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Commit 46f3b4d6 authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge pull request #884 from tucna:fuzzy_optimization

parents da6ecd30 e1f0f0c0
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modules/fuzzy/CMakeLists.txt
View file @ 46f3b4d6
set(the_description "Fuzzy mathematical image processing") set(the_description "Fuzzy mathematical image processing")
ocv_define_module(fuzzy opencv_imgproc opencv_core) ocv_define_module(fuzzy opencv_imgproc opencv_core WRAP python)
modules/fuzzy/include/opencv2/fuzzy/fuzzy_F0_math.hpp
View file @ 46f3b4d6
...@@ -54,7 +54,7 @@ namespace ft ...@@ -54,7 +54,7 @@ namespace ft
//! @{ //! @{
/** @brief Computes components of the array using direct F0-transform. /** @brief Computes components of the array using direct F0-transform.
@param matrix Input 1-channel array. @param matrix Input array.
@param kernel Kernel used for processing. Function **createKernel** can be used. @param kernel Kernel used for processing. Function **createKernel** can be used.
@param components Output 32-bit array for the components. @param components Output 32-bit array for the components.
@param mask Mask can be used for unwanted area marking. @param mask Mask can be used for unwanted area marking.
...@@ -64,10 +64,10 @@ namespace ft ...@@ -64,10 +64,10 @@ namespace ft
@note @note
F-transform technique is described in paper @cite Perf:FT. F-transform technique is described in paper @cite Perf:FT.
*/ */
CV_EXPORTS void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components, InputArray mask); CV_EXPORTS_AS(FT02D_components1) void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components, InputArray mask);
/** @brief Computes components of the array using direct F0-transform. /** @brief Computes components of the array using direct F0-transform.
@param matrix Input 1-channel array. @param matrix Input array.
@param kernel Kernel used for processing. Function **createKernel** can be used. @param kernel Kernel used for processing. Function **createKernel** can be used.
@param components Output 32-bit array for the components. @param components Output 32-bit array for the components.
...@@ -76,10 +76,10 @@ namespace ft ...@@ -76,10 +76,10 @@ namespace ft
@note @note
F-transform technique is described in paper @cite Perf:FT. F-transform technique is described in paper @cite Perf:FT.
*/ */
CV_EXPORTS void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components); CV_EXPORTS_W void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components);
/** @brief Computes inverse F0-transfrom. /** @brief Computes inverse F0-transfrom.
@param components Input 32-bit array for the components. @param components Input 32-bit single channel array for the components.
@param kernel Kernel used for processing. Function **createKernel** can be used. @param kernel Kernel used for processing. Function **createKernel** can be used.
@param output Output 32-bit array. @param output Output 32-bit array.
@param width Width of the output array. @param width Width of the output array.
...@@ -88,29 +88,38 @@ namespace ft ...@@ -88,29 +88,38 @@ namespace ft
@note @note
F-transform technique is described in paper @cite Perf:FT. F-transform technique is described in paper @cite Perf:FT.
*/ */
CV_EXPORTS void FT02D_inverseFT(InputArray components, InputArray kernel, OutputArray output, int width, int height); CV_EXPORTS_W void FT02D_inverseFT(InputArray components, InputArray kernel, OutputArray output, int width, int height);
/** @brief Computes F0-transfrom and inverse F0-transfrom at once. /** @brief Computes F0-transfrom and inverse F0-transfrom at once.
@param image Input image. @param matrix Input matrix.
@param kernel Kernel used for processing. Function **createKernel** can be used. @param kernel Kernel used for processing. Function **createKernel** can be used.
@param output Output 32-bit array. @param output Output 32-bit array.
@param mask Mask used for unwanted area marking. @param mask Mask used for unwanted area marking.
This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**. This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**.
*/ */
CV_EXPORTS void FT02D_process(const Mat &image, const Mat &kernel, Mat &output, const Mat &mask); CV_EXPORTS_AS(FT02D_process1) void FT02D_process(InputArray matrix, InputArray kernel, OutputArray output, InputArray mask);
/** @brief Computes F0-transfrom and inverse F0-transfrom at once.
@param matrix Input matrix.
@param kernel Kernel used for processing. Function **createKernel** can be used.
@param output Output 32-bit array.
This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**.
*/
CV_EXPORTS_W void FT02D_process(InputArray matrix, InputArray kernel, OutputArray output);
/** @brief Computes F0-transfrom and inverse F0-transfrom at once and return state. /** @brief Computes F0-transfrom and inverse F0-transfrom at once and return state.
@param image Input image. @param matrix Input matrix.
@param kernel Kernel used for processing. Function **createKernel** can be used. @param kernel Kernel used for processing. Function **createKernel** can be used.
@param imageOutput Output 32-bit array. @param output Output 32-bit array.
@param mask Mask used for unwanted area marking. @param mask Mask used for unwanted area marking.
@param maskOutput Mask after one iteration. @param maskOutput Mask after one iteration.
@param firstStop If **true** function returns -1 when first problem appears. In case of **false**, the process is completed and summation of all problems returned. @param firstStop If **true** function returns -1 when first problem appears. In case of **false**, the process is completed and summation of all problems returned.
This function computes iteration of F-transfrom and inverse F-transfotm and handle image and mask change. The function is used in *inpaint* function. This function computes iteration of F-transfrom and inverse F-transfotm and handle image and mask change. The function is used in *inpaint* function.
*/ */
CV_EXPORTS int FT02D_iteration(const Mat &image, const Mat &kernel, Mat &imageOutput, const Mat &mask, Mat &maskOutput, bool firstStop = true); CV_EXPORTS_W int FT02D_iteration(InputArray matrix, InputArray kernel, OutputArray output, InputArray mask, OutputArray maskOutput, bool firstStop);
//! @} //! @}
} }
......
modules/fuzzy/include/opencv2/fuzzy/fuzzy_image.hpp
View file @ 46f3b4d6
...@@ -61,7 +61,7 @@ namespace ft ...@@ -61,7 +61,7 @@ namespace ft
The function creates kernel usable for latter fuzzy image processing. The function creates kernel usable for latter fuzzy image processing.
*/ */
CV_EXPORTS void createKernel(cv::InputArray A, cv::InputArray B, cv::OutputArray kernel, const int chn = 1); CV_EXPORTS_AS(createKernel1) void createKernel(InputArray A, InputArray B, OutputArray kernel, const int chn);
/** @brief Creates kernel from general functions. /** @brief Creates kernel from general functions.
@param function Function type could be one of the following: @param function Function type could be one of the following:
...@@ -72,7 +72,7 @@ namespace ft ...@@ -72,7 +72,7 @@ namespace ft
The function creates kernel from predefined functions. The function creates kernel from predefined functions.
*/ */
CV_EXPORTS void createKernel(int function, int radius, cv::OutputArray kernel, const int chn = 1); CV_EXPORTS_W void createKernel(int function, int radius, OutputArray kernel, const int chn);
/** @brief Image inpainting /** @brief Image inpainting
@param image Input image. @param image Input image.
...@@ -91,16 +91,16 @@ namespace ft ...@@ -91,16 +91,16 @@ namespace ft
@note @note
The algorithms are described in paper @cite Perf:rec. The algorithms are described in paper @cite Perf:rec.
*/ */
CV_EXPORTS void inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int radius = 2, int function = ft::LINEAR, int algorithm = ft::ONE_STEP); CV_EXPORTS_W void inpaint(InputArray image, InputArray mask, OutputArray output, int radius, int function, int algorithm);
/** @brief Image filtering /** @brief Image filtering
@param image Input image. @param image Input image.
@param kernel Final 32-b kernel. @param kernel Final 32-bit kernel.
@param output Output 32-bit image. @param output Output 32-bit image.
Filtering of the input image by means of F-transform. Filtering of the input image by means of F-transform.
*/ */
CV_EXPORTS void filter(const cv::Mat &image, const cv::Mat &kernel, cv::Mat &output); CV_EXPORTS_W void filter(InputArray image, InputArray kernel, OutputArray output);
//! @} //! @}
} }
......
modules/fuzzy/src/fuzzy_F0_math.cpp
View file @ 46f3b4d6
This diff is collapsed.
modules/fuzzy/src/fuzzy_image.cpp
View file @ 46f3b4d6
...@@ -58,7 +58,7 @@ void ft::createKernel(InputArray A, InputArray B, OutputArray kernel, const int ...@@ -58,7 +58,7 @@ void ft::createKernel(InputArray A, InputArray B, OutputArray kernel, const int
merge(channels, kernel); merge(channels, kernel);
} }
void ft::createKernel(int function, int radius, cv::OutputArray kernel, const int chn) void ft::createKernel(int function, int radius, OutputArray kernel, const int chn)
{ {
int basicFunctionWidth = 2 * radius + 1; int basicFunctionWidth = 2 * radius + 1;
Mat kernelOneChannel; Mat kernelOneChannel;
...@@ -91,7 +91,7 @@ void ft::createKernel(int function, int radius, cv::OutputArray kernel, const in ...@@ -91,7 +91,7 @@ void ft::createKernel(int function, int radius, cv::OutputArray kernel, const in
merge(channels, kernel); merge(channels, kernel);
} }
void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int radius, int function, int algorithm) void ft::inpaint(InputArray image, InputArray mask, OutputArray output, int radius, int function, int algorithm)
{ {
if (algorithm == ft::ONE_STEP) if (algorithm == ft::ONE_STEP)
{ {
...@@ -99,42 +99,32 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int ...@@ -99,42 +99,32 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int
ft::createKernel(function, radius, kernel, image.channels()); ft::createKernel(function, radius, kernel, image.channels());
Mat processingInput; Mat processingInput;
image.convertTo(processingInput, CV_32F); image.getMat().convertTo(processingInput, CV_32F);
Mat processingOutput; ft::FT02D_process(image, kernel, output, mask);
ft::FT02D_process(processingInput, kernel, processingOutput, mask);
processingInput.copyTo(processingOutput, mask);
output = processingOutput; processingInput.copyTo(output, mask);
} }
else if (algorithm == ft::MULTI_STEP) else if (algorithm == ft::MULTI_STEP)
{ {
Mat kernel; Mat kernel;
Mat processingOutput;
Mat outpuMask;
int state = 0; int state = 0;
int currentRadius = radius; int currentRadius = radius;
Mat processingInput; Mat processingInput;
image.convertTo(processingInput, CV_32F); image.getMat().convertTo(processingInput, CV_32F);
Mat processingMask;
cvtColor(mask, processingMask, COLOR_BGR2GRAY);
do do
{ {
ft::createKernel(function, currentRadius, kernel, image.channels()); ft::createKernel(function, currentRadius, kernel, image.channels());
state = ft::FT02D_iteration(processingInput, kernel, processingOutput, processingMask, outpuMask, true); state = ft::FT02D_iteration(image, kernel, output, mask, noArray(), true);
currentRadius++; currentRadius++;
} }
while(state != 0); while(state != 0);
processingInput.copyTo(processingOutput, mask); processingInput.copyTo(output, mask);
output = processingOutput;
} }
else if (algorithm == ft::ITERATIVE) else if (algorithm == ft::ITERATIVE)
{ {
...@@ -144,14 +134,11 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int ...@@ -144,14 +134,11 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int
int state = 0; int state = 0;
int currentRadius = radius; int currentRadius = radius;
Mat originalImage;
image.convertTo(originalImage, CV_32F);
Mat processingInput; Mat processingInput;
image.convertTo(processingInput, CV_32F); image.getMat().convertTo(processingInput, CV_32F);
Mat processingMask; Mat processingMask;
cvtColor(mask, processingMask, COLOR_BGR2GRAY); mask.copyTo(processingMask);
do do
{ {
...@@ -168,11 +155,11 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int ...@@ -168,11 +155,11 @@ void ft::inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int
} }
while(state != 0); while(state != 0);
output = processingInput; processingInput.copyTo(output);
} }
} }
void ft::filter(const cv::Mat &image, const cv::Mat &kernel, cv::Mat &output) void ft::filter(InputArray image, InputArray kernel, OutputArray output)
{ {
Mat mask = Mat::ones(image.size(), CV_8U); Mat mask = Mat::ones(image.size(), CV_8U);
......
modules/fuzzy/test/test_f0.cpp 0 → 100644
View file @ 46f3b4d6
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// 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) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling,
// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of the copyright holders may not 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 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 std;
using namespace cv;
TEST(fuzzy_f0, components)
{
float arI[16][16] =
{
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 }
};
Mat I = Mat(16, 16, CV_32F, arI);
float arDemandedComp[9][9] =
{
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 }
};
Mat demandedComp = Mat(9, 9, CV_32F, arDemandedComp);
Mat kernel;
ft::createKernel(ft::LINEAR, 2, kernel, 1);
Mat f0comp;
ft::FT02D_components(I, kernel, f0comp);
double n1 = cvtest::norm(demandedComp, f0comp, NORM_INF);
EXPECT_DOUBLE_EQ(n1, 0);
}
TEST(fuzzy_f0, inversion)
{
float arI[16][16] =
{
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 },
{ 0, 0, 0, 10, 34, 57, 80, 104, 127, 150, 174, 197, 221, 244, 255, 255 }
};
Mat I = Mat(16, 16, CV_32F, arI);
float arDemandedO[16][16] =
{
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 },
{ 0, 1.25, 2.5, 18.125, 33.75, 57, 80.25, 103.625, 127, 150.375, 173.75, 197.25, 220.75, 236.5, 252.25, 253.625 }
};
Mat demandedO = Mat(16, 16, CV_32F, arDemandedO);
float arComp[9][9] =
{
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 },
{ 0, 2.5, 33.75, 80.25, 127, 173.75, 220.75, 252.25, 255 }
};
Mat comp = Mat(9, 9, CV_32F, arComp);
Mat kernel;
ft::createKernel(ft::LINEAR, 2, kernel, 1);
Mat O;
ft::FT02D_inverseFT(comp, kernel, O, 16, 16);
double n1 = cvtest::norm(demandedO, O, NORM_INF);
EXPECT_DOUBLE_EQ(n1, 0);
}
\ No newline at end of file
modules/fuzzy/test/test_image.cpp
View file @ 46f3b4d6
...@@ -40,84 +40,75 @@ ...@@ -40,84 +40,75 @@
//M*/ //M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
#include <string> #include <string>
using namespace std; using namespace std;
using namespace cv; using namespace cv;
class CV_FuzzyImageTest : public cvtest::BaseTest TEST(fuzzy_image, inpainting)
{
public:
CV_FuzzyImageTest();
~CV_FuzzyImageTest();
protected:
void run(int);
};
CV_FuzzyImageTest::CV_FuzzyImageTest()
{
}
CV_FuzzyImageTest::~CV_FuzzyImageTest() {}
void CV_FuzzyImageTest::run( int )
{ {
string folder = string(ts->get_data_path()) + "fuzzy/"; string folder = string(cvtest::TS::ptr()->get_data_path()) + "fuzzy/";
Mat orig = imread(folder + "orig.png"); Mat orig = imread(folder + "orig.png");
Mat exp1 = imread(folder + "exp1.png"); Mat exp1 = imread(folder + "exp1.png");
Mat exp2 = imread(folder + "exp2.png"); Mat exp2 = imread(folder + "exp2.png");
Mat exp3 = imread(folder + "exp3.png"); Mat exp3 = imread(folder + "exp3.png");
Mat mask1 = imread(folder + "mask1.png"); Mat mask1 = imread(folder + "mask1.png", IMREAD_GRAYSCALE);
Mat mask2 = imread(folder + "mask2.png"); Mat mask2 = imread(folder + "mask2.png", IMREAD_GRAYSCALE);
if (orig.empty() || exp1.empty() || exp2.empty() || mask1.empty() || mask2.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
// Conversion because of comparison. EXPECT_TRUE(!orig.empty() && !exp1.empty() && !exp2.empty() && !exp3.empty() && !mask1.empty() && !mask2.empty());
orig.convertTo(orig, CV_32F); Mat res1, res2, res3;
exp1.convertTo(exp1, CV_32F);
exp2.convertTo(exp2, CV_32F);
exp3.convertTo(exp3, CV_32F);
Mat res1, res2,res3;
ft::inpaint(orig, mask1, res1, 2, ft::LINEAR, ft::ONE_STEP); ft::inpaint(orig, mask1, res1, 2, ft::LINEAR, ft::ONE_STEP);
ft::inpaint(orig, mask2, res2, 2, ft::LINEAR, ft::MULTI_STEP); ft::inpaint(orig, mask2, res2, 2, ft::LINEAR, ft::MULTI_STEP);
ft::inpaint(orig, mask2, res3, 2, ft::LINEAR, ft::ITERATIVE); ft::inpaint(orig, mask2, res3, 2, ft::LINEAR, ft::ITERATIVE);
Mat diff1, diff2, diff3; res1.convertTo(res1, CV_8UC3);
absdiff(orig, res1, diff1); res2.convertTo(res2, CV_8UC3);
absdiff(orig, res2, diff2); res3.convertTo(res3, CV_8UC3);
absdiff(orig, res3, diff3);
double n1 = cvtest::norm(exp1, res1, NORM_INF);
double n1 = cvtest::norm(diff1.reshape(1), NORM_INF, mask1.reshape(1)); double n2 = cvtest::norm(exp2, res2, NORM_INF);
double n2 = cvtest::norm(diff2.reshape(1), NORM_INF, mask2.reshape(1)); double n3 = cvtest::norm(exp3, res3, NORM_INF);
double n3 = cvtest::norm(diff3.reshape(1), NORM_INF, mask2.reshape(1));
EXPECT_LE(n1, 1);
if (n1 != 0 || n2 != 0 || n3 != 0) EXPECT_LE(n2, 1);
{ EXPECT_LE(n3, 1);
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH ); }
return;
} TEST(fuzzy_image, filtering)
{
absdiff(exp1, res1, diff1); string folder = string(cvtest::TS::ptr()->get_data_path()) + "fuzzy/";
absdiff(exp2, res2, diff2); Mat orig = imread(folder + "orig.png");
absdiff(exp3, res3, diff3); Mat exp4 = imread(folder + "exp4.png");
n1 = cvtest::norm(diff1.reshape(1), NORM_INF, mask1.reshape(1)); EXPECT_TRUE(!orig.empty() && !exp4.empty());
n2 = cvtest::norm(diff2.reshape(1), NORM_INF, mask2.reshape(1));
n3 = cvtest::norm(diff3.reshape(1), NORM_INF, mask2.reshape(1)); Mat kernel;
ft::createKernel(ft::LINEAR, 20, kernel, 3);
const int jpeg_thres = 3;
if (n1 > jpeg_thres || n2 > jpeg_thres || n3 > jpeg_thres) Mat res4;
{ ft::filter(orig, kernel, res4);
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return; res4.convertTo(res4, CV_8UC3);
}
double n1 = cvtest::norm(exp4, res4, NORM_INF);
ts->set_failed_test_info(cvtest::TS::OK);
EXPECT_LE(n1, 1);
} }
TEST(Fuzzy_image, regression) { CV_FuzzyImageTest test; test.safe_run(); } TEST(fuzzy_image, kernel)
{
Mat kernel1;
ft::createKernel(ft::LINEAR, 2, kernel1, 1);
Mat vector1 = (Mat_<float>(5, 1) << 0, 0.5, 1, 0.5, 0);
Mat vector2 = (Mat_<float>(1, 5) << 0, 0.5, 1, 0.5, 0);
Mat kernel2;
ft::createKernel(vector1, vector2, kernel2, 1);
double diff = cvtest::norm(kernel1, kernel2, NORM_INF);
EXPECT_DOUBLE_EQ(diff, 0);
}
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