"""
PyFibre
Image Segmentation Library
Created by: Frank Longford
Created on: 18/02/2019
Last Modified: 18/02/2019
"""
import logging
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.morphology import binary_dilation
from skimage import measure
from skimage.morphology import remove_small_objects, remove_small_holes
logger = logging.getLogger(__name__)
[docs]def smooth_binary(binary, sigma=None):
"""Smooths binary image based on Guassian filter with
sigma standard deviation"""
if sigma is not None:
smoothed = gaussian_filter(
binary.astype(float), sigma=sigma
)
# Convert float image back to binary
binary = np.where(smoothed, 1, 0)
return binary
[docs]def region_check(region, min_size=0, min_frac=0,
edges=False, max_x=0, max_y=0):
"""Return whether input region passes minimum area and average
intensity checks"""
check = True
minr, minc, maxr, maxc = region.bbox
if edges:
edge_check = (minr != 0) * (minc != 0)
edge_check *= (maxr != max_x)
edge_check *= (maxc != max_y)
check *= edge_check
check *= region.filled_area >= min_size
if region._intensity_image is not None:
region_filter = (region.image * region.intensity_image)
region_frac = region_filter.sum() / region.filled_area
check *= (region_frac >= min_frac)
return check
[docs]def region_swap(masks, images, min_sizes, min_fracs):
"""Performs a region_check on each region present in masks using images as
intensity image. If check fails, removes region from mask and performs
another region_check using same region with other image as
intensity image. If this check passes, assigns region onto other mask."""
for i, j in [[0, 1], [1, 0]]:
labels = measure.label(masks[i].astype(np.int))
for region_1 in measure.regionprops(
labels, intensity_image=images[i]):
if not region_check(region_1, min_sizes[i], min_fracs[i]):
minr, minc, maxr, maxc = region_1.bbox
indices = np.mgrid[minr:maxr, minc:maxc]
masks[i][np.where(labels == region_1.label)] = False
region_2 = measure.regionprops(
np.array(region_1.image, dtype=int),
intensity_image=images[j][(indices[0], indices[1])])[0]
if region_check(region_2, 0, min_fracs[j]):
masks[j][np.where(labels == region_1.label)] = True
[docs]def mean_binary(binaries, image, iterations=1, min_intensity=0,
area_threshold=0, sigma=None):
"""Compares two binary of image and produces a
filter based on the overlap"""
intensity_map = image * np.mean(binaries, axis=0)
intensity_mask = np.where(intensity_map > min_intensity, True, False)
# Remove small holes and objects from masks
intensity_mask = remove_small_holes(
intensity_mask, area_threshold=area_threshold)
intensity_mask = remove_small_objects(
intensity_mask, min_size=area_threshold)
# Dilate image
binary = binary_dilation(intensity_mask, iterations=iterations)
smooth_binary(binary, sigma)
return binary.astype(int)