bool Calibration::load(string filename, bool absolute) {
imagePoints.clear();
//FileStorage fs(ofToDataPath(filename, absolute), FileStorage::READ);
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
return false;
cv::Size imageSize, sensorSize;
Mat cameraMatrix;
fs["cameraMatrix"] >> cameraMatrix;
fs["imageSize_width"] >> imageSize.width;
fs["imageSize_height"] >> imageSize.height;
fs["sensorSize_width"] >> sensorSize.width;
fs["sensorSize_height"] >> sensorSize.height;
fs["distCoeffs"] >> distCoeffs;
fs["reprojectionError"] >> reprojectionError;
FileNode features = fs["features"];
for(FileNodeIterator it = features.begin(); it != features.end(); it++) {
vector<Point2f> cur;
(*it) >> cur;
imagePoints.push_back(cur);
}
addedImageSize = imageSize;
distortedIntrinsics.setup(cameraMatrix, imageSize, sensorSize);
updateUndistortion();
ready = true;
return true;
}
void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
CvDTreeTrainData* _data )
{
int maxCatCount = ((CvCascadeBoostTrainData*)_data)->featureEvaluator->getMaxCatCount();
int subsetN = (maxCatCount + 31)/32;
int step = 3 + ( maxCatCount>0 ? subsetN : 1 );
queue<CvDTreeNode*> internalNodesQueue;
FileNodeIterator internalNodesIt, leafValsuesIt;
CvDTreeNode* prntNode, *cldNode;
clear();
data = _data;
ensemble = _ensemble;
pruned_tree_idx = 0;
// read tree nodes
FileNode rnode = node[CC_INTERNAL_NODES];
internalNodesIt = rnode.end();
leafValsuesIt = node[CC_LEAF_VALUES].end();
internalNodesIt--; leafValsuesIt--;
for( size_t i = 0; i < rnode.size()/step; i++ )
{
prntNode = data->new_node( 0, 0, 0, 0 );
if ( maxCatCount > 0 )
{
prntNode->split = data->new_split_cat( 0, 0 );
for( int j = subsetN-1; j>=0; j--)
{
*internalNodesIt >> prntNode->split->subset[j]; internalNodesIt--;
}
}
else
{
void ReadRightRects(vector<ImageRecognition::SlidingRect> &rightRects, const string &xml_filename, RecognitionStatistics &stat)
{
using namespace Utils;
FileStorage file_storage(xml_filename, FileStorage::READ);
FileNode images = file_storage["images"];
rightRects.reserve(images.size());
for (FileNodeIterator it = images.begin(); it != images.end(); ++it)
{
string part_filename = string(*it);
int dot_pos = part_filename.find_first_of('.');
if (dot_pos != -1)
part_filename = part_filename.substr(0, dot_pos);
stringstream ss(part_filename);
vector<string> parts;
string part;
while (getline(ss, part, '_'))
parts.push_back(part);
rightRects.push_back(ImageRecognition::SlidingRect());
int last = parts.size() - 1;
rightRects.back().rect.x = str2int(parts[last - 3]);
rightRects.back().rect.y = str2int(parts[last - 2]);
rightRects.back().rect.width = str2int(parts[last - 1]);
rightRects.back().rect.height = str2int(parts[last]);
}
}
static bool readModelViews( const string& filename, vector<Point3f>& box,
vector<string>& imagelist,
vector<Rect>& roiList, vector<Vec6f>& poseList )
{
imagelist.resize(0);
roiList.resize(0);
poseList.resize(0);
box.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
fs["box"] >> box;
FileNode all = fs["views"];
if( all.type() != FileNode::SEQ )
return false;
FileNodeIterator it = all.begin(), it_end = all.end();
for(; it != it_end; ++it)
{
FileNode n = *it;
imagelist.push_back((string)n["image"]);
FileNode nr = n["rect"];
roiList.push_back(Rect((int)nr[0], (int)nr[1], (int)nr[2], (int)nr[3]));
FileNode np = n["pose"];
poseList.push_back(Vec6f((float)np[0], (float)np[1], (float)np[2],
(float)np[3], (float)np[4], (float)np[5]));
}
return true;
}
/*
* Like runTests, but using the quantized lookup table
* instead of the support vector machine directly.
*/
void runTestsSVMTable()
{
ColonyCounter colonyCounter;
colonyCounter.loadTrainingQuantized(svmLookup, svmQuants);
FileStorage fs("samples/tests.yml", FileStorage::READ);
double absErrorSum = 0;
FileNode features = fs["tests"];
FileNodeIterator it = features.begin(), it_end = features.end();
int idx = 0;
for( ; it != it_end; ++it, idx++ )
{
string path;
(*it)["path"] >> path;
int red = (int)(*it)["red"];
int blue = (int)(*it)["blue"];
double error;
runTest(colonyCounter, "samples/" + path, red, blue, error);
absErrorSum += fabs(error);
}
fs.release();
printf("Error %f\n", absErrorSum);
}
int main(int, char** argv){
FileStorage fs2("test.xml", FileStorage::READ);
if (!fs2.isOpened()){
cout << "failed to open test.xml :(" << endl;
return 1;
}
// first method: use (type) operator on FileNode.
int frameCount = (int)fs2["frameCount"];
std::string date;
// second method: use FileNode::operator >>
fs2["calibrationDate"] >> date;
Mat camera_intrinsics_1, camera_intrinsics_2, camera_intrinsics_3, camera_intrinsics_4;
Mat camera_extrinsics_1, camera_extrinsics_2, camera_extrinsics_3, camera_extrinsics_4;
fs2["camera_intrinsics_1"] >> camera_intrinsics_1;
fs2["camera_intrinsics_2"] >> camera_intrinsics_2;
fs2["camera_intrinsics_3"] >> camera_intrinsics_3;
fs2["camera_intrinsics_4"] >> camera_intrinsics_4;
fs2["camera_extrinsics_1"] >> camera_extrinsics_1;
fs2["camera_extrinsics_2"] >> camera_extrinsics_2;
fs2["camera_extrinsics_3"] >> camera_extrinsics_3;
fs2["camera_extrinsics_4"] >> camera_extrinsics_4;
cout << camera_extrinsics_4 << endl;
cout << camera_intrinsics_4 << endl;
//fs2["distCoeffs"] >> distCoeffs2;
//cout << "frameCount: " << frameCount << endl
// << "calibration date: " << date << endl
// << "camera matrix: " << cameraMatrix2 << endl
// << "distortion coeffs: " << distCoeffs2 << endl;
FileNode features = fs2["features"];
FileNodeIterator it = features.begin(), it_end = features.end();
int idx = 0;
std::vector<uchar> lbpval;
// iterate through a sequence using FileNodeIterator
for( ; it != it_end; ++it, idx++ )
{
cout << "feature #" << idx << ": ";
cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";
// you can also easily read numerical arrays using FileNode >> std::vector operator.
(*it)["lbp"] >> lbpval;
for( int i = 0; i < (int)lbpval.size(); i++ )
cout << " " << (int)lbpval[i];
cout << ")" << endl;
}
fs2.release();
}
inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
if (fn.type() == FileNode::SEQ) {
for (FileNodeIterator it = fn.begin(); it != fn.end();) {
_Tp item;
it >> item;
result.push_back(item);
}
}
}
void read(const FileNode& node, vector<KeyPoint>& keypoints)
{
keypoints.resize(0);
FileNodeIterator it = node.begin(), it_end = node.end();
for( ; it != it_end; )
{
KeyPoint kpt;
it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave;
keypoints.push_back(kpt);
}
}
bool HOGEvaluator::read( const FileNode& node )
{
features->resize(node.size());
featuresPtr = &(*features)[0];
FileNodeIterator it = node.begin(), it_end = node.end();
for(int i = 0; it != it_end; ++it, i++)
{
if(!featuresPtr[i].read(*it))
return false;
}
return true;
}
static void readDetections( FileStorage fs, const string& nodeName, vector<LatentSvmDetector::ObjectDetection>& detections )
{
detections.clear();
FileNode fn = fs.root()[nodeName];
FileNodeIterator fni = fn.begin();
while( fni != fn.end() )
{
LatentSvmDetector::ObjectDetection d;
fni >> d.rect.x >> d.rect.y >> d.rect.width >> d.rect.height
>> d.score >> d.classID;
detections.push_back( d );
}
}
static bool readStringList( const string& filename, vector<string>& l )
{
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
int main( )
{
//改变console字体颜色
system("color 6F");
ShowHelpText();
//初始化
FileStorage fs2("test.yaml", FileStorage::READ);
// 第一种方法，对FileNode操作
int frameCount = (int)fs2["frameCount"];
std::string date;
// 第二种方法，使用FileNode运算符> >
fs2["calibrationDate"] >> date;
Mat cameraMatrix2, distCoeffs2;
fs2["cameraMatrix"] >> cameraMatrix2;
fs2["distCoeffs"] >> distCoeffs2;
cout << "frameCount: " << frameCount << endl
<< "calibration date: " << date << endl
<< "camera matrix: " << cameraMatrix2 << endl
<< "distortion coeffs: " << distCoeffs2 << endl;
FileNode features = fs2["features"];
FileNodeIterator it = features.begin(), it_end = features.end();
int idx = 0;
std::vector<uchar> lbpval;
//使用FileNodeIterator遍历序列
for( ; it != it_end; ++it, idx++ )
{
cout << "feature #" << idx << ": ";
cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";
// 我们也可以使用使用filenode > > std::vector操作符很容易的读数值阵列
(*it)["lbp"] >> lbpval;
for( int i = 0; i < (int)lbpval.size(); i++ )
cout << " " << (int)lbpval[i];
cout << ")" << endl;
}
fs2.release();
//程序结束，输出一些帮助文字
printf("\n文件读取完毕，请输入任意键结束程序~");
getchar();
return 0;
}
bool DataSetVOC::loadBBoxes(CStr &nameNE, vector<Vec4i> &boxes, vecI &clsIdx)
{
string fName = format(_S(annoPathW), _S(nameNE));
FileStorage fs(fName, FileStorage::READ);
FileNode fn = fs["annotation"]["object"];
boxes.clear();
clsIdx.clear();
if (fn.isSeq()){
for (FileNodeIterator it = fn.begin(), it_end = fn.end(); it != it_end; it++)
loadBox(*it, boxes, clsIdx);
}
else
loadBox(fn, boxes, clsIdx);
return true;
}
int Map::getMapFromFile(string filename)
{
FileStorage fs;
fs.open(filename, FileStorage::READ);
FileNode n = fs["Map"];
if (n.type() != FileNode::SEQ)
return -1; // Map n'est pas une séquence
FileNodeIterator it = n.begin(), it_end = n.end(); // Itérateur pour lire la séquence
for (; it != it_end; ++it)
cout << (string)*it << endl;
fs.release(); // énoncer la fermeture
return 0;
}
void readVectorOfVector(FileStorage &fns, string name, vector<vector<KeyPoint> > &vov)
{
vov.clear();
FileNode fn = fns[name];
if (fn.empty()){
return;
}
FileNodeIterator current = fn.begin(), it_end = fn.end(); // Go through the node
for (; current != it_end; ++current)
{
vector<KeyPoint> tmp;
FileNode item = *current;
read(item, tmp);
vov.push_back(tmp);
}
}
int imageDB::readKeyMap(const FileStorage& cvfs, const FileNode& node)
{
keypoint_map.clear();
int keypoint_id;
FileNodeIterator it = node.begin();
while(it != node.end()){
vector<KeyPoint> keypt_vec;
keypoint_id = (int)(*it)["keypoint_id"];
cv::read((*it)["KeyPoint"], keypt_vec);
keypoint_map.insert(pair<int,KeyPoint>(keypoint_id,keypt_vec[0]));
it++;
}
return 0;
}
int imageDB::readFeatureKptMap(const FileStorage& cvfs, const FileNode& node)
{
feature_KPT_map.clear();
int feature_id;
featureInfo feature_info;
FileNodeIterator it = node.begin();
while(it != node.end()){
feature_id = (int)(*it)["feature_id"];
feature_info.keypoint_id = (int)(*it)["keypoint_id"];
feature_info.img_id = (int)(*it)["img_id"];
feature_KPT_map.insert(pair<int, featureInfo>(feature_id,feature_info));
it++;
}
return 0;
}
void ICFDetector::read(const FileNode& node)
{
waldboost_ = Ptr<WaldBoost>(createWaldBoost(WaldBoostParams()));
String f_temp;
node["model_n_rows"] >> model_n_rows_;
node["model_n_cols"] >> model_n_cols_;
f_temp = (String)node["ftype"];
this->ftype_ = (string)f_temp.c_str();
waldboost_->read(node["waldboost"]);
FileNode features = node["features"];
features_.clear();
vector<int> p;
for( FileNodeIterator n = features.begin(); n != features.end(); ++n )
{
(*n) >> p;
features_.push_back(p);
}
}
static bool readStringList(const string &filename, vector<string> &l)
{
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
return false;
FileNode n = fs.getFirstTopLevelNode();
if (n.type() != FileNode::SEQ)
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
string temp = filename;
int pos = temp.find_last_of('/') + 1;
temp = temp.substr(0, pos);
for (; it != it_end; ++it)
l.push_back(temp + (string)*it);
return true;
}
bool load(const String &fn)
{
FileStorage fs(fn, FileStorage::READ);
if (! fs.isOpened())
return false;
bool ok = classifier->load(fs);
FileNode pers = fs["persons"];
FileNodeIterator it = pers.begin();
for( ; it != pers.end(); ++it )
{
int id;
(*it) >> id;
string s =(*it).name();
persons[id] = s;
}
fs.release();
return ok;
}
void UndistortImages::loadImages( char* images_list ){
FileStorage fs;
fs.open(images_list,FileStorage::READ);
if( !fs.isOpened() ){
cerr << " Fail to open " << images_list << endl;
exit(EXIT_FAILURE);
}
cout << " Images size = " << fs["images"].size() << endl;
FileNode imagesPathNode = fs["images"];
for(FileNodeIterator it = imagesPathNode.begin();
it != imagesPathNode.end(); ++it){
inputImagesPaths.push_back( *it );
}
fs.release();
}
void loadBins(float* out){
FileStorage fs("txtDict.xml", FileStorage::READ);
FileNode n = fs["binArray"];
if(n.type() != FileNode::SEQ){
cout << "incorrect filetype: " << n.type() << endl;
fs.release();
return;
}
FileNodeIterator it = n.begin(), it_end = n.end();
int cnt =0;
for(;it != it_end;++it){
out[cnt] = (float)*it;
cnt++;
}
cout << "finished reading Bins..\n\n";
fs.release();
}
void loadTex(vector<float>& out){
FileStorage fs("txtDict.xml", FileStorage::READ);
FileNode n = fs["TextonDictionary"];
if(n.type() != FileNode::SEQ){
cout << "incorrect filetype: " << n.type() << endl;
fs.release();
return;
}
FileNodeIterator it = n.begin(), it_end = n.end();
int cnt =0;
for(;it != it_end;++it){
out.push_back((float)*it);
cnt++;
}
cout << "finished reading Textons..\n\n";
fs.release();
}
bool HaarEvaluator::Feature :: read( const FileNode& node )
{
FileNode rnode = node[CC_RECTS];
FileNodeIterator it = rnode.begin(), it_end = rnode.end();
int ri;
for( ri = 0; ri < RECT_NUM; ri++ )
{
rect[ri].r = Rect();
rect[ri].weight = 0.f;
}
for(ri = 0; it != it_end; ++it, ri++)
{
FileNodeIterator it2 = (*it).begin();
it2 >> rect[ri].r.x >> rect[ri].r.y >>
rect[ri].r.width >> rect[ri].r.height >> rect[ri].weight;
}
tilted = (int)node[CC_TILTED] != 0;
return true;
}
bool ChessCalibration::loadExtrinsics() {
FileStorage fs(calibrationExtrinsicsFilePath, FileStorage::READ);
if (!fs.isOpened())
return false;
imagePoints.clear();
found = true;
fs["rvec"] >> rvec;
fs["tvec"] >> tvec;
FileNode features = fs["features"];
for(FileNodeIterator it = features.begin(); it != features.end(); it++) {
vector<Point2f> cur;
(*it) >> cur;
imagePoints.push_back(cur[0]);
}
fs["mapping"] >> mapping;
cout << "Extrinsics loaded from file" << endl;
// createTransformImageExtrinsicMap(camMat);
// fs.release();
// FileStorage fs2(calibrationExtrinsicsFilePath, FileStorage::APPEND);
// fs2 << "mapping" << mapping;
return true;
}
/**Reads board info from a file
*/
void BoardConfiguration::readFromFile(cv::FileStorage &fs) throw(cv::Exception) {
int aux = 0;
// look for the nmarkers
if (fs["aruco_bc_nmarkers"].name() != "aruco_bc_nmarkers")
throw cv::Exception(81818, "BoardConfiguration::readFromFile", "invalid file type", __FILE__, __LINE__);
fs["aruco_bc_nmarkers"] >> aux;
resize(aux);
fs["aruco_bc_mInfoType"] >> mInfoType;
cv::FileNode markers = fs["aruco_bc_markers"];
int i = 0;
for (FileNodeIterator it = markers.begin(); it != markers.end(); ++it, i++) {
at(i).id = (*it)["id"];
FileNode FnCorners = (*it)["corners"];
for (FileNodeIterator itc = FnCorners.begin(); itc != FnCorners.end(); ++itc) {
vector< float > coordinates3d;
(*itc) >> coordinates3d;
if (coordinates3d.size() != 3)
throw cv::Exception(81818, "BoardConfiguration::readFromFile", "invalid file type 3", __FILE__, __LINE__);
cv::Point3f point(coordinates3d[0], coordinates3d[1], coordinates3d[2]);
at(i).push_back(point);
}
}
}
static bool readStringList( const string& filename, const string& tempFile, vector<string>& l )
{
/* // Immediately generate the file name that we will read from
FileStorage fs(tempFile, FileStorage::WRITE);
fs << "images" << "[";
fs << string(filename);
fs << "]";
*/
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
void CameraCalibration::ReadMonoCalibParams( string &img_xml )
{
calib_params = new CalibParams;
FileStorage fs(img_xml, FileStorage::READ); // Read the settings
if (!fs.isOpened())
{
cout << "Could not open the configuration file: \"" << img_xml << "\"" << endl;
exit(EXIT_FAILURE);
}
BoardSize = Size((int)fs["BoardSizeWidth"], (int)fs["BoardSizeHeight"]);
SquareSize = (float)fs["SquareSize"];
BoardTexWdith = SquareSize*BoardSize.width;
BoardTexHeight = SquareSize*BoardSize.height;
FileNode imgs = fs["Images"];
for(FileNodeIterator itr = imgs.begin(); itr != imgs.end(); itr++)
calib_params->ImageList.push_back((string)*itr);
// Check if all image data have the same size.
Mat img = imread(calib_params->ImageList.front(), CV_LOAD_IMAGE_GRAYSCALE);
ImageSize = Size(img.rows, img.cols);
for(int i=1; i<calib_params->ImageList.size(); i++)
{
img = imread(calib_params->ImageList.at(i), CV_LOAD_IMAGE_GRAYSCALE);
assert(ImageSize == Size(img.rows, img.cols));
ImageSize = Size(img.rows, img.cols);
}
NumFrames = calib_params->ImageList.size();
fs.release();
}
/*
* Run tests to make sure that quantization is working.
*/
void runQuantTests()
{
ColonyCounter colonyCounter;
colonyCounter.loadTraining("svm_params.yml");
FileStorage fs("samples/tests.yml", FileStorage::READ);
FileNode features = fs["tests"];
FileNodeIterator it = features.begin(), it_end = features.end();
int idx = 0;
for( ; it != it_end; ++it, idx++ )
{
string path;
(*it)["path"] >> path;
Mat img = imread("samples/" + path);
// Find petri img
Rect petriRect = findPetriRect(img);
Mat petri = img(petriRect);
// Preprocess image
petri = colonyCounter.preprocessImage(petri);
colonyCounter.testQuantization(petri, quants);
// Classify image
Mat debugImg, debugImgq;
colonyCounter.classifyImage(petri, true, &debugImg);
colonyCounter.classifyImageQuant(petri, true, &debugImgq, quants);
imshow("normal", debugImg);
imshow("quant", debugImgq);
waitKey(0);
}
fs.release();
}
int imageDB::readImgInfoMap(const FileStorage& cvfs, const FileNode& node)
{
imgInfo_map.clear();
releaseImgVoteMap();
int img_id;
imageInfo img_info;
FileNodeIterator it = node.begin();
while(it != node.end()){
img_id = (int)(*it)[0];
img_info.feature_num = (int)(*it)[1];
img_info.img_size = Size((int)(*it)[2],(int)(*it)[3]);
imgInfo_map.insert(pair<int,imageInfo>(img_id,img_info));
// create voteTable
vector<featureVote>* voteTable = new vector<featureVote>;
imgVote_map.insert(pair<int,vector<featureVote>*>(img_id, voteTable));
it++;
}
return 0;
}
