generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

made everything compile and even run somehow

Browse Source
This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
View File

@@ -4,29 +4,29 @@
#include <opencv2/ml/ml.hpp>
using namespace cv;
using namespace cv::ml;
int main()
int main(int, char**)
{
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
// Set up training data
float labels[4] = {1.0, -1.0, -1.0, -1.0};
Mat labelsMat(4, 1, CV_32FC1, labels);
int labels[4] = {1, -1, -1, -1};
Mat labelsMat(4, 1, CV_32SC1, labels);
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
// Set up SVM's parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
SVM::Params params;
params.svmType = SVM::C_SVC;
params.kernelType = SVM::LINEAR;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6);
// Train the SVM
CvSVM SVM;
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
Ptr<SVM> svm = StatModel::train<SVM>(trainingDataMat, ROW_SAMPLE, labelsMat, params);
Vec3b green(0,255,0), blue (255,0,0);
// Show the decision regions given by the SVM
@@ -34,30 +34,30 @@ int main()
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << j,i);
float response = SVM.predict(sampleMat);
float response = svm->predict(sampleMat);
if (response == 1)
image.at<Vec3b>(i,j) = green;
else if (response == -1)
image.at<Vec3b>(i,j) = blue;
image.at<Vec3b>(i,j) = blue;
}
// Show the training data
int thickness = -1;
int lineType = 8;
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType );
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType );
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType );
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType );
// Show support vectors
thickness = 2;
lineType = 8;
int c = SVM.get_support_vector_count();
Mat sv = svm->getSupportVectors();
for (int i = 0; i < c; ++i)
for (int i = 0; i < sv.rows; ++i)
{
const float* v = SVM.get_support_vector(i);
const float* v = sv.ptr<float>(i);
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}

22
samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
View File

@@ -8,6 +8,7 @@
#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;
static void help()
@@ -30,7 +31,7 @@ int main()
//--------------------- 1. Set up training data randomly ---------------------------------------
Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32SC1);
RNG rng(100); // Random value generation class
@@ -71,16 +72,15 @@ int main()
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;
SVM::Params params;
params.svmType = SVM::C_SVC;
params.C = 0.1;
params.kernel_type = SVM::LINEAR;
params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);
params.kernelType = SVM::LINEAR;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6);
//------------------------ 3. Train the svm ----------------------------------------------------
cout << "Starting training process" << endl;
CvSVM svm;
svm.train(trainData, labels, Mat(), Mat(), params);
Ptr<SVM> svm = StatModel::train<SVM>(trainData, ROW_SAMPLE, labels, params);
cout << "Finished training process" << endl;
//------------------------ 4. Show the decision regions ----------------------------------------
@@ -89,7 +89,7 @@ int main()
for (int j = 0; j < I.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << i, j);
float response = svm.predict(sampleMat);
float response = svm->predict(sampleMat);
if (response == 1) I.at<Vec3b>(j, i) = green;
else if (response == 2) I.at<Vec3b>(j, i) = blue;
@@ -117,11 +117,11 @@ int main()
//------------------------- 6. Show support vectors --------------------------------------------
thick = 2;
lineType = 8;
int x = svm.get_support_vector_count();
Mat sv = svm->getSupportVectors();
for (int i = 0; i < x; ++i)
for (int i = 0; i < sv.rows; ++i)
{
const float* v = svm.get_support_vector(i);
const float* v = sv.ptr<float>(i);
circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);
}