NumPy is the fundamental building block of scientific computing in Python. It provides a data structure called an ndarray (n-dimensional array) that can efficiently hold and compute on numerical data. The rest of the scientific Python ecosystem is built on this.
Here, I present a brief introduction to NumPy arrays and their capabilities. A full NumPy tutorial would take all day, so this is just getting our feet wet. To delve deeper, have a look at the tentative numpy tutorial and the numpy API documentation
Image analysis is central to a boggling number of scientific endeavors. Google needs it for their self-driving cars and to match satellite imagery and mapping data. Neuroscientists need it to understand the brain. NASA needs it to map asteroids and save the human race. It is, however, a relatively underdeveloped area of scientific computing. In this tutorial I will introduce automated image analysis using the "images as numpy arrays" abstraction, explaining morphology and then running through a real-world example.
If we have time, raise your own analysis problems. They can make an interesting case study for the rest of the class! Even within the scikit-image team, we still surprise each other with the amazing diversity of applications we encounter!
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