bool findMatch(CvPoint &offset, FlannBasedMatcher matcher, SurfFeatureDetector detector, SurfDescriptorExtractor extractor, Mat des_object[])
{
bool noMatch = true;
Mat des_image, img_matches;
vector<KeyPoint> kp_image;
vector<vector<DMatch > > matches;
vector<DMatch > good_matches;
int iter = 0;
Mat image = imread("/home/pi/opencv/photo.jpg" , CV_LOAD_IMAGE_GRAYSCALE );
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
while ( noMatch )
{
//printf("before kp and des detection 2\n");
matcher.knnMatch(des_object[iter], des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//printf("Number of matches: %d\n", good_matches.size());
if (good_matches.size() >= 10)
{
CvPoint center = cvPoint(0,0);
for ( int z = 0 ; z < good_matches.size() ; z++ )
{
int index = good_matches.at(z).trainIdx;
center.x += kp_image.at(index).pt.x;
center.y += kp_image.at(index).pt.y;
}
center.x = center.x/good_matches.size();
center.y = center.y/good_matches.size();
int radius = 5;
circle( image, center, radius, {0,0,255}, 3, 8, 0 );
namedWindow("test");
imshow("test", image);
imwrite("centerPoint.jpg", image);
waitKey(5000);
int offsetX = center.x - image.cols/2;
int offsetY = center.y - image.rows/2;
offset = cvPoint(offsetX, offsetY);
noMatch = false;
}
//printf("draw good matches\n");
//Show detected matches
if ( iter++ == 3 || !noMatch )
break;
good_matches.clear();
}
return noMatch;
}
void tryFindImage_features(Mat input)
{
/* Сравниваем входящее изрображение с набором эталонов и выбираем наиболее подходящее */
resize(input, input, Size(SIGN_SIZE, SIGN_SIZE), 0, 0);
vector<KeyPoint> keyPoints;
_detector.detect(input, keyPoints);
Mat descriptors;
_extractor.compute(input, keyPoints, descriptors);
int max_value = 0, max_position = 0;
for(int i=0; i < 5; i++)
{
vector< vector<DMatch> > matches;
_matcher.knnMatch(descriptors, _train_descriptors[i], matches, 50);
int good_matches_count = 0;
for (size_t j = 0; j < matches.size(); ++j)
{
if (matches[j].size() < 2)
continue;
const DMatch &m1 = matches[j][0];
const DMatch &m2 = matches[j][1];
if(m1.distance <= 0.7 * m2.distance)
good_matches_count++;
}
if(good_matches_count > max_value)
{
max_value = good_matches_count;
max_position = i;
}
}
cout << STATUS_STR << "Detected sign: " << _train_sign_names[max_position] << endl;
}
//static void matchDescriptors( const Mat& queryDescriptors, const vector<Mat>& trainDescriptors,
//vector<DMatch>& matches, FlannBasedMatcher& descriptorMatcher )
static void matchDescriptors( const Mat& queryDescriptors, const vector<Mat>& trainDescriptors,
vector<DMatch>& matches, FlannBasedMatcher& descriptorMatcher, const vector<Mat>& trainImages, const vector<string>& trainImagesNames )
{
cout << "< Set train descriptors collection in the matcher and match query descriptors to them..." << endl;
descriptorMatcher.add( trainDescriptors );
descriptorMatcher.train();
descriptorMatcher.match( queryDescriptors, matches );
CV_Assert( queryDescriptors.rows == (int)matches.size() || matches.empty() );
cout << "Number of matches: " << matches.size() << endl;
cout << ">" << endl;
for( int i = 0; i < trainDescriptors.size(); i++){
std::vector< std::vector< DMatch> > matches2;
std::vector< DMatch > good_matches;
descriptorMatcher.knnMatch( queryDescriptors, trainDescriptors[i], matches2, 2);
CV_Assert( queryDescriptors.rows == (int)matches2.size() || matches2.empty() );
for (int j = 0; j < matches2.size(); ++j){
const float ratio = 0.8; // As in Lowe's paper; can be tuned
if (matches2[j][0].distance < ratio * matches2[j][1].distance){
good_matches.push_back(matches2[j][0]);
}
}
cout << "currentMatchSize : " << good_matches.size() << endl;
}
}
//--------------------------------------【main( )函数】-----------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始执行
//-----------------------------------------------------------------------------------------------
int main( )
{
//【0】改变console字体颜色
system("color 6F");
void ShowHelpText();
//【1】载入图像、显示并转化为灰度图
Mat trainImage = imread("1.jpg"), trainImage_gray;
imshow("原始图",trainImage);
cvtColor(trainImage, trainImage_gray, CV_BGR2GRAY);
//【2】检测Surf关键点、提取训练图像描述符
vector<KeyPoint> train_keyPoint;
Mat trainDescriptor;
SurfFeatureDetector featureDetector(80);
featureDetector.detect(trainImage_gray, train_keyPoint);
SurfDescriptorExtractor featureExtractor;
featureExtractor.compute(trainImage_gray, train_keyPoint, trainDescriptor);
//【3】创建基于FLANN的描述符匹配对象
FlannBasedMatcher matcher;
vector<Mat> train_desc_collection(1, trainDescriptor);
matcher.add(train_desc_collection);
matcher.train();
//【4】创建视频对象、定义帧率
VideoCapture cap(0);
unsigned int frameCount = 0;//帧数
//【5】不断循环，直到q键被按下
while(char(waitKey(1)) != 'q')
{
//<1>参数设置
int64 time0 = getTickCount();
Mat testImage, testImage_gray;
cap >> testImage;//采集视频到testImage中
if(testImage.empty())
continue;
//<2>转化图像到灰度
cvtColor(testImage, testImage_gray, CV_BGR2GRAY);
//<3>检测S关键点、提取测试图像描述符
vector<KeyPoint> test_keyPoint;
Mat testDescriptor;
featureDetector.detect(testImage_gray, test_keyPoint);
featureExtractor.compute(testImage_gray, test_keyPoint, testDescriptor);
//<4>匹配训练和测试描述符
vector<vector<DMatch> > matches;
matcher.knnMatch(testDescriptor, matches, 2);
// <5>根据劳氏算法（Lowe's algorithm），得到优秀的匹配点
vector<DMatch> goodMatches;
for(unsigned int i = 0; i < matches.size(); i++)
{
if(matches[i][0].distance < 0.6 * matches[i][1].distance)
goodMatches.push_back(matches[i][0]);
}
//<6>绘制匹配点并显示窗口
Mat dstImage;
drawMatches(testImage, test_keyPoint, trainImage, train_keyPoint, goodMatches, dstImage);
imshow("匹配窗口", dstImage);
//<7>输出帧率信息
cout << "当前帧率为：" << getTickFrequency() / (getTickCount() - time0) << endl;
}
return 0;
}
/** @function main */
int matchKeypoints( int argc, char** argv )
{
// if( argc != 3 )
// { readme(); return -1; }
cv::initModule_nonfree();
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
Codebook codebook;
//codebook.readInCSV(string(argv[3]));
if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 15000;
//SurfFeatureDetector detector( minHessian);
SURF* detector = new SURF(minHessian,1,4,true,true);
std::vector<KeyPoint> keypoints_1, keypoints_2;
assert(img_1.size[0]>0 && img_1.size[1]>0 && img_2.size[0]>0 && img_2.size[1]>0);
(*detector)( img_1, Mat(), keypoints_1 );
(*detector)( img_2, Mat(), keypoints_2 );
Mat img_keypoints_1; Mat img_keypoints_2;
// drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
// drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
cvtColor(img_1,img_keypoints_1,CV_GRAY2RGB);
for (KeyPoint k :keypoints_1)
{
// circle(img_keypoints_1,k.pt,k.size,Scalar(rand()%256,rand()%256,rand()%256));
// cout<<k.size<<endl;
Rect rec(k.pt.x-(k.size/2),k.pt.y-(k.size/2),k.size,k.size);
rectangle(img_keypoints_1,rec,Scalar(rand()%256,rand()%256,rand()%256));
}
cvtColor(img_2,img_keypoints_2,CV_GRAY2RGB);
for (KeyPoint k :keypoints_2)
{
// circle(img_keypoints_2,k.pt,k.size,Scalar(rand()%256,rand()%256,rand()%256));
Rect rec(k.pt.x-(k.size/2),k.pt.y-(k.size/2),k.size,k.size);
rectangle(img_keypoints_2,rec,Scalar(rand()%256,rand()%256,rand()%256));
}
//-- Show detected (drawn) keypoints
imshow("Keypoints 1", img_keypoints_1 );
imshow("Keypoints 2", img_keypoints_2 );
waitKey(0);
//-- Step 2: Calculate descriptors (feature vectors)
//SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;
detector->compute( img_1, keypoints_1, descriptors_1 );
detector->compute( img_2, keypoints_2, descriptors_2 );
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< std::vector< DMatch > > matches;
matcher.knnMatch( descriptors_1, descriptors_2, matches, 10 );
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < matches.size(); i++ )
{
for (int j=0; j < matches[i].size(); j++)
{
double dist = matches[i][j].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
}
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
//-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
//-- small)
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
for( int i = 0; i < matches.size(); i++ )
{
for (int j=0; j < matches[i].size(); j++)
//if( matches[i][j].distance <= max(2*min_dist, 0.02) )
if( matches[i][j].distance <= max((max_dist-min_dist)/4.0 + min_dist, 0.02) )
{ good_matches.push_back( matches[i][j]); }
else
printf("discard(%d,%d)\n",i,j);
}
//-- Draw only "good" matches
Mat img_matches;
drawMatches( img_1, keypoints_1, img_2, keypoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Show detected matches
imshow( ".... Matches", img_matches );
for( int i = 0; i < (int)good_matches.size(); i++ )
{ printf( "-- .... Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
waitKey(0);
// vector<Point2f> corners;
// double qualityLevel = 0.01;
// double minDistance = 10;
// int blockSize = 3;
// bool useHarrisDetector = false;
// double k = 0.04;
// int maxCorners = 23;
// int maxTrackbar = 100;
// goodFeaturesToTrack( src_gray,
// corners,
// maxCorners,
// qualityLevel,
// minDistance,
// Mat(),
// blockSize,
// useHarrisDetector,
// k );
return 0;
}
int main(int argc, char** argv)
{
sourceORIG = imread( argv[1] );
sourceORIG2 = imread( argv[2] );
cvtColor( sourceORIG, sourceGRAY, CV_BGR2GRAY );
cvtColor( sourceORIG2, sourceGRAY2, CV_BGR2GRAY );
GaussianBlur( sourceGRAY, sourceGRAY_BLUR, Size(3,3), 3.0 );
GaussianBlur( sourceGRAY2, sourceGRAY_BLUR2, Size(7,7), 3.0 );
Canny( sourceGRAY_BLUR, cannyOut, lowThreshold, lowThreshold*ratio, kernel_size );
cv::dilate(cannyOut, cannyOut, cv::Mat(), cv::Point(-1,-1));
findContours( cannyOut, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
drawing = Mat::zeros( cannyOut.size(), CV_8UC3 );
vector<Point> approxShape;
for(size_t i = 0; i < contours.size(); i++) {
approxPolyDP(contours[i], approxShape, 5, true);
drawContours(drawing, contours, i, Scalar(255, 255, 255), CV_FILLED);
}
bitwise_not ( drawing, drawing );
vector<vector<Point> > contours_poly( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{
double area = contourArea(contours[i]);
if(area > maxArea) {
maxArea = area;
approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
boundRect = boundingRect( Mat(contours_poly[i]) );
}
}
drawing = drawing(boundRect);
mytemplate = drawing;
// http://stackoverflow.com/questions/24539273/how-to-find-out-how-good-a-match-is-in-opencv
// GaussianBlur( mytemplate, mytemplate, Size(7,7), 3.0 );
detector.detect( mytemplate, keypointsTMPL );
detector.detect( sourceGRAY_BLUR2, keypointsSCENE );
extractor.compute( mytemplate, keypointsTMPL, descriptorsTMPL );
extractor.compute( sourceGRAY_BLUR2, keypointsSCENE, descriptorsSCENE );
obj_corners[0] = (cvPoint(0,0));
obj_corners[1] = (cvPoint(mytemplate.cols,0));
obj_corners[2] = (cvPoint(mytemplate.cols,mytemplate.rows));
obj_corners[3] = (cvPoint(0, mytemplate.rows));
if(descriptorsSCENE.type()!=CV_32F) {
descriptorsSCENE.convertTo(descriptorsSCENE, CV_32F);
}
if(descriptorsTMPL.type()!=CV_32F) {
descriptorsTMPL.convertTo(descriptorsTMPL, CV_32F);
}
// if ( descriptorsTMPL.empty() )
// cvError(0,"MatchFinder","1st descriptor empty",__FILE__,__LINE__);
// if ( descriptorsSCENE.empty() )
// cvError(0,"MatchFinder","2nd descriptor empty",__FILE__,__LINE__);
matcher.knnMatch( descriptorsTMPL, descriptorsSCENE, matches, 2);
for(int i = 0; i < cv::min(sourceGRAY_BLUR2.rows-1,(int) matches.size()); i++) {
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0)) {
good_matches.push_back(matches[i][0]);
cout << "GOOD MATCHES" << endl;
}
cout << "MATCHES: " << matches[i].size() << endl;
}
// for( int i = 0; i < descriptorsTMPL.rows; i++ )
// {
// if( matches[i][0].distance < 100 )
// {
// good_matches.push_back( matches[i][0]);
// }
// }
drawMatches( sourceGRAY_BLUR2, keypointsSCENE, mytemplate, keypointsTMPL, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4) {
for( int i = 0; i < good_matches.size(); i++ ) {
//Get the keypoints from the good matches
obj.push_back( keypointsTMPL[ good_matches[i].queryIdx ].pt );
scene.push_back( keypointsSCENE[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0], scene_corners[1], cvScalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1], scene_corners[2], cvScalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2], scene_corners[3], cvScalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3], scene_corners[0], cvScalar( 0, 255, 0), 4 );
}
imshow( "Good Matches & Object detection", img_matches );
// UIImage *resultimage = [UIImage imageWithCVMat:img_matches];
// UIImageView *imageview = [[UIImageView alloc] initWithImage:resultimage];
// imageview.frame = CGRectMake(0, 0, 320, 240);
// UIImage *resultimage2 = [UIImage imageWithCVMat:mytemplate];
// UIImageView *imageview2 = [[UIImageView alloc] initWithImage:resultimage2];
// imageview2.frame = CGRectMake(0, 240, 320, 240);
// GaussianBlur( sourceGRAY2, sourceGRAY_BLUR2, Size(7,7), 3.0 );
// imshow( "image", sourceGRAY_BLUR2 );
// track();
// imshow("image", sourceGRAY2);
// imshow(windowName,drawing);
waitKey(0);
return 0;
}
/** @function main */
int main(int argc, char** argv)
{
string filepath = "..\\Images\\IMG_20150313_152723.jpg";
bool vis = TRUE;
double margin = 0.1;
if (argc > 1){
filepath = argv[1];
string visualise = argv[2];
if (visualise == "1"){
vis = TRUE;
}
string marg = argv[3];
margin = ::atof(marg.c_str());
}
//string filepath = "..\\Images\\IMG_20150313_152723.jpg";
////////////////
Mat sceneMat = imread("..\\Images\\cover.jpg", IMREAD_GRAYSCALE);
sceneMat = imread(filepath, IMREAD_GRAYSCALE);
//objectMat = imread("..\\Images\\Photos\\IMG_20150311_154958.jpg", IMREAD_GRAYSCALE);
std::vector<KeyPoint> keypointsO; //keypoints for object
std::vector<KeyPoint> keypointsS; //keypoints for scene
//Descriptor matrices
Mat descriptors_object, descriptors_scene;
SurfFeatureDetector surf(1500);
surf.detect(sceneMat, keypointsS);
SurfDescriptorExtractor extractor;
extractor.compute(sceneMat, keypointsS, descriptors_scene);
printf("Keypoints in foto: %i \n", keypointsS.size());
if (vis){
cv::Mat output;
cv::drawKeypoints(sceneMat, keypointsS, output);
imshow("Keypoints on photo", output);
waitKey(0);
}
int most_amount_of_matches_overall = 0;
vector<std::string> folder_names = get_all_folder_names_within_folder("..\\Images\\Photos\\");
//vector<std::string> folder_names = get_all_folder_names_within_folder("C:\\temp\\");
vector< vector<int> > good_matches_per_folder(folder_names.size() - 2);
for (int i = 2; i < folder_names.size(); i++) {
std::string fon = folder_names[i];
printf("-- Folder : %s \n", fon.c_str());
char folder_path[80];
strcpy_s(folder_path, "..\\Images\\Photos\\");
strcat_s(folder_path, fon.c_str());
strcat_s(folder_path, "\\");
puts(folder_path);
vector<std::string> files_names = get_all_files_names_within_folder(folder_path);
for (int j = 0; j < files_names.size(); j++) {
std::string fn = files_names[j];
printf("-- Max dist : %s \n", fn.c_str());
char object_path[80];
strcpy_s(object_path, "..\\Images\\Photos\\");
strcat_s(object_path, fon.c_str());
strcat_s(object_path, "\\");
strcat_s(object_path, fn.c_str());
puts(object_path);
// Mat img_object = imread(object_path, IMREAD_GRAYSCALE);
Mat objectMat = imread(object_path, IMREAD_GRAYSCALE);
if (!objectMat.data || !sceneMat.data)
{
std::cout << " --(!) Error reading images " << std::endl; return -1;
}
//detector.detect(img_object, keypoints_object);
surf.detect(objectMat, keypointsO);
extractor.compute(objectMat, keypointsO, descriptors_object);
//Declering flann based matcher
FlannBasedMatcher matcher;
//BFMatcher for SURF algorithm can be either set to NORM_L1 or NORM_L2.
//But if you are using binary feature extractors like ORB, instead of NORM_L* you use "hamming"
// BFMatcher matcher(NORM_L1);
vector< vector< DMatch >> matches;
matcher.knnMatch(descriptors_object, descriptors_scene, matches, 2); // find the 2 nearest neighbors
vector< DMatch > good_matches;
good_matches.reserve(matches.size());
float nndrRatio = 0.7f;
for (size_t i = 0; i < matches.size(); ++i)
{
if (matches[i].size() < 2)
continue;
const DMatch &m1 = matches[i][0];
const DMatch &m2 = matches[i][1];
if (m1.distance <= nndrRatio * m2.distance)
good_matches.push_back(m1);
}
printf("-- Amount of good matches : %d \n", good_matches.size());
good_matches_per_folder[i - 2].push_back(good_matches.size());
if (good_matches.size() > most_amount_of_matches_overall){
most_amount_of_matches_overall = good_matches.size();
}
if (vis){
Mat img_matches;
drawMatches(objectMat, keypointsO, sceneMat, keypointsS,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
imshow("Good Matches & Object detection", img_matches);
waitKey(0);
}
}
}
vector<int> amount_of_good_matches_per_folder(folder_names.size() - 2);
for (int i = 0; i < good_matches_per_folder.size(); ++i)
{
vector<int> matches = good_matches_per_folder[i];
int good_matches = 0;
for (int j = 0; j < matches.size(); ++j)
{
if (matches[j]>0.8*most_amount_of_matches_overall){
good_matches = good_matches + 1;
}
}
amount_of_good_matches_per_folder[i] = good_matches;
}
if (most_amount_of_matches_overall > keypointsS.size()*margin){
auto biggest = std::max_element(std::begin(amount_of_good_matches_per_folder), std::end(amount_of_good_matches_per_folder));
std::cout << "Max element is " << *biggest
<< " at position " << std::distance(std::begin(amount_of_good_matches_per_folder), biggest) << std::endl;
int best = std::distance(std::begin(amount_of_good_matches_per_folder), biggest);
std::cout << "Best match is " << folder_names[best + 2];
}
else{
std::cout << "No match found";
}
return 0;
}
int main()
{
//for serial port
int fd = open_port();
fd_set readfs;
if (fd == -1)
{
std::cout << "Could not open /dev/ttyUSB1" << std::endl;
return -1;
}
struct timeval Timeout;
Timeout.tv_usec = 0; /* milliseconds */
Timeout.tv_sec = 0; /* seconds */
//for stereo
int w=640, h=480;
Mat M1, M2, D1, D2, R1, R2, P1, P2, mx1, mx2, my1, my2, Q;
FileStorage fs;
fs.open("intrinsics.xml", FileStorage::READ);
fs["M1"] >> M1;
fs["D1"] >> D1;
fs["M2"] >> M2;
fs["D2"] >> D2;
fs.release();
fs.open("extrinsics.xml", FileStorage::READ);
fs["R1"] >> R1;
fs["P1"] >> P1;
fs["R2"] >> R2;
fs["P2"] >> P2;
fs["Q"] >> Q;
fs.release();
Q.convertTo(Q, CV_32F);
initUndistortRectifyMap(M1, D1, R1, P1, Size(w, h), CV_32FC1, mx1, my1);
initUndistortRectifyMap(M2, D2, R2, P2, Size(w, h), CV_32FC1, mx2, my2);
int ndisp = 192; //288 96 192 48
StereoBM bm(StereoBM::BASIC_PRESET);
bm.state->preFilterCap = 31;
bm.state->SADWindowSize = 41;
bm.state->minDisparity = 48;
bm.state->numberOfDisparities = ndisp;
bm.state->textureThreshold = 10;
bm.state->uniquenessRatio = 15;
bm.state->speckleWindowSize = 100;
bm.state->speckleRange = 32;
bm.state->disp12MaxDiff = 1;
//for SURF
Mat object = imread("photo20.jpg", CV_LOAD_IMAGE_GRAYSCALE);
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0);
obj_corners[1] = cvPoint(object.cols, 0);
obj_corners[2] = cvPoint(object.cols, object.rows);
obj_corners[3] = cvPoint(0, object.rows);
SurfFeatureDetector detector(400);
SurfDescriptorExtractor extractor;
FlannBasedMatcher matcher;
std::vector<KeyPoint> kp_object;
Mat des_object;
detector.detect(object, kp_object);
extractor.compute(object, kp_object, des_object);
//miscellaneous
char key = 'a'; int framecount = 0, count = 0;
Camera cl("/dev/video2", w, h, 20);
Camera cr("/dev/video1", w, h, 20);
namedWindow("disparity");
namedWindow("object detection");
int window = 25, offset = 125; //35 pixels on either side, centre 25 pixels to the right of 240
bool is_open = false, isforward = false, nointerrupt = false;
while (key != 27)
{
//flags
bool ask = false, left = false, right = false, middle = false, inrange = false, detect = false, stop = false;
//serial receiving
FD_SET(fd, &readfs);
select(fd + 1, &readfs, NULL, NULL, &Timeout);
void *buf; int rx = 0;
if (FD_ISSET(fd, &readfs))
{
rx = read(fd, buf, 1);
tcflush(fd, TCIFLUSH);
ask = true; //isforward = false;
}
char extent = 0; float depth;
IplImage *l=cvCreateImage(Size(w, h), 8, 3);
IplImage *r=cvCreateImage(Size(w, h), 8, 3);
cl.Update(&cr);
if (framecount < 5)
{
framecount++;
continue;
}
cl.toIplImage(l);
cr.toIplImage(r);
Mat lg, rg, lge, rge;
cvtColor(Mat(l), lg, CV_RGB2GRAY);
cvtColor(Mat(r), rg, CV_RGB2GRAY);
equalizeHist(lg, lge);
equalizeHist(rg, rge);
line(lg, Point(w/2 - window + offset, 0), Point(w/2 - window + offset, h), Scalar(0, 255, 0), 2);
line(lg, Point(w/2 + window + offset, 0), Point(w/2 + window + offset, h), Scalar(0, 255, 0), 2);
line(rg, Point(w/2 - window - offset, 0), Point(w/2 - window - offset, h), Scalar(0, 255, 0), 2);
line(rg, Point(w/2 + window - offset, 0), Point(w/2 + window - offset, h), Scalar(0, 255, 0), 2);
Mat des_image, img_matches, H;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
detector.detect(lg, kp_image);
extractor.compute(lg, kp_image, des_image);
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < matches.size(); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if(matches[i].size()==2 && (matches[i][0].distance < 0.8*(matches[i][1].distance)) )
{
good_matches.push_back(matches[i][0]);
obj.push_back(kp_object[matches[i][0].queryIdx].pt);
scene.push_back(kp_image[matches[i][0].trainIdx].pt);
}
}
if (good_matches.size() >= 35)
{
H = findHomography(obj, scene, CV_RANSAC);
perspectiveTransform(obj_corners, scene_corners, H);
RotatedRect box = minAreaRect(scene_corners);
Point2f rect_corners[4];
box.points(rect_corners);
Rect roi = box.boundingRect();
if (roi.area() > 3000)
{
detect = true; count = 0;
for (int i = 0; i < 4; i++)
{
line(lg, rect_corners[i], rect_corners[(i+1)%4], Scalar(255, 255, 255), 4);
}
line(lg, box.center, box.center, Scalar(255, 255, 255), 8);
if (box.center.x < w/2 - window + offset)
{
left = true;
//write(fd, "a", 1);
extent = (w/2 + offset - box.center.x) / 5; //std::cout << int(extent) << std::endl;
if (extent < 6) extent = 6;
if (isforward == true) stop = true;
}
else if (box.center.x > w/2 + window + offset)
{
right = true;
//write(fd, "d", 1);
extent = (box.center.x - (w/2 + offset)) / 5; //std::cout << int(extent) << std::endl;
if (extent < 6) extent = 6;
if (isforward == true) stop = true;
}
else if (box.center.x > w/2 - window + offset && box.center.x < w/2 + window + offset)
{
middle = true;
Mat lr, rr;
//imshow("l", lg); imshow("r", rg);
remap(lge, lr, mx1, my1, INTER_LINEAR);
remap(rge, rr, mx2, my2, INTER_LINEAR);
Mat disp, vdisp, disp32;
bm(lr, rr, disp);
disp.convertTo(vdisp, CV_8U, 255/(ndisp*16.));
disp.convertTo(disp32, CV_32F, 1.f/16.f);
for (int i = 0; i < 4; i++)
{
line(vdisp, rect_corners[i], rect_corners[(i+1)%4], Scalar(255, 255, 255), 4);
}
line(vdisp, box.center, box.center, Scalar(255, 255, 255), 8);
imshow("disparity", vdisp);
Mat xyz;
reprojectImageTo3D(disp32, xyz, Q, true);
int ch[] = {2, 0};
Mat z(xyz.size(), CV_32FC1);
mixChannels(&xyz, 1, &z, 1, ch, 1);
float dist = 0; unsigned long int npts = 0;
for (int i = 0; i < z.rows; i++)
{
for (int j = 0; j < z.cols; j++)
{
if (roi.contains(Point(j, i)) && z.at<float>(i, j) > -500.0 && z.at<float>(i, j) < 500.0)
{
dist += z.at<float>(i, j);
npts++;
}
}
}
//std::cout << -dist/npts << std::endl;
depth = 1.1561 * ((-dist/npts) - 0.124);
std::cout << depth << std::endl;
extent = depth > 0 && depth < 45 ? (char(depth) - 6) / 3 : 2;
if (depth > 0 && depth < 35) inrange = true;
if (depth > 0 && depth < 30) nointerrupt = true;
}
}
}
//decisions and commanding
if (stop && !nointerrupt) {write(fd, "x", 1); write(fd, &extent, 1); isforward = false; std::cout << "stop" << std::endl;}
if (ask)
{
std::cout << "got" << std::endl;
if (left) {write(fd, "a", 1); write(fd, &extent, 1); tcflush(fd, TCIFLUSH);}
if (right) {write(fd, "d", 1); write(fd, &extent, 1); tcflush(fd, TCIFLUSH);}
//if ((middle && !inrange) || (middle && is_open)) {write(fd, "w", 1); write(fd, &extent, 1); tcflush(fd, TCIFLUSH); isforward = true;}
//if (inrange && !is_open){write(fd, "o", 1); write(fd, &extent, 1); is_open = true; std::cout << "open" << std::endl;}
if (middle)
{
if (!is_open) {write(fd, "o", 1); write(fd, &extent, 1); is_open = true; std::cout << "open" << std::endl;}
else {write(fd, "w", 1); write(fd, &extent, 1); tcflush(fd, TCIFLUSH); isforward = true;}
}
if (!detect)
{
count ++;
if (count > 14)
{
count = 0;
if (is_open) {write(fd, "c", 1); write(fd, &extent, 1); is_open = false; nointerrupt = false; std::cout << "close" << std::endl;}
}
}
}
imshow("object detection", lg);
key = cv::waitKey(1);
cvReleaseImage(&l);
cvReleaseImage(&r);
}
return 0;
}
JNIEXPORT bool JNICALL Java_com_example_mipatternrecognition_MainActivity_FindObject(
JNIEnv*, jobject, jlong addrGray, jlong addrRgba, jint TypeDetection) {
Mat& mGr = *(Mat*) addrGray;
Mat& mRgb = *(Mat*) addrRgba;
Mat& objeto = *(Mat*) objeto_long;
int minHessian = 500;
SurfFeatureDetector detector_Surf(minHessian);
SiftFeatureDetector detector_Sift(minHessian);
FastFeatureDetector detector_Fast(50);
OrbFeatureDetector detector_Orb(500, 1.2f, 8, 14, 0, 2, 0, 14);
MserFeatureDetector detector_Mser(5, 60, 14400, 0.25, 0.2, 200, 1.01, 0.003,
5);
int maxCorners = 1000;
double qualityLevel = 0.01;
double minDistance = 1.;
int blockSize = 3;
bool useHarrisDetector;
double k2 = 0.04;
useHarrisDetector = false;
GoodFeaturesToTrackDetector detector_Gftt(maxCorners, qualityLevel,
minDistance, blockSize, useHarrisDetector, k2);
useHarrisDetector = true;
GoodFeaturesToTrackDetector detector_Harris(maxCorners, qualityLevel,
minDistance, blockSize, useHarrisDetector, k2);
int maxSize = 45;
int responseThreshold = 30;
int lineThresholdProjected = 10;
int lineThresholdBinarized = 8;
int suppressNonmaxSize = 5;
StarFeatureDetector detector_Star(maxSize, responseThreshold,
lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize);
//http://stackoverflow.com/questions/14808429/classification-of-detectors-extractors-and-matchers
SurfDescriptorExtractor extractor_Surf;
SiftDescriptorExtractor extractor_Sift;
OrbDescriptorExtractor extractor_Orb;
FREAK extractor_Freak;
switch (TypeDetection) {
case SURF_DETECTION:
detector_Surf.detect(mGr, keyPoints_2);
extractor_Surf.compute(mGr, keyPoints_2, descriptors_2);
break;
case SIFT_DETECTION:
detector_Sift.detect(mGr, keyPoints_2);
extractor_Sift.compute(mGr, keyPoints_2, descriptors_2);
break;
case FAST_DETECTION:
detector_Fast.detect(mGr, keyPoints_2);
extractor_Freak.compute(mGr, keyPoints_2, descriptors_2);
break;
case ORB_DETECTION:
detector_Orb.detect(mGr, keyPoints_2);
extractor_Orb.compute(mGr, keyPoints_2, descriptors_2);
break;
case MSER_DETECTION:
detector_Mser.detect(mGr, keyPoints_2);
extractor_Surf.compute(mGr, keyPoints_2, descriptors_2);
break;
case GFTT_DETECTION:
detector_Gftt.detect(mGr, keyPoints_2);
extractor_Sift.compute(mGr, keyPoints_2, descriptors_2);
break;
case HARRIS_DETECTION:
detector_Harris.detect(mGr, keyPoints_2);
extractor_Orb.compute(mGr, keyPoints_2, descriptors_2);
break;
case STAR_DETECTION:
detector_Star.detect(mGr, keyPoints_2);
extractor_Orb.compute(mGr, keyPoints_2, descriptors_2);
break;
}
if (descriptors_2.rows == 0 || descriptors_1.rows == 0
|| keyPoints_2.size() == 0 || keyPoints_1.size() == 0) {
return false;
}
FlannBasedMatcher matcher;
vector<vector<DMatch> > matches;
matcher.knnMatch(descriptors_1, descriptors_2, matches, 2);
//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
//-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
//-- small)
//-- PS.- radiusMatch can also be used here.
vector<DMatch> good_matches;
for (int i = 0; i < min(descriptors_1.rows - 1, (int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if ((matches[i][0].distance < 0.6 * (matches[i][1].distance))
&& ((int) matches[i].size() <= 2 && (int) matches[i].size() > 0)) {
good_matches.push_back(matches[i][0]);
}
}
char cVal[50];
sprintf(cVal, "%i", good_matches.size());
putText(mRgb, cVal, Point2f(110, 100), FONT_HERSHEY_PLAIN, 2,
Scalar(0, 255, 0, 255), 2);
Mat H;
float reprojectionThreshold = 3;
try {
bool encontrado = refineMatchesWithHomography(keyPoints_1, keyPoints_2,
reprojectionThreshold, good_matches, H);
if (encontrado) {
vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0);
obj_corners[1] = cvPoint(objeto.cols, 0);
obj_corners[2] = cvPoint(objeto.cols, objeto.rows);
obj_corners[3] = cvPoint(0, objeto.rows);
vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
line(mRgb, scene_corners[0], scene_corners[1], Scalar(0, 255, 0),
4);
line(mRgb, scene_corners[1], scene_corners[2], Scalar(255, 0, 0),
4);
line(mRgb, scene_corners[2], scene_corners[3], Scalar(0, 0, 255),
4);
line(mRgb, scene_corners[3], scene_corners[0],
Scalar(255, 255, 255), 4);
for (unsigned int i = 0; i < scene.size(); i++) {
const Point2f& kp = scene[i];
circle(mRgb, Point(kp.x, kp.y), 10, Scalar(255, 0, 0, 255));
}
} else {
char cVal[50];
sprintf(cVal, "%i", good_matches.size());
putText(mRgb, cVal, Point2f(100, 100), FONT_HERSHEY_PLAIN, 2,
Scalar(0, 0, 255, 255), 2);
}
} catch (Exception e) {
}
//else
return false;
}
int main()
{
Mat object = imread( "photo.jpg", CV_LOAD_IMAGE_GRAYSCALE );
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kp_object;
detector.detect( object, kp_object );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( object, kp_object, des_object );
FlannBasedMatcher matcher;
VideoCapture cap(0);
namedWindow("Good Matches");
std::vector<Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
char key = 'a';
int framecount = 0;
while (key != 27)
{
Mat frame;
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
}
//Show detected matches
imshow( "Good Matches", img_matches );
key = waitKey(1);
}
return 0;
}
//Takes Mat object and finds its keypoints, then compares against the keypoints in segmentedCapture
//If there are 4 or more matching keypoints, then it reports a match
bool match(Mat object, IplImage* segmentedCapture, int i)
{
printf("Size check of segmented capture: height: %d, width: %d\n", segmentedCapture->height, segmentedCapture->width);
printf("attempting to read object now\n");
bool matchFound = false;
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
int minHessian = 500;
SurfFeatureDetector detector(minHessian);
//Detect the keypoints using SURF Detector
std::vector<KeyPoint> kp_object;
detector.detect( object, kp_object );
//Calculate descriptors (feature vectors)
Mat des_object;
SurfDescriptorExtractor extractor;
extractor.compute( object, kp_object, des_object );
printf("Number of descriptors found for initial object: %d\n", (int)kp_object.size());
FlannBasedMatcher matcher;
char *windowName = new char[20];
sprintf(windowName, "Match %d", i);
destroyWindow(windowName);
namedWindow(windowName);
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvResetImageROI(segmentedCapture);
printf("creating image to store it in");
// IplImage *image2 = cvCreateImage(cvSize(segmentedCapture->width, segmentedCapture->height), IPL_DEPTH_8U,1);
printf("about to convert to gray\n");
// cvCvtColor(segmentedCapture, image2, CV_BGR2GRAY);
//
// printf("converted to gray\n");
Mat matCon(segmentedCapture);
image = segmentedCapture;
// printf("before detection\n");
detector.detect( image, kp_image );
// printf("after detection, number of descriptors for detected object: %d\n", kp_image.size());
extractor.compute( image, kp_image, des_image );
// printf("after computation of extraction\n");
if(des_image.empty()){
printf("key points from capture frame are empty\n");
} else {
matcher.knnMatch(des_object, des_image, matches, 2);
// matcher.match(des_object, des_image, matches);
printf("after knnmatch: matches.size() is %d\n", matches.size());
for(int j = 0; j < min(des_image.rows-1,(int) matches.size()); j++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[j][0].distance < 0.5*(matches[j][1].distance)) && ((int) matches[j].size()<=2 && (int) matches[j].size()>0))
{
good_matches.push_back(matches[j][0]);
// printf("Outer loop is on: %d, Number of matches is: %d\n", i, (int)good_matches.size());
}
}
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
matchFound = true;
printf("Found %d matched points for detectedObject %d", good_matches.size(), i );
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
}
imshow( windowName, img_matches );
}
return matchFound;
}
bool detectLogo(Mat person, Mat desObject, Mat object, vector<KeyPoint> kpObject, vector<Point2f> objCorners)
{
// scale up the image
resize(person, person, Size(), 4, 4, CV_INTER_CUBIC);
// sharpen the image
Mat image;
GaussianBlur(person, image, cv::Size(0, 0), 3);
addWeighted(person, 1.75, image, -0.75, 0, image);
GaussianBlur(person, image, cv::Size(0, 0), 3);
addWeighted(person, 1.75, image, -0.75, 0, image);
// detect key points in the input frame
vector<KeyPoint> kpFrame;
detector.detect(person, kpFrame);
// extract feature descriptors for the detected key points
Mat desFrame;
extractor.compute(person, kpFrame, desFrame);
if(desFrame.empty() or desObject.empty())
return false;
// match the key points with object
FlannBasedMatcher matcher;
vector< vector <DMatch> > matches;
matcher.knnMatch(desObject, desFrame, matches, 2);
// compute the good matches among the matched key points
vector<DMatch> goodMatches;
for(int i=0; i<desObject.rows; i++)
{
if(matches[i][0].distance < 0.6 * matches[i][1].distance)
{
goodMatches.push_back(matches[i][0]);
}
}
if(goodMatches.size() >= 8)
{
vector<Point2f> obj;
vector<Point2f> scene;
for( int i = 0; i < goodMatches.size(); i++ )
{
// get the keypoints from the good matches
obj.push_back( kpObject[ goodMatches[i].queryIdx ].pt );
scene.push_back( kpFrame[ goodMatches[i].trainIdx ].pt );
}
Mat H;
H = findHomography(obj, scene);
vector<Point2f> sceneCorners(4);
perspectiveTransform( objCorners, sceneCorners, H);
// draw lines between the corners (the mapped object in the scene image )
line(person, sceneCorners[0], sceneCorners[1], Scalar(255, 255, 255), 4);
line(person, sceneCorners[1], sceneCorners[2], Scalar(255, 255, 255), 4);
line(person, sceneCorners[2], sceneCorners[3], Scalar(255, 255, 255), 4);
line(person, sceneCorners[3], sceneCorners[0], Scalar(255, 255, 255), 4);
imshow("Person", person);
cout << "[MESSAGE] LOGO DETECTED" << endl;
return true;
}
return false;
}
int main(int argc, char * argv[])
{
if(argc < 2)
{
std::cout << "Use: tracker <target_image>" << std::endl;
return -1;
}
Mat mTarget = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
if( !mTarget.data )
{
std::cout<< "Error reading target image." << std::endl;
return -1;
}
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kpTarget;
detector.detect( mTarget, kpTarget );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( mTarget, kpTarget, des_object );
FlannBasedMatcher matcher;
//VideoCapture cap("http://192.168.1.200/videostream.cgi?user=admin&pwd=31415LAS&resolution=32&dummy=.mjpg");
VideoCapture cap("http://nidq.no-ip.org/videostream.cgi?user=admin&pwd=31415LAS&resolution=32&dummy=.mjpg");
namedWindow("Capture");
std::vector<Point2f> tgt_corners(4);
//Get the corners from the object
tgt_corners[0] = cvPoint(0,0);
tgt_corners[1] = cvPoint( mTarget.cols, 0 );
tgt_corners[2] = cvPoint( mTarget.cols, mTarget.rows );
tgt_corners[3] = cvPoint( 0, mTarget.rows );
char key = 'a';
int framecount = 0;
while (key != 27)
{
Mat frame;
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
Mat des_image, img_matches;
std::vector<KeyPoint> kpImage;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kpImage );
extractor.compute( image, kpImage, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches( mTarget, kpTarget, image, kpImage, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kpTarget[ good_matches[i].queryIdx ].pt );
scene.push_back( kpImage[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( tgt_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( mTarget.cols, 0), scene_corners[1] + Point2f( mTarget.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( mTarget.cols, 0), scene_corners[2] + Point2f( mTarget.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( mTarget.cols, 0), scene_corners[3] + Point2f( mTarget.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( mTarget.cols, 0), scene_corners[0] + Point2f( mTarget.cols, 0), Scalar( 0, 255, 0), 4 );
}
//Show detected matches
imshow( "Capture", img_matches );
key = waitKey(1);
}
return 0;
}
/*
* @brief コールバック（動画処理の基本呼び出し部分)
*/
void imageCb(const sensor_msgs::ImageConstPtr &msg)
{
cv_bridge::CvImagePtr cap;
Mat in_img;
Mat grayframe;
Mat img_matches;
Mat cap_descriptors;
Mat H;
Mat lastimage;
std::vector<KeyPoint> cap_keypoint;
std::vector<std::vector<DMatch> > matches;
std::vector<DMatch> good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
//Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
FlannBasedMatcher matcher;
try{
cap = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
in_img = cap->image;
}
catch(cv_bridge::Exception& e){
ROS_ERROR("cv_brige exception: %s", e.what());
return;
}
if(in_img.empty()) {
std::cerr<<"No capture frame img"<<std::endl;
}
else{
cvtColor(cap->image, grayframe, CV_BGR2GRAY);
computeSURF(grayframe, cap_keypoint, cap_descriptors);
//drawAKAZEKeypoint(&cap->image, cap_keypoint, Scalar(0, 0, 255));
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( in_img.cols, 0 );
obj_corners[2] = cvPoint( in_img.cols, in_img.rows );
obj_corners[3] = cvPoint( 0, in_img.rows );
std::cout<<imagenum<<std::endl;
matcher.knnMatch(cap_descriptors, descriptors_0, matches, 2);
for(int i = 0; i < min(descriptors_0.rows-1,(int) matches.size()); i++){
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0)){
good_matches.push_back(matches[i][0]);
}
}
drawMatches(grayframe, cap_keypoint, object_img, keypoint_0, good_matches, img_matches);
if(good_matches.size() >= 4){
for(int i = 0; i < good_matches.size(); i++){
obj.push_back(keypoint_0[good_matches[i].queryIdx].pt);
scene.push_back(cap_keypoint[good_matches[i].trainIdx].pt);
}
}
H = findHomography(obj, scene, CV_RANSAC);
(obj_corners, scene_corners, H);
line( img_matches, scene_corners[0] + Point2f( object_img.cols, 0), scene_corners[1] + Point2f( object_img.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object_img.cols, 0), scene_corners[2] + Point2f( object_img.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object_img.cols, 0), scene_corners[3] + Point2f( object_img.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object_img.cols, 0), scene_corners[0] + Point2f( object_img.cols, 0), Scalar( 0, 255, 0), 4 );
#ifdef MULTI
for(int i = 0; i < IMAGENUM; i++){
matcher->match(cap_descriptors, trainDescCollection[i], matches);
drawMatches(cap->image, cap_keypoint, trainImgCollection[i], trainPointCollection[i], matches, img_matches);
}
#endif
}
imshow(OPENCV_WINDOW, img_matches);
waitKey(3);
image_pub_.publish(cap->toImageMsg());
}
void identifyObject( Mat& frame, Mat& object, const string& objectName ) {
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kp_object;
detector.detect( object, kp_object );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( object, kp_object, des_object );
FlannBasedMatcher matcher;
//Get the corners from the object
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
// Match descriptors to frame
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( frame, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 4 );
line( frame, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 4 );
line( frame, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 4 );
line( frame, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 4 );
}
//Show detected matches
Point2f textPoint = cvPoint( (scene_corners[0].x+scene_corners[1].x+scene_corners[2].x+scene_corners[3].x )/4.0 , (scene_corners[0].y+scene_corners[1].y+scene_corners[2].y+scene_corners[3].y )/4.0 );
putText( frame, objectName, textPoint, FONT_HERSHEY_COMPLEX_SMALL, 1.0, cvScalar(0,250,150), 1, CV_AA );
}
int main( int argc, char** argv ) {
if( argc != 3 )
{ readme(); return -1; }
int hessianValue = 400;
Mat objectMat = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat sceneMat = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
if( !objectMat.data || !sceneMat.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
bool objectFound = false;
float nndrRatio = 0.7f;
//vector of keypoints
vector< cv::KeyPoint > keypointsO;
vector< cv::KeyPoint > keypointsS;
Mat descriptors_object, descriptors_scene;
//-- Step 1: Extract keypoints
//SurfFeatureDetector surf(hessianValue);
Ptr<SURF> detector = SURF::create(1000);
detector->detect(sceneMat,keypointsS);
if(keypointsS.size() < 7) return false; //Not enough keypoints, object not found
detector->detect(objectMat,keypointsO);
if(keypointsO.size() < 7) return false; //Not enough keypoints, object not found
//-- Step 2: Calculate descriptors (feature vectors)
//SurfDescriptorExtractor extractor;
Ptr<SURF> extractor = SURF::create(1000);
extractor->compute( sceneMat, keypointsS, descriptors_scene );
extractor->compute( objectMat, keypointsO, descriptors_object );
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
descriptors_scene.size(), keypointsO.size(), keypointsS.size());
std::vector< vector< DMatch > > matches;
matcher.knnMatch( descriptors_object, descriptors_scene, matches, 2 );
vector< DMatch > good_matches;
good_matches.reserve(matches.size());
for (size_t i = 0; i < matches.size(); ++i)
{
if (matches[i].size() < 2)
continue;
const DMatch &m1 = matches[i][0];
const DMatch &m2 = matches[i][1];
if(m1.distance <= nndrRatio * m2.distance)
good_matches.push_back(m1);
}
if( (good_matches.size() >=7))
{
cout << "OBJECT FOUND!" << endl;
std::vector< Point2f > obj;
std::vector< Point2f > scene;
for( unsigned int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj.push_back( keypointsO[ good_matches[i].queryIdx ].pt );
scene.push_back( keypointsS[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector< Point2f > obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( objectMat.cols, 0 );
obj_corners[2] = cvPoint( objectMat.cols, objectMat.rows ); obj_corners[3] = cvPoint( 0, objectMat.rows );
std::vector< Point2f > scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( outImg, scene_corners[0] , scene_corners[1], color, 2 ); //TOP line
line( outImg, scene_corners[1] , scene_corners[2], color, 2 );
line( outImg, scene_corners[2] , scene_corners[3], color, 2 );
line( outImg, scene_corners[3] , scene_corners[0] , color, 2 );
objectFound=true;
}
else {
cout << "OBJECT NOT FOUND!" << endl;
}
cout << "Matches found: " << matches.size() << endl;
cout << "Good matches found: " << good_matches.size() << endl;
return 0;
}
int main()
{
Mat object = imread( "/home/pi/opencv/darkL2.jpg", CV_LOAD_IMAGE_GRAYSCALE );
//Mat inputImg = imread( "/home/pi/opencv/block.jpg", CV_LOAD_IMAGE_GRAYSCALE );
//Mat object;
//resize(inputImg,object,Size(0,0),0.03,0.03);
if( !object.data )
{
cout<< "Error reading object " << endl;
return -1;
}
printf("read image\n");
//Detect the keypoints using SURF Detector
int minHessian = 300;
SurfFeatureDetector detector( minHessian );
vector<KeyPoint> kp_object;
detector.detect( object, kp_object );
//printf("detect keypoints\n");
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( object, kp_object, des_object );
FlannBasedMatcher matcher;
//VideoCapture cap(0);
//VideoCapture cap("/home/pi/opencv/mouse2.mp4" );
Mat cap = imread( "/home/pi/opencv/photo.jpg", CV_LOAD_IMAGE_COLOR );
//printf("%f \n",cap.get(CV_CAP_PROP_FPS));
namedWindow("Good Matches");
vector<Point2f> obj_corners(4);
//printf("calculate descriptors\n");
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
//printf("get corners\n");
char key = 'a';
int framecount = 0;
int angle = 0;
Mat frame;
int numCycle = 0;
while (key != 27)
{
/*
Mat inputVid;
Mat frame;
cap.read(inputVid);
resize(inputVid, frame, Size(0,0), 0.3,0.3);
angle += 10;
printf("while loop\n");
int cols = frame.cols;
int rows = frame.rows;
Point2f abc(cols/2,rows/2);
Mat M = getRotationMatrix2D(abc,angle,1);
warpAffine(cap,frame,M,Size(cols,rows));
*/
frame = cap;
if (framecount < 5 )
{
framecount++;
continue;
}
Mat des_image, img_matches;
vector<KeyPoint> kp_image;
vector<vector<DMatch > > matches;
vector<DMatch > good_matches;
vector<Point2f> obj;
vector<Point2f> scene;
vector<Point2f> scene_corners(4);
Mat H;
Mat image;
//printf("before color call\n");
cvtColor(frame, image, CV_BGR2GRAY);
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
//printf("before segfault loop\n");
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//printf("segfault sensitive loop\n");
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
printf("Number of matches: %d\n", good_matches.size());
if (good_matches.size() >= 10)
{
printf("good matches >= 10\n");
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
}
//printf("draw good matches\n");
//Show detected matches
imshow( "Good Matches", img_matches );
if ( numCycle < 20 )
{
stringstream name;
name << numCycle;
string filename = string("Match_")+name.str()+string(".jpg");
imwrite( filename, img_matches );
numCycle++;
}
key = waitKey(33);
}
return 0;
}
int main(int argc, char * argv[])
{
if(argc < 2)
{
std::cout << "Use: tracker <target_image>" << std::endl;
return -1;
}
std::cout << "Load target: " << std::endl;
Mat mTarget = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
if( !mTarget.data )
{
std::cout<< "Error reading target image." << std::endl;
return -1;
}
std::cout << "\t\t\tDone" << std::endl;
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kpTarget;
std::cout << "Analize target image: " << std::endl;
detector.detect( mTarget, kpTarget );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( mTarget, kpTarget, des_object );
std::cout << "\t\t\tDone" << std::endl;
std::cout << "Creating macher: " << std::endl;
FlannBasedMatcher matcher;
std::cout << "\t\t\tDone" << std::endl;
std::cout << "Init capture: " << std::endl;
VideoCapture cap("http://192.168.1.200/videostream.cgi?user=admin&pwd=31415LAS&resolution=32&dummy=.mjpg");
//VideoCapture cap("http://nidq.no-ip.org/videostream.cgi?user=admin&pwd=31415LAS&resolution=32&dummy=.mjpg");
std::cout << "\t\t\tDone" << std::endl;
namedWindow("Capture");
std::vector<Point2f> tgt_corners(4);
//Get the corners from the object
tgt_corners[0] = cvPoint(0,0);
tgt_corners[1] = cvPoint( mTarget.cols, 0 );
tgt_corners[2] = cvPoint( mTarget.cols, mTarget.rows );
tgt_corners[3] = cvPoint( 0, mTarget.rows );
char key = 'a';
while (key != 27)
{
Mat frame;
for(int i = 0; i < 5; i++) cap >> frame;
Mat show = frame.clone();
Mat des_image, img_matches;
std::vector<KeyPoint> kpImage;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kpImage );
extractor.compute( image, kpImage, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kpTarget[ good_matches[i].queryIdx ].pt );
scene.push_back( kpImage[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( tgt_corners, scene_corners, H);
Point2f center(0,0);
for(int i = 0; i < 4; i++)
{
center.x += scene_corners[i].x;
center.y += scene_corners[i].y;
}
center.x/=4; center.y/=4;
circle( show, center, 5, Scalar( 255, 255, 0) );
adjust(center, frame.cols, frame.rows);
}
line( show, Point2f( show.cols/2, 0), Point2f( show.cols/2, show.rows), Scalar(0, 255, 0), 4 );
line( show, Point2f( 0, show.rows/2), Point2f( show.cols, show.rows/2), Scalar(0, 255, 0), 4 );
//Show detected matches
imshow( "Capture", show );
key = waitKey(1);
}
return 0;
}