cv2cvtest part2
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@ -329,7 +329,7 @@ public:
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EXPECT_EQ(reference.depth(), actual.depth());
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EXPECT_EQ(reference.channels(), actual.channels());
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double psnr = PSNR(actual, reference);
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double psnr = cvtest::PSNR(actual, reference);
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if (psnr < eps)
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{
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#define SUM cvtest::TS::SUMMARY
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@ -198,7 +198,7 @@ void CV_HighGuiTest::ImageTest(const string& dir)
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}
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const double thresDbell = 20;
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double psnr = PSNR(loaded, image);
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double psnr = cvtest::PSNR(loaded, image);
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if (psnr < thresDbell)
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{
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ts->printf(ts->LOG, "Reading image from file: too big difference (=%g) with fmt=%s\n", psnr, ext.c_str());
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@ -235,7 +235,7 @@ void CV_HighGuiTest::ImageTest(const string& dir)
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continue;
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}
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psnr = PSNR(buf_loaded, image);
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psnr = cvtest::PSNR(buf_loaded, image);
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if (psnr < thresDbell)
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{
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@ -316,7 +316,7 @@ void CV_HighGuiTest::VideoTest(const string& dir, const cvtest::VideoFormat& fmt
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Mat img = frames[i];
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Mat img1 = cv::cvarrToMat(ipl1);
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double psnr = PSNR(img1, img);
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double psnr = cvtest::PSNR(img1, img);
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if (psnr < thresDbell)
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{
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ts->printf(ts->LOG, "Too low frame %d psnr = %gdb\n", i, psnr);
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@ -371,7 +371,7 @@ void CV_HighGuiTest::SpecificImageTest(const string& dir)
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}
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const double thresDbell = 20;
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double psnr = PSNR(loaded, image);
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double psnr = cvtest::PSNR(loaded, image);
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if (psnr < thresDbell)
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{
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ts->printf(ts->LOG, "Reading image from file: too big difference (=%g) with fmt=bmp\n", psnr);
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@ -408,7 +408,7 @@ void CV_HighGuiTest::SpecificImageTest(const string& dir)
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continue;
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}
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psnr = PSNR(buf_loaded, image);
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psnr = cvtest::PSNR(buf_loaded, image);
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if (psnr < thresDbell)
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{
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@ -521,7 +521,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor
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Mat img = images[i];
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const double thresDbell = 40;
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double psnr = PSNR(img, frame);
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double psnr = cvtest::PSNR(img, frame);
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if (psnr > thresDbell)
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{
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@ -160,7 +160,7 @@ public:
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return;
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}
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double err = PSNR(img, img0);
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double err = cvtest::PSNR(img, img0);
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if( err < 20 )
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{
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@ -278,7 +278,7 @@ float dispRMS( const Mat& computedDisp, const Mat& groundTruthDisp, const Mat& m
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checkTypeAndSizeOfMask( mask, sz );
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pointsCount = countNonZero(mask);
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}
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return 1.f/sqrt((float)pointsCount) * (float)norm(computedDisp, groundTruthDisp, NORM_L2, mask);
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return 1.f/sqrt((float)pointsCount) * (float)cvtest::norm(computedDisp, groundTruthDisp, NORM_L2, mask);
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}
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/*
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@ -41,7 +41,8 @@
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#include "test_precomp.hpp"
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#include "opencv2/highgui.hpp"
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void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_salt_ratio,cv::RNG& rng){
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void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_salt_ratio,cv::RNG& rng)
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{
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noisy.create(img.size(), img.type());
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cv::Mat noise(img.size(), img.type()), mask(img.size(), CV_8U);
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rng.fill(noise,cv::RNG::NORMAL,128.0,sigma);
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@ -54,34 +55,36 @@ void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_
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noise.setTo(128, mask);
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cv::addWeighted(noisy, 1, noise, 1, -128, noisy);
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}
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void make_spotty(cv::Mat& img,cv::RNG& rng, int r=3,int n=1000){
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for(int i=0;i<n;i++){
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void make_spotty(cv::Mat& img,cv::RNG& rng, int r=3,int n=1000)
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{
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for(int i=0;i<n;i++)
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{
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int x=rng(img.cols-r),y=rng(img.rows-r);
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if(rng(2)==0){
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if(rng(2)==0)
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img(cv::Range(y,y+r),cv::Range(x,x+r))=(uchar)0;
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}else{
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else
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img(cv::Range(y,y+r),cv::Range(x,x+r))=(uchar)255;
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}
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}
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}
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bool validate_pixel(const cv::Mat& image,int x,int y,uchar val){
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bool validate_pixel(const cv::Mat& image,int x,int y,uchar val)
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{
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printf("test: image(%d,%d)=%d vs %d - %s\n",x,y,(int)image.at<uchar>(x,y),val,(val==image.at<uchar>(x,y))?"true":"false");
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return (image.at<uchar>(x,y)==val);
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}
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TEST(Optim_denoise_tvl1, regression_basic){
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TEST(Optim_denoise_tvl1, regression_basic)
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{
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cv::RNG rng(42);
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cv::Mat img = cv::imread("lena.jpg", 0), noisy,res;
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if(img.rows!=512 || img.cols!=512){
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printf("\tplease, put lena.jpg from samples/c in the current folder\n");
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printf("\tnow, the test will fail...\n");
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ASSERT_TRUE(false);
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}
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cv::Mat img = cv::imread(cvtest::TS::ptr()->get_data_path() + "shared/lena.png", 0), noisy, res;
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ASSERT_FALSE(img.empty()) << "Error: can't open 'lena.png'";
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const int obs_num=5;
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std::vector<cv::Mat> images(obs_num,cv::Mat());
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for(int i=0;i<(int)images.size();i++){
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std::vector<cv::Mat> images(obs_num, cv::Mat());
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for(int i=0;i<(int)images.size();i++)
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{
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make_noisy(img,images[i], 20, 0.02,rng);
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//make_spotty(images[i],rng);
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}
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@ -73,7 +73,7 @@ TEST(Photo_DenoisingGrayscale, regression)
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DUMP(result, expected_path + ".res.png");
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ASSERT_EQ(0, norm(result != expected));
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
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}
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TEST(Photo_DenoisingColored, regression)
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@ -93,7 +93,7 @@ TEST(Photo_DenoisingColored, regression)
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DUMP(result, expected_path + ".res.png");
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ASSERT_EQ(0, norm(result != expected));
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
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}
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TEST(Photo_DenoisingGrayscaleMulti, regression)
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@ -118,7 +118,7 @@ TEST(Photo_DenoisingGrayscaleMulti, regression)
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DUMP(result, expected_path + ".res.png");
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ASSERT_EQ(0, norm(result != expected));
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
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}
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TEST(Photo_DenoisingColoredMulti, regression)
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@ -143,7 +143,7 @@ TEST(Photo_DenoisingColoredMulti, regression)
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DUMP(result, expected_path + ".res.png");
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ASSERT_EQ(0, norm(result != expected));
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
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}
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TEST(Photo_White, issue_2646)
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@ -91,8 +91,8 @@ void CV_InpaintTest::run( int )
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absdiff( orig, res1, diff1 );
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absdiff( orig, res2, diff2 );
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double n1 = norm(diff1.reshape(1), NORM_INF, inv_mask.reshape(1));
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double n2 = norm(diff2.reshape(1), NORM_INF, inv_mask.reshape(1));
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double n1 = cvtest::norm(diff1.reshape(1), NORM_INF, inv_mask.reshape(1));
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double n2 = cvtest::norm(diff2.reshape(1), NORM_INF, inv_mask.reshape(1));
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if (n1 != 0 || n2 != 0)
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{
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@ -103,8 +103,8 @@ void CV_InpaintTest::run( int )
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absdiff( exp1, res1, diff1 );
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absdiff( exp2, res2, diff2 );
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n1 = norm(diff1.reshape(1), NORM_INF, mask.reshape(1));
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n2 = norm(diff2.reshape(1), NORM_INF, mask.reshape(1));
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n1 = cvtest::norm(diff1.reshape(1), NORM_INF, mask.reshape(1));
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n2 = cvtest::norm(diff2.reshape(1), NORM_INF, mask.reshape(1));
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const int jpeg_thres = 3;
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if (n1 > jpeg_thres || n2 > jpeg_thres)
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@ -73,6 +73,6 @@ TEST(MultiBandBlender, CanBlendTwoImages)
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Mat result; result_s.convertTo(result, CV_8U);
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Mat expected = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/baboon_lena.png");
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double rmsErr = norm(expected, result, NORM_L2) / sqrt(double(expected.size().area()));
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double rmsErr = cvtest::norm(expected, result, NORM_L2) / sqrt(double(expected.size().area()));
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ASSERT_LT(rmsErr, 1e-3);
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}
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@ -129,6 +129,7 @@ CV_EXPORTS void minMaxLoc(const Mat& src, double* minval, double* maxval,
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CV_EXPORTS double norm(InputArray src, int normType, InputArray mask=noArray());
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CV_EXPORTS double norm(InputArray src1, InputArray src2, int normType, InputArray mask=noArray());
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CV_EXPORTS Scalar mean(const Mat& src, const Mat& mask=Mat());
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CV_EXPORTS double PSNR(InputArray src1, InputArray src2);
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CV_EXPORTS bool cmpUlps(const Mat& data, const Mat& refdata, int expMaxDiff, double* realMaxDiff, vector<int>* idx);
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@ -1399,6 +1399,12 @@ double norm(InputArray _src1, InputArray _src2, int normType, InputArray _mask)
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return isRelative ? result / (cvtest::norm(src2, normType) + DBL_EPSILON) : result;
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}
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double PSNR(InputArray _src1, InputArray _src2)
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{
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CV_Assert( _src1.depth() == CV_8U );
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double diff = std::sqrt(cvtest::norm(_src1, _src2, NORM_L2SQR)/(_src1.total()*_src1.channels()));
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return 20*log10(255./(diff+DBL_EPSILON));
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}
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template<typename _Tp> static double
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crossCorr_(const _Tp* src1, const _Tp* src2, size_t total)
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@ -109,8 +109,8 @@ bool CV_RigidTransform_Test::testNPoints(int from)
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Mat aff_est = estimateRigidTransform(fpts, tpts, true);
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double thres = 0.1*norm(aff);
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double d = norm(aff_est, aff, NORM_L2);
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double thres = 0.1*cvtest::norm(aff, NORM_L2);
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double d = cvtest::norm(aff_est, aff, NORM_L2);
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if (d > thres)
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{
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double dB=0, nB=0;
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@ -120,7 +120,7 @@ bool CV_RigidTransform_Test::testNPoints(int from)
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Mat B = A - repeat(A.row(0), 3, 1), Bt = B.t();
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B = Bt*B;
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dB = cv::determinant(B);
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nB = norm(B);
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nB = cvtest::norm(B, NORM_L2);
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if( fabs(dB) < 0.01*nB )
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continue;
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}
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@ -154,11 +154,11 @@ bool CV_RigidTransform_Test::testImage()
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Mat aff_est = estimateRigidTransform(img, rotated, true);
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const double thres = 0.033;
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if (norm(aff_est, aff, NORM_INF) > thres)
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if (cvtest::norm(aff_est, aff, NORM_INF) > thres)
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( cvtest::TS::LOG, "Threshold = %f, norm of difference = %f", thres,
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norm(aff_est, aff, NORM_INF) );
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cvtest::norm(aff_est, aff, NORM_INF) );
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return false;
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}
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