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

Fix new blank line at EOF.

Browse Source
This commit is contained in:
peng xiao 2013-09-17 08:50:13 +08:00
parent 422396ef6a
commit 315c054379
1 changed files with 177 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

176
modules/ocl/test/test_ml.cpp
View File

@ -121,4 +121,180 @@ TEST_P(KNN, Accuracy)
} }
INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)), INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
Values(4, 3), Values(false, true))); Values(4, 3), Values(false, true)));
////////////////////////////////SVM/////////////////////////////////////////////////
PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
cv::Size size;
int kernel_type;
int svm_type;
Mat src, labels, samples, labels_predict;
int K;
cv::RNG rng ;
virtual void SetUp()
{
kernel_type = GET_PARAM(0);
svm_type = GET_PARAM(1);
K = GET_PARAM(2);
rng = TS::ptr()->get_rng();
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src.create(size, CV_32FC1);
labels.create(1, size.height, CV_32SC1);
int row_idx = 0;
const int max_number = size.height / K - 1;
CV_Assert(K <= size.height);
for(int i = 0; i < K; i++ )
{
Mat center_row_header = src.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number) ||
(i == K - 1 && j < max_number + size.height % K); j ++)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number;
}
labels.convertTo(labels, CV_32FC1);
cv::Size test_size = cv::Size(MWIDTH, 100);
samples.create(test_size, CV_32FC1);
labels_predict.create(1, test_size.height, CV_32SC1);
const int max_number_test = test_size.height / K - 1;
row_idx = 0;
for(int i = 0; i < K; i++ )
{
Mat center_row_header = samples.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels_predict.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number_test) ||
(i == K - 1 && j < max_number_test + test_size.height % K); j ++)
{
Mat cur_row_header = samples.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels_predict.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number_test;
}
labels_predict.convertTo(labels_predict, CV_32FC1);
}
};
TEST_P(SVM_OCL, Accuracy)
{
CvSVMParams params;
params.degree = 0.4;
params.gamma = 1;
params.coef0 = 1;
params.C = 1;
params.nu = 0.5;
params.p = 1;
params.svm_type = svm_type;
params.kernel_type = kernel_type;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);
CvSVM SVM;
SVM.train(src, labels, Mat(), Mat(), params);
cv::ocl::CvSVM_OCL SVM_OCL;
SVM_OCL.train(src, labels, Mat(), Mat(), params);
int c = SVM.get_support_vector_count();
int c1 = SVM_OCL.get_support_vector_count();
Mat sv(c, MHEIGHT, CV_32FC1);
Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
for(int i = 0; i < c; i++)
{
const float* v = SVM.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv.at<float>(i, j) = v[j];
}
}
for(int i = 0; i < c1; i++)
{
const float* v_ocl = SVM_OCL.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv_ocl.at<float>(i, j) = v_ocl[j];
}
}
cv::BFMatcher matcher(cv::NORM_L2);
std::vector<cv::DMatch> matches;
matcher.match(sv, sv_ocl, matches);
int count = 0;
for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
{
if((*itr).distance < 0.1)
{
count ++;
}
}
if(c != 0)
{
float matchedRatio = (float)count / c;
EXPECT_GT(matchedRatio, 0.95);
}
if(c != 0)
{
CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
CvMat test_samples = samples;
CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);
SVM.predict(&test_samples, result);
SVM_OCL.predict(&test_samples, result_ocl);
int true_resp = 0, true_resp_ocl = 0;
for (int i = 0; i < samples.rows; i++)
{
if (result->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp++;
}
}
float matchedRatio = (float)true_resp / samples.rows;
for (int i = 0; i < samples.rows; i++)
{
if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp_ocl++;
}
}
float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;
if(matchedRatio != 0 && true_resp_ocl < true_resp)
{
EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
}
}
}
INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
Values(CvSVM::LINEAR, CvSVM::POLY, CvSVM::RBF, CvSVM::SIGMOID),
Values(CvSVM::C_SVC, CvSVM::NU_SVC, CvSVM::ONE_CLASS, CvSVM::EPS_SVR, CvSVM::NU_SVR),
Values(2, 3, 4)
));
#endif // HAVE_OPENCL #endif // HAVE_OPENCL