CV_EXPORTS_W void tonemap(InputArray src, OutputArray dst, int algorithm,
const std::vector<float>& params = std::vector<float>());
-CV_EXPORTS_W void exposureFusion(InputArrayOfArrays srcImgs, OutputArray dst, bool align = false, float wc = 1, float ws = 1, float we = 0);
+CV_EXPORTS_W void exposureFusion(InputArrayOfArrays srcImgs, OutputArray dst, bool align = false, float wc = 1.0f, float ws = 1.0f, float we = 0.0f);
CV_EXPORTS_W void shiftMat(InputArray src, Point shift, OutputArray dst);
static void generateResponce(float responce[])
{
- for(int i = 0; i < 256; i++) {
- responce[i] = log((float)i);
+ for(int i = 1; i < 256; i++) {
+ responce[i] = logf((float)i);
}
responce[0] = responce[1];
}
-static void checkImages(std::vector<Mat>& images, bool hdr, const std::vector<float>& _exp_times = std::vector<float>())
+static void checkImages(const std::vector<Mat>& images, bool hdr, const std::vector<float>& _exp_times = std::vector<float>())
{
if(images.empty()) {
CV_Error(Error::StsBadArg, "Need at least one image");
}
}
-static void alignImages(std::vector<Mat>& src, std::vector<Mat>& dst)
+static void alignImages(const std::vector<Mat>& src, std::vector<Mat>& dst)
{
dst.resize(src.size());
}
std::vector<float> exp_times(_exp_times.size());
for(size_t i = 0; i < exp_times.size(); i++) {
- exp_times[i] = log(_exp_times[i]);
+ exp_times[i] = logf(_exp_times[i]);
}
float weights[256], responce[256];
}
}
for(int channel = 0; channel < 3; channel++) {
- res_ptr[channel] = exp(sum[channel] / weight_sum);
+ res_ptr[channel] = expf(sum[channel] / weight_sum);
if(res_ptr[channel] > max) {
max = res_ptr[channel];
}
pow(deviation, 2.0, deviation);
saturation += deviation;
}
- sqrt(saturation, saturation);
+ sqrt(saturation, saturation);
wellexp = Mat::ones(gray.size(), CV_32FC1);
for(int i = 0; i < 3; i++) {
weights[im] = weights[im].mul(wellexp);
weight_sum += weights[im];
}
- int maxlevel = (int)(log((double)max(images[0].rows, images[0].cols)) / log(2.0)) - 1;
+ 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++) {
res_pyr[0].copyTo(result);
}
-};
\ No newline at end of file
+};
//
//M*/
+#include "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
return params[i];
} else {
return defval;
- }
-
+ }
}
static void DragoMap(Mat& src_img, Mat &dst_img, const std::vector<float>& params)
cvtColor(src_img, gray_img, COLOR_RGB2GRAY);
Mat log_img;
log(gray_img, log_img);
- float mean = exp((float)sum(log_img)[0] / log_img.total());
+ float mean = expf(static_cast<float>(sum(log_img)[0]) / log_img.total());
gray_img /= mean;
log_img.release();