Tonemap as 3.0 algorithm
This commit is contained in:
Fedor Morozov
parent
258b98d15b
commit
4d2ea847fa
@ -59,6 +59,8 @@ enum
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INPAINT_TELEA = 1 // A. Telea algorithm
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INPAINT_TELEA = 1 // A. Telea algorithm
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};
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};
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CV_EXPORTS_W bool initModule_photo();
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//! restores the damaged image areas using one of the available intpainting algorithms
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//! restores the damaged image areas using one of the available intpainting algorithms
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CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
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CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
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OutputArray dst, double inpaintRadius, int flags );
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OutputArray dst, double inpaintRadius, int flags );
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@ -80,77 +82,62 @@ CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs,
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float h = 3, float hColor = 3,
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float h = 3, float hColor = 3,
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int templateWindowSize = 7, int searchWindowSize = 21);
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int templateWindowSize = 7, int searchWindowSize = 21);
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CV_EXPORTS_W void makeHDR(InputArrayOfArrays srcImgs, const std::vector<float>& exp_times, OutputArray dst, Mat response = Mat());
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CV_EXPORTS_W void exposureFusion(InputArrayOfArrays srcImgs, OutputArray dst, float wc = 1.0f, float ws = 1.0f, float we = 0.0f);
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CV_EXPORTS_W void shiftMat(InputArray src, Point shift, OutputArray dst);
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CV_EXPORTS_W Point getExpShift(InputArray img0, InputArray img1, int max_bits = 6, int exclude_range = 4);
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CV_EXPORTS_W void estimateResponse(InputArrayOfArrays srcImgs, const std::vector<float>& exp_times, OutputArray dst, int samples = 50, float lambda = 10);
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CV_EXPORTS_W void alignImages(InputArrayOfArrays src, std::vector<Mat>& dst);
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class CV_EXPORTS_W Tonemap : public Algorithm
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class CV_EXPORTS_W Tonemap : public Algorithm
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{
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{
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public:
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public:
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Tonemap(float gamma);
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CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
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virtual ~Tonemap();
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void process(InputArray src, OutputArray dst);
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static Ptr<Tonemap> create(const String& name);
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protected:
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float gamma;
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Mat img;
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void linearMap();
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void gammaCorrection();
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virtual void tonemap() = 0;
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CV_WRAP virtual float getGamma() const = 0;
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CV_WRAP virtual void setGamma(float gamma) = 0;
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};
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};
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class CV_EXPORTS_W TonemapLinear : public Tonemap
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class CV_EXPORTS_W TonemapLinear : public Tonemap
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{
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{
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public:
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TonemapLinear(float gamma = 2.2f);
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AlgorithmInfo* info() const;
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protected:
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void tonemap();
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};
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};
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CV_EXPORTS_W Ptr<TonemapLinear> createTonemapLinear(float gamma = 1.0f);
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class CV_EXPORTS_W TonemapDrago : public Tonemap
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class CV_EXPORTS_W TonemapDrago : public Tonemap
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{
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{
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public:
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public:
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TonemapDrago(float gamma = 2.2f, float bias = 0.85f);
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CV_WRAP virtual float getBias() const = 0;
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AlgorithmInfo* info() const;
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CV_WRAP virtual void setBias(float bias) = 0;
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protected:
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float bias;
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void tonemap();
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};
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};
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CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float bias = 0.85f);
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class CV_EXPORTS_W TonemapDurand : public Tonemap
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class CV_EXPORTS_W TonemapDurand : public Tonemap
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{
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{
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public:
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public:
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TonemapDurand(float gamma = 2.2f, float contrast = 4.0f, float sigma_color = 2.0f, float sigma_space = 2.0f);
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CV_WRAP virtual float getContrast() const = 0;
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AlgorithmInfo* info() const;
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CV_WRAP virtual void setContrast(float contrast) = 0;
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protected:
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float contrast;
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CV_WRAP virtual float getSigmaSpace() const = 0;
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float sigma_color;
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CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0;
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float sigma_space;
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void tonemap();
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CV_WRAP virtual float getSigmaColor() const = 0;
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CV_WRAP virtual void setSigmaColor(float sigma_color) = 0;
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};
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};
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CV_EXPORTS_W Ptr<TonemapDurand>
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createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float sigma_space = 2.0f, float sigma_color = 2.0f);
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class CV_EXPORTS_W TonemapReinhardDevlin : public Tonemap
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class CV_EXPORTS_W TonemapReinhardDevlin : public Tonemap
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{
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{
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public:
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public:
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TonemapReinhardDevlin(float gamma = 2.2f, float intensity = 0.0f, float color_adapt = 0.0f, float light_adapt = 1.0f);
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CV_WRAP virtual float getIntensity() const = 0;
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AlgorithmInfo* info() const;
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CV_WRAP virtual void setIntensity(float intensity) = 0;
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protected:
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float intensity;
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CV_WRAP virtual float getLightAdaptation() const = 0;
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float color_adapt;
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CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
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float light_adapt;
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void tonemap();
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CV_WRAP virtual float getColorAdaptation() const = 0;
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CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
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};
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};
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CV_EXPORTS_W Ptr<TonemapReinhardDevlin>
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createTonemapReinhardDevlin(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
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} // cv
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} // cv
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#endif
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#endif
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@ -1,161 +0,0 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
|
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// this list of conditions and the following disclaimer.
|
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
|
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "opencv2/photo.hpp"
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#include "opencv2/imgproc.hpp"
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namespace cv
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{
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static void downsample(Mat& src, Mat& dst)
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{
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dst = Mat(src.rows / 2, src.cols / 2, CV_8UC1);
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int offset = src.cols * 2;
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uchar *src_ptr = src.ptr();
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uchar *dst_ptr = dst.ptr();
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for(int y = 0; y < dst.rows; y ++) {
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uchar *ptr = src_ptr;
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for(int x = 0; x < dst.cols; x++) {
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dst_ptr[0] = ptr[0];
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dst_ptr++;
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ptr += 2;
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}
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src_ptr += offset;
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}
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}
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static void buildPyr(Mat& img, std::vector<Mat>& pyr, int maxlevel)
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{
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pyr.resize(maxlevel + 1);
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pyr[0] = img.clone();
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for(int level = 0; level < maxlevel; level++) {
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downsample(pyr[level], pyr[level + 1]);
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}
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}
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static int getMedian(Mat& img)
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{
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int channels = 0;
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Mat hist;
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int hist_size = 256;
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float range[] = {0, 256} ;
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const float* ranges[] = {range};
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calcHist(&img, 1, &channels, Mat(), hist, 1, &hist_size, ranges);
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float *ptr = hist.ptr<float>();
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int median = 0, sum = 0;
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int thresh = img.total() / 2;
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while(sum < thresh && median < 256) {
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sum += (int)ptr[median];
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median++;
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}
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return median;
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}
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static void computeBitmaps(Mat& img, Mat& tb, Mat& eb, int exclude_range)
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{
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int median = getMedian(img);
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compare(img, median, tb, CMP_GT);
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compare(abs(img - median), exclude_range, eb, CMP_GT);
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}
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void shiftMat(InputArray _src, Point shift, OutputArray _dst)
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{
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Mat src = _src.getMat();
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_dst.create(src.size(), src.type());
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Mat dst = _dst.getMat();
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dst = Mat::zeros(src.size(), src.type());
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int width = src.cols - abs(shift.x);
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int height = src.rows - abs(shift.y);
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Rect dst_rect(max(shift.x, 0), max(shift.y, 0), width, height);
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Rect src_rect(max(-shift.x, 0), max(-shift.y, 0), width, height);
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src(src_rect).copyTo(dst(dst_rect));
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}
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Point getExpShift(InputArray _img0, InputArray _img1, int max_bits, int exclude_range)
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{
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Mat img0 = _img0.getMat();
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Mat img1 = _img1.getMat();
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CV_Assert(img0.type() == CV_8UC1 && img1.type() == CV_8UC1);
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CV_Assert(img0.size() == img0.size());
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int maxlevel = (int)(log((double)max(img0.rows, img0.cols)) / log(2.0)) - 1;
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maxlevel = min(maxlevel, max_bits - 1);
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std::vector<Mat> pyr0;
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std::vector<Mat> pyr1;
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buildPyr(img0, pyr0, maxlevel);
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buildPyr(img1, pyr1, maxlevel);
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Point shift(0, 0);
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for(int level = maxlevel; level >= 0; level--) {
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shift *= 2;
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Mat tb1, tb2, eb1, eb2;
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computeBitmaps(pyr0[level], tb1, eb1, exclude_range);
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computeBitmaps(pyr1[level], tb2, eb2, exclude_range);
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int min_err = pyr0[level].total();
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Point new_shift(shift);
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for(int i = -1; i <= 1; i++) {
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for(int j = -1; j <= 1; j++) {
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Point test_shift = shift + Point(i, j);
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Mat shifted_tb2, shifted_eb2, diff;
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shiftMat(tb2, test_shift, shifted_tb2);
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shiftMat(eb2, test_shift, shifted_eb2);
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bitwise_xor(tb1, shifted_tb2, diff);
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bitwise_and(diff, eb1, diff);
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bitwise_and(diff, shifted_eb2, diff);
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int err = countNonZero(diff);
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if(err < min_err) {
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new_shift = test_shift;
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min_err = err;
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}
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}
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}
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shift = new_shift;
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}
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return shift;
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}
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};
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@ -1,294 +0,0 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
|
|
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// By downloading, copying, installing or using the software you agree to this license.
|
|
||||||
// If you do not agree to this license, do not download, install,
|
|
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// copy or use the software.
|
|
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//
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//
|
|
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// License Agreement
|
|
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// For Open Source Computer Vision Library
|
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//
|
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
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// Third party copyrights are property of their respective owners.
|
|
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//
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|
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// Redistribution and use in source and binary forms, with or without modification,
|
|
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// are permitted provided that the following conditions are met:
|
|
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//
|
|
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// * Redistribution's of source code must retain the above copyright notice,
|
|
||||||
// this list of conditions and the following disclaimer.
|
|
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//
|
|
||||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
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// this list of conditions and the following disclaimer in the documentation
|
|
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// and/or other materials provided with the distribution.
|
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//
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// * The name of the copyright holders may not be used to endorse or promote products
|
|
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// derived from this software without specific prior written permission.
|
|
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//
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// This software is provided by the copyright holders and contributors "as is" and
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|
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// any express or implied warranties, including, but not limited to, the implied
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|
||||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
||||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
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|
||||||
// indirect, incidental, special, exemplary, or consequential damages
|
|
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// (including, but not limited to, procurement of substitute goods or services;
|
|
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// loss of use, data, or profits; or business interruption) however caused
|
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "opencv2/photo.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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namespace cv
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{
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static void triangleWeights(float weights[])
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{
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for(int i = 0; i < 128; i++) {
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weights[i] = i + 1.0f;
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}
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for(int i = 128; i < 256; i++) {
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weights[i] = 256.0f - i;
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}
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}
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static Mat linearResponse()
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{
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Mat response(256, 1, CV_32F);
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for(int i = 1; i < 256; i++) {
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response.at<float>(i) = logf((float)i);
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}
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response.at<float>(0) = response.at<float>(1);
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return response;
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}
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static void modifyCheckResponse(Mat &response)
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{
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if(response.empty()) {
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response = linearResponse();
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}
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CV_Assert(response.rows == 256 && (response.cols == 1 || response.cols == 3));
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response.convertTo(response, CV_32F);
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if(response.cols == 1) {
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Mat result(256, 3, CV_32F);
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for(int i = 0; i < 3; i++) {
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response.copyTo(result.col(i));
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}
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response = result;
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}
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}
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|
|
||||||
static void checkImages(const std::vector<Mat>& images, bool hdr, const std::vector<float>& _exp_times = std::vector<float>())
|
|
||||||
{
|
|
||||||
CV_Assert(!images.empty());
|
|
||||||
CV_Assert(!hdr || images.size() == _exp_times.size());
|
|
||||||
int width = images[0].cols;
|
|
||||||
int height = images[0].rows;
|
|
||||||
int channels = images[0].channels();
|
|
||||||
for(size_t i = 0; i < images.size(); i++) {
|
|
||||||
|
|
||||||
CV_Assert(images[i].cols == width && images[i].rows == height);
|
|
||||||
CV_Assert(images[i].channels() == channels && images[i].depth() == CV_8U);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
void alignImages(InputArrayOfArrays _src, std::vector<Mat>& dst)
|
|
||||||
{
|
|
||||||
std::vector<Mat> src;
|
|
||||||
_src.getMatVector(src);
|
|
||||||
checkImages(src, false);
|
|
||||||
dst.resize(src.size());
|
|
||||||
|
|
||||||
size_t pivot = src.size() / 2;
|
|
||||||
dst[pivot] = src[pivot];
|
|
||||||
Mat gray_base;
|
|
||||||
cvtColor(src[pivot], gray_base, COLOR_RGB2GRAY);
|
|
||||||
|
|
||||||
for(size_t i = 0; i < src.size(); i++) {
|
|
||||||
if(i == pivot) {
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
Mat gray;
|
|
||||||
cvtColor(src[i], gray, COLOR_RGB2GRAY);
|
|
||||||
Point shift = getExpShift(gray_base, gray);
|
|
||||||
shiftMat(src[i], shift, dst[i]);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
void makeHDR(InputArrayOfArrays _images, const std::vector<float>& _exp_times, OutputArray _dst, Mat response)
|
|
||||||
{
|
|
||||||
std::vector<Mat> images;
|
|
||||||
_images.getMatVector(images);
|
|
||||||
checkImages(images, true, _exp_times);
|
|
||||||
modifyCheckResponse(response);
|
|
||||||
_dst.create(images[0].size(), CV_MAKETYPE(CV_32F, images[0].channels()));
|
|
||||||
Mat result = _dst.getMat();
|
|
||||||
|
|
||||||
std::vector<float> exp_times(_exp_times.size());
|
|
||||||
for(size_t i = 0; i < exp_times.size(); i++) {
|
|
||||||
exp_times[i] = logf(_exp_times[i]);
|
|
||||||
}
|
|
||||||
|
|
||||||
float weights[256];
|
|
||||||
triangleWeights(weights);
|
|
||||||
|
|
||||||
int channels = images[0].channels();
|
|
||||||
float *res_ptr = result.ptr<float>();
|
|
||||||
for(size_t pos = 0; pos < result.total(); pos++, res_ptr += channels) {
|
|
||||||
|
|
||||||
std::vector<float> sum(channels, 0);
|
|
||||||
float weight_sum = 0;
|
|
||||||
for(size_t im = 0; im < images.size(); im++) {
|
|
||||||
|
|
||||||
uchar *img_ptr = images[im].ptr() + channels * pos;
|
|
||||||
float w = 0;
|
|
||||||
for(int channel = 0; channel < channels; channel++) {
|
|
||||||
w += weights[img_ptr[channel]];
|
|
||||||
}
|
|
||||||
w /= channels;
|
|
||||||
weight_sum += w;
|
|
||||||
for(int channel = 0; channel < channels; channel++) {
|
|
||||||
sum[channel] += w * (response.at<float>(img_ptr[channel], channel) - exp_times[im]);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
for(int channel = 0; channel < channels; channel++) {
|
|
||||||
res_ptr[channel] = exp(sum[channel] / weight_sum);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
void exposureFusion(InputArrayOfArrays _images, OutputArray _dst, float wc, float ws, float we)
|
|
||||||
{
|
|
||||||
std::vector<Mat> images;
|
|
||||||
_images.getMatVector(images);
|
|
||||||
checkImages(images, false);
|
|
||||||
|
|
||||||
std::vector<Mat> weights(images.size());
|
|
||||||
Mat weight_sum = Mat::zeros(images[0].size(), CV_32FC1);
|
|
||||||
for(size_t im = 0; im < images.size(); im++) {
|
|
||||||
Mat img, gray, contrast, saturation, wellexp;
|
|
||||||
std::vector<Mat> channels(3);
|
|
||||||
|
|
||||||
images[im].convertTo(img, CV_32FC3, 1.0/255.0);
|
|
||||||
cvtColor(img, gray, COLOR_RGB2GRAY);
|
|
||||||
split(img, channels);
|
|
||||||
|
|
||||||
Laplacian(gray, contrast, CV_32F);
|
|
||||||
contrast = abs(contrast);
|
|
||||||
|
|
||||||
Mat mean = (channels[0] + channels[1] + channels[2]) / 3.0f;
|
|
||||||
saturation = Mat::zeros(channels[0].size(), CV_32FC1);
|
|
||||||
for(int i = 0; i < 3; i++) {
|
|
||||||
Mat deviation = channels[i] - mean;
|
|
||||||
pow(deviation, 2.0, deviation);
|
|
||||||
saturation += deviation;
|
|
||||||
}
|
|
||||||
sqrt(saturation, saturation);
|
|
||||||
|
|
||||||
wellexp = Mat::ones(gray.size(), CV_32FC1);
|
|
||||||
for(int i = 0; i < 3; i++) {
|
|
||||||
Mat exp = channels[i] - 0.5f;
|
|
||||||
pow(exp, 2, exp);
|
|
||||||
exp = -exp / 0.08;
|
|
||||||
wellexp = wellexp.mul(exp);
|
|
||||||
}
|
|
||||||
|
|
||||||
pow(contrast, wc, contrast);
|
|
||||||
pow(saturation, ws, saturation);
|
|
||||||
pow(wellexp, we, wellexp);
|
|
||||||
|
|
||||||
weights[im] = contrast;
|
|
||||||
weights[im] = weights[im].mul(saturation);
|
|
||||||
weights[im] = weights[im].mul(wellexp);
|
|
||||||
weight_sum += weights[im];
|
|
||||||
}
|
|
||||||
int maxlevel = static_cast<int>(logf(static_cast<float>(max(images[0].rows, images[0].cols))) / logf(2.0)) - 1;
|
|
||||||
std::vector<Mat> res_pyr(maxlevel + 1);
|
|
||||||
|
|
||||||
for(size_t im = 0; im < images.size(); im++) {
|
|
||||||
weights[im] /= weight_sum;
|
|
||||||
Mat img;
|
|
||||||
images[im].convertTo(img, CV_32FC3, 1/255.0);
|
|
||||||
std::vector<Mat> img_pyr, weight_pyr;
|
|
||||||
buildPyramid(img, img_pyr, maxlevel);
|
|
||||||
buildPyramid(weights[im], 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> channels(3);
|
|
||||||
split(img_pyr[lvl], channels);
|
|
||||||
for(int i = 0; i < 3; i++) {
|
|
||||||
channels[i] = channels[i].mul(weight_pyr[lvl]);
|
|
||||||
}
|
|
||||||
merge(channels, 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(images[0].size(), CV_32FC3);
|
|
||||||
Mat result = _dst.getMat();
|
|
||||||
res_pyr[0].copyTo(result);
|
|
||||||
}
|
|
||||||
|
|
||||||
void estimateResponse(InputArrayOfArrays _images, const std::vector<float>& exp_times, OutputArray _dst, int samples, float lambda)
|
|
||||||
{
|
|
||||||
std::vector<Mat> images;
|
|
||||||
_images.getMatVector(images);
|
|
||||||
checkImages(images, true, exp_times);
|
|
||||||
_dst.create(256, images[0].channels(), CV_32F);
|
|
||||||
Mat response = _dst.getMat();
|
|
||||||
|
|
||||||
float w[256];
|
|
||||||
triangleWeights(w);
|
|
||||||
|
|
||||||
for(int channel = 0; channel < images[0].channels(); channel++) {
|
|
||||||
Mat A = Mat::zeros(samples * images.size() + 257, 256 + samples, CV_32F);
|
|
||||||
Mat B = Mat::zeros(A.rows, 1, CV_32F);
|
|
||||||
|
|
||||||
int eq = 0;
|
|
||||||
for(int i = 0; i < samples; i++) {
|
|
||||||
|
|
||||||
int pos = 3 * (rand() % images[0].total()) + channel;
|
|
||||||
for(size_t j = 0; j < images.size(); j++) {
|
|
||||||
|
|
||||||
int val = (images[j].ptr() + pos)[0];
|
|
||||||
A.at<float>(eq, val) = w[val];
|
|
||||||
A.at<float>(eq, 256 + i) = -w[val];
|
|
||||||
B.at<float>(eq, 0) = w[val] * log(exp_times[j]);
|
|
||||||
eq++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
A.at<float>(eq, 128) = 1;
|
|
||||||
eq++;
|
|
||||||
|
|
||||||
for(int i = 0; i < 254; i++) {
|
|
||||||
A.at<float>(eq, i) = lambda * w[i + 1];
|
|
||||||
A.at<float>(eq, i + 1) = -2 * lambda * w[i + 1];
|
|
||||||
A.at<float>(eq, i + 2) = lambda * w[i + 1];
|
|
||||||
eq++;
|
|
||||||
}
|
|
||||||
Mat solution;
|
|
||||||
solve(A, B, solution, DECOMP_SVD);
|
|
||||||
solution.rowRange(0, 256).copyTo(response.col(channel));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
};
|
|
||||||
|
|
||||||
@ -47,56 +47,77 @@
|
|||||||
namespace cv
|
namespace cv
|
||||||
{
|
{
|
||||||
|
|
||||||
Tonemap::Tonemap(float gamma) : gamma(gamma)
|
class TonemapLinearImpl : public TonemapLinear
|
||||||
{
|
{
|
||||||
}
|
public:
|
||||||
|
TonemapLinearImpl(float gamma) : gamma(gamma), name("TonemapLinear")
|
||||||
Tonemap::~Tonemap()
|
{
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
void Tonemap::process(InputArray src, OutputArray dst)
|
|
||||||
{
|
|
||||||
Mat srcMat = src.getMat();
|
|
||||||
CV_Assert(!srcMat.empty());
|
|
||||||
dst.create(srcMat.size(), CV_32FC3);
|
|
||||||
img = dst.getMat();
|
|
||||||
srcMat.copyTo(img);
|
|
||||||
linearMap();
|
|
||||||
tonemap();
|
|
||||||
gammaCorrection();
|
|
||||||
}
|
|
||||||
|
|
||||||
void Tonemap::linearMap()
|
|
||||||
{
|
|
||||||
double min, max;
|
|
||||||
minMaxLoc(img, &min, &max);
|
|
||||||
if(max - min > DBL_EPSILON) {
|
|
||||||
img = (img - min) / (max - min);
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
void process(InputArray _src, OutputArray _dst)
|
||||||
|
{
|
||||||
|
Mat src = _src.getMat();
|
||||||
|
CV_Assert(!src.empty());
|
||||||
|
_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 { return gamma; }
|
||||||
|
void setGamma(float val) { gamma = val; }
|
||||||
|
|
||||||
|
void write(FileStorage& fs) const
|
||||||
|
{
|
||||||
|
fs << "name" << name
|
||||||
|
<< "gamma" << gamma;
|
||||||
|
}
|
||||||
|
|
||||||
|
void read(const FileNode& fn)
|
||||||
|
{
|
||||||
|
FileNode n = fn["name"];
|
||||||
|
CV_Assert(n.isString() && String(n) == name);
|
||||||
|
gamma = fn["gamma"];
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
String name;
|
||||||
|
float gamma;
|
||||||
|
};
|
||||||
|
|
||||||
|
Ptr<TonemapLinear> createTonemapLinear(float gamma)
|
||||||
|
{
|
||||||
|
return new TonemapLinearImpl(gamma);
|
||||||
}
|
}
|
||||||
|
|
||||||
void Tonemap::gammaCorrection()
|
class TonemapDragoImpl : public TonemapDrago
|
||||||
{
|
{
|
||||||
pow(img, 1.0f / gamma, img);
|
public:
|
||||||
}
|
TonemapDragoImpl(float gamma, float bias) :
|
||||||
|
gamma(gamma),
|
||||||
|
bias(bias),
|
||||||
|
name("TonemapLinear")
|
||||||
|
{
|
||||||
|
}
|
||||||
|
|
||||||
void TonemapLinear::tonemap()
|
void process(InputArray _src, OutputArray _dst)
|
||||||
{
|
{
|
||||||
}
|
Mat src = _src.getMat();
|
||||||
|
CV_Assert(!src.empty());
|
||||||
|
_dst.create(src.size(), CV_32FC3);
|
||||||
|
Mat img = _dst.getMat();
|
||||||
|
|
||||||
TonemapLinear::TonemapLinear(float gamma) : Tonemap(gamma)
|
Ptr<TonemapLinear> linear = createTonemapLinear(1.0f);
|
||||||
{
|
linear->process(src, img);
|
||||||
}
|
|
||||||
|
|
||||||
TonemapDrago::TonemapDrago(float gamma, float bias) :
|
|
||||||
Tonemap(gamma),
|
|
||||||
bias(bias)
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
void TonemapDrago::tonemap()
|
|
||||||
{
|
|
||||||
Mat gray_img;
|
Mat gray_img;
|
||||||
cvtColor(img, gray_img, COLOR_RGB2GRAY);
|
cvtColor(img, gray_img, COLOR_RGB2GRAY);
|
||||||
Mat log_img;
|
Mat log_img;
|
||||||
@ -125,21 +146,63 @@ void TonemapDrago::tonemap()
|
|||||||
}
|
}
|
||||||
map.release();
|
map.release();
|
||||||
merge(channels, img);
|
merge(channels, img);
|
||||||
linearMap();
|
|
||||||
|
linear->setGamma(gamma);
|
||||||
|
linear->process(img, img);
|
||||||
|
}
|
||||||
|
|
||||||
|
float getGamma() const { return gamma; }
|
||||||
|
void setGamma(float val) { gamma = val; }
|
||||||
|
|
||||||
|
float getBias() const { return bias; }
|
||||||
|
void setBias(float val) { bias = val; }
|
||||||
|
|
||||||
|
void write(FileStorage& fs) const
|
||||||
|
{
|
||||||
|
fs << "name" << name
|
||||||
|
<< "gamma" << gamma
|
||||||
|
<< "bias" << bias;
|
||||||
|
}
|
||||||
|
|
||||||
|
void read(const FileNode& fn)
|
||||||
|
{
|
||||||
|
FileNode n = fn["name"];
|
||||||
|
CV_Assert(n.isString() && String(n) == name);
|
||||||
|
gamma = fn["gamma"];
|
||||||
|
bias = fn["bias"];
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
String name;
|
||||||
|
float gamma, bias;
|
||||||
|
};
|
||||||
|
|
||||||
|
Ptr<TonemapDrago> createTonemapDrago(float gamma, float bias)
|
||||||
|
{
|
||||||
|
return new TonemapDragoImpl(gamma, bias);
|
||||||
}
|
}
|
||||||
|
|
||||||
TonemapDurand::TonemapDurand(float gamma, float contrast, float sigma_color, float sigma_space) :
|
class TonemapDurandImpl : public TonemapDurand
|
||||||
Tonemap(gamma),
|
{
|
||||||
|
public:
|
||||||
|
TonemapDurandImpl(float gamma, float contrast, float sigma_color, float sigma_space) :
|
||||||
|
gamma(gamma),
|
||||||
contrast(contrast),
|
contrast(contrast),
|
||||||
sigma_color(sigma_color),
|
sigma_color(sigma_color),
|
||||||
sigma_space(sigma_space)
|
sigma_space(sigma_space),
|
||||||
{
|
name("TonemapDurand")
|
||||||
}
|
{
|
||||||
|
}
|
||||||
|
|
||||||
|
void process(InputArray _src, OutputArray _dst)
|
||||||
|
{
|
||||||
|
Mat src = _src.getMat();
|
||||||
|
CV_Assert(!src.empty());
|
||||||
|
_dst.create(src.size(), CV_32FC3);
|
||||||
|
Mat dst = _dst.getMat();
|
||||||
|
|
||||||
void TonemapDurand::tonemap()
|
|
||||||
{
|
|
||||||
Mat gray_img;
|
Mat gray_img;
|
||||||
cvtColor(img, gray_img, COLOR_RGB2GRAY);
|
cvtColor(src, gray_img, COLOR_RGB2GRAY);
|
||||||
Mat log_img;
|
Mat log_img;
|
||||||
log(gray_img, log_img);
|
log(gray_img, log_img);
|
||||||
Mat map_img;
|
Mat map_img;
|
||||||
@ -155,23 +218,77 @@ void TonemapDurand::tonemap()
|
|||||||
gray_img.release();
|
gray_img.release();
|
||||||
|
|
||||||
std::vector<Mat> channels(3);
|
std::vector<Mat> channels(3);
|
||||||
split(img, channels);
|
split(src, channels);
|
||||||
for(int i = 0; i < 3; i++) {
|
for(int i = 0; i < 3; i++) {
|
||||||
channels[i] = channels[i].mul(map_img);
|
channels[i] = channels[i].mul(map_img);
|
||||||
}
|
}
|
||||||
merge(channels, img);
|
merge(channels, dst);
|
||||||
|
pow(dst, 1.0f / gamma, dst);
|
||||||
|
}
|
||||||
|
|
||||||
|
float getGamma() const { return gamma; }
|
||||||
|
void setGamma(float val) { gamma = val; }
|
||||||
|
|
||||||
|
float getContrast() const { return contrast; }
|
||||||
|
void setContrast(float val) { contrast = val; }
|
||||||
|
|
||||||
|
float getSigmaColor() const { return sigma_color; }
|
||||||
|
void setSigmaColor(float val) { sigma_color = val; }
|
||||||
|
|
||||||
|
float getSigmaSpace() const { return sigma_space; }
|
||||||
|
void setSigmaSpace(float val) { sigma_space = val; }
|
||||||
|
|
||||||
|
void write(FileStorage& fs) const
|
||||||
|
{
|
||||||
|
fs << "name" << name
|
||||||
|
<< "gamma" << gamma
|
||||||
|
<< "contrast" << contrast
|
||||||
|
<< "sigma_color" << sigma_color
|
||||||
|
<< "sigma_space" << sigma_space;
|
||||||
|
}
|
||||||
|
|
||||||
|
void read(const FileNode& fn)
|
||||||
|
{
|
||||||
|
FileNode n = fn["name"];
|
||||||
|
CV_Assert(n.isString() && String(n) == name);
|
||||||
|
gamma = fn["gamma"];
|
||||||
|
contrast = fn["contrast"];
|
||||||
|
sigma_color = fn["sigma_color"];
|
||||||
|
sigma_space = fn["sigma_space"];
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
String name;
|
||||||
|
float gamma, contrast, sigma_color, sigma_space;
|
||||||
|
};
|
||||||
|
|
||||||
|
Ptr<TonemapDurand> createTonemapDurand(float gamma, float contrast, float sigma_color, float sigma_space)
|
||||||
|
{
|
||||||
|
return new TonemapDurandImpl(gamma, contrast, sigma_color, sigma_space);
|
||||||
}
|
}
|
||||||
|
|
||||||
TonemapReinhardDevlin::TonemapReinhardDevlin(float gamma, float intensity, float color_adapt, float light_adapt) :
|
class TonemapReinhardDevlinImpl : public TonemapReinhardDevlin
|
||||||
Tonemap(gamma),
|
{
|
||||||
|
public:
|
||||||
|
TonemapReinhardDevlinImpl(float gamma, float intensity, float light_adapt, float color_adapt) :
|
||||||
|
gamma(gamma),
|
||||||
intensity(intensity),
|
intensity(intensity),
|
||||||
|
light_adapt(light_adapt),
|
||||||
color_adapt(color_adapt),
|
color_adapt(color_adapt),
|
||||||
light_adapt(light_adapt)
|
name("TonemapReinhardDevlin")
|
||||||
{
|
{
|
||||||
}
|
}
|
||||||
|
|
||||||
|
void process(InputArray _src, OutputArray _dst)
|
||||||
|
{
|
||||||
|
Mat src = _src.getMat();
|
||||||
|
CV_Assert(!src.empty());
|
||||||
|
_dst.create(src.size(), CV_32FC3);
|
||||||
|
Mat img = _dst.getMat();
|
||||||
|
|
||||||
|
Ptr<TonemapLinear> linear = createTonemapLinear(1.0f);
|
||||||
|
linear->process(src, img);
|
||||||
|
|
||||||
void TonemapReinhardDevlin::tonemap()
|
|
||||||
{
|
|
||||||
Mat gray_img;
|
Mat gray_img;
|
||||||
cvtColor(img, gray_img, COLOR_RGB2GRAY);
|
cvtColor(img, gray_img, COLOR_RGB2GRAY);
|
||||||
Mat log_img;
|
Mat log_img;
|
||||||
@ -200,30 +317,50 @@ void TonemapReinhardDevlin::tonemap()
|
|||||||
}
|
}
|
||||||
gray_img.release();
|
gray_img.release();
|
||||||
merge(channels, img);
|
merge(channels, img);
|
||||||
linearMap();
|
|
||||||
}
|
|
||||||
|
|
||||||
Ptr<Tonemap> Tonemap::create(const String& TonemapType)
|
linear->setGamma(gamma);
|
||||||
|
linear->process(img, img);
|
||||||
|
}
|
||||||
|
|
||||||
|
float getGamma() const { return gamma; }
|
||||||
|
void setGamma(float val) { gamma = val; }
|
||||||
|
|
||||||
|
float getIntensity() const { return intensity; }
|
||||||
|
void setIntensity(float val) { intensity = val; }
|
||||||
|
|
||||||
|
float getLightAdaptation() const { return light_adapt; }
|
||||||
|
void setLightAdaptation(float val) { light_adapt = val; }
|
||||||
|
|
||||||
|
float getColorAdaptation() const { return color_adapt; }
|
||||||
|
void setColorAdaptation(float val) { color_adapt = val; }
|
||||||
|
|
||||||
|
void write(FileStorage& fs) const
|
||||||
|
{
|
||||||
|
fs << "name" << name
|
||||||
|
<< "gamma" << gamma
|
||||||
|
<< "intensity" << intensity
|
||||||
|
<< "light_adapt" << light_adapt
|
||||||
|
<< "color_adapt" << color_adapt;
|
||||||
|
}
|
||||||
|
|
||||||
|
void read(const FileNode& fn)
|
||||||
|
{
|
||||||
|
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<TonemapReinhardDevlin> createTonemapReinhardDevlin(float gamma, float contrast, float sigma_color, float sigma_space)
|
||||||
{
|
{
|
||||||
return Algorithm::create<Tonemap>("Tonemap." + TonemapType);
|
return new TonemapReinhardDevlinImpl(gamma, contrast, sigma_color, sigma_space);
|
||||||
}
|
}
|
||||||
|
|
||||||
CV_INIT_ALGORITHM(TonemapLinear, "Tonemap.Linear",
|
|
||||||
obj.info()->addParam(obj, "gamma", obj.gamma));
|
|
||||||
|
|
||||||
CV_INIT_ALGORITHM(TonemapDrago, "Tonemap.Drago",
|
|
||||||
obj.info()->addParam(obj, "gamma", obj.gamma);
|
|
||||||
obj.info()->addParam(obj, "bias", obj.bias));
|
|
||||||
|
|
||||||
CV_INIT_ALGORITHM(TonemapDurand, "Tonemap.Durand",
|
|
||||||
obj.info()->addParam(obj, "gamma", obj.gamma);
|
|
||||||
obj.info()->addParam(obj, "contrast", obj.contrast);
|
|
||||||
obj.info()->addParam(obj, "sigma_color", obj.sigma_color);
|
|
||||||
obj.info()->addParam(obj, "sigma_space", obj.sigma_space));
|
|
||||||
|
|
||||||
CV_INIT_ALGORITHM(TonemapReinhardDevlin, "Tonemap.ReinhardDevlin",
|
|
||||||
obj.info()->addParam(obj, "gamma", obj.gamma);
|
|
||||||
obj.info()->addParam(obj, "intensity", obj.intensity);
|
|
||||||
obj.info()->addParam(obj, "color_adapt", obj.color_adapt);
|
|
||||||
obj.info()->addParam(obj, "light_adapt", obj.light_adapt));
|
|
||||||
}
|
}
|
||||||
@ -61,98 +61,159 @@ void checkEqual(Mat img0, Mat img1, double threshold)
|
|||||||
ASSERT_FALSE(max > threshold);
|
ASSERT_FALSE(max > threshold);
|
||||||
}
|
}
|
||||||
|
|
||||||
TEST(Photo_HdrFusion, regression)
|
|
||||||
{
|
|
||||||
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
|
|
||||||
string fuse_path = test_path + "fusion/";
|
|
||||||
|
|
||||||
vector<float> times;
|
|
||||||
vector<Mat> images;
|
|
||||||
|
|
||||||
ifstream list_file(fuse_path + "list.txt");
|
|
||||||
ASSERT_TRUE(list_file.is_open());
|
|
||||||
string name;
|
|
||||||
float val;
|
|
||||||
while(list_file >> name >> val) {
|
|
||||||
Mat img = imread(fuse_path + name);
|
|
||||||
ASSERT_FALSE(img.empty()) << "Could not load input image " << fuse_path + name;
|
|
||||||
images.push_back(img);
|
|
||||||
times.push_back(1 / val);
|
|
||||||
}
|
|
||||||
list_file.close();
|
|
||||||
|
|
||||||
Mat response, expected(256, 3, CV_32F);
|
|
||||||
ifstream resp_file(test_path + "response.csv");
|
|
||||||
for(int i = 0; i < 256; i++) {
|
|
||||||
for(int channel = 0; channel < 3; channel++) {
|
|
||||||
resp_file >> expected.at<float>(i, channel);
|
|
||||||
resp_file.ignore(1);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
resp_file.close();
|
|
||||||
|
|
||||||
estimateResponse(images, times, response);
|
|
||||||
checkEqual(expected, response, 0.001);
|
|
||||||
|
|
||||||
Mat result;
|
|
||||||
loadImage(test_path + "no_calibration.hdr", expected);
|
|
||||||
makeHDR(images, times, result);
|
|
||||||
checkEqual(expected, result, 0.01);
|
|
||||||
|
|
||||||
loadImage(test_path + "rle.hdr", expected);
|
|
||||||
makeHDR(images, times, result, response);
|
|
||||||
checkEqual(expected, result, 0.01);
|
|
||||||
|
|
||||||
loadImage(test_path + "exp_fusion.png", expected);
|
|
||||||
exposureFusion(images, result);
|
|
||||||
result.convertTo(result, CV_8UC3, 255);
|
|
||||||
checkEqual(expected, result, 0);
|
|
||||||
}
|
|
||||||
|
|
||||||
TEST(Photo_Tonemap, regression)
|
TEST(Photo_Tonemap, regression)
|
||||||
{
|
{
|
||||||
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
|
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
|
||||||
|
|
||||||
Mat img;
|
Mat img, expected, result;
|
||||||
loadImage(test_path + "../rle.hdr", img);
|
loadImage(test_path + "rle.hdr", img);
|
||||||
ifstream list_file(test_path + "list.txt");
|
float gamma = 2.2f;
|
||||||
ASSERT_TRUE(list_file.is_open());
|
test_path += "tonemap/";
|
||||||
|
|
||||||
string name;
|
Ptr<TonemapLinear> linear = createTonemapLinear(gamma);
|
||||||
while(list_file >> name) {
|
linear->process(img, result);
|
||||||
Mat expected = imread(test_path + name + ".png");
|
loadImage(test_path + "linear.png", expected);
|
||||||
ASSERT_FALSE(img.empty()) << "Could not load input image " << test_path + name + ".png";
|
|
||||||
Ptr<Tonemap> mapper = Tonemap::create(name);
|
|
||||||
ASSERT_FALSE(mapper.empty()) << "Could not find mapper " << name;
|
|
||||||
Mat result;
|
|
||||||
mapper->process(img, result);
|
|
||||||
result.convertTo(result, CV_8UC3, 255);
|
result.convertTo(result, CV_8UC3, 255);
|
||||||
checkEqual(expected, result, 0);
|
checkEqual(result, expected, 0);
|
||||||
}
|
|
||||||
list_file.close();
|
Ptr<TonemapDrago> drago = createTonemapDrago(gamma);
|
||||||
|
drago->process(img, result);
|
||||||
|
loadImage(test_path + "drago.png", expected);
|
||||||
|
result.convertTo(result, CV_8UC3, 255);
|
||||||
|
checkEqual(result, expected, 0);
|
||||||
|
|
||||||
|
Ptr<TonemapDurand> durand = createTonemapDurand(gamma);
|
||||||
|
durand->process(img, result);
|
||||||
|
loadImage(test_path + "durand.png", expected);
|
||||||
|
result.convertTo(result, CV_8UC3, 255);
|
||||||
|
checkEqual(result, expected, 0);
|
||||||
|
|
||||||
|
Ptr<TonemapReinhardDevlin> reinhard_devlin = createTonemapReinhardDevlin(gamma);
|
||||||
|
reinhard_devlin->process(img, result);
|
||||||
|
loadImage(test_path + "reinhard_devlin.png", expected);
|
||||||
|
result.convertTo(result, CV_8UC3, 255);
|
||||||
|
checkEqual(result, expected, 0);
|
||||||
}
|
}
|
||||||
|
|
||||||
TEST(Photo_Align, regression)
|
|
||||||
{
|
|
||||||
const int TESTS_COUNT = 100;
|
|
||||||
string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/";
|
|
||||||
|
|
||||||
string file_name = folder + "lena.png";
|
|
||||||
Mat img = imread(file_name);
|
|
||||||
ASSERT_FALSE(img.empty()) << "Could not load input image " << file_name;
|
|
||||||
cvtColor(img, img, COLOR_RGB2GRAY);
|
|
||||||
|
|
||||||
int max_bits = 5;
|
//void loadExposureSeq(String fuse_path, vector<Mat>& images, vector<float>& times = vector<float>())
|
||||||
int max_shift = 32;
|
//{
|
||||||
srand(static_cast<unsigned>(time(0)));
|
// ifstream list_file(fuse_path + "list.txt");
|
||||||
int errors = 0;
|
// ASSERT_TRUE(list_file.is_open());
|
||||||
|
// string name;
|
||||||
for(int i = 0; i < TESTS_COUNT; i++) {
|
// float val;
|
||||||
Point shift(rand() % max_shift, rand() % max_shift);
|
// while(list_file >> name >> val) {
|
||||||
Mat res;
|
// Mat img = imread(fuse_path + name);
|
||||||
shiftMat(img, shift, res);
|
// ASSERT_FALSE(img.empty()) << "Could not load input image " << fuse_path + name;
|
||||||
Point calc = getExpShift(img, res, max_bits);
|
// images.push_back(img);
|
||||||
errors += (calc != -shift);
|
// times.push_back(1 / val);
|
||||||
}
|
// }
|
||||||
ASSERT_TRUE(errors < 5);
|
// list_file.close();
|
||||||
}
|
//}
|
||||||
|
////
|
||||||
|
////TEST(Photo_MergeMertens, regression)
|
||||||
|
////{
|
||||||
|
//// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
|
||||||
|
//// string fuse_path = test_path + "fusion/";
|
||||||
|
////
|
||||||
|
//// vector<Mat> images;
|
||||||
|
//// loadExposureSeq(fuse_path, images);
|
||||||
|
////
|
||||||
|
//// MergeMertens merge;
|
||||||
|
////
|
||||||
|
//// Mat result, expected;
|
||||||
|
//// loadImage(test_path + "exp_fusion.png", expected);
|
||||||
|
//// merge.process(images, result);
|
||||||
|
//// result.convertTo(result, CV_8UC3, 255);
|
||||||
|
//// checkEqual(expected, result, 0);
|
||||||
|
////}
|
||||||
|
//
|
||||||
|
//TEST(Photo_Debevec, regression)
|
||||||
|
//{
|
||||||
|
// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
|
||||||
|
// string fuse_path = test_path + "fusion/";
|
||||||
|
//
|
||||||
|
// vector<float> times;
|
||||||
|
// vector<Mat> images;
|
||||||
|
//
|
||||||
|
// loadExposureSeq(fuse_path, images, times);
|
||||||
|
//
|
||||||
|
// Mat response, expected(256, 3, CV_32F);
|
||||||
|
// ifstream resp_file(test_path + "response.csv");
|
||||||
|
// for(int i = 0; i < 256; i++) {
|
||||||
|
// for(int channel = 0; channel < 3; channel++) {
|
||||||
|
// resp_file >> expected.at<float>(i, channel);
|
||||||
|
// resp_file.ignore(1);
|
||||||
|
// }
|
||||||
|
// }
|
||||||
|
// resp_file.close();
|
||||||
|
//
|
||||||
|
// CalibrateDebevec calib;
|
||||||
|
// MergeDebevec merge;
|
||||||
|
//
|
||||||
|
// //calib.process(images, response, times);
|
||||||
|
// //checkEqual(expected, response, 0.001);
|
||||||
|
// //
|
||||||
|
// Mat result;
|
||||||
|
// loadImage(test_path + "no_calibration.hdr", expected);
|
||||||
|
// merge.process(images, result, times);
|
||||||
|
// checkEqual(expected, result, 0.01);
|
||||||
|
//
|
||||||
|
// //loadImage(test_path + "rle.hdr", expected);
|
||||||
|
// //merge.process(images, result, times, response);
|
||||||
|
// //checkEqual(expected, result, 0.01);
|
||||||
|
//}
|
||||||
|
//
|
||||||
|
//TEST(Photo_Tonemap, regression)
|
||||||
|
//{
|
||||||
|
// initModule_photo();
|
||||||
|
// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
|
||||||
|
// Mat img;
|
||||||
|
// loadImage(test_path + "../rle.hdr", img);
|
||||||
|
//
|
||||||
|
// vector<String> algorithms;
|
||||||
|
// Algorithm::getList(algorithms);
|
||||||
|
// for(size_t i = 0; i < algorithms.size(); i++) {
|
||||||
|
// String str = algorithms[i];
|
||||||
|
// size_t dot = str.find('.');
|
||||||
|
// if(dot != String::npos && str.substr(0, dot).compare("Tonemap") == 0) {
|
||||||
|
// String algo_name = str.substr(dot + 1, str.size());
|
||||||
|
// Mat expected;
|
||||||
|
// loadImage(test_path + algo_name.toLowerCase() + ".png", expected);
|
||||||
|
// Ptr<Tonemap> mapper = Tonemap::create(algo_name);
|
||||||
|
// ASSERT_FALSE(mapper.empty()) << algo_name;
|
||||||
|
// Mat result;
|
||||||
|
// mapper->process(img, result);
|
||||||
|
// result.convertTo(result, CV_8UC3, 255);
|
||||||
|
// checkEqual(expected, result, 0);
|
||||||
|
// }
|
||||||
|
// }
|
||||||
|
////}
|
||||||
|
////
|
||||||
|
////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 = imread(file_name);
|
||||||
|
//// ASSERT_FALSE(img.empty()) << "Could not load input image " << file_name;
|
||||||
|
//// cvtColor(img, img, COLOR_RGB2GRAY);
|
||||||
|
////
|
||||||
|
//// int max_bits = 5;
|
||||||
|
//// int max_shift = 32;
|
||||||
|
//// srand(static_cast<unsigned>(time(0)));
|
||||||
|
//// int errors = 0;
|
||||||
|
////
|
||||||
|
//// AlignMTB align(max_bits);
|
||||||
|
////
|
||||||
|
//// for(int i = 0; i < TESTS_COUNT; i++) {
|
||||||
|
//// Point shift(rand() % max_shift, rand() % max_shift);
|
||||||
|
//// Mat res;
|
||||||
|
//// align.shiftMat(img, shift, res);
|
||||||
|
//// Point calc = align.getExpShift(img, res);
|
||||||
|
//// errors += (calc != -shift);
|
||||||
|
//// }
|
||||||
|
//// ASSERT_TRUE(errors < 5);
|
||||||
|
////}
|
||||||
|
|||||||
Loading…
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Reference in New Issue
Block a user