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Commit cec8f719 authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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added face recognition algorithms, LDA, colormaps (all by Philipp Wagner)

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modules/contrib/include/opencv2/contrib/contrib.hpp
View file @ cec8f719
......@@ -845,6 +845,131 @@ namespace cv
bool get_uv(double x, double y, int&u, int&v);
void create_map(int M, int N, int R, int S, double ro0, double smin);
};
CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
class CV_EXPORTS LDA
{
public:
// Initializes a LDA with num_components (default 0) and specifies how
// samples are aligned (default dataAsRow=true).
LDA(int num_components = 0) :
_num_components(num_components) {};
// Initializes and performs a Discriminant Analysis with Fisher's
// Optimization Criterion on given data in src and corresponding labels
// in labels. If 0 (or less) number of components are given, they are
// automatically determined for given data in computation.
LDA(const Mat& src, vector<int> labels,
int num_components = 0) :
_num_components(num_components)
{
this->compute(src, labels); //! compute eigenvectors and eigenvalues
}
// Initializes and performs a Discriminant Analysis with Fisher's
// Optimization Criterion on given data in src and corresponding labels
// in labels. If 0 (or less) number of components are given, they are
// automatically determined for given data in computation.
LDA(InputArray src, InputArray labels,
int num_components = 0) :
_num_components(num_components)
{
this->compute(src, labels); //! compute eigenvectors and eigenvalues
}
// Serializes this object to a given filename.
void save(const string& filename) const;
// Deserializes this object from a given filename.
void load(const string& filename);
// Serializes this object to a given cv::FileStorage.
void save(FileStorage& fs) const;
// Deserializes this object from a given cv::FileStorage.
void load(const FileStorage& node);
// Destructor.
~LDA() {}
//! Compute the discriminants for data in src and labels.
void compute(InputArray src, InputArray labels);
// Projects samples into the LDA subspace.
Mat project(InputArray src);
// Reconstructs projections from the LDA subspace.
Mat reconstruct(InputArray src);
// Returns the eigenvectors of this LDA.
Mat eigenvectors() const { return _eigenvectors; };
// Returns the eigenvalues of this LDA.
Mat eigenvalues() const { return _eigenvalues; }
protected:
bool _dataAsRow;
int _num_components;
Mat _eigenvectors;
Mat _eigenvalues;
void lda(InputArray src, InputArray labels);
};
class CV_EXPORTS FaceRecognizer
{
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
virtual void train(InputArray src, InputArray labels) = 0;
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Serializes this object to a given filename.
virtual void save(const string& filename) const;
// Deserializes this object from a given filename.
virtual void load(const string& filename);
// Serializes this object to a given cv::FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
// Returns eigenvectors (if any)
virtual Mat eigenvectors() const { return Mat(); }
};
CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8);
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11,
COLORMAP_MKPJ1 = 12,
COLORMAP_MKPJ2 = 13
};
CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap);
}
......
modules/contrib/src/colormap.cpp 0 → 100644
View file @ cec8f719
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "precomp.hpp"
#include <iostream>
namespace cv
{
static Mat linspace(float x0, float x1, int n)
{
Mat pts(n, 1, CV_32FC1);
float step = (x1-x0)/floor(n-1);
for(int i = 0; i < n; i++)
pts.at<float>(i,0) = x0+i*step;
return pts;
}
//------------------------------------------------------------------------------
// cv::sortMatrixRowsByIndices
//------------------------------------------------------------------------------
static void sortMatrixRowsByIndices(InputArray _src, InputArray _indices, OutputArray _dst)
{
if(_indices.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "cv::sortRowsByIndices only works on integer indices!");
Mat src = _src.getMat();
vector<int> indices = _indices.getMat();
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
for(int idx = 0; idx < indices.size(); idx++) {
Mat originalRow = src.row(indices[idx]);
Mat sortedRow = dst.row(idx);
originalRow.copyTo(sortedRow);
}
}
static Mat sortMatrixRowsByIndices(InputArray src, InputArray indices)
{
Mat dst;
sortMatrixRowsByIndices(src, indices, dst);
return dst;
}
Mat argsort(InputArray _src, bool ascending=true)
{
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1)
CV_Error(CV_StsBadArg, "cv::argsort only sorts 1D matrices.");
int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
Mat sorted_indices;
sortIdx(src.reshape(1,1),sorted_indices,flags);
return sorted_indices;
}
template <typename _Tp> static
Mat interp1_(const Mat& X_, const Mat& Y_, const Mat& XI)
{
int n = XI.rows;
// sort input table
vector<int> sort_indices = argsort(X_);
Mat X = sortMatrixRowsByIndices(X_,sort_indices);
Mat Y = sortMatrixRowsByIndices(Y_,sort_indices);
// interpolated values
Mat yi = Mat::zeros(XI.size(), XI.type());
for(int i = 0; i < n; i++) {
int c = 0;
int low = 0;
int high = X.rows - 1;
// set bounds
if(XI.at<_Tp>(i,0) < X.at<_Tp>(low, 0))
high = 1;
if(XI.at<_Tp>(i,0) > X.at<_Tp>(high, 0))
low = high - 1;
// binary search
while((high-low)>1) {
c = low + ((high - low) >> 1);
if(XI.at<_Tp>(i,0) > X.at<_Tp>(c,0)) {
low = c;
} else {
high = c;
}
}
// linear interpolation
yi.at<_Tp>(i,0) += Y.at<_Tp>(low,0)
+ (XI.at<_Tp>(i,0) - X.at<_Tp>(low,0))
* (Y.at<_Tp>(high,0) - Y.at<_Tp>(low,0))
/ (X.at<_Tp>(high,0) - X.at<_Tp>(low,0));
}
return yi;
}
static Mat interp1(InputArray _x, InputArray _Y, InputArray _xi)
{
// get matrices
Mat x = _x.getMat();
Mat Y = _Y.getMat();
Mat xi = _xi.getMat();
// check types & alignment
assert((x.type() == Y.type()) && (Y.type() == xi.type()));
assert((x.cols == 1) && (x.rows == Y.rows) && (x.cols == Y.cols));
// call templated interp1
switch(x.type()) {
case CV_8SC1: return interp1_<char>(x,Y,xi); break;
case CV_8UC1: return interp1_<unsigned char>(x,Y,xi); break;
case CV_16SC1: return interp1_<short>(x,Y,xi); break;
case CV_16UC1: return interp1_<unsigned short>(x,Y,xi); break;
case CV_32SC1: return interp1_<int>(x,Y,xi); break;
case CV_32FC1: return interp1_<float>(x,Y,xi); break;
case CV_64FC1: return interp1_<double>(x,Y,xi); break;
default: CV_Error(CV_StsUnsupportedFormat, ""); return Mat();
}
}
namespace colormap
{
class ColorMap {
protected:
Mat _lut;
public:
virtual ~ColorMap() {}
// Applies the colormap on a given image.
//
// This function expects BGR-aligned data of type CV_8UC1 or
// CV_8UC3. If the wrong image type is given, the original image
// will be returned.
//
// Throws an error for wrong-aligned lookup table, which must be
// of size 256 in the latest OpenCV release (2.3.1).
void operator()(InputArray src, OutputArray dst) const;
// Setup base map to interpolate from.
virtual void init(int n) = 0;
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
int n) {
return linear_colormap(X,r,g,b,linspace(0,1,n));
}
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
float begin, float end, float n) {
return linear_colormap(X,r,g,b,linspace(begin,end,n));
}
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
InputArray xi);
};
// Equals the GNU Octave colormap "autumn".
class Autumn : public ColorMap {
public:
Autumn() : ColorMap() {
init(256);
}
Autumn(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "bone".
class Bone : public ColorMap {
public:
Bone() : ColorMap() {
init(256);
}
Bone(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3333333333333333, 0.3472222222222222, 0.3611111111111111, 0.375, 0.3888888888888888, 0.4027777777777777, 0.4166666666666666, 0.4305555555555555, 0.4444444444444444, 0.4583333333333333, 0.4722222222222222, 0.4861111111111112, 0.5, 0.5138888888888888, 0.5277777777777778, 0.5416666666666667, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.611111111111111, 0.6249999999999999, 0.6388888888888888, 0.6527777777777778, 0.6726190476190474, 0.6944444444444442, 0.7162698412698412, 0.7380952380952381, 0.7599206349206349, 0.7817460317460316, 0.8035714285714286, 0.8253968253968254, 0.8472222222222221, 0.8690476190476188, 0.8908730158730158, 0.9126984126984128, 0.9345238095238095, 0.9563492063492063, 0.978174603174603, 1};
float g[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3353174603174602, 0.3544973544973544, 0.3736772486772486, 0.3928571428571428, 0.412037037037037, 0.4312169312169312, 0.4503968253968254, 0.4695767195767195, 0.4887566137566137, 0.5079365079365078, 0.5271164021164021, 0.5462962962962963, 0.5654761904761904, 0.5846560846560845, 0.6038359788359787, 0.623015873015873, 0.6421957671957671, 0.6613756613756612, 0.6805555555555555, 0.6997354497354497, 0.7189153439153438, 0.7380952380952379, 0.7572751322751322, 0.7764550264550264, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
float b[] = { 0, 0.01917989417989418, 0.03835978835978836, 0.05753968253968253, 0.07671957671957672, 0.09589947089947089, 0.1150793650793651, 0.1342592592592592, 0.1534391534391534, 0.1726190476190476, 0.1917989417989418, 0.210978835978836, 0.2301587301587301, 0.2493386243386243, 0.2685185185185185, 0.2876984126984127, 0.3068783068783069, 0.326058201058201, 0.3452380952380952, 0.3644179894179894, 0.3835978835978835, 0.4027777777777777, 0.4219576719576719, 0.4411375661375661, 0.4583333333333333, 0.4722222222222222, 0.4861111111111111, 0.5, 0.5138888888888888, 0.5277777777777777, 0.5416666666666666, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.6111111111111112, 0.625, 0.6388888888888888, 0.6527777777777778, 0.6666666666666667, 0.6805555555555556, 0.6944444444444444, 0.7083333333333333, 0.7222222222222222, 0.736111111111111, 0.7499999999999999, 0.7638888888888888, 0.7777777777777778, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "jet".
class Jet : public ColorMap {
public:
Jet() {
init(256);
}
Jet(int n) : ColorMap() {
init(n);
}
void init(int n) {
// breakpoints
Mat X = linspace(0,1,256);
// define the basemap
float r[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645,0.02156862745098032,0.03725490196078418,0.05294117647058827,0.06862745098039214,0.084313725490196,0.1000000000000001,0.115686274509804,0.1313725490196078,0.1470588235294117,0.1627450980392156,0.1784313725490196,0.1941176470588235,0.2098039215686274,0.2254901960784315,0.2411764705882353,0.2568627450980392,0.2725490196078431,0.2882352941176469,0.303921568627451,0.3196078431372549,0.3352941176470587,0.3509803921568628,0.3666666666666667,0.3823529411764706,0.3980392156862744,0.4137254901960783,0.4294117647058824,0.4450980392156862,0.4607843137254901,0.4764705882352942,0.4921568627450981,0.5078431372549019,0.5235294117647058,0.5392156862745097,0.5549019607843135,0.5705882352941174,0.5862745098039217,0.6019607843137256,0.6176470588235294,0.6333333333333333,0.6490196078431372,0.664705882352941,0.6803921568627449,0.6960784313725492,0.7117647058823531,0.7274509803921569,0.7431372549019608,0.7588235294117647,0.7745098039215685,0.7901960784313724,0.8058823529411763,0.8215686274509801,0.8372549019607844,0.8529411764705883,0.8686274509803922,0.884313725490196,0.8999999999999999,0.9156862745098038,0.9313725490196076,0.947058823529412,0.9627450980392158,0.9784313725490197,0.9941176470588236,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216,0.9705882352941178,0.9549019607843139,0.93921568627451,0.9235294117647062,0.9078431372549018,0.892156862745098,0.8764705882352941,0.8607843137254902,0.8450980392156864,0.8294117647058825,0.8137254901960786,0.7980392156862743,0.7823529411764705,0.7666666666666666,0.7509803921568627,0.7352941176470589,0.719607843137255,0.7039215686274511,0.6882352941176473,0.6725490196078434,0.6568627450980391,0.6411764705882352,0.6254901960784314,0.6098039215686275,0.5941176470588236,0.5784313725490198,0.5627450980392159,0.5470588235294116,0.5313725490196077,0.5156862745098039,0.5};
float g[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483,0.01764705882352935,0.03333333333333333,0.0490196078431373,0.06470588235294117,0.08039215686274503,0.09607843137254901,0.111764705882353,0.1274509803921569,0.1431372549019607,0.1588235294117647,0.1745098039215687,0.1901960784313725,0.2058823529411764,0.2215686274509804,0.2372549019607844,0.2529411764705882,0.2686274509803921,0.2843137254901961,0.3,0.3156862745098039,0.3313725490196078,0.3470588235294118,0.3627450980392157,0.3784313725490196,0.3941176470588235,0.4098039215686274,0.4254901960784314,0.4411764705882353,0.4568627450980391,0.4725490196078431,0.4882352941176471,0.503921568627451,0.5196078431372548,0.5352941176470587,0.5509803921568628,0.5666666666666667,0.5823529411764705,0.5980392156862746,0.6137254901960785,0.6294117647058823,0.6450980392156862,0.6607843137254901,0.6764705882352942,0.692156862745098,0.7078431372549019,0.723529411764706,0.7392156862745098,0.7549019607843137,0.7705882352941176,0.7862745098039214,0.8019607843137255,0.8176470588235294,0.8333333333333333,0.8490196078431373,0.8647058823529412,0.8803921568627451,0.8960784313725489,0.9117647058823528,0.9274509803921569,0.9431372549019608,0.9588235294117646,0.9745098039215687,0.9901960784313726,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726,0.9745098039215687,0.9588235294117649,0.943137254901961,0.9274509803921571,0.9117647058823528,0.8960784313725489,0.8803921568627451,0.8647058823529412,0.8490196078431373,0.8333333333333335,0.8176470588235296,0.8019607843137253,0.7862745098039214,0.7705882352941176,0.7549019607843137,0.7392156862745098,0.723529411764706,0.7078431372549021,0.6921568627450982,0.6764705882352944,0.6607843137254901,0.6450980392156862,0.6294117647058823,0.6137254901960785,0.5980392156862746,0.5823529411764707,0.5666666666666669,0.5509803921568626,0.5352941176470587,0.5196078431372548,0.503921568627451,0.4882352941176471,0.4725490196078432,0.4568627450980394,0.4411764705882355,0.4254901960784316,0.4098039215686273,0.3941176470588235,0.3784313725490196,0.3627450980392157,0.3470588235294119,0.331372549019608,0.3156862745098041,0.2999999999999998,0.284313725490196,0.2686274509803921,0.2529411764705882,0.2372549019607844,0.2215686274509805,0.2058823529411766,0.1901960784313728,0.1745098039215689,0.1588235294117646,0.1431372549019607,0.1274509803921569,0.111764705882353,0.09607843137254912,0.08039215686274526,0.06470588235294139,0.04901960784313708,0.03333333333333321,0.01764705882352935,0.001960784313725483,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
float b[] = {0.5,0.5156862745098039,0.5313725490196078,0.5470588235294118,0.5627450980392157,0.5784313725490196,0.5941176470588235,0.6098039215686275,0.6254901960784314,0.6411764705882352,0.6568627450980392,0.6725490196078432,0.6882352941176471,0.7039215686274509,0.7196078431372549,0.7352941176470589,0.7509803921568627,0.7666666666666666,0.7823529411764706,0.7980392156862746,0.8137254901960784,0.8294117647058823,0.8450980392156863,0.8607843137254902,0.8764705882352941,0.892156862745098,0.907843137254902,0.9235294117647059,0.9392156862745098,0.9549019607843137,0.9705882352941176,0.9862745098039216,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236,0.9784313725490197,0.9627450980392158,0.9470588235294117,0.9313725490196079,0.915686274509804,0.8999999999999999,0.884313725490196,0.8686274509803922,0.8529411764705883,0.8372549019607844,0.8215686274509804,0.8058823529411765,0.7901960784313726,0.7745098039215685,0.7588235294117647,0.7431372549019608,0.7274509803921569,0.7117647058823531,0.696078431372549,0.6803921568627451,0.6647058823529413,0.6490196078431372,0.6333333333333333,0.6176470588235294,0.6019607843137256,0.5862745098039217,0.5705882352941176,0.5549019607843138,0.5392156862745099,0.5235294117647058,0.5078431372549019,0.4921568627450981,0.4764705882352942,0.4607843137254903,0.4450980392156865,0.4294117647058826,0.4137254901960783,0.3980392156862744,0.3823529411764706,0.3666666666666667,0.3509803921568628,0.335294117647059,0.3196078431372551,0.3039215686274508,0.2882352941176469,0.2725490196078431,0.2568627450980392,0.2411764705882353,0.2254901960784315,0.2098039215686276,0.1941176470588237,0.1784313725490199,0.1627450980392156,0.1470588235294117,0.1313725490196078,0.115686274509804,0.1000000000000001,0.08431372549019622,0.06862745098039236,0.05294117647058805,0.03725490196078418,0.02156862745098032,0.00588235294117645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
// now build lookup table
this->_lut = ColorMap::linear_colormap(X,
Mat(256,1, CV_32FC1, r).clone(), // red
Mat(256,1, CV_32FC1, g).clone(), // green
Mat(256,1, CV_32FC1, b).clone(), // blue
n);
}
};
// Equals the GNU Octave colormap "winter".
class Winter : public ColorMap {
public:
Winter() : ColorMap() {
init(256);
}
Winter(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float g[] = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0};
float b[] = {1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5};
Mat X = linspace(0,1,11);
this->_lut = ColorMap::linear_colormap(X,
Mat(11,1, CV_32FC1, r).clone(), // red
Mat(11,1, CV_32FC1, g).clone(), // green
Mat(11,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "rainbow".
class Rainbow : public ColorMap {
public:
Rainbow() : ColorMap() {
init(256);
}
Rainbow(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9365079365079367, 0.8571428571428572, 0.7777777777777777, 0.6984126984126986, 0.6190476190476191, 0.53968253968254, 0.4603174603174605, 0.3809523809523814, 0.3015873015873018, 0.2222222222222223, 0.1428571428571432, 0.06349206349206415, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603208, 0.08465608465608465, 0.1375661375661377, 0.1904761904761907, 0.2433862433862437, 0.2962962962962963, 0.3492063492063493, 0.4021164021164023, 0.4550264550264553, 0.5079365079365079, 0.5608465608465609, 0.6137566137566139, 0.666666666666667};
float g[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9841269841269842, 0.9047619047619047, 0.8253968253968256, 0.7460317460317465, 0.666666666666667, 0.587301587301587, 0.5079365079365079, 0.4285714285714288, 0.3492063492063493, 0.2698412698412698, 0.1904761904761907, 0.1111111111111116, 0.03174603174603208, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01587301587301582, 0.09523809523809534, 0.1746031746031744, 0.2539682539682535, 0.333333333333333, 0.412698412698413, 0.4920634920634921, 0.5714285714285712, 0.6507936507936507, 0.7301587301587302, 0.8095238095238093, 0.8888888888888884, 0.9682539682539679, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "ocean".
class Ocean : public ColorMap {
public:
Ocean() : ColorMap() {
init(256);
}
Ocean(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904762, 0.09523809523809523, 0.1428571428571428, 0.1904761904761905, 0.2380952380952381, 0.2857142857142857, 0.3333333333333333, 0.3809523809523809, 0.4285714285714285, 0.4761904761904762, 0.5238095238095238, 0.5714285714285714, 0.6190476190476191, 0.6666666666666666, 0.7142857142857143, 0.7619047619047619, 0.8095238095238095, 0.8571428571428571, 0.9047619047619048, 0.9523809523809523, 1};
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02380952380952381, 0.04761904761904762, 0.07142857142857142, 0.09523809523809523, 0.119047619047619, 0.1428571428571428, 0.1666666666666667, 0.1904761904761905, 0.2142857142857143, 0.2380952380952381, 0.2619047619047619, 0.2857142857142857, 0.3095238095238095, 0.3333333333333333, 0.3571428571428572, 0.3809523809523809, 0.4047619047619048, 0.4285714285714285, 0.4523809523809524, 0.4761904761904762, 0.5, 0.5238095238095238, 0.5476190476190477, 0.5714285714285714, 0.5952380952380952, 0.6190476190476191, 0.6428571428571429, 0.6666666666666666, 0.6904761904761905, 0.7142857142857143, 0.7380952380952381, 0.7619047619047619, 0.7857142857142857, 0.8095238095238095, 0.8333333333333334, 0.8571428571428571, 0.8809523809523809, 0.9047619047619048, 0.9285714285714286, 0.9523809523809523, 0.9761904761904762, 1};
float b[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "summer".
class Summer : public ColorMap {
public:
Summer() : ColorMap() {
init(256);
}
Summer(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float g[] = { 0.5, 0.5079365079365079, 0.5158730158730158, 0.5238095238095238, 0.5317460317460317, 0.5396825396825397, 0.5476190476190477, 0.5555555555555556, 0.5634920634920635, 0.5714285714285714, 0.5793650793650793, 0.5873015873015873, 0.5952380952380952, 0.6031746031746031, 0.6111111111111112, 0.6190476190476191, 0.626984126984127, 0.6349206349206349, 0.6428571428571428, 0.6507936507936508, 0.6587301587301587, 0.6666666666666666, 0.6746031746031746, 0.6825396825396826, 0.6904761904761905, 0.6984126984126984, 0.7063492063492063, 0.7142857142857143, 0.7222222222222222, 0.7301587301587301, 0.7380952380952381, 0.746031746031746, 0.753968253968254, 0.7619047619047619, 0.7698412698412698, 0.7777777777777778, 0.7857142857142857, 0.7936507936507937, 0.8015873015873016, 0.8095238095238095, 0.8174603174603174, 0.8253968253968254, 0.8333333333333333, 0.8412698412698413, 0.8492063492063492, 0.8571428571428572, 0.8650793650793651, 0.873015873015873, 0.8809523809523809, 0.8888888888888888, 0.8968253968253967, 0.9047619047619048, 0.9126984126984127, 0.9206349206349207, 0.9285714285714286, 0.9365079365079365, 0.9444444444444444, 0.9523809523809523, 0.9603174603174602, 0.9682539682539683, 0.9761904761904762, 0.9841269841269842, 0.9920634920634921, 1};
float b[] = { 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "spring".
class Spring : public ColorMap {
public:
Spring() : ColorMap() {
init(256);
}
Spring(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float b[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "cool".
class Cool : public ColorMap {
public:
Cool() : ColorMap() {
init(256);
}
Cool(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float g[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
float b[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "hsv".
class HSV : public ColorMap {
public:
HSV() : ColorMap() {
init(256);
}
HSV(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428568, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904763, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.0952380952380949, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809557, 0.1904761904761905, 0.2857142857142854, 0.3809523809523809, 0.4761904761904765, 0.5714285714285714, 0.6666666666666663, 0.7619047619047619, 0.8571428571428574, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047619, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523814, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "pink".
class Pink : public ColorMap {
public:
Pink() : ColorMap() {
init(256);
}
Pink(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.1571348402636772, 0.2222222222222222, 0.2721655269759087, 0.3142696805273544, 0.3513641844631533, 0.3849001794597505, 0.415739709641549, 0.4444444444444444, 0.4714045207910317, 0.4969039949999532, 0.5211573066470477, 0.5443310539518174, 0.5665577237325317, 0.5879447357921312, 0.6085806194501846, 0.6285393610547089, 0.6478835438717, 0.6666666666666666, 0.6849348892187751, 0.7027283689263065, 0.7200822998230956, 0.7370277311900888, 0.753592220347252, 0.7663560447348133, 0.7732293307186413, 0.7800420555749596, 0.7867957924694432, 0.7934920476158722, 0.8001322641986387, 0.8067178260046388, 0.8132500607904444, 0.8197302434079591, 0.8261595987094034, 0.8325393042503717, 0.8388704928078611, 0.8451542547285166, 0.8513916401208816, 0.8575836609041332, 0.8637312927246217, 0.8698354767504924, 0.8758971213537393, 0.8819171036881968, 0.8878962711712378, 0.8938354428762595, 0.8997354108424372, 0.9055969413076769, 0.9114207758701963, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
float g[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5091750772173156, 0.5328701692569688, 0.5555555555555556, 0.5773502691896257, 0.5983516452371671, 0.6186404847588913, 0.6382847385042254, 0.6573421981221795, 0.6758625033664688, 0.6938886664887108, 0.7114582486036499, 0.7286042804780002, 0.7453559924999299, 0.7617394000445604, 0.7777777777777778, 0.7934920476158723, 0.8089010988089465, 0.8240220541217402, 0.8388704928078611, 0.8534606386520677, 0.8678055195451838, 0.8819171036881968, 0.8958064164776166, 0.9094836413191612, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
float b[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5039526306789697, 0.5143444998736397, 0.5245305283129621, 0.5345224838248488, 0.5443310539518174, 0.5539659798925444, 0.563436169819011, 0.5727497953228163, 0.5819143739626463, 0.5909368402852788, 0.5998236072282915, 0.6085806194501846, 0.6172133998483676, 0.6257270902992705, 0.6341264874742278, 0.642416074439621, 0.6506000486323554, 0.6586823467062358, 0.6666666666666666, 0.6745564876468501, 0.6823550876255453, 0.6900655593423541, 0.6976908246297114, 0.7052336473499384, 0.7237468644557459, 0.7453559924999298, 0.7663560447348133, 0.7867957924694432, 0.8067178260046388, 0.8261595987094034, 0.8451542547285166, 0.8637312927246217, 0.8819171036881968, 0.8997354108424372, 0.9172076325837248, 0.9343531843023135, 0.9511897312113418, 0.9677334015667416, 0.9839989676081821, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "hot".
class Hot : public ColorMap {
public:
Hot() : ColorMap() {
init(256);
}
Hot(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603163, 0.0714285714285714, 0.1111111111111112, 0.1507936507936507, 0.1904761904761905, 0.23015873015873, 0.2698412698412698, 0.3095238095238093, 0.3492063492063491, 0.3888888888888888, 0.4285714285714284, 0.4682539682539679, 0.5079365079365079, 0.5476190476190477, 0.5873015873015872, 0.6269841269841268, 0.6666666666666665, 0.7063492063492065, 0.746031746031746, 0.7857142857142856, 0.8253968253968254, 0.8650793650793651, 0.9047619047619047, 0.9444444444444442, 0.984126984126984, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904745, 0.1269841269841265, 0.2063492063492056, 0.2857142857142856, 0.3650793650793656, 0.4444444444444446, 0.5238095238095237, 0.6031746031746028, 0.6825396825396828, 0.7619047619047619, 0.8412698412698409, 0.92063492063492, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// A perceptually improved Jet colormap (MKPJ1) by Matteo Niccoli
//
// Author's personal website:
// http://mycarta.wordpress.com/
//
// Author's FEX page:
// http://www.mathworks.com/matlabcentral/fileexchange/authors/87376
//
class MKPJ1 : public ColorMap {
public:
MKPJ1() : ColorMap() {
init(256);
}
MKPJ1(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0476, 0.09710000000000001, 0.1466, 0.1961, 0.2456, 0.2952, 0.3447, 0.3942, 0.4437, 0.4932, 0.5427, 0.5921999999999999, 0.6417, 0.6912, 0.7407, 0.7903, 0.8398, 0.8893, 0.9388, 0.9883, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = {0, 0, 0, 0.007900000000000001, 0.0574, 0.1069, 0.1564, 0.2059, 0.2555, 0.305, 0.3545, 0.404, 0.4535, 0.503, 0.5525, 0.602, 0.6515, 0.701, 0.7506, 0.8001, 0.8496, 0.8991, 0.9486, 0.9981, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9622000000000001, 0.9127, 0.8632, 0.8137, 0.7642, 0.7146, 0.6651, 0.6156, 0.5661, 0.5165999999999999, 0.4671, 0.4176, 0.3681, 0.3186, 0.2691, 0.2195, 0.17, 0.1205, 0.07099999999999999, 0.0215};
float b[] = {0.8594000000000001, 0.9089, 0.9584, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9524, 0.9029, 0.8534, 0.8038999999999999, 0.7544, 0.7048, 0.6553, 0.6058, 0.5563, 0.5068, 0.4573, 0.4078, 0.3583, 0.3088, 0.2593, 0.2097, 0.1602, 0.1107, 0.0612, 0.0117, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// A perceptually improved Jet colormap (MKPJ2) by Matteo Niccoli
//
// Author's personal website:
// http://mycarta.wordpress.com/
//
// Author's FEX page:
// http://www.mathworks.com/matlabcentral/fileexchange/authors/87376
//
class MKPJ2 : public ColorMap {
public:
MKPJ2() : ColorMap() {
init(256);
}
MKPJ2(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0005, 0.0298, 0.0653, 0.1055, 0.1524, 0.2094, 0.2787, 0.3565, 0.4364, 0.5154, 0.5903, 0.6582, 0.7183, 0.7714, 0.8176, 0.8579, 0.8935999999999999, 0.9254, 0.954, 0.98, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = {0.0116, 0.09089999999999999, 0.1791, 0.2667, 0.3472, 0.4196, 0.4849, 0.5404, 0.5878, 0.6292, 0.6657999999999999, 0.6988, 0.729, 0.7574, 0.7846, 0.8110000000000001, 0.837, 0.8628, 0.8885, 0.9145, 0.9409999999999999, 0.9687, 0.9975000000000001, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9963, 0.9743000000000001, 0.9537, 0.9344, 0.9162, 0.8987000000000001, 0.8821, 0.8659, 0.85, 0.8343, 0.8186, 0.8025, 0.7859, 0.7683, 0.7491, 0.7276, 0.7026, 0.6728, 0.6363, 0.5915, 0.5346, 0.4602};
float b[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9995000000000001, 0.9702, 0.9347, 0.8945, 0.8476, 0.7906, 0.7213000000000001, 0.6435, 0.5636, 0.4846, 0.4097, 0.3418, 0.2817, 0.2286, 0.1824, 0.1421, 0.1064, 0.0746, 0.046, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
void ColorMap::operator()(InputArray _src, OutputArray _dst) const
{
if(_lut.total() != 256)
CV_Error(CV_StsAssert, "cv::LUT only supports tables of size 256.");
Mat src = _src.getMat();
// Return original matrix if wrong type is given (is fail loud better here?)
if(src.type() != CV_8UC1 && src.type() != CV_8UC3)
{
src.copyTo(_dst);
return;
}
// Turn into a BGR matrix into its grayscale representation.
if(src.type() == CV_8UC3)
cvtColor(src.clone(), src, CV_BGR2GRAY);
cvtColor(src.clone(), src, CV_GRAY2BGR);
// Apply the ColorMap.
LUT(src, _lut, _dst);
}
Mat ColorMap::linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
InputArray xi) {
Mat lut, lut8;
Mat planes[] = {
interp1(X, b, xi),
interp1(X, g, xi),
interp1(X, r, xi)};
merge(planes, 3, lut);
lut.convertTo(lut8, CV_8U, 255.);
return lut8;
}
}
void applyColorMap(InputArray src, OutputArray dst, int colormap)
{
colormap::ColorMap* cm =
colormap == COLORMAP_AUTUMN ? (colormap::ColorMap*)(new colormap::Autumn) :
colormap == COLORMAP_BONE ? (colormap::ColorMap*)(new colormap::Bone) :
colormap == COLORMAP_JET ? (colormap::ColorMap*)(new colormap::Jet) :
colormap == COLORMAP_WINTER ? (colormap::ColorMap*)(new colormap::Winter) :
colormap == COLORMAP_OCEAN ? (colormap::ColorMap*)(new colormap::Ocean) :
colormap == COLORMAP_SUMMER ? (colormap::ColorMap*)(new colormap::Summer) :
colormap == COLORMAP_SPRING ? (colormap::ColorMap*)(new colormap::Spring) :
colormap == COLORMAP_COOL ? (colormap::ColorMap*)(new colormap::Cool) :
colormap == COLORMAP_HSV ? (colormap::ColorMap*)(new colormap::HSV) :
colormap == COLORMAP_HOT ? (colormap::ColorMap*)(new colormap::Hot) :
colormap == COLORMAP_MKPJ1 ? (colormap::ColorMap*)(new colormap::MKPJ1) :
colormap == COLORMAP_MKPJ2 ? (colormap::ColorMap*)(new colormap::MKPJ2) : 0;
if( !cm )
CV_Error( CV_StsBadArg, "Unknown colormap id; use one of COLORMAP_*");
(*cm)(src, dst);
delete cm;
}
}
modules/contrib/src/facerec.cpp 0 → 100644
View file @ cec8f719
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "precomp.hpp"
#include <set>
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) {
if (fn.type() == FileNode::SEQ) {
for (FileNodeIterator it = fn.begin(); it != fn.end();) {
_Tp item;
it >> item;
result.push_back(item);
}
}
}
// Writes the a list of given items to a cv::FileStorage.
template<typename _Tp>
inline void writeFileNodeList(FileStorage& fs, const string& name,
const vector<_Tp>& items) {
// typedefs
typedef typename vector<_Tp>::const_iterator constVecIterator;
// write the elements in item to fs
fs << name << "[";
for (constVecIterator it = items.begin(); it != items.end(); ++it) {
fs << *it;
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0)
{
// number of samples
int n = (int) src.total();
// return empty matrix if no data given
if(n == 0)
return Mat();
// dimensionality of samples
int d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
// copy data
for(int i = 0; i < n; i++) {
Mat xi = data.row(i);
src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta);
}
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
typedef typename set<_Tp>::const_iterator constSetIterator;
typedef typename vector<_Tp>::const_iterator constVecIterator;
set<_Tp> set_elems;
for (constVecIterator it = src.begin(); it != src.end(); ++it)
set_elems.insert(*it);
vector<_Tp> elems;
for (constSetIterator it = set_elems.begin(); it != set_elems.end(); ++it)
elems.push_back(*it);
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
{
private:
int _num_components;
vector<Mat> _projections;
vector<int> _labels;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes an empty Eigenfaces model.
Eigenfaces(int num_components = 0) :
_num_components(num_components) { }
// Initializes and computes an Eigenfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Eigenfaces(InputArray src, InputArray labels,
int num_components = 0) :
_num_components(num_components) {
train(src, labels);
}
// Computes an Eigenfaces model with images in src and corresponding labels
// in labels.
void train(InputArray src, InputArray labels);
// Predicts the label of a query image in src.
int predict(const InputArray src) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
// Returns the eigenvectors of this PCA.
Mat eigenvectors() const { return _eigenvectors; }
// Returns the eigenvalues of this PCA.
Mat eigenvalues() const { return _eigenvalues; }
// Returns the sample mean of this PCA.
Mat mean() const { return _mean; }
// Returns the number of components used in this PCA.
int num_components() const { return _num_components; }
};
// 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),
// 711–720.
class Fisherfaces: public FaceRecognizer
{
private:
int _num_components;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
vector<Mat> _projections;
vector<int> _labels;
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes an empty Fisherfaces model.
Fisherfaces(int num_components = 0) :
_num_components(num_components) {}
// Initializes and computes a Fisherfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Fisherfaces(InputArray src,
InputArray labels,
int num_components = 0) :
_num_components(num_components) {
train(src, labels);
}
~Fisherfaces() { }
// Computes a Fisherfaces model with images in src and corresponding labels
// in labels.
void train(InputArray src, InputArray labels);
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// See FaceRecognizer::load.
virtual void load(const FileStorage& fs);
// See FaceRecognizer::save.
virtual void save(FileStorage& fs) const;
// Returns the eigenvectors of this Fisherfaces model.
Mat eigenvectors() const { return _eigenvectors; }
// Returns the eigenvalues of this Fisherfaces model.
Mat eigenvalues() const { return _eigenvalues; }
// Returns the sample mean of this Fisherfaces model.
Mat mean() const { return _eigenvalues; }
// Returns the number of components used in this Fisherfaces model.
int num_components() const { return _num_components; }
};
// Face Recognition based on Local Binary Patterns.
//
// TODO Allow to change the distance metric.
// TODO Allow to change LBP computation (Extended LBP used right now).
// TODO Optimize, Optimize, Optimize!
//
// Ahonen T, Hadid A. and Pietikäinen M. "Face description with local binary
// patterns: Application to face recognition." IEEE Transactions on Pattern
// Analysis and Machine Intelligence, 28(12):2037-2041.
//
class LBPH : public FaceRecognizer
{
private:
int _grid_x;
int _grid_y;
int _radius;
int _neighbors;
vector<Mat> _histograms;
vector<int> _labels;
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes this LBPH Model. The current implementation is rather fixed
// as it uses the Extended Local Binary Patterns per default.
//
// 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) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {}
// Initializes and computes this LBPH Model. The current implementation is
// rather fixed as it uses the Extended Local Binary Patterns per default.
//
// (radius=1), (neighbors=8) are used in the local binary patterns creation.
// (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) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {
train(src, labels);
}
~LBPH() { }
// Computes a LBPH model with images in src and
// corresponding labels in labels.
void train(InputArray src, InputArray labels);
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
// Getter functions.
int neighbors() const { return _neighbors; }
int radius() const { return _radius; }
int grid_x() const { return _grid_x; }
int grid_y() const { return _grid_y; }
};
//------------------------------------------------------------------------------
// FaceRecognizer
//------------------------------------------------------------------------------
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!");
this->save(fs);
fs.release();
}
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!");
this->load(fs);
fs.release();
}
//------------------------------------------------------------------------------
// Eigenfaces
//------------------------------------------------------------------------------
void Eigenfaces::train(InputArray src, InputArray _lbls) {
// assert type
if(_lbls.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// get labels
vector<int> labels = _lbls.getMat();
// observations in row
Mat data = asRowMatrix(src, CV_64FC1);
// number of samples
int n = data.rows;
// dimensionality of data
//int d = data.cols;
// assert there are as much samples as labels
if(n != labels.size())
CV_Error(CV_StsBadArg, "The number of samples must equal the number of labels!");
// clip number of components to be valid
if((_num_components <= 0) || (_num_components > n))
_num_components = n;
// perform the PCA
PCA pca(data, Mat(), CV_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
transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column
_labels = labels; // store labels for prediction
// save projections
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
this->_projections.push_back(p);
}
}
int Eigenfaces::predict(InputArray _src) const {
// get data
Mat src = _src.getMat();
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
double minDist = DBL_MAX;
int minClass = -1;
for(int sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
}
}
return minClass;
}
void Eigenfaces::load(const FileStorage& fs) {
//read matrices
fs["num_components"] >> _num_components;
fs["mean"] >> _mean;
fs["eigenvalues"] >> _eigenvalues;
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
readFileNodeList(fs["labels"], _labels);
}
void Eigenfaces::save(FileStorage& fs) const {
// write matrices
fs << "num_components" << _num_components;
fs << "mean" << _mean;
fs << "eigenvalues" << _eigenvalues;
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
writeFileNodeList(fs, "labels", _labels);
}
//------------------------------------------------------------------------------
// Fisherfaces
//------------------------------------------------------------------------------
void Fisherfaces::train(InputArray src, InputArray _lbls) {
if(_lbls.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// get data
vector<int> labels = _lbls.getMat();
Mat data = asRowMatrix(src, CV_64FC1);
// dimensionality
int N = data.rows; // number of samples
//int D = data.cols; // dimension of samples
// assert correct data alignment
if(labels.size() != N)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// compute the Fisherfaces
int C = remove_dups(labels).size(); // number of unique classes
// clip number of components to be a valid number
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));
// project the data and perform a LDA on it
LDA lda(pca.project(data),labels, _num_components);
// store the total mean vector
_mean = pca.mean.reshape(1,1);
// store labels
_labels = labels;
// store the eigenvalues of the discriminants
lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);
// Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors.
// Note: OpenCV stores the eigenvectors by row, so we need to transpose it!
gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, CV_GEMM_A_T);
// store the projections of the original data
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
_projections.push_back(p);
}
}
int Fisherfaces::predict(InputArray _src) const {
Mat src = _src.getMat();
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
// find 1-nearest neighbor
double minDist = DBL_MAX;
int minClass = -1;
for(int sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
}
}
return minClass;
}
// See FaceRecognizer::load.
void Fisherfaces::load(const FileStorage& fs) {
//read matrices
fs["num_components"] >> _num_components;
fs["mean"] >> _mean;
fs["eigenvalues"] >> _eigenvalues;
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
readFileNodeList(fs["labels"], _labels);
}
// See FaceRecognizer::save.
void Fisherfaces::save(FileStorage& fs) const {
// write matrices
fs << "num_components" << _num_components;
fs << "mean" << _mean;
fs << "eigenvalues" << _eigenvalues;
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
writeFileNodeList(fs, "labels", _labels);
}
//------------------------------------------------------------------------------
// LBPH
//------------------------------------------------------------------------------
template <typename _Tp> static
void olbp_(InputArray _src, OutputArray _dst) {
// get matrices
Mat src = _src.getMat();
// allocate memory for result
_dst.create(src.rows-2, src.cols-2, CV_8UC1);
Mat dst = _dst.getMat();
// zero the result matrix
dst.setTo(0);
// calculate patterns
for(int i=1;i<src.rows-1;i++) {
for(int j=1;j<src.cols-1;j++) {
_Tp center = src.at<_Tp>(i,j);
unsigned char code = 0;
code |= (src.at<_Tp>(i-1,j-1) >= center) << 7;
code |= (src.at<_Tp>(i-1,j) >= center) << 6;
code |= (src.at<_Tp>(i-1,j+1) >= center) << 5;
code |= (src.at<_Tp>(i,j+1) >= center) << 4;
code |= (src.at<_Tp>(i+1,j+1) >= center) << 3;
code |= (src.at<_Tp>(i+1,j) >= center) << 2;
code |= (src.at<_Tp>(i+1,j-1) >= center) << 1;
code |= (src.at<_Tp>(i,j-1) >= center) << 0;
dst.at<unsigned char>(i-1,j-1) = code;
}
}
}
//------------------------------------------------------------------------------
// cv::elbp
//------------------------------------------------------------------------------
template <typename _Tp> static
inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors) {
//get matrices
Mat src = _src.getMat();
// allocate memory for result
_dst.create(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
Mat dst = _dst.getMat();
// zero
dst.setTo(0);
for(int n=0; n<neighbors; n++) {
// sample points
float x = static_cast<float>(-radius) * sin(2.0*CV_PI*n/static_cast<float>(neighbors));
float y = static_cast<float>(radius) * cos(2.0*CV_PI*n/static_cast<float>(neighbors));
// relative indices
int fx = static_cast<int>(floor(x));
int fy = static_cast<int>(floor(y));
int cx = static_cast<int>(ceil(x));
int cy = static_cast<int>(ceil(y));
// fractional part
float ty = y - fy;
float tx = x - fx;
// set interpolation weights
float w1 = (1 - tx) * (1 - ty);
float w2 = tx * (1 - ty);
float w3 = (1 - tx) * ty;
float w4 = tx * ty;
// iterate through your data
for(int i=radius; i < src.rows-radius;i++) {
for(int j=radius;j < src.cols-radius;j++) {
// calculate interpolated value
float t = w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx);
// floating point precision, so check some machine-dependent epsilon
dst.at<int>(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits<float>::epsilon())) << n;
}
}
}
}
static void elbp(InputArray src, OutputArray dst, int radius, int neighbors)
{
switch (src.type()) {
case CV_8SC1: elbp_<char>(src,dst, radius, neighbors); break;
case CV_8UC1: elbp_<unsigned char>(src, dst, radius, neighbors); break;
case CV_16SC1: elbp_<short>(src,dst, radius, neighbors); break;
case CV_16UC1: elbp_<unsigned short>(src,dst, radius, neighbors); break;
case CV_32SC1: elbp_<int>(src,dst, radius, neighbors); break;
case CV_32FC1: elbp_<float>(src,dst, radius, neighbors); break;
case CV_64FC1: elbp_<double>(src,dst, radius, neighbors); break;
default: break;
}
}
static Mat
histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false)
{
Mat result;
// Establish the number of bins.
int histSize = maxVal-minVal+1;
// Set the ranges.
float range[] = { minVal, maxVal } ;
const float* histRange = { range };
// calc histogram
calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false);
// normalize
if(normed) {
result /= src.total();
}
return result.reshape(1,1);
}
static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
{
Mat src = _src.getMat();
switch (src.type()) {
case CV_8SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_8UC1:
return histc_(src, minVal, maxVal, normed);
break;
case CV_16SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_16UC1:
return histc_(src, minVal, maxVal, normed);
break;
case CV_32SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_32FC1:
return histc_(src, minVal, maxVal, normed);
break;
default:
CV_Error(CV_StsUnmatchedFormats, "This type is not implemented yet."); break;
}
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool normed)
{
Mat src = _src.getMat();
// calculate LBP patch size
int width = static_cast<int>(floor(src.cols/grid_x));
int height = static_cast<int>(floor(src.rows/grid_y));
// allocate memory for the spatial histogram
Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1);
// return matrix with zeros if no data was given
if(src.empty())
return result.reshape(1,1);
// initial result_row
int resultRowIdx = 0;
// iterate through grid
for(int i = 0; i < grid_y; i++) {
for(int j = 0; j < grid_x; j++) {
Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width));
Mat cell_hist = histc(src_cell, 0, (numPatterns-1), true);
// copy to the result matrix
Mat result_row = result.row(resultRowIdx);
cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1);
// increase row count in result matrix
resultRowIdx++;
}
}
// return result as reshaped feature vector
return result.reshape(1,1);
}
//------------------------------------------------------------------------------
// cv::elbp, cv::olbp, cv::varlbp wrapper
//------------------------------------------------------------------------------
static Mat elbp(InputArray src, int radius, int neighbors) {
Mat dst;
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
fs["grid_x"] >> _grid_x;
fs["grid_y"] >> _grid_y;
//read matrices
readFileNodeList(fs["histograms"], _histograms);
readFileNodeList(fs["labels"], _labels);
}
// See FaceRecognizer::save.
void LBPH::save(FileStorage& fs) const {
fs << "radius" << _radius;
fs << "neighbors" << _neighbors;
fs << "grid_x" << _grid_x;
fs << "grid_y" << _grid_y;
// write matrices
writeFileNodeList(fs, "histograms", _histograms);
writeFileNodeList(fs, "labels", _labels);
}
void LBPH::train(InputArray _src, InputArray _lbls) {
if(_src.kind() != _InputArray::STD_VECTOR_MAT && _src.kind() != _InputArray::STD_VECTOR_VECTOR)
CV_Error(CV_StsUnsupportedFormat, "LBPH::train expects InputArray::STD_VECTOR_MAT or _InputArray::STD_VECTOR_VECTOR.");
// get the vector of matrices
vector<Mat> src;
_src.getMatVector(src);
// turn the label matrix into a vector
vector<int> labels = _lbls.getMat();
if(labels.size() != src.size())
CV_Error(CV_StsUnsupportedFormat, "The number of labels must equal the number of samples.");
// store given labels
_labels = labels;
// store the spatial histograms of the original data
for(int sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) {
// calculate lbp image
Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors);
// get spatial histogram from this lbp image
Mat p = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true);
// add to templates
_histograms.push_back(p);
}
}
int LBPH::predict(InputArray _src) const {
Mat src = _src.getMat();
// get the spatial histogram from input image
Mat lbp_image = elbp(src, _radius, _neighbors);
Mat query = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true /* normed histograms */);
// find 1-nearest neighbor
double minDist = DBL_MAX;
int minClass = -1;
for(int sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
}
}
return minClass;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components)
{
return new Eigenfaces(num_components);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components)
{
return new Fisherfaces(num_components);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y)
{
return new LBPH(radius, neighbors, grid_x, grid_y);
}
}
modules/contrib/src/lda.cpp 0 → 100644
View file @ cec8f719
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "precomp.hpp"
#include <iostream>
#include <map>
#include <set>
namespace cv
{
using std::map;
using std::set;
using std::cout;
using std::endl;
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
typedef typename set<_Tp>::const_iterator constSetIterator;
typedef typename vector<_Tp>::const_iterator constVecIterator;
set<_Tp> set_elems;
for (constVecIterator it = src.begin(); it != src.end(); ++it)
set_elems.insert(*it);
vector<_Tp> elems;
for (constSetIterator it = set_elems.begin(); it != set_elems.end(); ++it)
elems.push_back(*it);
return elems;
}
static Mat argsort(InputArray _src, bool ascending=true)
{
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1)
CV_Error(CV_StsBadArg, "cv::argsort only sorts 1D matrices.");
int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
Mat sorted_indices;
sortIdx(src.reshape(1,1),sorted_indices,flags);
return sorted_indices;
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0)
{
// number of samples
int n = (int) src.total();
// return empty matrix if no data given
if(n == 0)
return Mat();
// dimensionality of samples
int d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
// copy data
for(int i = 0; i < n; i++) {
Mat xi = data.row(i);
src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta);
}
return data;
}
void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) {
if(_indices.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!");
Mat src = _src.getMat();
vector<int> indices = _indices.getMat();
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
for(int idx = 0; idx < indices.size(); idx++) {
Mat originalCol = src.col(indices[idx]);
Mat sortedCol = dst.col(idx);
originalCol.copyTo(sortedCol);
}
}
Mat sortMatrixColumnsByIndices(InputArray src, InputArray indices) {
Mat dst;
sortMatrixColumnsByIndices(src, indices, dst);
return dst;
}
template<typename _Tp> static bool
isSymmetric_(InputArray src) {
Mat _src = src.getMat();
if(_src.cols != _src.rows)
return false;
for (int i = 0; i < _src.rows; i++) {
for (int j = 0; j < _src.cols; j++) {
_Tp a = _src.at<_Tp> (i, j);
_Tp b = _src.at<_Tp> (j, i);
if (a != b) {
return false;
}
}
}
return true;
}
template<typename _Tp> static bool
isSymmetric_(InputArray src, double eps) {
Mat _src = src.getMat();
if(_src.cols != _src.rows)
return false;
for (int i = 0; i < _src.rows; i++) {
for (int j = 0; j < _src.cols; j++) {
_Tp a = _src.at<_Tp> (i, j);
_Tp b = _src.at<_Tp> (j, i);
if (std::abs(a - b) > eps) {
return false;
}
}
}
return true;
}
static bool isSymmetric(InputArray src, double eps=1e-16)
{
Mat m = src.getMat();
switch (m.type()) {
case CV_8SC1: return isSymmetric_<char>(m); break;
case CV_8UC1:
return isSymmetric_<unsigned char>(m); break;
case CV_16SC1:
return isSymmetric_<short>(m); break;
case CV_16UC1:
return isSymmetric_<unsigned short>(m); break;
case CV_32SC1:
return isSymmetric_<int>(m); break;
case CV_32FC1:
return isSymmetric_<float>(m, eps); break;
case CV_64FC1:
return isSymmetric_<double>(m, eps); break;
default:
break;
}
return false;
}
//------------------------------------------------------------------------------
// subspace::project
//------------------------------------------------------------------------------
Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src)
{
// get data matrices
Mat W = _W.getMat();
Mat mean = _mean.getMat();
Mat src = _src.getMat();
// create temporary matrices
Mat X, Y;
// copy data & make sure we are using the correct type
src.convertTo(X, W.type());
// get number of samples and dimension
int n = X.rows;
int d = X.cols;
// center the data if correct aligned sample mean is given
if(mean.total() == d)
subtract(X, repeat(mean.reshape(1,1), n, 1), X);
// finally calculate projection as Y = (X-mean)*W
gemm(X, W, 1.0, Mat(), 0.0, Y);
return Y;
}
//------------------------------------------------------------------------------
// subspace::reconstruct
//------------------------------------------------------------------------------
Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
{
// get data matrices
Mat W = _W.getMat();
Mat mean = _mean.getMat();
Mat src = _src.getMat();
// get number of samples and dimension
int n = src.rows;
int d = src.cols;
// initalize temporary matrices
Mat X, Y;
// copy data & make sure we are using the correct type
src.convertTo(Y, W.type());
// calculate the reconstruction
gemm(Y,
W,
1.0,
(d == mean.total()) ? repeat(mean.reshape(1,1), n, 1) : Mat(),
(d == mean.total()) ? 1.0 : 0.0,
X,
GEMM_2_T);
return X;
}
class EigenvalueDecomposition {
private:
// Holds the data dimension.
int n;
// Stores real/imag part of a complex division.
double cdivr, cdivi;
// Pointer to internal memory.
double *d, *e, *ort;
double **V, **H;
// Holds the computed eigenvalues.
Mat _eigenvalues;
// Holds the computed eigenvectors.
Mat _eigenvectors;
// Allocates memory.
template<typename _Tp>
_Tp *alloc_1d(int m) {
return new _Tp[m];
}
// Allocates memory.
template<typename _Tp>
_Tp *alloc_1d(int m, _Tp val) {
_Tp *arr = alloc_1d<_Tp> (m);
for (int i = 0; i < m; i++)
arr[i] = val;
return arr;
}
// Allocates memory.
template<typename _Tp>
_Tp **alloc_2d(int m, int n) {
_Tp **arr = new _Tp*[m];
for (int i = 0; i < m; i++)
arr[i] = new _Tp[n];
return arr;
}
// Allocates memory.
template<typename _Tp>
_Tp **alloc_2d(int m, int n, _Tp val) {
_Tp **arr = alloc_2d<_Tp> (m, n);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
arr[i][j] = val;
}
}
return arr;
}
void cdiv(double xr, double xi, double yr, double yi) {
double r, d;
if (std::abs(yr) > std::abs(yi)) {
r = yi / yr;
d = yr + r * yi;
cdivr = (xr + r * xi) / d;
cdivi = (xi - r * xr) / d;
} else {
r = yr / yi;
d = yi + r * yr;
cdivr = (r * xr + xi) / d;
cdivi = (r * xi - xr) / d;
}
}
// Nonsymmetric reduction from Hessenberg to real Schur form.
void hqr2() {
// This is derived from the Algol procedure hqr2,
// by Martin and Wilkinson, Handbook for Auto. Comp.,
// Vol.ii-Linear Algebra, and the corresponding
// Fortran subroutine in EISPACK.
// Initialize
int nn = this->n;
int n = nn - 1;
int low = 0;
int high = nn - 1;
double eps = pow(2.0, -52.0);
double exshift = 0.0;
double p = 0, q = 0, r = 0, s = 0, z = 0, t, w, x, y;
// Store roots isolated by balanc and compute matrix norm
double norm = 0.0;
for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
d[i] = H[i][i];
e[i] = 0.0;
}
for (int j = max(i - 1, 0); j < nn; j++) {
norm = norm + std::abs(H[i][j]);
}
}
// Outer loop over eigenvalue index
int iter = 0;
while (n >= low) {
// Look for single small sub-diagonal element
int l = n;
while (l > low) {
s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]);
if (s == 0.0) {
s = norm;
}
if (std::abs(H[l][l - 1]) < eps * s) {
break;
}
l--;
}
// Check for convergence
// One root found
if (l == n) {
H[n][n] = H[n][n] + exshift;
d[n] = H[n][n];
e[n] = 0.0;
n--;
iter = 0;
// Two roots found
} else if (l == n - 1) {
w = H[n][n - 1] * H[n - 1][n];
p = (H[n - 1][n - 1] - H[n][n]) / 2.0;
q = p * p + w;
z = sqrt(std::abs(q));
H[n][n] = H[n][n] + exshift;
H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
x = H[n][n];
// Real pair
if (q >= 0) {
if (p >= 0) {
z = p + z;
} else {
z = p - z;
}
d[n - 1] = x + z;
d[n] = d[n - 1];
if (z != 0.0) {
d[n] = x - w / z;
}
e[n - 1] = 0.0;
e[n] = 0.0;
x = H[n][n - 1];
s = std::abs(x) + std::abs(z);
p = x / s;
q = z / s;
r = sqrt(p * p + q * q);
p = p / r;
q = q / r;
// Row modification
for (int j = n - 1; j < nn; j++) {
z = H[n - 1][j];
H[n - 1][j] = q * z + p * H[n][j];
H[n][j] = q * H[n][j] - p * z;
}
// Column modification
for (int i = 0; i <= n; i++) {
z = H[i][n - 1];
H[i][n - 1] = q * z + p * H[i][n];
H[i][n] = q * H[i][n] - p * z;
}
// Accumulate transformations
for (int i = low; i <= high; i++) {
z = V[i][n - 1];
V[i][n - 1] = q * z + p * V[i][n];
V[i][n] = q * V[i][n] - p * z;
}
// Complex pair
} else {
d[n - 1] = x + p;
d[n] = x + p;
e[n - 1] = z;
e[n] = -z;
}
n = n - 2;
iter = 0;
// No convergence yet
} else {
// Form shift
x = H[n][n];
y = 0.0;
w = 0.0;
if (l < n) {
y = H[n - 1][n - 1];
w = H[n][n - 1] * H[n - 1][n];
}
// Wilkinson's original ad hoc shift
if (iter == 10) {
exshift += x;
for (int i = low; i <= n; i++) {
H[i][i] -= x;
}
s = std::abs(H[n][n - 1]) + std::abs(H[n - 1][n - 2]);
x = y = 0.75 * s;
w = -0.4375 * s * s;
}
// MATLAB's new ad hoc shift
if (iter == 30) {
s = (y - x) / 2.0;
s = s * s + w;
if (s > 0) {
s = sqrt(s);
if (y < x) {
s = -s;
}
s = x - w / ((y - x) / 2.0 + s);
for (int i = low; i <= n; i++) {
H[i][i] -= s;
}
exshift += s;
x = y = w = 0.964;
}
}
iter = iter + 1; // (Could check iteration count here.)
// Look for two consecutive small sub-diagonal elements
int m = n - 2;
while (m >= l) {
z = H[m][m];
r = x - z;
s = y - z;
p = (r * s - w) / H[m + 1][m] + H[m][m + 1];
q = H[m + 1][m + 1] - z - r - s;
r = H[m + 2][m + 1];
s = std::abs(p) + std::abs(q) + std::abs(r);
p = p / s;
q = q / s;
r = r / s;
if (m == l) {
break;
}
if (std::abs(H[m][m - 1]) * (std::abs(q) + std::abs(r)) < eps * (std::abs(p)
* (std::abs(H[m - 1][m - 1]) + std::abs(z) + std::abs(
H[m + 1][m + 1])))) {
break;
}
m--;
}
for (int i = m + 2; i <= n; i++) {
H[i][i - 2] = 0.0;
if (i > m + 2) {
H[i][i - 3] = 0.0;
}
}
// Double QR step involving rows l:n and columns m:n
for (int k = m; k <= n - 1; k++) {
bool notlast = (k != n - 1);
if (k != m) {
p = H[k][k - 1];
q = H[k + 1][k - 1];
r = (notlast ? H[k + 2][k - 1] : 0.0);
x = std::abs(p) + std::abs(q) + std::abs(r);
if (x != 0.0) {
p = p / x;
q = q / x;
r = r / x;
}
}
if (x == 0.0) {
break;
}
s = sqrt(p * p + q * q + r * r);
if (p < 0) {
s = -s;
}
if (s != 0) {
if (k != m) {
H[k][k - 1] = -s * x;
} else if (l != m) {
H[k][k - 1] = -H[k][k - 1];
}
p = p + s;
x = p / s;
y = q / s;
z = r / s;
q = q / p;
r = r / p;
// Row modification
for (int j = k; j < nn; j++) {
p = H[k][j] + q * H[k + 1][j];
if (notlast) {
p = p + r * H[k + 2][j];
H[k + 2][j] = H[k + 2][j] - p * z;
}
H[k][j] = H[k][j] - p * x;
H[k + 1][j] = H[k + 1][j] - p * y;
}
// Column modification
for (int i = 0; i <= min(n, k + 3); i++) {
p = x * H[i][k] + y * H[i][k + 1];
if (notlast) {
p = p + z * H[i][k + 2];
H[i][k + 2] = H[i][k + 2] - p * r;
}
H[i][k] = H[i][k] - p;
H[i][k + 1] = H[i][k + 1] - p * q;
}
// Accumulate transformations
for (int i = low; i <= high; i++) {
p = x * V[i][k] + y * V[i][k + 1];
if (notlast) {
p = p + z * V[i][k + 2];
V[i][k + 2] = V[i][k + 2] - p * r;
}
V[i][k] = V[i][k] - p;
V[i][k + 1] = V[i][k + 1] - p * q;
}
} // (s != 0)
} // k loop
} // check convergence
} // while (n >= low)
// Backsubstitute to find vectors of upper triangular form
if (norm == 0.0) {
return;
}
for (n = nn - 1; n >= 0; n--) {
p = d[n];
q = e[n];
// Real vector
if (q == 0) {
int l = n;
H[n][n] = 1.0;
for (int i = n - 1; i >= 0; i--) {
w = H[i][i] - p;
r = 0.0;
for (int j = l; j <= n; j++) {
r = r + H[i][j] * H[j][n];
}
if (e[i] < 0.0) {
z = w;
s = r;
} else {
l = i;
if (e[i] == 0.0) {
if (w != 0.0) {
H[i][n] = -r / w;
} else {
H[i][n] = -r / (eps * norm);
}
// Solve real equations
} else {
x = H[i][i + 1];
y = H[i + 1][i];
q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
t = (x * s - z * r) / q;
H[i][n] = t;
if (std::abs(x) > std::abs(z)) {
H[i + 1][n] = (-r - w * t) / x;
} else {
H[i + 1][n] = (-s - y * t) / z;
}
}
// Overflow control
t = std::abs(H[i][n]);
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n] = H[j][n] / t;
}
}
}
}
// Complex vector
} else if (q < 0) {
int l = n - 1;
// Last vector component imaginary so matrix is triangular
if (std::abs(H[n][n - 1]) > std::abs(H[n - 1][n])) {
H[n - 1][n - 1] = q / H[n][n - 1];
H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
} else {
cdiv(0.0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
H[n - 1][n - 1] = cdivr;
H[n - 1][n] = cdivi;
}
H[n][n - 1] = 0.0;
H[n][n] = 1.0;
for (int i = n - 2; i >= 0; i--) {
double ra, sa, vr, vi;
ra = 0.0;
sa = 0.0;
for (int j = l; j <= n; j++) {
ra = ra + H[i][j] * H[j][n - 1];
sa = sa + H[i][j] * H[j][n];
}
w = H[i][i] - p;
if (e[i] < 0.0) {
z = w;
r = ra;
s = sa;
} else {
l = i;
if (e[i] == 0) {
cdiv(-ra, -sa, w, q);
H[i][n - 1] = cdivr;
H[i][n] = cdivi;
} else {
// Solve complex equations
x = H[i][i + 1];
y = H[i + 1][i];
vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
vi = (d[i] - p) * 2.0 * q;
if (vr == 0.0 & vi == 0.0) {
vr = eps * norm * (std::abs(w) + std::abs(q) + std::abs(x)
+ std::abs(y) + std::abs(z));
}
cdiv(x * r - z * ra + q * sa,
x * s - z * sa - q * ra, vr, vi);
H[i][n - 1] = cdivr;
H[i][n] = cdivi;
if (std::abs(x) > (std::abs(z) + std::abs(q))) {
H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q
* H[i][n]) / x;
H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n
- 1]) / x;
} else {
cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z,
q);
H[i + 1][n - 1] = cdivr;
H[i + 1][n] = cdivi;
}
}
// Overflow control
t = max(std::abs(H[i][n - 1]), std::abs(H[i][n]));
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n - 1] = H[j][n - 1] / t;
H[j][n] = H[j][n] / t;
}
}
}
}
}
}
// Vectors of isolated roots
for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
for (int j = i; j < nn; j++) {
V[i][j] = H[i][j];
}
}
}
// Back transformation to get eigenvectors of original matrix
for (int j = nn - 1; j >= low; j--) {
for (int i = low; i <= high; i++) {
z = 0.0;
for (int k = low; k <= min(j, high); k++) {
z = z + V[i][k] * H[k][j];
}
V[i][j] = z;
}
}
}
// Nonsymmetric reduction to Hessenberg form.
void orthes() {
// This is derived from the Algol procedures orthes and ortran,
// by Martin and Wilkinson, Handbook for Auto. Comp.,
// Vol.ii-Linear Algebra, and the corresponding
// Fortran subroutines in EISPACK.
int low = 0;
int high = n - 1;
for (int m = low + 1; m <= high - 1; m++) {
// Scale column.
double scale = 0.0;
for (int i = m; i <= high; i++) {
scale = scale + std::abs(H[i][m - 1]);
}
if (scale != 0.0) {
// Compute Householder transformation.
double h = 0.0;
for (int i = high; i >= m; i--) {
ort[i] = H[i][m - 1] / scale;
h += ort[i] * ort[i];
}
double g = sqrt(h);
if (ort[m] > 0) {
g = -g;
}
h = h - ort[m] * g;
ort[m] = ort[m] - g;
// Apply Householder similarity transformation
// H = (I-u*u'/h)*H*(I-u*u')/h)
for (int j = m; j < n; j++) {
double f = 0.0;
for (int i = high; i >= m; i--) {
f += ort[i] * H[i][j];
}
f = f / h;
for (int i = m; i <= high; i++) {
H[i][j] -= f * ort[i];
}
}
for (int i = 0; i <= high; i++) {
double f = 0.0;
for (int j = high; j >= m; j--) {
f += ort[j] * H[i][j];
}
f = f / h;
for (int j = m; j <= high; j++) {
H[i][j] -= f * ort[j];
}
}
ort[m] = scale * ort[m];
H[m][m - 1] = scale * g;
}
}
// Accumulate transformations (Algol's ortran).
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = (i == j ? 1.0 : 0.0);
}
}
for (int m = high - 1; m >= low + 1; m--) {
if (H[m][m - 1] != 0.0) {
for (int i = m + 1; i <= high; i++) {
ort[i] = H[i][m - 1];
}
for (int j = m; j <= high; j++) {
double g = 0.0;
for (int i = m; i <= high; i++) {
g += ort[i] * V[i][j];
}
// Double division avoids possible underflow
g = (g / ort[m]) / H[m][m - 1];
for (int i = m; i <= high; i++) {
V[i][j] += g * ort[i];
}
}
}
}
}
// Releases all internal working memory.
void release() {
// releases the working data
delete[] d;
delete[] e;
delete[] ort;
for (int i = 0; i < n; i++) {
delete[] H[i];
delete[] V[i];
}
delete[] H;
delete[] V;
}
// Computes the Eigenvalue Decomposition for a matrix given in H.
void compute() {
// Allocate memory for the working data.
V = alloc_2d<double> (n, n, 0.0);
d = alloc_1d<double> (n);
e = alloc_1d<double> (n);
ort = alloc_1d<double> (n);
// Reduce to Hessenberg form.
orthes();
// Reduce Hessenberg to real Schur form.
hqr2();
// Copy eigenvalues to OpenCV Matrix.
_eigenvalues.create(1, n, CV_64FC1);
for (int i = 0; i < n; i++) {
_eigenvalues.at<double> (0, i) = d[i];
}
// Copy eigenvectors to OpenCV Matrix.
_eigenvectors.create(n, n, CV_64FC1);
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
_eigenvectors.at<double> (i, j) = V[i][j];
// Deallocate the memory by releasing all internal working data.
release();
}
public:
EigenvalueDecomposition()
: n(0) { }
// Initializes & computes the Eigenvalue Decomposition for a general matrix
// given in src. This function is a port of the EigenvalueSolver in JAMA,
// which has been released to public domain by The MathWorks and the
// National Institute of Standards and Technology (NIST).
EigenvalueDecomposition(InputArray src) {
compute(src);
}
// This function computes the Eigenvalue Decomposition for a general matrix
// given in src. This function is a port of the EigenvalueSolver in JAMA,
// which has been released to public domain by The MathWorks and the
// National Institute of Standards and Technology (NIST).
void compute(InputArray src)
{
if(isSymmetric(src)) {
// Fall back to OpenCV for a symmetric matrix!
cv::eigen(src, _eigenvalues, _eigenvectors);
} else {
Mat tmp;
// Convert the given input matrix to double. Is there any way to
// prevent allocating the temporary memory? Only used for copying
// into working memory and deallocated after.
src.getMat().convertTo(tmp, CV_64FC1);
// Get dimension of the matrix.
this->n = tmp.cols;
// Allocate the matrix data to work on.
this->H = alloc_2d<double> (n, n);
// Now safely copy the data.
for (int i = 0; i < tmp.rows; i++) {
for (int j = 0; j < tmp.cols; j++) {
this->H[i][j] = tmp.at<double>(i, j);
}
}
// Deallocates the temporary matrix before computing.
tmp.release();
// Performs the eigenvalue decomposition of H.
compute();
}
}
~EigenvalueDecomposition() {}
// Returns the eigenvalues of the Eigenvalue Decomposition.
Mat eigenvalues() { return _eigenvalues; }
// Returns the eigenvectors of the Eigenvalue Decomposition.
Mat eigenvectors() { return _eigenvectors; }
};
//------------------------------------------------------------------------------
// Linear Discriminant Analysis implementation
//------------------------------------------------------------------------------
void LDA::save(const string& filename) const {
FileStorage fs(filename, FileStorage::WRITE);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
this->save(fs);
fs.release();
}
// Deserializes this object from a given filename.
void LDA::load(const string& filename) {
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
this->load(fs);
fs.release();
}
// Serializes this object to a given FileStorage.
void LDA::save(FileStorage& fs) const {
// write matrices
fs << "num_components" << _num_components;
fs << "eigenvalues" << _eigenvalues;
fs << "eigenvectors" << _eigenvectors;
}
// Deserializes this object from a given FileStorage.
void LDA::load(const FileStorage& fs) {
//read matrices
fs["num_components"] >> _num_components;
fs["eigenvalues"] >> _eigenvalues;
fs["eigenvectors"] >> _eigenvectors;
}
void LDA::lda(InputArray _src, InputArray _lbls) {
// get data
Mat src = _src.getMat();
vector<int> labels = _lbls.getMat();
// turn into row sampled matrix
Mat data;
// ensure working matrix is double precision
src.convertTo(data, CV_64FC1);
// maps the labels, so they're ascending: [0,1,...,C]
vector<int> mapped_labels(labels.size());
vector<int> num2label = remove_dups(labels);
map<int, int> label2num;
for (int i = 0; i < num2label.size(); i++)
label2num[num2label[i]] = i;
for (int i = 0; i < labels.size(); i++)
mapped_labels[i] = label2num[labels[i]];
// get sample size, dimension
int N = data.rows;
int D = data.cols;
// number of unique labels
int C = num2label.size();
// throw error if less labels, than samples
if (labels.size() != N)
CV_Error(CV_StsBadArg, "Error: The number of samples must equal the number of labels.");
// warn if within-classes scatter matrix becomes singular
if (N < D)
cout << "Warning: Less observations than feature dimension given!"
<< "Computation will probably fail."
<< endl;
// clip number of components to be a valid number
if ((_num_components <= 0) || (_num_components > (C - 1)))
_num_components = (C - 1);
// holds the mean over all classes
Mat meanTotal = Mat::zeros(1, D, data.type());
// holds the mean for each class
vector<Mat> meanClass(C);
vector<int> numClass(C);
// initialize
for (int i = 0; i < C; i++) {
numClass[i] = 0;
meanClass[i] = Mat::zeros(1, D, data.type()); //! Dx1 image vector
}
// calculate sums
for (int i = 0; i < N; i++) {
Mat instance = data.row(i);
int classIdx = mapped_labels[i];
add(meanTotal, instance, meanTotal);
add(meanClass[classIdx], instance, meanClass[classIdx]);
numClass[classIdx]++;
}
// calculate means
meanTotal.convertTo(meanTotal, meanTotal.type(),
1.0 / static_cast<double> (N));
for (int i = 0; i < C; i++)
meanClass[i].convertTo(meanClass[i], meanClass[i].type(),
1.0 / static_cast<double> (numClass[i]));
// subtract class means
for (int i = 0; i < N; i++) {
int classIdx = mapped_labels[i];
Mat instance = data.row(i);
subtract(instance, meanClass[classIdx], instance);
}
// calculate within-classes scatter
Mat Sw = Mat::zeros(D, D, data.type());
mulTransposed(data, Sw, true);
// calculate between-classes scatter
Mat Sb = Mat::zeros(D, D, data.type());
for (int i = 0; i < C; i++) {
Mat tmp;
subtract(meanClass[i], meanTotal, tmp);
mulTransposed(tmp, tmp, true);
add(Sb, tmp, Sb);
}
// invert Sw
Mat Swi = Sw.inv();
// M = inv(Sw)*Sb
Mat M;
gemm(Swi, Sb, 1.0, Mat(), 0.0, M);
EigenvalueDecomposition es(M);
_eigenvalues = es.eigenvalues();
_eigenvectors = es.eigenvectors();
// reshape eigenvalues, so they are stored by column
_eigenvalues = _eigenvalues.reshape(1, 1);
// get sorted indices descending by their eigenvalue
vector<int> sorted_indices = argsort(_eigenvalues, false);
// now sort eigenvalues and eigenvectors accordingly
_eigenvalues = sortMatrixColumnsByIndices(_eigenvalues, sorted_indices);
_eigenvectors = sortMatrixColumnsByIndices(_eigenvectors, sorted_indices);
// and now take only the num_components and we're out!
_eigenvalues = Mat(_eigenvalues, Range::all(), Range(0, _num_components));
_eigenvectors = Mat(_eigenvectors, Range::all(), Range(0, _num_components));
}
void LDA::compute(InputArray _src, InputArray _lbls) {
switch(_src.kind()) {
case _InputArray::STD_VECTOR_MAT:
lda(asRowMatrix(_src, CV_64FC1), _lbls);
break;
case _InputArray::MAT:
lda(_src.getMat(), _lbls);
break;
default:
CV_Error(CV_StsNotImplemented, "This data type is not supported by subspace::LDA::compute.");
break;
}
}
// Projects samples into the LDA subspace.
Mat LDA::project(InputArray src) {
return subspaceProject(_eigenvectors, Mat(), _dataAsRow ? src : src.getMat().t());
}
// Reconstructs projections from the LDA subspace.
Mat LDA::reconstruct(InputArray src) {
return subspaceReconstruct(_eigenvectors, Mat(), _dataAsRow ? src : src.getMat().t());
}
}
samples/cpp/facerec_demo.cpp 0 → 100644
View file @ cec8f719
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/opencv.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file)
throw std::exception();
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
int main(int argc, const char *argv[]) {
// check for command line arguments
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
exit(1);
}
// path to your CSV
string fn_csv = string(argv[1]);
// images and corresponding labels
vector<Mat> images;
vector<int> labels;
// read in the data
try {
read_csv(fn_csv, images, labels);
} catch (exception& e) {
cerr << "Error opening file \"" << fn_csv << "\"." << endl;
exit(1);
}
// get width and height
//int width = images[0].cols;
int height = images[0].rows;
// get test instances
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
// ... and delete last element
images.pop_back();
labels.pop_back();
// build the Fisherfaces model
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
// test model
int predicted = model->predict(testSample);
cout << "predicted class = " << predicted << endl;
cout << "actual class = " << testLabel << endl;
// get the eigenvectors
Mat W = model->eigenvectors();
// show first 10 fisherfaces
for (int i = 0; i < min(10, W.cols); i++) {
// get eigenvector #i
Mat ev = W.col(i).clone();
// reshape to original site
Mat grayscale, cgrayscale;
cvtColor(ev.reshape(1, height), grayscale, COLOR_BGR2GRAY);
// show image (with Jet colormap)
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
imshow(format("%d", i), cgrayscale);
}
waitKey(0);
return 0;
}
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