Reshape (M, N, 3) numpy array to (M*N, 3)

Question

Suppose I have an RGB (or HSV) image represented by an (M, N, 3) numpy array wherein each dimension ( [x, y, 0] or [x, y, 1] ) represents a color channel value at a specific pixel. I want to reshape the array to (M*N, 3) where the color channels are combined ( [R1, G1, B1], [R2, G2, B2] ...) into a flat list (is that the correct terminology in this case?). I understand that one must use the reshape function, but I'm having difficulty understanding how to use the function. Any help is appreciated.

EDIT: Here is an example of what I would like to happen.

Input: (640 x 640 x 3) array representative of image where [40, 40, 1] would be the G value for a specific pixel. I want to take all 3 color channels and combine them into the following output.

Output: ([R, G, B], [R, G, B], [R, G, B]...)

Answer 1

If img is your array, you can use img.reshape(-1, 3) .

For example,

In [50]: img.shape
Out[50]: (5, 2, 3)

In [51]: img
Out[51]: 
array([[[2, 0, 4],
        [1, 4, 3]],

       [[2, 1, 4],
        [3, 2, 2]],

       [[2, 4, 1],
        [4, 0, 2]],

       [[1, 4, 2],
        [3, 2, 2]],

       [[3, 2, 1],
        [2, 1, 0]]])


In [53]: x = img.reshape(-1, 3)

In [54]: x.shape
Out[54]: (10, 3)

In [55]: x
Out[55]: 
array([[2, 0, 4],
       [1, 4, 3],
       [2, 1, 4],
       [3, 2, 2],
       [2, 4, 1],
       [4, 0, 2],
       [1, 4, 2],
       [3, 2, 2],
       [3, 2, 1],
       [2, 1, 0]])

Answer 2

If I understood you correctly, then, I would do something like this:

## loading the image...
import matplotlib.pyplot as plt   
import matplotlib.image as mpimg  
image = mpimg.imread("my_image.jpg")  

## extracting R, G, B and flatten them separately and then make
## an array out of them. 
res = np.transpose(np.array([image[...,0].flatten(), image[...,1].flatten(), image[...,2].flatten()]))

This might not be the most elegant way, but it will work. Actually, this do not give (M, 3) but it give (MxN, 3) . This should be actually desired, because with (M, 3) you are loosing some data!