Normalising an image in opencv

I have a RGB image stored in a Mat datastructure. I am converting the image into grayscale using cvtColor function in opencv. After that I am trying to normalise the image to the range [0,1]. I am using the default normalize function of opencv. To check the correctness, I tried printing the pixel values and equate it with matlab values(Matlab values are already in the range [0,1]). But the values differ a lot. Help me to make both results almost same. Below are the opencv and matlab codes.

Mat img1 = imread("D:/input.png", CV_LOAD_IMAGE_COLOR);
cvtColor(img1, img1, CV_BGR2GRAY);
img1.convertTo(img1, CV_32FC1);
cv::normalize(img1, img1, 0.0, 1.0, NORM_MINMAX, CV_32FC1);
for (int i = 0; i < img1.rows; i++)
{
    for (int j = 0; j < img1.cols; j++)
    {
       cout << img1.at<float>(i, j) << endl;
    }
}

Matlab code:

 I=im2double(imread('input.png'));
 gI=rgb2gray(I);
 display(gI)

---

I don't think you want to normalize here. The Matlab conversion rgb2gray uses this equation: 0.2989 * R + 0.5870 * G + 0.1140 * B. So there's no expectation that you have the minimum value of 0.0 or the maximum value of 1.0 in your output greyscale image. You would only expect 0 and 1 if you had pure white (255,255,255) and pure black (0,0,0) pixels.

Try this:

img *= 1./255;
cvtColor(img, img, CV_BGR2GRAY);

链接地址: http://www.djcxy.com/p/89792.html

上一篇: 在c ++中规格化直方图

下一篇: 在opencv中规范图像