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seg1.py
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seg1.py
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import numpy as np
from matplotlib import pyplot as plt
import cv2 as cv2
import tifffile
import PIL
from PIL import Image, ImageEnhance, ImageFilter
import time
import imutils
from array2gif import write_gif
import os
from scipy import ndimage as ndi
from scipy.signal import convolve2d as conv2
from skimage import feature, filters, color, data, restoration
from skimage.segmentation import watershed
from skimage.filters import sobel
from skimage.morphology import skeletonize
from skimage.util import invert
import sys
import warnings
warnings.simplefilter("ignore", ResourceWarning)
data_folders = []
for i in os.listdir("./workspace"):
data_folders.append(i)
def segment(data_folder):
data_files = {}
for file in os.listdir("./workspace/" + data_folder):
time_stamp = int(file.split("_")[-1].split(".tif")[0])
data_files[time_stamp] = file
tif0 = Image.open(("./workspace/" + data_folder + "/" + data_files[10]))
channels = 2
nslices = tif0.n_frames / channels
nframes = len(data_files)
slice_layer = int(nslices // 2)
frame_skip = 5
output_set = []
original_set = []
x_mid = int(tif0.size[0]/2)
y_mid = int(tif0.size[1]/2)
frame = 1
# while frame < nframes:
while frame <= nframes:
tif = tifffile.TiffFile(("./workspace/" + data_folder + "/" + data_files[frame]))
nucleus_array = tif.pages[(slice_layer)*channels + 1].asarray()
median = np.median(nucleus_array)
nucleus_array = nucleus_array - median
nucleus_array = nucleus_array/np.max(nucleus_array)*255 # 0-255 scaling
nucleus_array[nucleus_array<1] = 1
zero_array = np.zeros_like(nucleus_array)
membrane_array = tif.pages[(slice_layer)*channels].asarray()
median = np.median(membrane_array)
membrane_array = membrane_array - median
membrane_array = np.floor(membrane_array/np.max(membrane_array)*255) # 0-255 scaling
membrane_array[membrane_array<1] = 1
membrane_array = membrane_array.astype(np.uint8)
# membrane_array = cv2.convertScaleAbs(membrane_array, alpha=3, beta=0)
img_array = membrane_array
original_array = membrane_array + nucleus_array
original_array[original_array > 255] = 255
y_size = img_array.shape[0]
x_size = img_array.shape[1]
denoise_method = 0
if denoise_method == 0:
psf = np.ones((5, 5)) / 25
denoised_membrane = restoration.richardson_lucy(membrane_array, psf, iterations=10, clip=False)
elif denoise_method == 1:
denoised_membrane = cv2.fastNlMeansDenoising(img_array, h=10)
denoised_membrane = denoised_membrane.astype(np.uint8)
# plt.imshow(denoised_membrane, cmap = 'gray')
# Adaptive Thresholding
th2 = cv2.adaptiveThreshold(membrane_array,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
### Thresholded Membranes
membrane_array2 = th2.copy()
membrane_array2[membrane_array <= 10] = 0
membrane_array2[denoised_membrane <= 10] = 0
min_size = 5
membrane2_label, _ = ndi.label(membrane_array2)
membrane2_id, membrane2_counts = np.unique(membrane2_label, return_counts=True)
membrane2_id = membrane2_id[membrane2_counts >= min_size]
for i in range(membrane2_label.shape[0]):
for j in range(membrane2_label.shape[1]):
if membrane2_label[i][j] in membrane2_id and membrane2_label[i][j] != 0:
membrane2_label[i][j] = 255
else:
membrane2_label[i][j] = 0
### Thresholded Labelled Cells
cell_array = th2.copy()
cell_array[membrane_array > 10] = 0
# Labelling & Cleaning (cell)
min_size = 150
cell_array_label, _ = ndi.label(cell_array)
cell_id, counts = np.unique(cell_array_label, return_counts=True)
cell_id = cell_id[counts >= min_size]
for i in range(cell_array_label.shape[0]):
for j in range(cell_array_label.shape[1]):
if cell_array_label[i][j] in cell_id and cell_array_label[i][j] != 0:
cell_array_label[i][j] = 255
else:
cell_array_label[i][j] = 0
cell_array_label = ndi.binary_fill_holes(cell_array_label)
cell_array = cell_array.astype(np.uint8)
cell_array_clean = cell_array_label.astype(np.uint8)
# plt.imshow(cv2.addWeighted(cell_array, 0.5, membrane_array2, 1, 0))
# plt.imshow(cell_array_clean, cmap = 'gray')
### Void detection
void = np.zeros_like(cell_array_clean)
void[cell_array_clean == 0] = 1
void[cell_array_clean > 0] = 0
inverse_mask = np.zeros_like(membrane_array2)
inverse_mask[membrane_array2 <= 10] = 1
inverse_mask[membrane_array2 >= 10] = 0
D = ndi.distance_transform_edt(inverse_mask)
D_void = ndi.distance_transform_edt(void)
D_void[D_void <= 15] = 0 # Retract void to min dist = n away from existing seeds
D_void[D_void > 0] = D[D_void > 0] # Set void dist to membrane distance
D_void[D_void < 4] = 0 # Remove weak bridges between membrane distance
D_void[D_void > 0] = 1
combined_seeds = cell_array_clean + D_void
seedLabel, _ = ndi.label(combined_seeds)
labels = watershed(-D, seedLabel, compactness=2)
labels[labels > 0] = labels[labels > 0] + 50
labels = labels / np.max(labels) * 240
center_label = labels[y_mid][x_mid]
highlight_r = labels.copy().astype(np.uint8)
highlight_r[labels == center_label] = 255
highlight_r[membrane_array2 >= 10] = 255
highlight_gb = labels.copy().astype(np.uint8)
highlight_gb[membrane_array2 >= 10] = 255
# output_b = cv2.addWeighted(labels.astype(np.uint8), 10, membrane_array2.astype(np.uint8), 0.1, 0)
output_rgb = np.array([highlight_r, highlight_gb, highlight_gb])
output_set.append(output_rgb)
membrane_array[membrane_array>10] = 255
nucleus_array[nucleus_array>10] = 255
original_set.append(np.array([membrane_array.astype(np.uint8), nucleus_array.astype(np.uint8), zero_array.astype(np.uint8)]))
if frame % 10 == 0:
print(frame)
frame += frame_skip
file_output_name = data_folder.split("\\")[-1].split("/")[-1]
write_gif(output_set, "./raw/" + file_output_name + "_raw.gif", fps=1)
write_gif(original_set, './original/' + file_output_name + "_original.gif", fps=1)
print("Auto segmentation complete for: " + file_output_name)
fol_num = 0
for data_folder in data_folders:
fol_num += 1
file_output_name = data_folder.split("\\")[-1].split("/")[-1]
print("Job {}/{}: ".format(fol_num, len(data_folders)), file_output_name)
segment(data_folder)