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Rewrite distanceToCenters.

Browse Source 
It supports NORM_L1 distance types now and can
use user provided indices.
Also fixed a bug of kmeans where distance pointers should be float instead
 of double.

NORM_L2 changed to NORM_L2SQR, Accuracy and Perf tests are added

added ROI support in accuracy test of distanceToCenters
This commit is contained in:
peng xiao
2013-10-08 15:49:40 +08:00
committed by Konstantin Matskevich
parent 2279c209c8
commit 68a8a11161
6 changed files with 300 additions and 58 deletions
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91
modules/ocl/test/test_kmeans.cpp
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@@ -99,7 +99,6 @@ PARAM_TEST_CASE(Kmeans, int, int, int)
}
};
OCL_TEST_P(Kmeans, Mat){
if(flags & KMEANS_USE_INITIAL_LABELS)
{
// inital a given labels
@@ -116,11 +115,9 @@ OCL_TEST_P(Kmeans, Mat){
kmeans(src, K, labels,
TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
1, flags, centers);
ocl::kmeans(d_src, K, d_labels,
TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
1, flags, d_centers);
Mat dd_labels(d_labels);
Mat dd_centers(d_centers);
if(flags & KMEANS_USE_INITIAL_LABELS)
@@ -153,9 +150,97 @@ OCL_TEST_P(Kmeans, Mat){
}
}
}
INSTANTIATE_TEST_CASE_P(OCL_ML, Kmeans, Combine(
Values(3, 5, 8),
Values(CV_32FC1, CV_32FC2, CV_32FC4),
Values(OCL_KMEANS_USE_INITIAL_LABELS/*, OCL_KMEANS_PP_CENTERS*/)));
/////////////////////////////// DistanceToCenters //////////////////////////////////////////
CV_ENUM(DistType, NORM_L1, NORM_L2SQR);
PARAM_TEST_CASE(distanceToCenters, DistType, bool)
{
cv::Size size;
int distType;
bool useRoi;
cv::Mat src, centers, src_roi, centers_roi;
cv::ocl::oclMat ocl_src, ocl_centers, ocl_src_roi, ocl_centers_roi;
virtual void SetUp()
{
distType = GET_PARAM(0);
useRoi = GET_PARAM(1);
}
void random_roi()
{
Size roiSize_src = randomSize(10,1000);
Size roiSize_centers = randomSize(10, 1000);
roiSize_src.width = roiSize_centers.width;
Border srcBorder = randomBorder(0, useRoi ? 500 : 0);
randomSubMat(src, src_roi, roiSize_src, srcBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
Border centersBorder = randomBorder(0, useRoi ? 500 : 0);
randomSubMat(centers, centers_roi, roiSize_centers, centersBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
for(int i = 0; i<centers.rows; i++)
centers.at<float>(i, randomInt(0,centers.cols-1)) = (float)randomDouble(SHRT_MAX, INT_MAX);
generateOclMat(ocl_src, ocl_src_roi, src, roiSize_src, srcBorder);
generateOclMat(ocl_centers, ocl_centers_roi, centers, roiSize_centers, centersBorder);
}
};
OCL_TEST_P(distanceToCenters, Accuracy)
{
for(int j = 0; j< LOOP_TIMES; j++)
{
random_roi();
cv::ocl::oclMat ocl_dists;
cv::ocl::oclMat ocl_labels;
cv::ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src_roi, ocl_centers_roi, distType);
Mat labels, dists;
ocl_labels.download(labels);
ocl_dists.download(dists);
ASSERT_EQ(ocl_dists.cols, ocl_labels.rows);
Mat batch_dists;
cv::batchDistance(src_roi, centers_roi, batch_dists, CV_32FC1, noArray(), distType);
std::vector<double> gold_dists_v;
for(int i = 0; i<batch_dists.rows; i++)
{
Mat r = batch_dists.row(i);
double mVal;
Point mLoc;
minMaxLoc(r, &mVal, NULL, &mLoc, NULL);
int ocl_label = *(int*)labels.row(i).col(0).data;
ASSERT_EQ(mLoc.x, ocl_label);
gold_dists_v.push_back(mVal);
}
Mat gold_dists(gold_dists_v);
dists.convertTo(dists, CV_64FC1);
double relative_error = cv::norm(gold_dists.t(), dists, NORM_INF|NORM_RELATIVE);
ASSERT_LE(relative_error, 1e-5);
}
}
INSTANTIATE_TEST_CASE_P (OCL_ML, distanceToCenters, Combine(DistType::all(), Bool()) );
#endif