void addTrainImage(string name)
{
/* Добавление нового эталонного изображения и вычисление его дескриптора */
Mat train_img = imread(_train_img_dir + "template_" + name + ".jpg");
if(!train_img.empty())
{
resize(train_img, train_img, Size(SIGN_SIZE, SIGN_SIZE), 0, 0);
_train_images.push_back(train_img);
_train_sign_names.push_back(name);
vector<KeyPoint> points;
_detector.detect( train_img, points );
_train_keypoints.push_back(points);
Mat descriptors;
_extractor.compute( train_img, points, descriptors);
_train_descriptors.push_back(descriptors);
}
else
{
cout << ERROR_STR << "Could not load train image " << _train_img_dir << name << ".jpg" << endl;
}
}
void init(Mat img){
Pose = Mat::eye(4, 4, CV_64F);
PreviousImageGrayScale = img;
PreviousFeatures.clear();
SurfDetector.detect(img, PreviousFeatures);
SurfDescriptor.compute(img, PreviousFeatures, PreviousFeatureDescriptors);
}
void imageCallback(const sensor_msgs::ImageConstPtr& msg)
{
//std_msgs::String imsignal;
//std_msgs::String comsignal;
cv_bridge::CvImageConstPtr cv_ptr;
//char filename[40];
cv_ptr = cv_bridge::toCvShare(msg, enc::BGR8);
Mat im,biimage;
im = cv_ptr->image;
//cvtColor(cv_ptr->image,im,CV_BGR2GRAY);
inRange(im,Scalar(80,0,0), Scalar(255,255,50),biimage); //(85,80,80);
// p.Uppercolor = Scalar(125,255,255);
//imshow( "before", im );
//waitKey(1);
//imshow("after",biimage);
SimpleBlobDetector::Params params;
params.filterByColor =true;
params.filterByCircularity =false;
params.filterByConvexity =false;
params.filterByInertia = false;
params.blobColor = 255;
// Filter by Area.
params.filterByArea = true;
params.minArea = 10000;
params.maxArea = 800000;
// Filter by Inertia
params.minInertiaRatio = 10;
params.minDistBetweenBlobs = 10;
SimpleBlobDetector detector(params);
std::vector<KeyPoint> keypoints;
detector.detect(biimage,keypoints);
Mat im_with_keypoints;
drawKeypoints( biimage, keypoints, im_with_keypoints, Scalar(0,0,255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS );
imshow("test",im_with_keypoints);
waitKey(1);
for
(
std::vector<KeyPoint>::iterator it = keypoints.begin();
it != keypoints.end();
++it
)
{
KeyPoint k = *it;
cout << k.pt << endl;
x_bar=k.pt.x;
y_bar=k.pt.y;
if(x_bar>((im_with_keypoints.cols/2)-(im_with_keypoints.cols/15)) && x_bar<(im_with_keypoints.cols/2)+(im_with_keypoints.cols/15))
{
std_msgs::String sent;
sent.data = "getposition";
com_pub.publish(sent);
ros::Duration(0.05).sleep();
sent.data = "BR";
com_pub.publish(sent);
ros::Duration(2).sleep();
}
}
}
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;
}
virtual void process() {
std::vector<KeyPoint> ipts;
detector.detect(inputImage,ipts);
Mat descriptors;
extractor.compute( inputImage, ipts, descriptors );
// printf("num points %d\n",(int)ipts.size());
}
bool TrackerForProject::init( const cv::Mat& frame, const cv::Rect& initial_position )
{
position_ = initial_position;
cv::cvtColor(frame, prevFrame_, CV_BGR2GRAY);
Mat prev_(prevFrame_(position_));
SurfFeatureDetector detector;
detector.detect(prev_, keypoints1);
return true;
}
/*
* Test Calonder classifier to match keypoints on given image:
* classifierFilename - name of file from which classifier will be read,
* imgFilename - test image filename.
*
* To calculate keypoint descriptors you may use RTreeClassifier class (as to train),
* but it is convenient to use CalonderDescriptorExtractor class which is wrapper of
* RTreeClassifier.
*/
static void testCalonderClassifier( const string& classifierFilename, const string& imgFilename )
{
Mat img1 = imread( imgFilename, IMREAD_GRAYSCALE ), img2, H12;
if( img1.empty() )
{
cout << "Test image can not be read." << endl;
exit(-1);
}
warpPerspectiveRand( img1, img2, H12, theRNG() );
// Exstract keypoints from test images
SurfFeatureDetector detector;
vector<KeyPoint> keypoints1; detector.detect( img1, keypoints1 );
vector<KeyPoint> keypoints2; detector.detect( img2, keypoints2 );
// Compute descriptors
CalonderDescriptorExtractor<float> de( classifierFilename );
Mat descriptors1; de.compute( img1, keypoints1, descriptors1 );
Mat descriptors2; de.compute( img2, keypoints2, descriptors2 );
// Match descriptors
BFMatcher matcher(NORM_L1);
vector<DMatch> matches;
matcher.match( descriptors1, descriptors2, matches );
// Prepare inlier mask
vector<char> matchesMask( matches.size(), 0 );
vector<Point2f> points1; KeyPoint::convert( keypoints1, points1 );
vector<Point2f> points2; KeyPoint::convert( keypoints2, points2 );
Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
for( size_t mi = 0; mi < matches.size(); mi++ )
{
if( norm(points2[matches[mi].trainIdx] - points1t.at<Point2f>((int)mi,0)) < 4 ) // inlier
matchesMask[mi] = 1;
}
// Draw
Mat drawImg;
drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask );
string winName = "Matches";
namedWindow( winName, WINDOW_AUTOSIZE );
imshow( winName, drawImg );
waitKey();
}
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;
}
/*
* Trains Calonder classifier and writes trained classifier in file:
* imgFilename - name of .txt file which contains list of full filenames of train images,
* classifierFilename - name of binary file in which classifier will be written.
*
* To train Calonder classifier RTreeClassifier class need to be used.
*/
static void trainCalonderClassifier( const string& classifierFilename, const string& imgFilename )
{
// Reads train images
ifstream is( imgFilename.c_str(), ifstream::in );
vector<Mat> trainImgs;
while( !is.eof() )
{
string str;
getline( is, str );
if (str.empty()) break;
Mat img = imread( str, IMREAD_GRAYSCALE );
if( !img.empty() )
trainImgs.push_back( img );
}
if( trainImgs.empty() )
{
cout << "All train images can not be read." << endl;
exit(-1);
}
cout << trainImgs.size() << " train images were read." << endl;
// Extracts keypoints from train images
SurfFeatureDetector detector;
vector<BaseKeypoint> trainPoints;
vector<IplImage> iplTrainImgs(trainImgs.size());
for( size_t imgIdx = 0; imgIdx < trainImgs.size(); imgIdx++ )
{
iplTrainImgs[imgIdx] = trainImgs[imgIdx];
vector<KeyPoint> kps; detector.detect( trainImgs[imgIdx], kps );
for( size_t pointIdx = 0; pointIdx < kps.size(); pointIdx++ )
{
Point2f p = kps[pointIdx].pt;
trainPoints.push_back( BaseKeypoint(cvRound(p.x), cvRound(p.y), &iplTrainImgs[imgIdx]) );
}
}
// Trains Calonder classifier on extracted points
RTreeClassifier classifier;
classifier.train( trainPoints, theRNG(), 48, 9, 100 );
// Writes classifier
classifier.write( classifierFilename.c_str() );
}
Mat find_next_homography(Mat image, Mat image_next, vector<KeyPoint> keypoints_0, Mat descriptors_0,
SurfFeatureDetector detector, SurfDescriptorExtractor extractor,
BFMatcher matcher, vector<KeyPoint>& keypoints_next, Mat& descriptors_next)
{
//step 1 detect feature points in next image
vector<KeyPoint> keypoints_1;
detector.detect(image_next, keypoints_1);
Mat img_keypoints_surf0, img_keypoints_surf1;
drawKeypoints(image, keypoints_0, img_keypoints_surf0);
drawKeypoints(image_next, keypoints_1, img_keypoints_surf1);
//cout << "# im0 keypoints" << keypoints_0.size() << endl;
//cout << "# im1 keypoints" << keypoints_1.size() << endl;
imshow("surf 0", img_keypoints_surf0);
imshow("surf 1", img_keypoints_surf1);
//step 2: extract feature descriptors from feature points
Mat descriptors_1;
extractor.compute(image_next, keypoints_1, descriptors_1);
//step 3: feature matching
//cout << "fd matching" << endl;
vector<DMatch> matches;
vector<Point2f> matched_0;
vector<Point2f> matched_1;
matcher.match(descriptors_0, descriptors_1, matches);
Mat img_feature_matches;
drawMatches(image, keypoints_0, image_next, keypoints_1, matches, img_feature_matches );
imshow("Matches", img_feature_matches);
for (int i = 0; i < matches.size(); i++ )
{
matched_0.push_back(keypoints_0[matches[i].queryIdx].pt);
matched_1.push_back(keypoints_1[matches[i].trainIdx].pt);
}
keypoints_next = keypoints_1;
descriptors_next = descriptors_1;
return findHomography(matched_0, matched_1, RANSAC);
}
vector<Mat> getHistAndLabels(SurfFeatureDetector &detector, BOWImgDescriptorExtractor &bowDE, int dictionarySize) {
// setup variable and object I need
IplImage *img2;
Mat labels(0, 1, CV_32FC1);
Mat trainingData(0, dictionarySize, CV_32FC1);
vector<KeyPoint> keypoint1;
Mat bowDescriptor1;
Helper helper;
vector<string> files = vector<string>();
helper.GetFileList(EVAL_DIR, files);
float labelVal;
for (unsigned int iz = 0; iz < files.size(); iz++) {
int isImage = helper.instr(files[iz], "jpg", 0, true);
if (isImage > 0) {
string sFileName = TRAINING_DIR;
sFileName.append(files[iz]);
const char * imageName = sFileName.c_str ();
img2 = cvLoadImage(imageName,0);
if (img2) {
detector.detect(img2, keypoint1);
bowDE.compute(img2, keypoint1, bowDescriptor1);
trainingData.push_back(bowDescriptor1);
labelVal = iz+1;
labels.push_back(labelVal);
}
}
}
vector<Mat> retVec;
retVec.push_back(trainingData);
retVec.push_back(labels);
return retVec;
}
void collectclasscentroids() {
IplImage *img;
int samplesOnGroup = 60;
int trainingGroups = 4;
int allSamples = samplesOnGroup * trainingGroups;
for (int j = 1; j <= trainingGroups; j++)
for (int i = 1; i <= samplesOnGroup; i++) {
sprintf(ch, "%s%d%s%d%s", "train/", j, " (", i, ").jpg");
//cout << ch << endl;
printf("\rTraining : %3d %%",((((j-1)*samplesOnGroup)+i)*100/allSamples));
const char* imageName = ch;
img = cvLoadImage(imageName, 0);
vector<KeyPoint> keypoint;
detector.detect(img, keypoint);
Mat features;
extractor->compute(img, keypoint, features);
bowTrainer.add(features);
}
printf("\n");
return;
}
Mat getSingleImageHistogram(SurfFeatureDetector &detector, BOWImgDescriptorExtractor &bowDE, string evalFile) {
// setup variable and object I need
IplImage *img2;
vector<KeyPoint> keypoint1;
Mat bowDescriptor1;
Helper helper;
int isImage = helper.instr(evalFile, "jpg", 0, true);
if (isImage > 0) {
const char * imageName = evalFile.c_str ();
img2 = cvLoadImage(imageName,0);
if (img2) {
detector.detect(img2, keypoint1);
bowDE.compute(img2, keypoint1, bowDescriptor1);
}
}
return bowDescriptor1;
}
float getClassMatch(SurfFeatureDetector &detector, BOWImgDescriptorExtractor &bowDE, IplImage* &img2, int dictionarySize, string sFileName, CvSVM &svm) {
float response;
vector<KeyPoint> keypoint2;
Mat bowDescriptor2;
Mat evalData(0, dictionarySize, CV_32FC1);
Mat groundTruth(0, 1, CV_32FC1);
Mat results(0, 1, CV_32FC1);
detector.detect(img2, keypoint2);
bowDE.compute(img2, keypoint2, bowDescriptor2);
//evalData.push_back(bowDescriptor2);
//groundTruth.push_back((float) classID);
response = svm.predict(bowDescriptor2);
//results.push_back(response);
return response;
}
KDvoid SURF_Descriptor ( KDint nIdx )
{
Mat tDst;
Mat tImg1;
Mat tImg2;
tImg1 = imread ( "/res/image/box.png", CV_LOAD_IMAGE_GRAYSCALE );
tImg2 = imread ( "/res/image/box_in_scene.png", CV_LOAD_IMAGE_GRAYSCALE );
// -- Step 1: Detect the keypoints using SURF Detector
KDint nMinHessian = 400;
SurfFeatureDetector tDetector ( nMinHessian );
std::vector<KeyPoint> aKeypoints1, aKeypoints2;
tDetector.detect ( tImg1, aKeypoints1 );
tDetector.detect ( tImg2, aKeypoints2 );
// -- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor tExtractor;
Mat tDescriptors1, tDescriptors2;
tExtractor.compute ( tImg1, aKeypoints1, tDescriptors1 );
tExtractor.compute ( tImg2, aKeypoints2, tDescriptors2 );
/*
// -- Step 3: Matching descriptor vectors with a brute force matcher
BruteForceMatcher< L2<KDfloat> > tMatcher;
std::vector< DMatch > aMatches;
tMatcher.match ( tDescriptors1, tDescriptors2, aMatches );
// -- Draw matches
drawMatches ( tImg1, aKeypoints1, tImg2, aKeypoints2, aMatches, tDst );
g_pController->setFrame ( 0, tDst );
*/
}
Mat getHistograms(SurfFeatureDetector &detector, BOWImgDescriptorExtractor &bowDE, int dictionarySize, vector<string> &collectionFilenames, string evalDir) {
// setup variable and object I need
IplImage *img2;
Mat trainingData(0, dictionarySize, CV_32FC1);
vector<KeyPoint> keypoint1;
Mat bowDescriptor1;
Helper helper;
vector<string> files = vector<string>();
helper.GetFileList(evalDir, files);
cout << "Number of Collection Files to Process: " << files.size()-2 << endl;
for (unsigned int iz = 0; iz < files.size(); iz++) {
int isImage = helper.instr(files[iz], "jpg", 0, true);
if (isImage > 0) {
cout << " Processing " << files[iz] << endl;
collectionFilenames.push_back(files[iz]);
string sFileName = EVAL_DIR;
sFileName.append(files[iz]);
const char * imageName = sFileName.c_str ();
img2 = cvLoadImage(imageName,0);
if (img2) {
detector.detect(img2, keypoint1);
bowDE.compute(img2, keypoint1, bowDescriptor1);
trainingData.push_back(bowDescriptor1);
}
}
}
return trainingData;
}
int main(int argc, char** argv)
{
if( argc < 2 )
{
printPrompt( argv[0] );
return -1;
}
initModule_nonfree();
// Get Input Data
ifstream file(argv[1]);
if ( !file.is_open() )
return false;
string str;
// Image Name
getline( file, str ); getline( file, str );
string image_name = str;
// Cloud Name
getline( file, str ); getline( file, str );
string cloud_name = str;
// width of images to be created.
getline( file, str ); getline( file, str );
int w = atoi(str.c_str());
// height of images to be created
getline( file, str ); getline( file, str );
int h = atoi(str.c_str());
// resolution of voxel grids
getline( file, str ); getline( file, str );
float r = atof(str.c_str());
// f (distance from pinhole)
getline( file, str ); getline( file, str );
float f = atof(str.c_str());
// thetax (initial rotation about X Axis of map)
getline( file, str ); getline( file, str );
float thetaX = atof(str.c_str());
// thetay (initial rotation about Y Axis of map)
getline( file, str ); getline( file, str );
float thetaY = atof(str.c_str());
// number of points to go to
getline( file, str ); getline( file, str );
float nop = atoi(str.c_str());
// Number of divisions
getline( file, str ); getline( file, str );
float divs = atoi(str.c_str());
// Number of images to return
getline( file, str ); getline( file, str );
int numtoreturn = atoi(str.c_str());
// Should we load or create photos?
getline( file, str ); getline( file, str );
string lorc =str.c_str();
// Directory to look for photos
getline( file, str ); getline( file, str );
string dir =str.c_str();
// Directory to look for kp and descriptors
getline( file, str ); getline( file, str );
string kdir =str.c_str();
// save photos?
getline( file, str ); getline( file, str );
string savePhotos =str.c_str();
file.close();
// Done Getting Input Data
map<vector<float>, Mat> imagemap;
map<vector<float>, Mat> surfmap;
map<vector<float>, Mat> siftmap;
map<vector<float>, Mat> orbmap;
map<vector<float>, Mat> fastmap;
imagemap.clear();
vector<KeyPoint> SurfKeypoints;
vector<KeyPoint> SiftKeypoints;
vector<KeyPoint> OrbKeypoints;
vector<KeyPoint> FastKeypoints;
Mat SurfDescriptors;
Mat SiftDescriptors;
Mat OrbDescriptors;
Mat FastDescriptors;
int minHessian = 300;
SurfFeatureDetector SurfDetector (minHessian);
SiftFeatureDetector SiftDetector (minHessian);
OrbFeatureDetector OrbDetector (minHessian);
FastFeatureDetector FastDetector (minHessian);
SurfDescriptorExtractor SurfExtractor;
SiftDescriptorExtractor SiftExtractor;
OrbDescriptorExtractor OrbExtractor;
if ( !fs::exists( dir ) || lorc == "c" )
{ // Load Point Cloud and render images
PointCloud<PT>::Ptr cloud (new pcl::PointCloud<PT>);
io::loadPCDFile<PT>(cloud_name, *cloud);
Eigen::Affine3f tf = Eigen::Affine3f::Identity();
tf.rotate (Eigen::AngleAxisf (thetaX, Eigen::Vector3f::UnitX()));
pcl::transformPointCloud (*cloud, *cloud, tf);
tf = Eigen::Affine3f::Identity();
tf.rotate (Eigen::AngleAxisf (thetaY, Eigen::Vector3f::UnitY()));
pcl::transformPointCloud (*cloud, *cloud, tf);
// Create images from point cloud
imagemap = render::createImages(cloud, nop, w, h, r, f);
if (savePhotos == "y")
{
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
// Create image name and storagename
string imfn = dir + "/";
string kpfn = kdir + "/";
for (int j = 0; j < i->first.size(); j++)
{
imfn += boost::to_string(i->first[j]) + " ";
kpfn += boost::to_string(i->first[j]) + " ";
}
imfn += ".jpg";
imwrite(imfn, i->second);
// Detect keypoints, add to keypoint map. Same with descriptors
SurfDetector.detect(i->second, SurfKeypoints);
SiftDetector.detect(i->second, SiftKeypoints);
OrbDetector.detect(i->second, OrbKeypoints);
FastDetector.detect(i->second, FastKeypoints);
SurfExtractor.compute(i->second, SurfKeypoints, SurfDescriptors);
SiftExtractor.compute(i->second, SiftKeypoints, SiftDescriptors);
OrbExtractor.compute(i->second, OrbKeypoints, OrbDescriptors);
SiftExtractor.compute(i->second, FastKeypoints, FastDescriptors);
// Store KP and Descriptors in yaml file.
kpfn += ".yml";
FileStorage store(kpfn, cv::FileStorage::WRITE);
write(store,"SurfKeypoints",SurfKeypoints);
write(store,"SiftKeypoints",SiftKeypoints);
write(store,"OrbKeypoints", OrbKeypoints);
write(store,"FastKeypoints",FastKeypoints);
write(store,"SurfDescriptors",SurfDescriptors);
write(store,"SiftDescriptors",SiftDescriptors);
write(store,"OrbDescriptors", OrbDescriptors);
write(store,"FastDescriptors",FastDescriptors);
store.release();
surfmap[i->first] = SurfDescriptors;
siftmap[i->first] = SiftDescriptors;
orbmap[i->first] = OrbDescriptors;
fastmap[i->first] = FastDescriptors;
}
}
}
else
{ // load images from the folder dir
// First look into the folder to get a list of filenames
vector<fs::path> ret;
const char * pstr = dir.c_str();
fs::path p(pstr);
get_all(pstr, ret);
for (int i = 0; i < ret.size(); i++)
{
// Load Image via filename
string fn = ret[i].string();
istringstream iss(fn);
vector<string> tokens;
copy(istream_iterator<string>(iss), istream_iterator<string>(), back_inserter<vector<string> >(tokens));
// Construct ID from filename
vector<float> ID;
for (int i = 0; i < 6; i++) // 6 because there are three location floats and three direction floats
ID.push_back(::atof(tokens[i].c_str()));
string imfn = dir + "/" + fn;
// Read image and add to imagemap.
Mat m = imread(imfn);
imagemap[ID] = m;
// Create Filename for loading Keypoints and descriptors
string kpfn = kdir + "/";
for (int j = 0; j < ID.size(); j++)
{
kpfn += boost::to_string(ID[j]) + " ";
}
kpfn = kpfn+ ".yml";
// Create filestorage item to read from and add to map.
FileStorage store(kpfn, cv::FileStorage::READ);
FileNode n1 = store["SurfKeypoints"];
read(n1,SurfKeypoints);
FileNode n2 = store["SiftKeypoints"];
read(n2,SiftKeypoints);
FileNode n3 = store["OrbKeypoints"];
read(n3,OrbKeypoints);
FileNode n4 = store["FastKeypoints"];
read(n4,FastKeypoints);
FileNode n5 = store["SurfDescriptors"];
read(n5,SurfDescriptors);
FileNode n6 = store["SiftDescriptors"];
read(n6,SiftDescriptors);
FileNode n7 = store["OrbDescriptors"];
read(n7,OrbDescriptors);
FileNode n8 = store["FastDescriptors"];
read(n8,FastDescriptors);
store.release();
surfmap[ID] = SurfDescriptors;
siftmap[ID] = SiftDescriptors;
orbmap[ID] = OrbDescriptors;
fastmap[ID] = FastDescriptors;
}
}
TickMeter tm;
tm.reset();
cout << "<\n Analyzing Images ..." << endl;
// We have a bunch of images, now we compute their grayscale and black and white.
map<vector<float>, Mat> gsmap;
map<vector<float>, Mat> bwmap;
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
vector<float> ID = i->first;
Mat Image = i-> second;
GaussianBlur( Image, Image, Size(5,5), 0, 0, BORDER_DEFAULT );
gsmap[ID] = averageImage::getPixSumFromImage(Image, divs);
bwmap[ID] = averageImage::aboveBelow(gsmap[ID]);
}
Mat image = imread(image_name);
Mat gsimage = averageImage::getPixSumFromImage(image, divs);
Mat bwimage = averageImage::aboveBelow(gsimage);
// cout << gsimage <<endl;
imwrite("GS.png", gsimage);
namedWindow("GSIMAGE (Line 319)");
imshow("GSIMAGE (Line 319)", gsimage);
waitKey(0);
vector<KeyPoint> imgSurfKeypoints;
vector<KeyPoint> imgSiftKeypoints;
vector<KeyPoint> imgOrbKeypoints;
vector<KeyPoint> imgFastKeypoints;
Mat imgSurfDescriptors;
Mat imgSiftDescriptors;
Mat imgOrbDescriptors;
Mat imgFastDescriptors;
SurfDetector.detect(image, imgSurfKeypoints);
SiftDetector.detect(image, imgSiftKeypoints);
OrbDetector.detect(image, imgOrbKeypoints);
FastDetector.detect(image, imgFastKeypoints);
SurfExtractor.compute(image, imgSurfKeypoints, imgSurfDescriptors);
SiftExtractor.compute(image, imgSiftKeypoints, imgSiftDescriptors);
OrbExtractor.compute(image, imgOrbKeypoints, imgOrbDescriptors);
SiftExtractor.compute(image, imgFastKeypoints, imgFastDescriptors);
tm.start();
cout << ">\n<\n Comparing Images ..." << endl;
// We have their features, now compare them!
map<vector<float>, float> gssim; // Gray Scale Similarity
map<vector<float>, float> bwsim; // Above Below Similarity
map<vector<float>, float> surfsim;
map<vector<float>, float> siftsim;
map<vector<float>, float> orbsim;
map<vector<float>, float> fastsim;
for (map<vector<float>, Mat>::iterator i = gsmap.begin(); i != gsmap.end(); ++i)
{
vector<float> ID = i->first;
gssim[ID] = similarities::getSimilarity(i->second, gsimage);
bwsim[ID] = similarities::getSimilarity(bwmap[ID], bwimage);
surfsim[ID] = similarities::compareDescriptors(surfmap[ID], imgSurfDescriptors);
siftsim[ID] = similarities::compareDescriptors(siftmap[ID], imgSiftDescriptors);
orbsim[ID] = 0;//similarities::compareDescriptors(orbmap[ID], imgOrbDescriptors);
fastsim[ID] = 0;//similarities::compareDescriptors(fastmap[ID], imgFastDescriptors);
}
map<vector<float>, int> top;
bool gotone = false;
typedef map<vector<float>, int>::iterator iter;
// Choose the best ones!
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
vector<float> ID = i->first;
int sim = /* gssim[ID] + 0.5*bwsim[ID] + */ 5*surfsim[ID] + 0.3*siftsim[ID] + orbsim[ID] + fastsim[ID];
// cout << surfsim[ID] << "\t";
// cout << siftsim[ID] << "\t";
// cout << orbsim[ID] << "\t";
// cout << fastsim[ID] << endl;
if (!gotone)
{
top[ID] = sim;
gotone = true;
}
iter it = top.begin();
iter end = top.end();
int max_value = it->second;
vector<float> max_ID = it->first;
for( ; it != end; ++it)
{
int current = it->second;
if(current > max_value)
{
max_value = it->second;
max_ID = it->first;
}
}
// cout << "Sim: " << sim << "\tmax_value: " << max_value << endl;
if (top.size() < numtoreturn)
top[ID] = sim;
else
{
if (sim < max_value)
{
top[ID] = sim;
top.erase(max_ID);
}
}
}
tm.stop();
double s = tm.getTimeSec();
cout << ">\n<\n Writing top " << numtoreturn << " images ..." << endl;
int count = 1;
namedWindow("Image");
namedWindow("Match");
namedWindow("ImageBW");
namedWindow("MatchBW");
namedWindow("ImageGS");
namedWindow("MatchGS");
imshow("Image", image);
imshow("ImageBW", bwimage);
imshow("ImageGS", gsimage);
vector<KeyPoint> currentPoints;
for (iter i = top.begin(); i != top.end(); ++i)
{
vector<float> ID = i->first;
cout << " Score: "<< i->second << "\tGrayScale: " << gssim[ID] << "\tBW: " << bwsim[ID] << " \tSURF: " << surfsim[ID] << "\tSIFT: " << siftsim[ID] << endl;
string fn = "Sim_" + boost::to_string(count) + "_" + boost::to_string(i->second) + ".png";
imwrite(fn, imagemap[ID]);
count++;
normalize(bwmap[ID], bwmap[ID], 0, 255, NORM_MINMAX, CV_64F);
normalize(gsmap[ID], gsmap[ID], 0, 255, NORM_MINMAX, CV_64F);
imshow("Match", imagemap[ID]);
imshow("MatchBW", bwmap[ID]);
imshow("MatchGS", gsmap[ID]);
waitKey(0);
}
cout << ">\nComparisons took " << s << " seconds for " << imagemap.size() << " images ("
<< (int) imagemap.size()/s << " images per second)." << endl;
return 0;
}
bool compute(Mat CurrentImageGrayScale, Mat Kinverse, const int iteration){
vector<KeyPoint> CurrentFeatures;
SurfDetector.detect(CurrentImageGrayScale, CurrentFeatures);
Mat CurrentFeatureDescriptors;
SurfDescriptor.compute(CurrentImageGrayScale, CurrentFeatures, CurrentFeatureDescriptors);
vector<DMatch> matches;
matcher.match(PreviousFeatureDescriptors, CurrentFeatureDescriptors, matches);
if (matches.size() > 200){
nth_element(matches.begin(), matches.begin()+ 200, matches.end());
matches.erase(matches.begin() + 201, matches.end());
}
//Debug(matches, PreviousImageGrayScale, CurrentImageGrayScale, PreviousFeatures, CurrentFeatures);
vector< pair<double,double> > FirstImageFeatures;
vector< pair<double,double> > SecondImageFeatures;
for(int i = 0; i < matches.size(); i++){
Point2f myft = PreviousFeatures[matches[i].queryIdx].pt;
Mat FtMatForm = (Mat_<double>(3,1) << (double)myft.x, (double)myft.y, 1.0);
FtMatForm = Kinverse*FtMatForm;
pair<double,double> tmp = make_pair(FtMatForm.at<double>(0,0), FtMatForm.at<double>(1,0));
FirstImageFeatures.push_back(tmp);
myft = CurrentFeatures[matches[i].trainIdx].pt;
FtMatForm = (Mat_<double>(3,1) << (double)myft.x, (double)myft.y, 1.0);
FtMatForm = Kinverse*FtMatForm;
tmp = make_pair(FtMatForm.at<double>(0,0), FtMatForm.at<double>(1,0));
SecondImageFeatures.push_back(tmp);
}
vector<int> inliers_indexes;
Mat RobustEssentialMatrix= Ransac(FirstImageFeatures, SecondImageFeatures, 0.00001, 8, 2000, inliers_indexes);
//cout << RobustEssentialMatrix << endl;
//Debug2(matches, PreviousImageGrayScale, CurrentImageGrayScale, PreviousFeatures, CurrentFeatures, inliers_indexes);
Mat P = Mat::eye(3,4,CV_64F);
if (!GetRotationAndTraslation(RobustEssentialMatrix, FirstImageFeatures, SecondImageFeatures, inliers_indexes, P)){
cerr << "Recovering Translation and Rotation: Failed" << endl;
return false;
}
//cout << P << endl;
Mat Transformation = Mat::zeros(4,4, CV_64F);
Transformation.at<double>(3,3) = 1.0;
for(int i = 0 ; i < 3; i++)
for(int j = 0; j < 4; j++)
Transformation.at<double>(i, j) = P.at<double>(i, j);
Mat TransformationInverse = Transformation.inv();
Pose = Pose * TransformationInverse;
cerr << Pose.at<double>(0, 3) << " " << Pose.at<double>(1, 3) << " " << Pose.at<double>(2, 3) << endl;
PreviousImageGrayScale = CurrentImageGrayScale;
PreviousFeatures = CurrentFeatures;
PreviousFeatureDescriptors = CurrentFeatureDescriptors;
//viejo
// vector< pair<int,int> > correspondences = harrisFeatureMatcherMCC(PreviousImageGrayScale, CurrentImageGrayScale, PreviousFeatures, CurrentFeatures);
// cout << "Iteracion" << iteration << "Cantidad de correspondencias " << correspondences.size() << endl;
// vector< pair<double,double> > FirstImageFeatures;
// vector< pair<double,double> > SecondImageFeatures;
// for(int i = 0; i < correspondences.size(); i++){
// pair<int,int> myft = PreviousFeatures[correspondences[i].first];
// Mat FtMatForm = (Mat_<double>(3,1) << (double)myft.first, (double)myft.second, 1.0);
// FtMatForm = Kinverse*FtMatForm;
// pair<double,double> tmp = make_pair(FtMatForm.at<double>(0,0), FtMatForm.at<double>(1,0));
// FirstImageFeatures.push_back(tmp);
//
// myft = CurrentFeatures[correspondences[i].second];
// FtMatForm = (Mat_<double>(3,1) << (double)myft.first, (double)myft.second, 1.0);
// FtMatForm = Kinverse*FtMatForm;
// tmp = make_pair(FtMatForm.at<double>(0,0), FtMatForm.at<double>(1,0));
// SecondImageFeatures.push_back(tmp);
// }
// vector<int> inliers_indexes;
// Mat RobustEssentialMatrix= Ransac(FirstImageFeatures, SecondImageFeatures, 0.98, 0.00001, 0.5, 8, FirstImageFeatures.size()/2, inliers_indexes);
// cout << "Iteration" << iteration << "Final EssentialMatrix" << endl;
// cout << RobustEssentialMatrix << endl;
//
//
// vector<pair<int, int> > correspondences_inliers;
// for(int i = 0; i < inliers_indexes.size(); i++)
// correspondences_inliers.push_back(correspondences[inliers_indexes[i]]);
// debugging2(PreviousImageGrayScale, CurrentImageGrayScale, PreviousFeatures, CurrentFeatures, correspondences_inliers);
//
// Mat P = Mat::eye(3,4,CV_64F);
// if (!GetRotationAndTraslation(RobustEssentialMatrix, FirstImageFeatures, SecondImageFeatures, inliers_indexes, P))
// return false;
// cout << "Iteration" << iteration << "Camera Matrix" << endl;
// cout << P << endl;
// Mat Transformation = Mat::zeros(4,4, CV_64F);
// Transformation.at<double>(3,3) = 1.0;
// for(int i = 0 ; i < 3; i++)
// for(int j = 0; j < 4; j++)
// Transformation.at<double>(i, j) = P.at<double>(i, j);
// Mat TransformationInverse = Transformation.inv();
// Pose = Pose * TransformationInverse;
// PreviousImageGrayScale = CurrentImageGrayScale;
// PreviousFeatures = CurrentFeatures;
// cerr << Pose.at<double>(0, 4) << Pose.at<double>(1, 4) << Pose.at<double>(2, 4) << endl;
}
void collectclasscentroids(SurfFeatureDetector &detector, Ptr<DescriptorExtractor> &extractor, BOWKMeansTrainer &bowTrainer, string trainingDir, bool runInBackground, bool writelog) {
IplImage *img;
vector<string> files = vector<string>();
Helper helper;
string event;
char ch[30];
// should put error correction here to check if directory exists
helper.GetFileList(trainingDir, files);
for (unsigned int iz = 0; iz < files.size(); iz++) {
int isImage = helper.instr(files[iz], "jpg", 0, true);
if (isImage > 0) {
string sFileName = trainingDir;
string sFeaturesDir = "/usr/local/share/archive-vision/build/features/";
string sOutputImageFilename = "/usr/local/share/archive-vision/build/feature_point_images/";
sFileName.append(files[iz]);
sOutputImageFilename.append(files[iz]);
sFeaturesDir.append(files[iz]);
sFeaturesDir.append(".txt");
const char * imageName = sFileName.c_str ();
img = cvLoadImage(imageName,0);
if (img) {
string workingFile = files[iz];
vector<KeyPoint> keypoint;
detector.detect(img, keypoint);
if (keypoint.size()) {
Mat features;
extractor->compute(img, keypoint, features);
event = "Processing " + workingFile;
helper.logEvent(event, 2, runInBackground, writelog);
//try to write out an image with the features highlighted
// Add results to image and save.
// Mat output;
// drawKeypoints(img, keypoint, output, Scalar(0, 128, 0), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
// imwrite(sOutputImageFilename, output);
// try writing out all the feature, each to its own YML file and see what
// they look like
// helper.WriteToFile(sFeaturesDir, features, "features");
bowTrainer.add(features);
} else {
event = workingFile + "contains no keypoints.";
helper.logEvent(event, 1, runInBackground, writelog);
}
}
}
}
return;
}
void read (Mat rgbimg, Mat depimg, Mat mask, PointCloud &pointcloud,
PointCloud &keypoints, vector<KeyPoint> &key, int x1, int y1, double minHessian)
{
vector<KeyPoint> keytemp;
SurfFeatureDetector detector (minHessian);
detector.detect (rgbimg, keytemp);
// convert the feature points in the smaller mask into point cloud
for (int k = 0; k < keytemp.size (); k++)
{
int i = (int) keytemp[k].pt.y;
int j = (int) keytemp[k].pt.x;
unsigned short depth = depimg.at<unsigned short> (i, j);
if (mask.at<bool> (i, j) != 0 && depth != 0 && depth / MM_PER_M < DEPTH_THRESHOLD)
{
PointT point;
double x = (WIDTH-(j + x1)-WIDTH/2) * depth / FOCAL / MM_PER_M;
double y = (HEIGHT-(i + y1)-HEIGHT/2) * depth / FOCAL / MM_PER_M;
double z = depth / MM_PER_M;
point.x = x;
point.y = y;
point.z = z;
Vec3b rgb = rgbimg.at<Vec3b> (i, j);
point.b = (uint8_t) rgb[0];
point.g = (uint8_t) rgb[1];
point.r = (uint8_t) rgb[2];
keypoints.points.push_back (point);
//update the keypoints deleting some points outside the mask;
key.push_back (keytemp[k]);
}
}
// convert all the points in the mask into point cloud
for (int i = 0; i < rgbimg.rows; i++)
{
for (int j = 0; j < rgbimg.cols; j++)
{
unsigned short depth = depimg.at<unsigned short> (i, j);
if (mask.at<bool> (i, j) != 0 && depth != 0 && depth / MM_PER_M < DEPTH_THRESHOLD)
{
PointT point;
double x = (WIDTH-(j + x1)-WIDTH/2) * depth / FOCAL / MM_PER_M;
double y = (HEIGHT-(i + y1)-HEIGHT/2) * depth / FOCAL / MM_PER_M;
double z = depth / MM_PER_M;
point.x = x;
point.y = y;
point.z = z;
Vec3b rgb = rgbimg.at<Vec3b> (i, j);
point.b = (uint8_t) rgb[0];
point.g = (uint8_t) rgb[1];
point.r = (uint8_t) rgb[2];
pointcloud.points.push_back (point);
}
}
}
pointcloud.width = (uint32_t) pointcloud.points.size ();
pointcloud.height = 1;
keypoints.width = (uint32_t) keypoints.points.size ();
keypoints.height = 1;
}
bool TrackerForProject::filterRANSAC(cv::Mat newFrame_, vector<Point2f> &corners, vector<Point2f> &nextCorners)
{
int ransacReprojThreshold = 3;
cv::Mat prev_(prevFrame_(position_));
cv::Mat new_(newFrame_);
// detecting keypoints
SurfFeatureDetector detector;
detector.detect(prev_, keypoints1);
vector<KeyPoint> keypoints2;
detector.detect(new_, keypoints2);
// computing descriptors
SurfDescriptorExtractor extractor;
Mat descriptors1;
extractor.compute(prev_, keypoints1, descriptors1);
Mat descriptors2;
extractor.compute(newFrame_, keypoints2, descriptors2);
// matching descriptors
BFMatcher matcher;
vector<DMatch> matches;
matcher.match(descriptors1, descriptors2, matches);
std::cout << matches.size() << std::endl;
vector<Point2f> points1, points2;
// fill the arrays with the points
for (int i = 0; i < matches.size(); i++)
{
points1.push_back(keypoints1[matches[i].queryIdx].pt);
}
for (int i = 0; i < matches.size(); i++)
{
points2.push_back(keypoints2[matches[i].trainIdx].pt);
}
Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
Mat points1Projected;
perspectiveTransform(Mat(points1), points1Projected, H);
vector<KeyPoint> keypoints3;
for(int i = 0; i < matches.size(); i++)
{
Point2f p1 = points1Projected.at<Point2f>(matches[i].queryIdx);
Point2f p2 = keypoints2.at(matches[i].trainIdx).pt;
if(((p2.x - p1.x) * (p2.x - p1.x) +
(p2.y - p1.y) * (p2.y - p1.y) <= ransacReprojThreshold * ransacReprojThreshold)&& ((p2.x > position_.x - 10)
&& (p2.x < position_.x + position_.width + 10) && (p2.y > position_.y - 10) &&(p2.y < position_.y + position_.height + 10)) )
{
corners.push_back(keypoints1.at(matches[i].queryIdx).pt);
nextCorners.push_back(keypoints2.at(matches[i].trainIdx).pt);
keypoints3.push_back(keypoints2.at(matches[i].trainIdx));
}
}
for(int i = 0; i < corners.size(); i++)
{
corners[i].x += position_.x;
corners[i].y += position_.y;
}
keypoints1 = keypoints3;
for(int i = 0; i < keypoints1.size(); i++)
{
keypoints1[i].pt.x -= position_.x;
keypoints1[i].pt.y -= position_.y;
}
if (keypoints1.empty())
{
return false;
}
return true;
}
int main(int argc, char* argv[])
{
int i, j;
int samplesOnGroup = 60;
int trainingGroups = 4;
int allSamples = samplesOnGroup * trainingGroups;
IplImage *img2;
cout << "Vector quantization..." << endl;
collectclasscentroids();
vector<Mat> descriptors = bowTrainer.getDescriptors();
int count = 0;
for (vector<Mat>::iterator iter = descriptors.begin(); iter != descriptors.end(); iter++)
{
count += iter->rows;
}
cout << "Clustering " << count << " features" << endl;
//choosing cluster's centroids as dictionary's words
Mat dictionary = bowTrainer.cluster();
bowDE.setVocabulary(dictionary);
cout << "Extracting histograms in the form of BOW for each image " << endl;
Mat labels(0, 1, CV_32FC1);
Mat trainingData(0, dictionarySize, CV_32FC1);
int k = 0;
vector<KeyPoint> keypoint1;
Mat bowDescriptor1;
//extracting histogram in the form of bow for each image
for (j = 1; j <= trainingGroups; j++)
for (i = 1; i <= samplesOnGroup; i++)
{
sprintf(ch, "%s%d%s%d%s", "eval/", j, " (", i, ").jpg");
const char* imageName = ch;
img2 = cvLoadImage(imageName, 0);
detector.detect(img2, keypoint1);
bowDE.compute(img2, keypoint1, bowDescriptor1);
trainingData.push_back(bowDescriptor1);
labels.push_back((float)j);
}
//Setting up SVM parameters
CvSVMParams params;
params.kernel_type = CvSVM::RBF;
params.svm_type = CvSVM::C_SVC;
params.gamma = 0.50625000000000009;
params.C = 312.50000000000000;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 0.000001);
CvSVM svm;
printf("%s\n", "Training SVM classifier");
bool res = svm.train(trainingData, labels, cv::Mat(), cv::Mat(), params);
cout << "Processing evaluation data..." << endl;
Mat groundTruth(0, 1, CV_32FC1);
Mat evalData(0, dictionarySize, CV_32FC1);
k = 0;
vector<KeyPoint> keypoint2;
Mat bowDescriptor2;
Mat results(0, 1, CV_32FC1);;
for (j = 1; j <= trainingGroups; j++)
for (i = 1; i <= samplesOnGroup; i++)
{
sprintf(ch, "%s%d%s%d%s", "eval/", j, " (", i, ").jpg");
printf("\rEvaluating : %3d %%", ((((j - 1)*samplesOnGroup) + i) * 100 / allSamples));
const char* imageName = ch;
img2 = cvLoadImage(imageName, 0);
detector.detect(img2, keypoint2);
bowDE.compute(img2, keypoint2, bowDescriptor2);
evalData.push_back(bowDescriptor2);
groundTruth.push_back((float)j);
float response = svm.predict(bowDescriptor2);
results.push_back(response);
}
printf("\n");
//calculate the number of unmatched classes
double errorRate = (double)countNonZero(groundTruth - results) / evalData.rows;
printf("Error rate = %.2f %% \n", errorRate);
printf("Press ENTER to exit...");
getchar();
return 0;
}
int main( int argc, char* argv[])
{
// jmena souboru pro zpracovani
string imageName1;
string imageName2;
// zpracovani parametru prikazove radky
for( int i = 1; i < argc; i++){
if( string(argv[ i]) == "-i1" && i + 1 < argc){
imageName1 = argv[ ++i];
} else if( string(argv[ i]) == "-i2" && i + 1 < argc){
imageName2 = argv[ ++i];
} else if( string(argv[ i]) == "-h"){
cout << "Use: " << argv[0] << " -i1 imageName1 -i2 imageName2" << endl;
cout << "Merges two images into one. The images have to share some common area and have to be taken from one location." << endl;
return 0;
} else {
cerr << "Error: Unrecognized command line parameter \"" << argv[ i] << "\" use -h to get more information." << endl;
}
}
// kontrola zadani parametru
if( imageName1.empty() || imageName2.empty()){
cerr << "Error: Some mandatory command line options were not specified. Use -h for more information." << endl;
return -1;
}
// nacteni sedotonovych obrazku
Mat img1 = imread( imageName1, 0);
Mat img2 = imread( imageName2, 0);
if( img1.data == NULL || img2.data == NULL){
cerr << "Error: Failed to read input image files." << endl;
return -1;
}
// SURF detektor lokalnich oblasti
SurfFeatureDetector detector;
// samotna detekce lokalnich priznaku
vector< KeyPoint> keyPoints1, keyPoints2;
detector.detect( img1, keyPoints1);
detector.detect( img2, keyPoints2);
cout << keyPoints1.size() << " " << keyPoints2.size();
// extraktor SURF descriptoru
SurfDescriptorExtractor descriptorExtractor;
// samonty vypocet SURF descriptoru
Mat descriptors1, descriptors2;
descriptorExtractor.compute( img1, keyPoints1, descriptors1);
descriptorExtractor.compute( img2, keyPoints2, descriptors2);
// tento vektor je pouze pro ucely funkce hledajici korespondence
vector< Mat> descriptorVector2;
descriptorVector2.push_back( descriptors2);
// objekt, ktery dokaze snad pomerne efektivne vyhledavat podebne vektory v prostorech s vysokym poctem dimenzi
FlannBasedMatcher matcher;
// Pridani deskriptoru, mezi kterymi budeme pozdeji hledat nejblizsi sousedy
matcher.add( descriptorVector2);
// Vytvoreni vyhledavaci struktury nad vlozenymi descriptory
matcher.train();
// nalezeni nejpodobnejsich descriptoru (z obrazku 2) pro descriptors1 (oblasti z obrazku 1)
vector<cv::DMatch > matches;
matcher.match( descriptors1, matches);
// serazeni korespondenci od nejlepsi (ma nejmensi vzajemnou vzdalenost v prostoru descriptoru)
sort( matches.begin(), matches.end(), compareDMatch);
// pouzijeme jen 200 nejlepsich korespondenci
matches.resize( min( 200, (int) matches.size()));
// pripraveni korespondujicich dvojic
Mat img1Pos( matches.size(), 1, CV_32FC2);
Mat img2Pos( matches.size(), 1, CV_32FC2);
// naplneni matic pozicemi
for( int i = 0; i < (int)matches.size(); i++){
img1Pos.at< Vec2f>( i)[0] = keyPoints1[ matches[ i].queryIdx].pt.x;
img1Pos.at< Vec2f>( i)[1] = keyPoints1[ matches[ i].queryIdx].pt.y;
img2Pos.at< Vec2f>( i)[0] = keyPoints2[ matches[ i].trainIdx].pt.x;
img2Pos.at< Vec2f>( i)[1] = keyPoints2[ matches[ i].trainIdx].pt.y;
}
// Doplnte vypocet 3x3 matice homografie s vyuzitim algoritmu RANSAC. Pouzijte jdenu funkci knihovny OpenCV.
/** FILL DONE **/
Mat homography = findHomography( img1Pos, img2Pos, CV_RANSAC );
// vystupni buffer pro vykresleni spojenych obrazku
Mat outputBuffer( 1024, 1280, CV_8UC1);
// Vysledny spojeny obraz budeme chtit vykreslit do outputBuffer tak, aby se dotykal okraju, ale nepresahoval je.
// "Prilepime" obrazek 2 k prvnimu. Tuto "slepeninu" je potreba zvetsit a posunout, aby byla na pozadovane pozici.
// K tomuto potrebujeme zjistit maximalni a minimalni souradnice vykreslenych obrazu. U obrazu 1 je to jednoduche, minima a maxima se
// ziskaji primo z rozmeru obrazu. U obrazku 2 musime pomoci drive ziskane homografie promitnout do prostoru obrazku 1 jeho rohove body.
float minX = 0;
float minY = 0;
float maxX = (float) img1.cols;
float maxY = (float) img1.rows;
// rohy obrazku 2
vector< Vec3d> corners;
corners.push_back( Vec3d( 0, 0, 1));
corners.push_back( Vec3d( img2.cols, 0, 1));
corners.push_back( Vec3d( img2.cols, img2.rows, 1));
corners.push_back( Vec3d( 0, img2.rows, 1));
// promitnuti rohu obrazku 2 do prosotoru obrazku 1 a upraveni minim a maxim
for( int i = 0; i < (int)corners.size();i ++){
// Doplnte transformaci Mat( corners[ i]) do prostoru obrazku 1 pomoci homography.
// Dejte si pozor odkud kam homography je. Podle toho pouzijte homography, nebo homography.inv().
/**FILL ALMOST DONE**/
Mat projResult = homography.inv() * Mat( corners[ i]);// * homography;
minX = std::min( minX, (float) (projResult.at<double>( 0) / projResult.at<double>( 2)));
maxX = std::max( maxX, (float) (projResult.at<double>( 0) / projResult.at<double>( 2)));
minY = std::min( minY, (float) (projResult.at<double>( 1) / projResult.at<double>( 2)));
maxY = std::max( maxY, (float) (projResult.at<double>( 1) / projResult.at<double>( 2)));
}
// Posuneme a zvetseme/zmenseme vysledny spojeny obrazek tak, by vysledny byl co nejvetsi, ale aby byl uvnitr vystupniho bufferu.
// Zmena velikosti musi byt takova, aby se nam vysledek vesel na vysku i na sirku
double scaleFactor = min( outputBuffer.cols / ( maxX - minX), outputBuffer.rows / ( maxY - minY));
// Doplnte pripraveni matice, ktera zmeni velikost (scaleMatrix) o scaleFactor a druhe (translateMatrix), ktera posune vysledek o -minX a -minY.
// Po tomto bude obrazek ve vystupnim bufferu.
Mat scaleMatrix = Mat::eye( 3, 3, CV_64F);
Mat translateMatrix = Mat::eye( 3, 3, CV_64F);
/**FILL DONE**/
scaleMatrix.at<double>(0,0) = scaleFactor;
scaleMatrix.at<double>(1,1) = scaleFactor;
translateMatrix.at<double>(0,2) = -(double)minX;
translateMatrix.at<double>(1,2) = -(double)minY;
cout << endl << minX << " " << minY << endl << translateMatrix << endl << endl;
Mat centerMatrix = scaleMatrix * translateMatrix;
// Transformace obrazku 1
warpPerspective( img1, outputBuffer, centerMatrix, outputBuffer.size(), 1, BORDER_TRANSPARENT);
// Transformace obrazku 2
warpPerspective( img2, outputBuffer, centerMatrix * homography.inv(), outputBuffer.size(), 1, BORDER_TRANSPARENT);
cout << "normMatrix" << endl;
cout << centerMatrix << endl << endl;
cout << "normMatrix" << endl;
cout << homography << endl << endl;
#if VISUAL_OUTPUT
imshow( "IMG1", img1);
imshow( "IMG2", img2);
imshow( "MERGED", outputBuffer);
waitKey();
#endif
}
void regressionTest()
{
assert( !dextractor.empty() );
// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename );
if( img.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
vector<KeyPoint> keypoints;
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
if( fs.isOpened() )
{
read( fs.getFirstTopLevelNode(), keypoints );
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows );
if( calcDescriptors.rows != (int)keypoints.size() )
{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
else
{
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
SurfFeatureDetector fd;
fd.detect(img, keypoints);
write( fs, "keypoints", keypoints );
}
else
{
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
void SURFfeature::SURFfeaturematch(vector<CvPoint2D32f> &match_query, vector<CvPoint2D32f> &match_train)
{
std::vector<cv::KeyPoint> kpts;
std::vector<cv::KeyPoint> Quepts;
SurfFeatureDetector surf (Th1 );
surf.detect ( ImageGray1 , kpts ) ;
surf.detect ( ImageGray2 , Quepts ) ;
// extract descriptors
SurfDescriptorExtractor fde ;
cv::Mat desc;
cv::Mat src;
fde . compute ( ImageGray1 , kpts , desc ) ;
fde . compute ( ImageGray2 , Quepts , src );
//brute−force matching of des criptors
//BruteForceMatcher<L2<float>> matcher ; //L2 norm
std::vector<cv::DMatch> vec_matches;
//matcher. match ( desc , src , vec_matches ) ;
cv::Mat distance;
int k=2; // find two neighbors
cv::Mat results;
cv::flann::Index flannIndex(desc, cv::flann::KDTreeIndexParams(), cvflann::FLANN_DIST_EUCLIDEAN);
// search (nearest neighbor)
flannIndex.knnSearch(src, results, distance, k, cv::flann::SearchParams() );
float ErroRatio = 0.8;
for(unsigned int i=0; i<kpts.size(); i++)
{
// Apply NNDR
//if(results.at<int>(i,0) >= 0 && results.at<int>(i,1) >= 0 && distance.at<float>(i,0) <= ErroRatio * distance.at<float>(i,1))
//{
match_query.push_back(kpts.at(i).pt);
int idx =results.at<int>(i,0);
match_train.push_back(Quepts.at(idx).pt);
//}
}
// for (size_t i = 0; i < vec_matches.size(); ++i)
// {
// const DMatch& match = vec_matches[i];
// CvPoint2D32f pointA;
// CvPoint2D32f pointB;
// pointA.x=kpts[match.queryIdx].pt.x;
// pointA.y=kpts[match.queryIdx].pt.y;
//
// pointB.x=Quepts[match.trainIdx].pt.x;
// pointB.y=Quepts[match.trainIdx].pt.y;
//
// match_query.push_back(pointA);
// match_train.push_back(pointB);
// }
desc.release();
src.release();
}
void sift_demo( Mat dst,Mat dst2 ){
SurfFeatureDetector detector (1500);
std::vector<KeyPoint> keypoints_1,keypoints_2;
detector.detect(dst, keypoints_1);
detector.detect(dst2, keypoints_2);
//drawKeypoints(dst, keypoints_1, dst);
SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;
extractor.compute( dst, keypoints_1, descriptors_1 );
extractor.compute( dst2, keypoints_2, descriptors_2 );
//-- Step 3: Matching descriptor vectors with a brute force matcher
BFMatcher matcher(NORM_L2);
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );
pcl::PointXYZRGBA point;
double max_dist = 0; double min_dist = 100;
//filtrage des associations ratées
for( int i = 0; i < descriptors_1.rows; i++ )
{ double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ ){
if( matches[i].distance < 3*min_dist ){
good_matches.push_back( matches[i]);
}
}
nuage2.clear();
//nuage2=nuage;
for(int i=0;i<dst.cols;i++){
for (int j=0;j<dst.rows;j++){
//for(int i=0;i<matches.size;i++){
point.x=i;
point.y=j;
point.g=nuage.at(i,j).g;//dst.at<cv::Vec3b>(i,j)[1];
point.b=nuage.at(i,j).b;//dst.at<cv::Vec3b>(i,j)[2];
point.r=nuage.at(i,j).r;
//if (depth.at<float>(i,j)==depth.at<float>(i,j)){
if (nuage.at(i,j).z==nuage.at(i,j).z){
// std::cout<<depth.at<float>(i,j)<<endl;
point.z=nuage.at(i,j).z*100;//depth.at<float>(j,i)*100;
}
nuage2.push_back(point);
}
}
//-- Draw matches
Mat img_matches;
drawMatches( dst, keypoints_1, dst2, keypoints_2, good_matches, img_matches );
imshow("Matches", img_matches );
}