imadjust进行对比度增强。在OpenCV中是否有等效的函数？imadjust而不是直方图均衡化，而阈值似乎不能执行对比度增强。OpenCV中没有内置的解决方案来执行直方图拉伸，但是您可以在循环中轻松实现它。
imadjust允许选择上限和下限的容差，或直接选择边界，因此您需要比简单的for循环更多的逻辑。
您可以使用下面的示例作为参考来实现自己的直方图拉伸：
#include <opencv2\opencv.hpp>
#include <vector>
#include <algorithm>
using namespace std;
using namespace cv;
void imadjust(const Mat1b& src, Mat1b& dst, int tol = 1, Vec2i in = Vec2i(0, 255), Vec2i out = Vec2i(0, 255))
{
// src : input CV_8UC1 image
// dst : output CV_8UC1 imge
// tol : tolerance, from 0 to 100.
// in : src image bounds
// out : dst image buonds
dst = src.clone();
tol = max(0, min(100, tol));
if (tol > 0)
{
// Compute in and out limits
// Histogram
vector<int> hist(256, 0);
for (int r = 0; r < src.rows; ++r) {
for (int c = 0; c < src.cols; ++c) {
hist[src(r,c)]++;
}
}
// Cumulative histogram
vector<int> cum = hist;
for (int i = 1; i < hist.size(); ++i) {
cum[i] = cum[i - 1] + hist[i];
}
// Compute bounds
int total = src.rows * src.cols;
int low_bound = total * tol / 100;
int upp_bound = total * (100-tol) / 100;
in[0] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), low_bound));
in[1] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), upp_bound));
}
// Stretching
float scale = float(out[1] - out[0]) / float(in[1] - in[0]);
for (int r = 0; r < dst.rows; ++r)
{
for (int c = 0; c < dst.cols; ++c)
{
int vs = max(src(r, c) - in[0], 0);
int vd = min(int(vs * scale + 0.5f) + out[0], out[1]);
dst(r, c) = saturate_cast<uchar>(vd);
}
}
}
int main()
{
Mat3b img = imread("path_to_image");
Mat1b gray;
cvtColor(img, gray, COLOR_RGB2GRAY);
Mat1b adjusted;
imadjust(gray, adjusted);
// int low_in, high_in, low_out, high_out
// imadjust(gray, adjusted, 0, Vec2i(low_in, high_in), Vec2i(low_out, high_out));
return 0;
}
输入图像：
输出调整后的图像：
cv::calcHist计算直方图，然后使用std::vector计算CDF并找到上下限，最后使用cv::threshold将源代码夹在上下限之间，然后使用cv::normalize和cv::NORM_MINMAX进行缩放。这种方法的优点是可以重复使用CV原语，支持所有支持的数据类型，并且所有这些都可以推送到GPU。 - WillY这里有一个 imadjust 和 stretchlim 的实现：
你可以尝试向这位用户提问： http://opencv-users.1802565.n2.nabble.com/imadjust-matlab-function-with-stretchlim-OpenCV-implementation-td6253242.html
他的实现是基于这个的： http://www.mathworks.com/matlabcentral/fileexchange/12191-bilateral-filtering
文件应该是下面这个，但我不完全确定它是否有效：
void
getOptimalImgAdjustParamsFromHist (IplImage* p_img,unsigned int* p_optminmaxidx, int p_count)
{
int numBins = 256;
CvMat* bins = cvCreateMat(1,numBins,CV_8UC1);
calcHistogram(p_img,bins,numBins);
int sumlow = 0, sumhigh = 0;
int low_idx = 0, high_idx = 0;
for (unsigned int i = 0; i < numBins; i++) {
float curval = (float) cvGetReal1D (bins, (i));
sumlow += curval;
if (sumlow >= p_count) {
low_idx = i;
break;
}
}
for (unsigned int i = numBins - 1 ; i >= 0; i--) {
float curval = (float) cvGetReal1D (bins, (i));
sumhigh += curval;
if (sumhigh >= p_count) {
high_idx = i;
break;
}
}
cvReleaseMat(&bins);
p_optminmaxidx[OPTMINIDX] = low_idx;
p_optminmaxidx[OPTMAXIDX] = high_idx;
}
IplImage *
imageAdjust (IplImage * p_img)
{
CvSize framesize = cvGetSize (p_img);
int low_count = round (framesize.width * framesize.height * 0.01);
unsigned int *optminmaxidx = new unsigned int [2];
getOptimalImgAdjustParamsFromHist (p_img, optminmaxidx,low_count);
int range = optminmaxidx[OPTMAXIDX] - optminmaxidx[OPTMINIDX];
IplImage *adjustedImg = p_img;
for (int i = 0; i < framesize.height; i++)
for (int j = 0; j < framesize.width; j++) {
unsigned int val = (unsigned int) getData (p_img, i, j);
unsigned int newval = 0;
if (val <= optminmaxidx[OPTMINIDX]) {
newval = 0;
setData (adjustedImg, i, j, (uchar) newval);
} else if (val >= optminmaxidx[OPTMAXIDX]) {
newval = 255;
setData (adjustedImg, i, j, (uchar) newval);
} else {
newval =
(unsigned int) round ((double) (((double) val -
(double) optminmaxidx[OPTMINIDX]) * (double) (255.0 /
(double) range)));
setData (adjustedImg, i, j, (uchar) newval);
}
}
delete[]optminmaxidx;
return adjustedImg;
}
希望能对你有所帮助。 Fab。
cv::xphoto::SimpleWB类，它执行类似于imadjust的直方图拉伸。以下是实现代码：https://github.com/opencv/opencv_contrib/blob/3.2.0/modules/xphoto/src/simple_color_balance.cpp - Amro