#### Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
NumPy Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Winding Along with IPython
Get to Grips with Commonly Used Functions
Connecting NumPy with the Rest of the World
Audio and Image Processing
Special Arrays and Universal Functions
Profiling and Debugging
Quality Assurance
Speed Up Code with Cython
Index

## Detecting edges

Edge detection is another popular image processing technique (http://en.wikipedia.org/wiki/Edge_detection ). scikits-image has a Canny filter implementation, based on the standard deviation of the Gaussian distribution, which can perform edge detection out of the box. In addition to the image data as a 2D array, this filter accepts the following parameters:

• Standard deviation of the Gaussian distribution

• Lower bound threshold

• Upper bound threshold

### How to do it...

We will use the same image as in the previous recipe. The code is almost the same. You should pay extra attention to the one line where we call the Canny filter function:

```from sklearn.datasets import load_sample_images
from matplotlib.pyplot import imshow, show, axis
import numpy
import skimage.filter