def expand_binary_mask_for_window(mask, height, width):
'''Returns a new mask including window around each pixel in source mask.
Arguments:
`mask` (2D ndarray):
An ndarray whose non-zero elements define a mask.
`height` (int):
Height of the window.
`width` (int):
Width of the window
Returns a new mask of ones and zeros with same shape as `mask`. For each
non-zero element in mask, the returned mask will contain a value of one
for all pixels in the `height`x`width` window about the pixel and zeros
elsewhere.
'''
from spectral.algorithms.algorithms import iterator_ij
m = np.zeros_like(mask)
(mask_height, mask_width) = mask.shape
for (i, j) in iterator_ij(mask):
(r0, r1, c0, c1) = get_window_bounds_clipped(mask_height, mask_width,
height, width, i, j)
m[r0:r1, c0:c1] = 1
return m
def expand_binary_mask_for_window(mask, height, width):
'''Returns a new mask including window around each pixel in source mask.
Arguments:
`mask` (2D ndarray):
An ndarray whose non-zero elements define a mask.
`height` (int):
Height of the window.
`width` (int):
Width of the window
Returns a new mask of ones and zeros with same shape as `mask`. For each
non-zero element in mask, the returned mask will contain a value of one
for all pixels in the `height`x`width` window about the pixel and zeros
elsewhere.
'''
from spectral.algorithms.algorithms import iterator_ij
m = np.zeros_like(mask)
(mask_height, mask_width) = mask.shape
for (i, j) in iterator_ij(mask):
(r0, r1, c0, c1) = get_window_bounds_clipped(mask_height, mask_width,
height, width, i, j)
m[r0:r1, c0:c1] = 1
return m
def test_iterator_ij_nonzero(self):
'''Iteration over all non-background pixels.'''
data = self.image.load()
classes = self.gt.ravel()
pixels = data.reshape((-1, data.shape[-1]))
sum = np.sum(pixels[classes > 0], 0)
itsum = np.sum(np.array([data[ij] for ij in iterator_ij(self.gt)]), 0)
assert_allclose(sum, itsum)
def test_iterator_ij_index(self):
'''Iteration over single ground truth index'''
cls = 5
data = self.image.load()
classes = self.gt.ravel()
pixels = data.reshape((-1, data.shape[-1]))
sum = np.sum(pixels[classes == cls], 0)
itsum = np.sum(
np.array([data[ij] for ij in iterator_ij(self.gt, cls)]), 0)
assert_allclose(sum, itsum)