Convert multi-dimensional Numpy array to 2-dimensional array based on color values

Question

I have an image which is read as a uint8 array with the shape (512,512,3). Now I would like to convert this array to a uint8 array of shape (512,512,1)

Answer 1

Here's one based on views -

# https://stackoverflow.com/a/45313353/ @Divakar
def view1D(a, b): # a, b are arrays
    # This function gets 1D view into 2D input arrays
    a = np.ascontiguousarray(a)
    b = np.ascontiguousarray(b)
    void_dt = np.dtype((np.void, a.dtype.itemsize * a.shape[-1]))
    return a.view(void_dt).ravel(),  b.view(void_dt).ravel()

def img2label(a, maps):
    # Get one-dimension reduced view into input image and map arrays.
    # We need to reshape image to 2D, then feed it to view1D to get 1D
    # outputs and then reshape 1D image to 2D 
    A,B = view1D(a.reshape(-1,a.shape[-1]),maps)
    A = A.reshape(a.shape[:2])

    # Trace back positions of A in B using searchsorted. This gives us
    # original order, which is the final output.
    sidx = B.argsort()
    return sidx[np.searchsorted(B,A,sorter=sidx)]

Given that your labels start from 1, you might want to add 1 to the output.

Sample run -

In [100]: # Mapping array
     ...: maps = np.array([[0, 0, 0],[0, 0, 255],\
     ...:                  [255, 0, 0],[150, 30, 150]],dtype=np.uint8)
     ...: 
     ...: # Setup random image array
     ...: idx = np.array([[0,2,1,3],[1,3,2,0]])
     ...: img = maps[idx]

In [101]: img2label(img, maps) # should retrieve back idx
Out[101]: 
array([[0, 2, 1, 3],
       [1, 3, 2, 0]])

Answer 2

You could use giant lookup table. Let cls be [[0,0,0], [0,0,255], ...] of dtype=np.uint8.

LUT = np.zeros(size=(256,256,256), dtype='u1')
LUT[cls[:,0],cls[:,1],cls[:,2]] = np.arange(cls.shape[1])+1
img_as_cls = LUT[img[...,0],img[...,1], img[...,2]]

This solution is O(1) per pixel. It is also quite cache efficient because a small part of entries in LUT are actually used. It takes circa 10ms to process 1000x1000 image on my machine.

The solution can be slightly improved by converting 3-color channels to 24-bit integers. Here is the code

def scalarize(x):
    # compute x[...,2]*65536+x[...,1]*256+x[...,0] in efficient way
    y = x[...,2].astype('u4')
    y <<= 8
    y +=x[...,1]
    y <<= 8
    y += x[...,0]
    return y
LUT = np.zeros(2**24, dtype='u1')
LUT[scalarize(cls)] = 1 + np.arange(cls.shape[0])
simg = scalarize(img)
img_to_cls = LUT[simg]

After optimization it takes about 5ms to process 1000x1000 image.

Answer 3

One way: separately create the boolean arrays with True values where the input's pixel value matches one of the palette values, and then use arithmetic to combine them. Thus:

palette = [
    [0, 0, 0], 
    [0, 0, 255], 
    [255, 0, 0],
    # etc.
]

def palettized(data, palette):
    # Initialize result array
    shape = list(data.shape)
    shape[-1] = 1
    result = np.zeros(shape)
    # Loop and add each palette index component.
    for value, colour in enumerate(palette, 1):
        result += (data == colour).all(axis=2) * value
    return result