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| // For Open Source Computer Vision Library | |
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| //M*/ | |
| namespace opencv_test { namespace { | |
| void multiBandBlend(const cv::Mat& im1, const cv::Mat& im2, const cv::Mat& mask1, const cv::Mat& mask2, cv::Mat& result, bool try_cuda) | |
| { | |
| detail::MultiBandBlender blender(try_cuda, 5); | |
| blender.prepare(Rect(0, 0, max(im1.cols, im2.cols), max(im1.rows, im2.rows))); | |
| // If using cuda try blending multiple times without calling prepare inbetween | |
| for (int i = 0; i < (try_cuda ? 10 : 1); ++i) { | |
| blender.feed(im1, mask1, Point(0, 0)); | |
| blender.feed(im2, mask2, Point(0, 0)); | |
| Mat result_s, result_mask; | |
| blender.blend(result_s, result_mask); | |
| result_s.convertTo(result, CV_8U); | |
| } | |
| } | |
| TEST(CUDA_MultiBandBlender, Accuracy) | |
| { | |
| Mat image1 = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/baboon.png"); | |
| Mat image2 = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/lena.png"); | |
| ASSERT_EQ(image1.rows, image2.rows); ASSERT_EQ(image1.cols, image2.cols); | |
| Mat image1s, image2s; | |
| image1.convertTo(image1s, CV_16S); | |
| image2.convertTo(image2s, CV_16S); | |
| Mat mask1(image1s.size(), CV_8U); | |
| mask1(Rect(0, 0, mask1.cols/2, mask1.rows)).setTo(255); | |
| mask1(Rect(mask1.cols/2, 0, mask1.cols - mask1.cols/2, mask1.rows)).setTo(0); | |
| Mat mask2(image2s.size(), CV_8U); | |
| mask2(Rect(0, 0, mask2.cols/2, mask2.rows)).setTo(0); | |
| mask2(Rect(mask2.cols/2, 0, mask2.cols - mask2.cols/2, mask2.rows)).setTo(255); | |
| cv::Mat result; | |
| multiBandBlend(image1s, image2s, mask1, mask2, result, false); | |
| cv::Mat result_cuda; | |
| multiBandBlend(image1s, image2s, mask1, mask2, result_cuda, true); | |
| EXPECT_MAT_NEAR(result, result_cuda, 3); | |
| } | |
| }} // namespace | |