GitHub

1. Installation

Create the conda environment by typing:

conda env create -f binder/environment.yml

To use JupyterLab, install extensions by executing:

chmod u+x installation/install_jlab_extensions.sh
./installation/install_jlab_extensions.sh

Whenever you want to run a course notebook, first activate the environment:

conda activate improc_env

or if that doesn't work try:

source activate improc_env

Then start a Jupyter instance and move to the course folder:

jupyter notebook

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
Exercises		Exercises
binder		binder
illustrations		illustrations
installation		installation
01-Introduction.ipynb		01-Introduction.ipynb
02-Numpy_images.ipynb		02-Numpy_images.ipynb
03-Image_import.ipynb		03-Image_import.ipynb
04-Filtering_thresholding.ipynb		04-Filtering_thresholding.ipynb
05-Binary_operations.ipynb		05-Binary_operations.ipynb
06-Application_satellite_image.ipynb		06-Application_satellite_image.ipynb
07-Functions.ipynb		07-Functions.ipynb
08-Pattern_matching.ipynb		08-Pattern_matching.ipynb
09-Watershed.ipynb		09-Watershed.ipynb
10-3D_case.ipynb		10-3D_case.ipynb
11-Complete_analysis.ipynb		11-Complete_analysis.ipynb
12-Registration.ipynb		12-Registration.ipynb
13-Pixel_classification.ipynb		13-Pixel_classification.ipynb
14-OCR.ipynb		14-OCR.ipynb
15-DeepLearning.ipynb		15-DeepLearning.ipynb
16-Image_classification.ipynb		16-Image_classification.ipynb
17-Semantic_segmentation.ipynb		17-Semantic_segmentation.ipynb
18-Application_DICOM.ipynb		18-Application_DICOM.ipynb
README.md		README.md
course_functions.py		course_functions.py
deeplearning.py		deeplearning.py
move_repos.ipynb		move_repos.ipynb