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feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

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1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
This commit is contained in:
wangzhengyang
2022-05-10 09:54:44 +08:00
parent ecdd171c6f
commit 718c41634f
10018 changed files with 3593797 additions and 186748 deletions
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3rdparty/opencv-4.5.4/modules/photo/CMakeLists.txt vendored Normal file
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set(the_description "Computational Photography")
if(HAVE_CUDA)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations -Wshadow)
endif()
ocv_define_module(photo opencv_imgproc OPTIONAL opencv_cudaarithm opencv_cudaimgproc WRAP java objc python js)

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3rdparty/opencv-4.5.4/modules/photo/include/opencv2/photo.hpp vendored Normal file
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/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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*/
#ifndef OPENCV_PHOTO_HPP
#define OPENCV_PHOTO_HPP
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
/**
@defgroup photo Computational Photography
This module includes photo processing algorithms
@{
@defgroup photo_inpaint Inpainting
@defgroup photo_denoise Denoising
@defgroup photo_hdr HDR imaging
This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
camera calibration with multiple exposures and exposure fusion.
@defgroup photo_decolor Contrast Preserving Decolorization
Useful links:
http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
@defgroup photo_clone Seamless Cloning
Useful links:
https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp
@defgroup photo_render Non-Photorealistic Rendering
Useful links:
http://www.inf.ufrgs.br/~eslgastal/DomainTransform
https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/
@defgroup photo_c C API
@}
*/
namespace cv
{
//! @addtogroup photo
//! @{
//! @addtogroup photo_inpaint
//! @{
//! the inpainting algorithm
enum
{
INPAINT_NS = 0, //!< Use Navier-Stokes based method
INPAINT_TELEA = 1 //!< Use the algorithm proposed by Alexandru Telea @cite Telea04
};
/** @brief Restores the selected region in an image using the region neighborhood.
@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
needs to be inpainted.
@param dst Output image with the same size and type as src .
@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
by the algorithm.
@param flags Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA
The function reconstructs the selected image area from the pixel near the area boundary. The
function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
@note
- An example using the inpainting technique can be found at
opencv_source_code/samples/cpp/inpaint.cpp
- (Python) An example using the inpainting technique can be found at
opencv_source_code/samples/python/inpaint.py
*/
CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
OutputArray dst, double inpaintRadius, int flags );
//! @} photo_inpaint
//! @addtogroup photo_denoise
//! @{
/** @brief Perform image denoising using Non-local Means Denoising algorithm
<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
optimizations. Noise expected to be a gaussian white noise
@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise
This function expected to be applied to grayscale images. For colored images look at
fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
image to CIELAB colorspace and then separately denoise L and AB components with different h
parameter.
*/
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Perform image denoising using Non-local Means Denoising algorithm
<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
optimizations. Noise expected to be a gaussian white noise
@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
2-channel, 3-channel or 4-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Array of parameters regulating filter strength, either one
parameter applied to all channels or one per channel in dst. Big h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
This function expected to be applied to grayscale images. For colored images look at
fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
image to CIELAB colorspace and then separately denoise L and AB components with different h
parameter.
*/
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
const std::vector<float>& h,
int templateWindowSize = 7, int searchWindowSize = 21,
int normType = NORM_L2);
/** @brief Modification of fastNlMeansDenoising function for colored images
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
removes noise but also removes image details, smaller h value preserves details but also preserves
some noise
@param hColor The same as h but for color components. For most images value equals 10
will be enough to remove colored noise and do not distort colors
The function converts image to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using fastNlMeansDenoising function.
*/
CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
captured in small period of time. For example video. This version of the function is for grayscale
images or for manual manipulation with colorspaces. For more details see
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
4-channel images sequence. All images should have the same type and
size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength. Bigger h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
*/
CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
captured in small period of time. For example video. This version of the function is for grayscale
images or for manual manipulation with colorspaces. For more details see
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
2-channel, 3-channel or 4-channel images sequence. All images should
have the same type and size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Array of parameters regulating filter strength, either one
parameter applied to all channels or one per channel in dst. Big h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
*/
CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
const std::vector<float>& h,
int templateWindowSize = 7, int searchWindowSize = 21,
int normType = NORM_L2);
/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
removes noise but also removes image details, smaller h value preserves details but also preserves
some noise.
@param hColor The same as h but for color components.
The function converts images to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using fastNlMeansDenoisingMulti function.
*/
CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
finding a function to minimize some functional). As the image denoising, in particular, may be seen
as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
exactly what is implemented.
It should be noted, that this implementation was taken from the July 2013 blog entry
@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
of July 2013 and finally it was slightly adapted by later authors.
Although the thorough discussion and justification of the algorithm involved may be found in
@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
pixels (it may be seen as set
\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
@param observations This array should contain one or more noised versions of the image that is to
be restored.
@param result Here the denoised image will be stored. There is no need to do pre-allocation of
storage space, as it will be automatically allocated, if necessary.
@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
removed.
@param niters Number of iterations that the algorithm will run. Of course, as more iterations as
better, but it is hard to quantitatively refine this statement, so just use the default and
increase it if the results are poor.
*/
CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
//! @} photo_denoise
//! @addtogroup photo_hdr
//! @{
enum { LDR_SIZE = 256 };
/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
*/
class CV_EXPORTS_W Tonemap : public Algorithm
{
public:
/** @brief Tonemaps image
@param src source image - CV_32FC3 Mat (float 32 bits 3 channels)
@param dst destination image - CV_32FC3 Mat with values in [0, 1] range
*/
CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
CV_WRAP virtual float getGamma() const = 0;
CV_WRAP virtual void setGamma(float gamma) = 0;
};
/** @brief Creates simple linear mapper with gamma correction
@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
equal to 2.2f is suitable for most displays.
Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
*/
CV_EXPORTS_W Ptr<Tonemap> createTonemap(float gamma = 1.0f);
/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
logarithmic domain.
Since it's a global operator the same function is applied to all the pixels, it is controlled by the
bias parameter.
Optional saturation enhancement is possible as described in @cite FL02 .
For more information see @cite DM03 .
*/
class CV_EXPORTS_W TonemapDrago : public Tonemap
{
public:
CV_WRAP virtual float getSaturation() const = 0;
CV_WRAP virtual void setSaturation(float saturation) = 0;
CV_WRAP virtual float getBias() const = 0;
CV_WRAP virtual void setBias(float bias) = 0;
};
/** @brief Creates TonemapDrago object
@param gamma gamma value for gamma correction. See createTonemap
@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
than 1 increase saturation and values less than 1 decrease it.
@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
results, default value is 0.85.
*/
CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f);
/** @brief This is a global tonemapping operator that models human visual system.
Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
color adaptation.
For more information see @cite RD05 .
*/
class CV_EXPORTS_W TonemapReinhard : public Tonemap
{
public:
CV_WRAP virtual float getIntensity() const = 0;
CV_WRAP virtual void setIntensity(float intensity) = 0;
CV_WRAP virtual float getLightAdaptation() const = 0;
CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
CV_WRAP virtual float getColorAdaptation() const = 0;
CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
};
/** @brief Creates TonemapReinhard object
@param gamma gamma value for gamma correction. See createTonemap
@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
value, if 0 it's global, otherwise it's a weighted mean of this two cases.
@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
if 0 adaptation level is the same for each channel.
*/
CV_EXPORTS_W Ptr<TonemapReinhard>
createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
transforms contrast values to HVS response and scales the response. After this the image is
reconstructed from new contrast values.
For more information see @cite MM06 .
*/
class CV_EXPORTS_W TonemapMantiuk : public Tonemap
{
public:
CV_WRAP virtual float getScale() const = 0;
CV_WRAP virtual void setScale(float scale) = 0;
CV_WRAP virtual float getSaturation() const = 0;
CV_WRAP virtual void setSaturation(float saturation) = 0;
};
/** @brief Creates TonemapMantiuk object
@param gamma gamma value for gamma correction. See createTonemap
@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
dynamic range. Values from 0.6 to 0.9 produce best results.
@param saturation saturation enhancement value. See createTonemapDrago
*/
CV_EXPORTS_W Ptr<TonemapMantiuk>
createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f);
/** @brief The base class for algorithms that align images of the same scene with different exposures
*/
class CV_EXPORTS_W AlignExposures : public Algorithm
{
public:
/** @brief Aligns images
@param src vector of input images
@param dst vector of aligned images
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.
*/
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
InputArray times, InputArray response) = 0;
};
/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
It is invariant to exposure, so exposure values and camera response are not necessary.
In this implementation new image regions are filled with zeros.
For more information see @cite GW03 .
*/
class CV_EXPORTS_W AlignMTB : public AlignExposures
{
public:
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
InputArray times, InputArray response) CV_OVERRIDE = 0;
/** @brief Short version of process, that doesn't take extra arguments.
@param src vector of input images
@param dst vector of aligned images
*/
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst) = 0;
/** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
first.
@param img0 first image
@param img1 second image
*/
CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0;
/** @brief Helper function, that shift Mat filling new regions with zeros.
@param src input image
@param dst result image
@param shift shift value
*/
CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0;
/** @brief Computes median threshold and exclude bitmaps of given image.
@param img input image
@param tb median threshold bitmap
@param eb exclude bitmap
*/
CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0;
CV_WRAP virtual int getMaxBits() const = 0;
CV_WRAP virtual void setMaxBits(int max_bits) = 0;
CV_WRAP virtual int getExcludeRange() const = 0;
CV_WRAP virtual void setExcludeRange(int exclude_range) = 0;
CV_WRAP virtual bool getCut() const = 0;
CV_WRAP virtual void setCut(bool value) = 0;
};
/** @brief Creates AlignMTB object
@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
usually good enough (31 and 63 pixels shift respectively).
@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
median value.
@param cut if true cuts images, otherwise fills the new regions with zeros.
*/
CV_EXPORTS_W Ptr<AlignMTB> createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true);
/** @brief The base class for camera response calibration algorithms.
*/
class CV_EXPORTS_W CalibrateCRF : public Algorithm
{
public:
/** @brief Recovers inverse camera response.
@param src vector of input images
@param dst 256x1 matrix with inverse camera response function
@param times vector of exposure time values for each image
*/
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
};
/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system. Objective function is constructed using pixel values on the same position
in all images, extra term is added to make the result smoother.
For more information see @cite DM97 .
*/
class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF
{
public:
CV_WRAP virtual float getLambda() const = 0;
CV_WRAP virtual void setLambda(float lambda) = 0;
CV_WRAP virtual int getSamples() const = 0;
CV_WRAP virtual void setSamples(int samples) = 0;
CV_WRAP virtual bool getRandom() const = 0;
CV_WRAP virtual void setRandom(bool random) = 0;
};
/** @brief Creates CalibrateDebevec object
@param samples number of pixel locations to use
@param lambda smoothness term weight. Greater values produce smoother results, but can alter the
response.
@param random if true sample pixel locations are chosen at random, otherwise they form a
rectangular grid.
*/
CV_EXPORTS_W Ptr<CalibrateDebevec> createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false);
/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system. This algorithm uses all image pixels.
For more information see @cite RB99 .
*/
class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF
{
public:
CV_WRAP virtual int getMaxIter() const = 0;
CV_WRAP virtual void setMaxIter(int max_iter) = 0;
CV_WRAP virtual float getThreshold() const = 0;
CV_WRAP virtual void setThreshold(float threshold) = 0;
CV_WRAP virtual Mat getRadiance() const = 0;
};
/** @brief Creates CalibrateRobertson object
@param max_iter maximal number of Gauss-Seidel solver iterations.
@param threshold target difference between results of two successive steps of the minimization.
*/
CV_EXPORTS_W Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f);
/** @brief The base class algorithms that can merge exposure sequence to a single image.
*/
class CV_EXPORTS_W MergeExposures : public Algorithm
{
public:
/** @brief Merges images.
@param src vector of input images
@param dst result image
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.
*/
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
InputArray times, InputArray response) = 0;
};
/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
For more information see @cite DM97 .
*/
class CV_EXPORTS_W MergeDebevec : public MergeExposures
{
public:
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
InputArray times, InputArray response) CV_OVERRIDE = 0;
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
};
/** @brief Creates MergeDebevec object
*/
CV_EXPORTS_W Ptr<MergeDebevec> createMergeDebevec();
/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
combined using laplacian pyramids.
The resulting image weight is constructed as weighted average of contrast, saturation and
well-exposedness measures.
The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
For more information see @cite MK07 .
*/
class CV_EXPORTS_W MergeMertens : public MergeExposures
{
public:
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
InputArray times, InputArray response) CV_OVERRIDE = 0;
/** @brief Short version of process, that doesn't take extra arguments.
@param src vector of input images
@param dst result image
*/
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0;
CV_WRAP virtual float getContrastWeight() const = 0;
CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0;
CV_WRAP virtual float getSaturationWeight() const = 0;
CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0;
CV_WRAP virtual float getExposureWeight() const = 0;
CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0;
};
/** @brief Creates MergeMertens object
@param contrast_weight contrast measure weight. See MergeMertens.
@param saturation_weight saturation measure weight
@param exposure_weight well-exposedness measure weight
*/
CV_EXPORTS_W Ptr<MergeMertens>
createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f);
/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
For more information see @cite RB99 .
*/
class CV_EXPORTS_W MergeRobertson : public MergeExposures
{
public:
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
InputArray times, InputArray response) CV_OVERRIDE = 0;
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
};
/** @brief Creates MergeRobertson object
*/
CV_EXPORTS_W Ptr<MergeRobertson> createMergeRobertson();
//! @} photo_hdr
//! @addtogroup photo_decolor
//! @{
/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
black-and-white photograph rendering, and in many single channel image processing applications
@cite CL12 .
@param src Input 8-bit 3-channel image.
@param grayscale Output 8-bit 1-channel image.
@param color_boost Output 8-bit 3-channel image.
This function is to be applied on color images.
*/
CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost);
//! @} photo_decolor
//! @addtogroup photo_clone
//! @{
//! seamlessClone algorithm flags
enum
{
/** The power of the method is fully expressed when inserting objects with complex outlines into a new background*/
NORMAL_CLONE = 1,
/** The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable
halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective.*/
MIXED_CLONE = 2,
/** Monochrome transfer allows the user to easily replace certain features of one object by alternative features.*/
MONOCHROME_TRANSFER = 3};
/** @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp
An example using seamlessClone function
*/
/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
deformations) or local changes concerned to a selection. Here we are interested in achieving local
changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
manner. The extent of the changes ranges from slight distortions to complete replacement by novel
content @cite PM03 .
@param src Input 8-bit 3-channel image.
@param dst Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param p Point in dst image where object is placed.
@param blend Output image with the same size and type as dst.
@param flags Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER
*/
CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p,
OutputArray blend, int flags);
/** @brief Given an original color image, two differently colored versions of this image can be mixed
seamlessly.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src .
@param red_mul R-channel multiply factor.
@param green_mul G-channel multiply factor.
@param blue_mul B-channel multiply factor.
Multiplication factor is between .5 to 2.5.
*/
CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f,
float green_mul = 1.0f, float blue_mul = 1.0f);
/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src.
@param alpha Value ranges between 0-2.
@param beta Value ranges between 0-2.
This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
*/
CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst,
float alpha = 0.2f, float beta = 0.4f);
/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src.
@param low_threshold %Range from 0 to 100.
@param high_threshold Value \> 100.
@param kernel_size The size of the Sobel kernel to be used.
@note
The algorithm assumes that the color of the source image is close to that of the destination. This
assumption means that when the colors don't match, the source image color gets tinted toward the
color of the destination image.
*/
CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
float low_threshold = 30, float high_threshold = 45,
int kernel_size = 3);
//! @} photo_clone
//! @addtogroup photo_render
//! @{
//! Edge preserving filters
enum
{
RECURS_FILTER = 1, //!< Recursive Filtering
NORMCONV_FILTER = 2 //!< Normalized Convolution Filtering
};
/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
filters are used in many different applications @cite EM11 .
@param src Input 8-bit 3-channel image.
@param dst Output 8-bit 3-channel image.
@param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
@param sigma_s %Range between 0 to 200.
@param sigma_r %Range between 0 to 1.
*/
CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
float sigma_s = 60, float sigma_r = 0.4f);
/** @brief This filter enhances the details of a particular image.
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src.
@param sigma_s %Range between 0 to 200.
@param sigma_r %Range between 0 to 1.
*/
CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
float sigma_r = 0.15f);
/** @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp
An example using non-photorealistic line drawing functions
*/
/** @brief Pencil-like non-photorealistic line drawing
@param src Input 8-bit 3-channel image.
@param dst1 Output 8-bit 1-channel image.
@param dst2 Output image with the same size and type as src.
@param sigma_s %Range between 0 to 200.
@param sigma_r %Range between 0 to 1.
@param shade_factor %Range between 0 to 0.1.
*/
CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2,
float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f);
/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
contrast while preserving, or enhancing, high-contrast features.
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src.
@param sigma_s %Range between 0 to 200.
@param sigma_r %Range between 0 to 1.
*/
CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60,
float sigma_r = 0.45f);
//! @} photo_render
//! @} photo
} // cv
#endif

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/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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*/
#ifndef OPENCV_PHOTO_CUDA_HPP
#define OPENCV_PHOTO_CUDA_HPP
#include "opencv2/core/cuda.hpp"
namespace cv { namespace cuda {
//! @addtogroup photo_denoise
//! @{
/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast.
@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
@param dst Destination image.
@param h Filter sigma regulating filter strength for color.
@param search_window Size of search window.
@param block_size Size of block used for computing weights.
@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
@param stream Stream for the asynchronous version.
@sa
fastNlMeansDenoising
*/
CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
float h,
int search_window = 21,
int block_size = 7,
int borderMode = BORDER_DEFAULT,
Stream& stream = Stream::Null());
/** @brief Perform image denoising using Non-local Means Denoising algorithm
<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
optimizations. Noise expected to be a gaussian white noise
@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
@param dst Output image with the same size and type as src .
@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param stream Stream for the asynchronous invocations.
This function expected to be applied to grayscale images. For colored images look at
FastNonLocalMeansDenoising::labMethod.
@sa
fastNlMeansDenoising
*/
CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
float h,
int search_window = 21,
int block_size = 7,
Stream& stream = Stream::Null());
/** @brief Modification of fastNlMeansDenoising function for colored images
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src .
@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
also removes image details, smaller h value preserves details but also preserves some noise
@param photo_render float The same as h but for color components. For most images value equals 10 will be
enough to remove colored noise and do not distort colors
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param stream Stream for the asynchronous invocations.
The function converts image to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
@sa
fastNlMeansDenoisingColored
*/
CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
float h_luminance, float photo_render,
int search_window = 21,
int block_size = 7,
Stream& stream = Stream::Null());
//! @} photo
}} // namespace cv { namespace cuda {
#endif /* OPENCV_PHOTO_CUDA_HPP */

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_PHOTO_LEGACY_CONSTANTS_H
#define OPENCV_PHOTO_LEGACY_CONSTANTS_H
enum InpaintingModes
{
CV_INPAINT_NS =0,
CV_INPAINT_TELEA =1
};
#endif // OPENCV_PHOTO_LEGACY_CONSTANTS_H

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/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/photo.hpp"

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package org.opencv.test.photo;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Point;
import org.opencv.photo.Photo;
import org.opencv.test.OpenCVTestCase;
import org.opencv.imgproc.Imgproc;
public class PhotoTest extends OpenCVTestCase {
public void testInpaint() {
Point p = new Point(matSize / 2, matSize / 2);
Imgproc.circle(gray255, p, 2, colorBlack, Imgproc.FILLED);
Imgproc.circle(gray0, p, 2, colorWhite, Imgproc.FILLED);
Photo.inpaint(gray255, gray0, dst, 3, Photo.INPAINT_TELEA);
assertMatEqual(getMat(CvType.CV_8U, 255), dst);
}
}

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{
"func_arg_fix" : {
"Photo": {
"(void)fastNlMeansDenoising:(Mat*)src dst:(Mat*)dst h:(FloatVector*)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize normType:(int)normType" : { "h" : { "name" : "hVector" } },
"(void)fastNlMeansDenoisingMulti:(NSArray<Mat*>*)srcImgs dst:(Mat*)dst imgToDenoiseIndex:(int)imgToDenoiseIndex temporalWindowSize:(int)temporalWindowSize h:(FloatVector*)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize normType:(int)normType" : { "h" : { "name" : "hVector" } }
}
}
}

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
OCL_PERF_TEST(Photo, DenoisingGrayscale)
{
Mat _original = imread(getDataPath("cv/denoising/lena_noised_gaussian_sigma=10.png"), IMREAD_GRAYSCALE);
ASSERT_FALSE(_original.empty()) << "Could not load input image";
UMat result(_original.size(), _original.type()), original;
_original.copyTo(original);
declare.in(original).out(result).iterations(10);
OCL_TEST_CYCLE()
cv::fastNlMeansDenoising(original, result, 10);
SANITY_CHECK(result, 1);
}
OCL_PERF_TEST(Photo, DenoisingColored)
{
Mat _original = imread(getDataPath("cv/denoising/lena_noised_gaussian_sigma=10.png"));
ASSERT_FALSE(_original.empty()) << "Could not load input image";
UMat result(_original.size(), _original.type()), original;
_original.copyTo(original);
declare.in(original).out(result).iterations(10);
OCL_TEST_CYCLE()
cv::fastNlMeansDenoisingColored(original, result, 10, 10);
SANITY_CHECK(result, 2);
}
OCL_PERF_TEST(Photo, DISABLED_DenoisingGrayscaleMulti)
{
const int imgs_count = 3;
vector<UMat> original(imgs_count);
Mat tmp;
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("cv/denoising/lena_noised_gaussian_sigma=20_multi_%d.png", i);
tmp = imread(getDataPath(original_path), IMREAD_GRAYSCALE);
ASSERT_FALSE(tmp.empty()) << "Could not load input image " << original_path;
tmp.copyTo(original[i]);
declare.in(original[i]);
}
UMat result(tmp.size(), tmp.type());
declare.out(result).iterations(10);
OCL_TEST_CYCLE()
cv::fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15);
SANITY_CHECK(result);
}
OCL_PERF_TEST(Photo, DISABLED_DenoisingColoredMulti)
{
const int imgs_count = 3;
vector<UMat> original(imgs_count);
Mat tmp;
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("cv/denoising/lena_noised_gaussian_sigma=20_multi_%d.png", i);
tmp = imread(getDataPath(original_path), IMREAD_COLOR);
ASSERT_FALSE(tmp.empty()) << "Could not load input image " << original_path;
tmp.copyTo(original[i]);
declare.in(original[i]);
}
UMat result(tmp.size(), tmp.type());
declare.out(result).iterations(10);
OCL_TEST_CYCLE()
cv::fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15);
SANITY_CHECK(result);
}
} } // namespace opencv_test::ocl
#endif // HAVE_OPENCL

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/*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) 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.
//
// 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 "perf_precomp.hpp"
#include "opencv2/photo/cuda.hpp"
#include "opencv2/ts/cuda_perf.hpp"
#include "opencv2/opencv_modules.hpp"
#if defined (HAVE_CUDA) && defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAIMGPROC)
namespace opencv_test { namespace {
using namespace perf;
#define CUDA_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
// nonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, CUDA_NonLocalMeans,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
CUDA_CHANNELS_1_3,
Values(21),
Values(5)))
{
declare.time(600.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int search_widow_size = GET_PARAM(3);
const int block_size = GET_PARAM(4);
const float h = 10;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode);
CUDA_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, CUDA_FastNonLocalMeans,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
CUDA_CHANNELS_1_3,
Values(21),
Values(7)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::fastNlMeansDenoising(d_src, dst, h, search_widow_size, block_size);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans (colored)
DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int);
PERF_TEST_P(Sz_Depth_WinSz_BlockSz, CUDA_FastNonLocalMeansColored,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
Values(21),
Values(7)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 3);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::fastNlMeansDenoisingColored(d_src, dst, h, h, search_widow_size, block_size);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}
}} // namespace
#endif

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#include "perf_precomp.hpp"
namespace opencv_test
{
CV_ENUM(InpaintingMethod, INPAINT_NS, INPAINT_TELEA)
typedef tuple<Size, InpaintingMethod> InpaintArea_InpaintingMethod_t;
typedef perf::TestBaseWithParam<InpaintArea_InpaintingMethod_t> InpaintArea_InpaintingMethod;
PERF_TEST_P(InpaintArea_InpaintingMethod, inpaint,
testing::Combine(
testing::Values(::perf::szSmall24, ::perf::szSmall32, ::perf::szSmall64),
InpaintingMethod::all()
)
)
{
Mat src = imread(getDataPath("gpu/hog/road.png"));
Size sz = get<0>(GetParam());
int inpaintingMethod = get<1>(GetParam());
Mat mask(src.size(), CV_8UC1, Scalar(0));
Mat result(src.size(), src.type());
Rect inpaintArea(src.cols/3, src.rows/3, sz.width, sz.height);
mask(inpaintArea).setTo(255);
declare.in(src, mask).out(result).time(120);
TEST_CYCLE() inpaint(src, mask, result, 10.0, inpaintingMethod);
Mat inpaintedArea = result(inpaintArea);
SANITY_CHECK(inpaintedArea);
}
} // namespace

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#include "perf_precomp.hpp"
#include "opencv2/ts/cuda_perf.hpp"
static const char * impls[] = {
#ifdef HAVE_CUDA
"cuda",
#endif
"plain"
};
#if defined(HAVE_HPX)
#include <hpx/hpx_main.hpp>
#endif
CV_PERF_TEST_MAIN_WITH_IMPLS(photo, impls, perf::printCudaInfo())

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#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/photo.hpp"
#endif

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
#include "hdr_common.hpp"
namespace cv
{
class AlignMTBImpl CV_FINAL : public AlignMTB
{
public:
AlignMTBImpl(int _max_bits, int _exclude_range, bool _cut) :
name("AlignMTB"),
max_bits(_max_bits),
exclude_range(_exclude_range),
cut(_cut)
{
}
void process(InputArrayOfArrays src, std::vector<Mat>& dst,
InputArray, InputArray) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
process(src, dst);
}
void process(InputArrayOfArrays _src, std::vector<Mat>& dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
std::vector<Mat> src;
_src.getMatVector(src);
checkImageDimensions(src);
dst.resize(src.size());
size_t pivot = src.size() / 2;
dst[pivot] = src[pivot];
Mat gray_base;
cvtColor(src[pivot], gray_base, COLOR_RGB2GRAY);
std::vector<Point> shifts;
for(size_t i = 0; i < src.size(); i++) {
if(i == pivot) {
shifts.push_back(Point(0, 0));
continue;
}
Mat gray;
cvtColor(src[i], gray, COLOR_RGB2GRAY);
Point shift = calculateShift(gray_base, gray);
shifts.push_back(shift);
shiftMat(src[i], dst[i], shift);
}
if(cut) {
Point max(0, 0), min(0, 0);
for(size_t i = 0; i < shifts.size(); i++) {
if(shifts[i].x > max.x) {
max.x = shifts[i].x;
}
if(shifts[i].y > max.y) {
max.y = shifts[i].y;
}
if(shifts[i].x < min.x) {
min.x = shifts[i].x;
}
if(shifts[i].y < min.y) {
min.y = shifts[i].y;
}
}
Point size = dst[0].size();
for(size_t i = 0; i < dst.size(); i++) {
dst[i] = dst[i](Rect(max, min + size));
}
}
}
Point calculateShift(InputArray _img0, InputArray _img1) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat img0 = _img0.getMat();
Mat img1 = _img1.getMat();
CV_Assert(img0.channels() == 1 && img0.type() == img1.type());
CV_Assert(img0.size() == img1.size());
int maxlevel = static_cast<int>(log((double)max(img0.rows, img0.cols)) / log(2.0)) - 1;
maxlevel = min(maxlevel, max_bits - 1);
std::vector<Mat> pyr0;
std::vector<Mat> pyr1;
buildPyr(img0, pyr0, maxlevel);
buildPyr(img1, pyr1, maxlevel);
Point shift(0, 0);
for(int level = maxlevel; level >= 0; level--) {
shift *= 2;
Mat tb1, tb2, eb1, eb2;
computeBitmaps(pyr0[level], tb1, eb1);
computeBitmaps(pyr1[level], tb2, eb2);
int min_err = (int)pyr0[level].total();
Point new_shift(shift);
for(int i = -1; i <= 1; i++) {
for(int j = -1; j <= 1; j++) {
Point test_shift = shift + Point(i, j);
Mat shifted_tb2, shifted_eb2, diff;
shiftMat(tb2, shifted_tb2, test_shift);
shiftMat(eb2, shifted_eb2, test_shift);
bitwise_xor(tb1, shifted_tb2, diff);
bitwise_and(diff, eb1, diff);
bitwise_and(diff, shifted_eb2, diff);
int err = countNonZero(diff);
if(err < min_err) {
new_shift = test_shift;
min_err = err;
}
}
}
shift = new_shift;
}
return shift;
}
void shiftMat(InputArray _src, OutputArray _dst, const Point shift) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();
Mat res = Mat::zeros(src.size(), src.type());
int width = src.cols - abs(shift.x);
int height = src.rows - abs(shift.y);
Rect dst_rect(max(shift.x, 0), max(shift.y, 0), width, height);
Rect src_rect(max(-shift.x, 0), max(-shift.y, 0), width, height);
src(src_rect).copyTo(res(dst_rect));
res.copyTo(dst);
}
int getMaxBits() const CV_OVERRIDE { return max_bits; }
void setMaxBits(int val) CV_OVERRIDE { max_bits = val; }
int getExcludeRange() const CV_OVERRIDE { return exclude_range; }
void setExcludeRange(int val) CV_OVERRIDE { exclude_range = val; }
bool getCut() const CV_OVERRIDE { return cut; }
void setCut(bool val) CV_OVERRIDE { cut = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "max_bits" << max_bits
<< "exclude_range" << exclude_range
<< "cut" << static_cast<int>(cut);
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
max_bits = fn["max_bits"];
exclude_range = fn["exclude_range"];
int cut_val = fn["cut"];
cut = (cut_val != 0);
}
void computeBitmaps(InputArray _img, OutputArray _tb, OutputArray _eb) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat img = _img.getMat();
_tb.create(img.size(), CV_8U);
_eb.create(img.size(), CV_8U);
Mat tb = _tb.getMat(), eb = _eb.getMat();
int median = getMedian(img);
compare(img, median, tb, CMP_GT);
compare(abs(img - median), exclude_range, eb, CMP_GT);
}
protected:
String name;
int max_bits, exclude_range;
bool cut;
void downsample(Mat& src, Mat& dst)
{
dst = Mat(src.rows / 2, src.cols / 2, CV_8UC1);
int offset = src.cols * 2;
uchar *src_ptr = src.ptr();
uchar *dst_ptr = dst.ptr();
for(int y = 0; y < dst.rows; y ++) {
uchar *ptr = src_ptr;
for(int x = 0; x < dst.cols; x++) {
dst_ptr[0] = ptr[0];
dst_ptr++;
ptr += 2;
}
src_ptr += offset;
}
}
void buildPyr(Mat& img, std::vector<Mat>& pyr, int maxlevel)
{
pyr.resize(maxlevel + 1);
pyr[0] = img.clone();
for(int level = 0; level < maxlevel; level++) {
downsample(pyr[level], pyr[level + 1]);
}
}
int getMedian(Mat& img)
{
int channels = 0;
Mat hist;
int hist_size = LDR_SIZE;
float range[] = {0, LDR_SIZE} ;
const float* ranges[] = {range};
calcHist(&img, 1, &channels, Mat(), hist, 1, &hist_size, ranges);
float *ptr = hist.ptr<float>();
int median = 0, sum = 0;
int thresh = (int)img.total() / 2;
while(sum < thresh && median < LDR_SIZE) {
sum += static_cast<int>(ptr[median]);
median++;
}
return median;
}
};
Ptr<AlignMTB> createAlignMTB(int max_bits, int exclude_range, bool cut)
{
return makePtr<AlignMTBImpl>(max_bits, exclude_range, cut);
}
}

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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 "opencv2/core/base.hpp"
#ifndef __OPENCV_DENOISING_ARRAYS_HPP__
#define __OPENCV_DENOISING_ARRAYS_HPP__
namespace cv
{
template <class T>
struct Array2d
{
T* a;
int n1,n2;
bool needToDeallocArray;
Array2d(const Array2d& array2d):
a(array2d.a), n1(array2d.n1), n2(array2d.n2), needToDeallocArray(false)
{
if (array2d.needToDeallocArray)
{
CV_Error(Error::BadDataPtr, "Copy constructor for self allocating arrays not supported");
}
}
Array2d(T* _a, int _n1, int _n2):
a(_a), n1(_n1), n2(_n2), needToDeallocArray(false)
{
}
Array2d(int _n1, int _n2):
n1(_n1), n2(_n2), needToDeallocArray(true)
{
a = new T[n1*n2];
}
~Array2d()
{
if (needToDeallocArray)
delete[] a;
}
T* operator [] (int i)
{
return a + i*n2;
}
inline T* row_ptr(int i)
{
return (*this)[i];
}
};
template <class T>
struct Array3d
{
T* a;
int n1,n2,n3;
bool needToDeallocArray;
Array3d(T* _a, int _n1, int _n2, int _n3):
a(_a), n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(false)
{
}
Array3d(int _n1, int _n2, int _n3):
n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(true)
{
a = new T[n1*n2*n3];
}
~Array3d()
{
if (needToDeallocArray)
delete[] a;
}
Array2d<T> operator [] (int i)
{
Array2d<T> array2d(a + i*n2*n3, n2, n3);
return array2d;
}
inline T* row_ptr(int i1, int i2)
{
return a + i1*n2*n3 + i2*n3;
}
};
template <class T>
struct Array4d
{
T* a;
int n1,n2,n3,n4;
bool needToDeallocArray;
int steps[4];
void init_steps()
{
steps[0] = n2*n3*n4;
steps[1] = n3*n4;
steps[2] = n4;
steps[3] = 1;
}
Array4d(T* _a, int _n1, int _n2, int _n3, int _n4) :
a(_a), n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(false)
{
init_steps();
}
Array4d(int _n1, int _n2, int _n3, int _n4) :
n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(true)
{
a = new T[n1*n2*n3*n4];
init_steps();
}
~Array4d()
{
if (needToDeallocArray)
delete[] a;
}
Array3d<T> operator [] (int i)
{
Array3d<T> array3d(a + i*n2*n3*n4, n2, n3, n4);
return array3d;
}
inline T* row_ptr(int i1, int i2, int i3)
{
return a + i1*n2*n3*n4 + i2*n3*n4 + i3*n4;
}
inline int step_size(int dimension)
{
return steps[dimension];
}
};
}
#endif

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/*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) 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.
//
// 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
#include "hdr_common.hpp"
namespace cv
{
class CalibrateDebevecImpl CV_FINAL : public CalibrateDebevec
{
public:
CalibrateDebevecImpl(int _samples, float _lambda, bool _random) :
name("CalibrateDebevec"),
samples(_samples),
lambda(_lambda),
random(_random),
w(triangleWeights())
{
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray _times) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
// check inputs
std::vector<Mat> images;
src.getMatVector(images);
Mat times = _times.getMat();
CV_Assert(images.size() == times.total());
checkImageDimensions(images);
CV_Assert(images[0].depth() == CV_8U);
CV_Assert(times.type() == CV_32FC1);
// create output
int channels = images[0].channels();
int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
int rows = images[0].rows;
int cols = images[0].cols;
dst.create(LDR_SIZE, 1, CV_32FCC);
Mat result = dst.getMat();
// pick pixel locations (either random or in a rectangular grid)
std::vector<Point> points;
points.reserve(samples);
if(random) {
for(int i = 0; i < samples; i++) {
points.push_back(Point(rand() % cols, rand() % rows));
}
} else {
int x_points = static_cast<int>(sqrt(static_cast<double>(samples) * cols / rows));
CV_Assert(0 < x_points && x_points <= cols);
int y_points = samples / x_points;
CV_Assert(0 < y_points && y_points <= rows);
int step_x = cols / x_points;
int step_y = rows / y_points;
for(int i = 0, x = step_x / 2; i < x_points; i++, x += step_x) {
for(int j = 0, y = step_y / 2; j < y_points; j++, y += step_y) {
if( 0 <= x && x < cols && 0 <= y && y < rows ) {
points.push_back(Point(x, y));
}
}
}
// we can have slightly less grid points than specified
//samples = static_cast<int>(points.size());
}
// we need enough equations to ensure a sufficiently overdetermined system
// (maybe only as a warning)
//CV_Assert(points.size() * (images.size() - 1) >= LDR_SIZE);
// solve for imaging system response function, over each channel separately
std::vector<Mat> result_split(channels);
for(int ch = 0; ch < channels; ch++) {
// initialize system of linear equations
Mat A = Mat::zeros((int)points.size() * (int)images.size() + LDR_SIZE + 1,
LDR_SIZE + (int)points.size(), CV_32F);
Mat B = Mat::zeros(A.rows, 1, CV_32F);
// include the data-fitting equations
int k = 0;
for(size_t i = 0; i < points.size(); i++) {
for(size_t j = 0; j < images.size(); j++) {
// val = images[j].at<Vec3b>(points[i].y, points[i].x)[ch]
int val = images[j].ptr()[channels*(points[i].y * cols + points[i].x) + ch];
float wij = w.at<float>(val);
A.at<float>(k, val) = wij;
A.at<float>(k, LDR_SIZE + (int)i) = -wij;
B.at<float>(k, 0) = wij * log(times.at<float>((int)j));
k++;
}
}
// fix the curve by setting its middle value to 0
A.at<float>(k, LDR_SIZE / 2) = 1;
k++;
// include the smoothness equations
for(int i = 0; i < (LDR_SIZE - 2); i++) {
float wi = w.at<float>(i + 1);
A.at<float>(k, i) = lambda * wi;
A.at<float>(k, i + 1) = -2 * lambda * wi;
A.at<float>(k, i + 2) = lambda * wi;
k++;
}
// solve the overdetermined system using SVD (least-squares problem)
Mat solution;
solve(A, B, solution, DECOMP_SVD);
solution.rowRange(0, LDR_SIZE).copyTo(result_split[ch]);
}
// combine log-exposures and take its exponent
merge(result_split, result);
exp(result, result);
}
int getSamples() const CV_OVERRIDE { return samples; }
void setSamples(int val) CV_OVERRIDE { samples = val; }
float getLambda() const CV_OVERRIDE { return lambda; }
void setLambda(float val) CV_OVERRIDE { lambda = val; }
bool getRandom() const CV_OVERRIDE { return random; }
void setRandom(bool val) CV_OVERRIDE { random = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "samples" << samples
<< "lambda" << lambda
<< "random" << static_cast<int>(random);
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
samples = fn["samples"];
lambda = fn["lambda"];
int random_val = fn["random"];
random = (random_val != 0);
}
protected:
String name; // calibration algorithm identifier
int samples; // number of pixel locations to sample
float lambda; // constant that determines the amount of smoothness
bool random; // whether to sample locations randomly or in a grid shape
Mat w; // weighting function for corresponding pixel values
};
Ptr<CalibrateDebevec> createCalibrateDebevec(int samples, float lambda, bool random)
{
return makePtr<CalibrateDebevecImpl>(samples, lambda, random);
}
class CalibrateRobertsonImpl CV_FINAL : public CalibrateRobertson
{
public:
CalibrateRobertsonImpl(int _max_iter, float _threshold) :
name("CalibrateRobertson"),
max_iter(_max_iter),
threshold(_threshold),
weight(RobertsonWeights())
{
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray _times) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
std::vector<Mat> images;
src.getMatVector(images);
Mat times = _times.getMat();
CV_Assert(images.size() == times.total());
checkImageDimensions(images);
CV_Assert(images[0].depth() == CV_8U);
int channels = images[0].channels();
int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
CV_Assert(channels >= 1 && channels <= 3);
dst.create(LDR_SIZE, 1, CV_32FCC);
Mat response = dst.getMat();
response = linearResponse(3) / (LDR_SIZE / 2.0f);
Mat card = Mat::zeros(LDR_SIZE, 1, CV_32FCC);
for(size_t i = 0; i < images.size(); i++) {
uchar *ptr = images[i].ptr();
for(size_t pos = 0; pos < images[i].total(); pos++) {
for(int c = 0; c < channels; c++, ptr++) {
card.at<Vec3f>(*ptr)[c] += 1;
}
}
}
card = 1.0 / card;
Ptr<MergeRobertson> merge = createMergeRobertson();
for(int iter = 0; iter < max_iter; iter++) {
radiance = Mat::zeros(images[0].size(), CV_32FCC);
merge->process(images, radiance, times, response);
Mat new_response = Mat::zeros(LDR_SIZE, 1, CV_32FC3);
for(size_t i = 0; i < images.size(); i++) {
uchar *ptr = images[i].ptr();
float* rad_ptr = radiance.ptr<float>();
for(size_t pos = 0; pos < images[i].total(); pos++) {
for(int c = 0; c < channels; c++, ptr++, rad_ptr++) {
new_response.at<Vec3f>(*ptr)[c] += times.at<float>((int)i) * *rad_ptr;
}
}
}
new_response = new_response.mul(card);
for(int c = 0; c < 3; c++) {
float middle = new_response.at<Vec3f>(LDR_SIZE / 2)[c];
for(int i = 0; i < LDR_SIZE; i++) {
new_response.at<Vec3f>(i)[c] /= middle;
}
}
float diff = static_cast<float>(sum(sum(abs(new_response - response)))[0] / channels);
new_response.copyTo(response);
if(diff < threshold) {
break;
}
}
}
int getMaxIter() const CV_OVERRIDE { return max_iter; }
void setMaxIter(int val) CV_OVERRIDE { max_iter = val; }
float getThreshold() const CV_OVERRIDE { return threshold; }
void setThreshold(float val) CV_OVERRIDE { threshold = val; }
Mat getRadiance() const CV_OVERRIDE { return radiance; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "max_iter" << max_iter
<< "threshold" << threshold;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
max_iter = fn["max_iter"];
threshold = fn["threshold"];
}
protected:
String name;
int max_iter;
float threshold;
Mat weight, radiance;
};
Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter, float threshold)
{
return makePtr<CalibrateRobertsonImpl>(max_iter, threshold);
}
}

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include <cmath>
#include <vector>
#include <limits>
#include "contrast_preserve.hpp"
using namespace std;
using namespace cv;
void cv::decolor(InputArray _src, OutputArray _dst, OutputArray _color_boost)
{
CV_INSTRUMENT_REGION();
Mat I = _src.getMat();
_dst.create(I.size(), CV_8UC1);
Mat dst = _dst.getMat();
_color_boost.create(I.size(), CV_8UC3);
Mat color_boost = _color_boost.getMat();
CV_Assert(!I.empty() && (I.channels()==3));
// Parameter Setting
const int maxIter = 15;
const double tol = .0001;
int iterCount = 0;
double E = 0;
double pre_E = std::numeric_limits<double>::infinity();
Mat img;
I.convertTo(img, CV_32FC3, 1.0/255.0);
// Initialization
Decolor obj;
vector <double> Cg;
vector < vector <double> > polyGrad;
vector <Vec3i> comb;
vector <double> alf;
obj.grad_system(img,polyGrad,Cg,comb);
obj.weak_order(img,alf);
// Solver
Mat Mt = Mat(int(polyGrad.size()),int(polyGrad[0].size()), CV_32FC1);
obj.wei_update_matrix(polyGrad,Cg,Mt);
vector <double> wei;
obj.wei_inti(comb,wei);
//////////////////////////////// main loop starting ////////////////////////////////////////
vector <double> G_pos(alf.size());
vector <double> G_neg(alf.size());
vector <double> EXPsum(G_pos.size());
vector <double> EXPterm(G_pos.size());
vector <double> temp(polyGrad[0].size());
vector <double> temp1(polyGrad[0].size());
vector <double> temp2(EXPsum.size());
vector <double> wei1(polyGrad.size());
while(sqrt(pow(E-pre_E,2)) > tol)
{
iterCount +=1;
pre_E = E;
for(size_t i=0; i<polyGrad[0].size(); i++)
{
double val = 0.0;
for(size_t j=0; j<polyGrad.size(); j++)
val = val + (polyGrad[j][i] * wei[j]);
temp[i] = val - Cg[i];
temp1[i] = val + Cg[i];
}
for(size_t i=0; i<alf.size(); i++)
{
const double sqSigma = obj.sigma * obj.sigma;
const double pos = ((1 + alf[i])/2) * exp(-1.0 * 0.5 * (temp[i] * temp[i]) / sqSigma);
const double neg = ((1 - alf[i])/2) * exp(-1.0 * 0.5 * (temp1[i] * temp1[i]) / sqSigma);
G_pos[i] = pos;
G_neg[i] = neg;
}
for(size_t i=0; i<G_pos.size(); i++)
EXPsum[i] = G_pos[i]+G_neg[i];
for(size_t i=0; i<EXPsum.size(); i++)
temp2[i] = (EXPsum[i] == 0) ? 1.0 : 0.0;
for(size_t i=0; i<G_pos.size(); i++)
EXPterm[i] = (G_pos[i] - G_neg[i])/(EXPsum[i] + temp2[i]);
for(int i=0; i<int(polyGrad.size()); i++)
{
double val1 = 0.0;
for(int j=0; j<int(polyGrad[0].size()); j++)
{
val1 = val1 + (Mt.at<float>(i,j) * EXPterm[j]);
}
wei1[i] = val1;
}
for(size_t i=0; i<wei.size(); i++)
wei[i] = wei1[i];
E = obj.energyCalcu(Cg, polyGrad, wei);
if(iterCount > maxIter)
break;
}
Mat Gray = Mat::zeros(img.size(),CV_32FC1);
obj.grayImContruct(wei, img, Gray);
Gray.convertTo(dst,CV_8UC1,255);
/////////////////////////////////// Contrast Boosting /////////////////////////////////
Mat lab;
cvtColor(I,lab,COLOR_BGR2Lab);
vector <Mat> lab_channel;
split(lab,lab_channel);
dst.copyTo(lab_channel[0]);
merge(lab_channel,lab);
cvtColor(lab,color_boost,COLOR_Lab2BGR);
}

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include <cmath>
#include <vector>
using namespace std;
using namespace cv;
class Decolor
{
private:
Mat kernelx;
Mat kernely;
int order;
public:
float sigma;
Decolor();
static vector<double> product(const vector <Vec3i> &comb, const double initRGB[3]);
double energyCalcu(const vector <double> &Cg, const vector < vector <double> > &polyGrad, const vector <double> &wei) const;
void singleChannelGradx(const Mat &img, Mat& dest) const;
void singleChannelGrady(const Mat &img, Mat& dest) const;
void gradvector(const Mat &img, vector <double> &grad) const;
void colorGrad(const Mat &img, vector <double> &Cg) const;
static void add_vector(vector <Vec3i> &comb, int &idx, int r,int g,int b);
static void add_to_vector_poly(vector < vector <double> > &polyGrad, const vector <double> &curGrad, int &idx1);
void weak_order(const Mat &img, vector <double> &alf) const;
void grad_system(const Mat &img, vector < vector < double > > &polyGrad,
vector < double > &Cg, vector <Vec3i>& comb) const;
static void wei_update_matrix(const vector < vector <double> > &poly, const vector <double> &Cg, Mat &X);
static void wei_inti(const vector <Vec3i> &comb, vector <double> &wei);
void grayImContruct(vector <double> &wei, const Mat &img, Mat &Gray) const;
};
double Decolor::energyCalcu(const vector <double> &Cg, const vector < vector <double> > &polyGrad, const vector <double> &wei) const
{
const size_t size = polyGrad[0].size();
vector <double> energy(size);
vector <double> temp(size);
vector <double> temp1(size);
for(size_t i=0;i< polyGrad[0].size();i++)
{
double val = 0.0;
for(size_t j =0;j<polyGrad.size();j++)
val = val + (polyGrad[j][i] * wei[j]);
temp[i] = val - Cg[i];
temp1[i] = val + Cg[i];
}
for(size_t i=0;i<polyGrad[0].size();i++)
energy[i] = -1.0*log(exp(-1.0*pow(temp[i],2)/sigma) + exp(-1.0*pow(temp1[i],2)/sigma));
double sum = 0.0;
for(size_t i=0;i<polyGrad[0].size();i++)
sum +=energy[i];
return (sum/polyGrad[0].size());
}
Decolor::Decolor()
{
kernelx = Mat(1,2, CV_32FC1);
kernely = Mat(2,1, CV_32FC1);
kernelx.at<float>(0,0)=1.0;
kernelx.at<float>(0,1)=-1.0;
kernely.at<float>(0,0)=1.0;
kernely.at<float>(1,0)=-1.0;
order = 2;
sigma = 0.02f;
}
vector<double> Decolor::product(const vector <Vec3i> &comb, const double initRGB[3])
{
vector <double> res(comb.size());
for (size_t i=0;i<comb.size();i++)
{
double dp = 0.0;
for(int j=0;j<3;j++)
dp = dp + (comb[i][j] * initRGB[j]);
res[i] = dp;
}
return res;
}
void Decolor::singleChannelGradx(const Mat &img, Mat& dest) const
{
const int w = img.size().width;
const Point anchor(kernelx.cols - kernelx.cols/2 - 1, kernelx.rows - kernelx.rows/2 - 1);
filter2D(img, dest, -1, kernelx, anchor, 0.0, BORDER_CONSTANT);
dest.col(w - 1) = 0.0;
}
void Decolor::singleChannelGrady(const Mat &img, Mat& dest) const
{
const int h = img.size().height;
const Point anchor(kernely.cols - kernely.cols/2 - 1, kernely.rows - kernely.rows/2 - 1);
filter2D(img, dest, -1, kernely, anchor, 0.0, BORDER_CONSTANT);
dest.row(h - 1) = 0.0;
}
void Decolor::gradvector(const Mat &img, vector <double> &grad) const
{
Mat dest;
Mat dest1;
singleChannelGradx(img,dest);
singleChannelGrady(img,dest1);
Mat d_trans=dest.t();
Mat d1_trans=dest1.t();
const int height = d_trans.size().height;
const int width = d_trans.size().width;
grad.resize(width * height * 2);
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
grad[i*width + j] = d_trans.at<float>(i, j);
const int offset = width * height;
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
grad[offset + i * width + j] = d1_trans.at<float>(i, j);
}
void Decolor::colorGrad(const Mat &img, vector <double> &Cg) const
{
Mat lab;
cvtColor(img,lab,COLOR_BGR2Lab);
vector <Mat> lab_channel;
split(lab,lab_channel);
vector <double> ImL;
vector <double> Ima;
vector <double> Imb;
gradvector(lab_channel[0],ImL);
gradvector(lab_channel[1],Ima);
gradvector(lab_channel[2],Imb);
Cg.resize(ImL.size());
for(size_t i=0;i<ImL.size();i++)
{
const double res = sqrt(pow(ImL[i],2) + pow(Ima[i],2) + pow(Imb[i],2))/100;
Cg[i] = res;
}
}
void Decolor::add_vector(vector <Vec3i> &comb, int &idx, int r,int g,int b)
{
comb.push_back(Vec3i(r, g, b));
idx++;
}
void Decolor::add_to_vector_poly(vector < vector <double> > &polyGrad, const vector <double> &curGrad, int &idx1)
{
polyGrad.push_back(curGrad);
idx1++;
}
void Decolor::weak_order(const Mat &im, vector <double> &alf) const
{
Mat img;
const int h = im.size().height;
const int w = im.size().width;
if((h + w) > 800)
{
const double sizefactor = double(800)/(h+w);
resize(im, img, Size(cvRound(w*sizefactor), cvRound(h*sizefactor)));
}
else
{
img = im;
}
Mat curIm = Mat(img.size(),CV_32FC1);
vector <Mat> rgb_channel;
split(img,rgb_channel);
vector <double> Rg, Gg, Bg;
gradvector(rgb_channel[2],Rg);
gradvector(rgb_channel[1],Gg);
gradvector(rgb_channel[0],Bg);
vector <double> t1(Rg.size()), t2(Rg.size()), t3(Rg.size());
vector <double> tmp1(Rg.size()), tmp2(Rg.size()), tmp3(Rg.size());
const double level = .05;
for(size_t i=0;i<Rg.size();i++)
{
t1[i] = (Rg[i] > level) ? 1.0 : 0.0;
t2[i] = (Gg[i] > level) ? 1.0 : 0.0;
t3[i] = (Bg[i] > level) ? 1.0 : 0.0;
tmp1[i] = (Rg[i] < -1.0*level) ? 1.0 : 0.0;
tmp2[i] = (Gg[i] < -1.0*level) ? 1.0 : 0.0;
tmp3[i] = (Bg[i] < -1.0*level) ? 1.0 : 0.0;
}
alf.resize(Rg.size());
for(size_t i =0 ;i < Rg.size();i++)
alf[i] = (t1[i] * t2[i] * t3[i]);
for(size_t i =0 ;i < Rg.size();i++)
alf[i] -= tmp1[i] * tmp2[i] * tmp3[i];
}
void Decolor::grad_system(const Mat &im, vector < vector < double > > &polyGrad,
vector < double > &Cg, vector <Vec3i>& comb) const
{
Mat img;
int h = im.size().height;
int w = im.size().width;
if((h + w) > 800)
{
const double sizefactor = double(800)/(h+w);
resize(im, img, Size(cvRound(w*sizefactor), cvRound(h*sizefactor)));
}
else
{
img = im;
}
h = img.size().height;
w = img.size().width;
colorGrad(img,Cg);
Mat curIm = Mat(img.size(),CV_32FC1);
vector <Mat> rgb_channel;
split(img,rgb_channel);
int idx = 0, idx1 = 0;
for(int r=0 ;r <=order; r++)
for(int g=0; g<=order;g++)
for(int b =0; b <=order;b++)
{
if((r+g+b)<=order && (r+g+b) > 0)
{
add_vector(comb,idx,r,g,b);
for(int i = 0;i<h;i++)
for(int j=0;j<w;j++)
curIm.at<float>(i,j)=static_cast<float>(
pow(rgb_channel[2].at<float>(i,j),r)*pow(rgb_channel[1].at<float>(i,j),g)*
pow(rgb_channel[0].at<float>(i,j),b));
vector <double> curGrad;
gradvector(curIm,curGrad);
add_to_vector_poly(polyGrad,curGrad,idx1);
}
}
}
void Decolor::wei_update_matrix(const vector < vector <double> > &poly, const vector <double> &Cg, Mat &X)
{
const int size = static_cast<int>(poly.size());
const int size0 = static_cast<int>(poly[0].size());
Mat P = Mat(size, size0, CV_32FC1);
for (int i = 0; i < size; i++)
for (int j = 0; j < size0;j++)
P.at<float>(i,j) = static_cast<float>(poly[i][j]);
const Mat P_trans = P.t();
Mat B = Mat(size, size0, CV_32FC1);
for(int i =0;i < size;i++)
{
for(int j = 0, end = int(Cg.size()); j < end;j++)
B.at<float>(i,j) = static_cast<float>(poly[i][j] * Cg[j]);
}
Mat A = P*P_trans;
solve(A, B, X, DECOMP_NORMAL);
}
void Decolor::wei_inti(const vector <Vec3i> &comb, vector <double> &wei)
{
double initRGB[3] = { .33, .33, .33 };
wei = product(comb,initRGB);
vector <int> sum(comb.size());
for(size_t i=0;i<comb.size();i++)
sum[i] = (comb[i][0] + comb[i][1] + comb[i][2]);
for(size_t i=0;i<sum.size();i++)
{
if(sum[i] == 1)
wei[i] = wei[i] * double(1);
else
wei[i] = wei[i] * double(0);
}
sum.clear();
}
void Decolor::grayImContruct(vector <double> &wei, const Mat &img, Mat &Gray) const
{
const int h = img.size().height;
const int w = img.size().width;
vector <Mat> rgb_channel;
split(img,rgb_channel);
int kk =0;
for(int r =0;r<=order;r++)
for(int g=0;g<=order;g++)
for(int b=0;b<=order;b++)
if((r + g + b) <=order && (r+g+b) > 0)
{
for(int i = 0;i<h;i++)
for(int j=0;j<w;j++)
Gray.at<float>(i,j)=static_cast<float>(Gray.at<float>(i,j) +
static_cast<float>(wei[kk])*pow(rgb_channel[2].at<float>(i,j),r)*pow(rgb_channel[1].at<float>(i,j),g)*
pow(rgb_channel[0].at<float>(i,j),b));
kk=kk+1;
}
double minval, maxval;
minMaxLoc(Gray, &minval, &maxval);
Gray -= minval;
Gray /= maxval - minval;
}

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/*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) 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.
//
// 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 "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/border_interpolate.hpp"
using namespace cv::cuda;
typedef unsigned char uchar;
typedef unsigned short ushort;
//////////////////////////////////////////////////////////////////////////////////
//// Non Local Means Denosing
namespace cv { namespace cuda { namespace device
{
namespace imgproc
{
__device__ __forceinline__ float norm2(const float& v) { return v*v; }
__device__ __forceinline__ float norm2(const float2& v) { return v.x*v.x + v.y*v.y; }
__device__ __forceinline__ float norm2(const float3& v) { return v.x*v.x + v.y*v.y + v.z*v.z; }
__device__ __forceinline__ float norm2(const float4& v) { return v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w; }
template<typename T, typename B>
__global__ void nlm_kernel(const PtrStep<T> src, PtrStepSz<T> dst, const B b, int search_radius, int block_radius, float noise_mult)
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type value_type;
const int i = blockDim.y * blockIdx.y + threadIdx.y;
const int j = blockDim.x * blockIdx.x + threadIdx.x;
if (j >= dst.cols || i >= dst.rows)
return;
int bsize = search_radius + block_radius;
int search_window = 2 * search_radius + 1;
float minus_search_window2_inv = -1.f/(search_window * search_window);
value_type sum1 = VecTraits<value_type>::all(0);
float sum2 = 0.f;
if (j - bsize >= 0 && j + bsize < dst.cols && i - bsize >= 0 && i + bsize < dst.rows)
{
for(float y = -search_radius; y <= search_radius; ++y)
for(float x = -search_radius; x <= search_radius; ++x)
{
float dist2 = 0;
for(float ty = -block_radius; ty <= block_radius; ++ty)
for(float tx = -block_radius; tx <= block_radius; ++tx)
{
value_type bv = saturate_cast<value_type>(src(i + y + ty, j + x + tx));
value_type av = saturate_cast<value_type>(src(i + ty, j + tx));
dist2 += norm2(av - bv);
}
float w = __expf(dist2 * noise_mult + (x * x + y * y) * minus_search_window2_inv);
/*if (i == 255 && j == 255)
printf("%f %f\n", w, dist2 * minus_h2_inv + (x * x + y * y) * minus_search_window2_inv);*/
sum1 = sum1 + w * saturate_cast<value_type>(src(i + y, j + x));
sum2 += w;
}
}
else
{
for(float y = -search_radius; y <= search_radius; ++y)
for(float x = -search_radius; x <= search_radius; ++x)
{
float dist2 = 0;
for(float ty = -block_radius; ty <= block_radius; ++ty)
for(float tx = -block_radius; tx <= block_radius; ++tx)
{
value_type bv = saturate_cast<value_type>(b.at(i + y + ty, j + x + tx, src));
value_type av = saturate_cast<value_type>(b.at(i + ty, j + tx, src));
dist2 += norm2(av - bv);
}
float w = __expf(dist2 * noise_mult + (x * x + y * y) * minus_search_window2_inv);
sum1 = sum1 + w * saturate_cast<value_type>(b.at(i + y, j + x, src));
sum2 += w;
}
}
dst(i, j) = saturate_cast<T>(sum1 / sum2);
}
template<typename T, template <typename> class B>
void nlm_caller(const PtrStepSzb src, PtrStepSzb dst, int search_radius, int block_radius, float h, cudaStream_t stream)
{
dim3 block (32, 8);
dim3 grid (divUp (src.cols, block.x), divUp (src.rows, block.y));
B<T> b(src.rows, src.cols);
int block_window = 2 * block_radius + 1;
float minus_h2_inv = -1.f/(h * h * VecTraits<T>::cn);
float noise_mult = minus_h2_inv/(block_window * block_window);
cudaSafeCall( cudaFuncSetCacheConfig (nlm_kernel<T, B<T> >, cudaFuncCachePreferL1) );
nlm_kernel<<<grid, block>>>((PtrStepSz<T>)src, (PtrStepSz<T>)dst, b, search_radius, block_radius, noise_mult);
cudaSafeCall ( cudaGetLastError () );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template<typename T>
void nlm_bruteforce_gpu(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream)
{
typedef void (*func_t)(const PtrStepSzb src, PtrStepSzb dst, int search_radius, int block_radius, float h, cudaStream_t stream);
static func_t funcs[] =
{
nlm_caller<T, BrdConstant>,
nlm_caller<T, BrdReplicate>,
nlm_caller<T, BrdReflect>,
nlm_caller<T, BrdWrap>,
nlm_caller<T, BrdReflect101>
};
funcs[borderMode](src, dst, search_radius, block_radius, h, stream);
}
template void nlm_bruteforce_gpu<uchar>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
template void nlm_bruteforce_gpu<uchar2>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
template void nlm_bruteforce_gpu<uchar3>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
}
}}}
//////////////////////////////////////////////////////////////////////////////////
//// Non Local Means Denosing (fast approximate version)
namespace cv { namespace cuda { namespace device
{
namespace imgproc
{
template <int cn> struct Unroll;
template <> struct Unroll<1>
{
template <int BLOCK_SIZE>
static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*> smem_tuple(float* smem)
{
return cv::cuda::device::smem_tuple(smem, smem + BLOCK_SIZE);
}
static __device__ __forceinline__ thrust::tuple<float&, float&> tie(float& val1, float& val2)
{
return thrust::tie(val1, val2);
}
static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float> > op()
{
plus<float> op;
return thrust::make_tuple(op, op);
}
};
template <> struct Unroll<2>
{
template <int BLOCK_SIZE>
static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
{
return cv::cuda::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE);
}
static __device__ __forceinline__ thrust::tuple<float&, float&, float&> tie(float& val1, float2& val2)
{
return thrust::tie(val1, val2.x, val2.y);
}
static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float> > op()
{
plus<float> op;
return thrust::make_tuple(op, op, op);
}
};
template <> struct Unroll<3>
{
template <int BLOCK_SIZE>
static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
{
return cv::cuda::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE, smem + 3 * BLOCK_SIZE);
}
static __device__ __forceinline__ thrust::tuple<float&, float&, float&, float&> tie(float& val1, float3& val2)
{
return thrust::tie(val1, val2.x, val2.y, val2.z);
}
static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float>, plus<float> > op()
{
plus<float> op;
return thrust::make_tuple(op, op, op, op);
}
};
template <> struct Unroll<4>
{
template <int BLOCK_SIZE>
static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
{
return cv::cuda::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE, smem + 3 * BLOCK_SIZE, smem + 4 * BLOCK_SIZE);
}
static __device__ __forceinline__ thrust::tuple<float&, float&, float&, float&, float&> tie(float& val1, float4& val2)
{
return thrust::tie(val1, val2.x, val2.y, val2.z, val2.w);
}
static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float>, plus<float>, plus<float> > op()
{
plus<float> op;
return thrust::make_tuple(op, op, op, op, op);
}
};
__device__ __forceinline__ int calcDist(const uchar& a, const uchar& b) { return (a-b)*(a-b); }
__device__ __forceinline__ int calcDist(const uchar2& a, const uchar2& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y); }
__device__ __forceinline__ int calcDist(const uchar3& a, const uchar3& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y) + (a.z-b.z)*(a.z-b.z); }
template <class T> struct FastNonLocalMeans
{
enum
{
CTA_SIZE = 128,
TILE_COLS = 128,
TILE_ROWS = 32,
STRIDE = CTA_SIZE
};
struct plus
{
__device__ __forceinline__ float operator()(float v1, float v2) const { return v1 + v2; }
};
int search_radius;
int block_radius;
int search_window;
int block_window;
float minus_h2_inv;
FastNonLocalMeans(int search_window_, int block_window_, float h) : search_radius(search_window_/2), block_radius(block_window_/2),
search_window(search_window_), block_window(block_window_), minus_h2_inv(-1.f/(h * h * VecTraits<T>::cn)) {}
PtrStep<T> src;
mutable PtrStepi buffer;
__device__ __forceinline__ void initSums_BruteForce(int i, int j, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
{
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
{
dist_sums[index] = 0;
for(int tx = 0; tx < block_window; ++tx)
col_sums(tx, index) = 0;
int y = index / search_window;
int x = index - y * search_window;
int ay = i;
int ax = j;
int by = i + y - search_radius;
int bx = j + x - search_radius;
#if 1
for (int tx = -block_radius; tx <= block_radius; ++tx)
{
int col_sum = 0;
for (int ty = -block_radius; ty <= block_radius; ++ty)
{
int dist = calcDist(src(ay + ty, ax + tx), src(by + ty, bx + tx));
dist_sums[index] += dist;
col_sum += dist;
}
col_sums(tx + block_radius, index) = col_sum;
}
#else
for (int ty = -block_radius; ty <= block_radius; ++ty)
for (int tx = -block_radius; tx <= block_radius; ++tx)
{
int dist = calcDist(src(ay + ty, ax + tx), src(by + ty, bx + tx));
dist_sums[index] += dist;
col_sums(tx + block_radius, index) += dist;
}
#endif
up_col_sums(j, index) = col_sums(block_window - 1, index);
}
}
__device__ __forceinline__ void shiftRight_FirstRow(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
{
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
{
int y = index / search_window;
int x = index - y * search_window;
int ay = i;
int ax = j + block_radius;
int by = i + y - search_radius;
int bx = j + x - search_radius + block_radius;
int col_sum = 0;
for (int ty = -block_radius; ty <= block_radius; ++ty)
col_sum += calcDist(src(ay + ty, ax), src(by + ty, bx));
dist_sums[index] += col_sum - col_sums(first, index);
col_sums(first, index) = col_sum;
up_col_sums(j, index) = col_sum;
}
}
__device__ __forceinline__ void shiftRight_UpSums(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
{
int ay = i;
int ax = j + block_radius;
T a_up = src(ay - block_radius - 1, ax);
T a_down = src(ay + block_radius, ax);
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
{
int y = index / search_window;
int x = index - y * search_window;
int by = i + y - search_radius;
int bx = j + x - search_radius + block_radius;
T b_up = src(by - block_radius - 1, bx);
T b_down = src(by + block_radius, bx);
int col_sum = up_col_sums(j, index) + calcDist(a_down, b_down) - calcDist(a_up, b_up);
dist_sums[index] += col_sum - col_sums(first, index);
col_sums(first, index) = col_sum;
up_col_sums(j, index) = col_sum;
}
}
__device__ __forceinline__ void convolve_window(int i, int j, const int* dist_sums, T& dst) const
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_type;
float weights_sum = 0;
sum_type sum = VecTraits<sum_type>::all(0);
float bw2_inv = 1.f/(block_window * block_window);
int sx = j - search_radius;
int sy = i - search_radius;
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
{
int y = index / search_window;
int x = index - y * search_window;
float avg_dist = dist_sums[index] * bw2_inv;
float weight = __expf(avg_dist * minus_h2_inv);
weights_sum += weight;
sum = sum + weight * saturate_cast<sum_type>(src(sy + y, sx + x));
}
__shared__ float cta_buffer[CTA_SIZE * (VecTraits<T>::cn + 1)];
reduce<CTA_SIZE>(Unroll<VecTraits<T>::cn>::template smem_tuple<CTA_SIZE>(cta_buffer),
Unroll<VecTraits<T>::cn>::tie(weights_sum, sum),
threadIdx.x,
Unroll<VecTraits<T>::cn>::op());
if (threadIdx.x == 0)
dst = saturate_cast<T>(sum / weights_sum);
}
__device__ __forceinline__ void operator()(PtrStepSz<T>& dst) const
{
int tbx = blockIdx.x * TILE_COLS;
int tby = blockIdx.y * TILE_ROWS;
int tex = ::min(tbx + TILE_COLS, dst.cols);
int tey = ::min(tby + TILE_ROWS, dst.rows);
PtrStepi col_sums;
col_sums.data = buffer.ptr(dst.cols + blockIdx.x * block_window) + blockIdx.y * search_window * search_window;
col_sums.step = buffer.step;
PtrStepi up_col_sums;
up_col_sums.data = buffer.data + blockIdx.y * search_window * search_window;
up_col_sums.step = buffer.step;
extern __shared__ int dist_sums[]; //search_window * search_window
int first = 0;
for (int i = tby; i < tey; ++i)
for (int j = tbx; j < tex; ++j)
{
__syncthreads();
if (j == tbx)
{
initSums_BruteForce(i, j, dist_sums, col_sums, up_col_sums);
first = 0;
}
else
{
if (i == tby)
shiftRight_FirstRow(i, j, first, dist_sums, col_sums, up_col_sums);
else
shiftRight_UpSums(i, j, first, dist_sums, col_sums, up_col_sums);
first = (first + 1) % block_window;
}
__syncthreads();
convolve_window(i, j, dist_sums, dst(i, j));
}
}
};
template<typename T>
__global__ void fast_nlm_kernel(const FastNonLocalMeans<T> fnlm, PtrStepSz<T> dst) { fnlm(dst); }
void nln_fast_get_buffer_size(const PtrStepSzb& src, int search_window, int block_window, int& buffer_cols, int& buffer_rows)
{
typedef FastNonLocalMeans<uchar> FNLM;
dim3 grid(divUp(src.cols, FNLM::TILE_COLS), divUp(src.rows, FNLM::TILE_ROWS));
buffer_cols = search_window * search_window * grid.y;
buffer_rows = src.cols + block_window * grid.x;
}
template<typename T>
void nlm_fast_gpu(const PtrStepSzb& src, PtrStepSzb dst, PtrStepi buffer,
int search_window, int block_window, float h, cudaStream_t stream)
{
typedef FastNonLocalMeans<T> FNLM;
FNLM fnlm(search_window, block_window, h);
fnlm.src = (PtrStepSz<T>)src;
fnlm.buffer = buffer;
dim3 block(FNLM::CTA_SIZE, 1);
dim3 grid(divUp(src.cols, FNLM::TILE_COLS), divUp(src.rows, FNLM::TILE_ROWS));
int smem = search_window * search_window * sizeof(int);
fast_nlm_kernel<<<grid, block, smem>>>(fnlm, (PtrStepSz<T>)dst);
cudaSafeCall ( cudaGetLastError () );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void nlm_fast_gpu<uchar>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
template void nlm_fast_gpu<uchar2>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
template void nlm_fast_gpu<uchar3>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
__global__ void fnlm_split_kernel(const PtrStepSz<uchar3> lab, PtrStepb l, PtrStep<uchar2> ab)
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < lab.cols && y < lab.rows)
{
uchar3 p = lab(y, x);
ab(y,x) = make_uchar2(p.y, p.z);
l(y,x) = p.x;
}
}
void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream)
{
dim3 b(32, 8);
dim3 g(divUp(lab.cols, b.x), divUp(lab.rows, b.y));
fnlm_split_kernel<<<g, b>>>(lab, l, ab);
cudaSafeCall ( cudaGetLastError () );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void fnlm_merge_kernel(const PtrStepb l, const PtrStep<uchar2> ab, PtrStepSz<uchar3> lab)
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < lab.cols && y < lab.rows)
{
uchar2 p = ab(y, x);
lab(y, x) = make_uchar3(l(y, x), p.x, p.y);
}
}
void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream)
{
dim3 b(32, 8);
dim3 g(divUp(lab.cols, b.x), divUp(lab.rows, b.y));
fnlm_merge_kernel<<<g, b>>>(l, ab, lab);
cudaSafeCall ( cudaGetLastError () );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
}}}

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/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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 "precomp.hpp"
#include <vector>
#include <algorithm>
#define ABSCLIP(val,threshold) MIN(MAX((val),-(threshold)),(threshold))
namespace cv{
class AddFloatToCharScaled{
public:
AddFloatToCharScaled(double scale):_scale(scale){}
inline double operator()(double a,uchar b){
return a+_scale*((double)b);
}
private:
double _scale;
};
using std::transform;
void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){
CV_Assert(observations.size()>0 && niters>0 && lambda>0);
const double L2 = 8.0, tau = 0.02, sigma = 1./(L2*tau), theta = 1.0;
double clambda = (double)lambda;
double s=0;
const int workdepth = CV_64F;
int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count;
for(i=1;i<(int)observations.size();i++){
CV_Assert(observations[i].rows==rows && observations[i].cols==cols);
}
Mat X, P = Mat::zeros(rows, cols, CV_MAKETYPE(workdepth, 2));
observations[0].convertTo(X, workdepth, 1./255);
std::vector< Mat_<double> > Rs(observations.size());
for(count=0;count<(int)Rs.size();count++){
Rs[count]=Mat::zeros(rows,cols,workdepth);
}
for( i = 0; i < niters; i++ )
{
double currsigma = i == 0 ? 1 + sigma : sigma;
// P_ = P + sigma*nabla(X)
// P(x,y) = P_(x,y)/max(||P(x,y)||,1)
for( y = 0; y < rows; y++ )
{
const double* x_curr = X.ptr<double>(y);
const double* x_next = X.ptr<double>(std::min(y+1, rows-1));
Point2d* p_curr = P.ptr<Point2d>(y);
double dx, dy, m;
for( x = 0; x < cols-1; x++ )
{
dx = (x_curr[x+1] - x_curr[x])*currsigma + p_curr[x].x;
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
m = 1.0/std::max(std::sqrt(dx*dx + dy*dy), 1.0);
p_curr[x].x = dx*m;
p_curr[x].y = dy*m;
}
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
m = 1.0/std::max(std::abs(dy), 1.0);
p_curr[x].x = 0.0;
p_curr[x].y = dy*m;
}
//Rs = clip(Rs + sigma*(X-imgs), -clambda, clambda)
for(count=0;count<(int)Rs.size();count++){
transform<MatIterator_<double>,MatConstIterator_<uchar>,MatIterator_<double>,AddFloatToCharScaled>(
Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>(),
Rs[count].begin(),AddFloatToCharScaled(-sigma/255.0));
Rs[count]+=sigma*X;
min(Rs[count],clambda,Rs[count]);
max(Rs[count],-clambda,Rs[count]);
}
for( y = 0; y < rows; y++ )
{
double* x_curr = X.ptr<double>(y);
const Point2d* p_curr = P.ptr<Point2d>(y);
const Point2d* p_prev = P.ptr<Point2d>(std::max(y - 1, 0));
// X1 = X + tau*(-nablaT(P))
x = 0;
s=0.0;
for(count=0;count<(int)Rs.size();count++){
s=s+Rs[count](y,x);
}
double x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
// X = X2 + theta*(X2 - X)
x_curr[x] = x_new + theta*(x_new - x_curr[x]);
for(x = 1; x < cols; x++ )
{
s=0.0;
for(count=0;count<(int)Rs.size();count++){
s+=Rs[count](y,x);
}
// X1 = X + tau*(-nablaT(P))
x_new = x_curr[x] + tau*(p_curr[x].x - p_curr[x-1].x + p_curr[x].y - p_prev[x].y)-tau*s;
// X = X2 + theta*(X2 - X)
x_curr[x] = x_new + theta*(x_new - x_curr[x]);
}
}
}
result.create(X.rows,X.cols,CV_8U);
X.convertTo(result, CV_8U, 255);
}
}

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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 "precomp.hpp"
#include "fast_nlmeans_denoising_invoker.hpp"
#include "fast_nlmeans_multi_denoising_invoker.hpp"
#include "fast_nlmeans_denoising_opencl.hpp"
template<typename ST, typename IT, typename UIT, typename D>
static void fastNlMeansDenoising_( const Mat& src, Mat& dst, const std::vector<float>& h,
int templateWindowSize, int searchWindowSize)
{
int hn = (int)h.size();
double granularity = (double)std::max(1., (double)dst.total()/(1 << 17));
switch (CV_MAT_CN(src.type())) {
case 1:
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<ST, IT, UIT, D, int>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case 2:
if (hn == 1)
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case 3:
if (hn == 1)
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case 4:
if (hn == 1)
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, src.rows),
FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
src, dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported number of channels! Only 1, 2, 3, and 4 are supported");
}
}
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
int templateWindowSize, int searchWindowSize)
{
CV_INSTRUMENT_REGION();
fastNlMeansDenoising(_src, _dst, std::vector<float>(1, h),
templateWindowSize, searchWindowSize);
}
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, const std::vector<float>& h,
int templateWindowSize, int searchWindowSize, int normType)
{
CV_INSTRUMENT_REGION();
int hn = (int)h.size(), type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_Assert(!_src.empty());
CV_Assert(hn == 1 || hn == cn);
Size src_size = _src.size();
CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) &&
src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
ocl_fastNlMeansDenoising(_src, _dst, &h[0], hn,
templateWindowSize, searchWindowSize, normType))
Mat src = _src.getMat();
_dst.create(src_size, src.type());
Mat dst = _dst.getMat();
switch (normType) {
case NORM_L2:
switch (depth) {
case CV_8U:
fastNlMeansDenoising_<uchar, int, unsigned, DistSquared>(src, dst, h,
templateWindowSize,
searchWindowSize);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported depth! Only CV_8U is supported for NORM_L2");
}
break;
case NORM_L1:
switch (depth) {
case CV_8U:
fastNlMeansDenoising_<uchar, int, unsigned, DistAbs>(src, dst, h,
templateWindowSize,
searchWindowSize);
break;
case CV_16U:
fastNlMeansDenoising_<ushort, int64, uint64, DistAbs>(src, dst, h,
templateWindowSize,
searchWindowSize);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported depth! Only CV_8U and CV_16U are supported for NORM_L1");
}
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported norm type! Only NORM_L2 and NORM_L1 are supported");
}
}
void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
CV_INSTRUMENT_REGION();
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
Size src_size = _src.size();
if (type != CV_8UC3 && type != CV_8UC4)
{
CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3 or CV_8UC4!");
return;
}
CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()) &&
src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
ocl_fastNlMeansDenoisingColored(_src, _dst, h, hForColorComponents,
templateWindowSize, searchWindowSize))
Mat src = _src.getMat();
_dst.create(src_size, type);
Mat dst = _dst.getMat();
Mat src_lab;
cvtColor(src, src_lab, COLOR_LBGR2Lab);
Mat l(src_size, CV_MAKE_TYPE(depth, 1));
Mat ab(src_size, CV_MAKE_TYPE(depth, 2));
Mat l_ab[] = { l, ab };
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
Mat l_ab_denoised[] = { l, ab };
Mat dst_lab(src_size, CV_MAKE_TYPE(depth, 3));
mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR, cn);
}
static void fastNlMeansDenoisingMultiCheckPreconditions(
const std::vector<Mat>& srcImgs,
int imgToDenoiseIndex, int temporalWindowSize,
int templateWindowSize, int searchWindowSize)
{
int src_imgs_size = static_cast<int>(srcImgs.size());
if (src_imgs_size == 0)
{
CV_Error(Error::StsBadArg, "Input images vector should not be empty!");
}
if (temporalWindowSize % 2 == 0 ||
searchWindowSize % 2 == 0 ||
templateWindowSize % 2 == 0) {
CV_Error(Error::StsBadArg, "All windows sizes should be odd!");
}
int temporalWindowHalfSize = temporalWindowSize / 2;
if (imgToDenoiseIndex - temporalWindowHalfSize < 0 ||
imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size)
{
CV_Error(Error::StsBadArg,
"imgToDenoiseIndex and temporalWindowSize "
"should be chosen corresponding srcImgs size!");
}
for (int i = 1; i < src_imgs_size; i++)
if (srcImgs[0].size() != srcImgs[i].size() || srcImgs[0].type() != srcImgs[i].type())
{
CV_Error(Error::StsBadArg, "Input images should have the same size and type!");
}
}
template<typename ST, typename IT, typename UIT, typename D>
static void fastNlMeansDenoisingMulti_( const std::vector<Mat>& srcImgs, Mat& dst,
int imgToDenoiseIndex, int temporalWindowSize,
const std::vector<float>& h,
int templateWindowSize, int searchWindowSize)
{
int hn = (int)h.size();
double granularity = (double)std::max(1., (double)dst.total()/(1 << 16));
switch (srcImgs[0].type())
{
case CV_8U:
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<uchar, IT, UIT, D, int>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case CV_8UC2:
if (hn == 1)
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case CV_8UC3:
if (hn == 1)
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
case CV_8UC4:
if (hn == 1)
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
else
parallel_for_(cv::Range(0, srcImgs[0].rows),
FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
srcImgs, imgToDenoiseIndex, temporalWindowSize,
dst, templateWindowSize, searchWindowSize, &h[0]),
granularity);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3 and CV_8UC4 are supported");
}
}
void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
int imgToDenoiseIndex, int temporalWindowSize,
float h, int templateWindowSize, int searchWindowSize)
{
CV_INSTRUMENT_REGION();
fastNlMeansDenoisingMulti(_srcImgs, _dst, imgToDenoiseIndex, temporalWindowSize,
std::vector<float>(1, h), templateWindowSize, searchWindowSize);
}
void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
int imgToDenoiseIndex, int temporalWindowSize,
const std::vector<float>& h,
int templateWindowSize, int searchWindowSize, int normType)
{
CV_INSTRUMENT_REGION();
std::vector<Mat> srcImgs;
_srcImgs.getMatVector(srcImgs);
fastNlMeansDenoisingMultiCheckPreconditions(
srcImgs, imgToDenoiseIndex,
temporalWindowSize, templateWindowSize, searchWindowSize);
int hn = (int)h.size();
int type = srcImgs[0].type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_Assert(hn == 1 || hn == cn);
_dst.create(srcImgs[0].size(), srcImgs[0].type());
Mat dst = _dst.getMat();
switch (normType) {
case NORM_L2:
switch (depth) {
case CV_8U:
fastNlMeansDenoisingMulti_<uchar, int, unsigned,
DistSquared>(srcImgs, dst,
imgToDenoiseIndex, temporalWindowSize,
h,
templateWindowSize, searchWindowSize);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported depth! Only CV_8U is supported for NORM_L2");
}
break;
case NORM_L1:
switch (depth) {
case CV_8U:
fastNlMeansDenoisingMulti_<uchar, int, unsigned,
DistAbs>(srcImgs, dst,
imgToDenoiseIndex, temporalWindowSize,
h,
templateWindowSize, searchWindowSize);
break;
case CV_16U:
fastNlMeansDenoisingMulti_<ushort, int64, uint64,
DistAbs>(srcImgs, dst,
imgToDenoiseIndex, temporalWindowSize,
h,
templateWindowSize, searchWindowSize);
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported depth! Only CV_8U and CV_16U are supported for NORM_L1");
}
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported norm type! Only NORM_L2 and NORM_L1 are supported");
}
}
void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
int imgToDenoiseIndex, int temporalWindowSize,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
CV_INSTRUMENT_REGION();
std::vector<Mat> srcImgs;
_srcImgs.getMatVector(srcImgs);
fastNlMeansDenoisingMultiCheckPreconditions(
srcImgs, imgToDenoiseIndex,
temporalWindowSize, templateWindowSize, searchWindowSize);
_dst.create(srcImgs[0].size(), srcImgs[0].type());
Mat dst = _dst.getMat();
int type = srcImgs[0].type(), depth = CV_MAT_DEPTH(type);
int src_imgs_size = static_cast<int>(srcImgs.size());
if (type != CV_8UC3)
{
CV_Error(Error::StsBadArg, "Type of input images should be CV_8UC3!");
return;
}
int from_to[] = { 0,0, 1,1, 2,2 };
// TODO convert only required images
std::vector<Mat> src_lab(src_imgs_size);
std::vector<Mat> l(src_imgs_size);
std::vector<Mat> ab(src_imgs_size);
for (int i = 0; i < src_imgs_size; i++)
{
src_lab[i] = Mat::zeros(srcImgs[0].size(), type);
l[i] = Mat::zeros(srcImgs[0].size(), CV_MAKE_TYPE(depth, 1));
ab[i] = Mat::zeros(srcImgs[0].size(), CV_MAKE_TYPE(depth, 2));
cvtColor(srcImgs[i], src_lab[i], COLOR_LBGR2Lab);
Mat l_ab[] = { l[i], ab[i] };
mixChannels(&src_lab[i], 1, l_ab, 2, from_to, 3);
}
Mat dst_l;
Mat dst_ab;
fastNlMeansDenoisingMulti(
l, dst_l, imgToDenoiseIndex, temporalWindowSize,
h, templateWindowSize, searchWindowSize);
fastNlMeansDenoisingMulti(
ab, dst_ab, imgToDenoiseIndex, temporalWindowSize,
hForColorComponents, templateWindowSize, searchWindowSize);
Mat l_ab_denoised[] = { dst_l, dst_ab };
Mat dst_lab(srcImgs[0].size(), srcImgs[0].type());
mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR);
}

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/*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) 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.
//
// 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 "precomp.hpp"
#include "opencv2/photo/cuda.hpp"
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_CUDAARITHM
# include "opencv2/cudaarithm.hpp"
#endif
#ifdef HAVE_OPENCV_CUDAIMGPROC
# include "opencv2/cudaimgproc.hpp"
#endif
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || !defined(HAVE_OPENCV_CUDAARITHM) || !defined(HAVE_OPENCV_CUDAIMGPROC)
void cv::cuda::nonLocalMeans(InputArray, OutputArray, float, int, int, int, Stream&) { throw_no_cuda(); }
void cv::cuda::fastNlMeansDenoising(InputArray, OutputArray, float, int, int, Stream&) { throw_no_cuda(); }
void cv::cuda::fastNlMeansDenoisingColored(InputArray, OutputArray, float, float, int, int, Stream&) { throw_no_cuda(); }
#else
//////////////////////////////////////////////////////////////////////////////////
//// Non Local Means Denosing (brute force)
namespace cv { namespace cuda { namespace device
{
namespace imgproc
{
template<typename T>
void nlm_bruteforce_gpu(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
}
}}}
void cv::cuda::nonLocalMeans(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, int borderMode, Stream& stream)
{
using cv::cuda::device::imgproc::nlm_bruteforce_gpu;
typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
static const func_t funcs[4] = { nlm_bruteforce_gpu<uchar>, nlm_bruteforce_gpu<uchar2>, nlm_bruteforce_gpu<uchar3>, 0/*nlm_bruteforce_gpu<uchar4>,*/ };
const GpuMat src = _src.getGpuMat();
CV_Assert(src.type() == CV_8U || src.type() == CV_8UC2 || src.type() == CV_8UC3);
const func_t func = funcs[src.channels() - 1];
CV_Assert(func != 0);
int b = borderMode;
CV_Assert(b == BORDER_REFLECT101 || b == BORDER_REPLICATE || b == BORDER_CONSTANT || b == BORDER_REFLECT || b == BORDER_WRAP);
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
func(src, dst, search_window/2, block_window/2, h, borderMode, StreamAccessor::getStream(stream));
}
namespace cv { namespace cuda { namespace device
{
namespace imgproc
{
void nln_fast_get_buffer_size(const PtrStepSzb& src, int search_window, int block_window, int& buffer_cols, int& buffer_rows);
template<typename T>
void nlm_fast_gpu(const PtrStepSzb& src, PtrStepSzb dst, PtrStepi buffer,
int search_window, int block_window, float h, cudaStream_t stream);
void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream);
void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream);
}
}}}
void cv::cuda::fastNlMeansDenoising(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, Stream& stream)
{
const GpuMat src = _src.getGpuMat();
CV_Assert(src.depth() == CV_8U && src.channels() < 4);
int border_size = search_window/2 + block_window/2;
Size esize = src.size() + Size(border_size, border_size) * 2;
BufferPool pool(stream);
GpuMat extended_src = pool.getBuffer(esize, src.type());
cv::cuda::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), stream);
GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
int bcols, brows;
device::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
GpuMat buffer = pool.getBuffer(brows, bcols, CV_32S);
using namespace cv::cuda::device::imgproc;
typedef void (*nlm_fast_t)(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0};
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(stream));
}
void cv::cuda::fastNlMeansDenoisingColored(InputArray _src, OutputArray _dst, float h_luminance, float h_color, int search_window, int block_window, Stream& stream)
{
const GpuMat src = _src.getGpuMat();
CV_Assert(src.type() == CV_8UC3);
BufferPool pool(stream);
GpuMat lab = pool.getBuffer(src.size(), src.type());
cv::cuda::cvtColor(src, lab, cv::COLOR_BGR2Lab, 0, stream);
GpuMat l = pool.getBuffer(src.size(), CV_8U);
GpuMat ab = pool.getBuffer(src.size(), CV_8UC2);
device::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(stream));
fastNlMeansDenoising(l, l, h_luminance, search_window, block_window, stream);
fastNlMeansDenoising(ab, ab, h_color, search_window, block_window, stream);
device::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(stream));
cv::cuda::cvtColor(lab, _dst, cv::COLOR_Lab2BGR, 0, stream);
}
#endif

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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*/
#ifndef __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_HPP__
#define __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_HPP__
#include "precomp.hpp"
#include <limits>
#include "fast_nlmeans_denoising_invoker_commons.hpp"
#include "arrays.hpp"
using namespace cv;
template <typename T, typename IT, typename UIT, typename D, typename WT>
struct FastNlMeansDenoisingInvoker :
public ParallelLoopBody
{
public:
FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
int template_window_size, int search_window_size, const float *h);
void operator() (const Range& range) const CV_OVERRIDE;
private:
void operator= (const FastNlMeansDenoisingInvoker&);
const Mat& src_;
Mat& dst_;
Mat extended_src_;
int border_size_;
int template_window_size_;
int search_window_size_;
int template_window_half_size_;
int search_window_half_size_;
typename pixelInfo<WT>::sampleType fixed_point_mult_;
int almost_template_window_size_sq_bin_shift_;
std::vector<WT> almost_dist2weight_;
void calcDistSumsForFirstElementInRow(
int i, Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const;
void calcDistSumsForElementInFirstRow(
int i, int j, int first_col_num,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const;
};
inline int getNearestPowerOf2(int value)
{
int p = 0;
while( 1 << p < value)
++p;
return p;
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
FastNlMeansDenoisingInvoker<T, IT, UIT, D, WT>::FastNlMeansDenoisingInvoker(
const Mat& src, Mat& dst,
int template_window_size,
int search_window_size,
const float *h) :
src_(src), dst_(dst)
{
CV_Assert(src.channels() == pixelInfo<T>::channels);
template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2;
template_window_size_ = template_window_half_size_ * 2 + 1;
search_window_size_ = search_window_half_size_ * 2 + 1;
border_size_ = search_window_half_size_ + template_window_half_size_;
copyMakeBorder(src_, extended_src_, border_size_, border_size_, border_size_, border_size_, BORDER_DEFAULT);
const IT max_estimate_sum_value =
(IT)search_window_size_ * (IT)search_window_size_ * (IT)pixelInfo<T>::sampleMax();
fixed_point_mult_ = (int)std::min<IT>(std::numeric_limits<IT>::max() / max_estimate_sum_value,
pixelInfo<WT>::sampleMax());
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
CV_Assert(template_window_size_ <= 46340); // sqrt(INT_MAX)
int template_window_size_sq = template_window_size_ * template_window_size_;
almost_template_window_size_sq_bin_shift_ = getNearestPowerOf2(template_window_size_sq);
double almost_dist2actual_dist_multiplier = ((double)(1 << almost_template_window_size_sq_bin_shift_)) / template_window_size_sq;
int max_dist = D::template maxDist<T>();
int almost_max_dist = (int)(max_dist / almost_dist2actual_dist_multiplier + 1);
almost_dist2weight_.resize(almost_max_dist);
for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++)
{
double dist = almost_dist * almost_dist2actual_dist_multiplier;
almost_dist2weight_[almost_dist] =
D::template calcWeight<T, WT>(dist, h, fixed_point_mult_);
}
// additional optimization init end
if (dst_.empty())
dst_ = Mat::zeros(src_.size(), src_.type());
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
void FastNlMeansDenoisingInvoker<T, IT, UIT, D, WT>::operator() (const Range& range) const
{
int row_from = range.start;
int row_to = range.end - 1;
// sums of cols anf rows for current pixel p
Array2d<int> dist_sums(search_window_size_, search_window_size_);
// for lazy calc optimization (sum of cols for current pixel)
Array3d<int> col_dist_sums(template_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1;
// last elements of column sum (for each element in row)
Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_);
for (int i = row_from; i <= row_to; i++)
{
for (int j = 0; j < src_.cols; j++)
{
int search_window_y = i - search_window_half_size_;
int search_window_x = j - search_window_half_size_;
// calc dist_sums
if (j == 0)
{
calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
first_col_num = 0;
}
else
{
// calc cur dist_sums using previous dist_sums
if (i == row_from)
{
calcDistSumsForElementInFirstRow(i, j, first_col_num,
dist_sums, col_dist_sums, up_col_dist_sums);
}
else
{
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by = border_size_ + i - search_window_half_size_;
int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
T a_up = extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
T a_down = extended_src_.at<T>(ay + template_window_half_size_, ax);
// copy class member to local variable for optimization
int search_window_size = search_window_size_;
for (int y = 0; y < search_window_size; y++)
{
int * dist_sums_row = dist_sums.row_ptr(y);
int * col_dist_sums_row = col_dist_sums.row_ptr(first_col_num, y);
int * up_col_dist_sums_row = up_col_dist_sums.row_ptr(j, y);
const T * b_up_ptr = extended_src_.ptr<T>(start_by - template_window_half_size_ - 1 + y);
const T * b_down_ptr = extended_src_.ptr<T>(start_by + template_window_half_size_ + y);
// MSVC 2015 generates unaligned destination for "movaps" instruction for 32-bit builds
#if defined _MSC_VER && _MSC_VER == 1900 && !defined _WIN64
#pragma loop(no_vector)
#endif
for (int x = 0; x < search_window_size; x++)
{
// remove from current pixel sum column sum with index "first_col_num"
dist_sums_row[x] -= col_dist_sums_row[x];
int bx = start_bx + x;
col_dist_sums_row[x] = up_col_dist_sums_row[x] + D::template calcUpDownDist<T>(a_up, a_down, b_up_ptr[bx], b_down_ptr[bx]);
dist_sums_row[x] += col_dist_sums_row[x];
up_col_dist_sums_row[x] = col_dist_sums_row[x];
}
}
}
first_col_num = (first_col_num + 1) % template_window_size_;
}
// calc weights
IT estimation[pixelInfo<T>::channels], weights_sum[pixelInfo<WT>::channels];
for (int channel_num = 0; channel_num < pixelInfo<T>::channels; channel_num++)
estimation[channel_num] = 0;
for (int channel_num = 0; channel_num < pixelInfo<WT>::channels; channel_num++)
weights_sum[channel_num] = 0;
for (int y = 0; y < search_window_size_; y++)
{
const T* cur_row_ptr = extended_src_.ptr<T>(border_size_ + search_window_y + y);
int* dist_sums_row = dist_sums.row_ptr(y);
for (int x = 0; x < search_window_size_; x++)
{
int almostAvgDist = dist_sums_row[x] >> almost_template_window_size_sq_bin_shift_;
WT weight = almost_dist2weight_[almostAvgDist];
T p = cur_row_ptr[border_size_ + search_window_x + x];
incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p);
}
}
divByWeightsSum<IT, UIT, pixelInfo<T>::channels, pixelInfo<WT>::channels>(estimation,
weights_sum);
dst_.at<T>(i,j) = saturateCastFromArray<T, IT>(estimation);
}
}
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
inline void FastNlMeansDenoisingInvoker<T, IT, UIT, D, WT>::calcDistSumsForFirstElementInRow(
int i,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const
{
int j = 0;
for (int y = 0; y < search_window_size_; y++)
for (int x = 0; x < search_window_size_; x++)
{
dist_sums[y][x] = 0;
for (int tx = 0; tx < template_window_size_; tx++)
col_dist_sums[tx][y][x] = 0;
int start_y = i + y - search_window_half_size_;
int start_x = j + x - search_window_half_size_;
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++)
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++)
{
int dist = D::template calcDist<T>(extended_src_,
border_size_ + i + ty, border_size_ + j + tx,
border_size_ + start_y + ty, border_size_ + start_x + tx);
dist_sums[y][x] += dist;
col_dist_sums[tx + template_window_half_size_][y][x] += dist;
}
up_col_dist_sums[j][y][x] = col_dist_sums[template_window_size_ - 1][y][x];
}
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
inline void FastNlMeansDenoisingInvoker<T, IT, UIT, D, WT>::calcDistSumsForElementInFirstRow(
int i, int j, int first_col_num,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const
{
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by = border_size_ + i - search_window_half_size_;
int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
int new_last_col_num = first_col_num;
for (int y = 0; y < search_window_size_; y++)
for (int x = 0; x < search_window_size_; x++)
{
dist_sums[y][x] -= col_dist_sums[first_col_num][y][x];
col_dist_sums[new_last_col_num][y][x] = 0;
int by = start_by + y;
int bx = start_bx + x;
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++)
col_dist_sums[new_last_col_num][y][x] += D::template calcDist<T>(extended_src_, ay + ty, ax, by + ty, bx);
dist_sums[y][x] += col_dist_sums[new_last_col_num][y][x];
up_col_dist_sums[j][y][x] = col_dist_sums[new_last_col_num][y][x];
}
}
#endif

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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*/
#ifndef __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_COMMONS_HPP__
#define __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_COMMONS_HPP__
using namespace cv;
// std::isnan is a part of C++11 and it is not supported in MSVS2010/2012
#if defined _MSC_VER && _MSC_VER < 1800 /* MSVC 2013 */
#include <float.h>
namespace std {
template <typename T> bool isnan(T value) { return _isnan(value) != 0; }
}
#endif
template <typename T> struct pixelInfo_
{
static const int channels = 1;
typedef T sampleType;
};
template <typename ET, int n> struct pixelInfo_<Vec<ET, n> >
{
static const int channels = n;
typedef ET sampleType;
};
template <typename T> struct pixelInfo: public pixelInfo_<T>
{
typedef typename pixelInfo_<T>::sampleType sampleType;
static inline sampleType sampleMax()
{
return std::numeric_limits<sampleType>::max();
}
static inline sampleType sampleMin()
{
return std::numeric_limits<sampleType>::min();
}
static inline size_t sampleBytes()
{
return sizeof(sampleType);
}
static inline size_t sampleBits()
{
return 8*sampleBytes();
}
};
class DistAbs
{
template <typename T> struct calcDist_
{
static inline int f(const T a, const T b)
{
return std::abs((int)(a-b));
}
};
template <typename ET> struct calcDist_<Vec<ET, 2> >
{
static inline int f(const Vec<ET, 2> a, const Vec<ET, 2> b)
{
return std::abs((int)(a[0]-b[0])) + std::abs((int)(a[1]-b[1]));
}
};
template <typename ET> struct calcDist_<Vec<ET, 3> >
{
static inline int f(const Vec<ET, 3> a, const Vec<ET, 3> b)
{
return
std::abs((int)(a[0]-b[0])) +
std::abs((int)(a[1]-b[1])) +
std::abs((int)(a[2]-b[2]));
}
};
template <typename ET> struct calcDist_<Vec<ET, 4> >
{
static inline int f(const Vec<ET, 4> a, const Vec<ET, 4> b)
{
return
std::abs((int)(a[0]-b[0])) +
std::abs((int)(a[1]-b[1])) +
std::abs((int)(a[2]-b[2])) +
std::abs((int)(a[3]-b[3]));
}
};
template <typename T, typename WT> struct calcWeight_
{
static inline WT f(double dist, const float *h, WT fixed_point_mult)
{
double w = std::exp(-dist*dist / (h[0]*h[0] * pixelInfo<T>::channels));
if (std::isnan(w)) w = 1.0; // Handle h = 0.0
static const double WEIGHT_THRESHOLD = 0.001;
WT weight = (WT)cvRound(fixed_point_mult * w);
if (weight < WEIGHT_THRESHOLD * fixed_point_mult) weight = 0;
return weight;
}
};
template <typename T, typename ET, int n> struct calcWeight_<T, Vec<ET, n> >
{
static inline Vec<ET, n> f(double dist, const float *h, ET fixed_point_mult)
{
Vec<ET, n> res;
for (int i=0; i<n; i++)
res[i] = calcWeight<T, ET>(dist, &h[i], fixed_point_mult);
return res;
}
};
public:
template <typename T> static inline int calcDist(const T a, const T b)
{
return calcDist_<T>::f(a, b);
}
template <typename T>
static inline int calcDist(const Mat& m, int i1, int j1, int i2, int j2)
{
const T a = m.at<T>(i1, j1);
const T b = m.at<T>(i2, j2);
return calcDist<T>(a,b);
}
template <typename T>
static inline int calcUpDownDist(T a_up, T a_down, T b_up, T b_down)
{
return calcDist<T>(a_down, b_down) - calcDist<T>(a_up, b_up);
};
template <typename T, typename WT>
static inline WT calcWeight(double dist, const float *h,
typename pixelInfo<WT>::sampleType fixed_point_mult)
{
return calcWeight_<T, WT>::f(dist, h, fixed_point_mult);
}
template <typename T>
static inline int maxDist()
{
return (int)pixelInfo<T>::sampleMax() * pixelInfo<T>::channels;
}
};
class DistSquared
{
template <typename T> struct calcDist_
{
static inline int f(const T a, const T b)
{
return (int)(a-b) * (int)(a-b);
}
};
template <typename ET> struct calcDist_<Vec<ET, 2> >
{
static inline int f(const Vec<ET, 2> a, const Vec<ET, 2> b)
{
return (int)(a[0]-b[0])*(int)(a[0]-b[0]) + (int)(a[1]-b[1])*(int)(a[1]-b[1]);
}
};
template <typename ET> struct calcDist_<Vec<ET, 3> >
{
static inline int f(const Vec<ET, 3> a, const Vec<ET, 3> b)
{
return
(int)(a[0]-b[0])*(int)(a[0]-b[0]) +
(int)(a[1]-b[1])*(int)(a[1]-b[1]) +
(int)(a[2]-b[2])*(int)(a[2]-b[2]);
}
};
template <typename ET> struct calcDist_<Vec<ET, 4> >
{
static inline int f(const Vec<ET, 4> a, const Vec<ET, 4> b)
{
return
(int)(a[0]-b[0])*(int)(a[0]-b[0]) +
(int)(a[1]-b[1])*(int)(a[1]-b[1]) +
(int)(a[2]-b[2])*(int)(a[2]-b[2]) +
(int)(a[3]-b[3])*(int)(a[3]-b[3]);
}
};
template <typename T> struct calcUpDownDist_
{
static inline int f(T a_up, T a_down, T b_up, T b_down)
{
int A = a_down - b_down;
int B = a_up - b_up;
return (A-B)*(A+B);
}
};
template <typename ET, int n> struct calcUpDownDist_<Vec<ET, n> >
{
private:
typedef Vec<ET, n> T;
public:
static inline int f(T a_up, T a_down, T b_up, T b_down)
{
return calcDist<T>(a_down, b_down) - calcDist<T>(a_up, b_up);
}
};
template <typename T, typename WT> struct calcWeight_
{
static inline WT f(double dist, const float *h, WT fixed_point_mult)
{
double w = std::exp(-dist / (h[0]*h[0] * pixelInfo<T>::channels));
if (std::isnan(w)) w = 1.0; // Handle h = 0.0
static const double WEIGHT_THRESHOLD = 0.001;
WT weight = (WT)cvRound(fixed_point_mult * w);
if (weight < WEIGHT_THRESHOLD * fixed_point_mult) weight = 0;
return weight;
}
};
template <typename T, typename ET, int n> struct calcWeight_<T, Vec<ET, n> >
{
static inline Vec<ET, n> f(double dist, const float *h, ET fixed_point_mult)
{
Vec<ET, n> res;
for (int i=0; i<n; i++)
res[i] = calcWeight<T, ET>(dist, &h[i], fixed_point_mult);
return res;
}
};
public:
template <typename T> static inline int calcDist(const T a, const T b)
{
return calcDist_<T>::f(a, b);
}
template <typename T>
static inline int calcDist(const Mat& m, int i1, int j1, int i2, int j2)
{
const T a = m.at<T>(i1, j1);
const T b = m.at<T>(i2, j2);
return calcDist<T>(a,b);
}
template <typename T>
static inline int calcUpDownDist(T a_up, T a_down, T b_up, T b_down)
{
return calcUpDownDist_<T>::f(a_up, a_down, b_up, b_down);
};
template <typename T, typename WT>
static inline WT calcWeight(double dist, const float *h,
typename pixelInfo<WT>::sampleType fixed_point_mult)
{
return calcWeight_<T, WT>::f(dist, h, fixed_point_mult);
}
template <typename T>
static inline int maxDist()
{
return (int)pixelInfo<T>::sampleMax() * (int)pixelInfo<T>::sampleMax() *
pixelInfo<T>::channels;
}
};
template <typename T, typename IT, typename WT> struct incWithWeight_
{
static inline void f(IT* estimation, IT* weights_sum, WT weight, T p)
{
estimation[0] += (IT)weight * p;
weights_sum[0] += (IT)weight;
}
};
template <typename ET, typename IT, typename WT> struct incWithWeight_<Vec<ET, 2>, IT, WT>
{
static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 2> p)
{
estimation[0] += (IT)weight * p[0];
estimation[1] += (IT)weight * p[1];
weights_sum[0] += (IT)weight;
}
};
template <typename ET, typename IT, typename WT> struct incWithWeight_<Vec<ET, 3>, IT, WT>
{
static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 3> p)
{
estimation[0] += (IT)weight * p[0];
estimation[1] += (IT)weight * p[1];
estimation[2] += (IT)weight * p[2];
weights_sum[0] += (IT)weight;
}
};
template <typename ET, typename IT, typename WT> struct incWithWeight_<Vec<ET, 4>, IT, WT>
{
static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 4> p)
{
estimation[0] += (IT)weight * p[0];
estimation[1] += (IT)weight * p[1];
estimation[2] += (IT)weight * p[2];
estimation[3] += (IT)weight * p[3];
weights_sum[0] += (IT)weight;
}
};
template <typename ET, typename IT, typename EW> struct incWithWeight_<Vec<ET, 2>, IT, Vec<EW, 2> >
{
static inline void f(IT* estimation, IT* weights_sum, Vec<EW, 2> weight, Vec<ET, 2> p)
{
estimation[0] += (IT)weight[0] * p[0];
estimation[1] += (IT)weight[1] * p[1];
weights_sum[0] += (IT)weight[0];
weights_sum[1] += (IT)weight[1];
}
};
template <typename ET, typename IT, typename EW> struct incWithWeight_<Vec<ET, 3>, IT, Vec<EW, 3> >
{
static inline void f(IT* estimation, IT* weights_sum, Vec<EW, 3> weight, Vec<ET, 3> p)
{
estimation[0] += (IT)weight[0] * p[0];
estimation[1] += (IT)weight[1] * p[1];
estimation[2] += (IT)weight[2] * p[2];
weights_sum[0] += (IT)weight[0];
weights_sum[1] += (IT)weight[1];
weights_sum[2] += (IT)weight[2];
}
};
template <typename ET, typename IT, typename EW> struct incWithWeight_<Vec<ET, 4>, IT, Vec<EW, 4> >
{
static inline void f(IT* estimation, IT* weights_sum, Vec<EW, 4> weight, Vec<ET, 4> p)
{
estimation[0] += (IT)weight[0] * p[0];
estimation[1] += (IT)weight[1] * p[1];
estimation[2] += (IT)weight[2] * p[2];
estimation[3] += (IT)weight[3] * p[3];
weights_sum[0] += (IT)weight[0];
weights_sum[1] += (IT)weight[1];
weights_sum[2] += (IT)weight[2];
weights_sum[3] += (IT)weight[3];
}
};
template <typename T, typename IT, typename WT>
static inline void incWithWeight(IT* estimation, IT* weights_sum, WT weight, T p)
{
incWithWeight_<T, IT, WT>::f(estimation, weights_sum, weight, p);
}
template <typename IT, typename UIT, int nc, int nw> struct divByWeightsSum_
{
static inline void f(IT* estimation, IT* weights_sum);
};
template <typename IT, typename UIT> struct divByWeightsSum_<IT, UIT, 1, 1>
{
static inline void f(IT* estimation, IT* weights_sum)
{
estimation[0] = (static_cast<UIT>(estimation[0]) + weights_sum[0]/2) / weights_sum[0];
}
};
template <typename IT, typename UIT, int n> struct divByWeightsSum_<IT, UIT, n, 1>
{
static inline void f(IT* estimation, IT* weights_sum)
{
for (size_t i = 0; i < n; i++)
estimation[i] = (static_cast<UIT>(estimation[i]) + weights_sum[0]/2) / weights_sum[0];
}
};
template <typename IT, typename UIT, int n> struct divByWeightsSum_<IT, UIT, n, n>
{
static inline void f(IT* estimation, IT* weights_sum)
{
for (size_t i = 0; i < n; i++)
estimation[i] = (static_cast<UIT>(estimation[i]) + weights_sum[i]/2) / weights_sum[i];
}
};
template <typename IT, typename UIT, int nc, int nw>
static inline void divByWeightsSum(IT* estimation, IT* weights_sum)
{
divByWeightsSum_<IT, UIT, nc, nw>::f(estimation, weights_sum);
}
template <typename T, typename IT> struct saturateCastFromArray_
{
static inline T f(IT* estimation)
{
return saturate_cast<T>(estimation[0]);
}
};
template <typename ET, typename IT> struct saturateCastFromArray_<Vec<ET, 2>, IT>
{
static inline Vec<ET, 2> f(IT* estimation)
{
Vec<ET, 2> res;
res[0] = saturate_cast<ET>(estimation[0]);
res[1] = saturate_cast<ET>(estimation[1]);
return res;
}
};
template <typename ET, typename IT> struct saturateCastFromArray_<Vec<ET, 3>, IT>
{
static inline Vec<ET, 3> f(IT* estimation)
{
Vec<ET, 3> res;
res[0] = saturate_cast<ET>(estimation[0]);
res[1] = saturate_cast<ET>(estimation[1]);
res[2] = saturate_cast<ET>(estimation[2]);
return res;
}
};
template <typename ET, typename IT> struct saturateCastFromArray_<Vec<ET, 4>, IT>
{
static inline Vec<ET, 4> f(IT* estimation)
{
Vec<ET, 4> res;
res[0] = saturate_cast<ET>(estimation[0]);
res[1] = saturate_cast<ET>(estimation[1]);
res[2] = saturate_cast<ET>(estimation[2]);
res[3] = saturate_cast<ET>(estimation[3]);
return res;
}
};
template <typename T, typename IT> static inline T saturateCastFromArray(IT* estimation)
{
return saturateCastFromArray_<T, IT>::f(estimation);
}
#endif

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
#include "precomp.hpp"
#ifndef __OPENCV_FAST_NLMEANS_DENOISING_OPENCL_HPP__
#define __OPENCV_FAST_NLMEANS_DENOISING_OPENCL_HPP__
#include "opencl_kernels_photo.hpp"
#ifdef HAVE_OPENCL
namespace cv {
enum
{
BLOCK_ROWS = 32,
BLOCK_COLS = 32,
CTA_SIZE_INTEL = 64,
CTA_SIZE_DEFAULT = 256
};
template <typename FT, typename ST, typename WT>
static bool ocl_calcAlmostDist2Weight(UMat & almostDist2Weight,
int searchWindowSize, int templateWindowSize,
const FT *h, int hn, int cn, int normType,
int & almostTemplateWindowSizeSqBinShift)
{
const WT maxEstimateSumValue = searchWindowSize * searchWindowSize *
std::numeric_limits<ST>::max();
int fixedPointMult = (int)std::min<WT>(std::numeric_limits<WT>::max() / maxEstimateSumValue,
std::numeric_limits<int>::max());
int depth = DataType<FT>::depth;
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
if (depth == CV_64F && !doubleSupport)
return false;
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
CV_Assert(templateWindowSize <= 46340); // sqrt(INT_MAX)
int templateWindowSizeSq = templateWindowSize * templateWindowSize;
almostTemplateWindowSizeSqBinShift = getNearestPowerOf2(templateWindowSizeSq);
FT almostDist2ActualDistMultiplier = (FT)(1 << almostTemplateWindowSizeSqBinShift) / templateWindowSizeSq;
const FT WEIGHT_THRESHOLD = 1e-3f;
WT maxDist = normType == NORM_L1 ? (WT)std::numeric_limits<ST>::max() * cn :
(WT)std::numeric_limits<ST>::max() * (WT)std::numeric_limits<ST>::max() * cn;
int almostMaxDist = (int)(maxDist / almostDist2ActualDistMultiplier + 1);
FT den[4];
CV_Assert(hn > 0 && hn <= 4);
for (int i=0; i<hn; i++)
den[i] = 1.0f / (h[i] * h[i] * cn);
almostDist2Weight.create(1, almostMaxDist, CV_32SC(hn == 3 ? 4 : hn));
char buf[40];
ocl::Kernel k("calcAlmostDist2Weight", ocl::photo::nlmeans_oclsrc,
format("-D OP_CALC_WEIGHTS -D FT=%s -D w_t=%s"
" -D wlut_t=%s -D convert_wlut_t=%s%s%s",
ocl::typeToStr(depth), ocl::typeToStr(CV_MAKE_TYPE(depth, hn)),
ocl::typeToStr(CV_32SC(hn)), ocl::convertTypeStr(depth, CV_32S, hn, buf),
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
normType == NORM_L1 ? " -D ABS" : ""));
if (k.empty())
return false;
k.args(ocl::KernelArg::PtrWriteOnly(almostDist2Weight), almostMaxDist,
almostDist2ActualDistMultiplier, fixedPointMult,
ocl::KernelArg::Constant(den, (hn == 3 ? 4 : hn)*sizeof(FT)), WEIGHT_THRESHOLD);
size_t globalsize[1] = { (size_t)almostMaxDist };
return k.run(1, globalsize, NULL, false);
}
static bool ocl_fastNlMeansDenoising(InputArray _src, OutputArray _dst, const float *h, int hn,
int templateWindowSize, int searchWindowSize, int normType)
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
int ctaSize = ocl::Device::getDefault().isIntel() ? CTA_SIZE_INTEL : CTA_SIZE_DEFAULT;
Size size = _src.size();
if (cn < 1 || cn > 4 || ((normType != NORM_L2 || depth != CV_8U) &&
(normType != NORM_L1 || (depth != CV_8U && depth != CV_16U))))
return false;
int templateWindowHalfWize = templateWindowSize / 2;
int searchWindowHalfSize = searchWindowSize / 2;
templateWindowSize = templateWindowHalfWize * 2 + 1;
searchWindowSize = searchWindowHalfSize * 2 + 1;
int nblocksx = divUp(size.width, BLOCK_COLS), nblocksy = divUp(size.height, BLOCK_ROWS);
int almostTemplateWindowSizeSqBinShift = -1;
char buf[4][40];
const unsigned psz = (depth == CV_8U ? sizeof(uchar) : sizeof(ushort)) * (cn == 3 ? 4 : cn);
String opts = format("-D OP_CALC_FASTNLMEANS -D TEMPLATE_SIZE=%d -D SEARCH_SIZE=%d"
" -D pixel_t=%s -D int_t=%s -D wlut_t=%s"
" -D weight_t=%s -D convert_weight_t=%s -D sum_t=%s -D convert_sum_t=%s"
" -D BLOCK_COLS=%d -D BLOCK_ROWS=%d"
" -D CTA_SIZE=%d -D TEMPLATE_SIZE2=%d -D SEARCH_SIZE2=%d"
" -D convert_int_t=%s -D cn=%d -D psz=%u -D convert_pixel_t=%s%s",
templateWindowSize, searchWindowSize,
ocl::typeToStr(type), ocl::typeToStr(CV_32SC(cn)),
ocl::typeToStr(CV_32SC(hn)),
depth == CV_8U ? ocl::typeToStr(CV_32SC(hn)) :
format("long%s", hn > 1 ? format("%d", hn).c_str() : "").c_str(),
depth == CV_8U ? ocl::convertTypeStr(CV_32S, CV_32S, hn, buf[0]) :
format("convert_long%s", hn > 1 ? format("%d", hn).c_str() : "").c_str(),
depth == CV_8U ? ocl::typeToStr(CV_32SC(cn)) :
format("long%s", cn > 1 ? format("%d", cn).c_str() : "").c_str(),
depth == CV_8U ? ocl::convertTypeStr(depth, CV_32S, cn, buf[1]) :
format("convert_long%s", cn > 1 ? format("%d", cn).c_str() : "").c_str(),
BLOCK_COLS, BLOCK_ROWS,
ctaSize, templateWindowHalfWize, searchWindowHalfSize,
ocl::convertTypeStr(depth, CV_32S, cn, buf[2]), cn,
psz,
ocl::convertTypeStr(CV_32S, depth, cn, buf[3]),
normType == NORM_L1 ? " -D ABS" : "");
ocl::Kernel k("fastNlMeansDenoising", ocl::photo::nlmeans_oclsrc, opts);
if (k.empty())
return false;
UMat almostDist2Weight;
if ((depth == CV_8U &&
!ocl_calcAlmostDist2Weight<float, uchar, int>(almostDist2Weight,
searchWindowSize, templateWindowSize,
h, hn, cn, normType,
almostTemplateWindowSizeSqBinShift)) ||
(depth == CV_16U &&
!ocl_calcAlmostDist2Weight<float, ushort, int64>(almostDist2Weight,
searchWindowSize, templateWindowSize,
h, hn, cn, normType,
almostTemplateWindowSizeSqBinShift)))
return false;
CV_Assert(almostTemplateWindowSizeSqBinShift >= 0);
UMat srcex;
int borderSize = searchWindowHalfSize + templateWindowHalfWize;
if (cn == 3) {
srcex.create(size.height + 2*borderSize, size.width + 2*borderSize, CV_MAKE_TYPE(depth, 4));
UMat src(srcex, Rect(borderSize, borderSize, size.width, size.height));
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(std::vector<UMat>(1, _src.getUMat()), std::vector<UMat>(1, src), from_to, 3);
copyMakeBorder(src, srcex, borderSize, borderSize, borderSize, borderSize,
BORDER_DEFAULT|BORDER_ISOLATED); // create borders in place
}
else
copyMakeBorder(_src, srcex, borderSize, borderSize, borderSize, borderSize, BORDER_DEFAULT);
_dst.create(size, type);
UMat dst;
if (cn == 3)
dst.create(size, CV_MAKE_TYPE(depth, 4));
else
dst = _dst.getUMat();
int searchWindowSizeSq = searchWindowSize * searchWindowSize;
Size upColSumSize(size.width, searchWindowSizeSq * nblocksy);
Size colSumSize(nblocksx * templateWindowSize, searchWindowSizeSq * nblocksy);
UMat buffer(upColSumSize + colSumSize, CV_32SC(cn));
srcex = srcex(Rect(Point(borderSize, borderSize), size));
k.args(ocl::KernelArg::ReadOnlyNoSize(srcex), ocl::KernelArg::WriteOnly(dst),
ocl::KernelArg::PtrReadOnly(almostDist2Weight),
ocl::KernelArg::PtrReadOnly(buffer), almostTemplateWindowSizeSqBinShift);
size_t globalsize[2] = { (size_t)nblocksx * ctaSize, (size_t)nblocksy }, localsize[2] = { (size_t)ctaSize, 1 };
if (!k.run(2, globalsize, localsize, false)) return false;
if (cn == 3) {
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(std::vector<UMat>(1, dst), std::vector<UMat>(1, _dst.getUMat()), from_to, 3);
}
return true;
}
static bool ocl_fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
UMat src = _src.getUMat();
_dst.create(src.size(), src.type());
UMat dst = _dst.getUMat();
UMat src_lab;
cvtColor(src, src_lab, COLOR_LBGR2Lab);
UMat l(src.size(), CV_8U);
UMat ab(src.size(), CV_8UC2);
std::vector<UMat> l_ab(2), l_ab_denoised(2);
l_ab[0] = l;
l_ab[1] = ab;
l_ab_denoised[0].create(src.size(), CV_8U);
l_ab_denoised[1].create(src.size(), CV_8UC2);
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(std::vector<UMat>(1, src_lab), l_ab, from_to, 3);
fastNlMeansDenoising(l_ab[0], l_ab_denoised[0], h, templateWindowSize, searchWindowSize);
fastNlMeansDenoising(l_ab[1], l_ab_denoised[1], hForColorComponents, templateWindowSize, searchWindowSize);
UMat dst_lab(src.size(), CV_8UC3);
mixChannels(l_ab_denoised, std::vector<UMat>(1, dst_lab), from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR, src.channels());
return true;
}
}
#endif
#endif

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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*/
#ifndef __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
#define __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
#include "precomp.hpp"
#include <limits>
#include "fast_nlmeans_denoising_invoker_commons.hpp"
#include "arrays.hpp"
using namespace cv;
template <typename T, typename IT, typename UIT, typename D, typename WT>
struct FastNlMeansMultiDenoisingInvoker :
ParallelLoopBody
{
public:
FastNlMeansMultiDenoisingInvoker(const std::vector<Mat>& srcImgs, int imgToDenoiseIndex,
int temporalWindowSize, Mat& dst, int template_window_size,
int search_window_size, const float *h);
void operator() (const Range& range) const CV_OVERRIDE;
private:
void operator= (const FastNlMeansMultiDenoisingInvoker&);
int rows_;
int cols_;
Mat& dst_;
std::vector<Mat> extended_srcs_;
Mat main_extended_src_;
int border_size_;
int template_window_size_;
int search_window_size_;
int temporal_window_size_;
int template_window_half_size_;
int search_window_half_size_;
int temporal_window_half_size_;
typename pixelInfo<WT>::sampleType fixed_point_mult_;
int almost_template_window_size_sq_bin_shift;
std::vector<WT> almost_dist2weight;
void calcDistSumsForFirstElementInRow(int i, Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const;
void calcDistSumsForElementInFirstRow(int i, int j, int first_col_num,
Array3d<int>& dist_sums, Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const;
};
template <typename T, typename IT, typename UIT, typename D, typename WT>
FastNlMeansMultiDenoisingInvoker<T, IT, UIT, D, WT>::FastNlMeansMultiDenoisingInvoker(
const std::vector<Mat>& srcImgs,
int imgToDenoiseIndex,
int temporalWindowSize,
cv::Mat& dst,
int template_window_size,
int search_window_size,
const float *h) :
dst_(dst), extended_srcs_(srcImgs.size())
{
CV_Assert(srcImgs.size() > 0);
CV_Assert(srcImgs[0].channels() == pixelInfo<T>::channels);
rows_ = srcImgs[0].rows;
cols_ = srcImgs[0].cols;
template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2;
temporal_window_half_size_ = temporalWindowSize / 2;
template_window_size_ = template_window_half_size_ * 2 + 1;
search_window_size_ = search_window_half_size_ * 2 + 1;
temporal_window_size_ = temporal_window_half_size_ * 2 + 1;
border_size_ = search_window_half_size_ + template_window_half_size_;
for (int i = 0; i < temporal_window_size_; i++)
copyMakeBorder(srcImgs[imgToDenoiseIndex - temporal_window_half_size_ + i], extended_srcs_[i],
border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
main_extended_src_ = extended_srcs_[temporal_window_half_size_];
const IT max_estimate_sum_value =
(IT)temporal_window_size_ * (IT)search_window_size_ * (IT)search_window_size_ * (IT)pixelInfo<T>::sampleMax();
fixed_point_mult_ = (int)std::min<IT>(std::numeric_limits<IT>::max() / max_estimate_sum_value,
pixelInfo<WT>::sampleMax());
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
int template_window_size_sq = template_window_size_ * template_window_size_;
almost_template_window_size_sq_bin_shift = 0;
while (1 << almost_template_window_size_sq_bin_shift < template_window_size_sq)
almost_template_window_size_sq_bin_shift++;
int almost_template_window_size_sq = 1 << almost_template_window_size_sq_bin_shift;
double almost_dist2actual_dist_multiplier = (double) almost_template_window_size_sq / template_window_size_sq;
int max_dist = D::template maxDist<T>();
int almost_max_dist = (int)(max_dist / almost_dist2actual_dist_multiplier + 1);
almost_dist2weight.resize(almost_max_dist);
for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++)
{
double dist = almost_dist * almost_dist2actual_dist_multiplier;
almost_dist2weight[almost_dist] =
D::template calcWeight<T, WT>(dist, h, fixed_point_mult_);
}
// additional optimization init end
if (dst_.empty())
dst_ = Mat::zeros(srcImgs[0].size(), srcImgs[0].type());
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
void FastNlMeansMultiDenoisingInvoker<T, IT, UIT, D, WT>::operator() (const Range& range) const
{
int row_from = range.start;
int row_to = range.end - 1;
Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
// for lazy calc optimization
Array4d<int> col_dist_sums(template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1;
Array4d<int> up_col_dist_sums(cols_, temporal_window_size_, search_window_size_, search_window_size_);
for (int i = row_from; i <= row_to; i++)
{
for (int j = 0; j < cols_; j++)
{
int search_window_y = i - search_window_half_size_;
int search_window_x = j - search_window_half_size_;
// calc dist_sums
if (j == 0)
{
calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
first_col_num = 0;
}
else
{
// calc cur dist_sums using previous dist_sums
if (i == row_from)
{
calcDistSumsForElementInFirstRow(i, j, first_col_num,
dist_sums, col_dist_sums, up_col_dist_sums);
}
else
{
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by =
border_size_ + i - search_window_half_size_;
int start_bx =
border_size_ + j - search_window_half_size_ + template_window_half_size_;
T a_up = main_extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
T a_down = main_extended_src_.at<T>(ay + template_window_half_size_, ax);
// copy class member to local variable for optimization
int search_window_size = search_window_size_;
for (int d = 0; d < temporal_window_size_; d++)
{
Mat cur_extended_src = extended_srcs_[d];
Array2d<int> cur_dist_sums = dist_sums[d];
Array2d<int> cur_col_dist_sums = col_dist_sums[first_col_num][d];
Array2d<int> cur_up_col_dist_sums = up_col_dist_sums[j][d];
for (int y = 0; y < search_window_size; y++)
{
int* dist_sums_row = cur_dist_sums.row_ptr(y);
int* col_dist_sums_row = cur_col_dist_sums.row_ptr(y);
int* up_col_dist_sums_row = cur_up_col_dist_sums.row_ptr(y);
const T* b_up_ptr = cur_extended_src.ptr<T>(start_by - template_window_half_size_ - 1 + y);
const T* b_down_ptr = cur_extended_src.ptr<T>(start_by + template_window_half_size_ + y);
for (int x = 0; x < search_window_size; x++)
{
dist_sums_row[x] -= col_dist_sums_row[x];
col_dist_sums_row[x] = up_col_dist_sums_row[x] +
D::template calcUpDownDist<T>(a_up, a_down, b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]);
dist_sums_row[x] += col_dist_sums_row[x];
up_col_dist_sums_row[x] = col_dist_sums_row[x];
}
}
}
}
first_col_num = (first_col_num + 1) % template_window_size_;
}
// calc weights
IT estimation[pixelInfo<T>::channels], weights_sum[pixelInfo<WT>::channels];
for (size_t channel_num = 0; channel_num < pixelInfo<T>::channels; channel_num++)
estimation[channel_num] = 0;
for (size_t channel_num = 0; channel_num < pixelInfo<WT>::channels; channel_num++)
weights_sum[channel_num] = 0;
for (int d = 0; d < temporal_window_size_; d++)
{
const Mat& esrc_d = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++)
{
const T* cur_row_ptr = esrc_d.ptr<T>(border_size_ + search_window_y + y);
int* dist_sums_row = dist_sums.row_ptr(d, y);
for (int x = 0; x < search_window_size_; x++)
{
int almostAvgDist = dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
WT weight = almost_dist2weight[almostAvgDist];
T p = cur_row_ptr[border_size_ + search_window_x + x];
incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p);
}
}
}
divByWeightsSum<IT, UIT, pixelInfo<T>::channels, pixelInfo<WT>::channels>(estimation,
weights_sum);
dst_.at<T>(i,j) = saturateCastFromArray<T, IT>(estimation);
}
}
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
inline void FastNlMeansMultiDenoisingInvoker<T, IT, UIT, D, WT>::calcDistSumsForFirstElementInRow(
int i, Array3d<int>& dist_sums, Array4d<int>& col_dist_sums, Array4d<int>& up_col_dist_sums) const
{
int j = 0;
for (int d = 0; d < temporal_window_size_; d++)
{
Mat cur_extended_src = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++)
for (int x = 0; x < search_window_size_; x++)
{
dist_sums[d][y][x] = 0;
for (int tx = 0; tx < template_window_size_; tx++)
col_dist_sums[tx][d][y][x] = 0;
int start_y = i + y - search_window_half_size_;
int start_x = j + x - search_window_half_size_;
int* dist_sums_ptr = &dist_sums[d][y][x];
int* col_dist_sums_ptr = &col_dist_sums[0][d][y][x];
int col_dist_sums_step = col_dist_sums.step_size(0);
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++)
{
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++)
{
int dist = D::template calcDist<T>(
main_extended_src_.at<T>(border_size_ + i + ty, border_size_ + j + tx),
cur_extended_src.at<T>(border_size_ + start_y + ty, border_size_ + start_x + tx));
*dist_sums_ptr += dist;
*col_dist_sums_ptr += dist;
}
col_dist_sums_ptr += col_dist_sums_step;
}
up_col_dist_sums[j][d][y][x] = col_dist_sums[template_window_size_ - 1][d][y][x];
}
}
}
template <typename T, typename IT, typename UIT, typename D, typename WT>
inline void FastNlMeansMultiDenoisingInvoker<T, IT, UIT, D, WT>::calcDistSumsForElementInFirstRow(
int i, int j, int first_col_num, Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums, Array4d<int>& up_col_dist_sums) const
{
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by = border_size_ + i - search_window_half_size_;
int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
int new_last_col_num = first_col_num;
for (int d = 0; d < temporal_window_size_; d++)
{
Mat cur_extended_src = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++)
for (int x = 0; x < search_window_size_; x++)
{
dist_sums[d][y][x] -= col_dist_sums[first_col_num][d][y][x];
col_dist_sums[new_last_col_num][d][y][x] = 0;
int by = start_by + y;
int bx = start_bx + x;
int* col_dist_sums_ptr = &col_dist_sums[new_last_col_num][d][y][x];
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++)
{
*col_dist_sums_ptr += D::template calcDist<T>(
main_extended_src_.at<T>(ay + ty, ax),
cur_extended_src.at<T>(by + ty, bx));
}
dist_sums[d][y][x] += col_dist_sums[new_last_col_num][d][y][x];
up_col_dist_sums[j][d][y][x] = col_dist_sums[new_last_col_num][d][y][x];
}
}
}
#endif

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "hdr_common.hpp"
namespace cv
{
void checkImageDimensions(const std::vector<Mat>& images)
{
CV_Assert(!images.empty());
int width = images[0].cols;
int height = images[0].rows;
int type = images[0].type();
for(size_t i = 0; i < images.size(); i++) {
CV_Assert(images[i].cols == width && images[i].rows == height);
CV_Assert(images[i].type() == type);
}
}
Mat triangleWeights()
{
// hat function
Mat w(LDR_SIZE, 1, CV_32F);
int half = LDR_SIZE / 2;
for(int i = 0; i < LDR_SIZE; i++) {
w.at<float>(i) = i < half ? i + 1.0f : LDR_SIZE - i;
}
return w;
}
Mat RobertsonWeights()
{
Mat weight(LDR_SIZE, 1, CV_32FC3);
float q = (LDR_SIZE - 1) / 4.0f;
float e4 = exp(4.f);
float scale = e4/(e4 - 1.f);
float shift = 1 / (1.f - e4);
for(int i = 0; i < LDR_SIZE; i++) {
float value = i / q - 2.0f;
value = scale*exp(-value * value) + shift;
weight.at<Vec3f>(i) = Vec3f::all(value);
}
return weight;
}
void mapLuminance(Mat src, Mat dst, Mat lum, Mat new_lum, float saturation)
{
std::vector<Mat> channels(3);
split(src, channels);
for(int i = 0; i < 3; i++) {
channels[i] = channels[i].mul(1.0f / lum);
pow(channels[i], saturation, channels[i]);
channels[i] = channels[i].mul(new_lum);
}
merge(channels, dst);
}
Mat linearResponse(int channels)
{
Mat response = Mat(LDR_SIZE, 1, CV_MAKETYPE(CV_32F, channels));
for(int i = 0; i < LDR_SIZE; i++) {
response.at<Vec3f>(i) = Vec3f::all(static_cast<float>(i));
}
return response;
}
}

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/*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) 2013, OpenCV Foundation, 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*/
#ifndef __OPENCV_HDR_COMMON_HPP__
#define __OPENCV_HDR_COMMON_HPP__
#include "precomp.hpp"
#include "opencv2/photo.hpp"
namespace cv
{
void checkImageDimensions(const std::vector<Mat>& images);
Mat triangleWeights();
void mapLuminance(Mat src, Mat dst, Mat lum, Mat new_lum, float saturation);
Mat RobertsonWeights();
Mat linearResponse(int channels);
}
#endif

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective icvers.
//
// 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 Intel Corporation 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*/
/* ////////////////////////////////////////////////////////////////////
//
// Geometrical transforms on images and matrices: rotation, zoom etc.
//
// */
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/photo/legacy/constants_c.h"
#undef CV_MAT_ELEM_PTR_FAST
#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \
((mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col))
inline float
min4( float a, float b, float c, float d )
{
a = MIN(a,b);
c = MIN(c,d);
return MIN(a,c);
}
#define CV_MAT_3COLOR_ELEM(img,type,y,x,c) CV_MAT_ELEM(img,type,y,(x)*3+(c))
#define KNOWN 0 //known outside narrow band
#define BAND 1 //narrow band (known)
#define INSIDE 2 //unknown
#define CHANGE 3 //servise
typedef struct CvHeapElem
{
float T;
int i,j;
struct CvHeapElem* prev;
struct CvHeapElem* next;
}
CvHeapElem;
class CvPriorityQueueFloat
{
private:
CvPriorityQueueFloat(const CvPriorityQueueFloat & ); // copy disabled
CvPriorityQueueFloat& operator=(const CvPriorityQueueFloat &); // assign disabled
protected:
CvHeapElem *mem,*empty,*head,*tail;
int num,in;
public:
bool Init( const CvMat* f )
{
int i,j;
for( i = num = 0; i < f->rows; i++ )
{
for( j = 0; j < f->cols; j++ )
num += CV_MAT_ELEM(*f,uchar,i,j)!=0;
}
if (num<=0) return false;
mem = (CvHeapElem*)cvAlloc((num+2)*sizeof(CvHeapElem));
if (mem==NULL) return false;
head = mem;
head->i = head->j = -1;
head->prev = NULL;
head->next = mem+1;
head->T = -FLT_MAX;
empty = mem+1;
for (i=1; i<=num; i++) {
mem[i].prev = mem+i-1;
mem[i].next = mem+i+1;
mem[i].i = -1;
mem[i].T = FLT_MAX;
}
tail = mem+i;
tail->i = tail->j = -1;
tail->prev = mem+i-1;
tail->next = NULL;
tail->T = FLT_MAX;
return true;
}
bool Add(const CvMat* f) {
int i,j;
for (i=0; i<f->rows; i++) {
for (j=0; j<f->cols; j++) {
if (CV_MAT_ELEM(*f,uchar,i,j)!=0) {
if (!Push(i,j,0)) return false;
}
}
}
return true;
}
bool Push(int i, int j, float T) {
CvHeapElem *tmp=empty,*add=empty;
if (empty==tail) return false;
while (tmp->prev->T>T) tmp = tmp->prev;
if (tmp!=empty) {
add->prev->next = add->next;
add->next->prev = add->prev;
empty = add->next;
add->prev = tmp->prev;
add->next = tmp;
add->prev->next = add;
add->next->prev = add;
} else {
empty = empty->next;
}
add->i = i;
add->j = j;
add->T = T;
in++;
// printf("push i %3d j %3d T %12.4e in %4d\n",i,j,T,in);
return true;
}
bool Pop(int *i, int *j) {
CvHeapElem *tmp=head->next;
if (empty==tmp) return false;
*i = tmp->i;
*j = tmp->j;
tmp->prev->next = tmp->next;
tmp->next->prev = tmp->prev;
tmp->prev = empty->prev;
tmp->next = empty;
tmp->prev->next = tmp;
tmp->next->prev = tmp;
empty = tmp;
in--;
// printf("pop i %3d j %3d T %12.4e in %4d\n",tmp->i,tmp->j,tmp->T,in);
return true;
}
bool Pop(int *i, int *j, float *T) {
CvHeapElem *tmp=head->next;
if (empty==tmp) return false;
*i = tmp->i;
*j = tmp->j;
*T = tmp->T;
tmp->prev->next = tmp->next;
tmp->next->prev = tmp->prev;
tmp->prev = empty->prev;
tmp->next = empty;
tmp->prev->next = tmp;
tmp->next->prev = tmp;
empty = tmp;
in--;
// printf("pop i %3d j %3d T %12.4e in %4d\n",tmp->i,tmp->j,tmp->T,in);
return true;
}
CvPriorityQueueFloat(void) {
num=in=0;
mem=empty=head=tail=NULL;
}
~CvPriorityQueueFloat(void)
{
cvFree( &mem );
}
};
static inline float VectorScalMult(const cv::Point2f& v1, const cv::Point2f& v2)
{
return v1.x*v2.x+v1.y*v2.y;
}
static inline float VectorLength(const cv::Point2f& v1)
{
return v1.x*v1.x+v1.y*v1.y;
}
///////////////////////////////////////////////////////////////////////////////////////////
//HEAP::iterator Heap_Iterator;
//HEAP Heap;
static float FastMarching_solve(int i1,int j1,int i2,int j2, const CvMat* f, const CvMat* t)
{
double sol, a11, a22, m12;
a11=CV_MAT_ELEM(*t,float,i1,j1);
a22=CV_MAT_ELEM(*t,float,i2,j2);
m12=MIN(a11,a22);
if( CV_MAT_ELEM(*f,uchar,i1,j1) != INSIDE )
if( CV_MAT_ELEM(*f,uchar,i2,j2) != INSIDE )
if( fabs(a11-a22) >= 1.0 )
sol = 1+m12;
else
sol = (a11+a22+sqrt((double)(2-(a11-a22)*(a11-a22))))*0.5;
else
sol = 1+a11;
else if( CV_MAT_ELEM(*f,uchar,i2,j2) != INSIDE )
sol = 1+a22;
else
sol = 1+m12;
return (float)sol;
}
/////////////////////////////////////////////////////////////////////////////////////
static void
icvCalcFMM(const CvMat *f, CvMat *t, CvPriorityQueueFloat *Heap, bool negate) {
int i, j, ii = 0, jj = 0, q;
float dist;
while (Heap->Pop(&ii,&jj)) {
unsigned known=(negate)?CHANGE:KNOWN;
CV_MAT_ELEM(*f,uchar,ii,jj) = (uchar)known;
for (q=0; q<4; q++) {
i=0; j=0;
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>f->rows)||(j>f->cols)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
if (negate) {
for (i=0; i<f->rows; i++) {
for(j=0; j<f->cols; j++) {
if (CV_MAT_ELEM(*f,uchar,i,j) == CHANGE) {
CV_MAT_ELEM(*f,uchar,i,j) = KNOWN;
CV_MAT_ELEM(*t,float,i,j) = -CV_MAT_ELEM(*t,float,i,j);
}
}
}
}
}
template <typename data_type>
static void
icvTeleaInpaintFMM(const CvMat *f, CvMat *t, CvMat *out, int range, CvPriorityQueueFloat *Heap ) {
int i = 0, j = 0, ii = 0, jj = 0, k, l, q, color = 0;
float dist;
if (CV_MAT_CN(out->type)==3) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
cv::Point2f gradT[3];
for (color=0; color<=2; color++) {
if (CV_MAT_ELEM(*f,uchar,i,j+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT[color].x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j-1)))*0.5f;
} else {
gradT[color].x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT[color].x=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i,j-1)));
} else {
gradT[color].x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,i+1,j)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT[color].y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i-1,j)))*0.5f;
} else {
gradT[color].y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT[color].y=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i-1,j)));
} else {
gradT[color].y=0;
}
}
}
cv::Point2f gradI,r;
float Jx[3] = {0,0,0};
float Jy[3] = {0,0,0};
float Ia[3] = {0,0,0};
float s[3] = {1.0e-20f,1.0e-20f,1.0e-20f};
float w,dst,lev,dir,sat;
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
for (color=0; color<=2; color++) {
r.y = (float)(i-k);
r.x = (float)(j-l);
dst = (float)(1./(VectorLength(r)*sqrt((double)VectorLength(r))));
lev = (float)(1./(1+fabs(CV_MAT_ELEM(*t,float,k,l)-CV_MAT_ELEM(*t,float,i,j))));
dir=VectorScalMult(r,gradT[color]);
if (fabs(dir)<=0.01) dir=0.000001f;
w = (float)fabs(dst*lev*dir);
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)))*2.0f;
} else {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)));
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)))*2.0f;
} else {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)));
} else {
gradI.y=0;
}
}
Ia[color] += (float)w * (float)(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color));
Jx[color] -= (float)w * (float)(gradI.x*r.x);
Jy[color] -= (float)w * (float)(gradI.y*r.y);
s[color] += w;
}
}
}
}
}
for (color=0; color<=2; color++) {
sat = (float)((Ia[color]/s[color]+(Jx[color]+Jy[color])/(sqrt(Jx[color]*Jx[color]+Jy[color]*Jy[color])+1.0e-20f)+0.5f));
CV_MAT_3COLOR_ELEM(*out,uchar,i-1,j-1,color) = cv::saturate_cast<uchar>(sat);
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
} else if (CV_MAT_CN(out->type)==1) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
for (color=0; color<=0; color++) {
cv::Point2f gradI,gradT,r;
float Ia=0,Jx=0,Jy=0,s=1.0e-20f,w,dst,lev,dir,sat;
if (CV_MAT_ELEM(*f,uchar,i,j+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j-1)))*0.5f;
} else {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i,j-1)));
} else {
gradT.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,i+1,j)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i-1,j)))*0.5f;
} else {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i-1,j)));
} else {
gradT.y=0;
}
}
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y = (float)(i-k);
r.x = (float)(j-l);
dst = (float)(1./(VectorLength(r)*sqrt(VectorLength(r))));
lev = (float)(1./(1+fabs(CV_MAT_ELEM(*t,float,k,l)-CV_MAT_ELEM(*t,float,i,j))));
dir=VectorScalMult(r,gradT);
if (fabs(dir)<=0.01) dir=0.000001f;
w = (float)fabs(dst*lev*dir);
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_ELEM(*out,data_type,km,lp+1)-CV_MAT_ELEM(*out,data_type,km,lm-1)))*2.0f;
} else {
gradI.x=(float)((CV_MAT_ELEM(*out,data_type,km,lp+1)-CV_MAT_ELEM(*out,data_type,km,lm)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_ELEM(*out,data_type,km,lp)-CV_MAT_ELEM(*out,data_type,km,lm-1)));
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_ELEM(*out,data_type,kp+1,lm)-CV_MAT_ELEM(*out,data_type,km-1,lm)))*2.0f;
} else {
gradI.y=(float)((CV_MAT_ELEM(*out,data_type,kp+1,lm)-CV_MAT_ELEM(*out,data_type,km,lm)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_ELEM(*out,data_type,kp,lm)-CV_MAT_ELEM(*out,data_type,km-1,lm)));
} else {
gradI.y=0;
}
}
Ia += (float)w * (float)(CV_MAT_ELEM(*out,data_type,km,lm));
Jx -= (float)w * (float)(gradI.x*r.x);
Jy -= (float)w * (float)(gradI.y*r.y);
s += w;
}
}
}
}
sat = (float)((Ia/s+(Jx+Jy)/(sqrt(Jx*Jx+Jy*Jy)+1.0e-20f)+0.5f));
{
CV_MAT_ELEM(*out,data_type,i-1,j-1) = cv::saturate_cast<data_type>(sat);
}
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
}
}
template <typename data_type>
static void
icvNSInpaintFMM(const CvMat *f, CvMat *t, CvMat *out, int range, CvPriorityQueueFloat *Heap) {
int i = 0, j = 0, ii = 0, jj = 0, k, l, q, color = 0;
float dist;
if (CV_MAT_CN(out->type)==3) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
cv::Point2f gradI,r;
float Ia[3]={0,0,0};
float s[3]={1.0e-20f,1.0e-20f,1.0e-20f};
float w,dst,dir;
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==f->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==f->cols-2);
if (k>0&&l>0&&k<f->rows-1&&l<f->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
for (color=0; color<=2; color++) {
r.y=(float)(k-i);
r.x=(float)(l-j);
dst = 1/(VectorLength(r)*VectorLength(r)+1);
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color))+
abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)));
} else {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)))*2.0f;
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color))+
abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)));
} else {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)))*2.0f;
} else {
gradI.y=0;
}
}
gradI.x=-gradI.x;
dir=VectorScalMult(r,gradI);
if (fabs(dir)<=0.01) {
dir=0.000001f;
} else {
dir = (float)fabs(VectorScalMult(r,gradI)/sqrt(VectorLength(r)*VectorLength(gradI)));
}
w = dst*dir;
Ia[color] += (float)w * (float)(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color));
s[color] += w;
}
}
}
}
}
for (color=0; color<=2; color++) {
CV_MAT_3COLOR_ELEM(*out,uchar,i-1,j-1,color) = cv::saturate_cast<uchar>((double)Ia[color]/s[color]);
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
} else if (CV_MAT_CN(out->type)==1) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
{
cv::Point2f gradI,r;
float Ia=0,s=1.0e-20f,w,dst,dir;
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y=(float)(i-k);
r.x=(float)(j-l);
dst = 1/(VectorLength(r)*VectorLength(r)+1);
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(std::abs(CV_MAT_ELEM(*out,data_type,kp+1,lm)-CV_MAT_ELEM(*out,data_type,kp,lm))+
std::abs(CV_MAT_ELEM(*out,data_type,kp,lm)-CV_MAT_ELEM(*out,data_type,km-1,lm)));
} else {
gradI.x=(float)(std::abs(CV_MAT_ELEM(*out,data_type,kp+1,lm)-CV_MAT_ELEM(*out,data_type,kp,lm)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(std::abs(CV_MAT_ELEM(*out,data_type,kp,lm)-CV_MAT_ELEM(*out,data_type,km-1,lm)))*2.0f;
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(std::abs(CV_MAT_ELEM(*out,data_type,km,lp+1)-CV_MAT_ELEM(*out,data_type,km,lm))+
std::abs(CV_MAT_ELEM(*out,data_type,km,lm)-CV_MAT_ELEM(*out,data_type,km,lm-1)));
} else {
gradI.y=(float)(std::abs(CV_MAT_ELEM(*out,data_type,km,lp+1)-CV_MAT_ELEM(*out,data_type,km,lm)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(std::abs(CV_MAT_ELEM(*out,data_type,km,lm)-CV_MAT_ELEM(*out,data_type,km,lm-1)))*2.0f;
} else {
gradI.y=0;
}
}
gradI.x=-gradI.x;
dir=VectorScalMult(r,gradI);
if (fabs(dir)<=0.01) {
dir=0.000001f;
} else {
dir = (float)fabs(VectorScalMult(r,gradI)/sqrt(VectorLength(r)*VectorLength(gradI)));
}
w = dst*dir;
Ia += (float)w * (float)(CV_MAT_ELEM(*out,data_type,km,lm));
s += w;
}
}
}
}
CV_MAT_ELEM(*out,data_type,i-1,j-1) = cv::saturate_cast<data_type>((double)Ia/s);
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
}
}
#define SET_BORDER1_C1(image,type,value) {\
int i,j;\
for(j=0; j<image->cols; j++) {\
CV_MAT_ELEM(*image,type,0,j) = value;\
}\
for (i=1; i<image->rows-1; i++) {\
CV_MAT_ELEM(*image,type,i,0) = CV_MAT_ELEM(*image,type,i,image->cols-1) = value;\
}\
for(j=0; j<image->cols; j++) {\
CV_MAT_ELEM(*image,type,erows-1,j) = value;\
}\
}
#define COPY_MASK_BORDER1_C1(src,dst,type) {\
int i,j;\
for (i=0; i<src->rows; i++) {\
for(j=0; j<src->cols; j++) {\
if (CV_MAT_ELEM(*src,type,i,j)!=0)\
CV_MAT_ELEM(*dst,type,i+1,j+1) = INSIDE;\
}\
}\
}
namespace cv {
template<> struct DefaultDeleter<IplConvKernel>{ void operator ()(IplConvKernel* obj) const { cvReleaseStructuringElement(&obj); } };
}
static void
icvInpaint( const CvArr* _input_img, const CvArr* _inpaint_mask, CvArr* _output_img,
double inpaintRange, int flags )
{
cv::Ptr<CvMat> mask, band, f, t, out;
cv::Ptr<CvPriorityQueueFloat> Heap, Out;
cv::Ptr<IplConvKernel> el_cross, el_range;
CvMat input_hdr, mask_hdr, output_hdr;
CvMat* input_img, *inpaint_mask, *output_img;
int range=cvRound(inpaintRange);
int erows, ecols;
input_img = cvGetMat( _input_img, &input_hdr );
inpaint_mask = cvGetMat( _inpaint_mask, &mask_hdr );
output_img = cvGetMat( _output_img, &output_hdr );
if( !CV_ARE_SIZES_EQ(input_img,output_img) || !CV_ARE_SIZES_EQ(input_img,inpaint_mask))
CV_Error( CV_StsUnmatchedSizes, "All the input and output images must have the same size" );
if( (CV_MAT_TYPE(input_img->type) != CV_8U &&
CV_MAT_TYPE(input_img->type) != CV_16U &&
CV_MAT_TYPE(input_img->type) != CV_32F &&
CV_MAT_TYPE(input_img->type) != CV_8UC3) ||
!CV_ARE_TYPES_EQ(input_img,output_img) )
CV_Error( CV_StsUnsupportedFormat,
"8-bit, 16-bit unsigned or 32-bit float 1-channel and 8-bit 3-channel input/output images are supported" );
if( CV_MAT_TYPE(inpaint_mask->type) != CV_8UC1 )
CV_Error( CV_StsUnsupportedFormat, "The mask must be 8-bit 1-channel image" );
range = MAX(range,1);
range = MIN(range,100);
ecols = input_img->cols + 2;
erows = input_img->rows + 2;
f.reset(cvCreateMat(erows, ecols, CV_8UC1));
t.reset(cvCreateMat(erows, ecols, CV_32FC1));
band.reset(cvCreateMat(erows, ecols, CV_8UC1));
mask.reset(cvCreateMat(erows, ecols, CV_8UC1));
el_cross.reset(cvCreateStructuringElementEx(3,3,1,1,CV_SHAPE_CROSS,NULL));
cvCopy( input_img, output_img );
cvSet(mask,cvScalar(KNOWN,0,0,0));
COPY_MASK_BORDER1_C1(inpaint_mask,mask,uchar);
SET_BORDER1_C1(mask,uchar,0);
cvSet(f,cvScalar(KNOWN,0,0,0));
cvSet(t,cvScalar(1.0e6f,0,0,0));
cvDilate(mask,band,el_cross,1); // image with narrow band
Heap=cv::makePtr<CvPriorityQueueFloat>();
if (!Heap->Init(band))
return;
cvSub(band,mask,band,NULL);
SET_BORDER1_C1(band,uchar,0);
if (!Heap->Add(band))
return;
cvSet(f,cvScalar(BAND,0,0,0),band);
cvSet(f,cvScalar(INSIDE,0,0,0),mask);
cvSet(t,cvScalar(0,0,0,0),band);
if( flags == cv::INPAINT_TELEA )
{
out.reset(cvCreateMat(erows, ecols, CV_8UC1));
el_range.reset(cvCreateStructuringElementEx(2*range+1,2*range+1,
range,range,CV_SHAPE_RECT,NULL));
cvDilate(mask,out,el_range,1);
cvSub(out,mask,out,NULL);
Out=cv::makePtr<CvPriorityQueueFloat>();
if (!Out->Init(out))
return;
if (!Out->Add(band))
return;
cvSub(out,band,out,NULL);
SET_BORDER1_C1(out,uchar,0);
icvCalcFMM(out,t,Out,true);
switch(CV_MAT_DEPTH(output_img->type))
{
case CV_8U:
icvTeleaInpaintFMM<uchar>(mask,t,output_img,range,Heap);
break;
case CV_16U:
icvTeleaInpaintFMM<ushort>(mask,t,output_img,range,Heap);
break;
case CV_32F:
icvTeleaInpaintFMM<float>(mask,t,output_img,range,Heap);
break;
default:
CV_Error( cv::Error::StsBadArg, "Unsupportedformat of the input image" );
}
}
else if (flags == cv::INPAINT_NS) {
switch(CV_MAT_DEPTH(output_img->type))
{
case CV_8U:
icvNSInpaintFMM<uchar>(mask,t,output_img,range,Heap);
break;
case CV_16U:
icvNSInpaintFMM<ushort>(mask,t,output_img,range,Heap);
break;
case CV_32F:
icvNSInpaintFMM<float>(mask,t,output_img,range,Heap);
break;
default:
CV_Error( cv::Error::StsBadArg, "Unsupported format of the input image" );
}
} else {
CV_Error( cv::Error::StsBadArg, "The flags argument must be one of CV_INPAINT_TELEA or CV_INPAINT_NS" );
}
}
void cv::inpaint( InputArray _src, InputArray _mask, OutputArray _dst,
double inpaintRange, int flags )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat(), mask = _mask.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
CvMat c_src = cvMat(src), c_mask = cvMat(mask), c_dst = cvMat(dst);
icvInpaint( &c_src, &c_mask, &c_dst, inpaintRange, flags );
}

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@ -0,0 +1,367 @@
/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
#include "hdr_common.hpp"
namespace cv
{
class MergeDebevecImpl CV_FINAL : public MergeDebevec
{
public:
MergeDebevecImpl() :
name("MergeDebevec"),
weights(triangleWeights())
{
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray _times, InputArray input_response) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
std::vector<Mat> images;
src.getMatVector(images);
Mat times = _times.getMat();
CV_Assert(images.size() == times.total());
checkImageDimensions(images);
CV_Assert(images[0].depth() == CV_8U);
int channels = images[0].channels();
Size size = images[0].size();
int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
dst.create(images[0].size(), CV_32FCC);
Mat result = dst.getMat();
Mat response = input_response.getMat();
if(response.empty()) {
response = linearResponse(channels);
response.at<Vec3f>(0) = response.at<Vec3f>(1);
}
Mat log_response;
log(response, log_response);
CV_Assert(log_response.rows == LDR_SIZE && log_response.cols == 1 &&
log_response.channels() == channels);
Mat exp_values(times.clone());
log(exp_values, exp_values);
result = Mat::zeros(size, CV_32FCC);
std::vector<Mat> result_split;
split(result, result_split);
Mat weight_sum = Mat::zeros(size, CV_32F);
for(size_t i = 0; i < images.size(); i++) {
std::vector<Mat> splitted;
split(images[i], splitted);
Mat w = Mat::zeros(size, CV_32F);
for(int c = 0; c < channels; c++) {
LUT(splitted[c], weights, splitted[c]);
w += splitted[c];
}
w /= channels;
Mat response_img;
LUT(images[i], log_response, response_img);
split(response_img, splitted);
for(int c = 0; c < channels; c++) {
result_split[c] += w.mul(splitted[c] - exp_values.at<float>((int)i));
}
weight_sum += w;
}
weight_sum = 1.0f / weight_sum;
for(int c = 0; c < channels; c++) {
result_split[c] = result_split[c].mul(weight_sum);
}
merge(result_split, result);
exp(result, result);
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray times) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
process(src, dst, times, Mat());
}
protected:
String name;
Mat weights;
};
Ptr<MergeDebevec> createMergeDebevec()
{
return makePtr<MergeDebevecImpl>();
}
class MergeMertensImpl CV_FINAL : public MergeMertens
{
public:
MergeMertensImpl(float _wcon, float _wsat, float _wexp) :
name("MergeMertens"),
wcon(_wcon),
wsat(_wsat),
wexp(_wexp)
{
}
void process(InputArrayOfArrays src, OutputArrayOfArrays dst, InputArray, InputArray) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
process(src, dst);
}
void process(InputArrayOfArrays src, OutputArray dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
std::vector<Mat> images;
src.getMatVector(images);
checkImageDimensions(images);
int channels = images[0].channels();
CV_Assert(channels == 1 || channels == 3);
Size size = images[0].size();
int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
std::vector<Mat> weights(images.size());
Mat weight_sum = Mat::zeros(size, CV_32F);
for(size_t i = 0; i < images.size(); i++) {
Mat img, gray, contrast, saturation, wellexp;
std::vector<Mat> splitted(channels);
images[i].convertTo(img, CV_32F, 1.0f/255.0f);
if(channels == 3) {
cvtColor(img, gray, COLOR_RGB2GRAY);
} else {
img.copyTo(gray);
}
split(img, splitted);
Laplacian(gray, contrast, CV_32F);
contrast = abs(contrast);
Mat mean = Mat::zeros(size, CV_32F);
for(int c = 0; c < channels; c++) {
mean += splitted[c];
}
mean /= channels;
saturation = Mat::zeros(size, CV_32F);
for(int c = 0; c < channels; c++) {
Mat deviation = splitted[c] - mean;
pow(deviation, 2.0f, deviation);
saturation += deviation;
}
sqrt(saturation, saturation);
wellexp = Mat::ones(size, CV_32F);
for(int c = 0; c < channels; c++) {
Mat expo = splitted[c] - 0.5f;
pow(expo, 2.0f, expo);
expo = -expo / 0.08f;
exp(expo, expo);
wellexp = wellexp.mul(expo);
}
pow(contrast, wcon, contrast);
pow(saturation, wsat, saturation);
pow(wellexp, wexp, wellexp);
weights[i] = contrast;
if(channels == 3) {
weights[i] = weights[i].mul(saturation);
}
weights[i] = weights[i].mul(wellexp) + 1e-12f;
weight_sum += weights[i];
}
int maxlevel = static_cast<int>(logf(static_cast<float>(min(size.width, size.height))) / logf(2.0f));
std::vector<Mat> res_pyr(maxlevel + 1);
for(size_t i = 0; i < images.size(); i++) {
weights[i] /= weight_sum;
Mat img;
images[i].convertTo(img, CV_32F, 1.0f/255.0f);
std::vector<Mat> img_pyr, weight_pyr;
buildPyramid(img, img_pyr, maxlevel);
buildPyramid(weights[i], weight_pyr, maxlevel);
for(int lvl = 0; lvl < maxlevel; lvl++) {
Mat up;
pyrUp(img_pyr[lvl + 1], up, img_pyr[lvl].size());
img_pyr[lvl] -= up;
}
for(int lvl = 0; lvl <= maxlevel; lvl++) {
std::vector<Mat> splitted(channels);
split(img_pyr[lvl], splitted);
for(int c = 0; c < channels; c++) {
splitted[c] = splitted[c].mul(weight_pyr[lvl]);
}
merge(splitted, img_pyr[lvl]);
if(res_pyr[lvl].empty()) {
res_pyr[lvl] = img_pyr[lvl];
} else {
res_pyr[lvl] += img_pyr[lvl];
}
}
}
for(int lvl = maxlevel; lvl > 0; lvl--) {
Mat up;
pyrUp(res_pyr[lvl], up, res_pyr[lvl - 1].size());
res_pyr[lvl - 1] += up;
}
dst.create(size, CV_32FCC);
res_pyr[0].copyTo(dst);
}
float getContrastWeight() const CV_OVERRIDE { return wcon; }
void setContrastWeight(float val) CV_OVERRIDE { wcon = val; }
float getSaturationWeight() const CV_OVERRIDE { return wsat; }
void setSaturationWeight(float val) CV_OVERRIDE { wsat = val; }
float getExposureWeight() const CV_OVERRIDE { return wexp; }
void setExposureWeight(float val) CV_OVERRIDE { wexp = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "contrast_weight" << wcon
<< "saturation_weight" << wsat
<< "exposure_weight" << wexp;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
wcon = fn["contrast_weight"];
wsat = fn["saturation_weight"];
wexp = fn["exposure_weight"];
}
protected:
String name;
float wcon, wsat, wexp;
};
Ptr<MergeMertens> createMergeMertens(float wcon, float wsat, float wexp)
{
return makePtr<MergeMertensImpl>(wcon, wsat, wexp);
}
class MergeRobertsonImpl CV_FINAL : public MergeRobertson
{
public:
MergeRobertsonImpl() :
name("MergeRobertson"),
weight(RobertsonWeights())
{
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray _times, InputArray input_response) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
std::vector<Mat> images;
src.getMatVector(images);
Mat times = _times.getMat();
CV_Assert(images.size() == times.total());
checkImageDimensions(images);
CV_Assert(images[0].depth() == CV_8U);
int channels = images[0].channels();
int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
dst.create(images[0].size(), CV_32FCC);
Mat result = dst.getMat();
Mat response = input_response.getMat();
if(response.empty()) {
float middle = LDR_SIZE / 2.0f;
response = linearResponse(channels) / middle;
}
CV_Assert(response.rows == LDR_SIZE && response.cols == 1 &&
response.channels() == channels);
result = Mat::zeros(images[0].size(), CV_32FCC);
Mat wsum = Mat::zeros(images[0].size(), CV_32FCC);
for(size_t i = 0; i < images.size(); i++) {
Mat im, w;
LUT(images[i], weight, w);
LUT(images[i], response, im);
result += times.at<float>((int)i) * w.mul(im);
wsum += times.at<float>((int)i) * times.at<float>((int)i) * w;
}
result = result.mul(1 / (wsum + Scalar::all(DBL_EPSILON)));
}
void process(InputArrayOfArrays src, OutputArray dst, InputArray times) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
process(src, dst, times, Mat());
}
protected:
String name;
Mat weight;
};
Ptr<MergeRobertson> createMergeRobertson()
{
return makePtr<MergeRobertsonImpl>();
}
}

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include <iostream>
#include <stdlib.h>
#include "npr.hpp"
using namespace std;
using namespace cv;
void cv::edgePreservingFilter(InputArray _src, OutputArray dst, int flags, float sigma_s, float sigma_r)
{
CV_INSTRUMENT_REGION();
Mat I = _src.getMat();
Domain_Filter obj;
Mat img;
I.convertTo(img,CV_32FC3,1.0/255.0);
Mat res;
obj.filter(img, res, sigma_s, sigma_r, flags);
convertScaleAbs(res, dst, 255,0);
}
void cv::detailEnhance(InputArray _src, OutputArray dst, float sigma_s, float sigma_r)
{
CV_INSTRUMENT_REGION();
Mat I = _src.getMat();
float factor = 3.0f;
Mat img;
I.convertTo(img,CV_32FC3,1.0/255.0);
Mat lab;
vector <Mat> lab_channel;
cvtColor(img,lab,COLOR_BGR2Lab);
split(lab,lab_channel);
Mat L;
lab_channel[0].convertTo(L,CV_32FC1,1.0/255.0);
Domain_Filter obj;
Mat res;
obj.filter(L, res, sigma_s, sigma_r, 1);
Mat detail = L - res;
multiply(detail,factor,detail);
L = res + detail;
L.convertTo(lab_channel[0],CV_32FC1,255);
merge(lab_channel,lab);
cvtColor(lab,res,COLOR_Lab2BGR);
res.convertTo(dst,CV_8UC3,255);
}
void cv::pencilSketch(InputArray _src, OutputArray _dst1, OutputArray _dst2, float sigma_s, float sigma_r, float shade_factor)
{
CV_INSTRUMENT_REGION();
Mat I = _src.getMat();
_dst1.create(I.size(), CV_8UC1);
Mat dst1 = _dst1.getMat();
_dst2.create(I.size(), CV_8UC3);
Mat dst2 = _dst2.getMat();
Mat img = Mat(I.size(),CV_32FC3);
I.convertTo(img,CV_32FC3,1.0/255.0);
Domain_Filter obj;
Mat sketch = Mat(I.size(),CV_32FC1);
Mat color_sketch = Mat(I.size(),CV_32FC3);
obj.pencil_sketch(img, sketch, color_sketch, sigma_s, sigma_r, shade_factor);
sketch.convertTo(dst1,CV_8UC1,255);
color_sketch.convertTo(dst2,CV_8UC3,255);
}
void cv::stylization(InputArray _src, OutputArray _dst, float sigma_s, float sigma_r)
{
CV_INSTRUMENT_REGION();
Mat I = _src.getMat();
_dst.create(I.size(), CV_8UC3);
Mat dst = _dst.getMat();
Mat img;
I.convertTo(img,CV_32FC3,1.0/255.0);
int h = img.size().height;
int w = img.size().width;
Mat res;
Mat magnitude = Mat(h,w,CV_32FC1);
Domain_Filter obj;
obj.filter(img, res, sigma_s, sigma_r, NORMCONV_FILTER);
obj.find_magnitude(res,magnitude);
Mat stylized;
vector <Mat> temp;
split(res,temp);
multiply(temp[0],magnitude,temp[0]);
multiply(temp[1],magnitude,temp[1]);
multiply(temp[2],magnitude,temp[2]);
merge(temp,stylized);
stylized.convertTo(dst,CV_8UC3,255);
}

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include <iostream>
#include <stdlib.h>
#include <limits>
#include "math.h"
using namespace std;
using namespace cv;
double myinf = std::numeric_limits<double>::infinity();
class Domain_Filter
{
public:
Mat ct_H, ct_V, horiz, vert, O, O_t, lower_idx, upper_idx;
void init(const Mat &img, int flags, float sigma_s, float sigma_r);
void getGradientx( const Mat &img, Mat &gx);
void getGradienty( const Mat &img, Mat &gy);
void diffx(const Mat &img, Mat &temp);
void diffy(const Mat &img, Mat &temp);
void find_magnitude(Mat &img, Mat &mag);
void compute_boxfilter(Mat &output, Mat &hz, Mat &psketch, float radius);
void compute_Rfilter(Mat &O, Mat &horiz, float sigma_h);
void compute_NCfilter(Mat &O, Mat &horiz, Mat &psketch, float radius);
void filter(const Mat &img, Mat &res, float sigma_s, float sigma_r, int flags);
void pencil_sketch(const Mat &img, Mat &sketch, Mat &color_res, float sigma_s, float sigma_r, float shade_factor);
void Depth_of_field(const Mat &img, Mat &img1, float sigma_s, float sigma_r);
};
void Domain_Filter::diffx(const Mat &img, Mat &temp)
{
int channel = img.channels();
for(int i = 0; i < img.size().height; i++)
for(int j = 0; j < img.size().width-1; j++)
{
for(int c =0; c < channel; c++)
{
temp.at<float>(i,j*channel+c) =
img.at<float>(i,(j+1)*channel+c) - img.at<float>(i,j*channel+c);
}
}
}
void Domain_Filter::diffy(const Mat &img, Mat &temp)
{
int channel = img.channels();
for(int i = 0; i < img.size().height-1; i++)
for(int j = 0; j < img.size().width; j++)
{
for(int c =0; c < channel; c++)
{
temp.at<float>(i,j*channel+c) =
img.at<float>((i+1),j*channel+c) - img.at<float>(i,j*channel+c);
}
}
}
void Domain_Filter::getGradientx( const Mat &img, Mat &gx)
{
int w = img.cols;
int h = img.rows;
int channel = img.channels();
for(int i=0;i<h;i++)
for(int j=0;j<w;j++)
for(int c=0;c<channel;++c)
{
gx.at<float>(i,j*channel+c) =
img.at<float>(i,(j+1)*channel+c) - img.at<float>(i,j*channel+c);
}
}
void Domain_Filter::getGradienty( const Mat &img, Mat &gy)
{
int w = img.cols;
int h = img.rows;
int channel = img.channels();
for(int i=0;i<h;i++)
for(int j=0;j<w;j++)
for(int c=0;c<channel;++c)
{
gy.at<float>(i,j*channel+c) =
img.at<float>(i+1,j*channel+c) - img.at<float>(i,j*channel+c);
}
}
void Domain_Filter::find_magnitude(Mat &img, Mat &mag)
{
int h = img.rows;
int w = img.cols;
vector <Mat> planes;
split(img, planes);
Mat magXR = Mat(h, w, CV_32FC1);
Mat magYR = Mat(h, w, CV_32FC1);
Mat magXG = Mat(h, w, CV_32FC1);
Mat magYG = Mat(h, w, CV_32FC1);
Mat magXB = Mat(h, w, CV_32FC1);
Mat magYB = Mat(h, w, CV_32FC1);
Sobel(planes[0], magXR, CV_32FC1, 1, 0, 3);
Sobel(planes[0], magYR, CV_32FC1, 0, 1, 3);
Sobel(planes[1], magXG, CV_32FC1, 1, 0, 3);
Sobel(planes[1], magYG, CV_32FC1, 0, 1, 3);
Sobel(planes[2], magXB, CV_32FC1, 1, 0, 3);
Sobel(planes[2], magYB, CV_32FC1, 0, 1, 3);
Mat mag1 = Mat(h,w,CV_32FC1);
Mat mag2 = Mat(h,w,CV_32FC1);
Mat mag3 = Mat(h,w,CV_32FC1);
magnitude(magXR,magYR,mag1);
magnitude(magXG,magYG,mag2);
magnitude(magXB,magYB,mag3);
mag = mag1 + mag2 + mag3;
mag = 1.0f - mag;
}
void Domain_Filter::compute_Rfilter(Mat &output, Mat &hz, float sigma_h)
{
int h = output.rows;
int w = output.cols;
int channel = output.channels();
float a = (float) exp((-1.0 * sqrt(2.0)) / sigma_h);
Mat temp = Mat(h,w,CV_32FC3);
output.copyTo(temp);
Mat V = Mat(h,w,CV_32FC1);
for(int i=0;i<h;i++)
for(int j=0;j<w;j++)
V.at<float>(i,j) = pow(a,hz.at<float>(i,j));
for(int i=0; i<h; i++)
{
for(int j =1; j < w; j++)
{
for(int c = 0; c<channel; c++)
{
temp.at<float>(i,j*channel+c) = temp.at<float>(i,j*channel+c) +
(temp.at<float>(i,(j-1)*channel+c) - temp.at<float>(i,j*channel+c)) * V.at<float>(i,j);
}
}
}
for(int i=0; i<h; i++)
{
for(int j =w-2; j >= 0; j--)
{
for(int c = 0; c<channel; c++)
{
temp.at<float>(i,j*channel+c) = temp.at<float>(i,j*channel+c) +
(temp.at<float>(i,(j+1)*channel+c) - temp.at<float>(i,j*channel+c))*V.at<float>(i,j+1);
}
}
}
temp.copyTo(output);
}
void Domain_Filter::compute_boxfilter(Mat &output, Mat &hz, Mat &psketch, float radius)
{
int h = output.rows;
int w = output.cols;
Mat lower_pos = Mat(h,w,CV_32FC1);
Mat upper_pos = Mat(h,w,CV_32FC1);
lower_pos = hz - radius;
upper_pos = hz + radius;
lower_idx = Mat::zeros(h,w,CV_32FC1);
upper_idx = Mat::zeros(h,w,CV_32FC1);
Mat domain_row = Mat::zeros(1,w+1,CV_32FC1);
for(int i=0;i<h;i++)
{
for(int j=0;j<w;j++)
domain_row.at<float>(0,j) = hz.at<float>(i,j);
domain_row.at<float>(0,w) = (float) myinf;
Mat lower_pos_row = Mat::zeros(1,w,CV_32FC1);
Mat upper_pos_row = Mat::zeros(1,w,CV_32FC1);
for(int j=0;j<w;j++)
{
lower_pos_row.at<float>(0,j) = lower_pos.at<float>(i,j);
upper_pos_row.at<float>(0,j) = upper_pos.at<float>(i,j);
}
Mat temp_lower_idx = Mat::zeros(1,w,CV_32FC1);
Mat temp_upper_idx = Mat::zeros(1,w,CV_32FC1);
for(int j=0;j<w;j++)
{
if(domain_row.at<float>(0,j) > lower_pos_row.at<float>(0,0))
{
temp_lower_idx.at<float>(0,0) = (float) j;
break;
}
}
for(int j=0;j<w;j++)
{
if(domain_row.at<float>(0,j) > upper_pos_row.at<float>(0,0))
{
temp_upper_idx.at<float>(0,0) = (float) j;
break;
}
}
int temp = 0;
for(int j=1;j<w;j++)
{
int count=0;
for(int k=(int) temp_lower_idx.at<float>(0,j-1);k<w+1;k++)
{
if(domain_row.at<float>(0,k) > lower_pos_row.at<float>(0,j))
{
temp = count;
break;
}
count++;
}
temp_lower_idx.at<float>(0,j) = temp_lower_idx.at<float>(0,j-1) + temp;
count = 0;
for(int k=(int) temp_upper_idx.at<float>(0,j-1);k<w+1;k++)
{
if(domain_row.at<float>(0,k) > upper_pos_row.at<float>(0,j))
{
temp = count;
break;
}
count++;
}
temp_upper_idx.at<float>(0,j) = temp_upper_idx.at<float>(0,j-1) + temp;
}
for(int j=0;j<w;j++)
{
lower_idx.at<float>(i,j) = temp_lower_idx.at<float>(0,j) + 1;
upper_idx.at<float>(i,j) = temp_upper_idx.at<float>(0,j) + 1;
}
}
psketch = upper_idx - lower_idx;
}
void Domain_Filter::compute_NCfilter(Mat &output, Mat &hz, Mat &psketch, float radius)
{
int h = output.rows;
int w = output.cols;
int channel = output.channels();
compute_boxfilter(output,hz,psketch,radius);
Mat box_filter = Mat::zeros(h,w+1,CV_32FC3);
for(int i = 0; i < h; i++)
{
box_filter.at<float>(i,1*channel+0) = output.at<float>(i,0*channel+0);
box_filter.at<float>(i,1*channel+1) = output.at<float>(i,0*channel+1);
box_filter.at<float>(i,1*channel+2) = output.at<float>(i,0*channel+2);
for(int j = 2; j < w+1; j++)
{
for(int c=0;c<channel;c++)
box_filter.at<float>(i,j*channel+c) = output.at<float>(i,(j-1)*channel+c) + box_filter.at<float>(i,(j-1)*channel+c);
}
}
Mat indices = Mat::zeros(h,w,CV_32FC1);
Mat final = Mat::zeros(h,w,CV_32FC3);
for(int i=0;i<h;i++)
for(int j=0;j<w;j++)
indices.at<float>(i,j) = (float) i+1;
Mat a = Mat::zeros(h,w,CV_32FC1);
Mat b = Mat::zeros(h,w,CV_32FC1);
// Compute the box filter using a summed area table.
for(int c=0;c<channel;c++)
{
Mat flag = Mat::ones(h,w,CV_32FC1);
multiply(flag,c+1,flag);
Mat temp1, temp2;
multiply(flag - 1,h*(w+1),temp1);
multiply(lower_idx - 1,h,temp2);
a = temp1 + temp2 + indices;
multiply(flag - 1,h*(w+1),temp1);
multiply(upper_idx - 1,h,temp2);
b = temp1 + temp2 + indices;
int p,q,r,rem;
int p1,q1,r1,rem1;
// Calculating indices
for(int i=0;i<h;i++)
{
for(int j=0;j<w;j++)
{
r = (int) b.at<float>(i,j)/(h*(w+1));
rem = (int) b.at<float>(i,j) - r*h*(w+1);
q = rem/h;
p = rem - q*h;
if(q==0)
{
p=h;
q=w;
r=r-1;
}
if(p==0)
{
p=h;
q=q-1;
}
r1 = (int) a.at<float>(i,j)/(h*(w+1));
rem1 = (int) a.at<float>(i,j) - r1*h*(w+1);
q1 = rem1/h;
p1 = rem1 - q1*h;
if(p1==0)
{
p1=h;
q1=q1-1;
}
final.at<float>(i,j*channel+2-c) = (box_filter.at<float>(p-1,q*channel+(2-r)) - box_filter.at<float>(p1-1,q1*channel+(2-r1)))
/(upper_idx.at<float>(i,j) - lower_idx.at<float>(i,j));
}
}
}
final.copyTo(output);
}
void Domain_Filter::init(const Mat &img, int flags, float sigma_s, float sigma_r)
{
int h = img.size().height;
int w = img.size().width;
int channel = img.channels();
//////////////////////////////////// horizontal and vertical partial derivatives /////////////////////////////////
Mat derivx = Mat::zeros(h,w-1,CV_32FC3);
Mat derivy = Mat::zeros(h-1,w,CV_32FC3);
diffx(img,derivx);
diffy(img,derivy);
Mat distx = Mat::zeros(h,w,CV_32FC1);
Mat disty = Mat::zeros(h,w,CV_32FC1);
//////////////////////// Compute the l1-norm distance of neighbor pixels ////////////////////////////////////////////////
for(int i = 0; i < h; i++)
for(int j = 0,k=1; j < w-1; j++,k++)
for(int c = 0; c < channel; c++)
{
distx.at<float>(i,k) =
distx.at<float>(i,k) + abs(derivx.at<float>(i,j*channel+c));
}
for(int i = 0,k=1; i < h-1; i++,k++)
for(int j = 0; j < w; j++)
for(int c = 0; c < channel; c++)
{
disty.at<float>(k,j) =
disty.at<float>(k,j) + abs(derivy.at<float>(i,j*channel+c));
}
////////////////////// Compute the derivatives of the horizontal and vertical domain transforms. /////////////////////////////
horiz = Mat(h,w,CV_32FC1);
vert = Mat(h,w,CV_32FC1);
Mat final = Mat(h,w,CV_32FC3);
Mat tempx,tempy;
multiply(distx,sigma_s/sigma_r,tempx);
multiply(disty,sigma_s/sigma_r,tempy);
horiz = 1.0f + tempx;
vert = 1.0f + tempy;
O = Mat(h,w,CV_32FC3);
img.copyTo(O);
O_t = Mat(w,h,CV_32FC3);
if(flags == 2)
{
ct_H = Mat(h,w,CV_32FC1);
ct_V = Mat(h,w,CV_32FC1);
for(int i = 0; i < h; i++)
{
ct_H.at<float>(i,0) = horiz.at<float>(i,0);
for(int j = 1; j < w; j++)
{
ct_H.at<float>(i,j) = horiz.at<float>(i,j) + ct_H.at<float>(i,j-1);
}
}
for(int j = 0; j < w; j++)
{
ct_V.at<float>(0,j) = vert.at<float>(0,j);
for(int i = 1; i < h; i++)
{
ct_V.at<float>(i,j) = vert.at<float>(i,j) + ct_V.at<float>(i-1,j);
}
}
}
}
void Domain_Filter::filter(const Mat &img, Mat &res, float sigma_s = 60, float sigma_r = 0.4, int flags = 1)
{
int no_of_iter = 3;
int h = img.size().height;
int w = img.size().width;
float sigma_h = sigma_s;
init(img,flags,sigma_s,sigma_r);
if(flags == 1)
{
Mat vert_t = vert.t();
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
compute_Rfilter(O, horiz, sigma_h);
O_t = O.t();
compute_Rfilter(O_t, vert_t, sigma_h);
O = O_t.t();
}
}
else if(flags == 2)
{
Mat vert_t = ct_V.t();
Mat temp = Mat(h,w,CV_32FC1);
Mat temp1 = Mat(w,h,CV_32FC1);
float radius;
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
radius = (float) sqrt(3.0) * sigma_h;
compute_NCfilter(O, ct_H, temp,radius);
O_t = O.t();
compute_NCfilter(O_t, vert_t, temp1, radius);
O = O_t.t();
}
}
res = O.clone();
}
void Domain_Filter::pencil_sketch(const Mat &img, Mat &sketch, Mat &color_res, float sigma_s, float sigma_r, float shade_factor)
{
int no_of_iter = 3;
init(img,2,sigma_s,sigma_r);
int h = img.size().height;
int w = img.size().width;
/////////////////////// convert to YCBCR model for color pencil drawing //////////////////////////////////////////////////////
Mat color_sketch = Mat(h,w,CV_32FC3);
cvtColor(img,color_sketch,COLOR_BGR2YCrCb);
vector <Mat> YUV_channel;
Mat vert_t = ct_V.t();
float sigma_h = sigma_s;
Mat penx = Mat(h,w,CV_32FC1);
Mat pen_res = Mat::zeros(h,w,CV_32FC1);
Mat peny = Mat(w,h,CV_32FC1);
Mat peny_t;
float radius;
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
radius = (float) sqrt(3.0) * sigma_h;
compute_boxfilter(O, ct_H, penx, radius);
O_t = O.t();
compute_boxfilter(O_t, vert_t, peny, radius);
O = O_t.t();
peny_t = peny.t();
for(int k=0;k<h;k++)
for(int j=0;j<w;j++)
pen_res.at<float>(k,j) = (shade_factor * (penx.at<float>(k,j) + peny_t.at<float>(k,j)));
if(i==0)
{
sketch = pen_res.clone();
split(color_sketch,YUV_channel);
pen_res.copyTo(YUV_channel[0]);
merge(YUV_channel,color_sketch);
cvtColor(color_sketch,color_res,COLOR_YCrCb2BGR);
}
}
}

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
#ifdef cl_amd_printf
#pragma OPENCL_EXTENSION cl_amd_printf:enable
#endif
#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined cl_khr_fp64
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef OP_CALC_WEIGHTS
__kernel void calcAlmostDist2Weight(__global wlut_t * almostDist2Weight, int almostMaxDist,
FT almostDist2ActualDistMultiplier, int fixedPointMult,
w_t den, FT WEIGHT_THRESHOLD)
{
int almostDist = get_global_id(0);
if (almostDist < almostMaxDist)
{
FT dist = almostDist * almostDist2ActualDistMultiplier;
#ifdef ABS
w_t w = exp((w_t)(-dist*dist) * den);
#else
w_t w = exp((w_t)(-dist) * den);
#endif
wlut_t weight = convert_wlut_t(fixedPointMult * (isnan(w) ? (w_t)1.0 : w));
almostDist2Weight[almostDist] =
weight < (wlut_t)(WEIGHT_THRESHOLD * fixedPointMult) ? (wlut_t)0 : weight;
}
}
#elif defined OP_CALC_FASTNLMEANS
#define noconvert
#define SEARCH_SIZE_SQ (SEARCH_SIZE * SEARCH_SIZE)
inline int calcDist(pixel_t a, pixel_t b)
{
#ifdef ABS
int_t retval = convert_int_t(abs_diff(a, b));
#else
int_t diff = convert_int_t(a) - convert_int_t(b);
int_t retval = diff * diff;
#endif
#if cn == 1
return retval;
#elif cn == 2
return retval.x + retval.y;
#elif cn == 3
return retval.x + retval.y + retval.z;
#elif cn == 4
return retval.x + retval.y + retval.z + retval.w;
#else
#error "cn should be either 1, 2, 3 or 4"
#endif
}
#ifdef ABS
inline int calcDistUpDown(pixel_t down_value, pixel_t down_value_t, pixel_t up_value, pixel_t up_value_t)
{
return calcDist(down_value, down_value_t) - calcDist(up_value, up_value_t);
}
#else
inline int calcDistUpDown(pixel_t down_value, pixel_t down_value_t, pixel_t up_value, pixel_t up_value_t)
{
int_t A = convert_int_t(down_value) - convert_int_t(down_value_t);
int_t B = convert_int_t(up_value) - convert_int_t(up_value_t);
int_t retval = (A - B) * (A + B);
#if cn == 1
return retval;
#elif cn == 2
return retval.x + retval.y;
#elif cn == 3
return retval.x + retval.y + retval.z;
#elif cn == 4
return retval.x + retval.y + retval.z + retval.w;
#else
#error "cn should be either 1, 2, 3 or 4"
#endif
}
#endif
#define COND if (x == 0 && y == 0)
inline void calcFirstElementInRow(__global const uchar * src, int src_step, int src_offset,
__local int * dists, int y, int x, int id,
__global int * col_dists, __global int * up_col_dists)
{
y -= TEMPLATE_SIZE2;
int sx = x - SEARCH_SIZE2, sy = y - SEARCH_SIZE2;
int col_dists_current_private[TEMPLATE_SIZE];
for (int i = id; i < SEARCH_SIZE_SQ; i += CTA_SIZE)
{
int dist = 0, value;
__global const pixel_t * src_template = (__global const pixel_t *)(src +
mad24(sy + i / SEARCH_SIZE, src_step, mad24(psz, sx + i % SEARCH_SIZE, src_offset)));
__global const pixel_t * src_current = (__global const pixel_t *)(src + mad24(y, src_step, mad24(psz, x, src_offset)));
__global int * col_dists_current = col_dists + i * TEMPLATE_SIZE;
#pragma unroll
for (int j = 0; j < TEMPLATE_SIZE; ++j)
col_dists_current_private[j] = 0;
for (int ty = 0; ty < TEMPLATE_SIZE; ++ty)
{
#pragma unroll
for (int tx = -TEMPLATE_SIZE2; tx <= TEMPLATE_SIZE2; ++tx)
{
value = calcDist(src_template[tx], src_current[tx]);
col_dists_current_private[tx + TEMPLATE_SIZE2] += value;
dist += value;
}
src_current = (__global const pixel_t *)((__global const uchar *)src_current + src_step);
src_template = (__global const pixel_t *)((__global const uchar *)src_template + src_step);
}
#pragma unroll
for (int j = 0; j < TEMPLATE_SIZE; ++j)
col_dists_current[j] = col_dists_current_private[j];
dists[i] = dist;
up_col_dists[0 + i] = col_dists[TEMPLATE_SIZE - 1];
}
}
inline void calcElementInFirstRow(__global const uchar * src, int src_step, int src_offset,
__local int * dists, int y, int x0, int x, int id, int first,
__global int * col_dists, __global int * up_col_dists)
{
x += TEMPLATE_SIZE2;
y -= TEMPLATE_SIZE2;
int sx = x - SEARCH_SIZE2, sy = y - SEARCH_SIZE2;
for (int i = id; i < SEARCH_SIZE_SQ; i += CTA_SIZE)
{
__global const pixel_t * src_current = (__global const pixel_t *)(src + mad24(y, src_step, mad24(psz, x, src_offset)));
__global const pixel_t * src_template = (__global const pixel_t *)(src +
mad24(sy + i / SEARCH_SIZE, src_step, mad24(psz, sx + i % SEARCH_SIZE, src_offset)));
__global int * col_dists_current = col_dists + TEMPLATE_SIZE * i;
int col_dist = 0;
#pragma unroll
for (int ty = 0; ty < TEMPLATE_SIZE; ++ty)
{
col_dist += calcDist(src_current[0], src_template[0]);
src_current = (__global const pixel_t *)((__global const uchar *)src_current + src_step);
src_template = (__global const pixel_t *)((__global const uchar *)src_template + src_step);
}
dists[i] += col_dist - col_dists_current[first];
col_dists_current[first] = col_dist;
up_col_dists[mad24(x0, SEARCH_SIZE_SQ, i)] = col_dist;
}
}
inline void calcElement(__global const uchar * src, int src_step, int src_offset,
__local int * dists, int y, int x0, int x, int id, int first,
__global int * col_dists, __global int * up_col_dists)
{
int sx = x + TEMPLATE_SIZE2;
int sy_up = y - TEMPLATE_SIZE2 - 1;
int sy_down = y + TEMPLATE_SIZE2;
pixel_t up_value = *(__global const pixel_t *)(src + mad24(sy_up, src_step, mad24(psz, sx, src_offset)));
pixel_t down_value = *(__global const pixel_t *)(src + mad24(sy_down, src_step, mad24(psz, sx, src_offset)));
sx -= SEARCH_SIZE2;
sy_up -= SEARCH_SIZE2;
sy_down -= SEARCH_SIZE2;
for (int i = id; i < SEARCH_SIZE_SQ; i += CTA_SIZE)
{
int wx = i % SEARCH_SIZE, wy = i / SEARCH_SIZE;
pixel_t up_value_t = *(__global const pixel_t *)(src + mad24(sy_up + wy, src_step, mad24(psz, sx + wx, src_offset)));
pixel_t down_value_t = *(__global const pixel_t *)(src + mad24(sy_down + wy, src_step, mad24(psz, sx + wx, src_offset)));
__global int * col_dists_current = col_dists + mad24(i, TEMPLATE_SIZE, first);
__global int * up_col_dists_current = up_col_dists + mad24(x0, SEARCH_SIZE_SQ, i);
int col_dist = up_col_dists_current[0] + calcDistUpDown(down_value, down_value_t, up_value, up_value_t);
dists[i] += col_dist - col_dists_current[0];
col_dists_current[0] = col_dist;
up_col_dists_current[0] = col_dist;
}
}
inline void convolveWindow(__global const uchar * src, int src_step, int src_offset,
__local int * dists, __global const wlut_t * almostDist2Weight,
__global uchar * dst, int dst_step, int dst_offset,
int y, int x, int id, __local weight_t * weights_local,
__local sum_t * weighted_sum_local, int almostTemplateWindowSizeSqBinShift)
{
int sx = x - SEARCH_SIZE2, sy = y - SEARCH_SIZE2;
weight_t weights = (weight_t)0;
sum_t weighted_sum = (sum_t)0;
for (int i = id; i < SEARCH_SIZE_SQ; i += CTA_SIZE)
{
int src_index = mad24(sy + i / SEARCH_SIZE, src_step, mad24(i % SEARCH_SIZE + sx, psz, src_offset));
sum_t src_value = convert_sum_t(*(__global const pixel_t *)(src + src_index));
int almostAvgDist = dists[i] >> almostTemplateWindowSizeSqBinShift;
weight_t weight = convert_weight_t(almostDist2Weight[almostAvgDist]);
weights += weight;
weighted_sum += (sum_t)weight * src_value;
}
weights_local[id] = weights;
weighted_sum_local[id] = weighted_sum;
barrier(CLK_LOCAL_MEM_FENCE);
for (int lsize = CTA_SIZE >> 1; lsize > 2; lsize >>= 1)
{
if (id < lsize)
{
int id2 = lsize + id;
weights_local[id] += weights_local[id2];
weighted_sum_local[id] += weighted_sum_local[id2];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (id == 0)
{
int dst_index = mad24(y, dst_step, mad24(psz, x, dst_offset));
sum_t weighted_sum_local_0 = weighted_sum_local[0] + weighted_sum_local[1] +
weighted_sum_local[2] + weighted_sum_local[3];
weight_t weights_local_0 = weights_local[0] + weights_local[1] + weights_local[2] + weights_local[3];
*(__global pixel_t *)(dst + dst_index) = convert_pixel_t(weighted_sum_local_0 / (sum_t)weights_local_0);
}
}
__kernel void fastNlMeansDenoising(__global const uchar * src, int src_step, int src_offset,
__global uchar * dst, int dst_step, int dst_offset, int dst_rows, int dst_cols,
__global const wlut_t * almostDist2Weight, __global uchar * buffer,
int almostTemplateWindowSizeSqBinShift)
{
int block_x = get_group_id(0), nblocks_x = get_num_groups(0);
int block_y = get_group_id(1);
int id = get_local_id(0), first;
__local int dists[SEARCH_SIZE_SQ];
__local weight_t weights[CTA_SIZE];
__local sum_t weighted_sum[CTA_SIZE];
int x0 = block_x * BLOCK_COLS, x1 = min(x0 + BLOCK_COLS, dst_cols);
int y0 = block_y * BLOCK_ROWS, y1 = min(y0 + BLOCK_ROWS, dst_rows);
// for each group we need SEARCH_SIZE_SQ * TEMPLATE_SIZE integer buffer for storing part column sum for current element
// and SEARCH_SIZE_SQ * BLOCK_COLS integer buffer for storing last column sum for each element of search window of up row
int block_data_start = SEARCH_SIZE_SQ * (mad24(block_y, dst_cols, x0) + mad24(block_y, nblocks_x, block_x) * TEMPLATE_SIZE);
__global int * col_dists = (__global int *)(buffer + block_data_start * sizeof(int));
__global int * up_col_dists = col_dists + SEARCH_SIZE_SQ * TEMPLATE_SIZE;
for (int y = y0; y < y1; ++y)
for (int x = x0; x < x1; ++x)
{
if (x == x0)
{
calcFirstElementInRow(src, src_step, src_offset, dists, y, x, id, col_dists, up_col_dists);
first = 0;
}
else
{
if (y == y0)
calcElementInFirstRow(src, src_step, src_offset, dists, y, x - x0, x, id, first, col_dists, up_col_dists);
else
calcElement(src, src_step, src_offset, dists, y, x - x0, x, id, first, col_dists, up_col_dists);
first = (first + 1) % TEMPLATE_SIZE;
}
convolveWindow(src, src_step, src_offset, dists, almostDist2Weight, dst, dst_step, dst_offset,
y, x, id, weights, weighted_sum, almostTemplateWindowSizeSqBinShift);
}
}
#endif

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/*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) 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.
//
// 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*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include "opencv2/core/private.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/imgproc.hpp"
#endif

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "seamless_cloning.hpp"
using namespace std;
using namespace cv;
static Mat checkMask(InputArray _mask, Size size)
{
Mat mask = _mask.getMat();
Mat gray;
if (mask.channels() > 1)
cvtColor(mask, gray, COLOR_BGRA2GRAY);
else
{
if (mask.empty())
gray = Mat(size.height, size.width, CV_8UC1, Scalar(255));
else
mask.copyTo(gray);
}
return gray;
}
void cv::seamlessClone(InputArray _src, InputArray _dst, InputArray _mask, Point p, OutputArray _blend, int flags)
{
CV_INSTRUMENT_REGION();
const Mat src = _src.getMat();
const Mat dest = _dst.getMat();
Mat mask = checkMask(_mask, src.size());
dest.copyTo(_blend);
Mat blend = _blend.getMat();
Mat mask_inner = mask(Rect(1, 1, mask.cols - 2, mask.rows - 2));
copyMakeBorder(mask_inner, mask, 1, 1, 1, 1, BORDER_ISOLATED | BORDER_CONSTANT, Scalar(0));
Rect roi_s = boundingRect(mask);
if (roi_s.empty()) return;
Rect roi_d(p.x - roi_s.width / 2, p.y - roi_s.height / 2, roi_s.width, roi_s.height);
Mat destinationROI = dest(roi_d).clone();
Mat sourceROI = Mat::zeros(roi_s.height, roi_s.width, src.type());
src(roi_s).copyTo(sourceROI,mask(roi_s));
Mat maskROI = mask(roi_s);
Mat recoveredROI = blend(roi_d);
Cloning obj;
obj.normalClone(destinationROI,sourceROI,maskROI,recoveredROI,flags);
}
void cv::colorChange(InputArray _src, InputArray _mask, OutputArray _dst, float red, float green, float blue)
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
Mat mask = checkMask(_mask, src.size());
_dst.create(src.size(), src.type());
Mat blend = _dst.getMat();
Mat cs_mask = Mat::zeros(src.size(), src.type());
src.copyTo(cs_mask, mask);
Cloning obj;
obj.localColorChange(src, cs_mask, mask, blend, red, green, blue);
}
void cv::illuminationChange(InputArray _src, InputArray _mask, OutputArray _dst, float alpha, float beta)
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
Mat mask = checkMask(_mask, src.size());
_dst.create(src.size(), src.type());
Mat blend = _dst.getMat();
Mat cs_mask = Mat::zeros(src.size(), src.type());
src.copyTo(cs_mask, mask);
Cloning obj;
obj.illuminationChange(src, cs_mask, mask, blend, alpha, beta);
}
void cv::textureFlattening(InputArray _src, InputArray _mask, OutputArray _dst,
float low_threshold, float high_threshold, int kernel_size)
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
Mat mask = checkMask(_mask, src.size());
_dst.create(src.size(), src.type());
Mat blend = _dst.getMat();
Mat cs_mask = Mat::zeros(src.size(), src.type());
src.copyTo(cs_mask, mask);
Cloning obj;
obj.textureFlatten(src, cs_mask, mask, low_threshold, high_threshold, kernel_size, blend);
}

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/*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) 2013, OpenCV Foundation, 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*/
#ifndef CV_SEAMLESS_CLONING_HPP___
#define CV_SEAMLESS_CLONING_HPP___
#include "precomp.hpp"
#include "opencv2/photo.hpp"
#include <vector>
namespace cv
{
class Cloning
{
public:
void normalClone(const cv::Mat& destination, const cv::Mat &mask, const cv::Mat &wmask, cv::Mat &cloned, int flag);
void illuminationChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float alpha, float beta);
void localColorChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float red_mul, float green_mul, float blue_mul);
void textureFlatten(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, float low_threshold, float high_threhold, int kernel_size, cv::Mat &cloned);
protected:
void initVariables(const cv::Mat &destination, const cv::Mat &binaryMask);
void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, const cv::Mat &binaryMask);
void scalarProduct(cv::Mat mat, float r, float g, float b);
void poisson(const cv::Mat &destination);
void evaluate(const cv::Mat &I, const cv::Mat &wmask, const cv::Mat &cloned);
void dst(const Mat& src, Mat& dest, bool invert = false);
void solve(const Mat &img, Mat& mod_diff, Mat &result);
void poissonSolver(const cv::Mat &img, cv::Mat &gxx , cv::Mat &gyy, cv::Mat &result);
void arrayProduct(const cv::Mat& lhs, const cv::Mat& rhs, cv::Mat& result) const;
void computeGradientX(const cv::Mat &img, cv::Mat &gx);
void computeGradientY(const cv::Mat &img, cv::Mat &gy);
void computeLaplacianX(const cv::Mat &img, cv::Mat &gxx);
void computeLaplacianY(const cv::Mat &img, cv::Mat &gyy);
private:
std::vector <cv::Mat> rgbx_channel, rgby_channel, output;
cv::Mat destinationGradientX, destinationGradientY;
cv::Mat patchGradientX, patchGradientY;
cv::Mat binaryMaskFloat, binaryMaskFloatInverted;
std::vector<float> filter_X, filter_Y;
};
}
#endif

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/*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) 2013, OpenCV Foundation, 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 "seamless_cloning.hpp"
using namespace cv;
using namespace std;
void Cloning::computeGradientX( const Mat &img, Mat &gx)
{
Mat kernel = Mat::zeros(1, 3, CV_8S);
kernel.at<char>(0,2) = 1;
kernel.at<char>(0,1) = -1;
if(img.channels() == 3)
{
filter2D(img, gx, CV_32F, kernel);
}
else if (img.channels() == 1)
{
filter2D(img, gx, CV_32F, kernel);
cvtColor(gx, gx, COLOR_GRAY2BGR);
}
}
void Cloning::computeGradientY( const Mat &img, Mat &gy)
{
Mat kernel = Mat::zeros(3, 1, CV_8S);
kernel.at<char>(2,0) = 1;
kernel.at<char>(1,0) = -1;
if(img.channels() == 3)
{
filter2D(img, gy, CV_32F, kernel);
}
else if (img.channels() == 1)
{
filter2D(img, gy, CV_32F, kernel);
cvtColor(gy, gy, COLOR_GRAY2BGR);
}
}
void Cloning::computeLaplacianX( const Mat &img, Mat &laplacianX)
{
Mat kernel = Mat::zeros(1, 3, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(0,1) = 1;
filter2D(img, laplacianX, CV_32F, kernel);
}
void Cloning::computeLaplacianY( const Mat &img, Mat &laplacianY)
{
Mat kernel = Mat::zeros(3, 1, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(1,0) = 1;
filter2D(img, laplacianY, CV_32F, kernel);
}
void Cloning::dst(const Mat& src, Mat& dest, bool invert)
{
Mat temp = Mat::zeros(src.rows, 2 * src.cols + 2, CV_32F);
int flag = invert ? DFT_ROWS + DFT_SCALE + DFT_INVERSE: DFT_ROWS;
src.copyTo(temp(Rect(1,0, src.cols, src.rows)));
for(int j = 0 ; j < src.rows ; ++j)
{
float * tempLinePtr = temp.ptr<float>(j);
const float * srcLinePtr = src.ptr<float>(j);
for(int i = 0 ; i < src.cols ; ++i)
{
tempLinePtr[src.cols + 2 + i] = - srcLinePtr[src.cols - 1 - i];
}
}
Mat planes[] = {temp, Mat::zeros(temp.size(), CV_32F)};
Mat complex;
merge(planes, 2, complex);
dft(complex, complex, flag);
split(complex, planes);
temp = Mat::zeros(src.cols, 2 * src.rows + 2, CV_32F);
for(int j = 0 ; j < src.cols ; ++j)
{
float * tempLinePtr = temp.ptr<float>(j);
for(int i = 0 ; i < src.rows ; ++i)
{
float val = planes[1].ptr<float>(i)[j + 1];
tempLinePtr[i + 1] = val;
tempLinePtr[temp.cols - 1 - i] = - val;
}
}
Mat planes2[] = {temp, Mat::zeros(temp.size(), CV_32F)};
merge(planes2, 2, complex);
dft(complex, complex, flag);
split(complex, planes2);
temp = planes2[1].t();
temp(Rect( 0, 1, src.cols, src.rows)).copyTo(dest);
}
void Cloning::solve(const Mat &img, Mat& mod_diff, Mat &result)
{
const int w = img.cols;
const int h = img.rows;
Mat res;
dst(mod_diff, res);
for(int j = 0 ; j < h-2; j++)
{
float * resLinePtr = res.ptr<float>(j);
for(int i = 0 ; i < w-2; i++)
{
resLinePtr[i] /= (filter_X[i] + filter_Y[j] - 4);
}
}
dst(res, mod_diff, true);
unsigned char * resLinePtr = result.ptr<unsigned char>(0);
const unsigned char * imgLinePtr = img.ptr<unsigned char>(0);
const float * interpLinePtr = NULL;
//first col
for(int i = 0 ; i < w ; ++i)
result.ptr<unsigned char>(0)[i] = img.ptr<unsigned char>(0)[i];
for(int j = 1 ; j < h-1 ; ++j)
{
resLinePtr = result.ptr<unsigned char>(j);
imgLinePtr = img.ptr<unsigned char>(j);
interpLinePtr = mod_diff.ptr<float>(j-1);
//first row
resLinePtr[0] = imgLinePtr[0];
for(int i = 1 ; i < w-1 ; ++i)
{
//saturate cast is not used here, because it behaves differently from the previous implementation
//most notable, saturate_cast rounds before truncating, here it's the opposite.
float value = interpLinePtr[i-1];
if(value < 0.)
resLinePtr[i] = 0;
else if (value > 255.0)
resLinePtr[i] = 255;
else
resLinePtr[i] = static_cast<unsigned char>(value);
}
//last row
resLinePtr[w-1] = imgLinePtr[w-1];
}
//last col
resLinePtr = result.ptr<unsigned char>(h-1);
imgLinePtr = img.ptr<unsigned char>(h-1);
for(int i = 0 ; i < w ; ++i)
resLinePtr[i] = imgLinePtr[i];
}
void Cloning::poissonSolver(const Mat &img, Mat &laplacianX , Mat &laplacianY, Mat &result)
{
const int w = img.cols;
const int h = img.rows;
Mat lap = laplacianX + laplacianY;
Mat bound = img.clone();
rectangle(bound, Point(1, 1), Point(img.cols-2, img.rows-2), Scalar::all(0), -1);
Mat boundary_points;
Laplacian(bound, boundary_points, CV_32F);
boundary_points = lap - boundary_points;
Mat mod_diff = boundary_points(Rect(1, 1, w-2, h-2));
solve(img,mod_diff,result);
}
void Cloning::initVariables(const Mat &destination, const Mat &binaryMask)
{
destinationGradientX = Mat(destination.size(),CV_32FC3);
destinationGradientY = Mat(destination.size(),CV_32FC3);
patchGradientX = Mat(destination.size(),CV_32FC3);
patchGradientY = Mat(destination.size(),CV_32FC3);
binaryMaskFloat = Mat(binaryMask.size(),CV_32FC1);
binaryMaskFloatInverted = Mat(binaryMask.size(),CV_32FC1);
//init of the filters used in the dst
const int w = destination.cols;
filter_X.resize(w - 2);
double scale = CV_PI / (w - 1);
for(int i = 0 ; i < w-2 ; ++i)
filter_X[i] = 2.0f * (float)std::cos(scale * (i + 1));
const int h = destination.rows;
filter_Y.resize(h - 2);
scale = CV_PI / (h - 1);
for(int j = 0 ; j < h - 2 ; ++j)
filter_Y[j] = 2.0f * (float)std::cos(scale * (j + 1));
}
void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, const Mat &binaryMask)
{
initVariables(destination, binaryMask);
computeGradientX(destination, destinationGradientX);
computeGradientY(destination, destinationGradientY);
computeGradientX(patch, patchGradientX);
computeGradientY(patch, patchGradientY);
Mat Kernel(Size(3, 3), CV_8UC1);
Kernel.setTo(Scalar(1));
erode(binaryMask, binaryMask, Kernel, Point(-1,-1), 3);
binaryMask.convertTo(binaryMaskFloat, CV_32FC1, 1.0/255.0);
}
void Cloning::scalarProduct(Mat mat, float r, float g, float b)
{
vector <Mat> channels;
split(mat,channels);
multiply(channels[2],r,channels[2]);
multiply(channels[1],g,channels[1]);
multiply(channels[0],b,channels[0]);
merge(channels,mat);
}
void Cloning::arrayProduct(const cv::Mat& lhs, const cv::Mat& rhs, cv::Mat& result) const
{
vector <Mat> lhs_channels;
vector <Mat> result_channels;
split(lhs,lhs_channels);
split(result,result_channels);
for(int chan = 0 ; chan < 3 ; ++chan)
multiply(lhs_channels[chan],rhs,result_channels[chan]);
merge(result_channels,result);
}
void Cloning::poisson(const Mat &destination)
{
Mat laplacianX = destinationGradientX + patchGradientX;
Mat laplacianY = destinationGradientY + patchGradientY;
computeLaplacianX(laplacianX,laplacianX);
computeLaplacianY(laplacianY,laplacianY);
split(laplacianX,rgbx_channel);
split(laplacianY,rgby_channel);
split(destination,output);
for(int chan = 0 ; chan < 3 ; ++chan)
{
poissonSolver(output[chan], rgbx_channel[chan], rgby_channel[chan], output[chan]);
}
}
void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned)
{
bitwise_not(wmask,wmask);
wmask.convertTo(binaryMaskFloatInverted,CV_32FC1,1.0/255.0);
arrayProduct(destinationGradientX, binaryMaskFloatInverted, destinationGradientX);
arrayProduct(destinationGradientY, binaryMaskFloatInverted, destinationGradientY);
poisson(I);
merge(output,cloned);
}
void Cloning::normalClone(const Mat &destination, const Mat &patch, const Mat &binaryMask, Mat &cloned, int flag)
{
const int w = destination.cols;
const int h = destination.rows;
const int channel = destination.channels();
const int n_elem_in_line = w * channel;
computeDerivatives(destination,patch,binaryMask);
switch(flag)
{
case NORMAL_CLONE:
arrayProduct(patchGradientX, binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY, binaryMaskFloat, patchGradientY);
break;
case MIXED_CLONE:
{
AutoBuffer<int> maskIndices(n_elem_in_line);
for (int i = 0; i < n_elem_in_line; ++i)
maskIndices[i] = i / channel;
for(int i=0;i < h; i++)
{
float * patchXLinePtr = patchGradientX.ptr<float>(i);
float * patchYLinePtr = patchGradientY.ptr<float>(i);
const float * destinationXLinePtr = destinationGradientX.ptr<float>(i);
const float * destinationYLinePtr = destinationGradientY.ptr<float>(i);
const float * binaryMaskLinePtr = binaryMaskFloat.ptr<float>(i);
for(int j=0; j < n_elem_in_line; j++)
{
int maskIndex = maskIndices[j];
if(abs(patchXLinePtr[j] - patchYLinePtr[j]) >
abs(destinationXLinePtr[j] - destinationYLinePtr[j]))
{
patchXLinePtr[j] *= binaryMaskLinePtr[maskIndex];
patchYLinePtr[j] *= binaryMaskLinePtr[maskIndex];
}
else
{
patchXLinePtr[j] = destinationXLinePtr[j]
* binaryMaskLinePtr[maskIndex];
patchYLinePtr[j] = destinationYLinePtr[j]
* binaryMaskLinePtr[maskIndex];
}
}
}
}
break;
case MONOCHROME_TRANSFER:
Mat gray;
cvtColor(patch, gray, COLOR_BGR2GRAY );
computeGradientX(gray,patchGradientX);
computeGradientY(gray,patchGradientY);
arrayProduct(patchGradientX, binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY, binaryMaskFloat, patchGradientY);
break;
}
evaluate(destination,binaryMask,cloned);
}
void Cloning::localColorChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float red_mul=1.0,
float green_mul=1.0, float blue_mul=1.0)
{
computeDerivatives(I,mask,wmask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
scalarProduct(patchGradientX,red_mul,green_mul,blue_mul);
scalarProduct(patchGradientY,red_mul,green_mul,blue_mul);
evaluate(I,wmask,cloned);
}
void Cloning::illuminationChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float alpha, float beta)
{
CV_INSTRUMENT_REGION();
computeDerivatives(I,mask,wmask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
Mat mag;
magnitude(patchGradientX,patchGradientY,mag);
Mat multX, multY, multx_temp, multy_temp;
multiply(patchGradientX,pow(alpha,beta),multX);
pow(mag,-1*beta, multx_temp);
multiply(multX,multx_temp, patchGradientX);
patchNaNs(patchGradientX);
multiply(patchGradientY,pow(alpha,beta),multY);
pow(mag,-1*beta, multy_temp);
multiply(multY,multy_temp,patchGradientY);
patchNaNs(patchGradientY);
Mat zeroMask = (patchGradientX != 0);
patchGradientX.copyTo(patchGradientX, zeroMask);
patchGradientY.copyTo(patchGradientY, zeroMask);
evaluate(I,wmask,cloned);
}
void Cloning::textureFlatten(Mat &I, Mat &mask, Mat &wmask, float low_threshold,
float high_threshold, int kernel_size, Mat &cloned)
{
computeDerivatives(I,mask,wmask);
Mat out;
Canny(mask,out,low_threshold,high_threshold,kernel_size);
Mat zeros = Mat::zeros(patchGradientX.size(), CV_32FC3);
Mat zerosMask = (out != 255);
zeros.copyTo(patchGradientX, zerosMask);
zeros.copyTo(patchGradientY, zerosMask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
evaluate(I,wmask,cloned);
}

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/*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) 2013, OpenCV Foundation, 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 "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
#include "hdr_common.hpp"
namespace cv
{
inline void log_(const Mat& src, Mat& dst)
{
max(src, Scalar::all(1e-4), dst);
log(dst, dst);
}
class TonemapImpl CV_FINAL : public Tonemap
{
public:
TonemapImpl(float _gamma) : name("Tonemap"), gamma(_gamma)
{
}
void process(InputArray _src, OutputArray _dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert(!src.empty());
CV_Assert(_src.dims() == 2 && _src.type() == CV_32FC3);
_dst.create(src.size(), CV_32FC3);
Mat dst = _dst.getMat();
double min, max;
minMaxLoc(src, &min, &max);
if(max - min > DBL_EPSILON) {
dst = (src - min) / (max - min);
} else {
src.copyTo(dst);
}
pow(dst, 1.0f / gamma, dst);
}
float getGamma() const CV_OVERRIDE { return gamma; }
void setGamma(float val) CV_OVERRIDE { gamma = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "gamma" << gamma;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
gamma = fn["gamma"];
}
protected:
String name;
float gamma;
};
Ptr<Tonemap> createTonemap(float gamma)
{
return makePtr<TonemapImpl>(gamma);
}
class TonemapDragoImpl CV_FINAL : public TonemapDrago
{
public:
TonemapDragoImpl(float _gamma, float _saturation, float _bias) :
name("TonemapDrago"),
gamma(_gamma),
saturation(_saturation),
bias(_bias)
{
}
void process(InputArray _src, OutputArray _dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert(!src.empty());
_dst.create(src.size(), CV_32FC3);
Mat img = _dst.getMat();
Ptr<Tonemap> linear = createTonemap(1.0f);
linear->process(src, img);
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log_(gray_img, log_img);
float mean = expf(static_cast<float>(sum(log_img)[0]) / log_img.total());
gray_img /= mean;
log_img.release();
double max;
minMaxLoc(gray_img, NULL, &max);
CV_Assert(max > 0);
Mat map;
log(gray_img + 1.0f, map);
Mat div;
pow(gray_img / static_cast<float>(max), logf(bias) / logf(0.5f), div);
log(2.0f + 8.0f * div, div);
map = map.mul(1.0f / div);
div.release();
mapLuminance(img, img, gray_img, map, saturation);
linear->setGamma(gamma);
linear->process(img, img);
}
float getGamma() const CV_OVERRIDE { return gamma; }
void setGamma(float val) CV_OVERRIDE { gamma = val; }
float getSaturation() const CV_OVERRIDE { return saturation; }
void setSaturation(float val) CV_OVERRIDE { saturation = val; }
float getBias() const CV_OVERRIDE { return bias; }
void setBias(float val) CV_OVERRIDE { bias = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "gamma" << gamma
<< "bias" << bias
<< "saturation" << saturation;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
gamma = fn["gamma"];
bias = fn["bias"];
saturation = fn["saturation"];
}
protected:
String name;
float gamma, saturation, bias;
};
Ptr<TonemapDrago> createTonemapDrago(float gamma, float saturation, float bias)
{
return makePtr<TonemapDragoImpl>(gamma, saturation, bias);
}
class TonemapReinhardImpl CV_FINAL : public TonemapReinhard
{
public:
TonemapReinhardImpl(float _gamma, float _intensity, float _light_adapt, float _color_adapt) :
name("TonemapReinhard"),
gamma(_gamma),
intensity(_intensity),
light_adapt(_light_adapt),
color_adapt(_color_adapt)
{
}
void process(InputArray _src, OutputArray _dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert(!src.empty());
_dst.create(src.size(), CV_32FC3);
Mat img = _dst.getMat();
Ptr<Tonemap> linear = createTonemap(1.0f);
linear->process(src, img);
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log_(gray_img, log_img);
float log_mean = static_cast<float>(sum(log_img)[0] / log_img.total());
double log_min, log_max;
minMaxLoc(log_img, &log_min, &log_max);
log_img.release();
double key = static_cast<float>((log_max - log_mean) / (log_max - log_min));
float map_key = 0.3f + 0.7f * pow(static_cast<float>(key), 1.4f);
intensity = exp(-intensity);
Scalar chan_mean = mean(img);
float gray_mean = static_cast<float>(mean(gray_img)[0]);
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
float global = color_adapt * static_cast<float>(chan_mean[i]) + (1.0f - color_adapt) * gray_mean;
Mat adapt = color_adapt * channels[i] + (1.0f - color_adapt) * gray_img;
adapt = light_adapt * adapt + (1.0f - light_adapt) * global;
pow(intensity * adapt, map_key, adapt);
channels[i] = channels[i].mul(1.0f / (adapt + channels[i]));
}
gray_img.release();
merge(channels, img);
linear->setGamma(gamma);
linear->process(img, img);
}
float getGamma() const CV_OVERRIDE { return gamma; }
void setGamma(float val) CV_OVERRIDE { gamma = val; }
float getIntensity() const CV_OVERRIDE { return intensity; }
void setIntensity(float val) CV_OVERRIDE { intensity = val; }
float getLightAdaptation() const CV_OVERRIDE { return light_adapt; }
void setLightAdaptation(float val) CV_OVERRIDE { light_adapt = val; }
float getColorAdaptation() const CV_OVERRIDE { return color_adapt; }
void setColorAdaptation(float val) CV_OVERRIDE { color_adapt = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "gamma" << gamma
<< "intensity" << intensity
<< "light_adapt" << light_adapt
<< "color_adapt" << color_adapt;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
gamma = fn["gamma"];
intensity = fn["intensity"];
light_adapt = fn["light_adapt"];
color_adapt = fn["color_adapt"];
}
protected:
String name;
float gamma, intensity, light_adapt, color_adapt;
};
Ptr<TonemapReinhard> createTonemapReinhard(float gamma, float contrast, float sigma_color, float sigma_space)
{
return makePtr<TonemapReinhardImpl>(gamma, contrast, sigma_color, sigma_space);
}
class TonemapMantiukImpl CV_FINAL : public TonemapMantiuk
{
public:
TonemapMantiukImpl(float _gamma, float _scale, float _saturation) :
name("TonemapMantiuk"),
gamma(_gamma),
scale(_scale),
saturation(_saturation)
{
}
void process(InputArray _src, OutputArray _dst) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert(!src.empty());
_dst.create(src.size(), CV_32FC3);
Mat img = _dst.getMat();
Ptr<Tonemap> linear = createTonemap(1.0f);
linear->process(src, img);
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log_(gray_img, log_img);
std::vector<Mat> x_contrast, y_contrast;
getContrast(log_img, x_contrast, y_contrast);
for(size_t i = 0; i < x_contrast.size(); i++) {
mapContrast(x_contrast[i]);
mapContrast(y_contrast[i]);
}
Mat right(src.size(), CV_32F);
calculateSum(x_contrast, y_contrast, right);
Mat p, r, product, x = log_img;
calculateProduct(x, r);
r = right - r;
r.copyTo(p);
const float target_error = 1e-3f;
float target_norm = static_cast<float>(right.dot(right)) * powf(target_error, 2.0f);
int max_iterations = 100;
float rr = static_cast<float>(r.dot(r));
for(int i = 0; i < max_iterations; i++)
{
calculateProduct(p, product);
double dprod = p.dot(product);
CV_Assert(fabs(dprod) > 0);
float alpha = rr / static_cast<float>(dprod);
r -= alpha * product;
x += alpha * p;
float new_rr = static_cast<float>(r.dot(r));
CV_Assert(fabs(rr) > 0);
p = r + (new_rr / rr) * p;
rr = new_rr;
if(rr < target_norm) {
break;
}
}
exp(x, x);
mapLuminance(img, img, gray_img, x, saturation);
linear = createTonemap(gamma);
linear->process(img, img);
}
float getGamma() const CV_OVERRIDE { return gamma; }
void setGamma(float val) CV_OVERRIDE { gamma = val; }
float getScale() const CV_OVERRIDE { return scale; }
void setScale(float val) CV_OVERRIDE { scale = val; }
float getSaturation() const CV_OVERRIDE { return saturation; }
void setSaturation(float val) CV_OVERRIDE { saturation = val; }
void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name
<< "gamma" << gamma
<< "scale" << scale
<< "saturation" << saturation;
}
void read(const FileNode& fn) CV_OVERRIDE
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
gamma = fn["gamma"];
scale = fn["scale"];
saturation = fn["saturation"];
}
protected:
String name;
float gamma, scale, saturation;
void signedPow(Mat src, float power, Mat& dst)
{
Mat sign = (src > 0);
sign.convertTo(sign, CV_32F, 1.0f/255.0f);
sign = sign * 2.0f - 1.0f;
pow(abs(src), power, dst);
dst = dst.mul(sign);
}
void mapContrast(Mat& contrast)
{
const float response_power = 0.4185f;
signedPow(contrast, response_power, contrast);
contrast *= scale;
signedPow(contrast, 1.0f / response_power, contrast);
}
void getGradient(Mat src, Mat& dst, int pos)
{
dst = Mat::zeros(src.size(), CV_32F);
Mat a, b;
Mat grad = src.colRange(1, src.cols) - src.colRange(0, src.cols - 1);
grad.copyTo(dst.colRange(pos, src.cols + pos - 1));
if(pos == 1) {
src.col(0).copyTo(dst.col(0));
}
}
void getContrast(Mat src, std::vector<Mat>& x_contrast, std::vector<Mat>& y_contrast)
{
int levels = static_cast<int>(logf(static_cast<float>(min(src.rows, src.cols))) / logf(2.0f));
x_contrast.resize(levels);
y_contrast.resize(levels);
Mat layer;
src.copyTo(layer);
for(int i = 0; i < levels; i++) {
getGradient(layer, x_contrast[i], 0);
getGradient(layer.t(), y_contrast[i], 0);
resize(layer, layer, Size(layer.cols / 2, layer.rows / 2), 0, 0, INTER_LINEAR);
}
}
void calculateSum(std::vector<Mat>& x_contrast, std::vector<Mat>& y_contrast, Mat& sum)
{
if (x_contrast.empty())
return;
const int last = (int)x_contrast.size() - 1;
sum = Mat::zeros(x_contrast[last].size(), CV_32F);
for(int i = last; i >= 0; i--)
{
Mat grad_x, grad_y;
getGradient(x_contrast[i], grad_x, 1);
getGradient(y_contrast[i], grad_y, 1);
resize(sum, sum, x_contrast[i].size(), 0, 0, INTER_LINEAR);
sum += grad_x + grad_y.t();
}
}
void calculateProduct(Mat src, Mat& dst)
{
std::vector<Mat> x_contrast, y_contrast;
getContrast(src, x_contrast, y_contrast);
calculateSum(x_contrast, y_contrast, dst);
}
};
Ptr<TonemapMantiuk> createTonemapMantiuk(float gamma, float scale, float saturation)
{
return makePtr<TonemapMantiukImpl>(gamma, scale, saturation);
}
}

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
PARAM_TEST_CASE(FastNlMeansDenoisingTestBase, Channels, int, bool, bool)
{
int cn, normType, templateWindowSize, searchWindowSize;
std::vector<float> h;
bool use_roi, use_image;
TEST_DECLARE_INPUT_PARAMETER(src);
TEST_DECLARE_OUTPUT_PARAMETER(dst);
virtual void SetUp()
{
cn = GET_PARAM(0);
normType = GET_PARAM(1);
use_roi = GET_PARAM(2);
use_image = GET_PARAM(3);
templateWindowSize = 7;
searchWindowSize = 21;
h.resize(cn);
for (int i=0; i<cn; i++)
h[i] = 3.0f + 0.5f*i;
}
void generateTestData()
{
const int type = CV_8UC(cn);
Mat image;
if (use_image) {
image = readImage("denoising/lena_noised_gaussian_sigma=10.png",
cn == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
ASSERT_FALSE(image.empty());
}
Size roiSize = use_image ? image.size() : randomSize(1, MAX_VALUE);
Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src, src_roi, roiSize, srcBorder, type, 0, 255);
if (use_image) {
ASSERT_TRUE(cn > 0 && cn <= 4);
if (cn == 2) {
int from_to[] = { 0,0, 1,1 };
src_roi.create(roiSize, type);
mixChannels(&image, 1, &src_roi, 1, from_to, 2);
}
else if (cn == 4) {
int from_to[] = { 0,0, 1,1, 2,2, 1,3};
src_roi.create(roiSize, type);
mixChannels(&image, 1, &src_roi, 1, from_to, 4);
}
else image.copyTo(src_roi);
}
Border dstBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 0, 255);
UMAT_UPLOAD_INPUT_PARAMETER(src);
UMAT_UPLOAD_OUTPUT_PARAMETER(dst);
}
};
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoising;
OCL_TEST_P(FastNlMeansDenoising, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
OCL_OFF(cv::fastNlMeansDenoising(src_roi, dst_roi, std::vector<float>(1, h[0]), templateWindowSize, searchWindowSize, normType));
OCL_ON(cv::fastNlMeansDenoising(usrc_roi, udst_roi, std::vector<float>(1, h[0]), templateWindowSize, searchWindowSize, normType));
OCL_EXPECT_MATS_NEAR(dst, 1);
}
}
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoising_hsep;
OCL_TEST_P(FastNlMeansDenoising_hsep, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
OCL_OFF(cv::fastNlMeansDenoising(src_roi, dst_roi, h, templateWindowSize, searchWindowSize, normType));
OCL_ON(cv::fastNlMeansDenoising(usrc_roi, udst_roi, h, templateWindowSize, searchWindowSize, normType));
OCL_EXPECT_MATS_NEAR(dst, 1);
}
}
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingColored;
OCL_TEST_P(FastNlMeansDenoisingColored, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
OCL_OFF(cv::fastNlMeansDenoisingColored(src_roi, dst_roi, h[0], h[0], templateWindowSize, searchWindowSize));
OCL_ON(cv::fastNlMeansDenoisingColored(usrc_roi, udst_roi, h[0], h[0], templateWindowSize, searchWindowSize));
OCL_EXPECT_MATS_NEAR(dst, 1);
}
}
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising,
Combine(Values(1, 2, 3, 4), Values((int)NORM_L2, (int)NORM_L1),
Bool(), Values(true)));
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising_hsep,
Combine(Values(1, 2, 3, 4), Values((int)NORM_L2, (int)NORM_L1),
Bool(), Values(true)));
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingColored,
Combine(Values(3, 4), Values((int)NORM_L2), Bool(), Values(false)));
} } // namespace opencv_test::ocl
#endif // HAVE_OPENCL

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/*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) 2013, OpenCV Foundation, 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"
namespace opencv_test { namespace {
#define OUTPUT_SAVING 0
#if OUTPUT_SAVING
#define SAVE(x) std::vector<int> params;\
params.push_back(16);\
params.push_back(0);\
imwrite(folder + "output.png", x ,params);
#else
#define SAVE(x)
#endif
static const double numerical_precision = 0.05; // 95% of pixels should have exact values
TEST(Photo_SeamlessClone_normal, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Normal_Cloning/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 1);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
SAVE(result);
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
mask = Scalar(0, 0, 0);
seamlessClone(source, destination, mask, p, result, 1);
reference = destination;
errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
TEST(Photo_SeamlessClone_mixed, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Mixed_Cloning/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 2);
SAVE(result);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
TEST(Photo_SeamlessClone_featureExchange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Monochrome_Transfer/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 3);
SAVE(result);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
TEST(Photo_SeamlessClone_colorChange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/color_change/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
colorChange(source, mask, result, 1.5, .5, .5);
SAVE(result);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
TEST(Photo_SeamlessClone_illuminationChange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Illumination_Change/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
illuminationChange(source, mask, result, 0.2f, 0.4f);
SAVE(result);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
TEST(Photo_SeamlessClone_textureFlattening, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Texture_Flattening/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
string reference_path = folder + "reference.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
textureFlattening(source, mask, result, 30, 45, 3);
SAVE(result);
Mat reference = imread(reference_path);
ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
double errorINF = cvtest::norm(reference, result, NORM_INF);
EXPECT_LE(errorINF, 1);
double errorL1 = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
}
}} // namespace

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/*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) 2013, OpenCV Foundation, 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"
namespace opencv_test { namespace {
TEST(Photo_Decolor, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "decolor/";
string original_path = folder + "color_image_1.png";
Mat original = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
ASSERT_EQ(3, original.channels()) << "Load color input image " << original_path;
Mat grayscale, color_boost;
decolor(original, grayscale, color_boost);
Mat reference_grayscale = imread(folder + "grayscale_reference.png", 0 /* == grayscale image*/);
double gray_psnr = cvtest::PSNR(reference_grayscale, grayscale);
EXPECT_GT(gray_psnr, 60.0);
Mat reference_boost = imread(folder + "boost_reference.png");
double boost_psnr = cvtest::PSNR(reference_boost, color_boost);
EXPECT_GT(boost_psnr, 60.0);
}
}} // namespace

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/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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"
namespace opencv_test { namespace {
void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_salt_ratio,cv::RNG& rng)
{
noisy.create(img.size(), img.type());
cv::Mat noise(img.size(), img.type()), mask(img.size(), CV_8U);
rng.fill(noise,cv::RNG::NORMAL,128.0,sigma);
cv::addWeighted(img, 1, noise, 1, -128, noisy);
cv::randn(noise, cv::Scalar::all(0), cv::Scalar::all(2));
noise *= 255;
cv::randu(mask, 0, cvRound(1./pepper_salt_ratio));
cv::Mat half = mask.colRange(0, img.cols/2);
half = cv::Scalar::all(1);
noise.setTo(128, mask);
cv::addWeighted(noisy, 1, noise, 1, -128, noisy);
}
#if 0
void make_spotty(cv::Mat& img,cv::RNG& rng, int r=3,int n=1000)
{
for(int i=0;i<n;i++)
{
int x=rng(img.cols-r),y=rng(img.rows-r);
if(rng(2)==0)
img(cv::Range(y,y+r),cv::Range(x,x+r))=(uchar)0;
else
img(cv::Range(y,y+r),cv::Range(x,x+r))=(uchar)255;
}
}
#endif
bool validate_pixel(const cv::Mat& image,int x,int y,uchar val)
{
bool ok = std::abs(image.at<uchar>(x,y) - val) < 10;
printf("test: image(%d,%d)=%d vs %d - %s\n",x,y,(int)image.at<uchar>(x,y),val,ok?"ok":"bad");
return ok;
}
TEST(Optim_denoise_tvl1, regression_basic)
{
cv::RNG rng(42);
cv::Mat img = cv::imread(cvtest::TS::ptr()->get_data_path() + "shared/lena.png", 0), noisy, res;
ASSERT_FALSE(img.empty()) << "Error: can't open 'lena.png'";
const int obs_num=5;
std::vector<cv::Mat> images(obs_num, cv::Mat());
for(int i=0;i<(int)images.size();i++)
{
make_noisy(img,images[i], 20, 0.02,rng);
//make_spotty(images[i],rng);
}
//cv::imshow("test", images[0]);
cv::denoise_TVL1(images, res);
//cv::imshow("denoised", res);
//cv::waitKey();
#if 0
ASSERT_TRUE(validate_pixel(res,248,334,179));
ASSERT_TRUE(validate_pixel(res,489,333,172));
ASSERT_TRUE(validate_pixel(res,425,507,104));
ASSERT_TRUE(validate_pixel(res,489,486,105));
ASSERT_TRUE(validate_pixel(res,223,208,64));
ASSERT_TRUE(validate_pixel(res,418,3,78));
ASSERT_TRUE(validate_pixel(res,63,76,97));
ASSERT_TRUE(validate_pixel(res,29,134,126));
ASSERT_TRUE(validate_pixel(res,219,291,174));
ASSERT_TRUE(validate_pixel(res,384,124,76));
#endif
#if 1
ASSERT_TRUE(validate_pixel(res,248,334,194));
ASSERT_TRUE(validate_pixel(res,489,333,171));
ASSERT_TRUE(validate_pixel(res,425,507,103));
ASSERT_TRUE(validate_pixel(res,489,486,109));
ASSERT_TRUE(validate_pixel(res,223,208,72));
ASSERT_TRUE(validate_pixel(res,418,3,58));
ASSERT_TRUE(validate_pixel(res,63,76,93));
ASSERT_TRUE(validate_pixel(res,29,134,127));
ASSERT_TRUE(validate_pixel(res,219,291,180));
ASSERT_TRUE(validate_pixel(res,384,124,80));
#endif
}
}} // namespace

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/*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) 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.
//
// 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"
namespace opencv_test { namespace {
//#define DUMP_RESULTS
#ifdef DUMP_RESULTS
# define DUMP(image, path) imwrite(path, image)
#else
# define DUMP(image, path)
#endif
TEST(Photo_DenoisingGrayscale, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string original_path = folder + "lena_noised_gaussian_sigma=10.png";
string expected_path = folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png";
Mat original = imread(original_path, IMREAD_GRAYSCALE);
Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
Mat result;
fastNlMeansDenoising(original, result, 10);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
}
TEST(Photo_DenoisingColored, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string original_path = folder + "lena_noised_gaussian_sigma=10.png";
string expected_path = folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png";
Mat original = imread(original_path, IMREAD_COLOR);
Mat expected = imread(expected_path, IMREAD_COLOR);
ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
Mat result;
fastNlMeansDenoisingColored(original, result, 10, 10);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
}
TEST(Photo_DenoisingGrayscaleMulti, regression)
{
const int imgs_count = 3;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string expected_path = folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png";
Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
vector<Mat> original(imgs_count);
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
original[i] = imread(original_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
}
Mat result;
fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
}
TEST(Photo_DenoisingColoredMulti, regression)
{
const int imgs_count = 3;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string expected_path = folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png";
Mat expected = imread(expected_path, IMREAD_COLOR);
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
vector<Mat> original(imgs_count);
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
original[i] = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
}
Mat result;
fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2));
}
TEST(Photo_White, issue_2646)
{
cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255));
cv::Mat filtered;
cv::fastNlMeansDenoising(img, filtered);
int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img);
ASSERT_EQ(0, nonWhitePixelsCount);
}
TEST(Photo_Denoising, speed)
{
string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png";
Mat src = imread(imgname, 0), dst;
double t = (double)getTickCount();
fastNlMeansDenoising(src, dst, 5, 7, 21);
t = (double)getTickCount() - t;
printf("execution time: %gms\n", t*1000./getTickFrequency());
}
}} // namespace

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/*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) 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.
//
// 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 "opencv2/photo/cuda.hpp"
#include "opencv2/ts/cuda_test.hpp"
#include "opencv2/opencv_modules.hpp"
#include "cvconfig.h"
#if defined (HAVE_CUDA) && defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAIMGPROC)
namespace opencv_test { namespace {
////////////////////////////////////////////////////////
// Brute Force Non local means
TEST(CUDA_BruteForceNonLocalMeans, Regression)
{
using cv::cuda::GpuMat;
cv::Mat bgr = readImage("../gpu/denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
ASSERT_FALSE(bgr.empty());
cv::resize(bgr, bgr, cv::Size(256, 256));
cv::Mat gray;
cv::cvtColor(bgr, gray, cv::COLOR_BGR2GRAY);
GpuMat dbgr, dgray;
cv::cuda::nonLocalMeans(GpuMat(bgr), dbgr, 20);
cv::cuda::nonLocalMeans(GpuMat(gray), dgray, 20);
#if 0
dumpImage("../gpu/denoising/nlm_denoised_lena_bgr.png", cv::Mat(dbgr));
dumpImage("../gpu/denoising/nlm_denoised_lena_gray.png", cv::Mat(dgray));
#endif
cv::Mat bgr_gold = readImage("../gpu/denoising/nlm_denoised_lena_bgr.png", cv::IMREAD_COLOR);
cv::Mat gray_gold = readImage("../gpu/denoising/nlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty());
cv::resize(bgr_gold, bgr_gold, cv::Size(256, 256));
cv::resize(gray_gold, gray_gold, cv::Size(256, 256));
EXPECT_MAT_NEAR(bgr_gold, dbgr, 1);
EXPECT_MAT_NEAR(gray_gold, dgray, 1);
}
////////////////////////////////////////////////////////
// Fast Force Non local means
TEST(CUDA_FastNonLocalMeans, Regression)
{
using cv::cuda::GpuMat;
cv::Mat bgr = readImage("../gpu/denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
ASSERT_FALSE(bgr.empty());
cv::Mat gray;
cv::cvtColor(bgr, gray, cv::COLOR_BGR2GRAY);
GpuMat dbgr, dgray;
cv::cuda::fastNlMeansDenoising(GpuMat(gray), dgray, 20);
cv::cuda::fastNlMeansDenoisingColored(GpuMat(bgr), dbgr, 20, 10);
#if 0
dumpImage("../gpu/denoising/fnlm_denoised_lena_bgr.png", cv::Mat(dbgr));
dumpImage("../gpu/denoising/fnlm_denoised_lena_gray.png", cv::Mat(dgray));
#endif
cv::Mat bgr_gold = readImage("../gpu/denoising/fnlm_denoised_lena_bgr.png", cv::IMREAD_COLOR);
cv::Mat gray_gold = readImage("../gpu/denoising/fnlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty());
EXPECT_MAT_NEAR(bgr_gold, dbgr, 1);
EXPECT_MAT_NEAR(gray_gold, dgray, 1);
}
}} // namespace
#endif // HAVE_CUDA

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/*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) 2013, OpenCV Foundation, 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"
namespace opencv_test { namespace {
void loadImage(string path, Mat &img)
{
img = imread(path, -1);
ASSERT_FALSE(img.empty()) << "Could not load input image " << path;
}
void checkEqual(Mat img0, Mat img1, double threshold, const string& name)
{
double max = 1.0;
minMaxLoc(abs(img0 - img1), NULL, &max);
ASSERT_FALSE(max > threshold) << "max=" << max << " threshold=" << threshold << " method=" << name;
}
static vector<float> DEFAULT_VECTOR;
void loadExposureSeq(String path, vector<Mat>& images, vector<float>& times = DEFAULT_VECTOR)
{
std::ifstream list_file((path + "list.txt").c_str());
ASSERT_TRUE(list_file.is_open());
string name;
float val;
while(list_file >> name >> val) {
Mat img = imread(path + name);
ASSERT_FALSE(img.empty()) << "Could not load input image " << path + name;
images.push_back(img);
times.push_back(1 / val);
}
list_file.close();
}
void loadResponseCSV(String path, Mat& response)
{
response = Mat(256, 1, CV_32FC3);
std::ifstream resp_file(path.c_str());
for(int i = 0; i < 256; i++) {
for(int c = 0; c < 3; c++) {
resp_file >> response.at<Vec3f>(i)[c];
resp_file.ignore(1);
}
}
resp_file.close();
}
TEST(Photo_Tonemap, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
Mat img, expected, result;
loadImage(test_path + "image.hdr", img);
float gamma = 2.2f;
Ptr<Tonemap> linear = createTonemap(gamma);
linear->process(img, result);
loadImage(test_path + "linear.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 3, "Simple");
Ptr<TonemapDrago> drago = createTonemapDrago(gamma);
drago->process(img, result);
loadImage(test_path + "drago.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 3, "Drago");
Ptr<TonemapReinhard> reinhard = createTonemapReinhard(gamma);
reinhard->process(img, result);
loadImage(test_path + "reinhard.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 3, "Reinhard");
Ptr<TonemapMantiuk> mantiuk = createTonemapMantiuk(gamma);
mantiuk->process(img, result);
loadImage(test_path + "mantiuk.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 3, "Mantiuk");
}
TEST(Photo_AlignMTB, regression)
{
const int TESTS_COUNT = 100;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/";
string file_name = folder + "lena.png";
Mat img;
loadImage(file_name, img);
cvtColor(img, img, COLOR_RGB2GRAY);
int max_bits = 5;
int max_shift = 32;
srand(static_cast<unsigned>(time(0)));
int errors = 0;
Ptr<AlignMTB> align = createAlignMTB(max_bits);
RNG rng = theRNG();
for(int i = 0; i < TESTS_COUNT; i++) {
Point shift(rng.uniform(0, max_shift), rng.uniform(0, max_shift));
Mat res;
align->shiftMat(img, res, shift);
Point calc = align->calculateShift(img, res);
errors += (calc != -shift);
}
ASSERT_TRUE(errors < 5) << errors << " errors";
}
TEST(Photo_MergeMertens, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images;
loadExposureSeq((test_path + "exposures/").c_str() , images);
Ptr<MergeMertens> merge = createMergeMertens();
Mat result, expected;
loadImage(test_path + "merge/mertens.png", expected);
merge->process(images, result);
result.convertTo(result, CV_8UC3, 255);
checkEqual(expected, result, 3, "Mertens");
Mat uniform(100, 100, CV_8UC3);
uniform = Scalar(0, 255, 0);
images.clear();
images.push_back(uniform);
merge->process(images, result);
result.convertTo(result, CV_8UC3, 255);
checkEqual(uniform, result, 1e-2f, "Mertens");
}
TEST(Photo_MergeDebevec, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images;
vector<float> times;
Mat response;
loadExposureSeq(test_path + "exposures/", images, times);
loadResponseCSV(test_path + "exposures/response.csv", response);
Ptr<MergeDebevec> merge = createMergeDebevec();
Mat result, expected;
loadImage(test_path + "merge/debevec.hdr", expected);
merge->process(images, result, times, response);
Ptr<Tonemap> map = createTonemap();
map->process(result, result);
map->process(expected, expected);
checkEqual(expected, result, 1e-2f, "Debevec");
}
TEST(Photo_MergeRobertson, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images;
vector<float> times;
loadExposureSeq(test_path + "exposures/", images, times);
Ptr<MergeRobertson> merge = createMergeRobertson();
Mat result, expected;
loadImage(test_path + "merge/robertson.hdr", expected);
merge->process(images, result, times);
const float eps = 6.f;
checkEqual(expected, result, eps, "MergeRobertson");
}
TEST(Photo_CalibrateDebevec, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images;
vector<float> times;
Mat response, expected;
loadExposureSeq(test_path + "exposures/", images, times);
loadResponseCSV(test_path + "calibrate/debevec.csv", expected);
Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec();
calibrate->process(images, response, times);
Mat diff = abs(response - expected);
diff = diff.mul(1.0f / response);
double max;
minMaxLoc(diff, NULL, &max);
#if defined(__arm__) || defined(__aarch64__)
ASSERT_LT(max, 0.131);
#else
ASSERT_LT(max, 0.1);
#endif
}
TEST(Photo_CalibrateRobertson, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images;
vector<float> times;
Mat response, expected;
loadExposureSeq(test_path + "exposures/", images, times);
loadResponseCSV(test_path + "calibrate/robertson.csv", expected);
Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson();
calibrate->process(images, response, times);
checkEqual(expected, response, 1e-1f, "CalibrateRobertson");
}
TEST(Photo_CalibrateRobertson, bug_18180)
{
vector<Mat> images;
vector<cv::String> fn;
string test_path = cvtest::TS::ptr()->get_data_path() + "hdr/exposures/bug_18180/";
for(int i = 1; i <= 4; ++i)
images.push_back(imread(test_path + std::to_string(i) + ".jpg"));
vector<float> times {15.0f, 2.5f, 0.25f, 0.33f};
Mat response, expected;
Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson(2, 0.01f);
calibrate->process(images, response, times);
Mat response_no_nans = response.clone();
patchNaNs(response_no_nans);
// since there should be no NaNs, original response vs. response with NaNs patched should be identical
EXPECT_EQ(0.0, cv::norm(response, response_no_nans, NORM_L2));
}
}} // namespace

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/*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) 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.
//
// 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"
namespace opencv_test { namespace {
class CV_InpaintTest : public cvtest::BaseTest
{
public:
CV_InpaintTest();
~CV_InpaintTest();
protected:
void run(int);
};
CV_InpaintTest::CV_InpaintTest()
{
}
CV_InpaintTest::~CV_InpaintTest() {}
void CV_InpaintTest::run( int )
{
string folder = string(ts->get_data_path()) + "inpaint/";
Mat orig = imread(folder + "orig.png");
Mat exp1 = imread(folder + "exp1.png");
Mat exp2 = imread(folder + "exp2.png");
Mat mask = imread(folder + "mask.png");
if (orig.empty() || exp1.empty() || exp2.empty() || mask.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
Mat inv_mask;
mask.convertTo(inv_mask, CV_8UC3, -1.0, 255.0);
Mat mask1ch;
cv::cvtColor(mask, mask1ch, COLOR_BGR2GRAY);
Mat test = orig.clone();
test.setTo(Scalar::all(255), mask1ch);
Mat res1, res2;
inpaint( test, mask1ch, res1, 5, INPAINT_NS );
inpaint( test, mask1ch, res2, 5, INPAINT_TELEA );
Mat diff1, diff2;
absdiff( orig, res1, diff1 );
absdiff( orig, res2, diff2 );
double n1 = cvtest::norm(diff1.reshape(1), NORM_INF, inv_mask.reshape(1));
double n2 = cvtest::norm(diff2.reshape(1), NORM_INF, inv_mask.reshape(1));
if (n1 != 0 || n2 != 0)
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
return;
}
absdiff( exp1, res1, diff1 );
absdiff( exp2, res2, diff2 );
n1 = cvtest::norm(diff1.reshape(1), NORM_INF, mask.reshape(1));
n2 = cvtest::norm(diff2.reshape(1), NORM_INF, mask.reshape(1));
const int jpeg_thres = 3;
if (n1 > jpeg_thres || n2 > jpeg_thres)
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
ts->set_failed_test_info(cvtest::TS::OK);
}
TEST(Photo_Inpaint, regression) { CV_InpaintTest test; test.safe_run(); }
typedef testing::TestWithParam<tuple<int> > formats;
TEST_P(formats, 1c)
{
const int type = get<0>(GetParam());
Mat src(100, 100, type);
src.setTo(Scalar::all(128));
Mat ref = src.clone();
Mat dst, mask = Mat::zeros(src.size(), CV_8U);
circle(src, Point(50, 50), 5, Scalar(200), 6);
circle(mask, Point(50, 50), 5, Scalar(200), 6);
inpaint(src, mask, dst, 10, INPAINT_NS);
Mat dst2;
inpaint(src, mask, dst2, 10, INPAINT_TELEA);
ASSERT_LE(cv::norm(dst, ref, NORM_INF), 3.);
ASSERT_LE(cv::norm(dst2, ref, NORM_INF), 3.);
}
INSTANTIATE_TEST_CASE_P(Photo_Inpaint, formats, testing::Values(CV_32F, CV_16U, CV_8U));
TEST(Photo_InpaintBorders, regression)
{
Mat img(64, 64, CV_8U);
img = 128;
img(Rect(0, 0, 16, 64)) = 0;
Mat mask(64, 64, CV_8U);
mask = 0;
mask(Rect(0, 0, 16, 64)) = 255;
Mat inpainted;
inpaint(img, mask, inpainted, 1, INPAINT_TELEA);
Mat diff;
cv::absdiff(inpainted, 128*Mat::ones(inpainted.size(), inpainted.type()), diff);
ASSERT_TRUE(countNonZero(diff) == 0);
}
}} // namespace

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#if defined(HAVE_HPX)
#include <hpx/hpx_main.hpp>
#endif
CV_TEST_MAIN("cv")

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/*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) 2013, OpenCV Foundation, 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"
namespace opencv_test { namespace {
static const double numerical_precision = 100.;
TEST(Photo_NPR_EdgePreserveSmoothing_RecursiveFilter, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
string original_path = folder + "test1.png";
Mat source = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat result;
edgePreservingFilter(source,result,1);
Mat reference = imread(folder + "smoothened_RF_reference.png");
double psnr = cvtest::PSNR(reference, result);
EXPECT_GT(psnr, 60.0);
}
TEST(Photo_NPR_EdgePreserveSmoothing_NormConvFilter, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
string original_path = folder + "test1.png";
Mat source = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat result;
edgePreservingFilter(source,result,2);
Mat reference = imread(folder + "smoothened_NCF_reference.png");
double psnr = cvtest::PSNR(reference, result);
EXPECT_GT(psnr, 60.0);
}
TEST(Photo_NPR_DetailEnhance, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
string original_path = folder + "test1.png";
Mat source = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat result;
detailEnhance(source,result);
Mat reference = imread(folder + "detail_enhanced_reference.png");
double psnr = cvtest::PSNR(reference, result);
EXPECT_GT(psnr, 60.0);
}
TEST(Photo_NPR_PencilSketch, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
string original_path = folder + "test1.png";
Mat source = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat pencil_result, color_pencil_result;
pencilSketch(source,pencil_result, color_pencil_result, 10, 0.1f, 0.03f);
Mat pencil_reference = imread(folder + "pencil_sketch_reference.png", 0 /* == grayscale*/);
double pencil_error = cvtest::norm(pencil_reference, pencil_result, NORM_L1);
EXPECT_LE(pencil_error, numerical_precision);
Mat color_pencil_reference = imread(folder + "color_pencil_sketch_reference.png");
double color_pencil_error = cvtest::norm(color_pencil_reference, color_pencil_result, NORM_L1);
EXPECT_LE(color_pencil_error, numerical_precision);
}
TEST(Photo_NPR_Stylization, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
string original_path = folder + "test1.png";
Mat source = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat result;
stylization(source,result);
Mat stylized_reference = imread(folder + "stylized_reference.png");
double stylized_error = cvtest::norm(stylized_reference, result, NORM_L1);
EXPECT_LE(stylized_error, numerical_precision);
}
}} // namespace

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/photo.hpp"
#endif