By Kurt Demaagd
Learn find out how to construct your personal machine imaginative and prescient (CV) functions speedy and simply with SimpleCV, an open resource framework written in Python. via examples of real-world functions, this hands-on advisor introduces you to uncomplicated CV strategies for gathering, processing, and studying streaming electronic pictures. You’ll then easy methods to practice those equipment with SimpleCV, utilizing pattern Python code. All you must start is a home windows, Mac, or Linux procedure, and a willingness to place CV to paintings in a number of methods. Programming adventure is optional.
- Capture photos from numerous assets, together with webcams, smartphones, and Kinect
- Filter photograph enter so your software tactics in basic terms important info
- Manipulate photos by means of appearing easy mathematics on pixel values
- Use characteristic detection ideas to target fascinating components of a picture
- Work with numerous positive aspects in one photograph, utilizing the NumPy and SciPy Python libraries
- Learn approximately optical stream to spot items that modify among snapshot frames
- Use SimpleCV’s command line and code editor to run examples and attempt options
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Additional info for Practical Computer Vision with SimpleCV: The Simple Way to Make Technology See
When the program terminates, it will naturally clean up its windows. To control the closing of a window based on the user interaction with the window, use the Display object’s isDone() function. 1)” is indented after the while statement? That indentation matters. Python groups statements together into a block of code based on the indentation. 1)” statement as the body of the while loop. The print statement in the example above outputs to the command prompt, and not the image. The drawText function is used for writing text on the image.
PUCE is a built-in color— it’s a shade of red—simply type help Color at the SimpleCV shell prompt, and it will list all available colors. Many functions include a color parameter, and color is an important tool for segmenting images. It would be worthwhile to take a moment and review the predefined color codes provided by the SimpleCV framework. Images With these preliminaries covered, it is now time to dive into working with images themselves. This section covers how color pixels are assembled into images and how to work with those images inside the SimpleCV framework.
Objects farther away from the camera will have less of a problem with this. Figure 3-5. Example result of using depth for segmentation. From L–R: The 2D image Center; the depth information; the final result This is the first of several tricks for extracting important features from an image. It demonstrates how the depth information can be used to reduce the image to only its key components. As the book progresses, we cover these concepts in greater detail, and introduce a variety of different tips and tricks.