Image processing

Note: Original analysis run using jupyter notebooks, transcribed for website presentation.

import czifile
from skimage import io
import numpy as np
import matplotlib.pyplot as plt
import napari
from glob import glob
from os import path
import scipy
import pandas as pd

#function for reading czi files
def read_czi(file_path):
    img = czifile.imread(file_path)
    img = img[0,0,:,0,0,:,:,0] # remove extra dimensions
    return (img)

img_files = sorted(glob("../data/*.czi"))
file_names = [path.basename(a) for a in img_files]

print(file_names)

#generate sample matrix with file name and condition

condition = ["IWR", "IWR", "IWR", "PBS", "PBS", "PBS"]

sample_matrix = pd.DataFrame({'file':file_names, 'condition': condition})
sample_matrix.to_csv("sample_matrix.csv")

#view the sample matrix
sample_matrix

# This block loads each image, checks if an labels annotation file already exists and create one if not.
# A napari window is then loaded allowing manual labelling of the beads in each each image.
# Finally, the code chunk below is run to save the annotation file.

# file index below corresponds to the index in the sample matrix above
file_idx = 0
labels_file = 'labels_' + str(file_idx) + ".tif"

print(img_files[file_idx])
img = read_czi(img_files[file_idx])
print(img.shape)

#if file exists load, else generate ...
if path.exists(labels_file):
    print('file already exists')
    labels = io.imread(labels_file)
else:
    labels = np.zeros_like(img[0,:,:])

print(labels.shape)

viewer = napari.view_image(img, channel_axis=0)

labels_layer = viewer.add_labels(labels, name="segmentation")

napari.run()

# Code to save labels - run this when annotation complete

print (labels_file)

preserve = True

if path.exists(labels_file) and preserve:
    print('file already exists')
else:
    print('creating new labels file')
    io.imsave(labels_file, labels)

Session information

sessionInfo()