opencv学习笔记-openCV2与opencv3机器学习库MLL

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参考：http://www.aiuxian.com/article/p-620963.html

http://blog.csdn.net/lifeitengup/article/details/8866078

opencv2机器学习库MLL

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#define NTRAINING_SAMPLES 100 // Number of training samples per class
#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part
using namespace cv;
using namespace std;
void help()
{
cout<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl
<< "Usage:" << endl
<< "./non_linear_svms" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main()
{
help();
// Data for visual representation
const int WIDTH = 512, HEIGHT = 512;
Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
//--------------------- 1. Set up training data randomly ---------------------------------------
Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);
RNG rng(100); // Random value generation class
// Set up the linearly separable part of the training data
int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
// Generate random points for the class 1
Mat trainClass = trainData.rowRange(0, nLinearSamples);
// The x coordinate of the points is in [0, 0.4)
Mat c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
// Generate random points for the class 2
trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
// The x coordinate of the points is in [0.6, 1]
c = trainClass.colRange(0 , 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//------------------ Set up the non-linearly separable part of the training data ---------------
// Generate random points for the classes 1 and 2
trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
// The x coordinate of the points is in [0.4, 0.6)
c = trainClass.colRange(0,1);
rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//------------------------- Set up the labels for the classes ---------------------------------
labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
//------------------------ 2. Set up the support vector machines parameters --------------------
CvSVMParams params;
params.svm_type = SVM::C_SVC;
params.C = 0.1;
params.kernel_type = SVM::LINEAR;
params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);
//------------------------ 3. Train the svm ----------------------------------------------------
cout << "Starting training process" << endl;
CvSVM svm;
svm.train(trainData, labels, Mat(), Mat(), params);
cout << "Finished training process" << endl;
//------------------------ 4. Show the decision regions ----------------------------------------
Vec3b green(0,100,0), blue (100,0,0);
for (int i = 0; i < I.rows; ++i)
for (int j = 0; j < I.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << i, j);
float response = svm.predict(sampleMat);
if (response == 1) I.at<Vec3b>(j, i) = green;
else if (response == 2) I.at<Vec3b>(j, i) = blue;
}
//----------------------- 5. Show the training data --------------------------------------------
int thick = -1;
int lineType = 8;
float px, py;
// Class 1
for (int i = 0; i < NTRAINING_SAMPLES; ++i)
{
px = trainData.at<float>(i,0);
py = trainData.at<float>(i,1);
circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick, lineType);
}
// Class 2
for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; ++i)
{
px = trainData.at<float>(i,0);
py = trainData.at<float>(i,1);
circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick, lineType);
}
//------------------------- 6. Show support vectors --------------------------------------------
thick = 2;
lineType = 8;
int x = svm.get_support_vector_count();
for (int i = 0; i < x; ++i)
{
const float* v = svm.get_support_vector(i);
circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);
}
imwrite("result.png", I); // save the Image
imshow("SVM for Non-Linear Training Data", I); // show it to the user
waitKey(0);
}

opencv3 svm非线性分类

#include "opencv2/opencv.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/ml.hpp"
using namespace cv;
using namespace cv::ml;
int main(int, char**)
{
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3); //创建窗口可视化
// 设置训练数据
int labels[10] = { 1, -1, 1, 1,-1,1,-1,1,-1,-1 };
Mat labelsMat(10, 1, CV_32SC1, labels);
float trainingData[10][2] = { { 501, 150 }, { 255, 10 }, { 501, 255 }, { 10, 501 }, { 25, 80 },
{ 150, 300 }, { 77, 200 } , { 300, 300 } , { 45, 250 } , { 200, 200 } };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
// 创建分类器并设置参数
Ptr<SVM> model =SVM::create();
model->setType(SVM::C_SVC);
model->setKernel(SVM::LINEAR); //核函数
//设置训练数据
Ptr<TrainData> tData =TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
// 训练分类器
model->train(tData);
Vec3b green(0, 255, 0), blue(255, 0, 0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i); //生成测试数据
float response = model->predict(sampleMat); //进行预测，返回1或-1
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
// 显示训练数据
int thickness = -1;
int lineType = 8;
Scalar c1 = Scalar::all(0); //标记为1的显示成黑点
Scalar c2 = Scalar::all(255); //标记成-1的显示成白点
//绘图时，先宽后高，对应先列后行
for (int i = 0; i < labelsMat.rows; i++)
{
const float* v = trainingDataMat.ptr<float>(i); //取出每行的头指针
Point pt = Point((int)v[0], (int)v[1]);
if (labels[i] == 1)
circle(image, pt, 5, c1, thickness, lineType);
else
circle(image, pt, 5, c2, thickness, lineType);
}
imshow("SVM Simple Example", image);
waitKey(0);
}

最初，支持向量机（SVM）是构建最优二进制（2类）分类器的技术。后来该技术扩展到回归和聚类问题。 SVM是基于内核的方法的一部分。它使用内核函数将特征向量映射到更高维的空间，并在该空间中构建最佳线性鉴别函数或适合训练数据的最优超平面。在SVM的情况下，内核没有明确定义。相反，需要定义超空间中任何2个点之间的距离。

解决方案是最优的，这意味着分离超平面与两类的最近特征向量之间的边界（在2类分类器的情况下）是最大的。最靠近超平面的特征向量称为支持向量，这意味着其他向量的位置不会影响超平面（决策函数）。

OpenCV3 SVM是基于 [LibSVM]的。

[Burges98]

Burges. A tutorial on support vector machines for pattern recognition, Knowledge Discovery and Data Mining 2(2), 1998 (available online at http://citeseer.ist.psu.edu/burges98tutorial.html)

[LibSVM](1, 2) C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. (http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf)

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