Normalizing color channels of and image by intensity values, OpenCV
I have split an image into 3 separate color channels - one blue, one green, and one red. I would like to normalize each of these channels by the image's intensity, where intensity = (red + blue + green)/3. To be clear, I am trying to make an image that is composed of one of the three color channels, divided by the image's intensity, where the intensity is described by the equation above. I am new to OpenCV and I do not think I am doing this correctly; when the images are displayed, all the pixels appear to be black. I am new to OpenCV (I have worked through the tutorials that come with the documentation, but that is it) - any advice about how to go about this normalization would be extremely helpful.
Thanks!
Here is my attempt:
int main(int argc, char** argv){
Mat sourceImage, I;
const char* redWindow = "Red Color Channel";
const char* greenWindow = "Green Color Channel";
const char* blueWindow = "Blue Color Channel";
if(argc != 2)
{
cout << "Incorrect number of arguments" << endl;
}
/* Load the image */
sourceImage = imread(argv[1], 1);
if(!sourceImage.data)
{
cout << "Image failed to load" << endl;
}
/* First, we have to allocate the new channels */
Mat r(sourceImage.rows, sourceImage.cols, CV_8UC1);
Mat b(sourceImage.rows, sourceImage.cols, CV_8UC1);
Mat g(sourceImage.rows, sourceImage.cols, CV_8UC1);
/* Now we put these into a matrix */
Mat out[] = {b, g, r};
/* Split the image into the three color channels */
split(sourceImage, out);
/* I = (r + b + g)/3 */
add(b, g, I);
add(I, r, I);
I = I/3;
Mat red = r/I;
Mat blue = b/I;
Mat green = g/I;
/* Create the windows */
namedWindow(blueWindow, 0);
namedWindow(greenWindow, 0);
namedWindow(redWindow, 0);
/* Show the images */
imshow(blueWindow, blue);
imshow(greenWindow, green);
imshow(redWindow, red);
waitKey(0);
return 0;
}
Once you divide by the intensity the pixel values will be in the range [0, 1], except since they are integers they will be 0 or 1. For a display image white is 255 and 0 is black, so this is why everything appears black to you. You need to use floating point to get an accurate result, and you need to scale the result by 255 to see it. Doing that results in this (which I an not sure is particularly useful)
(Image source: BSDS500)
And here is the code that generated it:
#include <opencv2/core/core.hpp>
#include <vector>
int main(int argc, char** argv)
{
// READ RGB color image and convert it to Lab
cv::Mat bgr_image = cv::imread("208001.jpg"); // BSDS500 mushroom
cv::imshow("original image", bgr_image);
cv::Mat bgr_image_f;
bgr_image.convertTo(bgr_image_f, CV_32FC3);
// Extract the color planes and calculate I = (r + g + b) / 3
std::vector<cv::Mat> planes(3);
cv::split(bgr_image_f, planes);
cv::Mat intensity_f((planes[0] + planes[1] + planes[2]) / 3.0f);
cv::Mat intensity;
intensity_f.convertTo(intensity, CV_8UC1);
cv::imshow("intensity", intensity);
//void divide(InputArray src1, InputArray src2, OutputArray dst, double scale=1, int dtype=-1)
cv::Mat b_normalized_f;
cv::divide(planes[0], intensity_f, b_normalized_f);
cv::Mat b_normalized;
b_normalized_f.convertTo(b_normalized, CV_8UC1, 255.0);
cv::imshow("b_normalized", b_normalized);
cv::Mat g_normalized_f;
cv::divide(planes[1], intensity_f, g_normalized_f);
cv::Mat g_normalized;
g_normalized_f.convertTo(g_normalized, CV_8UC1, 255.0);
cv::imshow("g_normalized", g_normalized);
cv::Mat r_normalized_f;
cv::divide(planes[2], intensity_f, r_normalized_f);
cv::Mat r_normalized;
r_normalized_f.convertTo(r_normalized, CV_8UC1, 255.0);
cv::imshow("r_normalized", r_normalized);
cv::waitKey();
}
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