def sized_mask(
arr: Union[np.ndarray, coo_matrix], shape: Tuple[int, int] = None,
full: bool = False):
"""return a 2D dense array representation of the mask, optionally
cropped and padded
Parameters
----------
arr: numpy.ndarray or scipy.sparse.coo_matrix:
a representation of the mask
shape: tuple(int, int)
[h, w] for padded shape. If None, cropped to existing values
full: bool
if True, the full-frame array is returned
Returns
-------
mask: numpy.ndarray
2D dense matrix representation of the mask
"""
if isinstance(arr, coo_matrix):
mask = arr.toarray()
else:
mask = arr
if not full:
mask = crop_2d_array(mask)
if shape is not None:
mask = center_pad_2d(mask, shape)
return mask
def postage_stamp(data: coo_matrix,
canvas_size: Optional[Tuple[int, int]] = None):
"""
Generate a "postage stamp" image data for a matrix in coo_format.
Parameters
==========
coo_matrix: scipy.sparse.coo_matrix representation of an image to
represent as a png
canvas_size: tuple of canvas size in pixels, where each row and
column is one pixel (rows, columns). If None, will fit tight to
outer boundaries of the data. If larger than the image data,
will place the image in the center of the canvas (as closely as
possible given dimensions).
Returns
=======
2-dimensional array representing PNG data normalized for grayscale
(values 0-255).
"""
# Crop image down to minimum-sized rectangle containing all nonzero data
arr = crop_2d_array(data)
img_size = arr.shape
# Normalize to grayscale based on data values
gray = (arr / arr.max() * 255).astype(np.uint8)
# Don't need to do any transforms if fitting tightly to image data
if canvas_size is None or canvas_size == img_size:
return gray
else:
return center_pad_2d(gray, canvas_size, 0)
def test_compare_crop_pad_and_extents(arr, shape):
"""check that cropping and padding an image
is the same as getting the extents and
indexing the array (i.e. as would be applied to video frame)
"""
cropped_padded = au.center_pad_2d(au.crop_2d_array(arr), shape)
extents, pad_width = au.content_extents(arr, shape)
indexed = np.pad(
arr[extents[0]:extents[1], extents[2]:extents[3]],
pad_width)
assert np.all(cropped_padded == indexed)
def test_crop_array_raises_error(arr):
with pytest.raises(ValueError):
au.crop_2d_array(arr)
def test_crop_array(arr, expected):
np.testing.assert_array_equal(expected, au.crop_2d_array(arr))