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# -*- coding: utf-8 -*-
# Copyright 2007-2023 The HyperSpy developers
#
# This file is part of RosettaSciIO.
#
# RosettaSciIO is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RosettaSciIO is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with RosettaSciIO. If not, see <https://www.gnu.org/licenses/#GPL>.
import numpy as np
from dask.array import Array
from dask.diagnostics import ProgressBar
from rsciio._docstrings import SHOW_PROGRESSBAR_DOC
from rsciio.utils.array import get_numpy_kwargs
from rsciio.utils.tools import dummy_context_manager
rgba8 = np.dtype({"names": ["R", "G", "B", "A"], "formats": ["u1", "u1", "u1", "u1"]})
rgb8 = np.dtype({"names": ["R", "G", "B"], "formats": ["u1", "u1", "u1"]})
rgba16 = np.dtype({"names": ["R", "G", "B", "A"], "formats": ["u2", "u2", "u2", "u2"]})
rgb16 = np.dtype({"names": ["R", "G", "B"], "formats": ["u2", "u2", "u2"]})
rgb_dtypes = {"rgb8": rgb8, "rgb16": rgb16, "rgba8": rgba8, "rgba16": rgba16}
def is_rgba(array):
if array.dtype in (rgba8, rgba16):
return True
else:
return False
def is_rgb(array):
if array.dtype in (rgb8, rgb16):
return True
else:
return False
def is_rgbx(array):
if is_rgb(array) or is_rgba(array):
return True
else:
return False
def rgbx2regular_array(data, plot_friendly=False, show_progressbar=True):
"""Transforms a RGBx array into a standard one
Parameters
----------
data : numpy array of RGBx dtype
plot_friendly : bool
If True change the dtype to float when dtype is not uint8 and
normalize the array so that it is ready to be plotted by matplotlib.
%s
"""
# Make sure that the data is contiguous
if isinstance(data, Array):
cm = ProgressBar if show_progressbar else dummy_context_manager
with cm():
data = data.compute()
if data.flags["C_CONTIGUOUS"] is False:
if np.ma.is_masked(data):
data = data.copy(order="C")
else:
data = np.ascontiguousarray(data, **get_numpy_kwargs(data))
if is_rgba(data) is True:
dt = data.dtype.fields["B"][0]
data = data.view((dt, 4))
elif is_rgb(data) is True:
dt = data.dtype.fields["B"][0]
data = data.view((dt, 3))
else:
return data
if plot_friendly is True and data.dtype == np.dtype("uint16"):
data = data.astype("float")
data /= 2**16 - 1
return data
rgbx2regular_array.__doc__ %= (SHOW_PROGRESSBAR_DOC,)
def regular_array2rgbx(data):
# Make sure that the data is contiguous
if data.flags["C_CONTIGUOUS"] is False:
if np.ma.is_masked(data):
data = data.copy(order="C")
else:
data = np.ascontiguousarray(data, **get_numpy_kwargs(data))
if data.shape[-1] == 3:
names = rgb8.names
elif data.shape[-1] == 4:
names = rgba8.names
else:
raise ValueError("The last dimension size of the array must be 3 or 4")
if data.dtype in (np.dtype("u1"), np.dtype("u2")):
formats = [data.dtype] * len(names)
else:
raise ValueError("The data dtype must be uint16 or uint8")
return data.view(np.dtype({"names": names, "formats": formats})).reshape(
data.shape[:-1]
)
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