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Tonemap as 3.0 algorithm

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This commit is contained in:
Fedor Morozov 2013-07-31 16:05:31 +04:00
parent 258b98d15b
commit 4d2ea847fa
5 changed files with 474 additions and 744 deletions
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79
modules/photo/include/opencv2/photo.hpp
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@ -59,6 +59,8 @@ enum
INPAINT_TELEA = 1 // A. Telea algorithm
};
CV_EXPORTS_W bool initModule_photo();
//! restores the damaged image areas using one of the available intpainting algorithms
CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
OutputArray dst, double inpaintRadius, int flags );
@ -80,77 +82,62 @@ CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs,
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
CV_EXPORTS_W void makeHDR(InputArrayOfArrays srcImgs, const std::vector<float>& exp_times, OutputArray dst, Mat response = Mat());
CV_EXPORTS_W void exposureFusion(InputArrayOfArrays srcImgs, OutputArray dst, float wc = 1.0f, float ws = 1.0f, float we = 0.0f);
CV_EXPORTS_W void shiftMat(InputArray src, Point shift, OutputArray dst);
CV_EXPORTS_W Point getExpShift(InputArray img0, InputArray img1, int max_bits = 6, int exclude_range = 4);
CV_EXPORTS_W void estimateResponse(InputArrayOfArrays srcImgs, const std::vector<float>& exp_times, OutputArray dst, int samples = 50, float lambda = 10);
CV_EXPORTS_W void alignImages(InputArrayOfArrays src, std::vector<Mat>& dst);
class CV_EXPORTS_W Tonemap : public Algorithm
{
public:
Tonemap(float gamma);
virtual ~Tonemap();
void process(InputArray src, OutputArray dst);
static Ptr<Tonemap> create(const String& name);
protected:
float gamma;
Mat img;
void linearMap();
void gammaCorrection();
CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
virtual void tonemap() = 0;
CV_WRAP virtual float getGamma() const = 0;
CV_WRAP virtual void setGamma(float gamma) = 0;
};
class CV_EXPORTS_W TonemapLinear : public Tonemap
{
public:
TonemapLinear(float gamma = 2.2f);
AlgorithmInfo* info() const;
protected:
void tonemap();
};
CV_EXPORTS_W Ptr<TonemapLinear> createTonemapLinear(float gamma = 1.0f);
class CV_EXPORTS_W TonemapDrago : public Tonemap
{
public:
TonemapDrago(float gamma = 2.2f, float bias = 0.85f);
AlgorithmInfo* info() const;
protected:
float bias;
void tonemap();
CV_WRAP virtual float getBias() const = 0;
CV_WRAP virtual void setBias(float bias) = 0;
};
CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float bias = 0.85f);
class CV_EXPORTS_W TonemapDurand : public Tonemap
{
public:
TonemapDurand(float gamma = 2.2f, float contrast = 4.0f, float sigma_color = 2.0f, float sigma_space = 2.0f);
AlgorithmInfo* info() const;
protected:
float contrast;
float sigma_color;
float sigma_space;
void tonemap();
CV_WRAP virtual float getContrast() const = 0;
CV_WRAP virtual void setContrast(float contrast) = 0;
CV_WRAP virtual float getSigmaSpace() const = 0;
CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0;
CV_WRAP virtual float getSigmaColor() const = 0;
CV_WRAP virtual void setSigmaColor(float sigma_color) = 0;
};
CV_EXPORTS_W Ptr<TonemapDurand>
createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float sigma_space = 2.0f, float sigma_color = 2.0f);
class CV_EXPORTS_W TonemapReinhardDevlin : public Tonemap
{
public:
TonemapReinhardDevlin(float gamma = 2.2f, float intensity = 0.0f, float color_adapt = 0.0f, float light_adapt = 1.0f);
AlgorithmInfo* info() const;
protected:
float intensity;
float color_adapt;
float light_adapt;
void tonemap();
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;
};
CV_EXPORTS_W Ptr<TonemapReinhardDevlin>
createTonemapReinhardDevlin(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
} // cv
#endif

161
modules/photo/src/align.cpp
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@ -1,161 +0,0 @@
/*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"
namespace cv
{
static 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;
}
}
static 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]);
}
}
static int getMedian(Mat& img)
{
int channels = 0;
Mat hist;
int hist_size = 256;
float range[] = {0, 256} ;
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 = img.total() / 2;
while(sum < thresh && median < 256) {
sum += (int)ptr[median];
median++;
}
return median;
}
static void computeBitmaps(Mat& img, Mat& tb, Mat& eb, int exclude_range)
{
int median = getMedian(img);
compare(img, median, tb, CMP_GT);
compare(abs(img - median), exclude_range, eb, CMP_GT);
}
void shiftMat(InputArray _src, Point shift, OutputArray _dst)
{
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();
dst = 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(dst(dst_rect));
}
Point getExpShift(InputArray _img0, InputArray _img1, int max_bits, int exclude_range)
{
Mat img0 = _img0.getMat();
Mat img1 = _img1.getMat();
CV_Assert(img0.type() == CV_8UC1 && img1.type() == CV_8UC1);
CV_Assert(img0.size() == img0.size());
int maxlevel = (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, exclude_range);
computeBitmaps(pyr1[level], tb2, eb2, exclude_range);
int min_err = 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, test_shift, shifted_tb2);
shiftMat(eb2, test_shift, shifted_eb2);
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;
}
};

294
modules/photo/src/hdr_fusion.cpp
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@ -1,294 +0,0 @@
/*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/photo.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
namespace cv
{
static void triangleWeights(float weights[])
{
for(int i = 0; i < 128; i++) {
weights[i] = i + 1.0f;
}
for(int i = 128; i < 256; i++) {
weights[i] = 256.0f - i;
}
}
static Mat linearResponse()
{
Mat response(256, 1, CV_32F);
for(int i = 1; i < 256; i++) {
response.at<float>(i) = logf((float)i);
}
response.at<float>(0) = response.at<float>(1);
return response;
}
static void modifyCheckResponse(Mat &response)
{
if(response.empty()) {
response = linearResponse();
}
CV_Assert(response.rows == 256 && (response.cols == 1 || response.cols == 3));
response.convertTo(response, CV_32F);
if(response.cols == 1) {
Mat result(256, 3, CV_32F);
for(int i = 0; i < 3; i++) {
response.copyTo(result.col(i));
}
response = result;
}
}
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));
}
}
};

445
modules/photo/src/tonemap.cpp
View File

@ -47,183 +47,320 @@
namespace cv
{
Tonemap::Tonemap(float gamma) : gamma(gamma)
class TonemapLinearImpl : public TonemapLinear
{
}
public:
TonemapLinearImpl(float gamma) : gamma(gamma), name("TonemapLinear")
{
}
Tonemap::~Tonemap()
{
}
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);
}
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();
}
pow(dst, 1.0f / gamma, dst);
}
void Tonemap::linearMap()
{
double min, max;
minMaxLoc(img, &min, &max);
if(max - min > DBL_EPSILON) {
img = (img - min) / (max - min);
float getGamma() const { return gamma; }
void setGamma(float val) { gamma = val; }
void write(FileStorage& fs) const
{
fs << "name" << name
<< "gamma" << gamma;
}
}
void Tonemap::gammaCorrection()
{
pow(img, 1.0f / gamma, img);
}
void TonemapLinear::tonemap()
{
}
TonemapLinear::TonemapLinear(float gamma) : Tonemap(gamma)
{
}
TonemapDrago::TonemapDrago(float gamma, float bias) :
Tonemap(gamma),
bias(bias)
{
}
void TonemapDrago::tonemap()
{
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);
Mat map;
log(gray_img + 1.0f, map);
Mat div;
pow(gray_img / (float)max, logf(bias) / logf(0.5f), div);
log(2.0f + 8.0f * div, div);
map = map.mul(1.0f / div);
map = map.mul(1.0f / gray_img);
div.release();
gray_img.release();
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
channels[i] = channels[i].mul(map);
void read(const FileNode& fn)
{
FileNode n = fn["name"];
CV_Assert(n.isString() && String(n) == name);
gamma = fn["gamma"];
}
map.release();
merge(channels, img);
linearMap();
protected:
String name;
float gamma;
};
Ptr<TonemapLinear> createTonemapLinear(float gamma)
{
return new TonemapLinearImpl(gamma);
}
TonemapDurand::TonemapDurand(float gamma, float contrast, float sigma_color, float sigma_space) :
Tonemap(gamma),
contrast(contrast),
sigma_color(sigma_color),
sigma_space(sigma_space)
class TonemapDragoImpl : public TonemapDrago
{
}
public:
TonemapDragoImpl(float gamma, float bias) :
gamma(gamma),
bias(bias),
name("TonemapLinear")
{
}
void TonemapDurand::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();
Ptr<TonemapLinear> linear = createTonemapLinear(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);
Mat map;
log(gray_img + 1.0f, map);
Mat div;
pow(gray_img / (float)max, logf(bias) / logf(0.5f), div);
log(2.0f + 8.0f * div, div);
map = map.mul(1.0f / div);
map = map.mul(1.0f / gray_img);
div.release();
gray_img.release();
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
channels[i] = channels[i].mul(map);
}
map.release();
merge(channels, img);
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)
{
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log(gray_img, log_img);
Mat map_img;
bilateralFilter(log_img, map_img, -1, sigma_color, sigma_space);
return new TonemapDragoImpl(gamma, bias);
}
class TonemapDurandImpl : public TonemapDurand
{
public:
TonemapDurandImpl(float gamma, float contrast, float sigma_color, float sigma_space) :
gamma(gamma),
contrast(contrast),
sigma_color(sigma_color),
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();
Mat gray_img;
cvtColor(src, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log(gray_img, log_img);
Mat map_img;
bilateralFilter(log_img, map_img, -1, sigma_color, sigma_space);
double min, max;
minMaxLoc(map_img, &min, &max);
float scale = contrast / (float)(max - min);
double min, max;
minMaxLoc(map_img, &min, &max);
float scale = contrast / (float)(max - min);
exp(map_img * (scale - 1.0f) + log_img, map_img);
log_img.release();
map_img = map_img.mul(1.0f / gray_img);
gray_img.release();
exp(map_img * (scale - 1.0f) + log_img, map_img);
log_img.release();
map_img = map_img.mul(1.0f / gray_img);
gray_img.release();
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
channels[i] = channels[i].mul(map_img);
std::vector<Mat> channels(3);
split(src, channels);
for(int i = 0; i < 3; i++) {
channels[i] = channels[i].mul(map_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;
}
merge(channels, img);
}
TonemapReinhardDevlin::TonemapReinhardDevlin(float gamma, float intensity, float color_adapt, float light_adapt) :
Tonemap(gamma),
intensity(intensity),
color_adapt(color_adapt),
light_adapt(light_adapt)
{
}
void TonemapReinhardDevlin::tonemap()
{
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log(gray_img, log_img);
float log_mean = (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 = (float)((log_max - log_mean) / (log_max - log_min));
float map_key = 0.3f + 0.7f * pow((float)key, 1.4f);
intensity = exp(-intensity);
Scalar chan_mean = mean(img);
float gray_mean = (float)mean(gray_img)[0];
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
float global = color_adapt * (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]));
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"];
}
gray_img.release();
merge(channels, img);
linearMap();
}
Ptr<Tonemap> Tonemap::create(const String& TonemapType)
protected:
String name;
float gamma, contrast, sigma_color, sigma_space;
};
Ptr<TonemapDurand> createTonemapDurand(float gamma, float contrast, float sigma_color, float sigma_space)
{
return Algorithm::create<Tonemap>("Tonemap." + TonemapType);
return new TonemapDurandImpl(gamma, contrast, sigma_color, sigma_space);
}
CV_INIT_ALGORITHM(TonemapLinear, "Tonemap.Linear",
obj.info()->addParam(obj, "gamma", obj.gamma));
class TonemapReinhardDevlinImpl : public TonemapReinhardDevlin
{
public:
TonemapReinhardDevlinImpl(float gamma, float intensity, float light_adapt, float color_adapt) :
gamma(gamma),
intensity(intensity),
light_adapt(light_adapt),
color_adapt(color_adapt),
name("TonemapReinhardDevlin")
{
}
CV_INIT_ALGORITHM(TonemapDrago, "Tonemap.Drago",
obj.info()->addParam(obj, "gamma", obj.gamma);
obj.info()->addParam(obj, "bias", obj.bias));
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);
Mat gray_img;
cvtColor(img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log(gray_img, log_img);
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));
float log_mean = (float)sum(log_img)[0] / log_img.total();
double log_min, log_max;
minMaxLoc(log_img, &log_min, &log_max);
log_img.release();
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));
double key = (float)((log_max - log_mean) / (log_max - log_min));
float map_key = 0.3f + 0.7f * pow((float)key, 1.4f);
intensity = exp(-intensity);
Scalar chan_mean = mean(img);
float gray_mean = (float)mean(gray_img)[0];
std::vector<Mat> channels(3);
split(img, channels);
for(int i = 0; i < 3; i++) {
float global = color_adapt * (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 { 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 new TonemapReinhardDevlinImpl(gamma, contrast, sigma_color, sigma_space);
}
}

239
modules/photo/test/test_hdr.cpp
View File

@ -61,98 +61,159 @@ void checkEqual(Mat img0, Mat img1, double 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)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
Mat img;
loadImage(test_path + "../rle.hdr", img);
ifstream list_file(test_path + "list.txt");
ASSERT_TRUE(list_file.is_open());
string name;
while(list_file >> name) {
Mat expected = imread(test_path + name + ".png");
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);
checkEqual(expected, result, 0);
}
list_file.close();
}
TEST(Photo_Align, regression)
{
const int TESTS_COUNT = 100;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/";
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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);
Mat img, expected, result;
loadImage(test_path + "rle.hdr", img);
float gamma = 2.2f;
test_path += "tonemap/";
Ptr<TonemapLinear> linear = createTonemapLinear(gamma);
linear->process(img, result);
loadImage(test_path + "linear.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 0);
int max_bits = 5;
int max_shift = 32;
srand(static_cast<unsigned>(time(0)));
int errors = 0;
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);
for(int i = 0; i < TESTS_COUNT; i++) {
Point shift(rand() % max_shift, rand() % max_shift);
Mat res;
shiftMat(img, shift, res);
Point calc = getExpShift(img, res, max_bits);
errors += (calc != -shift);
}
ASSERT_TRUE(errors < 5);
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);
}
//void loadExposureSeq(String fuse_path, vector<Mat>& images, vector<float>& times = vector<float>())
//{
// 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();
//}
////
////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);
////}