def read(self, *args, **kwargs): return ap.read(self.filename(*args, **kwargs));
def _test():
import numpy as np
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
## Lookup table processing
#apply_lut
x = np.random.randint(0, 100, size=(20, 30))
lut = np.arange(100) + 1
y = ap.apply_lut(x, lut)
assert np.all(y == x + 1)
#apply_lut_to_index
import ClearMap.ImageProcessing.Topology.Topology3d as t3d
kernel = t3d.index_kernel(dtype=int)
import ClearMap.ImageProcessing.Binary.Smoothing as sm
lut = sm.initialize_lookup_table()
data = np.array(np.random.rand(150, 30, 40) > 0.75, order='F')
result = ap.apply_lut_to_index(data, kernel, lut, sink=None, verbose=True)
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([[data, result]])
### Correlation
#correlate1d
kernel = np.array(range(11), dtype='uint32')
data = np.array(np.random.randint(0,
2**27, (300, 400, 1500),
dtype='uint32'),
order='F')
#data = np.array(np.random.rand(3,4,5), order='F');
data = np.empty((300, 400, 1500), order='F')
kernel = np.array([1, 2, 3, 4, 5], dtype='uint8')
sink = 'test.npy'
import ClearMap.Utils.Timer as tmr
import scipy.ndimage as ndi
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr_ndi = ndi.correlate1d(data, axis=axis, mode='constant', cval=0)
timer.print_elapsed_time('ndi')
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr = ap.correlate1d(data,
sink=sink,
kernel=kernel,
axis=axis,
verbose=False,
processes=None)
timer.print_elapsed_time('ap')
assert np.allclose(corr.array, corr_ndi)
# IO
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
import numpy as np
reload(ap)
data = np.random.rand(10, 200, 10)
sink = ap.write('test.npy', data, verbose=True)
assert (np.all(sink.array == data))
read = ap.read('test.npy', verbose=True)
assert (np.all(read.array == data))
ap.io.delete_file('test.npy')
# where
reload(ap)
data = np.random.rand(30, 20, 40) > 0.5
where_np = np.array(np.where(data)).T
where = ap.where(data, cutoff=2**0)
check_np = np.zeros(data.shape, dtype=bool)
check = np.zeros(data.shape, dtype=bool)
check_np[tuple(where_np.T)] = True
check[tuple(where.array.T)] = True
assert (np.all(check_np == check))
def skeletonize(source,
sink=None,
points=None,
method='PK12i',
steps=None,
in_place=False,
verbose=True,
**kwargs):
"""Skeletonize 3d binary arrays.
Arguments
---------
source : array or source
Binary image to skeletonize.
sink : sink specification
Optional sink.
points : array or None
Optional point list of the foreground points in the binary.
method : str
'PK12' or faster index version 'PK12i'.
steps : int or None
Number of maximal iteration steps. If None, maximal thinning.
in_place : bool
If True, the skeletonization is done directly on the input array.
Returns
-------
skeleton : Source
The skeletonized array.
"""
if verbose:
timer = tmr.Timer()
if not in_place and io.is_file(source):
binary_buffer = ap.read(source).as_buffer()
else:
binary, binary_buffer = ap.initialize_source(source)
if not in_place:
binary_buffer = np.array(binary_buffer)
if method == 'PK12':
result = PK12.skeletonize(binary_buffer,
points=points,
steps=steps,
verbose=verbose,
**kwargs)
elif method == 'PK12i':
result = PK12.skeletonize_index(binary_buffer,
points=points,
steps=steps,
verbose=verbose,
**kwargs)
else:
raise RuntimeError('Skeletonizaton method %r is not valid!' % method)
if verbose:
timer.print_elapsed_time(head='Skeletonization')
if sink is None:
sink = ap.io.as_source(result)
elif isinstance(sink, str):
sink = ap.write(sink, result)
else:
sink = io.write(sink, result)
return sink