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