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refactor CUDA ORB feature detector/extractor algorithm:

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use new abstract interface and hidden implementation
This commit is contained in:
Vladislav Vinogradov
2015-01-13 10:40:58 +03:00
parent 554ddd2ec4
commit f960a5707d
5 changed files with 447 additions and 442 deletions
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modules/cudafeatures2d/test/test_features2d.cpp
+6 -5
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@@ -122,7 +122,7 @@ namespace
IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool)
}
CV_ENUM(ORB_ScoreType, ORB::HARRIS_SCORE, ORB::FAST_SCORE)
CV_ENUM(ORB_ScoreType, cv::ORB::HARRIS_SCORE, cv::ORB::FAST_SCORE)
PARAM_TEST_CASE(ORB, cv::cuda::DeviceInfo, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold, ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
{
@@ -162,8 +162,9 @@ CUDA_TEST_P(ORB, Accuracy)
cv::Mat mask(image.size(), CV_8UC1, cv::Scalar::all(1));
mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
cv::cuda::ORB_CUDA orb(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
orb.blurForDescriptor = blurForDescriptor;
cv::Ptr<cv::cuda::ORB> orb =
cv::cuda::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel,
WTA_K, scoreType, patchSize, 20, blurForDescriptor);
if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
{
@@ -171,7 +172,7 @@ CUDA_TEST_P(ORB, Accuracy)
{
std::vector<cv::KeyPoint> keypoints;
cv::cuda::GpuMat descriptors;
orb(loadMat(image), loadMat(mask), keypoints, descriptors);
orb->detectAndComputeAsync(loadMat(image), loadMat(mask), keypoints, descriptors);
}
catch (const cv::Exception& e)
{
@@ -182,7 +183,7 @@ CUDA_TEST_P(ORB, Accuracy)
{
std::vector<cv::KeyPoint> keypoints;
cv::cuda::GpuMat descriptors;
orb(loadMat(image), loadMat(mask), keypoints, descriptors);
orb->detectAndCompute(loadMat(image), loadMat(mask), keypoints, descriptors);
cv::Ptr<cv::ORB> orb_gold = cv::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);