t i=0; i<outs.size();i++)
{
float* pdata = (float*)outs[i].data;
for(int j=0;j<outs[i].rows;j++, pdata += outs[i].cols)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classId;
double confidence;
minMaxLoc(scores, 0, &confidence, 0, &classId);
if(confidence>this->confThreshold)
{
int centerX = (int)(pdata[0]*img.cols);
int centerY = (int)(pdata[1]*img.rows);
int width = (int)(pdata[2]*img.cols);
int height = (int)(pdata[3]*img.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classids.push_back(classId.x);
confidences.push_back(confidence);
boxes.push_back(Rect(left, top, width, height));
}
}
}
dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, this->indexes);
for(size_t i=0;i<indexes.size();i++)
{
int idx = indexes[i];
Rect box = boxes[idx];
this->drawImg(classids[idx], confidences[idx], box.x, box.y, box.x+box.width, box.y+box.height, img);
}
}
void YOLOV3::drawImg(int classId, float confidence, int left, int top, int right, int bottom, Mat &img)
{
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
rectangle(img, Point(left, top), Point(right, bottom), color, 2);
string label = format("%.2f", confidence);
label = this->classes[classId] + ":" + label;
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, 0.75, color, 2, 8);
}
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