bnsreenu/python_for_microscopists: A Notebook Companion to the DigitalSreeni Tutorials

What the repository is, and the problem it solves

The README describes this as the supporting code for the tutorials on the DigitalSreeni YouTube channel, and states that the channel walks through learning to code in Python from basics to advanced machine learning and deep learning, with primary emphasis on image processing. That framing explains the shape of the repository. It is not a library you install; it is a numbered sequence of scripts and notebooks that map onto videos. A reader who has watched an episode can open the matching file and see the same operations in runnable form.

The problem it solves is therefore pedagogical and practical. Microscopy image analysis sits between two communities that rarely share vocabulary: biologists who need measurements from images, and programmers who know the tooling. The repository bridges that gap by keeping everything in Python and Jupyter Notebook, using the libraries a working analyst would already have. The top-level entries show the progression: reading images in Python, then Pillow, SciPy, scikit-image and OpenCV, then segmentation, then grain size analysis, then classical machine learning with scikit-learn, then deep learning with Keras. The audience is the person who has a folder of micrographs and needs to count nuclei, measure grains, or train a segmentation model, and who learns by following along rather than by reading API docs.

How the numbered scripts and notebooks are organised

The mechanism is a flat, number-prefixed file layout. Entries such as 017-Reading_Images_in_Python.py, 021-scratch_assay_using_scikit_image.py and 033-grain_size_analysis_using_wateshed_segmentation.py sit beside later deep learning work like 074-Defining U-net in Python using Keras.py and 091_intro_to_transfer_learning_VGG16.py. The number is the index into the video series, not a version. There is no package directory, no setup.py, no pyproject.toml and no __init__.py in the listing, which means nothing here is importable as a module. Each file is meant to be read and run on its own.

Several entries come in pairs or families, and the naming carries the intent. 033, 034a and 034b all perform grain size analysis using watershed segmentation; the a and b suffixes indicate a single-file version and a multi-file version with functions factored out. 062-066-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest.py and 067-ML_06_05_PREDICT_ML_segmentation_All_filters_RForest.py split training and prediction into separate scripts, which is how you would actually run a filter-bank plus random forest pipeline on a dataset larger than memory. The same split appears in 069a-Train_BOVW_V1.0.py and 069b-Validate_BOVW_V1.0.py for bag-of-visual-words. Reading the pairs in order is the intended path.

The data flow in the classical machine learning scripts is consistent: load an image, compute a feature representation (Gabor filter responses, or a bag of visual words), train a pixel or patch classifier, then apply it to new images. 057-ML_06_02_what are features.py and 058-ML_06_03_what is gabor filter.py are the conceptual setup for 061-Gabor_Filter_Banks.py and the two random forest scripts. That is a genuine pipeline, not a set of disconnected demos, and it is the part of the repository most likely to be useful outside a classroom.

Running your first script: setup and a real example

There is no install procedure in the README. The repository says nothing about a package, a requirements file, or a supported Python version, so the setup is whatever your environment already provides. The practical route is to clone the repository and open the file that matches the tutorial you are following.

git clone https://github.com/bnsreenu/python_for_microscopists.git
cd python_for_microscopists
jupyter notebook

The Jupyter server starts and lists the repository contents in the browser. Open 017-Reading_Images_in_Python.py and run the cells; the script demonstrates reading an image into an array, which is the base operation every later tutorial assumes. Because the file extension is .py but the content is notebook-style, Jupyter will open it as a notebook if jupytext or a similar tool is configured, and otherwise you can copy the cells into a new notebook manually.

For a more substantive first run, 033-grain_size_analysis_using_wateshed_segmentation.py is the classic workflow: threshold the image, separate touching grains with a distance transform and watershed, then measure each labelled region. The script writes results to CSV, and 032-reading_and_writing_csv.py covers the pandas side if you need to inspect the output. Expect to edit the image path at the top of the script before it runs, since the sample paths point at the author's local files. That is the single most common first error with this repository.

Where the repository stops being the right tool

The most important limitation is that nothing here is maintained as software. The last push was on 2026-04-21, so the code is recent, but there are no releases and no dependency pinning in the README. A script written against an older scikit-image or Keras API may fail on a current install, and there is no changelog to tell you which tutorial has drifted. If you need a component you can depend on, this is the wrong source.

The second limitation is scale. The scripts are written for teaching, which means they favour clarity over memory efficiency. Loading a full image stack into a NumPy array, computing Gabor responses for every pixel, and training a random forest on the result works on the tutorial datasets and becomes painful on a whole-slide image. The train and predict scripts are separated precisely because that is the point at which the workflow stops fitting in one process, but even the predict script assumes the feature extraction step is tractable in memory.

The third limitation is reproducibility. The README gives citation formats for the videos and for the repository, which is a sign the author expects academic use, but it does not document dataset provenance, random seeds, or expected output values. If you need to reproduce a published number, you cannot do it from this repository alone. And if you need a supported, versioned, tested image analysis library, the correct move is to use one, not to vendor these scripts.

How it differs from pyclesperanto and other microscopy libraries

The related searches point at pyclesperanto_prototype, and the comparison is instructive. pyclesperanto is a GPU-accelerated library for image processing aimed at microscopy, with a defined function surface and a versioned release. This repository is the opposite kind of artifact: a teaching corpus with no function surface at all. You cannot call python_for_microscopists from another program. You can read a script, understand the operation, and then either adapt the code or call the equivalent function in a library that does ship as a package.

That difference matters when the task is production analysis of thousands of images. A library gives you a stable API, documented behaviour, and a bug tracker. A tutorial repository gives you a worked example and the reasoning behind it, which is often more valuable while you are deciding what pipeline to build, and much less valuable once the pipeline exists. The sensible pattern is to learn the operation here, then implement it against a supported library.

There is also a difference in scope. This repository covers the full arc from reading a single image through classical segmentation, classical machine learning, and Keras-based deep learning, including U-Net and autoencoders. A focused microscopy library will cover the segmentation and measurement stages in more depth and will not attempt to teach you pandas, seaborn, or the difference between linear and logistic regression. The breadth is the point here, and it is also why no single file is a finished tool.

Licence, citation and the cost of upgrading

The repository is MIT licensed, which permits reuse, modification and redistribution provided the copyright notice and permission notice are retained. That is permissive enough for commercial use, but the licence covers the repository as distributed; it says nothing about the third-party libraries the scripts import, and those carry their own terms. If you copy a script into a product, you are responsible for the licences of scikit-image, OpenCV, Keras and the rest, not just this one. None of this is legal advice; check the terms of each dependency you actually ship.

The README provides citation formats for both the videos and the repository, with a worked example in APA style. If you use the code in academic work, cite the repository or the corresponding video. That is the author's stated expectation.

Upgrade cost is the part the repository does not address. There is no dependency manifest, so there is no upgrade path to reason about. In practice, upgrading means opening the script you care about, running it, and fixing whatever breaks against your installed library versions. For a tutorial you are following once, that is acceptable. For anything you intend to run repeatedly, the absence of a pinned environment is a real cost you should price in before committing.

Editorial conclusion

Adopt it as a study corpus if you are learning Python image processing or want a working reference for watershed segmentation, Gabor filter banks, U-Net training or autoencoder denoising, and read the file whose number matches the video you are watching. Do not adopt it as a dependency: there is no package, no release, and no API. Before building on any single script, check whether it uses a pinned or unpinned import, whether the sample data path is hardcoded, and whether the licence header is present in the file you plan to copy.

Frequently asked questions

Official sources

1. bnsreenu/python_for_microscopists on GitHub
2. Issues
3. License: MIT
4. README