remove test

modules/imgproc/test/test_adpativethresh.cpp

@@ -1,113 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
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-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  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
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
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-// are permitted provided that the following conditions are met:
-//
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-//     this list of conditions and the following disclaimer.
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-//     derived from this software without specific prior written permission.
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-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation 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.
-//
-//M*/
-
-#include "test_precomp.hpp"
-#include <string>
-
-using namespace cv;
-using namespace std;
-
-class CV_Adaptivethresh : public cvtest::BaseTest
-{
-public:
-    CV_Adaptivethresh();
-    ~CV_Adaptivethresh();
-protected:
-    void run(int);
-};
-
-CV_Adaptivethresh::CV_Adaptivethresh() {}
-CV_Adaptivethresh::~CV_Adaptivethresh() {}
-
-void CV_Adaptivethresh::run( int /* start_from */)
-{
-    string exp_path = string(ts->get_data_path()) + "adaptivethresh/lena_orig.png";
-    Mat lena = imread(exp_path, 0); // CV_LOAD_IMAGE_GRAYSCALE=0
-    if (lena.empty() )
-    {
-        ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
-        return;
-    }
-    int sum=0;
-    for (int i = 0; i < lena.rows; i++)
-    {
-        unsigned char *ptr = lena.ptr(i);
-        for (int j=0;j<lena.cols;j++,ptr++)
-            sum+=*ptr;
-    }
-    if (sum!=31910861)
-    {
-        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-        return;
-    }
-    int windowSize[9] = {3,9,11,17,21,25,29,37,47};
-    int expectedValueMean[9] = {96138,121836,124499,129096,130538,131330,131743,131616,131223};
-    int expectedValueGaussNew[9] = {86308,112910,116197,122117,124672,126488,127855,129377,130387};
-    int expectedValueGaussOld[9] = {88583,81365,154081,98049,149357,106414,179701,168433,90250};
-    Mat im;
-    bool failed=false;
-    for(int i = 0; i<9; ++i )
-    {
-        adaptiveThreshold( lena, im, 255,cv::ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,windowSize[i],0);
-        int numberWhite=countNonZero(im);
-        if (numberWhite != expectedValueMean[i])
-        {
-            ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
-            return;
-        }
-        adaptiveThreshold( lena, im, 255,cv::ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,windowSize[i],0);
-        if (numberWhite != expectedValueGaussNew[i])
-        {   
-
-            if (numberWhite != expectedValueGaussOld[i])
-                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
-            else
-                ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
-        }
-    }
-    if (failed)
-        ts->set_failed_test_info(cvtest::TS::OK);
-    else
-        ts->set_failed_test_info(cvtest::TS::OK);
-}
-
-TEST(Imgproc_Adaptivethresh, regression) { CV_Adaptivethresh test; test.safe_run(); }