/* * By downloading, copying, installing or using the software you agree to this license. * If you do not agree to this license, do not download, install, * copy or use the software. * * * License Agreement * For Open Source Computer Vision Library * (3 - clause BSD License) * * Redistribution and use in source and binary forms, with or without modification, * are permitted provided that the following conditions are met : * * *Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * * * Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and / or other materials provided with the distribution. * * * Neither the names of the copyright holders nor the names of the contributors * may be used to endorse or promote products derived from this software * without specific prior written permission. * * This software is provided by the copyright holders and contributors "as is" and * any express or implied warranties, including, but not limited to, the implied * warranties of merchantability and fitness for a particular purpose are disclaimed. * In no event shall copyright holders or contributors be liable for any direct, * indirect, incidental, special, exemplary, or consequential damages * (including, but not limited to, procurement of substitute goods or services; * loss of use, data, or profits; or business interruption) however caused * and on any theory of liability, whether in contract, strict liability, * or tort(including negligence or otherwise) arising in any way out of * the use of this software, even if advised of the possibility of such damage. */ #include "perf_precomp.hpp" namespace cvtest { using std::tr1::tuple; using std::tr1::get; using namespace perf; using namespace testing; using namespace cv; void MakeArtificialExample(RNG rng, Mat& dst_left_view, Mat& dst_view); CV_ENUM(SGBMModes, StereoSGBM::MODE_SGBM, StereoSGBM::MODE_SGBM_3WAY, StereoSGBM::MODE_HH4); typedef tuple SGBMParams; typedef TestBaseWithParam TestStereoCorresp; PERF_TEST_P( TestStereoCorresp, SGBM, Combine(Values(Size(1280,720),Size(640,480)), Values(256,128), SGBMModes::all()) ) { RNG rng(0); SGBMParams params = GetParam(); Size sz = get<0>(params); int num_disparities = get<1>(params); int mode = get<2>(params); Mat src_left(sz, CV_8UC3); Mat src_right(sz, CV_8UC3); Mat dst(sz, CV_16S); MakeArtificialExample(rng,src_left,src_right); cv::setNumThreads(cv::getNumberOfCPUs()); int wsize = 3; int P1 = 8*src_left.channels()*wsize*wsize; TEST_CYCLE() { Ptr sgbm = StereoSGBM::create(0,num_disparities,wsize,P1,4*P1,1,63,25,0,0,mode); sgbm->compute(src_left,src_right,dst); } SANITY_CHECK(dst, .01, ERROR_RELATIVE); } void MakeArtificialExample(RNG rng, Mat& dst_left_view, Mat& dst_right_view) { int w = dst_left_view.cols; int h = dst_left_view.rows; //params: unsigned char bg_level = (unsigned char)rng.uniform(0.0,255.0); unsigned char fg_level = (unsigned char)rng.uniform(0.0,255.0); int rect_width = (int)rng.uniform(w/16,w/2); int rect_height = (int)rng.uniform(h/16,h/2); int rect_disparity = (int)(0.15*w); double sigma = 3.0; int rect_x_offset = (w-rect_width) /2; int rect_y_offset = (h-rect_height)/2; if(dst_left_view.channels()==3) { dst_left_view = Scalar(Vec3b(bg_level,bg_level,bg_level)); dst_right_view = Scalar(Vec3b(bg_level,bg_level,bg_level)); } else { dst_left_view = Scalar(bg_level); dst_right_view = Scalar(bg_level); } Mat dst_left_view_rect = Mat(dst_left_view, Rect(rect_x_offset,rect_y_offset,rect_width,rect_height)); if(dst_left_view.channels()==3) dst_left_view_rect = Scalar(Vec3b(fg_level,fg_level,fg_level)); else dst_left_view_rect = Scalar(fg_level); rect_x_offset-=rect_disparity; Mat dst_right_view_rect = Mat(dst_right_view, Rect(rect_x_offset,rect_y_offset,rect_width,rect_height)); if(dst_right_view.channels()==3) dst_right_view_rect = Scalar(Vec3b(fg_level,fg_level,fg_level)); else dst_right_view_rect = Scalar(fg_level); //add some gaussian noise: unsigned char *l, *r; for(int i=0;i(l[0] + rng.gaussian(sigma)); l[1] = saturate_cast(l[1] + rng.gaussian(sigma)); l[2] = saturate_cast(l[2] + rng.gaussian(sigma)); l+=3; r[0] = saturate_cast(r[0] + rng.gaussian(sigma)); r[1] = saturate_cast(r[1] + rng.gaussian(sigma)); r[2] = saturate_cast(r[2] + rng.gaussian(sigma)); r+=3; } } else { for(int j=0;j(l[0] + rng.gaussian(sigma)); l++; r[0] = saturate_cast(r[0] + rng.gaussian(sigma)); r++; } } } } }