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Add Java and Python code for ML tutorials.

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catree
2018-07-13 15:11:00 +02:00
parent 4dc7e617a4
commit 41b95cae38
15 changed files with 1141 additions and 180 deletions
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samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
+30 -29
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@@ -5,9 +5,6 @@
#include <opencv2/highgui.hpp>
#include <opencv2/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 cv::ml;
using namespace std;
@@ -16,8 +13,6 @@ static 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;
}
@@ -26,13 +21,16 @@ int main()
{
help();
const int NTRAINING_SAMPLES = 100; // Number of training samples per class
const float FRAC_LINEAR_SEP = 0.9f; // Fraction of samples which compose the linear separable part
// 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_32SC1);
Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32F);
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32S);
RNG rng(100); // Random value generation class
@@ -44,10 +42,10 @@ int main()
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));
rng.fill(c, RNG::UNIFORM, Scalar(0), 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));
rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
// Generate random points for the class 2
trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
@@ -56,26 +54,26 @@ int main()
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));
rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
//! [setup1]
//------------------ Set up the non-linearly separable part of the training data ---------------
//! [setup2]
// Generate random points for the classes 1 and 2
trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
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));
rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
//! [setup2]
//------------------------- 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 --------------------
//------------------------ 3. Train the svm ----------------------------------------------------
cout << "Starting training process" << endl;
//! [init]
Ptr<SVM> svm = SVM::create();
@@ -84,6 +82,8 @@ int main()
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
//! [init]
//------------------------ 3. Train the svm ----------------------------------------------------
//! [train]
svm->train(trainData, ROW_SAMPLE, labels);
//! [train]
@@ -91,53 +91,54 @@ int main()
//------------------------ 4. Show the decision regions ----------------------------------------
//! [show]
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)
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);
Mat sampleMat = (Mat_<float>(1,2) << j, i);
float response = svm->predict(sampleMat);
if (response == 1) I.at<Vec3b>(j, i) = green;
else if (response == 2) I.at<Vec3b>(j, i) = blue;
if (response == 1) I.at<Vec3b>(i,j) = green;
else if (response == 2) I.at<Vec3b>(i,j) = blue;
}
}
//! [show]
//----------------------- 5. Show the training data --------------------------------------------
//! [show_data]
int thick = -1;
int lineType = 8;
float px, py;
// Class 1
for (int i = 0; i < NTRAINING_SAMPLES; ++i)
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);
circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick);
}
// Class 2
for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; ++i)
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);
circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick);
}
//! [show_data]
//------------------------- 6. Show support vectors --------------------------------------------
//! [show_vectors]
thick = 2;
lineType = 8;
Mat sv = svm->getUncompressedSupportVectors();
for (int i = 0; i < sv.rows; ++i)
for (int i = 0; i < sv.rows; i++)
{
const float* v = sv.ptr<float>(i);
circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);
circle(I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick);
}
//! [show_vectors]
imwrite("result.png", I); // save the Image
imwrite("result.png", I); // save the Image
imshow("SVM for Non-Linear Training Data", I); // show it to the user
waitKey(0);
waitKey();
return 0;
}