OpenCV 3.x with Python By Example : Make the most of OpenCV and Python to build applications for object recognition and augmented reality.
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications...
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Full text (MCPHS users only) |
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| Main Author: | |
| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2018
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| Edition: | 2nd ed. |
| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover; Title Page; Copyright and Credits; Contributors; Packt Upsell; Table of Contents; Preface; Chapter 1: Applying Geometric Transformations to Images; Installing OpenCV-Python; Windows; macOS X; Linux (for Ubuntu); Virtual environments; Troubleshooting; OpenCV documentation; Reading, displaying, and saving images; What just happened?; Loading and saving an image; Changing image format; Image color spaces; Converting color spaces; What just happened?; Splitting image channels; Merging image channels; Image translation; What just happened?; Image rotation; What just happened?; Image scaling.
- What just happened?Affine transformations; What just happened?; Projective transformations; What just happened?; Image warping; Summary; Chapter 2: Detecting Edges and Applying Image Filters; 2D convolution; Blurring; Size of the kernel versus blurriness; Motion blur; Under the hood; Sharpening; Understanding the pattern; Embossing; Edge detection; Erosion and dilation; Afterthought; Creating a vignette filter; What's happening underneath?; How do we move the focus around?; Enhancing the contrast in an image; How do we handle color images?; Summary; Chapter 3: Cartoonizing an Image.
- Accessing the webcamUnder the hood; Extending capture options; Keyboard inputs; Interacting with the application; Mouse inputs; What's happening underneath?; Interacting with a live video stream; How did we do it?; Cartoonizing an image; Deconstructing the code; Summary; Chapter 4: Detecting and Tracking Different Body Parts; Using Haar cascades to detect things; What are integral images?; Detecting and tracking faces; Understanding it better; Fun with faces; Under the hood; Removing the alpha channel from the overlay image; Detecting eyes; Afterthought; Fun with eyes.
- Positioning the sunglassesDetecting ears; Detecting a mouth; It's time for a moustache; Detecting pupils; Deconstructing the code; Summary; Chapter 5: Extracting Features from an Image; Why do we care about keypoints?; What are keypoints?; Detecting the corners; Good features to track; Scale-invariant feature transform (SIFT); Speeded-up robust features (SURF); Features from accelerated segment test (FAST); Binary robust independent elementary features (BRIEF); Oriented FAST and Rotated BRIEF (ORB); Summary; Chapter 6: Seam Carving; Why do we care about seam carving?; How does it work?
- How do we define interesting?How do we compute the seams?; Can we expand an image?; Can we remove an object completely?; How did we do it?; Summary; Chapter 7: Detecting Shapes and Segmenting an Image; Contour analysis and shape matching; Approximating a contour; Identifying a pizza with a slice taken out; How to censor a shape?; What is image segmentation?; How does it work?; Watershed algorithm; Summary; Chapter 8: Object Tracking; Frame differencing; Colorspace based tracking; Building an interactive object tracker; Feature-based tracking; Background subtraction; Summary.
