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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 7 10:51:48 2022
@author: jma
"""
import numpy as np
from skimage import segmentation, measure, exposure, morphology
import scipy.ndimage as nd
from tqdm import tqdm
import skimage
import colorsys
def fill_holes(label_img, size=10, connectivity=1):
output_image = np.copy(label_img)
props = measure.regionprops(np.squeeze(label_img.astype('int')), cache=False)
for prop in props:
if prop.euler_number < 1:
patch = output_image[prop.slice]
filled = morphology.remove_small_holes(
ar=(patch == prop.label),
area_threshold=size,
connectivity=connectivity)
output_image[prop.slice] = np.where(filled, prop.label, patch)
return output_image
def watershed_post(distmaps, interiors, dist_thre=0.1, interior_thre=0.2):
"""
Parameters
----------
distmaps : float (N, H, W) N is the number of cells
distance transform map of cell/nuclear [0,1].
interiors : float (N, H, W)
interior map of cell/nuclear [0,1].
Returns
-------
label_images : uint (N, H, W)
cell/nuclear instance segmentation.
"""
label_images = []
for maxima, interior in zip(distmaps, interiors):# in interiors[0:num]:
interior = nd.gaussian_filter(interior.astype(np.float32), 2)
# find marker based on distance map
if skimage.__version__ > '0.18.2':
markers = measure.label(morphology.h_maxima(image=maxima, h=dist_thre, footprint=morphology.disk(2)))
else:
markers = measure.label(morphology.h_maxima(image=maxima, h=dist_thre, selem=morphology.disk(2)))
# print('distmap marker num:', np.max(markers), 'interior marker num:', np.max(makers_interior))
label_image = segmentation.watershed(-1 * interior, markers,
mask=interior > interior_thre, # 0.2/0.3
watershed_line=0)
label_image = morphology.remove_small_objects(label_image, min_size=15)
# fill in holes that lie completely within a segmentation label
label_image = fill_holes(label_image, size=15)
# Relabel the label image
label_image, _, _ = segmentation.relabel_sequential(label_image)
label_images.append(label_image)
label_images = np.stack(label_images, axis=0).astype(np.uint)
return label_images
def hsv_to_rgb(arr):
hsv_to_rgb_channels = np.vectorize(colorsys.hsv_to_rgb)
h, s, v = np.rollaxis(arr, axis=-1)
r, g, b = hsv_to_rgb_channels(h, s, v)
rgb = np.stack((r,g,b), axis=-1)
return rgb
def mask_overlay(img, masks):
""" overlay masks on image (set image to grayscale)
Adapted from https://github.com/MouseLand/cellpose/blob/06df602fbe074be02db3d716e280f0990816c726/cellpose/plot.py#L172
Parameters
----------------
img: int or float, 2D or 3D array
img is of size [Ly x Lx (x nchan)]
masks: int, 2D array
masks where 0=NO masks; 1,2,...=mask labels
Returns
----------------
RGB: uint8, 3D array
array of masks overlaid on grayscale image
"""
if img.ndim>2:
img = img.astype(np.float32).mean(axis=-1)
else:
img = img.astype(np.float32)
HSV = np.zeros((img.shape[0], img.shape[1], 3), np.float32)
HSV[:,:,2] = np.clip((img / 255. if img.max() > 1 else img) * 1.5, 0, 1)
hues = np.linspace(0, 1, masks.max()+1)[np.random.permutation(masks.max())]
for n in range(int(masks.max())):
ipix = (masks==n+1).nonzero()
HSV[ipix[0],ipix[1],0] = hues[n]
HSV[ipix[0],ipix[1],1] = 1.0
RGB = (hsv_to_rgb(HSV) * 255).astype(np.uint8)
return RGB
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