755 lines
24 KiB
C++
755 lines
24 KiB
C++
#include <iostream>
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#include <qthread.h>
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#include <qqueue.h>
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#include <qmutex.h>
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#include <iomanip>
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#include <sstream>
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#include <algo2.h>
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#include <filesystem>
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#include <opencv.hpp>
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#include <string>
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#include <handleai.h>
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#include <sqliteController/sqlControler.h>
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#include <QtConcurrent/qtconcurrent>.
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#include <threadPoolController.h>
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enum ngTypeEnum
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{
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OK,
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huaHeng,
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aoKeng,
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zangWu
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};
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//简单的图像相关算法
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class simpleAlgo
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{
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public:
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//融合两张图片白色区域
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static cv::Mat mergeBinaryMats(const cv::Mat& mat1, const cv::Mat& mat2)
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{
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// 确保两个输入图像具有相同的尺寸
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if (mat1.size() != mat2.size()) {
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std::cerr << "Error: Input images must have the same size." << std::endl;
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return cv::Mat();
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}
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// 创建一个输出图像，初始化为全黑
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cv::Mat merged = cv::Mat::zeros(mat1.size(), CV_8U);
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// 将两个输入图像中的白色部分合并到输出图像
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cv::bitwise_or(mat1, mat2, merged);
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return merged;
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}
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//Rect相加
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cv::Rect rectPlusRect(cv::Rect world, cv::Rect rect)
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{
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return cv::Rect(rect.x + world.x, rect.y + world.y, rect.width, rect.height);
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}
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static cv::Rect findMaxAreaContour(cv::Mat src)
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{
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int maxIndex = 0, maxArea = 0;
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vector<vector<cv::Point>>contours;
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cv::findContours(src, contours, cv::noArray(), cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
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if (contours.size() == 0)
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return cv::Rect(0, 0, 0, 0);
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for (int i = 0; i < contours.size(); i++)
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{
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int Area = contourArea(contours[i]);
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if (Area > maxArea)
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{
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maxArea = Area;
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maxIndex = i;
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}
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}
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if (contours.size() != 0)
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return boundingRect(contours[maxIndex]);
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return cv::Rect(0, 0, 0, 0);
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}
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static cv::Rect findMaxAreaContour(cv::Mat src, int& indexReturn)
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{
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int maxIndex = 0, maxArea = 0;
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vector<vector<cv::Point>>contours;
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cv::findContours(src, contours, cv::noArray(), cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
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if (contours.size() == 0)
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return cv::Rect(0, 0, 0, 0);
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for (int i = 0; i < contours.size(); i++)
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{
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int Area = contourArea(contours[i]);
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if (Area > maxArea)
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{
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maxArea = Area;
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maxIndex = i;
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}
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}
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if (contours.size() != 0)
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{
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indexReturn = maxIndex;
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return boundingRect(contours[maxIndex]);
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}
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}
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static cv::Mat cutImg2Cls(vector<cv::Point>contour, cv::Mat imgInput, cv::Size targetSize = cv::Size(128, 128))
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{
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if (contour.empty() || imgInput.empty()) {
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std::cerr << "Empty contour or input image." << std::endl;
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return cv::Mat();
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}
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cv::Rect boundingBox = cv::boundingRect(contour);
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cv::Point mid(boundingBox.x + boundingBox.width / 2, boundingBox.y + boundingBox.height / 2);
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int sideLength = std::max(std::max(boundingBox.width, boundingBox.height), 128);
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cv::Mat squareImage(sideLength, sideLength, imgInput.type(), cv::Scalar(0, 0, 0));
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cv::Rect expectRect(mid.x - sideLength / 2, mid.y - sideLength / 2, sideLength, sideLength);
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int xOffset = 0;
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int yOffset = 0;// (sideLength - boundingBox.height) / 2;
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//越界判断
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{
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if (expectRect.x < 0)
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{
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expectRect.width += expectRect.x;
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xOffset -= expectRect.x;
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expectRect.x = 0;
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}
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if (expectRect.y < 0)
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{
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expectRect.height += expectRect.y;
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yOffset -= expectRect.y;
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expectRect.y = 0;
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}
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if (expectRect.x + expectRect.width > imgInput.cols)
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expectRect.width = imgInput.cols - expectRect.x;
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if (expectRect.y + expectRect.height > imgInput.rows)
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expectRect.height = imgInput.rows - expectRect.y;
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}
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imgInput(expectRect).copyTo(squareImage(cv::Rect(xOffset, yOffset, expectRect.width, expectRect.height)));
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resize(squareImage, squareImage, targetSize);
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return squareImage;
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}
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static cv::Mat cutImg2Cls(cv::Rect boundingBox, cv::Mat imgInput, cv::Size targetSize = cv::Size(128, 128))
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{
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if (boundingBox.empty() || imgInput.empty()) {
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std::cerr << "Empty contour or input image." << std::endl;
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return cv::Mat();
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}
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cv::Point mid(boundingBox.x + boundingBox.width / 2, boundingBox.y + boundingBox.height / 2);
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int sideLength = std::max(std::max(boundingBox.width, boundingBox.height), 128);
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cv::Mat squareImage(sideLength, sideLength, imgInput.type(), cv::Scalar(0, 0, 0));
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cv::Rect expectRect(mid.x - sideLength / 2, mid.y - sideLength / 2, sideLength, sideLength);
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int xOffset = 0;
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int yOffset = 0;// (sideLength - boundingBox.height) / 2;
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//越界判断
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{
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if (expectRect.x < 0)
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{
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expectRect.width += expectRect.x;
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xOffset -= expectRect.x;
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expectRect.x = 0;
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}
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if (expectRect.y < 0)
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{
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expectRect.height += expectRect.y;
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yOffset -= expectRect.y;
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expectRect.y = 0;
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}
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if (expectRect.x + expectRect.width > imgInput.cols)
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expectRect.width = imgInput.cols - expectRect.x;
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if (expectRect.y + expectRect.height > imgInput.rows)
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expectRect.height = imgInput.rows - expectRect.y;
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}
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imgInput(expectRect).copyTo(squareImage(cv::Rect(xOffset, yOffset, expectRect.width, expectRect.height)));
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resize(squareImage, squareImage, targetSize);
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return squareImage;
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}
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static cv::Mat HObject2Mat(HObject Hobj) //HObject类型转Mat类型
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{
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HTuple htCh;
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HString cType;
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cv::Mat Image;
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ConvertImageType(Hobj, &Hobj, "byte");
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CountChannels(Hobj, &htCh);
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Hlong wid = 0;
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Hlong hgt = 0;
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if (htCh[0].I() == 1)
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{
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HImage hImg(Hobj);
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void* ptr = hImg.GetImagePointer1(&cType, &wid, &hgt);//GetImagePointer1(Hobj, &ptr, &cType, &wid, &hgt);
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int W = wid;
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int H = hgt;
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Image.create(H, W, CV_8UC1);
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unsigned char* pdata = static_cast<unsigned char*>(ptr);
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memcpy(Image.data, pdata, W * H);
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}
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else if (htCh[0].I() == 3)
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{
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void* Rptr;
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void* Gptr;
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void* Bptr;
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HImage hImg(Hobj);
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hImg.GetImagePointer3(&Rptr, &Gptr, &Bptr, &cType, &wid, &hgt);
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int W = wid;
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int H = hgt;
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Image.create(H, W, CV_8UC3);
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std::vector<cv::Mat> VecM(3);
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VecM[0].create(H, W, CV_8UC1);
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VecM[1].create(H, W, CV_8UC1);
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VecM[2].create(H, W, CV_8UC1);
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unsigned char* R2 = (unsigned char*)Rptr;
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unsigned char* G = (unsigned char*)Gptr;
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unsigned char* B = (unsigned char*)Bptr;
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memcpy(VecM[2].data, R2, W * H);
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memcpy(VecM[1].data, G, W * H);
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memcpy(VecM[0].data, B, W * H);
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cv::merge(VecM, Image);
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}
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return Image;
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}
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static HalconCpp::HObject MatToHObject(cv::Mat& image)
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{
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HalconCpp::HObject Hobj = HalconCpp::HObject();
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int hgt = image.rows;
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int wid = image.cols;
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int i;
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if (image.type() == CV_8UC3)
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{
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HalconCpp::HImage himage = HalconCpp::HImage();
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std::vector<cv::Mat> imgchannel;
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split(image, imgchannel);
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cv::Mat imgB = imgchannel[0];
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cv::Mat imgG = imgchannel[1];
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cv::Mat imgR = imgchannel[2];
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uchar* dataR = new uchar[hgt * wid];
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uchar* dataG = new uchar[hgt * wid];
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uchar* dataB = new uchar[hgt * wid];
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for (i = 0; i < hgt; i++)
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{
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memcpy(dataR + wid * i, imgR.data + imgR.step * i, wid);
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memcpy(dataG + wid * i, imgG.data + imgG.step * i, wid);
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memcpy(dataB + wid * i, imgB.data + imgB.step * i, wid);
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}
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himage.GenImage3("byte", wid, hgt, dataR, dataG, dataB);
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HalconCpp::HString tmpType;
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Hlong tmpW, tmpH;
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void* hImgPtR, * hImgPtG, * hImgPtB;
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himage.GetImagePointer3(&hImgPtR, &hImgPtG, &hImgPtB, &tmpType, &tmpW, &tmpH);
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GenImage3(&Hobj, "byte", wid, hgt, (Hlong)hImgPtR, (Hlong)hImgPtG, (Hlong)hImgPtB);
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delete[]dataR;
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delete[]dataG;
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delete[]dataB;
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dataR = NULL;
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dataG = NULL;
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dataB = NULL;
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}
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else if (image.type() == CV_8UC1)
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{
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HalconCpp::HImage himage = HalconCpp::HImage();
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uchar* data = new uchar[hgt * wid];
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for (i = 0; i < hgt; i++)
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{
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memcpy(data + wid * i, image.data + image.step * i, wid);
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}
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himage.GenImage1("byte", wid, hgt, data);
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HalconCpp::HString tmpType;
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Hlong tmpW, tmpH;
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void* hImgPt;
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hImgPt = himage.GetImagePointer1(&tmpType, &tmpW, &tmpH);
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GenImage1(&Hobj, "byte", wid, hgt, (Hlong)hImgPt);
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delete[] data;
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data = NULL;
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}
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else if (image.type() == CV_16UC1)
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{
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HalconCpp::HImage himage = HalconCpp::HImage();
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ushort* data = new ushort[hgt * wid * 2];
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memcpy(data, image.data, hgt * wid * 2);
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himage.GenImage1("uint2", wid, hgt, data);
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HalconCpp::HString tmpType;
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Hlong tmpW, tmpH;
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void* hImgPt;
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hImgPt = himage.GetImagePointer1(&tmpType, &tmpW, &tmpH);
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GenImage1(&Hobj, "uint2", wid, hgt, (Hlong)hImgPt);
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delete[] data;
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data = NULL;
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}
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else if (image.type() == CV_16SC1)
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{
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HalconCpp::HImage himage = HalconCpp::HImage();
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short* data = new short[hgt * wid * 2];
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memcpy(data, image.data, hgt * wid * 2);
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himage.GenImage1("int2", wid, hgt, data);
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HalconCpp::HString tmpType;
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Hlong tmpW, tmpH;
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void* hImgPt;
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hImgPt = himage.GetImagePointer1(&tmpType, &tmpW, &tmpH);
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GenImage1(&Hobj, "int2", wid, hgt, (Hlong)hImgPt);
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delete[] data;
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data = NULL;
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}
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return Hobj;
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}
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//获取两点之间的直线距离
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static double getDistance(cv::Point pointO, cv::Point pointA)
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{
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double distance;
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distance = powf((pointO.x - pointA.x), 2) + powf((pointO.y - pointA.y), 2);
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distance = sqrtf(distance);
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return distance;
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}
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//计算角度，pt2为顶点
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static double calculateAngle(cv::Point pt1, cv::Point pt2, cv::Point pt3)
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{
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// 计算两个向量
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cv::Point vector1 = pt1 - pt2;
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cv::Point vector2 = pt3 - pt2;
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// 计算两个向量的长度
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double length1 = norm(vector1);
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double length2 = norm(vector2);
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// 计算两个向量的点积
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double dotProduct = vector1.dot(vector2);
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// 计算夹角的余弦值
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double cosAngle = dotProduct / (length1 * length2);
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// 计算夹角的弧度
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double angleRadians = acos(cosAngle);
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// 将弧度转换为度数
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double angleDegrees = angleRadians * 180 / CV_PI;
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return angleDegrees;
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}
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//计算角度偏移量，pt1,pt2为第一条线段，pt3,pt4为第二条线段
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static double calculateAngleOffset(cv::Point pt11, cv::Point pt12, cv::Point pt21, cv::Point pt22)
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{
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// 计算第一条线段的向量
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cv::Point vec1(pt12.x - pt11.x, pt12.y - pt11.y);
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// 计算第二条线段的向量
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cv::Point vec2(pt22.x - pt21.x, pt22.y - pt21.y);
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// 计算向量的叉乘
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double crossProduct = vec1.x * vec2.y - vec1.y * vec2.x;
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// 计算角度偏移量
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double angleOffset = atan2(crossProduct, vec1.dot(vec2));
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// 将弧度转换为度数
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angleOffset = angleOffset * 180 / CV_PI;
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return angleOffset;
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}
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//通过角度计算圆上点的位置
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cv::Point calculatePointOnCircle(cv::Point center, double radius, double angleDegrees)
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{
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// 将角度转换为弧度
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double angleRadians = angleDegrees * CV_PI / 180.0;
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// 计算点的坐标
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int x = static_cast<int>(center.x + radius * cos(angleRadians));
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int y = static_cast<int>(center.y + radius * sin(angleRadians));
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return cv::Point(x, y);
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}
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/// <summary>
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/// 模板匹配，以MatchResult返回
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/// </summary>
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/// <param name="originalImage">图像</param>
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/// <param name="templateImage">模板图像</param>
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/// <returns></returns>
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static MatchResult templateMatching(const cv::Mat& originalImage, const cv::Mat& templateImage) {
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MatchResult result;
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// 创建结果图像
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int resultCols = originalImage.cols - templateImage.cols + 1;
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int resultRows = originalImage.rows - templateImage.rows + 1;
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cv::Mat resultImg(resultRows, resultCols, CV_32FC1);
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// 进行模板匹配
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cv::matchTemplate(originalImage, templateImage, resultImg, cv::TM_CCOEFF_NORMED);
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// 归一化结果图像
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cv::normalize(resultImg, resultImg, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
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// 寻找最佳匹配位置
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double minVal, maxVal;
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cv::Point minLoc, maxLoc;
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cv::minMaxLoc(resultImg, &minVal, &maxVal, &minLoc, &maxLoc);
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// 设置矩形框和可信度
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result.boundingRect = cv::Rect(maxLoc.x, maxLoc.y, templateImage.cols, templateImage.rows);
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result.confidence = maxVal;
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return result;
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}
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/// <summary>
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/// 模板匹配,返回Rect以及分数
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/// </summary>
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/// <param name="templateImg">模板图像</param>
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/// <param name="srcImg">匹配图像</param>
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/// <param name="sorce">分数</param>
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/// <returns></returns>
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static cv::Rect doMatchTemplate(const cv::Mat& templateImg, const cv::Mat& srcImg, double* sorce)
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{
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cv::Mat resultImage;
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matchTemplate(srcImg, templateImg, resultImage, cv::TM_CCOEFF_NORMED);
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double minVal = 0, maxVal = 0;
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cv::Point minLoc(0, 0), maxLoc(0, 0);
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minMaxLoc(resultImage, &minVal, &maxVal, &minLoc, &maxLoc);
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*sorce = maxVal;
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cv::Rect rect(maxLoc.x, maxLoc.y, templateImg.cols, templateImg.rows);
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return rect;
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}
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//计算contour是否重叠
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static bool isContoursOverlap(const vector<cv::Point>& contour1, const vector<cv::Point>& contour2, double thresholdPercent = 0) {
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cv::Rect rect1 = cv::boundingRect(contour1);
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cv::Rect rect2 = cv::boundingRect(contour2);
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cv::Size tempImgSize(max(rect1.x + rect1.width, rect2.x + rect2.width), max(rect1.y + rect1.height, rect2.y + rect2.height));
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cv::Mat binaryImage = cv::Mat::zeros(tempImgSize,CV_8U);
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// 画出目标轮廓
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drawContours(binaryImage, vector<vector<cv::Point>>{contour2}, 0, cv::Scalar(255), -1);
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double totalOverlap = 0.0;
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// 创建一个临时的二值图像
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cv::Mat tempBinaryImage = cv::Mat::zeros(tempImgSize, CV_8U);
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// 画出当前轮廓
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drawContours(tempBinaryImage, vector<vector<cv::Point>>{contour1}, 0, cv::Scalar(255), -1);
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// 计算重叠的像素数
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cv::Mat intersection = binaryImage & tempBinaryImage;
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int overlapPixelCount = countNonZero(intersection);
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// 计算当前轮廓的重叠百分比
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double overlapPercentage = static_cast<double>(overlapPixelCount) / countNonZero(tempBinaryImage);
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return overlapPercentage * 100.0> thresholdPercent; // 将结果转换为百分比
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}
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//计算rect是否重叠
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static bool isRectOverlap(const cv::Rect& rect1, const cv::Rect& rect2,double thresholdPercent=0) {
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// 计算两个矩形的重叠区域
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cv::Rect intersection = rect1 & rect2;
|
|
if (thresholdPercent != 0)//如果有阈值
|
|
{
|
|
int maxCnotourArea = max(rect1.area(), rect2.area());
|
|
if (intersection.area() / maxCnotourArea < thresholdPercent)
|
|
return false;
|
|
}
|
|
// 如果重叠区域的面积大于0，则说明两个矩形重叠
|
|
return intersection.area() > 0;
|
|
}
|
|
//将两个contour的区域合并
|
|
static vector<cv::Point> contourPlusContour(const vector<cv::Point>& contour1, const vector<cv::Point>& contour2)
|
|
{
|
|
vector<cv::Point>res;
|
|
res.insert(res.end(), contour1.begin(), contour1.end());
|
|
res.insert(res.end(), contour2.begin(), contour2.end());
|
|
return res;
|
|
}
|
|
};
|
|
struct rectAndData
|
|
{
|
|
cv::Rect rect;
|
|
double defectHeight;
|
|
double defectWidth;
|
|
int badType=0;
|
|
bool result;
|
|
ngTypeEnum ngType;
|
|
vector<cv::Point> contour;
|
|
rectAndData(cv::Rect rectInput, double defectHeightInput, double defectWidthInput,bool resultInput)
|
|
{
|
|
rect = rectInput;
|
|
defectHeight = defectHeightInput;
|
|
defectWidth = defectWidthInput;
|
|
result = resultInput;
|
|
}
|
|
rectAndData(cv::Rect rectInput, double defectHeightInput, double defectWidthInput, int n, bool resultInput)
|
|
{
|
|
rect = rectInput;
|
|
defectHeight = defectHeightInput;
|
|
defectWidth = defectWidthInput;
|
|
badType = n;
|
|
result = resultInput;
|
|
}
|
|
rectAndData(cv::Rect rectInput, double defectHeightInput, double defectWidthInput, int n, bool resultInput, vector<cv::Point>& contourInput)
|
|
{
|
|
rect = rectInput;
|
|
defectHeight = defectHeightInput;
|
|
defectWidth = defectWidthInput;
|
|
badType = n;
|
|
result = resultInput;
|
|
contour = contourInput;
|
|
}
|
|
rectAndData(cv::Rect rectInput, double defectHeightInput, double defectWidthInput, int n, bool resultInput, vector<cv::Point>&contourInput, ngTypeEnum inputNgType)
|
|
{
|
|
rect = rectInput;
|
|
defectHeight = defectHeightInput;
|
|
defectWidth = defectWidthInput;
|
|
badType = n;
|
|
result = resultInput;
|
|
contour = contourInput;
|
|
ngType = inputNgType;
|
|
}
|
|
};
|
|
|
|
//计算主线程
|
|
class algoThread : public QThread
|
|
{
|
|
Q_OBJECT
|
|
public:
|
|
bool m_runFlag = true;
|
|
QString threadName;
|
|
|
|
handleAiModel* modelPtr;
|
|
sqlController* sql;
|
|
threadConroller* tController;
|
|
subThread* oriSaveThread;
|
|
subThread* resSaveThread;
|
|
sncodeCls* snDatas;
|
|
cameraImages* cameraImgs;
|
|
|
|
QMutex detQueueMutex;
|
|
QQueue<detData*> detQueue;
|
|
QMutex currentMutex, nextMutex, globalMutex;
|
|
|
|
string snErrorsnError = string("snErrNoSn");
|
|
QMutex* snAndHashMutex;
|
|
|
|
int Index = 1;
|
|
int areaIndex = 0;
|
|
void run();
|
|
bool waitForImg(detData* data,int);
|
|
void getAllFiles(string path, vector<string>& files, string format);
|
|
void Init(threadConroller* threadConroller) {
|
|
tController = threadConroller;
|
|
modelPtr = &threadConroller->models;
|
|
sql = threadConroller->sql;
|
|
oriSaveThread = threadConroller->oriSaveThread;
|
|
resSaveThread = threadConroller->resSaveThread;
|
|
snDatas = &threadConroller->snDatas;
|
|
cameraImgs = &threadConroller->cameraImgs;
|
|
}
|
|
bool addDetTask(detData*);//添加任务
|
|
|
|
//算法
|
|
void _25D_1_Algo(detData*);
|
|
void _25D_2_Algo(detData*);
|
|
signals:
|
|
void subThreadStart();
|
|
};
|
|
|
|
//用于储存以及计算需要在图片上屏蔽的区域
|
|
class avoidArea
|
|
{
|
|
public:
|
|
std::vector<std::vector<cv::Point>>avoidContours;
|
|
bool addArea(cv::Mat& detImg, pSegmentation* segPtr)//只取最大的
|
|
{
|
|
cv::Mat resMat = ~segPtr->Evaluate(detImg);
|
|
int maxIndex = 0, maxArea = 0;
|
|
vector<vector<cv::Point>>contours;
|
|
cv::findContours(resMat, contours, cv::noArray(), cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
|
|
if (contours.size() == 0)
|
|
return false;
|
|
for (int i = 0; i < contours.size(); i++)
|
|
{
|
|
int Area = contourArea(contours[i]);
|
|
if (Area > maxArea)
|
|
{
|
|
maxArea = Area;
|
|
maxIndex = i;
|
|
}
|
|
}
|
|
if (contours.size() != 0)
|
|
{
|
|
avoidContours.push_back(contours[maxIndex]);
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
bool checkOverLap(cv::Point input)//检测与中心点是否重叠
|
|
{
|
|
for (auto& contour : avoidContours)
|
|
{
|
|
if (cv::pointPolygonTest(contour, input, true) >= 0)
|
|
{
|
|
return true;
|
|
}
|
|
}
|
|
return false;
|
|
}
|
|
bool checkOverLap(std::vector<cv::Point> contourInput)
|
|
{
|
|
for (auto& contour : avoidContours)
|
|
{
|
|
if (simpleAlgo::isContoursOverlap(contour, contourInput)==true)
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
};
|
|
|
|
//用法：先将contour用insertContours插入，然后调用fusionContourToMat生成Mat
|
|
class contour2Mat
|
|
{
|
|
public:
|
|
enum type
|
|
{
|
|
jiZhuPoSun,
|
|
aoKeng,
|
|
huaHeng,
|
|
max = huaHeng
|
|
};
|
|
struct _strcut
|
|
{
|
|
vector<cv::Point> contours;
|
|
cv::Point offset;
|
|
};
|
|
contour2Mat(cv::Size sizeInput)
|
|
{
|
|
contoursVec.resize(max+1);
|
|
offsets.resize(max + 1);
|
|
imgSize = sizeInput;
|
|
}
|
|
contour2Mat()
|
|
{
|
|
contoursVec.resize(max + 1);
|
|
offsets.resize(max + 1);
|
|
}
|
|
void insertContours(vector<cv::Point> contours, type t,cv::Point offset=cv::Point(0,0))
|
|
{
|
|
if (contours.size() == 0)
|
|
return;
|
|
contoursVec[t].push_back({ contours,offset });
|
|
}
|
|
cv::Mat fusionContourToMat(type t)
|
|
{
|
|
cv::Mat res;
|
|
res = cv::Mat(
|
|
cv::Size( imgSize.width,imgSize.height ),
|
|
CV_8UC1, cv::Scalar(0));
|
|
|
|
if (contoursVec[t].size() == 0)
|
|
{
|
|
cv::copyMakeBorder(res, res, offsets[t].x, 0, offsets[t].y, 0, 1, cv::Scalar(0));
|
|
return res;
|
|
}
|
|
for (auto& data : contoursVec[t])
|
|
{
|
|
cv::Rect rect = cv::boundingRect(data.contours);
|
|
if (res.cols < rect.x + rect.width)
|
|
cv::copyMakeBorder(res, res, 0, 0, 0, rect.x + rect.width - res.cols, cv::BORDER_CONSTANT, cv::Scalar(0));
|
|
if (res.rows < rect.y + rect.height)
|
|
cv::copyMakeBorder(res, res, 0, rect.y + rect.height - res.rows, 0, 0, cv::BORDER_CONSTANT, cv::Scalar(0));
|
|
}
|
|
for (auto& data : contoursVec[t])
|
|
{
|
|
cv::drawContours(res(cv::Rect(data.offset.x,data.offset.y,imgSize.width-data.offset.x,imgSize.height-data.offset.y)),
|
|
vector <vector<cv::Point>>(1,data.contours), -1, cv::Scalar(255), -1);
|
|
}
|
|
return res;
|
|
}
|
|
void setImgSize(cv::Size sizeInput)
|
|
{
|
|
imgSize = sizeInput;
|
|
}
|
|
void setOffSet(cv::Point offsetInput, type t)
|
|
{
|
|
offsets[t] = offsetInput;
|
|
}
|
|
private:
|
|
cv::Size imgSize;
|
|
std::vector<cv::Point>offsets;
|
|
std::vector<vector<_strcut>>contoursVec;
|
|
};
|
|
|
|
//用于储存检测结果,以及防止重复画图 //尚不启用
|
|
class rectAndDataCls
|
|
{
|
|
public:
|
|
vector<rectAndData>rectAndDatas;
|
|
void insert(vector<rectAndData>& input)
|
|
{
|
|
if (rectAndDatas.size() == 0)//第一次插入
|
|
{
|
|
rectAndDatas = input;
|
|
return;
|
|
}
|
|
else//第2+次插入 需要进行重合判定
|
|
{
|
|
for (auto& data : input)
|
|
{
|
|
bool flag = false;
|
|
for (auto& data2 : rectAndDatas)//检查是否重合
|
|
{
|
|
if (simpleAlgo::isContoursOverlap(data.contour, data2.contour, 50) == true)//重合部分超过30%
|
|
{
|
|
flag = true;
|
|
data2.contour = simpleAlgo::contourPlusContour(data.contour, data2.contour);//融合
|
|
data2.ngType = std::max(data.ngType, data2.ngType);
|
|
}
|
|
}
|
|
if (flag == false)// 否
|
|
rectAndDatas.push_back(data);
|
|
}
|
|
}
|
|
}
|
|
void insert(vector<rectAndData> input)
|
|
{
|
|
if (rectAndDatas.size() == 0)//第一次插入
|
|
{
|
|
rectAndDatas = input;
|
|
return;
|
|
}
|
|
else//第2+次插入 需要进行重合判定
|
|
{
|
|
for (auto& data : input)
|
|
{
|
|
bool flag = false;
|
|
for (auto& data2 : rectAndDatas)//检查是否重合
|
|
{
|
|
if (simpleAlgo::isContoursOverlap(data.contour, data2.contour, 50) == true)//重合部分超过30%
|
|
{
|
|
flag = true;
|
|
data2.contour = simpleAlgo::contourPlusContour(data.contour, data2.contour);//融合
|
|
data2.ngType = std::max(data.ngType, data2.ngType);
|
|
}
|
|
}
|
|
if (flag == false)// 否
|
|
rectAndDatas.push_back(data);
|
|
}
|
|
}
|
|
}
|
|
void clear()
|
|
{
|
|
rectAndDatas.clear();
|
|
}
|
|
private:
|
|
|
|
};
|
|
|
|
|
|
#pragma once
|