在OpenCV中比较强度像素值Vec3b

Question

I have a 3 channel Mat image, type is CV_8UC3 . I want to compare, in a loop, the intensity value of a pixel with its neighbours and then set 0 or 1 if the neighbour is greater or not. I can get the intensity calling Img.at<Vec3b>(x,y) .

But my question is: how can I compare two Vec3b ?

Should I compare pixels value for every channel (BGR or Vec3b[0] , Vec3b[1] and Vec3b[2] ), and then merge the three channels results into a single Mat object?

Answer 1

Me again :)

If you want to compare (greater or less) two RGB values you need to project the 3-dimensional RGB space onto a plane or axis.

Of course, there are many possibilities to do this, but an easy way would be to use the HSV color space. The hue ( H ), however, is not appropriate as a linear order function because it is circular (ie the value 1.0 is identical with 0.0, so you cannot decide if 0.5 > 0.0 or 0.5 < 0.0). However, the saturation ( S ) or the value ( V ) are appropriate projection functions for your purpose:

• If you want to have colored pixels "larger" than monochrome pixels, you will prefer S .
• If you want to have lighter pixels larger than darker pixels, you will probably prefer V .
• Also any combination of S and V would be a valid projection function, eg S+V .

Answer 2

As far as I understand, you want a measure to calculate distance/similarity between two Vec3b pixels. This can be reflected to the general problem of finding distance between two vectors in an n-mathematical space.

One of the famous measures (and I think this is what you're asking for), is the Euclidean distance.

If you are using Opencv then you can simply use:

cv::Vec3b a(1, 1, 1);
cv::Vec3b b(5, 5, 5);
double dist = cv::norm(a, b, CV_L2);

You can refer to this for reading about cv::norm and its options.

Edit: If you are doing this to measure color similarity, it's recommended to use the LAB color space as it's proved that Euclidean distance in LAB space is a good approximation for human perception of colors.

Edit 2: I see what you mean, for this you can get the magnitude of each vector and then compare them, something like this:

    double a_magnitude = cv::norm(a, CV_L2);
    double b_magnitude = cv::norm(b, CV_L2);
    if(a_magnitude > b_magnitude)
      // do something
    else
      // do something else.