def test_florinate():
plus_one = add(1)
data = 0
for i in range(1, 101):
data = plus_one(data)
assert data == i
worlder = concat('World')
data = 'Hello'
expected = 'Hello'
for _ in range(100):
data = worlder(data)
expected = ' '.join([expected, 'World'])
assert data == expected
data = np.zeros((7, 7), dtype=np.uint8)
selem = np.zeros((3, 3), dtype=np.uint8)
data[3, 3] = 1
selem[0, 1] = 1
selem[1, :] = 1
selem[2, 1] = 1
dilation = florinate(binary_dilation)(selem=selem)
erosion = florinate(binary_erosion)(selem=selem)
result = dilation(data)
assert np.all(result == binary_dilation(data, selem))
result2 = dilation(data)
assert np.all(result2 == result)
result_dil = dilation(result)
assert np.all(result_dil == binary_dilation(result, selem))
result2_dil = dilation(result2)
assert np.all(result2_dil == result_dil)
result = erosion(result_dil)
assert np.all(result == binary_erosion(result_dil, selem))
result2 = erosion(result2_dil)
assert np.all(result2 == result)
result = erosion(result)
assert np.all(result == data)
result2 = erosion(result2)
assert np.all(result2 == result)
assert np.all(result2 == data)
def __getattr__(self, key):
if key in self.__dict__['submodules']:
return self.__dict__['submodules'][key]
else:
try:
target = importlib.import_module('.'.join(
[self.inner_module.__name__, key]))
except ImportError:
target = getattr(self.inner_module, key)
if inspect.ismodule(target):
self.__dict__['submodules'][key] = ImportWrapper(target)
return self.__dict__['submodules'][key]
elif callable(target):
return florinate(target)
else:
return self.__dict__[key]
binary_erosion
Perform a binary morphological erosion on an image.
binary_opening
Perform a binary morphological opening on an image.
remove_small_holes
Fill in contiguous holes smaller than the specified size.
remove_small_objects
Remove contiguous objects smaller than the specified size.
"""
import skimage.morphology
from florin.closure import florinate
closing = florinate(skimage.morphology.closing)
dilation = florinate(skimage.morphology.dilation)
erosion = florinate(skimage.morphology.erosion)
opening = florinate(skimage.morphology.opening)
binary_closing = florinate(skimage.morphology.binary_closing)
binary_dilation = florinate(skimage.morphology.binary_dilation)
binary_erosion = florinate(skimage.morphology.binary_erosion)
binary_opening = florinate(skimage.morphology.binary_opening)
self.bounds[key] = (-float('inf'), val)
elif isinstance(val, str):
self.bounds[key] = ('', val)
else:
self.bounds[key] = (min(val), max(val))
def classify(self, obj):
"""Determine if an object is in this class.
Parameters
----------
obj : skimage.measure._regionprops.RegionProperties
The object to classify.
Returns
-------
bool
True if the object is within all defined boundaries else False. If
no boundaries were provided, return True (e.g., the default class).
"""
if len(self.bounds) > 0:
return all([
v[0] <= getattr(obj, k) <= v[1]
for k, v in self.bounds.items()
])
else:
return True
classify = florinate(classify)
Parameters
----------
tiles : collection of FlorinArray
The collection of tiles to join.
shape : tuple of int
The shape of the joined array.
Returns
-------
joined : array_like
The array created by joining the tiles and inserting them into the
correct positions.
"""
out = None
for tile, metadata in tiles:
if out is None:
out = np.zeros(metadata['original_shape'], dtype=tile.dtype)
start = np.asarray(metadata['origin'])
end = start + np.asarray(tile.shape)
slices = [slice(start[i], end[i]) for i in range(tile.ndim)]
out[tuple(slices)] += tile
return out.astype(tile.dtype)
tile = florinate(tile_generator)
join = florinate(join_tiles)
Performs Sigmoid Correction on the input image.
adjust_log
Determine if an image is low contrast.
Notes
-----
The functions defined here are wrappers for functions in the `skimage.exposure`
module. Full documentation may be found in the `scikit-image documentation`_.
.. _scikit-image documentation: https://scikit-image.org/docs/stable/api/skimage.exposure.html
"""
import skimage.exposure
from florin.closure import florinate
histogram = florinate(skimage.exposure.histogram)
equalize_hist = florinate(skimage.exposure.equalize_hist)
equalize_adapthist = florinate(skimage.exposure.equalize_adapthist)
rescale_intensity = florinate(skimage.exposure.rescale_intensity)
cumulative_distribution = florinate(skimage.exposure.cumulative_distribution)
adjust_gamma = florinate(skimage.exposure.adjust_gamma)
adjust_log = florinate(skimage.exposure.adjust_log)
__all__ = ['histogram', 'equalize_hist', 'equalize_adapthist',
'rescale_intensity', 'cumulative_distribution', 'adjust_gamma',
'adjust_log']
Invert an image. Notes ----- The functions defined here are wrappers for functions in the `skimage.util` module. Full documentation may be found in the `scikit-image documentation`_. .. _scikit-image documentation: https://scikit-image.org/docs/stable/api/skimage.util.html """ import skimage.util from florin.closure import florinate img_as_float32 = florinate(skimage.util.img_as_float32) img_as_float64 = florinate(skimage.util.img_as_float64) img_as_float = florinate(skimage.util.img_as_float) img_as_int = florinate(skimage.util.img_as_int) img_as_uint = florinate(skimage.util.img_as_uint) img_as_ubyte = florinate(skimage.util.img_as_ubyte) img_as_bool = florinate(skimage.util.img_as_bool) view_as_blocks = florinate(skimage.util.view_as_blocks) pad = florinate(skimage.util.pad) crop = florinate(skimage.util.crop) random_noise = florinate(skimage.util.random_noise) regular_grid = florinate(skimage.util.regular_grid) regular_seeds = florinate(skimage.util.regular_seeds) invert = florinate(skimage.util.invert) __all__ = [
"""Convenience functions for image thresholding operations.
Functions
---------
ndnt
Binarize data with N-Dimensional Neighborhood Thresholding.
"""
from florin.closure import florinate
from florin.ndnt import ndnt
ndnt = florinate(ndnt)