How to extend mask region (True) by 1 or 2 pixels?-CodePudding

I have a numpy mask of True and False values, detecting black regions in an image.

I want to extend the True regions by 1 or 2 pixel.

For example, considering this mask:

[[False False False False False]
 [False False TRUE  False False]
 [False TRUE  TRUE  TRUE  False]
 [False False TRUE  False False]
 [False False False False False]

I want to have:

[[False False TRUE  False False]
 [False TRUE  TRUE  TRUE  False]
 [TRUE  TRUE  TRUE  TRUE  TRUE ]
 [False TRUE  TRUE  TRUE  False]
 [False False TRUE  False False]

Actually I could have made a for loop, but in a big image it's to slow.

Any ideas ?

Thanks !

CodePudding user response：

Dilation is the easiest way to extend the "True" regions.

Consider the array:

a = np.array([[False, False, False, False, False],
              [False, False,  True, False, False],
              [False,  True,  True,  True, False],
              [False, False,  True, False, False],
              [False, False, False, False, False]])

Convert to integer data type

a = a.astype(np.uint8)

You get:

array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)

Perform dilation using ellipse kernel of size 3x3:

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
dilate = cv2.dilate(a, kernel, iterations=1)

dilate:

array([[0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [1, 1, 1, 1, 1],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0]], dtype=uint8)

Convert the result to boolean data type:

d = np.array(dilate, dtype=bool)

Resulting array:

array([[False, False,  True, False, False],
       [False,  True,  True,  True, False],
       [ True,  True,  True,  True,  True],
       [False,  True,  True,  True, False],
       [False, False,  True, False, False]])

To extend the True regions further:

increase the kernel size
repeat dilation operation using iteration

Note: Boolean array cannot be used as input with cv2.dilate(), or else it throws error: (-5:Bad argument). Hence we convert it to int data type, perform the opertion and convert it back to bool data type.

It seems

CodePudding user response：

You can use the boolean sum:

new_mask = mask.copy()
new_mask[:,1:] = mask[:,1:] mask[:,:-1]
new_mask[1:,:] = new_mask[1:,:] mask[:-1,:]

new_mask now is:

array([[False, False, False, False, False],
       [False, False,  True, False, False],
       [False,  True,  True,  True, True],
       [False,  True,  True,  True, False],
       [False, False,  True, False, False]])

the sum between two boolean works as a logical or, so in the previous lines of code you are saying: new_mask[i,j] = (mask[i,j] or mask[i,j-1] or mask[i-1,j])