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313 lines
7.6 KiB
Plaintext
313 lines
7.6 KiB
Plaintext
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace cv::cuda;
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using namespace cv::cudev;
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using namespace cvtest;
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TEST(Sum, GpuMat)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<float> dst = sum_(d_src);
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float res;
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dst.download(_OutputArray(&res, 1));
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Scalar dst_gold = cv::sum(src);
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ASSERT_FLOAT_EQ(static_cast<float>(dst_gold[0]), res);
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}
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TEST(Sum, Expr)
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{
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const Size size = randomSize(100, 400);
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Mat src1 = randomMat(size, CV_32FC1, 0, 1);
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Mat src2 = randomMat(size, CV_32FC1, 0, 1);
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GpuMat_<float> d_src1(src1), d_src2(src2);
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GpuMat_<float> dst = sum_(abs_(d_src1 - d_src2));
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float res;
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dst.download(_OutputArray(&res, 1));
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Scalar dst_gold = cv::norm(src1, src2, NORM_L1);
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ASSERT_FLOAT_EQ(static_cast<float>(dst_gold[0]), res);
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}
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TEST(MinVal, GpuMat)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<float> dst = minVal_(d_src);
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float res;
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dst.download(_OutputArray(&res, 1));
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double res_gold;
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cv::minMaxLoc(src, &res_gold, 0);
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ASSERT_FLOAT_EQ(static_cast<float>(res_gold), res);
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}
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TEST(MaxVal, Expr)
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{
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const Size size = randomSize(100, 400);
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Mat src1 = randomMat(size, CV_32SC1);
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Mat src2 = randomMat(size, CV_32SC1);
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GpuMat_<int> d_src1(src1), d_src2(src2);
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GpuMat_<float> dst = maxVal_(abs_(d_src1 - d_src2));
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float res;
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dst.download(_OutputArray(&res, 1));
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double res_gold = cv::norm(src1, src2, NORM_INF);
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ASSERT_FLOAT_EQ(static_cast<float>(res_gold), res);
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}
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TEST(MinMaxVal, GpuMat)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<float> dst = minMaxVal_(d_src);
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float res[2];
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dst.download(Mat(1, 2, CV_32FC1, res));
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double res_gold[2];
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cv::minMaxLoc(src, &res_gold[0], &res_gold[1]);
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ASSERT_FLOAT_EQ(static_cast<float>(res_gold[0]), res[0]);
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ASSERT_FLOAT_EQ(static_cast<float>(res_gold[1]), res[1]);
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}
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TEST(NonZeroCount, Accuracy)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1, 0, 5);
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GpuMat_<uchar> d_src(src);
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GpuMat_<int> dst1 = countNonZero_(d_src);
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GpuMat_<int> dst2 = sum_(cvt_<int>(d_src) != 0);
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EXPECT_MAT_NEAR(dst1, dst2, 0.0);
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}
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TEST(ReduceToRow, Sum)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<int> dst = reduceToRow_<Sum<int> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 0, REDUCE_SUM, CV_32S);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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TEST(ReduceToRow, Avg)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<float> dst = reduceToRow_<Avg<float> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 0, REDUCE_AVG, CV_32F);
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-4);
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}
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TEST(ReduceToRow, Min)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<uchar> dst = reduceToRow_<Min<uchar> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 0, REDUCE_MIN);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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TEST(ReduceToRow, Max)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<uchar> dst = reduceToRow_<Max<uchar> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 0, REDUCE_MAX);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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TEST(ReduceToColumn, Sum)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<int> dst = reduceToColumn_<Sum<int> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 1, REDUCE_SUM, CV_32S);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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TEST(ReduceToColumn, Avg)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<float> dst = reduceToColumn_<Avg<float> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 1, REDUCE_AVG, CV_32F);
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-4);
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}
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TEST(ReduceToColumn, Min)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<uchar> dst = reduceToColumn_<Min<uchar> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 1, REDUCE_MIN);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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TEST(ReduceToColumn, Max)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<uchar> dst = reduceToColumn_<Max<uchar> >(d_src);
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Mat dst_gold;
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cv::reduce(src, dst_gold, 1, REDUCE_MAX);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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static void calcHistGold(const cv::Mat& src, cv::Mat& hist)
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{
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hist.create(1, 256, CV_32SC1);
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hist.setTo(cv::Scalar::all(0));
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int* hist_row = hist.ptr<int>();
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for (int y = 0; y < src.rows; ++y)
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{
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const uchar* src_row = src.ptr(y);
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for (int x = 0; x < src.cols; ++x)
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++hist_row[src_row[x]];
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}
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}
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TEST(Histogram, GpuMat)
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{
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const Size size = randomSize(100, 400);
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Mat src = randomMat(size, CV_8UC1);
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GpuMat_<uchar> d_src(src);
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GpuMat_<int> dst = histogram_<256>(d_src);
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Mat dst_gold;
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calcHistGold(src, dst_gold);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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