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Fixed number of warnings. Fixed mingw64 build.

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This commit is contained in:
Andrey Kamaev
2012-06-12 14:46:12 +00:00
parent 02e3afae3a
commit c5aba337e9
110 changed files with 2232 additions and 2367 deletions
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64
modules/contrib/src/facerec.cpp
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@@ -22,7 +22,7 @@ namespace cv
{
using std::set;
// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
template<typename _Tp>
inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
@@ -48,7 +48,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) {
// 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) {
@@ -63,7 +63,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// dimensionality of (reshaped) samples
size_t d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
Mat data((int)n, (int)d, rtype);
// now copy data
for(unsigned int i = 0; i < n; i++) {
// make sure data can be reshaped, throw exception if not!
@@ -83,7 +83,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
@@ -98,7 +98,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
@@ -146,10 +146,10 @@ public:
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
AlgorithmInfo* info() const;
};
// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
// faces: Recognition using class specific linear projection.". IEEE
// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
@@ -232,13 +232,13 @@ public:
//
// radius, neighbors are used in the local binary patterns creation.
// grid_x, grid_y control the grid size of the spatial histograms.
LBPH(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8,
LBPH(int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors),
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {}
// Initializes and computes this LBPH Model. The current implementation is
@@ -248,13 +248,13 @@ public:
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArray src,
InputArray labels,
int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8,
int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors),
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {
train(src, labels);
}
@@ -367,7 +367,7 @@ void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -473,7 +473,7 @@ void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -641,9 +641,9 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool normed)
int grid_x, int grid_y, bool /*normed*/)
{
Mat src = _src.getMat();
// calculate LBP patch size
@@ -681,7 +681,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) {
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
@@ -758,7 +758,7 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -770,23 +770,23 @@ int LBPH::predict(InputArray _src) const {
return label;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
{
return new Eigenfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
{
return new Fisherfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y, double threshold)
{
return new LBPH(radius, neighbors, grid_x, grid_y, threshold);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
@@ -803,8 +803,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
obj.info()->addParam(obj, "mean", obj._mean, true));
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
obj.info()->addParam(obj, "neighbors", obj._neighbors);
@@ -813,7 +813,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));
bool initModule_contrib()
{
Ptr<Algorithm> efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH();