我们常见的图像位深一般是8bit，颜色范围[0, 255]，称为标准动态范围SDR(Standard Dynamic Range)。SDR的颜色值有限，如果要图像色彩更鲜艳，那么就需要10bit，甚至12bit，称为高动态范围HDR(High Dynamic Range)。OpenCV有提供SDR转HDR的方法，而逆转换是通过Tone mapping实现。
我们先看下SDR与HDR图像的对比，如下图所示：
一、核心函数
在OpenCV的photo模块提供SDR与HDR互转，还有图像曝光融合。
1、SDR转HDR
HDR算法需要CRF摄像头响应函数，计算CRF示例代码如下：
Mat image;
Mat response;
vector<float> times;
Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec();
calibrate->process(image, response, times);得到CRF响应函数后，使用MergeDebevec函数来转换HDR图像，C++代码：
Mat hdr;
Ptr<MergeDebevec> merge_debevec = createMergeDebevec();
merge_debevec->process(image, hdr, times, response);java版本代码：
Mat hdr = new Mat();
MergeDebevec mergeDebevec = Photo.createMergeDebevec();
mergeDebevec.process(image, hdr, matTime);python版本代码：
merge_debevec = cv.createMergeDebevec()
hdr = merge_debevec.process(image, time, response)2、HDR转SDR
HDR逆转SDR是通过Tonemap函数实现，其中2.2为Gamma矫正系数，C++代码如下：
Mat sdr;
float gamma = 2.2f;
Ptr<Tonemap> tonemap = createTonemap(gamma);
tonemap->process(hdr, sdr);java版本代码：
Mat ldr = new Mat();
Tonemap tonemap = Photo.createTonemap(2.2f);
tonemap.process(hdr, ldr);python版本代码：
tonemap = cv.createTonemap(2.2)
ldr = tonemap.process(hdr)3、图像曝光
在OpenCV中，使用MergeMertens进行图像的曝光融合，C++代码：
Mat exposure;
Ptr<MergeMertens> merge_mertens = createMergeMertens();
merge_mertens->process(image, exposure);java版本代码：
Mat exposure = new Mat();
MergeMertens mergeMertens = Photo.createMergeMertens();
mergeMertens.process(image, exposure);python版本代码：
merge_mertens = cv.createMergeMertens()
exposure = merge_mertens.process(image)二、实现代码
1、SDR转HDR源码
HDR图像转换的源码位于opencv/modules/photo/src/merge.cpp，首先是createMergeDebevec函数使用makePtr智能指针包裹：
Ptr<MergeDebevec> createMergeDebevec()
{return makePtr<MergeDebevecImpl>();
}核心代码在于MergeDebevecImpl类的process()，具体如下：
class MergeDebevecImpl CV_FINAL : public MergeDebevec
{
public:MergeDebevecImpl() :name("MergeDebevec"),weights(triangleWeights()){}void process(InputArrayOfArrays src, OutputArray dst, InputArray _times, InputArray input_response) CV_OVERRIDE{CV_INSTRUMENT_REGION();std::vector<Mat> images;src.getMatVector(images);Mat times = _times.getMat();CV_Assert(images.size() == times.total());checkImageDimensions(images);CV_Assert(images[0].depth() == CV_8U);int channels = images[0].channels();Size size = images[0].size();int CV_32FCC = CV_MAKETYPE(CV_32F, channels);dst.create(images[0].size(), CV_32FCC);Mat result = dst.getMat();Mat response = input_response.getMat();if(response.empty()) {response = linearResponse(channels);response.at<Vec3f>(0) = response.at<Vec3f>(1);}Mat log_response;log(response, log_response);CV_Assert(log_response.rows == LDR_SIZE && log_response.cols == 1 &&log_response.channels() == channels);Mat exp_values(times.clone());log(exp_values, exp_values);result = Mat::zeros(size, CV_32FCC);std::vector<Mat> result_split;split(result, result_split);Mat weight_sum = Mat::zeros(size, CV_32F);// 图像加权平均for(size_t i = 0; i < images.size(); i++) {std::vector<Mat> splitted;split(images[i], splitted);Mat w = Mat::zeros(size, CV_32F);for(int c = 0; c < channels; c++) {LUT(splitted[c], weights, splitted[c]);w += splitted[c];}w /= channels;Mat response_img;LUT(images[i], log_response, response_img);split(response_img, splitted);for(int c = 0; c < channels; c++) {result_split[c] += w.mul(splitted[c] - exp_values.at<float>((int)i));}weight_sum += w;}weight_sum = 1.0f / weight_sum;for(int c = 0; c < channels; c++) {result_split[c] = result_split[c].mul(weight_sum);}// 融合merge(result_split, result);// 求对数exp(result, result);}protected:String name;Mat weights;
};这里MergeDebevecImpl继承MergeDebevec父类，最终是继承Algorithm抽象类，位于photo.hpp：
class CV_EXPORTS_W MergeExposures : public Algorithm
{
public:CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,InputArray times, InputArray response) = 0;
};class CV_EXPORTS_W MergeDebevec : public MergeExposures
{
public:CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,InputArray times, InputArray response) CV_OVERRIDE = 0;CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
};2、HDR转SDR源码
前面我们有谈到，HDR转SDR是通过ToneMapping色调映射实现。位于photo模块的tonemap.cpp，入口是createTonemap()，也是使用智能指针包裹：
Ptr<Tonemap> createTonemap(float gamma)
{return makePtr<TonemapImpl>(gamma);
}接着我们继续看TonemapImpl核心代码：
class TonemapImpl CV_FINAL : public Tonemap
{
public:TonemapImpl(float _gamma) : name("Tonemap"), gamma(_gamma){}void process(InputArray _src, OutputArray _dst) CV_OVERRIDE{Mat src = _src.getMat();Mat dst = _dst.getMat();double min, max;// 获取图像像素最小值与最大值minMaxLoc(src, &min, &max);if(max - min > DBL_EPSILON) {dst = (src - min) / (max - min);} else {src.copyTo(dst);}// 幂运算，指数为gamma的倒数pow(dst, 1.0f / gamma, dst);}......protected:String name;float gamma;
};同时还提供Drago、Reinhard、Mantiuk算法进行色调映射，大家感兴趣可以去阅读源码。
3、图像曝光源码
图像曝光的源码同样位于merge.cpp，入口是createMergeMertens()，同样使用智能指针包裹：
Ptr<MergeMertens> createMergeMertens(float wcon, float wsat, float wexp)
{return makePtr<MergeMertensImpl>(wcon, wsat, wexp);
}核心源码在MergeMertensImpl类：
class MergeMertensImpl CV_FINAL : public MergeMertens
{
public:MergeMertensImpl(float _wcon, float _wsat, float _wexp) :name("MergeMertens"),wcon(_wcon),wsat(_wsat),wexp(_wexp){}void process(InputArrayOfArrays src, OutputArray dst) CV_OVERRIDE{......parallel_for_(Range(0, static_cast<int>(images.size())), [&](const Range& range) {for(int i = range.start; i < range.end; i++) {Mat img, gray, contrast, saturation, wellexp;std::vector<Mat> splitted(channels);images[i].convertTo(img, CV_32F, 1.0f/255.0f);if(channels == 3) {cvtColor(img, gray, COLOR_RGB2GRAY);} else {img.copyTo(gray);}images[i] = img;// 通道分离split(img, splitted);// 计算对比度：拉普拉斯变换Laplacian(gray, contrast, CV_32F);contrast = abs(contrast);// 通道求均值Mat mean = Mat::zeros(size, CV_32F);for(int c = 0; c < channels; c++) {mean += splitted[c];}mean /= channels;// 计算饱和度saturation = Mat::zeros(size, CV_32F);for(int c = 0; c < channels; c++) {Mat deviation = splitted[c] - mean;pow(deviation, 2.0f, deviation);saturation += deviation;}sqrt(saturation, saturation);// 计算曝光量wellexp = Mat::ones(size, CV_32F);for(int c = 0; c < channels; c++) {Mat expo = splitted[c] - 0.5f;pow(expo, 2.0f, expo);expo = -expo / 0.08f;exp(expo, expo);wellexp = wellexp.mul(expo);}pow(contrast, wcon, contrast);pow(saturation, wsat, saturation);pow(wellexp, wexp, wellexp);weights[i] = contrast;if(channels == 3) {weights[i] = weights[i].mul(saturation);}weights[i] = weights[i].mul(wellexp) + 1e-12f;AutoLock lock(weight_sum_mutex);weight_sum += weights[i];}});int maxlevel = static_cast<int>(logf(static_cast<float>(min(size.width, size.height))) / logf(2.0f));std::vector<Mat> res_pyr(maxlevel + 1);std::vector<Mutex> res_pyr_mutexes(maxlevel + 1);parallel_for_(Range(0, static_cast<int>(images.size())), [&](const Range& range) {for(int i = range.start; i < range.end; i++) {weights[i] /= weight_sum;std::vector<Mat> img_pyr, weight_pyr;// 分别构建image、weight图像金字塔buildPyramid(images[i], img_pyr, maxlevel);buildPyramid(weights[i], weight_pyr, maxlevel);for(int lvl = 0; lvl < maxlevel; lvl++) {Mat up;pyrUp(img_pyr[lvl + 1], up, img_pyr[lvl].size());img_pyr[lvl] -= up;}for(int lvl = 0; lvl <= maxlevel; lvl++) {std::vector<Mat> splitted(channels);// 通道分离，然后与weight权重相乘split(img_pyr[lvl], splitted);for(int c = 0; c < channels; c++) {splitted[c] = splitted[c].mul(weight_pyr[lvl]);}// 图像融合merge(splitted, img_pyr[lvl]);AutoLock lock(res_pyr_mutexes[lvl]);if(res_pyr[lvl].empty()) {res_pyr[lvl] = img_pyr[lvl];} else {res_pyr[lvl] += img_pyr[lvl];}}}});for(int lvl = maxlevel; lvl > 0; lvl--) {Mat up;pyrUp(res_pyr[lvl], up, res_pyr[lvl - 1].size());res_pyr[lvl - 1] += up;}dst.create(size, CV_32FCC);res_pyr[0].copyTo(dst);}......protected:String name;float wcon, wsat, wexp;
};
