177 lines
		
	
	
		
			6.8 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			177 lines
		
	
	
		
			6.8 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| /*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) 2010-2012, Multicoreware, Inc., all rights reserved.
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| // Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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| // Third party copyrights are property of their respective owners.
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| //
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| // @Authors
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| //    Nathan, liujun@multicorewareinc.com
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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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| #include <iomanip>
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| 
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| using namespace cv;
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| using namespace cv::ocl;
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| using namespace testing;
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| using namespace std;
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| 
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| template <typename T>
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| static void blendLinearGold(const Mat &img1, const Mat &img2,
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|                             const Mat &weights1, const Mat &weights2,
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|                             Mat &result_gold)
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| {
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|     CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
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|     CV_Assert(weights1.size() == weights2.size() && weights1.size() == img1.size() &&
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|               weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
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| 
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|     result_gold.create(img1.size(), img1.type());
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| 
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|     int cn = img1.channels();
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|     int step1 = img1.cols * img1.channels();
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| 
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|     for (int y = 0; y < img1.rows; ++y)
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|     {
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|         const float * const weights1_row = weights1.ptr<float>(y);
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|         const float * const weights2_row = weights2.ptr<float>(y);
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|         const T * const img1_row = img1.ptr<T>(y);
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|         const T * const img2_row = img2.ptr<T>(y);
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|         T * const result_gold_row = result_gold.ptr<T>(y);
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| 
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|         for (int x = 0; x < step1; ++x)
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|         {
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|             int x1 = x / cn;
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|             float w1 = weights1_row[x1], w2 = weights2_row[x1];
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|             result_gold_row[x] = saturate_cast<T>(((float)img1_row[x] * w1
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|                                                  + (float)img2_row[x] * w2) / (w1 + w2 + 1e-5f));
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|         }
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|     }
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| }
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| 
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| PARAM_TEST_CASE(Blend, MatDepth, int, bool)
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| {
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|     int depth, channels;
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|     bool useRoi;
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| 
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|     Mat src1, src2, weights1, weights2, dst;
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|     Mat src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi;
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|     oclMat gsrc1, gsrc2, gweights1, gweights2, gdst, gst;
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|     oclMat gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi;
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| 
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|     virtual void SetUp()
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|     {
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|         depth = GET_PARAM(0);
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|         channels = GET_PARAM(1);
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|         useRoi = GET_PARAM(2);
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|     }
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| 
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|     void random_roi()
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|     {
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|         const int type = CV_MAKE_TYPE(depth, channels);
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| 
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|         const double upValue = 256;
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|         const double sumMinValue = 0.01; // we don't want to divide by "zero"
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| 
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|         Size roiSize = randomSize(1, 20);
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|         Border src1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
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|         randomSubMat(src1, src1_roi, roiSize, src1Border, type, -upValue, upValue);
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| 
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|         Border src2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
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|         randomSubMat(src2, src2_roi, roiSize, src2Border, type, -upValue, upValue);
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| 
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|         Border weights1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
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|         randomSubMat(weights1, weights1_roi, roiSize, weights1Border, CV_32FC1, -upValue, upValue);
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| 
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|         Border weights2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
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|         randomSubMat(weights2, weights2_roi, roiSize, weights2Border, CV_32FC1, sumMinValue, upValue); // fill it as a (w1 + w12)
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| 
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|         weights2_roi = weights2_roi - weights1_roi;
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|         // check that weights2_roi is still a part of weights2 (not a new matrix)
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|         CV_Assert(checkNorm(weights2_roi,
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|             weights2(Rect(weights2Border.lef, weights2Border.top, roiSize.width, roiSize.height))) < 1e-6);
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| 
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|         Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
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|         randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 5, 16);
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| 
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|         generateOclMat(gsrc1, gsrc1_roi, src1, roiSize, src1Border);
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|         generateOclMat(gsrc2, gsrc2_roi, src2, roiSize, src2Border);
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|         generateOclMat(gweights1, gweights1_roi, weights1, roiSize, weights1Border);
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|         generateOclMat(gweights2, gweights2_roi, weights2, roiSize, weights2Border);
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|         generateOclMat(gdst, gdst_roi, dst, roiSize, dstBorder);
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|     }
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| 
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|     void Near(double eps = 0.0)
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|     {
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|         Mat whole, roi;
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|         gdst.download(whole);
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|         gdst_roi.download(roi);
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| 
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|         EXPECT_MAT_NEAR(dst, whole, eps);
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|         EXPECT_MAT_NEAR(dst_roi, roi, eps);
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|     }
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| };
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| 
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| typedef void (*blendLinearFunc)(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold);
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| 
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| OCL_TEST_P(Blend, Accuracy)
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| {
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|     for (int i = 0; i < LOOP_TIMES; ++i)
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|     {
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|         random_roi();
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| 
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|         cv::ocl::blendLinear(gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi);
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| 
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|         static blendLinearFunc funcs[] = {
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|             blendLinearGold<uchar>,
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|             blendLinearGold<schar>,
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|             blendLinearGold<ushort>,
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|             blendLinearGold<short>,
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|             blendLinearGold<int>,
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|             blendLinearGold<float>,
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|         };
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| 
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|         blendLinearFunc func = funcs[depth];
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|         func(src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi);
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| 
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|         Near(depth <= CV_32S ? 1.0 : 0.2);
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|     }
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| }
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| 
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| INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend,
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|                         Combine(testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F),
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|                                 testing::Range(1, 5), Bool()));
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