Numpy: Get a rectangle just the size of the mask

Question

I have an image and a mask. Both are numpy arrays. I get the mask through GraphSegmentation (cv2.ximgproc.segmentation), so the area is not rectangular, but not divided. I would like to get a rectangle the size of the masking area, but I don’t know an effective way.

In other words, unoiled pixels have a value of 0, and masked pixels have a value greater than 0, so I want to get a rectangle where ...

• top = lowest index of axis 0 whose value is> 0
• bottom = the largest index of the 0 axis whose value is> 0
• left = smallest axis of index 1 whose value is> 0
• right = the largest axis of index 1 whose value is> 0
• image = src [top, bottom, left: right]

My code is below

segmentation = cv2.ximgproc.segmentation.createGraphSegmentation()
src = cv2.imread('image_file')
segment = segmentation.processImage(src)
for i in range(np.max(segment)):
    dst = np.array(src)
    dst[segment != i] = 0
    cv2.imwrite('output_file', dst)

Answer 1

If you prefer pure Numpy, you can achieve this using np.whereand np.meshgrid:

i, j = np.where(mask)
indices = np.meshgrid(np.arange(min(i), max(i) + 1),
                      np.arange(min(j), max(j) + 1),
                      indexing='ij')
sub_image = image[indices]

np.where , , , mask. , np.arange, np.meshgrid , . , - index='ij', ( ).

, meshgrid indices , :

image[indices][a, b] == image[indices[0][a, b], indices[1][a, b]]

:

>>> image = np.arange(12).reshape((4, 3))
>>> image
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])

, [[3,4],[6,7]], :

>>> mask = np.array([[0,0,0],[0,1,0],[1,0,0],[0,0,0]])
>>> mask
array([[0, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 0, 0]])

, :

>>> i, j = np.where(mask)
>>> indices = np.meshgrid(np.arange(min(i), max(i) + 1), np.arange(min(j), max(j) + 1), indexing='ij')
>>> image[indices]
array([[3, 4],
       [6, 7]])

indices[0] - , indices[1] - :

>>> indices[0]
array([[1, 1],
       [2, 2]])
>>> indices[1]
array([[0, 1],
       [0, 1]])

Answer 2

, np.amax np.amin .

i, j = np.where(mask)
indices = np.meshgrid(np.arange(min(i), max(i) + 1),
              np.arange(min(j), max(j) + 1),
              indexing='ij')
sub_image = image[indices]

: 50

where = np.array(np.where(mask))

x1, y1 = np.amin(where, axis=1)
x2, y2 = np.amax(where, axis=1)
sub_image = image[x1:x2, y1:y2]

: 5.6