void CamCalib::CalibrateCamera(CvSize &image_size)
{
if (_intrinsic_matrix) cvReleaseMat(&_intrinsic_matrix);
if (_distortion_coeffs) cvReleaseMat(&_distortion_coeffs);
if (_mapx) cvReleaseImage(&_mapx);
if (_mapy) cvReleaseImage(&_mapy);
_intrinsic_matrix = cvCreateMat(3, 3, CV_32FC1);
_distortion_coeffs = cvCreateMat(4, 1, CV_32FC1);
// 초점 거리 비율을 1.0으로 설정하여 내부행렬을 초기화
CV_MAT_ELEM( *_intrinsic_matrix, float, 0, 0 ) = 1.0f;
CV_MAT_ELEM( *_intrinsic_matrix, float, 1, 1 ) = 1.0f;
// 실제 카메라 보정함수
cvCalibrateCamera2 (_object_points, _image_points, _point_counts, image_size, _intrinsic_matrix, _distortion_coeffs, NULL, NULL, 0);
// 내부 행렬과 왜곡 계수를 파일로 저장
cvSave("Intrinsics.xml", _intrinsic_matrix);
cvSave("Distortion.xml", _distortion_coeffs);
// 왜곡 제거를 위한 지도를 생성
_mapx = cvCreateImage( image_size, IPL_DEPTH_32F, 1 );
_mapy = cvCreateImage( image_size, IPL_DEPTH_32F, 1 );
// 왜곡 제거를 위한 지도를 구성
cvInitUndistortMap (_intrinsic_matrix, _distortion_coeffs, _mapx, _mapy);
}
bool Classifier::kmeans(DataSet *data)
{
cout << "------------------------------------------" << endl;
cout << "\t\tK-Means" << endl;
if (kmeans_load)
{
cout << "Loading..." << endl;
centers = (CvMat *)cvLoad("centers.dat");
data->samples = (CvMat *)cvLoad("samples.dat");
data->responses = (CvMat *)cvLoad("responses.dat");
data->centers = centers;
cout << "Loaded Successfully" << endl;
return true;
}
CvMat *desc = data->kmeans_input();
data->clusters = cvCreateMat(data->num_samples, 1, CV_32SC1);
centers = cvCreateMat(num_clusters, SURF_SIZE, CV_32FC1);
data->centers = centers;
cout << "Running with k = " << num_clusters << endl;
flush(cout);
cvKMeans2(
desc, // samples
num_clusters, // clusters
data->clusters, // labels
cvTermCriteria(
CV_TERMCRIT_EPS|CV_TERMCRIT_ITER, // End criteria
10, // Max iter
0.1), //accuracy
1, // attempts
&rng, //rng
0, // flags
centers, // centers
NULL // compactness
);
if (kmeans_save)
{
cvSave("centers.dat", centers);
cvSave("samples.dat", data->cluster_samples() );
cvSave("responses.dat", data->cluster_responses() );
cout << "Saved!" << endl;
}
cvReleaseMat(&desc);
data->to_kmeans = NULL;
return true;
}
int main(int argc, char * argv[])
{
char keys[1<<12];
sprintf(keys,
"{ s | solver | | location of solver file }"
"{ tr | trainsize | 5000 | number of training samples }"
"{ ts | testsize | 1000 | number of testing samples }"
"{ h | help | false | display this help message }");
CvCommandLineParser parser(argc,argv,keys);
const int display_help = parser.get<bool>("help");
if (display_help){parser.printParams();return 0;}
const char * solver_filename = parser.get<string>("solver").c_str();
Network * cnn = new Network();
cnn->loadSolver(solver_filename);
const char * training_filename = "data/svhn/train/%d.png";
const char * response_filename = "data/svhn/train/digitStruct.xml";
const char * testing_filename = "data/svhn/test/%d.png";
const char * expected_filename = "data/svhn/test/digitStruct.xml";
const char * training_filename_xml = cnn->solver()->training_filename();
const char * response_filename_xml = cnn->solver()->response_filename();
const char * testing_filename_xml = cnn->solver()->testing_filename();
const char * expected_filename_xml = cnn->solver()->expected_filename();
const int n_train_samples = parser.get<int>("trainsize");
const int n_test_samples = parser.get<int>("testsize");
const int ndigits = 3;
fprintf(stderr,"Loading SVHN Images ...\n");
CvMat * response = icvReadSVHNLabels((char*)response_filename,n_train_samples);
CvMat * training = icvReadSVHNImages((char*)training_filename,response);
assert(CV_MAT_TYPE(training->type)==CV_32F);
CvMat * expected = icvReadSVHNLabels((char*)expected_filename,n_test_samples);
CvMat * testing = icvReadSVHNImages((char*) testing_filename,expected);
CvMat * response_mat = icvConvertLabels(response);
CvMat * expected_mat = icvConvertLabels(expected);
fprintf(stderr,"%d training samples generated!\n", training->rows);
fprintf(stderr,"%d testing samples generated!\n", testing->rows);
cvSave(training_filename_xml,training);
cvSave(response_filename_xml,response_mat);
cvSave( testing_filename_xml,testing);
cvSave(expected_filename_xml,expected_mat);
cvReleaseMat(&training);
cvReleaseMat(&response);
cvReleaseMat(&response_mat);
cvReleaseMat(&testing);
cvReleaseMat(&expected);
cvReleaseMat(&expected_mat);
return 0;
}
int mixImages(char *filename, char *filename2, char *output) {
image1 = cvLoadImage(filename, 1);
assert( image1 != 0 );
image2 = cvLoadImage(filename2, 1);
assert( image2 != 0 );
// размер шаблона
int width = image2->width;
int height = image2->height;
// устанавливаем область интереса
cvSetImageROI(image1, cvRect(0, 0 , width, height));
// взвешенная сумма
cvAddWeighted(image1, 0.5, image2, 0.5, 0.0, result);
// освобождаем область интереса
cvResetImageROI(image1);
cvSave("output.jpeg", output);
// освобождаем ресурсы
cvReleaseImage( &image1 );
cvReleaseImage( &image2 );
cvReleaseImage( &result );
return 0;
}
int main( int argc, char** argv )
{
const char* size_opt = "--size=";
char comment[1024];
CvHaarClassifierCascade* cascade = 0;
CvSize size;
help();
if( argc != 4 || strncmp( argv[1], size_opt, strlen(size_opt) ) != 0 )
{
help();
return -1;
}
sscanf( argv[1], "--size=%ux%u", &size.width, &size.height );
cascade = cvLoadHaarClassifierCascade( argv[2], size );
if( !cascade )
{
fprintf( stderr, "Input cascade could not be found/opened\n" );
return -1;
}
sprintf( comment, "Automatically converted from %s, window size = %dx%d", argv[2], size.width, size.height );
cvSave( argv[3], cascade, 0, comment, cvAttrList(0,0) );
return 0;
}
CvMat* BackgroundModel::_computeHomography()
{
int n = 4; // 4 vertices
CvMat *src = cvCreateMat(n, 2, CV_32FC1);
CvMat *dst = cvCreateMat(n, 2, CV_32FC1);
CvMat *homography = cvCreateMat(3, 3, CV_32FC1);
CV_MAT_ELEM(*src, float, 0, 0) = _vertex[0].x; // topleft
CV_MAT_ELEM(*src, float, 0, 1) = _vertex[0].y;
CV_MAT_ELEM(*src, float, 1, 0) = _vertex[1].x; // topright
CV_MAT_ELEM(*src, float, 1, 1) = _vertex[1].y;
CV_MAT_ELEM(*src, float, 2, 0) = _vertex[2].x; // bottomright
CV_MAT_ELEM(*src, float, 2, 1) = _vertex[2].y;
CV_MAT_ELEM(*src, float, 3, 0) = _vertex[3].x; // bottomleft
CV_MAT_ELEM(*src, float, 3, 1) = _vertex[3].y;
CV_MAT_ELEM(*dst, float, 0, 0) = 0.0f; // topleft
CV_MAT_ELEM(*dst, float, 0, 1) = 0.0f;
CV_MAT_ELEM(*dst, float, 1, 0) = 1.0f; // topright
CV_MAT_ELEM(*dst, float, 1, 1) = 0.0f;
CV_MAT_ELEM(*dst, float, 2, 0) = 1.0f; // bottomright
CV_MAT_ELEM(*dst, float, 2, 1) = 1.0f;
CV_MAT_ELEM(*dst, float, 3, 0) = 0.0f; // bottomleft
CV_MAT_ELEM(*dst, float, 3, 1) = 1.0f;
cvFindHomography(src, dst, homography);
cvSave("data/bgmodel/homography.yaml", homography);
cvReleaseMat(&src);
cvReleaseMat(&dst);
return homography;
}
bool VisionPipeLine::storeLocalizationCalibrationData() {
if (_calibrationData.bindedPoints.size() < 4) {
return false;
}
cv::Mat_<float> mat(_calibrationData.bindedPoints.size(),2);
for (int pos = 0; pos < _calibrationData.bindedPoints.size() ;pos ++) {
mat(pos, 0) = _calibrationData.bindedPoints[pos].x;
mat(pos, 1) = _calibrationData.bindedPoints[pos].y;
}
std::string filepath = getPlatformConfigPrefix();
filepath+= FILEPATH_LOC_CALIB_FOLDER;
if (g_config_bundle.model_id == 1) {
filepath+= FILEPATH_CONFIG_NEWMODEL_PREFIX;
}
filepath+=FILEPATH_LOC_CALIB_DATA;
try {
CvMat tmpMat = (CvMat)mat;
cvSave(filepath.c_str(), &tmpMat);
} catch (...) {
printf("Cannot save the localization data to external files.\n");
}
return true;
}
void CvMatrix::save( const char* filename, const char* matname, const int* params )
{
if( !matrix )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, matrix, matname );
else
cvSaveImage( filename, matrix, params );
}
void CvImage::save( const char* filename, const char* imgname, const int* params )
{
if( !image )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, image, imgname );
else
cvSaveImage( filename, image, params );
}
int calcSSIM :: print_map()
{
if (ssim_map == NULL)
{
cout<<"Error>> No Index_map_created.\n";
return 0;
}
cvSave("imgSSIM.xml", ssim_map, NULL, "TESTing Index map");
return 1;
}
int calcQualityIndex :: print_map()
{
if (image_quality_map == NULL)
{
cout<<"Error>> No Index_map_created.\n";
return 0;
}
cvSave("imgQI.xml", image_quality_map, NULL, "TESTing Index map");
return 1;
}
int main( int argc, char* argv[] ) {
const char* filenameVi = argc == 2 ? argv[1] : "..\\..\\..\\MVI_0033.MOV";
captVi = cvCreateFileCapture( filenameVi );
CvMat* intrinsic = cvCreateMat( 3, 3, CV_32FC1 );
CvMat* distortion = cvCreateMat( 5, 1, CV_32FC1 );
const int board_w = /*atoi(argv[2])*/ 8;
const int board_h = /*atoi(argv[3])*/ 6;
int n_boards = /*atoi(argv[4])*/ (int)cvGetCaptureProperty( captVi, CV_CAP_PROP_FRAME_COUNT );
int nb = (int)(n_boards/1000);
calibrCam( captVi, board_w, board_h, nb, intrinsic, distortion );
cvSave( "Intrinsics_3500_18_fK3.xml", intrinsic );
cvSave( "Distortion_3500_18_fK3.xml", distortion );
return 0;
}
bool BackgroundModel::save()
{
ofstream f("data/bgmodel/vertices.txt");
if(!f.is_open()) {
std::cerr << "[error] BackgroundModel::save - can't open vertices file" << std::endl;
return false;
}
for(int i=0; i<4; i++)
f << _vertex[i].x << " " << _vertex[i].y << std::endl;
f << std::endl;
for(int i=0; i<4; i++)
f << _3dvertex[i].x << " " << _3dvertex[i].y << " " << _3dvertex[i].z << std::endl;
cvSave("data/bgmodel/image.yaml", _image);
cvSave("data/bgmodel/mask.yaml", _mask);
cvSave("data/bgmodel/mean.yaml", _mean);
cvSave("data/bgmodel/sd.yaml", _sd);
cvSave("data/bgmodel/unmasked.yaml", _unmaskedImage);
return true;
}
void CvMatrix::save( const char* filename, const char* matname, const int* params )
{
if( !matrix )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, matrix, matname );
#ifdef HAVE_OPENCV_HIGHGUI
else
cvSaveImage( filename, matrix, params );
#else
(void)params;
#endif
}
void CvImage::save( const char* filename, const char* imgname, const int* params )
{
if( !image )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, image, imgname );
#ifdef HAVE_OPENCV_HIGHGUI
else
cvSaveImage( filename, image, params );
#else
(void)params;
#endif
}
// Prints all index maps of all the levels into different xml files
int calcMSSSIM :: print_map()
{
if (ms_ssim_map == NULL)
{
cout<<"Error>> No Index_map_created.\n";
return 0;
}
char file_name[50];
// Printing the MS-SSIM_Map
for (int i=0; i < level; i++)
{
sprintf(file_name, "img_MS-SSIM_map_level_%d.xml", i);
cvSave(file_name, ms_ssim_map[i], NULL, "Testing MS-SSIM Index map");
}
return 1;
}
void CvMatrix::save( const char* filename, const char* matname, const int* params )
{
if( !matrix )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, matrix, matname );
else
{
if( save_image )
save_image( filename, matrix, params );
else
CV_Error( CV_StsNotImplemented,
"Saving a matrixe in such a format requires HigGUI.\n"
"Link it to your program and call any function from it\n" );
}
}
void ShapeClassifier::Save() {
if (!isTrained) return;
Classifier::Save();
USES_CONVERSION;
WCHAR filename[MAX_PATH];
// save the contour data
wcscpy(filename,directoryName);
wcscat(filename, FILE_CONTOUR_NAME);
const char* contour_attrs[] = {
"recursive", "1",
0
};
cvSave(W2A(filename), templateContours, 0, 0, cvAttrList(contour_attrs,0));
}
void saveFramePoses(const string& dirname, vector<FramePose>& framePoses) {
// TODO: for now, turn poses into a CvMat of numOfKeyFrames x 7 (index, rod[3], shift[3])
double _poses[framePoses.size()*7];
CvMat _framePoses = cvMat(framePoses.size(), 7, CV_64FC1, _poses);
int i=0;
for (vector<FramePose>::const_iterator iter= framePoses.begin(); iter!=framePoses.end(); iter++,i++) {
_poses[i*7 + 0] = iter->mIndex;
_poses[i*7 + 1] = iter->mRod.x;
_poses[i*7 + 2] = iter->mRod.y;
_poses[i*7 + 3] = iter->mRod.z;
_poses[i*7 + 4] = iter->mShift.x;
_poses[i*7 + 5] = iter->mShift.y;
_poses[i*7 + 6] = iter->mShift.z;
}
if (i>0) {
string framePosesFilename("framePoses.xml");
cvSave((dirname+framePosesFilename).c_str(), &_framePoses, "index-rod3-shift3", "indices, rodrigues and shifts w.r.t. starting frame");
}
}
void CvMatrix::save( const char* filename, const char* matname )
{
CV_FUNCNAME( "CvMatrix::write" );
__BEGIN__;
if( !matrix )
return;
if( icvIsXmlOrYaml( filename ) )
cvSave( filename, matrix, matname );
else
{
if( save_image )
save_image( filename, matrix );
else
CV_ERROR( CV_StsNotImplemented,
"Saving a matrixe in such a format requires HigGUI.\n"
"Link it to your program and call any function from it\n" );
}
__END__;
}
bool ImageIO::saveTexture(const string &filename, Texture *texture)
{
ensureImageSize(texture->getWidth(), texture->getHeight(), texture->getChannelCount());
bool saved = false;
if(GL_UNSIGNED_BYTE == texture->getDataType())
{
texture->transferFromTexture(m_imageHandle.get());
bool reorderChannels = texture->getFormat() == GL_RGBA || texture->getFormat() == GL_RGB;
cvFlip(m_imageHandle.get(), 0);
saved = saveImage(filename, m_imageHandle.get(), reorderChannels);
}
else if(GL_FLOAT == texture->getDataType())
{
shared_ptr<IplImage> floatImage = shared_ptr<IplImage>(
cvCreateImage(cvSize(texture->getWidth(), texture->getHeight()), IPL_DEPTH_32F, 3),
[](IplImage* ptr){ cvReleaseImage(&ptr); });
texture->transferFromTexture(floatImage.get());
cvSave(filename.c_str(), floatImage.get());
}
return saved;
}
/*void saveInputTraining()
*
*inputs: Cvmat trainInputs CvMat trainOutputs CvMat validation inputs CvMat Validation Outputs
*
*saves files with training data . Input file
*
*
*/
void saveInputAndOutput(CvMat* input,CvMat* output,char* filenameInput,char*filenameOutput ){
cvSave( filenameInput, input);
cvSave(filenameOutput, output);
}
void bird_eye() {
int board_n = board_w * board_h;
CvSize board_sz = cvSize(board_w, board_h);
CvMat *intrinsic = (CvMat*) cvLoad("Intrinsics.xml");
CvMat *distortion = (CvMat*) cvLoad("Distortion.xml");
IplImage* image = cvLoadImage("./Resource/bird-eye.jpg", 1);
IplImage* gray_image = cvCreateImage(cvGetSize(image), 8, 1);
cvCvtColor(image, gray_image, CV_BGR2GRAY);
IplImage* mapx = cvCreateImage(cvGetSize(image), IPL_DEPTH_32F, 1);
IplImage* mapy = cvCreateImage(cvGetSize(image), IPL_DEPTH_32F, 1);
cvInitUndistortMap(
intrinsic,
distortion,
mapx,
mapy
);
IplImage* t = cvCloneImage(image);
cvRemap(t, image, mapx, mapy);
cvNamedWindow("Chessboard");
cvShowImage("Chessboard", image);
int c = cvWaitKey(-1);
CvPoint2D32f* corners = new CvPoint2D32f[board_n];
int corner_count = 0;
int found = cvFindChessboardCorners(
image,
board_sz,
corners,
&corner_count,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS
);
if(!found){
printf("couldn't aquire chessboard!\n");
return;
}
cvFindCornerSubPix(
gray_image,
corners,
corner_count,
cvSize(11, 11),
cvSize(-1, -1),
cvTermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 30, 0.1)
);
CvPoint2D32f objPts[4], imgPts[4];
objPts[0].x = 0; objPts[0].y = 0;
objPts[1].x = board_w - 1; objPts[1].y = 0;
objPts[2].x = 0; objPts[2].y = board_h - 1;
objPts[3].x = board_w - 1; objPts[3].y = board_h - 1;
imgPts[0] = corners[0];
imgPts[1] = corners[board_w - 1];
imgPts[2] = corners[(board_h - 1) * board_w];
imgPts[3] = corners[(board_h - 1) * board_w + board_w - 1];
cvCircle(image, cvPointFrom32f(imgPts[0]), 9, CV_RGB(0, 0, 255), 3);
cvCircle(image, cvPointFrom32f(imgPts[1]), 9, CV_RGB(0, 255, 0), 3);
cvCircle(image, cvPointFrom32f(imgPts[2]), 9, CV_RGB(255, 0, 0), 3);
cvCircle(image, cvPointFrom32f(imgPts[3]), 9, CV_RGB(255, 255, 0), 3);
cvDrawChessboardCorners(
image,
board_sz,
corners,
corner_count,
found
);
cvShowImage("Chessboard", image);
CvMat *H = cvCreateMat(3, 3, CV_32F);
cvGetPerspectiveTransform(objPts, imgPts, H);
float z = 25;
int key = 0;
IplImage * birds_image = cvCloneImage(image);
cvNamedWindow("Birds_Eye");
while(key != 27) {
CV_MAT_ELEM(*H, float, 2, 2) = z;
cvWarpPerspective(
image,
birds_image,
H,
CV_INTER_LINEAR| CV_WARP_INVERSE_MAP | CV_WARP_FILL_OUTLIERS
);
cvShowImage("Birds_Eye", birds_image);
key = cvWaitKey();
if(key == 'u') z += 0.5;
if(key == 'd') z -= 0.5;
}
cvSave("H.xml", H);
}
void MyLable::set_scaled_button()
{
int i, j, comp_count = 0;
CvMat* color_tab = 0;
CvSeq* contours = 0;
cvSave("markes.xml",marker_mask);
cvClearMemStorage(storage);
cvFindContours( marker_mask, storage, &contours, sizeof(CvContour),
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
cvZero( markes );
for( ; contours != 0; contours = contours->h_next, comp_count++ )
{
//cvDrawContours(markers, contours, cvScalarAll(comp_count+1),
//cvScalarAll(comp_count+1), -1, -1, 8, cvPoint(0,0) );
cvDrawContours(markes, contours, cvScalarAll(comp_count+1),
cvScalarAll(comp_count+1), 1, -1, 8, cvPoint(0,0) );
}
color_tab = cvCreateMat( 1, comp_count, CV_8UC3 );//创建随机颜色列表
for( i = 0; i < comp_count; i++ ) //不同的整数标记
{
uchar* ptr = color_tab->data.ptr + i*3;
ptr[0] = (uchar)(cvRandInt(&rng)%180 + 50);
ptr[1] = (uchar)(cvRandInt(&rng)%180 + 50);
ptr[2] = (uchar)(cvRandInt(&rng)%180 + 50);
}
double t = (double)cvGetTickCount();
cvSave("img0.xml",markes);
cvWatershed( img0, markes );
cvSave("img1.xml",markes);
// cvShowImage("imgo",img0);
t = (double)cvGetTickCount() - t;
printf( "exec time = %gms\n", t/(cvGetTickFrequency()*1000.) );
/*********/
for( i = 0; i < markes->height; i++ )
for( j = 0; j < markes->width; j++ )
{
int idx = CV_IMAGE_ELEM( markes, int, i, j );//markers的数据类型为IPL_DEPTH_32S
uchar* dst = &CV_IMAGE_ELEM( wshed, uchar, i, j*3 );//BGR三个通道的数是一起的,故要j*3
uchar* t_body = &CV_IMAGE_ELEM( tongue_body, uchar, i, j*3 );
uchar* src_pic = &CV_IMAGE_ELEM( img0, uchar, i, j*3 );
if( idx == -1 ) //输出时若为-1，表示各个部分的边界
{
//dst[0] = dst[1] = dst[2] = (uchar)128;
dst[2] = (uchar)255;
}
else if( idx <= 0 || idx > comp_count ) //异常情况
{
//dst[0] = dst[1] = dst[2] = (uchar)0; // should not get here
}
else if( idx == 1 ) //异常情况
{
//green first
dst[0] = (uchar)255;
t_body[0] = src_pic[0];
t_body[1] = src_pic[1];
t_body[2] = src_pic[2];
}
else if( idx == 2 ) //异常情况
{
//blue second
dst[1] = (uchar)255;
}
else if( idx == 3 ) //异常情况
{
//red third
dst[2] = (uchar)255;
}
else //正常情况
{
uchar* ptr = color_tab->data.ptr + (idx-1)*3;
dst[0] = ptr[0]; dst[1] = ptr[1]; dst[2] = ptr[2];
}
}
cvShowImage("img_gray",img_gray);
cvShowImage("wshed",wshed);
cvAddWeighted( wshed, 0.5, img_gray, 0.5, 0, wshed );
//wshed.x.y=0.5*wshed.x.y+0.5*img_gray+0加权融合图像
//cvShowImage("swhed",wshed);
//cvShowImage("img_gray",img_gray);
//cvShowImage( "watershed transform", wshed );
//cvShowImage("img_final",img_final);
cvShowImage( "watershed transform", wshed );
img = Function::CjwIplToQImg(tongue_body);
image = *img;
cvReleaseMat( &color_tab );
update();
clear_list();
}
int main()
{
if(run_tests_only)
{
MyLine3D::runTest();
return 0;
}
//CvMat *camera_inner_calibration_matrix;
bool show_surf_example=false;
bool show_calibration_from_camera_and_undistortion=false;
if(show_calibration_from_camera_and_undistortion)
{
CvMat *object_points_all=0;
CvMat *image_points_all=0;
CvMat *points_count_all=0;
CvMat *camera_matr=0;
CvMat *distor_coefs=0;
CvMat *rotation_vecs=0;
CvMat *transpose_vecs=0;
vector<CvPoint2D32f> qu_calibr_points;
IplImage* frameCam1;
cvNamedWindow("WindowCam1",CV_WINDOW_KEEPRATIO);
CvCapture *captureCam1=cvCreateCameraCapture(0);
IplImage *quarterFrame;
CvPoint2D32f *cornersFounded= new CvPoint2D32f[100];
int cornersCount=0;
int result_Found=0;
// getting snapshots for inner camera calibration from video camera
bool capture_flag=false;
while(true)
{
frameCam1=cvQueryFrame(captureCam1);
quarterFrame=cvCreateImage(cvSize((frameCam1->width),(frameCam1->height)),IPL_DEPTH_8U,3);
cvCopy(frameCam1,quarterFrame);
if(capture_flag)
{
result_Found=cvFindChessboardCorners(quarterFrame,cvSize(chess_b_szW,chess_b_szH),cornersFounded,&cornersCount);//,CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS |CV_CALIB_CB_FAST_CHECK);
cvDrawChessboardCorners(quarterFrame,cvSize(chess_b_szW,chess_b_szH),cornersFounded,cornersCount,result_Found);
if(result_Found>0)
AddPointsToInnerCalibrate(qu_calibr_points,cornersFounded,cornersCount);
capture_flag=false;
cvShowImage("WindowCam1",quarterFrame);
if(result_Found>0)
cvWaitKey(0);
}
char c=cvWaitKey(33);
if(c==27)
break;
if(c==32 || c=='y' || c=='Y')
capture_flag=true;
cvShowImage("WindowCam1",quarterFrame);
cvReleaseImage(&quarterFrame);
}
cvReleaseImage(&quarterFrame);
cvReleaseCapture(&captureCam1);
cvDestroyWindow("WindowCam1");
PrintAllPointsForInnerCalibrate(qu_calibr_points,chess_b_szW*chess_b_szH);
InitCvMatPointsParametersForInnerCallibration_part1(qu_calibr_points,chess_b_szW*chess_b_szH,object_points_all,image_points_all,points_count_all,chess_b_szW,chess_b_szH);
InitOtherCameraParametersForInnerCallibration_part2(qu_calibr_points.size()/(chess_b_szW*chess_b_szH),camera_matr,distor_coefs,rotation_vecs,transpose_vecs);
double calibration_error_result=cvCalibrateCamera2(object_points_all,
image_points_all,
points_count_all,
cvSize(imgW,imgH),
camera_matr,
distor_coefs,
rotation_vecs,
transpose_vecs,
CV_CALIB_FIX_PRINCIPAL_POINT|CV_CALIB_FIX_ASPECT_RATIO|CV_CALIB_ZERO_TANGENT_DIST
);
WriteMatrixCoef(camera_matr);
WriteMatrixCoef(distor_coefs);
//camera_inner_calibration_matrix=cvCreateMat(3,3,CV_32FC1);
//cvCopy(camera_matr,camera_inner_calibration_matrix);
cvSave("camera_calibration_inner.txt",camera_matr,"camera_inner_calibration_matrix");
cvSave("camera_calibration_dist.txt",distor_coefs,"distor_coefs","coeficients of distortions");
cout<<"Total Error:"<<calibration_error_result<<endl;
cout<<"Average Calibration Error :"<<(calibration_error_result)/qu_calibr_points.size()<<endl;
//undistortion example
IplImage *frame_cur;
IplImage *undistor_image;
cvNamedWindow("cameraUndistor",CV_WINDOW_KEEPRATIO);
CvCapture *captureCam2=cvCreateCameraCapture(0);
bool undist_flag=false;
while(true)
{
frame_cur= cvQueryFrame(captureCam2);
undistor_image=cvCreateImage(cvSize((frame_cur->width),(frame_cur->height)),IPL_DEPTH_8U,3);
if(undist_flag)
{
cvUndistort2(frame_cur,undistor_image,camera_matr,distor_coefs);
}
else
{
cvCopy(frame_cur,undistor_image);
}
cvShowImage("cameraUndistor",undistor_image);
char c=cvWaitKey(33);
if(c==27)
break;
if(c=='u'||c=='U')
undist_flag=!undist_flag;
cvReleaseImage(&undistor_image);
}
cvReleaseImage(&undistor_image);
cvReleaseCapture(&captureCam2);
cvDestroyWindow("cameraUndistor");
}//ending undistortion_example
if(show_surf_example)
{
//using SURF
initModule_nonfree();// added at 16.04.2013
CvCapture* capture_cam_3=cvCreateCameraCapture(0);
cvNamedWindow("SURF from Cam",CV_WINDOW_KEEPRATIO);
cvCreateTrackbar("Hessian Level","SURF from Cam",0,1000,onTrackbarSlide1);
IplImage* buf_frame_3=0;
IplImage* gray_copy=0;
IplImage* buf_frame_3_copy=0;
CvSeq *kp1,*descr1;
CvMemStorage *storage=cvCreateMemStorage(0);
CvSURFPoint *surf_pt;
bool surf_flag=false;
while(true)
{
buf_frame_3=cvQueryFrame(capture_cam_3);
if(surf_flag)
{
surf_flag=false;
gray_copy=cvCreateImage(cvSize((buf_frame_3->width),(buf_frame_3->height)),IPL_DEPTH_8U,1);
buf_frame_3_copy=cvCreateImage(cvSize((buf_frame_3->width),(buf_frame_3->height)),IPL_DEPTH_8U,3);
cvCvtColor(buf_frame_3,gray_copy,CV_RGB2GRAY);
//cvSetImageROI(gray_copy,cvRect(280,200,40,40));
cvExtractSURF(gray_copy,NULL,&kp1,&descr1,storage,cvSURFParams(0.0,0));
cvReleaseImage(&gray_copy);
re_draw=true;
while(true)
{
if(re_draw)
{
cvCopy(buf_frame_3,buf_frame_3_copy);
double pi=acos(-1.0);
for(int i=0;i<kp1->total;i++)
{
surf_pt=(CvSURFPoint*)cvGetSeqElem(kp1,i);
if(surf_pt->hessian<min_hessian)
continue;
int pt_x,pt_y;
pt_x=(int)(surf_pt->pt.x);
pt_y=(int)(surf_pt->pt.y);
int sz=surf_pt->size;
int rad_angle=(surf_pt->dir*pi)/180;
cvCircle(buf_frame_3_copy,cvPoint(pt_x,pt_y),1/*sz*/,CV_RGB(0,255,0));
cvLine(buf_frame_3_copy,cvPoint(pt_x,pt_y),cvPoint(pt_x+sz*cosl(rad_angle),pt_y-sz*sinl(rad_angle)),CV_RGB(0,0,255));
}
cvShowImage("SURF from Cam",buf_frame_3_copy);
}
char c=cvWaitKey(33);
if(c==27)
{
break;
}
}
cvReleaseImage(&buf_frame_3_copy);
}
cvShowImage("SURF from Cam",buf_frame_3);
char ch=cvWaitKey(33);
if(ch==27)
break;
if(ch==32)
surf_flag=true;
}
if(gray_copy!=0)
cvReleaseImage(&gray_copy);
cvReleaseCapture(&capture_cam_3);
cvDestroyWindow("SURF from Cam");
}//ending SURF_example
CvFont my_font=cvFont(1,1);
cvInitFont(&my_font,CV_FONT_HERSHEY_SIMPLEX,1.0,1.0);
cvNamedWindow("twoSnapshots",CV_WINDOW_KEEPRATIO);
cvCreateTrackbar("Select LLine","twoSnapshots",0,1000,onTrackbarSlideSelectLine);
CvCapture *capture_4 = 0;
IplImage* left_img=0;
IplImage* right_img=0;
IplImage* cur_frame_buf=0;
IplImage* gray_img_left=0;
IplImage* gray_img_right=0;
IplImage* merged_images=0;
IplImage* merged_images_copy=0;
CvMat *fundamentalMatrix = 0;
vector<KeyPoint> key_points_left;
Mat descriptors_left;
vector<KeyPoint> key_points_right;
Mat descriptors_right;
//CvMemStorage *mem_stor=cvCreateMemStorage(0);*/
float min_hessian_value=1001.0f;
double startValueOfFocus = 350;
char* left_image_file_path = "camera_picture_left.png";
char* right_image_file_path = "camera_picture_right.png";
Array left_points, right_points;
left_points.init(1,1);
right_points.init(1,1);
Array forReconstructionLeftPoints, forReconstructionRightPoints;
forReconstructionLeftPoints.init(1,1);
forReconstructionRightPoints.init(1,1);
while(true)
{
char ch=cvWaitKey(33);
if(ch==27)
break;
// open left and right images
if(ch == 'o' || ch == 'O')
{
openTwoImages(left_image_file_path, right_image_file_path, left_img, right_img );
MergeTwoImages(left_img,right_img,merged_images);
}
// save both left and right images from camera
if(ch == 's' || ch == 'S')
{
if( left_img != 0 )
cvSaveImage(left_image_file_path, left_img);
if( right_img != 0)
cvSaveImage(right_image_file_path, right_img);
}
if(ch=='l'||ch=='L')
{
if(capture_4 == 0)
{
capture_4=cvCreateCameraCapture(0);
}
cur_frame_buf=cvQueryFrame(capture_4);
if(left_img==0)
left_img=cvCreateImage(cvSize(cur_frame_buf->width,cur_frame_buf->height),IPL_DEPTH_8U,3);
cvCopy(cur_frame_buf,left_img);
if(right_img == 0)
{
right_img=cvCreateImage(cvSize(cur_frame_buf->width,cur_frame_buf->height),IPL_DEPTH_8U,3);
cvCopy(cur_frame_buf,right_img);
}
MergeTwoImages(left_img,right_img,merged_images);
}
if(ch=='r'||ch=='R')
{
if(capture_4 == 0)
{
capture_4=cvCreateCameraCapture(0);
}
cur_frame_buf=cvQueryFrame(capture_4);
if(right_img==0)
right_img=cvCreateImage(cvSize(cur_frame_buf->width,cur_frame_buf->height),IPL_DEPTH_8U,3);
cvCopy(cur_frame_buf,right_img);
if(left_img == 0)
{
left_img=cvCreateImage(cvSize(cur_frame_buf->width,cur_frame_buf->height),IPL_DEPTH_8U,3);
cvCopy(cur_frame_buf,left_img);
}
MergeTwoImages(left_img,right_img,merged_images);
}
if(ch=='b'||ch=='B')
{
if(capture_4 == 0)
{
capture_4=cvCreateCameraCapture(0);
}
cur_frame_buf=cvQueryFrame(capture_4);
cvCopy(cur_frame_buf,left_img);
cvCopy(cur_frame_buf,right_img);
}
if(ch=='q'||ch=='Q' && left_img!=0)
{
//proceed left
extractFeaturesFromImage(left_img, min_hessian_value, gray_img_left, key_points_left, descriptors_left);
}
if(ch=='w'||ch=='W' && right_img!=0)
{
//proceed right
extractFeaturesFromImage(right_img, min_hessian_value, gray_img_right, key_points_right, descriptors_right);
}
if(ch=='m'||ch=='M' && left_img!=0 && right_img!=0)
{
//merge two images in to bigger one
MergeTwoImages(left_img,right_img,merged_images);
}
if(ch=='c'||ch=='C' && merged_images!=0)
{
//comparison of two images
if(fundamentalMatrix != 0)
{
cvReleaseMat(& fundamentalMatrix);
fundamentalMatrix = 0;
}
left_to_right_corresponding_points.clear();
right_to_left_corresponding_points.clear();
GetCorrespondingPointsForSURF(key_points_left,descriptors_left,key_points_right,descriptors_right,left_to_right_corresponding_points,right_to_left_corresponding_points);
}
if(ch == 'E' || ch == 'e')
{
//drawing lines for corresponding points
KeyPoint *leftPoint,*rightPoint,*leftPoint2,*rightPoint2;
int width_part=merged_images->width>>1;
/*for(int iL=0;iL<left_to_right_corresponding_points.size();iL++)
{
leftPoint=(CvSURFPoint*)cvGetSeqElem(key_points_left,left_to_right_corresponding_points[iL].first);
rightPoint=(CvSURFPoint*)cvGetSeqElem(key_points_right,left_to_right_corresponding_points[iL].second);
cvLine(merged_images,cvPoint(leftPoint->pt.x,leftPoint->pt.y),cvPoint(rightPoint->pt.x+width_part,rightPoint->pt.y),CV_RGB(255,0,0));
}*/
int sizeOfAccepptedLeftToRightCorrespondings = left_to_right_corresponding_points.size();
bool* acceptedLeftToRightCorrespondings = 0;
getAcceptedCorrespondingsForFindingModelParameters(left_to_right_corresponding_points,
key_points_left,
key_points_right,
fundamentalMatrix,
acceptedLeftToRightCorrespondings,
sizeOfAccepptedLeftToRightCorrespondings);
while(true)
{
merged_images_copy=cvCreateImage(cvSize(merged_images->width,merged_images->height),merged_images->depth,3);
cvCopy(merged_images,merged_images_copy);
int iL=selectedLeftLine;
int iR=iL;
if(iL>=left_to_right_corresponding_points.size())
iL=left_to_right_corresponding_points.size()-1;
if(iR>=right_to_left_corresponding_points.size())
iR=right_to_left_corresponding_points.size()-1;
char str[100]={0};
if(iL >= 0 )
{
bool isLeftToRightLineIsAccepted = acceptedLeftToRightCorrespondings[iL];
// difference value
sprintf(str,"%f",left_to_right_corresponding_points[iL].comparer_value);
cvPutText(merged_images_copy,str,cvPoint(0,merged_images_copy->height-40),&my_font,CV_RGB(0,255,0));
// count of Matches
sprintf(str,"%d",left_to_right_corresponding_points[iL].counterOfMatches);
cvPutText(merged_images_copy,str,cvPoint(200,merged_images_copy->height-40),&my_font,CV_RGB(255,255,0));
// median of compared values
sprintf(str,"%lf",left_to_right_corresponding_points[iL].medianOfComparedMatches);
cvPutText(merged_images_copy,str,cvPoint(250,merged_images_copy->height-40),&my_font,CV_RGB(255,0,0));
// Variance of compared values
sprintf(str,"V=%lf",left_to_right_corresponding_points[iL].Variance());
cvPutText(merged_images_copy,str,cvPoint(0,merged_images_copy->height-80),&my_font,CV_RGB(0,255,0));
// Standard deviation of compared values
sprintf(str,"SD=%lf",sqrt( left_to_right_corresponding_points[iL].Variance() ));
cvPutText(merged_images_copy,str,cvPoint(250,merged_images_copy->height-80),&my_font,CV_RGB(0,255,0));
double SD = sqrt( left_to_right_corresponding_points[iL].Variance() ) ;
double median = left_to_right_corresponding_points[iL].medianOfComparedMatches;
double compValue = left_to_right_corresponding_points[iL].comparer_value;
double mark_1_5 = median - 1.5 * SD - compValue;
// Mark 1.5
sprintf(str,"m1.5=%lf", mark_1_5);
cvPutText(merged_images_copy,str,cvPoint(0,merged_images_copy->height-120),&my_font,CV_RGB(0,255,0));
sprintf(str,"angle=%lf", left_to_right_corresponding_points[iL].degreesBetweenDeltaVector);
cvPutText(merged_images_copy,str,cvPoint(0,merged_images_copy->height-150),&my_font,CV_RGB(0,255,0));
leftPoint= &(key_points_left[ left_to_right_corresponding_points[iL].comp_pair.first ]);
rightPoint=&(key_points_right[ left_to_right_corresponding_points[iL].comp_pair.second ]);
cvLine(merged_images_copy,cvPoint(leftPoint->pt.x,leftPoint->pt.y),cvPoint(rightPoint->pt.x+width_part,rightPoint->pt.y),CV_RGB(0,255,0));
drawEpipolarLinesOnLeftAndRightImages(merged_images_copy, cvPoint(leftPoint->pt.x,leftPoint->pt.y),
cvPoint(rightPoint->pt.x,rightPoint->pt.y), fundamentalMatrix);
CvScalar color = CV_RGB(255, 0, 0);
if(isLeftToRightLineIsAccepted)
{
color = CV_RGB(0,255,0);
}
cvCircle(merged_images_copy, cvPoint(leftPoint->pt.x,leftPoint->pt.y), 5, color);
cvCircle(merged_images_copy, cvPoint(rightPoint->pt.x+width_part,rightPoint->pt.y), 5, color);
}
//cvLine(merged_images_copy,cvPoint(leftPoint->pt.x,leftPoint->pt.y),cvPoint(rightPoint->pt.x,rightPoint->pt.y),CV_RGB(255,0,255));
if(iR >= 0 )
{
sprintf(str,"%f",right_to_left_corresponding_points[iR].comparer_value);
cvPutText(merged_images_copy,str,cvPoint(width_part,merged_images_copy->height-40),&my_font,CV_RGB(255,0,0));
rightPoint2= &(key_points_right [right_to_left_corresponding_points[iR].comp_pair.first]);
leftPoint2= &(key_points_left [right_to_left_corresponding_points[iR].comp_pair.second]);
cvLine(merged_images_copy,cvPoint(leftPoint2->pt.x,leftPoint2->pt.y),cvPoint(rightPoint2->pt.x+width_part,rightPoint2->pt.y),CV_RGB(255,0,0));
}
//cvLine(merged_images_copy,cvPoint(leftPoint2->pt.x+width_part,leftPoint2->pt.y),cvPoint(rightPoint2->pt.x+width_part,rightPoint2->pt.y),CV_RGB(255,0,255));
cvShowImage("twoSnapshots",merged_images_copy);
cvReleaseImage(&merged_images_copy);
char ch2=cvWaitKey(33);
if(ch2==27)
break;
if(ch2=='z' && selectedLeftLine>0)
{
selectedLeftLine--;
}
if(ch2=='x' && selectedLeftLine<1000)
{
selectedLeftLine++;
}
if( ch2 == 'a' || ch2 == 'A')
{
acceptedLeftToRightCorrespondings[selectedLeftLine] = true;
}
if( ch2 == 'd' || ch2 == 'D')
{
acceptedLeftToRightCorrespondings[selectedLeftLine] = false;
}
}//end of while(true)
SaveAcceptedCorresspondings(
left_to_right_corresponding_points,
right_to_left_corresponding_points,
key_points_left,
key_points_right,
acceptedLeftToRightCorrespondings,
sizeOfAccepptedLeftToRightCorrespondings
);
ConvertAcceptedCorresspondingsToMyArray(left_to_right_corresponding_points,
right_to_left_corresponding_points,
key_points_left,
key_points_right,
acceptedLeftToRightCorrespondings,
sizeOfAccepptedLeftToRightCorrespondings,
left_points,
right_points
);
delete[] acceptedLeftToRightCorrespondings;
}
if( ch == 'T' || ch == 't')
{
clock_t startTime = clock();
openTwoImages(left_image_file_path, right_image_file_path, left_img, right_img );
// proceed left
extractFeaturesFromImage(left_img, min_hessian_value, gray_img_left, key_points_left, descriptors_left);
//proceed right
extractFeaturesFromImage(right_img, min_hessian_value, gray_img_right, key_points_right, descriptors_right);
//comparison of two images
if(fundamentalMatrix != 0)
{
cvReleaseMat(& fundamentalMatrix);
fundamentalMatrix = 0;
}
left_to_right_corresponding_points.clear();
right_to_left_corresponding_points.clear();
GetCorrespondingPointsForSURF(key_points_left,descriptors_left,key_points_right,descriptors_right,left_to_right_corresponding_points,right_to_left_corresponding_points);
// searching fundamental matrix and corresponding points
findFundamentalMatrixAndCorrespondingPointsForReconstruction(
left_to_right_corresponding_points,
right_to_left_corresponding_points,
fundamentalMatrix,
key_points_left,
key_points_right,
descriptors_left,
descriptors_right,
left_img,
right_img,
gray_img_left,
gray_img_right,
forReconstructionLeftPoints,
forReconstructionRightPoints,
min_hessian_value, 450);
// selecting points for finding model parameters
int sizeOfAccepptedLeftToRightCorrespondings = left_to_right_corresponding_points.size();
bool* acceptedLeftToRightCorrespondings = 0;
getAcceptedCorrespondingsForFindingModelParameters(left_to_right_corresponding_points,
key_points_left,
key_points_right,
fundamentalMatrix,
acceptedLeftToRightCorrespondings,
sizeOfAccepptedLeftToRightCorrespondings);
ConvertAcceptedCorresspondingsToMyArray(left_to_right_corresponding_points,
right_to_left_corresponding_points,
key_points_left,
key_points_right,
acceptedLeftToRightCorrespondings,
sizeOfAccepptedLeftToRightCorrespondings,
left_points,
right_points
);
delete[] acceptedLeftToRightCorrespondings;
// start process of determination parameters of model and reconstruction of scene
cv::Mat mat_left_img(left_img, true);
cv::Mat mat_right_img(right_img, true);
mainLevenbergMarkvardt_LMFIT(startValueOfFocus, "currentPLYExportFile", left_points, right_points,
mat_left_img, mat_right_img,
forReconstructionLeftPoints, forReconstructionRightPoints);
mat_left_img.release();
mat_right_img.release();
cout << "Code execution time: "<< double( clock() - startTime ) / (double)CLOCKS_PER_SEC<< " seconds." << endl;
}
if( ch == 'I' || ch == 'i')
{
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_left, descriptors_right, matches );
//double max_dist = 0; double min_dist = 100;
////-- Quick calculation of max and min distances between keypoints
//for( int i = 0; i < descriptors_left.rows; i++ )
//{ double dist = matches[i].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;
left_to_right_corresponding_points.clear();
right_to_left_corresponding_points.clear();
for( int i = 0; i < descriptors_left.rows; i++ )
{
//if( matches[i].distance <= max(2*min_dist, 0.02) )
{
//good_matches.push_back( matches[i]);
left_to_right_corresponding_points.push_back( ComparedIndexes(matches[i].distance, pair<int, int> (i, matches[i].trainIdx)) );
}
}
cout<< "Count of good matches :" << left_to_right_corresponding_points.size() << endl;
stable_sort(left_to_right_corresponding_points.begin(),left_to_right_corresponding_points.end(),my_comparator_for_stable_sort);
}
//if( ch == 'K' || ch == 'k')
//{
// CvSURFPoint *leftPoint;
// //proceed left
// gray_img_left=cvCreateImage(cvSize((left_img->width),(left_img->height)),IPL_DEPTH_8U,1);
// cvCvtColor(left_img,gray_img_left,CV_RGB2GRAY);
// cvExtractSURF(gray_img_left,NULL,&key_points_left,&descriptors_left,mem_stor,cvSURFParams(min_hessian_value,0));
// cv::Mat mat_gray_leftImage(gray_img_left, true);
// cvReleaseImage(&gray_img_left);
// // proceed right
// gray_img_right=cvCreateImage(cvSize((right_img->width),(right_img->height)),IPL_DEPTH_8U,1);
// cvCvtColor(right_img,gray_img_right,CV_RGB2GRAY);
// cv::Mat mat_gray_rightImage(gray_img_right, true);
// cvReleaseImage(&gray_img_right);
// vector<Point2f> LK_left_points;
// vector<Point2f> LK_right_points;
// LK_right_points.resize(key_points_left->total);
// for( int i = 0; i < key_points_left->total; i++)
// {
// leftPoint=(CvSURFPoint*)cvGetSeqElem(key_points_left, i);
// LK_left_points.push_back(Point2f( leftPoint->pt.x, leftPoint->pt.y));
// }
//
// vector<uchar> status;
// vector<float> err;
// cv::calcOpticalFlowPyrLK(
// mat_gray_leftImage,
// mat_gray_rightImage,
// LK_left_points,
// LK_right_points,
// status,
// err);
// int width_part=merged_images->width>>1;
//
// float minErr = err[0];
// for(int k = 0; k < err.size(); k++)
// {
// if(status[k] && err[k] < minErr)
// {
// minErr = err[k];
// }
// }
// cout<< "Lucass Kanade min error: " << minErr<< endl;
// int i = 0;
// merged_images_copy=cvCreateImage(cvSize(merged_images->width,merged_images->height),merged_images->depth,3);
// cvCopy(merged_images,merged_images_copy);
// for(; i < LK_left_points.size(); ++i)
// {
// if(err[i] < 5 * minErr && status[i])
// {
// cvLine(merged_images_copy,cvPoint(LK_left_points[i].x,LK_left_points[i].y),cvPoint(LK_right_points[i].x+width_part,LK_right_points[i].y),
// CV_RGB(100 + (( i *3) % 155), 100+ ((i*7)%155), 100+ ((i*13)%155)));
// }
// }
// cvShowImage("twoSnapshots",merged_images_copy);
//
// while(true)
// {
// char ch2=cvWaitKey(33);
// if(ch2==27)
// break;
//
// }
//
// cvReleaseImage(&merged_images_copy);
// status.clear();
// err.clear();
// LK_left_points.clear();
// LK_right_points.clear();
// mat_gray_leftImage.release();
// mat_gray_rightImage.release();
//}
if( ch == 'F' || ch == 'f')
{
findFundamentalMatrixAndCorrespondingPointsForReconstruction(
left_to_right_corresponding_points,
right_to_left_corresponding_points,
fundamentalMatrix,
key_points_left,
key_points_right,
descriptors_left,
descriptors_right,
left_img,
right_img,
gray_img_left,
gray_img_right,
forReconstructionLeftPoints,
forReconstructionRightPoints,
min_hessian_value);
}
if( ch == 'P' || ch == 'p')
{
cv::Mat mat_left_img(left_img, true);
cv::Mat mat_right_img(right_img, true);
mainLevenbergMarkvardt_LMFIT(startValueOfFocus, "currentPLYExportFile", left_points, right_points,
mat_left_img, mat_right_img,
forReconstructionLeftPoints, forReconstructionRightPoints);
mat_left_img.release();
mat_right_img.release();
}
if(merged_images!=0)
{
cvShowImage("twoSnapshots",merged_images);
}
}
int main(int argc, char *argv[])
{
if (argc != 6) {
printf("\nERROR: too few parameters\n");
help();
return -1;
}
help();
//INPUT PARAMETERS:
int board_w = atoi(argv[1]);
int board_h = atoi(argv[2]);
int board_n = board_w * board_h;
CvSize board_sz = cvSize(board_w, board_h);
CvMat *intrinsic = (CvMat *) cvLoad(argv[3]);
CvMat *distortion = (CvMat *) cvLoad(argv[4]);
IplImage *image = 0, *gray_image = 0;
if ((image = cvLoadImage(argv[5])) == 0) {
printf("Error: Couldn't load %s\n", argv[5]);
return -1;
}
gray_image = cvCreateImage(cvGetSize(image), 8, 1);
cvCvtColor(image, gray_image, CV_BGR2GRAY);
//UNDISTORT OUR IMAGE
IplImage *mapx = cvCreateImage(cvGetSize(image), IPL_DEPTH_32F, 1);
IplImage *mapy = cvCreateImage(cvGetSize(image), IPL_DEPTH_32F, 1);
cvInitUndistortMap(intrinsic, distortion, mapx, mapy);
IplImage *t = cvCloneImage(image);
cvRemap(t, image, mapx, mapy);
//GET THE CHECKERBOARD ON THE PLANE
cvNamedWindow("Checkers");
CvPoint2D32f *corners = new CvPoint2D32f[board_n];
int corner_count = 0;
int found = cvFindChessboardCorners(image,
board_sz,
corners,
&corner_count,
CV_CALIB_CB_ADAPTIVE_THRESH |
CV_CALIB_CB_FILTER_QUADS);
if (!found) {
printf
("Couldn't aquire checkerboard on %s, only found %d of %d corners\n",
argv[5], corner_count, board_n);
return -1;
}
//Get Subpixel accuracy on those corners
cvFindCornerSubPix(gray_image, corners, corner_count,
cvSize(11, 11), cvSize(-1, -1),
cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30,
0.1));
//GET THE IMAGE AND OBJECT POINTS:
//Object points are at (r,c): (0,0), (board_w-1,0), (0,board_h-1), (board_w-1,board_h-1)
//That means corners are at: corners[r*board_w + c]
CvPoint2D32f objPts[4], imgPts[4];
objPts[0].x = 0;
objPts[0].y = 0;
objPts[1].x = board_w - 1;
objPts[1].y = 0;
objPts[2].x = 0;
objPts[2].y = board_h - 1;
objPts[3].x = board_w - 1;
objPts[3].y = board_h - 1;
imgPts[0] = corners[0];
imgPts[1] = corners[board_w - 1];
imgPts[2] = corners[(board_h - 1) * board_w];
imgPts[3] = corners[(board_h - 1) * board_w + board_w - 1];
//DRAW THE POINTS in order: B,G,R,YELLOW
cvCircle(image, cvPointFrom32f(imgPts[0]), 9, CV_RGB(0, 0, 255), 3);
cvCircle(image, cvPointFrom32f(imgPts[1]), 9, CV_RGB(0, 255, 0), 3);
cvCircle(image, cvPointFrom32f(imgPts[2]), 9, CV_RGB(255, 0, 0), 3);
cvCircle(image, cvPointFrom32f(imgPts[3]), 9, CV_RGB(255, 255, 0), 3);
//DRAW THE FOUND CHECKERBOARD
cvDrawChessboardCorners(image, board_sz, corners, corner_count, found);
cvShowImage("Checkers", image);
//FIND THE HOMOGRAPHY
CvMat *H = cvCreateMat(3, 3, CV_32F);
CvMat *H_invt = cvCreateMat(3, 3, CV_32F);
cvGetPerspectiveTransform(objPts, imgPts, H);
//LET THE USER ADJUST THE Z HEIGHT OF THE VIEW
float Z = 25;
int key = 0;
IplImage *birds_image = cvCloneImage(image);
cvNamedWindow("Birds_Eye");
while (key != 27) { //escape key stops
CV_MAT_ELEM(*H, float, 2, 2) = Z;
// cvInvert(H,H_invt); //If you want to invert the homography directly
// cvWarpPerspective(image,birds_image,H_invt,CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS );
//USE HOMOGRAPHY TO REMAP THE VIEW
cvWarpPerspective(image, birds_image, H,
CV_INTER_LINEAR + CV_WARP_INVERSE_MAP +
CV_WARP_FILL_OUTLIERS);
cvShowImage("Birds_Eye", birds_image);
key = cvWaitKey();
if (key == 'u')
Z += 0.5;
if (key == 'd')
Z -= 0.5;
}
//SHOW ROTATION AND TRANSLATION VECTORS
CvMat *image_points = cvCreateMat(4, 1, CV_32FC2);
CvMat *object_points = cvCreateMat(4, 1, CV_32FC3);
for (int i = 0; i < 4; ++i) {
CV_MAT_ELEM(*image_points, CvPoint2D32f, i, 0) = imgPts[i];
CV_MAT_ELEM(*object_points, CvPoint3D32f, i, 0) =
cvPoint3D32f(objPts[i].x, objPts[i].y, 0);
}
CvMat *RotRodrigues = cvCreateMat(3, 1, CV_32F);
CvMat *Rot = cvCreateMat(3, 3, CV_32F);
CvMat *Trans = cvCreateMat(3, 1, CV_32F);
cvFindExtrinsicCameraParams2(object_points, image_points,
intrinsic, distortion, RotRodrigues, Trans);
cvRodrigues2(RotRodrigues, Rot);
//SAVE AND EXIT
cvSave("Rot.xml", Rot);
cvSave("Trans.xml", Trans);
cvSave("H.xml", H);
cvInvert(H, H_invt);
cvSave("H_invt.xml", H_invt); //Bottom row of H invert is horizon line
return 0;
}
int main(int argc, char* argv[])
{
int height,width,step,channels; //parameters of the image we are working on
int i,j,k,t1min=0,t1max=0,t2min=0,t2max=0,t3min=0,t3max=0; // other variables used
CvMat* threshold_matrix = cvCreateMat(2,3,CV_32FC1);
CvFileStorage* temp = cvOpenFileStorage("threshold_matrix.xml",NULL,CV_STORAGE_READ);
// Load the previous values of the threshold if they exist
if(temp)
{
threshold_matrix = (CvMat*)cvLoad("threshold_matrix.xml");
t1min =(int) CV_MAT_ELEM(*threshold_matrix,float,0,0) ;t2min =(int) CV_MAT_ELEM(*threshold_matrix,float,0,1) ;t3min =(int) CV_MAT_ELEM(*threshold_matrix,float,0,2);
t1max =(int) CV_MAT_ELEM(*threshold_matrix,float,1,0) ;t2max =(int) CV_MAT_ELEM(*threshold_matrix,float,1,1) ;t3max =(int) CV_MAT_ELEM(*threshold_matrix,float,1,2) ;
}
// Open capture device. 0 is /dev/video0, 1 is /dev/video1, etc.
CvCapture* capture = cvCaptureFromCAM( 1 );
if( !capture )
{
fprintf( stderr, "ERROR: capture is NULL \n" );
getchar();
return -1;
}
// grab an image from the capture
IplImage* frame = cvQueryFrame( capture );
// Create a window in which the captured images will be presented
cvNamedWindow( "Camera", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "HSV", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "F1", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "F2", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "F3", CV_WINDOW_AUTOSIZE );
//cvNamedWindow( "EdgeDetection", CV_WINDOW_AUTOSIZE );
/// Create Trackbars
char TrackbarName1[50]="t1min";
char TrackbarName2[50]="t1max";
char TrackbarName3[50]="t2min";
char TrackbarName4[50]="t2max";
char TrackbarName5[50]="t3min";
char TrackbarName6[50]="t3max";
cvCreateTrackbar( TrackbarName1, "F1", &t1min, 260 , NULL );
cvCreateTrackbar( TrackbarName2, "F1", &t1max, 260, NULL );
cvCreateTrackbar( TrackbarName3, "F2", &t2min, 260 , NULL );
cvCreateTrackbar( TrackbarName4, "F2", &t2max, 260, NULL );
cvCreateTrackbar( TrackbarName5, "F3", &t3min, 260 , NULL );
cvCreateTrackbar( TrackbarName6, "F3", &t3max, 260, NULL );
// Load threshold from the slider bars in these 2 parameters
CvScalar hsv_min = cvScalar(t1min, t2min, t3min, 0);
CvScalar hsv_max = cvScalar(t1max, t2max ,t3max, 0);
// get the image data
height = frame->height;
width = frame->width;
step = frame->widthStep;
// capture size -
CvSize size = cvSize(width,height);
// Initialize different images that are going to be used in the program
IplImage* hsv_frame = cvCreateImage(size, IPL_DEPTH_8U, 3); // image converted to HSV plane
IplImage* thresholded = cvCreateImage(size, IPL_DEPTH_8U, 1); // final thresholded image
IplImage* thresholded1 = cvCreateImage(size, IPL_DEPTH_8U, 1); // Component image threshold
IplImage* thresholded2 = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* thresholded3 = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* filtered = cvCreateImage(size, IPL_DEPTH_8U, 1); //smoothed image
while( 1 )
{
// Load threshold from the slider bars in these 2 parameters
hsv_min = cvScalar(t1min, t2min, t3min, 0);
hsv_max = cvScalar(t1max, t2max ,t3max, 0);
// Get one frame
frame = cvQueryFrame( capture );
if( !frame )
{
fprintf( stderr, "ERROR: frame is null...\n" );
getchar();
break;
}
// Covert color space to HSV as it is much easier to filter colors in the HSV color-space.
cvCvtColor(frame, hsv_frame, CV_BGR2HSV);
// Filter out colors which are out of range.
cvInRangeS(hsv_frame, hsv_min, hsv_max, thresholded);
// the below lines of code is for visual purpose only remove after calibration
//--------------FROM HERE-----------------------------------
//Split image into its 3 one dimensional images
cvSplit( hsv_frame,thresholded1, thresholded2, thresholded3, NULL );
// Filter out colors which are out of range.
cvInRangeS(thresholded1,cvScalar(t1min,0,0,0) ,cvScalar(t1max,0,0,0) ,thresholded1);
cvInRangeS(thresholded2,cvScalar(t2min,0,0,0) ,cvScalar(t2max,0,0,0) ,thresholded2);
cvInRangeS(thresholded3,cvScalar(t3min,0,0,0) ,cvScalar(t3max,0,0,0) ,thresholded3);
//-------------REMOVE OR COMMENT AFTER CALIBRATION TILL HERE ------------------
// Memory for hough circles
CvMemStorage* storage = cvCreateMemStorage(0);
// hough detector works better with some smoothing of the image
cvSmooth( thresholded, thresholded, CV_GAUSSIAN, 9, 9 );
//hough transform to detect circle
CvSeq* circles = cvHoughCircles(thresholded, storage, CV_HOUGH_GRADIENT, 2,
thresholded->height/4, 100, 50, 10, 400);
for (int i = 0; i < circles->total; i++)
{ //get the parameters of circles detected
float* p = (float*)cvGetSeqElem( circles, i );
printf("Ball! x=%f y=%f r=%f\n\r",p[0],p[1],p[2] );
// draw a circle with the centre and the radius obtained from the hough transform
cvCircle( frame, cvPoint(cvRound(p[0]),cvRound(p[1])), //plot centre
2, CV_RGB(255,255,255),-1, 8, 0 );
cvCircle( frame, cvPoint(cvRound(p[0]),cvRound(p[1])), //plot circle
cvRound(p[2]), CV_RGB(0,255,0), 2, 8, 0 );
}
/* for testing purpose you can show all the images but when done with calibration
only show frame to keep the screen clean */
cvShowImage( "Camera", frame ); // Original stream with detected ball overlay
cvShowImage( "HSV", hsv_frame); // Original stream in the HSV color space
cvShowImage( "After Color Filtering", thresholded ); // The stream after color filtering
cvShowImage( "F1", thresholded1 ); // individual filters
cvShowImage( "F2", thresholded2 );
cvShowImage( "F3", thresholded3 );
//cvShowImage( "filtered", thresholded );
cvReleaseMemStorage(&storage);
//If ESC key pressed, Key=0x10001B under OpenCV 0.9.7(linux version),
//remove higher bits using AND operator
if( (cvWaitKey(10) & 255) == 27 ) break;
}
//Save the threshold values before exiting
*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 0, 0 ) ) = t1min ;*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 0, 1 ) ) = t2min ;*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 0, 2 ) ) = t3min ;
*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 1, 0 ) ) = t1max ;*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 1, 1 ) ) = t2max ;*((float*)CV_MAT_ELEM_PTR( *threshold_matrix, 1, 2 ) ) = t3max ;
cvSave("threshold_matrix.xml",threshold_matrix);
// Release the capture device housekeeping
cvReleaseCapture( &capture );
cvDestroyWindow( "mywindow" );
return 0;
}
/*!
\fn CvBinGabAdaFeatureSelect::saveweights(const char* filename)
*/
void CvBinGabAdaFeatureSelect::saveweights(const char* filename)
{
cvSave( filename, weights, NULL, NULL, cvAttrList());
}
void PerformanceMatrix::saveDatabase(const char * fileName){
cvSave(fileName, pm);
}
int main(int argc, char* argv[])
{
int i, j;
CvMemStorage* storage = cvCreateMemStorage(0);
IplImage* img = cvCreateImage( cvSize(w,w), 8, 1 );
IplImage* img32f = cvCreateImage( cvSize(w,w), IPL_DEPTH_32F, 1 );
IplImage* img32s = cvCreateImage( cvSize(w,w), IPL_DEPTH_32S, 1 );
IplImage* img3 = cvCreateImage( cvSize(w,w), 8, 3 );
(void)argc; (void)argv;
help();
cvZero( img );
for( i=0; i < 6; i++ )
{
int dx = (i%2)*250 - 30;
int dy = (i/2)*150;
CvScalar white = cvRealScalar(255);
CvScalar black = cvRealScalar(0);
if( i == 0 )
{
for( j = 0; j <= 10; j++ )
{
double angle = (j+5)*CV_PI/21;
cvLine(img, cvPoint(cvRound(dx+100+j*10-80*cos(angle)),
cvRound(dy+100-90*sin(angle))),
cvPoint(cvRound(dx+100+j*10-30*cos(angle)),
cvRound(dy+100-30*sin(angle))), white, 3, 8, 0);
}
}
cvEllipse( img, cvPoint(dx+150, dy+100), cvSize(100,70), 0, 0, 360, white, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(30,20), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(30,20), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(15,15), 0, 0, 360, white, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(15,15), 0, 0, 360, white, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(5,5), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(5,5), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+150, dy+100), cvSize(10,5), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+150, dy+150), cvSize(40,10), 0, 0, 360, black, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+27, dy+100), cvSize(20,35), 0, 0, 360, white, -1, 8, 0 );
cvEllipse( img, cvPoint(dx+273, dy+100), cvSize(20,35), 0, 0, 360, white, -1, 8, 0 );
}
cvNamedWindow( "image", 1 );
cvShowImage( "image", img );
cvConvert( img, img32f );
findCComp( img32f );
cvConvert( img32f, img32s );
cvFindContours( img32s, storage, &contours, sizeof(CvContour),
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
//cvFindContours( img, storage, &contours, sizeof(CvContour),
// CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
{
const char* attrs[] = {"recursive", "1", 0};
cvSave("contours.xml", contours, 0, 0, cvAttrList(attrs, 0));
contours = (CvSeq*)cvLoad("contours.xml", storage, 0, 0);
}
// comment this out if you do not want approximation
contours = cvApproxPoly( contours, sizeof(CvContour), storage, CV_POLY_APPROX_DP, 3, 1 );
cvNamedWindow( "contours", 1 );
cvCreateTrackbar( "levels+3", "contours", &levels, 7, on_trackbar );
{
CvRNG rng = cvRNG(-1);
CvSeq* tcontours = contours;
cvCvtColor( img, img3, CV_GRAY2BGR );
while( tcontours->h_next )
tcontours = tcontours->h_next;
for( ; tcontours != 0; tcontours = tcontours->h_prev )
{
CvScalar color;
color.val[0] = cvRandInt(&rng) % 256;
color.val[1] = cvRandInt(&rng) % 256;
color.val[2] = cvRandInt(&rng) % 256;
color.val[3] = cvRandInt(&rng) % 256;
cvDrawContours(img3, tcontours, color, color, 0, -1, 8, cvPoint(0,0));
if( tcontours->v_next )
{
color.val[0] = cvRandInt(&rng) % 256;
color.val[1] = cvRandInt(&rng) % 256;
color.val[2] = cvRandInt(&rng) % 256;
color.val[3] = cvRandInt(&rng) % 256;
cvDrawContours(img3, tcontours->v_next, color, color, 1, -1, 8, cvPoint(0,0));
}
}
}
cvShowImage( "colored", img3 );
on_trackbar(0);
cvWaitKey(0);
cvReleaseMemStorage( &storage );
cvReleaseImage( &img );
cvReleaseImage( &img32f );
cvReleaseImage( &img32s );
cvReleaseImage( &img3 );
return 0;
}
