{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bi 1x: Image processing III\n", "\n", "(c) 2023 Justin Bois. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).\n", "\n", "*This tutorial was generated from a Jupyter notebook, which can be downloaded [here](image_processing_3.ipynb)*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we will step through an image processing **pipeline** for analysis of the movies of beads we take in the single molecule kinetics experiment. Many image processing tasks are constructed as pipelines, a set of well-defined image processing operations done in succession. Deciding on the steps in your pipeline is as important as implementing it.\n", "\n", "In the in-class tutorial, we will discuss options for a pipeline together. As we will see, processing the images present their own unique challenges. The steps in the pipeline can be somewhat complicated, so I have written code to do many of the steps for you. So, in this module, you will focus less on implementation and more on what we are doing to the images. We will rely on the modules I wrote for you to do the analysis.\n", "\n", "As you work up your own data, be sure to read the doc strings of the functions. You might need to tweak parameters for your data sets. Now that we have the module, we can import it and get to work on analyzing our images!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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