Can place jupyter notebook files here. Use the jupyter_to_md.sh script to convert the notebooks into Markdown files for the workbook.

bash jupyter_to_md.sh Tutorial1_Image_Processing_Essentials.ipynb
# Will generate Tutorial1_Image_Processing_Essentials.md

Remember to double check the following repos for updates. Notebook files are probably in the tutorials folder, also double check the repo branches.

Running or editing tutorials locally

cd tutorial         # folder containing jupyter notebooks
# optional, start your conda environment which has the jupyter notebook package
jupyter notebook    # Opens a browser window where you can open, edit and run a notebook file

Converting notebooks to python scripts

For example, Tutorial 1 loads python libraries, imports a file, and shows it in a plot. The main change is the fact that we are not showing in a plot but saving to an image. Therefore we can create a python script like the following:

#! /usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt 
#%matplotlib inline                  #<== notice how this needs to be commented out
import imageio
import skimage.color
import skimage.transform
import scipy.ndimage as ndimage

I_camera = np.asarray(imageio.imread('data/cameraman.png'))
plt.figure()                      # open a new figure window
plt.imshow(I_camera, cmap='gray') # visualize the I_camera image with a grayscale colormap
# plt.show()                        # show the plot   #<== comment out
plt.savefig('cameraman_grayscale.png')   #<== Add exported file name

Then this script tutorial1.py can be called from command-line.

python tutorial1.py
ls cameraman_grayscale.png

#> -rw-r--r--@ 1 jenchang  staff   131K Nov 10 11:36 cameraman_grayscale.png

This script should add argument handling (take a different png image), and probably be renamed to grayscale-ify.py, so you can reuse it.