Added to the tutorials the "Support Vector Machines for Non-Linearly Separable Data" contributed by Fernando Iglesias García. Corrected a mistake in the gpu-basics-similarity.rst file.
Bernat Gabor
14 years ago
parent
a51a8ad577
commit
fec995391f
9 changed files with 256 additions and 30 deletions
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#include <iostream> |
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#include <opencv2/core/core.hpp> |
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#include <opencv2/highgui/highgui.hpp> |
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#include <opencv2/ml/ml.hpp> |
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#define NTRAINING_SAMPLES 100 // Number of training samples per class
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#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part
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using namespace cv; |
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using namespace std; |
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void help() |
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{ |
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cout<< "\n--------------------------------------------------------------------------" << endl |
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<< "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl |
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<< "Usage:" << endl |
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<< "./non_linear_svms" << endl |
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<< "--------------------------------------------------------------------------" << endl |
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<< endl; |
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} |
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int main() |
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{ |
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help(); |
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// Data for visual representation
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const int WIDTH = 512, HEIGHT = 512; |
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Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3); |
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//--------------------- 1. Set up training data randomly ---------------------------------------
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Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1); |
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Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1); |
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RNG rng(100); // Random value generation class
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// Set up the linearly separable part of the training data
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int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES); |
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// Generate random points for the class 1
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Mat trainClass = trainData.rowRange(0, nLinearSamples); |
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// The x coordinate of the points is in [0, 0.4)
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Mat c = trainClass.colRange(0, 1); |
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH)); |
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2); |
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); |
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// Generate random points for the class 2
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trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES); |
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// The x coordinate of the points is in [0.6, 1]
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c = trainClass.colRange(0 , 1);
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rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH)); |
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2); |
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); |
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//------------------ Set up the non-linearly separable part of the training data ---------------
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// Generate random points for the classes 1 and 2
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trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples); |
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// The x coordinate of the points is in [0.4, 0.6)
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c = trainClass.colRange(0,1); |
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rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2); |
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); |
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//------------------------- Set up the labels for the classes ---------------------------------
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labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
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labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
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//------------------------ 2. Set up the support vector machines parameters --------------------
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CvSVMParams params; |
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params.svm_type = SVM::C_SVC; |
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params.C = 0.1; |
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params.kernel_type = SVM::LINEAR; |
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params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6); |
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//------------------------ 3. Train the svm ----------------------------------------------------
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cout << "Starting training process" << endl; |
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CvSVM svm; |
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svm.train(trainData, labels, Mat(), Mat(), params); |
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cout << "Finished training process" << endl; |
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//------------------------ 4. Show the decision regions ----------------------------------------
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Vec3b green(0,100,0), blue (100,0,0); |
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for (int i = 0; i < I.rows; ++i) |
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for (int j = 0; j < I.cols; ++j) |
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{ |
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Mat sampleMat = (Mat_<float>(1,2) << i, j); |
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float response = svm.predict(sampleMat); |
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if (response == 1) I.at<Vec3b>(j, i) = green; |
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else if (response == 2) I.at<Vec3b>(j, i) = blue; |
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} |
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//----------------------- 5. Show the training data --------------------------------------------
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int thick = -1; |
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int lineType = 8; |
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float px, py; |
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// Class 1
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for (int i = 0; i < NTRAINING_SAMPLES; ++i) |
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{ |
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px = trainData.at<float>(i,0); |
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py = trainData.at<float>(i,1); |
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circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick, lineType); |
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} |
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// Class 2
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for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; ++i) |
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{ |
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px = trainData.at<float>(i,0); |
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py = trainData.at<float>(i,1); |
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circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick, lineType); |
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} |
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//------------------------- 6. Show support vectors --------------------------------------------
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thick = 2; |
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lineType = 8; |
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int x = svm.get_support_vector_count(); |
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for (int i = 0; i < x; ++i) |
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{ |
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const float* v = svm.get_support_vector(i); |
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circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType); |
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} |
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imwrite("result.png", I); // save the Image
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imshow("SVM for Non-Linear Training Data", I); // show it to the user
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waitKey(0); |
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} |
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