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pythonopencv

convert True False matrix to image

I have a matrix consisting of True and False values. I want to print this as an image where all the True values are white and the False values are black. The matrix is called indices. I have tried the following:

indices = indices.astype(int)  #To convert the true to 1 and false to 0
indices*=255                   #To change all the 1's to 255
cv2.imshow('Indices',indices)
cv2.waitKey()

This is printing a fully black image. When I try, print (indices==255).sum(), it returns a values of 669 which means that there are 669 elements/pixels in the indices matrix which should be white. But I can only see a pure black image. How can I fix this?

Solution

  • As far as I know, opencv represents an image as a matrix of floats ranging from 0 to 1, or an integer with values between the minimum and the maximum of that type.

    An int has no bounds (except the boundaries of what can be represented with all available memory). If you however use np.uint8, that means you are working with (unsigned) bytes, where the minimum is 0 and the maximum 255.

    So there are several options. The two most popular would be:

    1. cast to np.uint8 and then multiply with 255:

      indices = indices.astype(np.uint8)  #convert to an unsigned byte
 indices*=255
 cv2.imshow('Indices',indices)
 cv2.waitKey()

    2. Use a float representation:

      indices = indices.astype(float)
 cv2.imshow('Indices',indices)
 cv2.waitKey()

    Note that you could also choose to use np.uint16 for instance to use unsigned 16-bit integers. In that case you will have to multiply with 65'535. The advantage of this approach is that you can use an arbitrary color depth (although most image formats use 24-bit colors (8 bits per channel), there is no reason not to use 48-bit colors. If you for instance are doing image processing for a glossy magazine, then it can be beneficial to work with more color depth.

    Furthermore even if the end result is a 24-bit colorpalette, one can sometimes better use a higher color depth for the different steps in image processing.