Source code for brainglobe_atlasapi.atlas_generation.annotation_utils
"""Helper functions to read annotation metadata from common formats."""
from pathlib import Path
import numpy as np
from scipy.ndimage import generic_filter
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def split_label_text(name: str, acronym_length=1) -> str:
"""Split label text into name + acronym.
If the label text ends with ')', extract the acronym inside parentheses.
Otherwise, you can specify how many first letters
you would like to use as acronym
"""
if name.endswith(")"):
name, acronym = name.split("(")
name = name[:-1] # ignore trailing space
acronym = acronym[:-1] # ignore trailing )
else:
if acronym_length > len(name):
raise ValueError(
"Acronym length cannot be longer than the name itself."
)
else:
acronym = name[:acronym_length]
return name, acronym
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def read_itk_labels(path: Path, acronym_length=1) -> dict:
"""Turn ITK label data from a file into a list of dictionaries."""
labels = []
with open(path) as labels_file:
for line in labels_file:
if not line.startswith("#"):
raw_values = line.split(maxsplit=7)
id = int(raw_values[0])
rgb = list((int(r) for r in raw_values[1:4]))
if raw_values[7][-1] == "\n":
raw_values[7] = raw_values[7][:-1]
label_text = raw_values[7][1:-1]
if label_text != "Clear Label":
name, acronym = split_label_text(
label_text, acronym_length
)
labels.append(
{
"id": id,
"name": name,
"rgb_triplet": rgb,
"acronym": acronym,
}
)
return labels
ITK_SNAP_HEADER = """################################################
# ITK-SnAP Label Description File
# File format:
# IDX -R- -G- -B- -A-- VIS MSH LABEL
# Fields:
# IDX: Zero-based index
# -R-: Red color component (0..255)
# -G-: Green color component (0..255)
# -B-: Blue color component (0..255)
# -A-: Label transparency (0.00 .. 1.00)
# VIS: Label visibility (0 or 1)
# IDX: Label mesh visibility (0 or 1)
# LABEL: Label description
################################################
"""
ITK_CLEAR_LABEL = '0 0 0 0 0 0 0 "Clear Label"\n'
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def write_itk_labels(path: Path, labels):
"""Write ITK label data to a file."""
with open(path, "w") as labels_file:
labels_file.write(ITK_SNAP_HEADER)
labels_file.write(ITK_CLEAR_LABEL)
for label in labels:
labels_file.write(
f"{label['id']} "
f"{label['rgb_triplet'][0]} "
f"{label['rgb_triplet'][1]} "
f"{label['rgb_triplet'][2]} 1.00 1 1 "
f'"{label["name"] + " (" + label["acronym"] + ")"}"\n'
)
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def modal_filter_ignore_zeros(window):
"""
Compute the mode of the window, ignoring zero values.
Parameters
----------
window : numpy.ndarray
The input window of values.
Returns
-------
int or float
The most common non-zero value in the window, or 0 if all values
are zero.
"""
# Remove zeros from the window
non_zero_values = window[window != 0]
if len(non_zero_values) == 0:
return 0 # If all values are zero, return 0
# Compute the mode (most common value)
values, counts = np.unique(non_zero_values, return_counts=True)
return values[np.argmax(counts)]
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def apply_modal_filter(image, filter_size=3):
"""Apply a modal filter to the image, ignoring zero neighbors.
Parameters
----------
image : numpy.ndarray
Input image as a 2D NumPy array.
filter_size : int
Size of the filtering window (must be odd).
Returns
-------
numpy.ndarray
Filtered image.
"""
# Apply the modal filter using a sliding window
filtered_image = generic_filter(
image, function=modal_filter_ignore_zeros, size=filter_size
)
return filtered_image
