def test_uint(dtype):
data = np.arange(50, dtype=dtype)
data_scaled = data * 256**(data.dtype.itemsize - 1)
assert convert_to_uint8(data_scaled).dtype == np.uint8
assert np.all(data == convert_to_uint8(data_scaled))
assert np.all(img_as_ubyte(data) == convert_to_uint8(data))
assert np.all(img_as_ubyte(data_scaled) == convert_to_uint8(data_scaled))
def test_float(dtype):
data = np.linspace(0, 0.5, 128, dtype=dtype, endpoint=False)
res = np.arange(128, dtype=np.uint8)
assert convert_to_uint8(data).dtype == np.uint8
assert np.all(convert_to_uint8(data) == res)
data = np.linspace(0, 1, 256, dtype=dtype)
res = np.arange(256, dtype=np.uint8)
assert np.all(convert_to_uint8(data) == res)
assert np.all(img_as_ubyte(data) == convert_to_uint8(data))
assert np.all(img_as_ubyte(data - 0.5) == convert_to_uint8(data - 0.5))
def make_thumbnail(image: np.ndarray, shape=(30, 30, 4)) -> np.ndarray:
"""Resizes an image to `shape` with padding"""
from napari.layers.utils.layer_utils import convert_to_uint8
from scipy import ndimage as ndi
scale_factor = np.min(np.divide(shape, image.shape))
intermediate_image = ndi.zoom(image, (scale_factor, scale_factor, 1))
padding_needed = np.subtract(shape, intermediate_image.shape)
pad_amounts = [(p // 2, (p + 1) // 2) for p in padding_needed]
thumbnail = np.pad(intermediate_image, pad_amounts, mode="constant")
thumbnail = convert_to_uint8(thumbnail)
# blend thumbnail with opaque black background
background = np.zeros(shape, dtype=np.uint8)
background[..., 3] = 255
f_dest = thumbnail[..., 3][..., None] / 255
f_source = 1 - f_dest
thumbnail = thumbnail * f_dest + background * f_source
return thumbnail.astype(np.uint8)
def test_bool():
data = np.zeros((10, 10), dtype=np.bool)
data[2:-2, 2:-2] = 1
converted = convert_to_uint8(data)
assert converted.dtype == np.uint8
assert np.all(img_as_ubyte(data) == converted)