How to increase brightness in specify part of image using Histogram Equalization - c++

Question

Here I did coding for increasing brightness using Histogram Equalization but it changes the overall brightness of images. And I need brightness in specify location.
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
int main( int argc, const char** argv {
Mat img = imread("MyPic.JPG", CV_LOAD_IMAGE_COLOR);
if (img.empty())
{
cout << "Image cannot be loaded..!!" << endl;
return -1;
}
cvtColor(img, img, CV_BGR2GRAY);
Mat img_hist_equalized;
equalizeHist(img, img_hist_equalized); //equalize the histogram
namedWindow("Original Image", CV_WINDOW_AUTOSIZE);
namedWindow("Histogram Equalized", CV_WINDOW_AUTOSIZE);
imshow("Original Image", img);
imshow("Histogram Equalized", img_hist_equalized);
waitKey(0); //wait for key press
destroyAllWindows(); //destroy all open windows
return 0;}
Input image:
Output I gets:
But I expected output
The above code is based on the Histogram Equalization.Is there any other approach means specify here.

Answer 1

The answer on your question is in the method, used for generation of last image with expected output. Which commands of graphical editor have you used to generate this result? Just repeat it with OpenCV.
You can try something like this:
increase contrast,
reduce number of colors to 4,
replace all three "near black" colors by real black, and replace one
"near white" color by real white.
You will have something like this (top image after second step, bottom image after third step):
Is it good enought for your task? Do you need better quality? Any way, try to process your image by any graphical editor before coding. And only when you understand, which operations you need to apply to your image, try to implement it using OpenCV.
To understand better the problem, let's see the histogram of your initial image:
We can see, that difference between "expected black" and "expected white" colors are very small. More over, inside "white circle" we can see pixels with "expected black colors". So it is not enough to change histogram for fixing of this mix of pixels. We need to analyze surroundings of each pixel (in fact, I done it in first step - when increased contrast of the image). We can discuss methods for it, but first of all we need more information and efforts from you. Editing of the pallet without analyzing of surroundings will give you strange results like this:
So, you need to open any graphical editor and find way to convert your input to expected output.