#include "DetectFunc.h"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"
#include <algorithm>
using pcb::UserConfig;
using pcb::RuntimeParams;
using std::vector;
using std::string;
using std::cout;
using std::to_string;
using cv::Mat;
using cv::Point;
using cv::Point2i;
using cv::Point2f;
using cv::Vec4i;
using cv::Rect;
using cv::Size;
using cv::Scalar;
using cv::Ptr;
using cv::KeyPoint;
using cv::DMatch;
using cv::FlannBasedMatcher;
using cv::xfeatures2d::SURF;
using cv::xfeatures2d::SIFT;
using cv::BFMatcher;
#define M_PI 3.14159265358979323846 // pi
DetectFunc::DetectFunc()
{
adminConfig = Q_NULLPTR; //系统参数
userConfig = Q_NULLPTR; //用户参数
runtimeParams = Q_NULLPTR; //运行参数
}
DetectFunc::~DetectFunc()
{
qDebug() << "~DetectFunc";
}
bool DetectFunc::alignImages_surf_load(vector<KeyPoint> &keypoints_1, Mat& descriptors_1, Mat &image_sample_gray, Mat &imgReg, Mat &H, Mat &imMatches)
{
Ptr<SURF> detector = SURF::create(100, 4, 4, true, true);
std::vector<KeyPoint> keypoints_2;
Mat descriptors_2;
double t1 = clock();
cv::Mat pyr;
cv::Size sz = image_sample_gray.size();
pyrDown(image_sample_gray, pyr);
if (userConfig->matchingAccuracyLevel == 2)//低精度
{
pyrDown(pyr, pyr);
}
detector->detectAndCompute(pyr, Mat(), keypoints_2, descriptors_2);
double t2 = clock();
cout << "获取特征点时间" << double(t2 - t1) / CLOCKS_PER_SEC << endl;
Ptr<cv::flann::IndexParams> indexParams = new cv::flann::KDTreeIndexParams(5);
Ptr<cv::flann::SearchParams> searchParams;
FlannBasedMatcher matcher(indexParams);
vector<DMatch> matches;
vector<vector<DMatch>> m_knnMatches;
/*const float minRatio = 1.f / 1.5f;*/
const float minRatio = 0.7;
matcher.knnMatch(descriptors_1, descriptors_2, m_knnMatches, 2);
////Mat outImg;
////drawKeypoints(image_template, keypoints_1, image_template,cv::Scalar::all(-1),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////imwrite("outImg.jpg", image_template);
std::sort(m_knnMatches.begin(), m_knnMatches.end(), [](const vector<DMatch> &m1, const vector<DMatch> &m2) {return m1[0].distance < m2[0].distance; });
for (int i = 0; i < m_knnMatches.size(); i++)
{
const DMatch& bestMatch = m_knnMatches[i][0];
const DMatch& betterMatch = m_knnMatches[i][1];
if (bestMatch.distance < 0.7*betterMatch.distance)
{
matches.push_back(bestMatch);
}
}
vector< DMatch > good_matches;
if (!matches.size())
{
cout << "matches is empty! " << endl;
}
else if (matches.size() < 4)
{
cout << matches.size() << " points matched is not enough " << endl;
}
else //单应性矩阵的计算最少得使用4个点
{
for (int i = 0; i < matches.size(); i++)
{
good_matches.push_back(matches[i]);
}
vector<Point2f> temp_points;
vector<Point2f> samp_points;
for (int i = 0; i < matches.size(); i++)
{
temp_points.push_back(keypoints_1[matches[i].queryIdx].pt);
samp_points.push_back(keypoints_2[matches[i].trainIdx].pt);
}
double t3 = clock();
cout << "匹配并获取变换矩阵时间" << double(t3 - t2) / CLOCKS_PER_SEC << endl;
H = findHomography(samp_points, temp_points, cv::RANSAC, 5.0);
int matrixAdj = 2 * (userConfig->matchingAccuracyLevel);
H.at<double>(0, 2) *= matrixAdj;
H.at<double>(1, 2) *= matrixAdj;
H.at<double>(2, 0) /= matrixAdj;
H.at<double>(2, 1) /= matrixAdj;
warpPerspective(image_sample_gray, imgReg, H, image_sample_gray.size());
}
return true;
}
bool DetectFunc::alignImages_sift_load(std::vector<cv::KeyPoint>& keypoints_1, cv::Mat & descriptors_1, cv::Mat & image_sample_gray, cv::Mat & imgReg, cv::Mat & H, cv::Mat & imMatches)
{
Ptr<SIFT> detector = SIFT::create();
std::vector<KeyPoint> keypoints_2;
Mat descriptors_2;
double t1 = clock();
cv::Mat pyr;
cv::Size sz = image_sample_gray.size();
pyrDown(image_sample_gray, pyr);
if (userConfig->matchingAccuracyLevel == 2)//低精度
{
pyrDown(pyr, pyr);
}
detector->detectAndCompute(pyr, Mat(), keypoints_2, descriptors_2);
double t2 = clock();
cout << "获取特征点时间" << double(t2 - t1) / CLOCKS_PER_SEC << endl;
BFMatcher matcher;
vector<DMatch> matches;
vector<vector<DMatch>> m_knnMatches;
/*const float minRatio = 1.f / 1.5f;*/
const float minRatio = 0.7;
matcher.knnMatch(descriptors_1, descriptors_2, m_knnMatches, 2);
////Mat outImg;
////drawKeypoints(image_template, keypoints_1, image_template,cv::Scalar::all(-1),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////imwrite("outImg.jpg", image_template);
std::sort(m_knnMatches.begin(), m_knnMatches.end(), [](const vector<DMatch> &m1, const vector<DMatch> &m2) {return m1[0].distance < m2[0].distance; });
for (int i = 0; i < m_knnMatches.size(); i++)
{
const DMatch& bestMatch = m_knnMatches[i][0];
const DMatch& betterMatch = m_knnMatches[i][1];
if (bestMatch.distance < 0.7*betterMatch.distance)
{
matches.push_back(bestMatch);
}
}
vector< DMatch > good_matches;
if (!matches.size())
{
cout << "matches is empty! " << endl;
}
else if (matches.size() < 4)
{
cout << matches.size() << " points matched is not enough " << endl;
}
else //单应性矩阵的计算最少得使用4个点
{
for (int i = 0; i < matches.size(); i++)
{
good_matches.push_back(matches[i]);
}
vector<Point2f> temp_points;
vector<Point2f> samp_points;
for (int i = 0; i < matches.size(); i++)
{
temp_points.push_back(keypoints_1[matches[i].queryIdx].pt);
samp_points.push_back(keypoints_2[matches[i].trainIdx].pt);
}
double t3 = clock();
cout << "匹配并获取变换矩阵时间" << double(t3 - t2) / CLOCKS_PER_SEC << endl;
H = findHomography(samp_points, temp_points, cv::RANSAC, 5.0);
int matrixAdj = 2 * (userConfig->matchingAccuracyLevel);
H.at<double>(0, 2) *= matrixAdj;
H.at<double>(1, 2) *= matrixAdj;
H.at<double>(2, 0) /= matrixAdj;
H.at<double>(2, 1) /= matrixAdj;
warpPerspective(image_sample_gray, imgReg, H, image_sample_gray.size());
}
return true;
}
Mat DetectFunc::myThresh(int curCol, int curRow, const cv::Mat & grayImg, cv::Point point_left, cv::Point point_right,int segThresh, bool threshFlag)
{
int totalCol = runtimeParams->nCamera - 1;//从0开始
int totalRow = runtimeParams->nPhotographing - 1;
Mat res = Mat::zeros(grayImg.size(), CV_8UC1);
Rect rect;//roi区域
if (curCol == 0 && curRow == 0)//左上
{
rect.x = point_left.x;
rect.y = point_right.y;
rect.width = grayImg.cols - point_left.x;
rect.height = grayImg.rows - point_right.y;
}
else if (curCol == 0 && curRow == totalRow)//左下
{
rect.x = point_left.x;
rect.y = 0;
rect.width = grayImg.cols - point_left.x;
rect.height = point_left.y;
}
else if (curCol == totalCol && curRow == 0)//右上
{
rect.x = 0;
rect.y = point_right.y;
rect.width = point_right.x;
rect.height = grayImg.rows - point_right.y;
}
else if (curCol == totalCol && curRow == totalRow)//右下
{
rect.x = 0;
rect.y = 0;
rect.width = point_right.x;
rect.height = point_left.y;
}
else if (curCol == 0 && 0 < curRow && curRow < totalRow)//左边
{
rect.x = point_left.x;
rect.y = 0;
rect.width = grayImg.cols - point_left.x;
rect.height = grayImg.rows;
}
else if (curCol == totalCol && 0 < curRow && curRow < totalRow)//右边
{
rect.x = 0;
rect.y = 0;
rect.width = point_right.x;
rect.height = grayImg.rows;
}
else if (curRow == 0 && 0 < curCol && curCol < totalCol)//上边
{
rect.x = 0;
rect.y = point_right.y;
rect.width = grayImg.cols;
rect.height = grayImg.rows - point_right.y;
}
else if (curRow == totalRow && 0 < curCol && curCol < totalCol)//下边
{
rect.x = 0;
rect.y = 0;
rect.width = grayImg.cols;
rect.height = point_left.y;
}
else if (0 < curCol && curCol < totalCol && 0 < curRow && curRow < totalRow)//中央
{
rect.x = 0;
rect.y = 0;
rect.width = grayImg.cols;
rect.height = grayImg.rows;
}
//int longSize = std::max(rect.width, rect.height);
//int blockSize = longSize / 4 * 2 + 1;
//cv::adaptiveThreshold(grayImg(rect), res(rect), 255, cv::ADAPTIVE_THRESH_MEAN_
基于OpenCV+Qt5开发的PCB缺陷检测系统源码(本科毕设).zip
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基于OpenCV+Qt5开发的PCB缺陷检测系统源码(本科毕设).zip （159个子文件） 
.admin.config 2KB .admin.config 2KB .admin.config 602B .user.config 580B .user.config 539B .admin.config 511B .user.config 448B .user.config 448B DetectFunc.cpp 62KB CameraControler.cpp 52KB Configurator.cpp 35KB DetectUI.cpp 25KB ExtractUI.cpp 25KB RecheckUI.cpp 22KB TemplSettingUI.cpp 19KB SettingUI.cpp 17KB DetectUnit.cpp 15KB ExtractFunc.cpp 14KB MotionControler.cpp 13KB PCBDetect.cpp 13KB RuntimeParams.cpp 13KB DefectDetecter.cpp 13KB TemplateExtractor.cpp 10KB SysInitThread.cpp 9KB AdminSettingUI.cpp 7KB ImgConvertThread.cpp 5KB LaunchUI.cpp 5KB pcbFuncLib.cpp 4KB ImageConverter.cpp 4KB FlickeringArrow.cpp 4KB FileSender.cpp 3KB PassWordUI.cpp 2KB ProductID.cpp 1KB DetectThread.cpp 926B FileSyncThread.cpp 788B ExtractThread.cpp 707B main.cpp 212B mfc100d.dll 8.71MB libtesseract-4.dll 8.61MB pvt.cppan.demo.danbloomberg.leptonica-1.74.1.dll 4.66MB msvcr100d.dll 1.77MB user32.dll 1.55MB pvt.cppan.demo.webp-0.6.0.dll 1.2MB MFC42D.DLL 908KB pvt.cppan.demo.tiff-4.0.7.dll 885KB MFCO42D.DLL 780KB pvt.cppan.demo.openjpeg.openjp2-2.1.2.dll 591KB pvt.cppan.demo.jpeg-9.2.0.dll 538KB pvt.cppan.demo.xz_utils.lzma-5.2.2.dll 446KB pvt.cppan.demo.png-1.6.28.dll 412KB MSVCRTD.DLL 376KB NETDLL.dll 224KB pvt.cppan.demo.madler.zlib-1.2.11.dll 204KB MOTIONDLL.dll 138KB PCBDetect.vcxproj.filters 8KB PCBDetect.vcxproj.filters 7KB PCBDetect.vcxproj.filters 7KB PCBDetect.vcxproj.filters 6KB Configurator.h 8KB CameraControler.h 5KB AMC98C.h 5KB MOTION_DLL.h 4KB TemplSettingUI.h 4KB DetectUI.h 4KB RuntimeParams.h 4KB ExtractUI.h 4KB RecheckUI.h 3KB DetectFunc.h 3KB DetectUnit.h 3KB DefectDetecter.h 3KB MotionControler.h 3KB SettingUI.h 2KB PCBDetect.h 2KB pcbFuncLib.h 2KB ImgConvertThread.h 2KB ExtractFunc.h 2KB LaunchUI.h 2KB SysInitThread.h 1KB TemplateExtractor.h 1KB AdminSettingUI.h 1KB FileSender.h 1KB ImageConverter.h 1KB FlickeringArrow.h 1KB DetectThread.h 1KB ExtractThread.h 933B FileSyncThread.h 803B ProductID.h 615B PassWordUI.h 526B MVCAMSDK.lib 82KB MVCAMSDK_X64.lib 77KB NETDLL.lib 14KB MOTIONDLL.lib 8KB 项目说明.md 4KB
screen2.png 1.05MB screen.png 935KB red.png 40KB grey.png 35KB green.png 34KB set.png 14KB exit.png 13KB共 159 条
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