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Move legacy part of opencv_contrib to separate header

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This commit is contained in:
Andrey Kamaev
2013-04-12 16:19:48 +04:00
parent 3b364330ad
commit 909d6fcf51
20 changed files with 1025 additions and 987 deletions
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54
modules/contrib/src/facerec.cpp
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@@ -51,7 +51,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// number of samples
size_t n = src.total();
@@ -67,7 +67,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure data can be reshaped, throw exception if not!
if(src.getMat(i).total() != d) {
String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// get a hold of the current row
Mat xi = data.row(i);
@@ -306,13 +306,13 @@ void FaceRecognizer::update(InputArrayOfArrays src, InputArray labels ) {
}
String error_msg = format("This FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.", this->name().c_str());
CV_Error(CV_StsNotImplemented, error_msg);
CV_Error(Error::StsNotImplemented, error_msg);
}
void FaceRecognizer::save(const String& filename) const {
FileStorage fs(filename, FileStorage::WRITE);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
this->save(fs);
fs.release();
}
@@ -320,7 +320,7 @@ void FaceRecognizer::save(const String& filename) const {
void FaceRecognizer::load(const String& filename) {
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
this->load(fs);
fs.release();
}
@@ -331,17 +331,17 @@ void FaceRecognizer::load(const String& filename) {
void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
if(_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(_local_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(_src.total() > 1) {
for(int i = 1; i < static_cast<int>(_src.total()); i++) {
if(_src.getMat(i-1).total() != _src.getMat(i).total()) {
String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
@@ -355,7 +355,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
// assert there are as much samples as labels
if(static_cast<int>(labels.total()) != n) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
@@ -365,7 +365,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
_num_components = n;
// perform the PCA
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components);
PCA pca(data, Mat(), PCA::DATA_AS_ROW, _num_components);
// copy the PCA results
_mean = pca.mean.reshape(1,1); // store the mean vector
_eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row
@@ -386,11 +386,11 @@ void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?";
CV_Error(CV_StsError, error_message);
CV_Error(Error::StsError, error_message);
} else if(_eigenvectors.rows != static_cast<int>(src.total())) {
// check data alignment just for clearer exception messages
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
@@ -440,17 +440,17 @@ void Eigenfaces::save(FileStorage& fs) const {
void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if(src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(_lbls.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(src.total() > 1) {
for(int i = 1; i < static_cast<int>(src.total()); i++) {
if(src.getMat(i-1).total() != src.getMat(i).total()) {
String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
@@ -462,10 +462,10 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
// make sure labels are passed in correct shape
if(labels.total() != (size_t) N) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(labels.rows != 1 && labels.cols != 1) {
String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols);
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
@@ -481,7 +481,7 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if((_num_components <= 0) || (_num_components > (C-1)))
_num_components = (C-1);
// perform a PCA and keep (N-C) components
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, (N-C));
PCA pca(data, Mat(), PCA::DATA_AS_ROW, (N-C));
// project the data and perform a LDA on it
LDA lda(pca.project(data),labels, _num_components);
// store the total mean vector
@@ -506,10 +506,10 @@ void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(src.total() != (size_t) _eigenvectors.rows) {
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
@@ -641,7 +641,7 @@ static void elbp(InputArray src, OutputArray dst, int radius, int neighbors)
case CV_64FC1: elbp_<double>(src,dst, radius, neighbors); break;
default:
String error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type);
CV_Error(CV_StsNotImplemented, error_msg);
CV_Error(Error::StsNotImplemented, error_msg);
break;
}
}
@@ -687,7 +687,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return histc_(src, minVal, maxVal, normed);
break;
default:
CV_Error(CV_StsUnmatchedFormats, "This type is not implemented yet."); break;
CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break;
}
return Mat();
}
@@ -769,14 +769,14 @@ void LBPH::update(InputArrayOfArrays _in_src, InputArray _in_labels) {
void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) {
if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
if(_in_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
} else if(_in_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
// get the vector of matrices
std::vector<Mat> src;
@@ -786,7 +786,7 @@ void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserv
// check if data is well- aligned
if(labels.total() != src.size()) {
String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// if this model should be trained without preserving old data, delete old model data
if(!preserveData) {
@@ -817,7 +817,7 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
if(_histograms.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This LBPH model is not computed yet. Did you call the train method?";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
Mat src = _src.getMat();
// get the spatial histogram from input image