import itertools
import numpy as np
import seaborn as sns
from dosma import defaults
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
__all__ = ["downsample_slice", "write_regions"]
[docs]def downsample_slice(img_array, ds_factor, is_mask=False):
"""
Takes in a 3D array and then downsamples in the z-direction by a
user-specified downsampling factor.
Args:
img_array (np.ndarray): 3D numpy array for now (xres x yres x zres)
ds_factor (int): Downsampling factor
is_mask (:obj:`bool`, optional): If ``True``, ``img_array`` is a mask and will be binarized
after downsampling. Defaults to `False`.
Returns:
np.ndarray: 3D numpy array of dimensions (xres x yres x zres//ds_factor)
Examples:
>>> input_image = numpy.random.rand(4,4,4)
>>> input_mask = (a > 0.5) * 1.0
>>> output_image = downsample_slice(input_mask, ds_factor = 2, is_mask = False)
>>> output_mask = downsample_slice(input_mask, ds_factor = 2, is_mask = True)
"""
img_array = np.transpose(img_array, (2, 0, 1))
L = list(img_array)
def grouper(iterable, n):
args = [iter(iterable)] * n
return itertools.zip_longest(fillvalue=0, *args)
final = np.array([sum(x) for x in grouper(L, ds_factor)])
final = np.transpose(final, (1, 2, 0))
# Binarize if it is a mask.
if is_mask is True:
final = (final >= 1) * 1
return final
[docs]def write_regions(file_path, arr, plt_dict=None):
"""Write 2D array to region image where colors correspond to the region.
All finite values should be >= 1.
nan/inf value are ignored - written as white.
Args:
file_path (str): File path to save image.
arr (np.ndarray): The 2D numpy array to convert to region image.
Unique non-zero values correspond to different regions.
Values that are `0` or `np.nan` will be written as white pixels.
plt_dict (:obj:`dict`, optional): Dictionary of values to use when plotting with
``matplotlib.pyplot``. Keys are strings like `xlabel`, `ylabel`, etc.
Use Key `labels` to specify a mapping from unique non-zero values in the array
to names for the legend.
"""
if len(arr.shape) != 2:
raise ValueError("`arr` must be a 2D numpy array")
unique_vals = np.unique(arr.flatten())
if 0 in unique_vals:
raise ValueError("All finite values in `arr` must be >=1")
unique_vals = unique_vals[np.isfinite(unique_vals)]
num_unique_vals = len(unique_vals)
plt_dict_int = {"xlabel": "", "ylabel": "", "title": "", "labels": None}
if plt_dict:
plt_dict_int.update(plt_dict)
plt_dict = plt_dict_int
labels = plt_dict["labels"]
if labels is None:
labels = list(unique_vals)
if len(labels) != num_unique_vals:
raise ValueError(
"len(labels) != num_unique_vals - %d != %d" % (len(labels), num_unique_vals)
)
cpal = sns.color_palette("pastel", num_unique_vals)
arr_c = np.array(arr)
arr_c = np.nan_to_num(arr_c)
arr_c[arr_c > np.max(unique_vals)] = 0
arr_rgb = np.ones([arr_c.shape[0], arr_c.shape[1], 3])
plt.figure()
plt.clf()
custom_lines = []
for i in range(num_unique_vals):
unique_val = unique_vals[i]
i0, i1 = np.where(arr_c == unique_val)
arr_rgb[i0, i1, ...] = np.asarray(cpal[i])
custom_lines.append(
Line2D([], [], color=cpal[i], marker="o", linestyle="None", markersize=5)
)
plt.xlabel(plt_dict["xlabel"])
plt.ylabel(plt_dict["ylabel"])
plt.title(plt_dict["title"])
lgd = plt.legend(
custom_lines,
labels,
loc="upper center",
bbox_to_anchor=(0.5, -defaults.DEFAULT_TEXT_SPACING),
fancybox=True,
shadow=True,
ncol=3,
)
plt.imshow(arr_rgb)
plt.savefig(file_path, bbox_extra_artists=(lgd,), bbox_inches="tight")