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import numpy as np
from . import normalize
class Nchannel2RGB(object):
"""Convert nchannel array to rgb by PCA.
Parameters
----------
pca: sklearn.decomposition.PCA
PCA.
"""
def __init__(self, pca=None):
self._pca = pca
# for uint8
self._min_max_value = (None, None)
@property
def pca(self):
"""PCA for N channel to 3."""
return self._pca
def __call__(self, nchannel, dtype=np.uint8):
"""Convert nchannel array to rgb by PCA.
Parameters
----------
nchannel: numpy.ndarray, (H, W, C), float
N channel image.
dtype: numpy.dtype
Dtype (default: numpy.uint8).
Returns
-------
dst: numpy.ndarray, (H, W, 3), numpy.uint8
Visualized image.
"""
import sklearn.decomposition
assert nchannel.ndim == 3, "nchannel.ndim must be 3"
assert np.issubdtype(
nchannel.dtype, np.floating
), "nchannel.dtype must be floating"
H, W, D = nchannel.shape
dst = nchannel.reshape(-1, D)
if self._pca is None:
self._pca = sklearn.decomposition.PCA(
n_components=3, random_state=1234
)
dst = self._pca.fit_transform(dst)
else:
dst = self._pca.transform(dst)
dst = dst.reshape(H, W, 3)
if dtype == np.uint8:
if self._min_max_value is None:
self._min_max_value = (
np.nanmin(dst, axis=(0, 1)),
np.nanmax(dst, axis=(0, 1)),
)
min_value, max_value = self._min_max_value
dst = normalize.normalize(dst, min_value, max_value)
dst = (dst * 255).round().astype(np.uint8)
else:
assert np.issubdtype(dtype, np.floating)
dst = dst.astype(dtype)
return dst
def nchannel2rgb(nchannel, dtype=np.uint8, pca=None):
"""Convert nchannel array to rgb by PCA.
Parameters
----------
nchannel: numpy.ndarray, (H, W, C), float
N channel image.
dtype: numpy.dtype
Dtype (default: numpy.uint8).
pca: sklearn.decomposition.PCA
PCA.
Returns
-------
dst: numpy.ndarray, (H, W, 3), numpy.uint8
Visualized image.
"""
return Nchannel2RGB(pca)(nchannel, dtype)
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