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Merge pull request #11967 from catree:add_tutorial_ml_java_python
This commit is contained in:
Alexander Alekhin
@@ -21,13 +21,9 @@ double getOrientation(const vector<Point> &, Mat&);
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*/
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void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
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{
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//! [visualization1]
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double angle;
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double hypotenuse;
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angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
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hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
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// double degrees = angle * 180 / CV_PI; // convert radians to degrees (0-180 range)
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// cout << "Degrees: " << abs(degrees - 180) << endl; // angle in 0-360 degrees range
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//! [visualization1]
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double angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
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double hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
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// Here we lengthen the arrow by a factor of scale
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q.x = (int) (p.x - scale * hypotenuse * cos(angle));
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@@ -42,7 +38,7 @@ void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2
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p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
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line(img, p, q, colour, 1, LINE_AA);
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//! [visualization1]
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//! [visualization1]
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}
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/**
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@@ -50,11 +46,11 @@ void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2
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*/
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double getOrientation(const vector<Point> &pts, Mat &img)
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{
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//! [pca]
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//! [pca]
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//Construct a buffer used by the pca analysis
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int sz = static_cast<int>(pts.size());
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Mat data_pts = Mat(sz, 2, CV_64FC1);
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for (int i = 0; i < data_pts.rows; ++i)
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Mat data_pts = Mat(sz, 2, CV_64F);
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for (int i = 0; i < data_pts.rows; i++)
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{
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data_pts.at<double>(i, 0) = pts[i].x;
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data_pts.at<double>(i, 1) = pts[i].y;
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@@ -70,16 +66,16 @@ double getOrientation(const vector<Point> &pts, Mat &img)
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//Store the eigenvalues and eigenvectors
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vector<Point2d> eigen_vecs(2);
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vector<double> eigen_val(2);
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for (int i = 0; i < 2; ++i)
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for (int i = 0; i < 2; i++)
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{
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eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
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pca_analysis.eigenvectors.at<double>(i, 1));
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eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
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}
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//! [pca]
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//! [pca]
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//! [visualization]
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//! [visualization]
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// Draw the principal components
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circle(img, cntr, 3, Scalar(255, 0, 255), 2);
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Point p1 = cntr + 0.02 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
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@@ -88,7 +84,7 @@ double getOrientation(const vector<Point> &pts, Mat &img)
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drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
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double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
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//! [visualization]
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//! [visualization]
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return angle;
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}
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@@ -98,10 +94,10 @@ double getOrientation(const vector<Point> &pts, Mat &img)
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*/
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int main(int argc, char** argv)
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{
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//! [pre-process]
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//! [pre-process]
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// Load image
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CommandLineParser parser(argc, argv, "{@input | ../data/pca_test1.jpg | input image}");
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parser.about( "This program demonstrates how to use OpenCV PCA to extract the orienation of an object.\n" );
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parser.about( "This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n" );
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parser.printMessage();
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Mat src = imread(parser.get<String>("@input"));
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@@ -122,14 +118,14 @@ int main(int argc, char** argv)
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// Convert image to binary
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Mat bw;
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threshold(gray, bw, 50, 255, THRESH_BINARY | THRESH_OTSU);
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//! [pre-process]
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//! [pre-process]
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//! [contours]
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//! [contours]
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// Find all the contours in the thresholded image
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vector<vector<Point> > contours;
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findContours(bw, contours, RETR_LIST, CHAIN_APPROX_NONE);
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for (size_t i = 0; i < contours.size(); ++i)
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for (size_t i = 0; i < contours.size(); i++)
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{
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// Calculate the area of each contour
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double area = contourArea(contours[i]);
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@@ -137,14 +133,14 @@ int main(int argc, char** argv)
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if (area < 1e2 || 1e5 < area) continue;
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// Draw each contour only for visualisation purposes
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drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2, LINE_8);
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drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2);
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// Find the orientation of each shape
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getOrientation(contours[i], src);
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}
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//! [contours]
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//! [contours]
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imshow("output", src);
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waitKey(0);
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waitKey();
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return 0;
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}
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@@ -1,6 +1,6 @@
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include "opencv2/imgcodecs.hpp"
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/ml.hpp>
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@@ -9,21 +9,16 @@ using namespace cv::ml;
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int main(int, char**)
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{
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// Data for visual representation
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int width = 512, height = 512;
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Mat image = Mat::zeros(height, width, CV_8UC3);
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// Set up training data
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//! [setup1]
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int labels[4] = {1, -1, -1, -1};
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float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
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//! [setup1]
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//! [setup2]
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Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
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Mat trainingDataMat(4, 2, CV_32F, trainingData);
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Mat labelsMat(4, 1, CV_32SC1, labels);
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//! [setup2]
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// Train the SVM
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//! [init]
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Ptr<SVM> svm = SVM::create();
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@@ -35,11 +30,16 @@ int main(int, char**)
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svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);
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//! [train]
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// Data for visual representation
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int width = 512, height = 512;
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Mat image = Mat::zeros(height, width, CV_8UC3);
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// Show the decision regions given by the SVM
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//! [show]
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Vec3b green(0,255,0), blue (255,0,0);
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for (int i = 0; i < image.rows; ++i)
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for (int j = 0; j < image.cols; ++j)
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Vec3b green(0,255,0), blue(255,0,0);
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for (int i = 0; i < image.rows; i++)
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{
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for (int j = 0; j < image.cols; j++)
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{
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Mat sampleMat = (Mat_<float>(1,2) << j,i);
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float response = svm->predict(sampleMat);
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@@ -49,34 +49,33 @@ int main(int, char**)
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else if (response == -1)
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image.at<Vec3b>(i,j) = blue;
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}
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}
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//! [show]
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// Show the training data
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//! [show_data]
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int thickness = -1;
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int lineType = 8;
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circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType );
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circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType );
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circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType );
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circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType );
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circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness );
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circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness );
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circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness );
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circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness );
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//! [show_data]
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// Show support vectors
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//! [show_vectors]
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thickness = 2;
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lineType = 8;
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Mat sv = svm->getUncompressedSupportVectors();
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for (int i = 0; i < sv.rows; ++i)
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for (int i = 0; i < sv.rows; i++)
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{
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const float* v = sv.ptr<float>(i);
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circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
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circle(image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness);
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}
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//! [show_vectors]
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imwrite("result.png", image); // save the image
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imshow("SVM Simple Example", image); // show it to the user
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waitKey(0);
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waitKey();
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return 0;
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}
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@@ -5,9 +5,6 @@
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#include <opencv2/highgui.hpp>
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#include <opencv2/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 cv::ml;
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using namespace std;
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@@ -16,8 +13,6 @@ static 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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@@ -26,13 +21,16 @@ int main()
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{
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help();
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const int NTRAINING_SAMPLES = 100; // Number of training samples per class
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const float FRAC_LINEAR_SEP = 0.9f; // Fraction of samples which compose the linear separable part
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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_32SC1);
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Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32F);
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Mat labels (2*NTRAINING_SAMPLES, 1, CV_32S);
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RNG rng(100); // Random value generation class
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@@ -44,10 +42,10 @@ int main()
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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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rng.fill(c, RNG::UNIFORM, Scalar(0), 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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rng.fill(c, RNG::UNIFORM, Scalar(0), 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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@@ -56,26 +54,26 @@ int main()
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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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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
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//! [setup1]
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//------------------ Set up the non-linearly separable part of the training data ---------------
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//! [setup2]
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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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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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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
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//! [setup2]
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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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//------------------------ 3. Train the svm ----------------------------------------------------
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cout << "Starting training process" << endl;
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//! [init]
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Ptr<SVM> svm = SVM::create();
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@@ -84,6 +82,8 @@ int main()
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svm->setKernel(SVM::LINEAR);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
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//! [init]
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//------------------------ 3. Train the svm ----------------------------------------------------
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//! [train]
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svm->train(trainData, ROW_SAMPLE, labels);
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//! [train]
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@@ -91,53 +91,54 @@ int main()
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//------------------------ 4. Show the decision regions ----------------------------------------
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//! [show]
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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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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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{
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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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Mat sampleMat = (Mat_<float>(1,2) << j, i);
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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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if (response == 1) I.at<Vec3b>(i,j) = green;
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else if (response == 2) I.at<Vec3b>(i,j) = blue;
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}
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}
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//! [show]
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//----------------------- 5. Show the training data --------------------------------------------
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//! [show_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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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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circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick);
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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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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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circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick);
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}
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//! [show_data]
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//------------------------- 6. Show support vectors --------------------------------------------
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//! [show_vectors]
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thick = 2;
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lineType = 8;
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Mat sv = svm->getUncompressedSupportVectors();
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for (int i = 0; i < sv.rows; ++i)
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for (int i = 0; i < sv.rows; i++)
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{
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const float* v = sv.ptr<float>(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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circle(I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick);
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}
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//! [show_vectors]
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imwrite("result.png", I); // save the Image
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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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waitKey();
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return 0;
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}
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@@ -0,0 +1,144 @@
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import java.util.ArrayList;
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import java.util.List;
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.MatOfPoint;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.highgui.HighGui;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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//This program demonstrates how to use OpenCV PCA to extract the orientation of an object.
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class IntroductionToPCA {
|
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private void drawAxis(Mat img, Point p_, Point q_, Scalar colour, float scale) {
|
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Point p = new Point(p_.x, p_.y);
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Point q = new Point(q_.x, q_.y);
|
||||
//! [visualization1]
|
||||
double angle = Math.atan2(p.y - q.y, p.x - q.x); // angle in radians
|
||||
double hypotenuse = Math.sqrt((p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
|
||||
|
||||
// Here we lengthen the arrow by a factor of scale
|
||||
q.x = (int) (p.x - scale * hypotenuse * Math.cos(angle));
|
||||
q.y = (int) (p.y - scale * hypotenuse * Math.sin(angle));
|
||||
Imgproc.line(img, p, q, colour, 1, Core.LINE_AA, 0);
|
||||
|
||||
// create the arrow hooks
|
||||
p.x = (int) (q.x + 9 * Math.cos(angle + Math.PI / 4));
|
||||
p.y = (int) (q.y + 9 * Math.sin(angle + Math.PI / 4));
|
||||
Imgproc.line(img, p, q, colour, 1, Core.LINE_AA, 0);
|
||||
|
||||
p.x = (int) (q.x + 9 * Math.cos(angle - Math.PI / 4));
|
||||
p.y = (int) (q.y + 9 * Math.sin(angle - Math.PI / 4));
|
||||
Imgproc.line(img, p, q, colour, 1, Core.LINE_AA, 0);
|
||||
//! [visualization1]
|
||||
}
|
||||
|
||||
private double getOrientation(MatOfPoint ptsMat, Mat img) {
|
||||
List<Point> pts = ptsMat.toList();
|
||||
//! [pca]
|
||||
// Construct a buffer used by the pca analysis
|
||||
int sz = pts.size();
|
||||
Mat dataPts = new Mat(sz, 2, CvType.CV_64F);
|
||||
double[] dataPtsData = new double[(int) (dataPts.total() * dataPts.channels())];
|
||||
for (int i = 0; i < dataPts.rows(); i++) {
|
||||
dataPtsData[i * dataPts.cols()] = pts.get(i).x;
|
||||
dataPtsData[i * dataPts.cols() + 1] = pts.get(i).y;
|
||||
}
|
||||
dataPts.put(0, 0, dataPtsData);
|
||||
|
||||
// Perform PCA analysis
|
||||
Mat mean = new Mat();
|
||||
Mat eigenvectors = new Mat();
|
||||
Mat eigenvalues = new Mat();
|
||||
Core.PCACompute2(dataPts, mean, eigenvectors, eigenvalues);
|
||||
double[] meanData = new double[(int) (mean.total() * mean.channels())];
|
||||
mean.get(0, 0, meanData);
|
||||
|
||||
// Store the center of the object
|
||||
Point cntr = new Point(meanData[0], meanData[1]);
|
||||
|
||||
// Store the eigenvalues and eigenvectors
|
||||
double[] eigenvectorsData = new double[(int) (eigenvectors.total() * eigenvectors.channels())];
|
||||
double[] eigenvaluesData = new double[(int) (eigenvalues.total() * eigenvalues.channels())];
|
||||
eigenvectors.get(0, 0, eigenvectorsData);
|
||||
eigenvalues.get(0, 0, eigenvaluesData);
|
||||
//! [pca]
|
||||
|
||||
//! [visualization]
|
||||
// Draw the principal components
|
||||
Imgproc.circle(img, cntr, 3, new Scalar(255, 0, 255), 2);
|
||||
Point p1 = new Point(cntr.x + 0.02 * eigenvectorsData[0] * eigenvaluesData[0],
|
||||
cntr.y + 0.02 * eigenvectorsData[1] * eigenvaluesData[0]);
|
||||
Point p2 = new Point(cntr.x - 0.02 * eigenvectorsData[2] * eigenvaluesData[1],
|
||||
cntr.y - 0.02 * eigenvectorsData[3] * eigenvaluesData[1]);
|
||||
drawAxis(img, cntr, p1, new Scalar(0, 255, 0), 1);
|
||||
drawAxis(img, cntr, p2, new Scalar(255, 255, 0), 5);
|
||||
|
||||
double angle = Math.atan2(eigenvectorsData[1], eigenvectorsData[0]); // orientation in radians
|
||||
//! [visualization]
|
||||
|
||||
return angle;
|
||||
}
|
||||
|
||||
public void run(String[] args) {
|
||||
//! [pre-process]
|
||||
// Load image
|
||||
String filename = args.length > 0 ? args[0] : "../data/pca_test1.jpg";
|
||||
Mat src = Imgcodecs.imread(filename);
|
||||
|
||||
// Check if image is loaded successfully
|
||||
if (src.empty()) {
|
||||
System.err.println("Cannot read image: " + filename);
|
||||
System.exit(0);
|
||||
}
|
||||
|
||||
Mat srcOriginal = src.clone();
|
||||
HighGui.imshow("src", srcOriginal);
|
||||
|
||||
// Convert image to grayscale
|
||||
Mat gray = new Mat();
|
||||
Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
|
||||
|
||||
// Convert image to binary
|
||||
Mat bw = new Mat();
|
||||
Imgproc.threshold(gray, bw, 50, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
|
||||
//! [pre-process]
|
||||
|
||||
//! [contours]
|
||||
// Find all the contours in the thresholded image
|
||||
List<MatOfPoint> contours = new ArrayList<>();
|
||||
Mat hierarchy = new Mat();
|
||||
Imgproc.findContours(bw, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_NONE);
|
||||
|
||||
for (int i = 0; i < contours.size(); i++) {
|
||||
// Calculate the area of each contour
|
||||
double area = Imgproc.contourArea(contours.get(i));
|
||||
// Ignore contours that are too small or too large
|
||||
if (area < 1e2 || 1e5 < area)
|
||||
continue;
|
||||
|
||||
// Draw each contour only for visualisation purposes
|
||||
Imgproc.drawContours(src, contours, i, new Scalar(0, 0, 255), 2);
|
||||
// Find the orientation of each shape
|
||||
getOrientation(contours.get(i), src);
|
||||
}
|
||||
//! [contours]
|
||||
|
||||
HighGui.imshow("output", src);
|
||||
|
||||
HighGui.waitKey();
|
||||
System.exit(0);
|
||||
}
|
||||
}
|
||||
|
||||
public class IntroductionToPCADemo {
|
||||
public static void main(String[] args) {
|
||||
// Load the native OpenCV library
|
||||
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
||||
|
||||
new IntroductionToPCA().run(args);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,99 @@
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.TermCriteria;
|
||||
import org.opencv.highgui.HighGui;
|
||||
import org.opencv.imgcodecs.Imgcodecs;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.ml.Ml;
|
||||
import org.opencv.ml.SVM;
|
||||
|
||||
public class IntroductionToSVMDemo {
|
||||
public static void main(String[] args) {
|
||||
// Load the native OpenCV library
|
||||
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
||||
|
||||
// Set up training data
|
||||
//! [setup1]
|
||||
int[] labels = { 1, -1, -1, -1 };
|
||||
float[] trainingData = { 501, 10, 255, 10, 501, 255, 10, 501 };
|
||||
//! [setup1]
|
||||
//! [setup2]
|
||||
Mat trainingDataMat = new Mat(4, 2, CvType.CV_32FC1);
|
||||
trainingDataMat.put(0, 0, trainingData);
|
||||
Mat labelsMat = new Mat(4, 1, CvType.CV_32SC1);
|
||||
labelsMat.put(0, 0, labels);
|
||||
//! [setup2]
|
||||
|
||||
// Train the SVM
|
||||
//! [init]
|
||||
SVM svm = SVM.create();
|
||||
svm.setType(SVM.C_SVC);
|
||||
svm.setKernel(SVM.LINEAR);
|
||||
svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 100, 1e-6));
|
||||
//! [init]
|
||||
//! [train]
|
||||
svm.train(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);
|
||||
//! [train]
|
||||
|
||||
// Data for visual representation
|
||||
int width = 512, height = 512;
|
||||
Mat image = Mat.zeros(height, width, CvType.CV_8UC3);
|
||||
|
||||
// Show the decision regions given by the SVM
|
||||
//! [show]
|
||||
byte[] imageData = new byte[(int) (image.total() * image.channels())];
|
||||
Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
|
||||
float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
|
||||
for (int i = 0; i < image.rows(); i++) {
|
||||
for (int j = 0; j < image.cols(); j++) {
|
||||
sampleMatData[0] = j;
|
||||
sampleMatData[1] = i;
|
||||
sampleMat.put(0, 0, sampleMatData);
|
||||
float response = svm.predict(sampleMat);
|
||||
|
||||
if (response == 1) {
|
||||
imageData[(i * image.cols() + j) * image.channels()] = 0;
|
||||
imageData[(i * image.cols() + j) * image.channels() + 1] = (byte) 255;
|
||||
imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
|
||||
} else if (response == -1) {
|
||||
imageData[(i * image.cols() + j) * image.channels()] = (byte) 255;
|
||||
imageData[(i * image.cols() + j) * image.channels() + 1] = 0;
|
||||
imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
image.put(0, 0, imageData);
|
||||
//! [show]
|
||||
|
||||
// Show the training data
|
||||
//! [show_data]
|
||||
int thickness = -1;
|
||||
int lineType = Core.LINE_8;
|
||||
Imgproc.circle(image, new Point(501, 10), 5, new Scalar(0, 0, 0), thickness, lineType, 0);
|
||||
Imgproc.circle(image, new Point(255, 10), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
|
||||
Imgproc.circle(image, new Point(501, 255), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
|
||||
Imgproc.circle(image, new Point(10, 501), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
|
||||
//! [show_data]
|
||||
|
||||
// Show support vectors
|
||||
//! [show_vectors]
|
||||
thickness = 2;
|
||||
Mat sv = svm.getUncompressedSupportVectors();
|
||||
float[] svData = new float[(int) (sv.total() * sv.channels())];
|
||||
sv.get(0, 0, svData);
|
||||
for (int i = 0; i < sv.rows(); ++i) {
|
||||
Imgproc.circle(image, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6,
|
||||
new Scalar(128, 128, 128), thickness, lineType, 0);
|
||||
}
|
||||
//! [show_vectors]
|
||||
|
||||
Imgcodecs.imwrite("result.png", image); // save the image
|
||||
|
||||
HighGui.imshow("SVM Simple Example", image); // show it to the user
|
||||
HighGui.waitKey();
|
||||
System.exit(0);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,186 @@
|
||||
import java.util.Random;
|
||||
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.TermCriteria;
|
||||
import org.opencv.highgui.HighGui;
|
||||
import org.opencv.imgcodecs.Imgcodecs;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.ml.Ml;
|
||||
import org.opencv.ml.SVM;
|
||||
|
||||
public class NonLinearSVMsDemo {
|
||||
public static final int NTRAINING_SAMPLES = 100;
|
||||
public static final float FRAC_LINEAR_SEP = 0.9f;
|
||||
|
||||
public static void main(String[] args) {
|
||||
// Load the native OpenCV library
|
||||
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
||||
|
||||
System.out.println("\n--------------------------------------------------------------------------");
|
||||
System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
|
||||
System.out.println("--------------------------------------------------------------------------\n");
|
||||
|
||||
// Data for visual representation
|
||||
int width = 512, height = 512;
|
||||
Mat I = Mat.zeros(height, width, CvType.CV_8UC3);
|
||||
|
||||
// --------------------- 1. Set up training data randomly---------------------------------------
|
||||
Mat trainData = new Mat(2 * NTRAINING_SAMPLES, 2, CvType.CV_32F);
|
||||
Mat labels = new Mat(2 * NTRAINING_SAMPLES, 1, CvType.CV_32S);
|
||||
|
||||
Random rng = new Random(100); // Random value generation class
|
||||
|
||||
// Set up the linearly separable part of the training data
|
||||
int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
|
||||
|
||||
//! [setup1]
|
||||
// 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);
|
||||
float[] cData = new float[(int) (c.total() * c.channels())];
|
||||
double[] cDataDbl = rng.doubles(cData.length, 0, 0.4f * width).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
// The y coordinate of the points is in [0, 1)
|
||||
c = trainClass.colRange(1, 2);
|
||||
cData = new float[(int) (c.total() * c.channels())];
|
||||
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
|
||||
// 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);
|
||||
cData = new float[(int) (c.total() * c.channels())];
|
||||
cDataDbl = rng.doubles(cData.length, 0.6 * width, width).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
// The y coordinate of the points is in [0, 1)
|
||||
c = trainClass.colRange(1, 2);
|
||||
cData = new float[(int) (c.total() * c.channels())];
|
||||
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
//! [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);
|
||||
// The x coordinate of the points is in [0.4, 0.6)
|
||||
c = trainClass.colRange(0, 1);
|
||||
cData = new float[(int) (c.total() * c.channels())];
|
||||
cDataDbl = rng.doubles(cData.length, 0.4 * width, 0.6 * width).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
// The y coordinate of the points is in [0, 1)
|
||||
c = trainClass.colRange(1, 2);
|
||||
cData = new float[(int) (c.total() * c.channels())];
|
||||
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
|
||||
for (int i = 0; i < cData.length; i++) {
|
||||
cData[i] = (float) cDataDbl[i];
|
||||
}
|
||||
c.put(0, 0, cData);
|
||||
//! [setup2]
|
||||
|
||||
// ------------------------- Set up the labels for the classes---------------------------------
|
||||
labels.rowRange(0, NTRAINING_SAMPLES).setTo(new Scalar(1)); // Class 1
|
||||
labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(new Scalar(2)); // Class 2
|
||||
|
||||
// ------------------------ 2. Set up the support vector machines parameters--------------------
|
||||
System.out.println("Starting training process");
|
||||
//! [init]
|
||||
SVM svm = SVM.create();
|
||||
svm.setType(SVM.C_SVC);
|
||||
svm.setC(0.1);
|
||||
svm.setKernel(SVM.LINEAR);
|
||||
svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, (int) 1e7, 1e-6));
|
||||
//! [init]
|
||||
|
||||
// ------------------------ 3. Train the svm----------------------------------------------------
|
||||
//! [train]
|
||||
svm.train(trainData, Ml.ROW_SAMPLE, labels);
|
||||
//! [train]
|
||||
System.out.println("Finished training process");
|
||||
|
||||
// ------------------------ 4. Show the decision regions----------------------------------------
|
||||
//! [show]
|
||||
byte[] IData = new byte[(int) (I.total() * I.channels())];
|
||||
Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
|
||||
float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
|
||||
for (int i = 0; i < I.rows(); i++) {
|
||||
for (int j = 0; j < I.cols(); j++) {
|
||||
sampleMatData[0] = j;
|
||||
sampleMatData[1] = i;
|
||||
sampleMat.put(0, 0, sampleMatData);
|
||||
float response = svm.predict(sampleMat);
|
||||
|
||||
if (response == 1) {
|
||||
IData[(i * I.cols() + j) * I.channels()] = 0;
|
||||
IData[(i * I.cols() + j) * I.channels() + 1] = 100;
|
||||
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
|
||||
} else if (response == 2) {
|
||||
IData[(i * I.cols() + j) * I.channels()] = 100;
|
||||
IData[(i * I.cols() + j) * I.channels() + 1] = 0;
|
||||
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
I.put(0, 0, IData);
|
||||
//! [show]
|
||||
|
||||
// ----------------------- 5. Show the training data--------------------------------------------
|
||||
//! [show_data]
|
||||
int thick = -1;
|
||||
int lineType = Core.LINE_8;
|
||||
float px, py;
|
||||
// Class 1
|
||||
float[] trainDataData = new float[(int) (trainData.total() * trainData.channels())];
|
||||
trainData.get(0, 0, trainDataData);
|
||||
for (int i = 0; i < NTRAINING_SAMPLES; i++) {
|
||||
px = trainDataData[i * trainData.cols()];
|
||||
py = trainDataData[i * trainData.cols() + 1];
|
||||
Imgproc.circle(I, new Point(px, py), 3, new Scalar(0, 255, 0), thick, lineType, 0);
|
||||
}
|
||||
// Class 2
|
||||
for (int i = NTRAINING_SAMPLES; i < 2 * NTRAINING_SAMPLES; ++i) {
|
||||
px = trainDataData[i * trainData.cols()];
|
||||
py = trainDataData[i * trainData.cols() + 1];
|
||||
Imgproc.circle(I, new Point(px, py), 3, new Scalar(255, 0, 0), thick, lineType, 0);
|
||||
}
|
||||
//! [show_data]
|
||||
|
||||
// ------------------------- 6. Show support vectors--------------------------------------------
|
||||
//! [show_vectors]
|
||||
thick = 2;
|
||||
Mat sv = svm.getUncompressedSupportVectors();
|
||||
float[] svData = new float[(int) (sv.total() * sv.channels())];
|
||||
sv.get(0, 0, svData);
|
||||
for (int i = 0; i < sv.rows(); i++) {
|
||||
Imgproc.circle(I, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6, new Scalar(128, 128, 128),
|
||||
thick, lineType, 0);
|
||||
}
|
||||
//! [show_vectors]
|
||||
|
||||
Imgcodecs.imwrite("result.png", I); // save the Image
|
||||
HighGui.imshow("SVM for Non-Linear Training Data", I); // show it to the user
|
||||
HighGui.waitKey();
|
||||
System.exit(0);
|
||||
}
|
||||
}
|
||||
+2
-2
@@ -25,8 +25,8 @@ def thresh_callback(val):
|
||||
boundRect = [None]*len(contours)
|
||||
centers = [None]*len(contours)
|
||||
radius = [None]*len(contours)
|
||||
for i in range(len(contours)):
|
||||
contours_poly[i] = cv.approxPolyDP(contours[i], 3, True)
|
||||
for i, c in enumerate(contours):
|
||||
contours_poly[i] = cv.approxPolyDP(c, 3, True)
|
||||
boundRect[i] = cv.boundingRect(contours_poly[i])
|
||||
centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])
|
||||
## [allthework]
|
||||
|
||||
+6
-6
@@ -22,22 +22,22 @@ def thresh_callback(val):
|
||||
# Find the rotated rectangles and ellipses for each contour
|
||||
minRect = [None]*len(contours)
|
||||
minEllipse = [None]*len(contours)
|
||||
for i in range(len(contours)):
|
||||
minRect[i] = cv.minAreaRect(contours[i])
|
||||
if contours[i].shape[0] > 5:
|
||||
minEllipse[i] = cv.fitEllipse(contours[i])
|
||||
for i, c in enumerate(contours):
|
||||
minRect[i] = cv.minAreaRect(c)
|
||||
if c.shape[0] > 5:
|
||||
minEllipse[i] = cv.fitEllipse(c)
|
||||
|
||||
# Draw contours + rotated rects + ellipses
|
||||
## [zeroMat]
|
||||
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
|
||||
## [zeroMat]
|
||||
## [forContour]
|
||||
for i in range(len(contours)):
|
||||
for i, c in enumerate(contours):
|
||||
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
|
||||
# contour
|
||||
cv.drawContours(drawing, contours, i, color)
|
||||
# ellipse
|
||||
if contours[i].shape[0] > 5:
|
||||
if c.shape[0] > 5:
|
||||
cv.ellipse(drawing, minEllipse[i], color, 2)
|
||||
# rotated rectangle
|
||||
box = cv.boxPoints(minRect[i])
|
||||
|
||||
@@ -0,0 +1,100 @@
|
||||
from __future__ import print_function
|
||||
from __future__ import division
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
import argparse
|
||||
from math import atan2, cos, sin, sqrt, pi
|
||||
|
||||
def drawAxis(img, p_, q_, colour, scale):
|
||||
p = list(p_)
|
||||
q = list(q_)
|
||||
## [visualization1]
|
||||
angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
|
||||
hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))
|
||||
|
||||
# Here we lengthen the arrow by a factor of scale
|
||||
q[0] = p[0] - scale * hypotenuse * cos(angle)
|
||||
q[1] = p[1] - scale * hypotenuse * sin(angle)
|
||||
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
|
||||
|
||||
# create the arrow hooks
|
||||
p[0] = q[0] + 9 * cos(angle + pi / 4)
|
||||
p[1] = q[1] + 9 * sin(angle + pi / 4)
|
||||
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
|
||||
|
||||
p[0] = q[0] + 9 * cos(angle - pi / 4)
|
||||
p[1] = q[1] + 9 * sin(angle - pi / 4)
|
||||
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
|
||||
## [visualization1]
|
||||
|
||||
def getOrientation(pts, img):
|
||||
## [pca]
|
||||
# Construct a buffer used by the pca analysis
|
||||
sz = len(pts)
|
||||
data_pts = np.empty((sz, 2), dtype=np.float64)
|
||||
for i in range(data_pts.shape[0]):
|
||||
data_pts[i,0] = pts[i,0,0]
|
||||
data_pts[i,1] = pts[i,0,1]
|
||||
|
||||
# Perform PCA analysis
|
||||
mean = np.empty((0))
|
||||
mean, eigenvectors, eigenvalues = cv.PCACompute2(data_pts, mean)
|
||||
|
||||
# Store the center of the object
|
||||
cntr = (int(mean[0,0]), int(mean[0,1]))
|
||||
## [pca]
|
||||
|
||||
## [visualization]
|
||||
# Draw the principal components
|
||||
cv.circle(img, cntr, 3, (255, 0, 255), 2)
|
||||
p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0])
|
||||
p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0])
|
||||
drawAxis(img, cntr, p1, (0, 255, 0), 1)
|
||||
drawAxis(img, cntr, p2, (255, 255, 0), 5)
|
||||
|
||||
angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians
|
||||
## [visualization]
|
||||
|
||||
return angle
|
||||
|
||||
## [pre-process]
|
||||
# Load image
|
||||
parser = argparse.ArgumentParser(description='Code for Introduction to Principal Component Analysis (PCA) tutorial.\
|
||||
This program demonstrates how to use OpenCV PCA to extract the orientation of an object.')
|
||||
parser.add_argument('--input', help='Path to input image.', default='../data/pca_test1.jpg')
|
||||
args = parser.parse_args()
|
||||
|
||||
src = cv.imread(args.input)
|
||||
# Check if image is loaded successfully
|
||||
if src is None:
|
||||
print('Could not open or find the image: ', args.input)
|
||||
exit(0)
|
||||
|
||||
cv.imshow('src', src)
|
||||
|
||||
# Convert image to grayscale
|
||||
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
|
||||
|
||||
# Convert image to binary
|
||||
_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
|
||||
## [pre-process]
|
||||
|
||||
## [contours]
|
||||
# Find all the contours in the thresholded image
|
||||
_, contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
|
||||
|
||||
for i, c in enumerate(contours):
|
||||
# Calculate the area of each contour
|
||||
area = cv.contourArea(c);
|
||||
# Ignore contours that are too small or too large
|
||||
if area < 1e2 or 1e5 < area:
|
||||
continue
|
||||
|
||||
# Draw each contour only for visualisation purposes
|
||||
cv.drawContours(src, contours, i, (0, 0, 255), 2);
|
||||
# Find the orientation of each shape
|
||||
getOrientation(c, src)
|
||||
## [contours]
|
||||
|
||||
cv.imshow('output', src)
|
||||
cv.waitKey()
|
||||
@@ -0,0 +1,62 @@
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
|
||||
# Set up training data
|
||||
## [setup1]
|
||||
labels = np.array([1, -1, -1, -1])
|
||||
trainingData = np.matrix([[501, 10], [255, 10], [501, 255], [10, 501]], dtype=np.float32)
|
||||
## [setup1]
|
||||
|
||||
# Train the SVM
|
||||
## [init]
|
||||
svm = cv.ml.SVM_create()
|
||||
svm.setType(cv.ml.SVM_C_SVC)
|
||||
svm.setKernel(cv.ml.SVM_LINEAR)
|
||||
svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, 100, 1e-6))
|
||||
## [init]
|
||||
## [train]
|
||||
svm.train(trainingData, cv.ml.ROW_SAMPLE, labels)
|
||||
## [train]
|
||||
|
||||
# Data for visual representation
|
||||
width = 512
|
||||
height = 512
|
||||
image = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
|
||||
# Show the decision regions given by the SVM
|
||||
## [show]
|
||||
green = (0,255,0)
|
||||
blue = (255,0,0)
|
||||
for i in range(image.shape[0]):
|
||||
for j in range(image.shape[1]):
|
||||
sampleMat = np.matrix([[j,i]], dtype=np.float32)
|
||||
response = svm.predict(sampleMat)[1]
|
||||
|
||||
if response == 1:
|
||||
image[i,j] = green
|
||||
elif response == -1:
|
||||
image[i,j] = blue
|
||||
## [show]
|
||||
|
||||
# Show the training data
|
||||
## [show_data]
|
||||
thickness = -1
|
||||
cv.circle(image, (501, 10), 5, ( 0, 0, 0), thickness)
|
||||
cv.circle(image, (255, 10), 5, (255, 255, 255), thickness)
|
||||
cv.circle(image, (501, 255), 5, (255, 255, 255), thickness)
|
||||
cv.circle(image, ( 10, 501), 5, (255, 255, 255), thickness)
|
||||
## [show_data]
|
||||
|
||||
# Show support vectors
|
||||
## [show_vectors]
|
||||
thickness = 2
|
||||
sv = svm.getUncompressedSupportVectors()
|
||||
|
||||
for i in range(sv.shape[0]):
|
||||
cv.circle(image, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thickness)
|
||||
## [show_vectors]
|
||||
|
||||
cv.imwrite('result.png', image) # save the image
|
||||
|
||||
cv.imshow('SVM Simple Example', image) # show it to the user
|
||||
cv.waitKey()
|
||||
@@ -0,0 +1,117 @@
|
||||
from __future__ import print_function
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
import random as rng
|
||||
|
||||
NTRAINING_SAMPLES = 100 # Number of training samples per class
|
||||
FRAC_LINEAR_SEP = 0.9 # Fraction of samples which compose the linear separable part
|
||||
|
||||
# Data for visual representation
|
||||
WIDTH = 512
|
||||
HEIGHT = 512
|
||||
I = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)
|
||||
|
||||
# --------------------- 1. Set up training data randomly ---------------------------------------
|
||||
trainData = np.empty((2*NTRAINING_SAMPLES, 2), dtype=np.float32)
|
||||
labels = np.empty((2*NTRAINING_SAMPLES, 1), dtype=np.int32)
|
||||
|
||||
rng.seed(100) # Random value generation class
|
||||
|
||||
# Set up the linearly separable part of the training data
|
||||
nLinearSamples = int(FRAC_LINEAR_SEP * NTRAINING_SAMPLES)
|
||||
|
||||
## [setup1]
|
||||
# Generate random points for the class 1
|
||||
trainClass = trainData[0:nLinearSamples,:]
|
||||
# The x coordinate of the points is in [0, 0.4)
|
||||
c = trainClass[:,0:1]
|
||||
c[:] = np.random.uniform(0.0, 0.4 * WIDTH, c.shape)
|
||||
# The y coordinate of the points is in [0, 1)
|
||||
c = trainClass[:,1:2]
|
||||
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
|
||||
|
||||
# Generate random points for the class 2
|
||||
trainClass = trainData[2*NTRAINING_SAMPLES-nLinearSamples:2*NTRAINING_SAMPLES,:]
|
||||
# The x coordinate of the points is in [0.6, 1]
|
||||
c = trainClass[:,0:1]
|
||||
c[:] = np.random.uniform(0.6*WIDTH, WIDTH, c.shape)
|
||||
# The y coordinate of the points is in [0, 1)
|
||||
c = trainClass[:,1:2]
|
||||
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
|
||||
## [setup1]
|
||||
|
||||
#------------------ Set up the non-linearly separable part of the training data ---------------
|
||||
## [setup2]
|
||||
# Generate random points for the classes 1 and 2
|
||||
trainClass = trainData[nLinearSamples:2*NTRAINING_SAMPLES-nLinearSamples,:]
|
||||
# The x coordinate of the points is in [0.4, 0.6)
|
||||
c = trainClass[:,0:1]
|
||||
c[:] = np.random.uniform(0.4*WIDTH, 0.6*WIDTH, c.shape)
|
||||
# The y coordinate of the points is in [0, 1)
|
||||
c = trainClass[:,1:2]
|
||||
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
|
||||
## [setup2]
|
||||
|
||||
#------------------------- Set up the labels for the classes ---------------------------------
|
||||
labels[0:NTRAINING_SAMPLES,:] = 1 # Class 1
|
||||
labels[NTRAINING_SAMPLES:2*NTRAINING_SAMPLES,:] = 2 # Class 2
|
||||
|
||||
#------------------------ 2. Set up the support vector machines parameters --------------------
|
||||
print('Starting training process')
|
||||
## [init]
|
||||
svm = cv.ml.SVM_create()
|
||||
svm.setType(cv.ml.SVM_C_SVC)
|
||||
svm.setC(0.1)
|
||||
svm.setKernel(cv.ml.SVM_LINEAR)
|
||||
svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, int(1e7), 1e-6))
|
||||
## [init]
|
||||
|
||||
#------------------------ 3. Train the svm ----------------------------------------------------
|
||||
## [train]
|
||||
svm.train(trainData, cv.ml.ROW_SAMPLE, labels)
|
||||
## [train]
|
||||
print('Finished training process')
|
||||
|
||||
#------------------------ 4. Show the decision regions ----------------------------------------
|
||||
## [show]
|
||||
green = (0,100,0)
|
||||
blue = (100,0,0)
|
||||
for i in range(I.shape[0]):
|
||||
for j in range(I.shape[1]):
|
||||
sampleMat = np.matrix([[j,i]], dtype=np.float32)
|
||||
response = svm.predict(sampleMat)[1]
|
||||
|
||||
if response == 1:
|
||||
I[i,j] = green
|
||||
elif response == 2:
|
||||
I[i,j] = blue
|
||||
## [show]
|
||||
|
||||
#----------------------- 5. Show the training data --------------------------------------------
|
||||
## [show_data]
|
||||
thick = -1
|
||||
# Class 1
|
||||
for i in range(NTRAINING_SAMPLES):
|
||||
px = trainData[i,0]
|
||||
py = trainData[i,1]
|
||||
cv.circle(I, (px, py), 3, (0, 255, 0), thick)
|
||||
|
||||
# Class 2
|
||||
for i in range(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES):
|
||||
px = trainData[i,0]
|
||||
py = trainData[i,1]
|
||||
cv.circle(I, (px, py), 3, (255, 0, 0), thick)
|
||||
## [show_data]
|
||||
|
||||
#------------------------- 6. Show support vectors --------------------------------------------
|
||||
## [show_vectors]
|
||||
thick = 2
|
||||
sv = svm.getUncompressedSupportVectors()
|
||||
|
||||
for i in range(sv.shape[0]):
|
||||
cv.circle(I, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thick)
|
||||
## [show_vectors]
|
||||
|
||||
cv.imwrite('result.png', I) # save the Image
|
||||
cv.imshow('SVM for Non-Linear Training Data', I) # show it to the user
|
||||
cv.waitKey()
|
||||
Reference in New Issue
Block a user