
OpenCV: Computer Vision Projects with Python
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Persons
Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world.Beyeler Michael :
Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.Howse Joseph :
Joseph Howse lives in a Canadian fishing village, where he chats with his cats, crafts his books, and nurtures an orchard of hardy fruit trees. He is President of Nummist Media Corporation, which exists to support his books and to provide mentoring and consulting services, with a specialty in computer vision. On average, in 2015-2022, Joseph has written 1.4 new books or new editions per year for Packt. He also writes fiction, including an upcoming novel about the lives of a group of young people in the last days of the Soviet Union.
Content
- Cover
- Copyright
- Credits
- Table of Contents
- Module 1: OpenCV Computer Vision with Python
- Chapter 1: Setting up OpenCV
- Choosing and using the right setup tools
- Running samples
- Finding documentation, help, and updates
- Summary
- Chapter 2: Handling Files, Cameras, and GUIs
- Basic I/O scripts
- Project concept
- An object-oriented design
- Summary
- Chapter 3: Filtering Images
- Creating modules
- Channel mixing - seeing in Technicolor
- Curves - bending color space
- Highlighting edges
- Custom kernels - getting convoluted
- Modifying the application
- Summary
- Chapter 4: Tracking Faces with Haar Cascades
- Conceptualizing Haar cascades
- Getting Haar cascade data
- Creating modules
- Defining a face as a hierarchy of rectangles
- Tracing, cutting, and pasting rectangles
- Adding more utility functions
- Tracking faces
- Modifying the application
- Summary
- Chapter 5: Detecting Foreground/Background Regions and Depth
- Creating modules
- Capturing frames from a depth camera
- Creating a mask from a disparity map
- Masking a copy operation
- Modifying the application
- Summary
- Appendix A: Integrating with Pygame
- Installing Pygame
- Documentation and tutorials
- Subclassing managers.WindowManager
- Modifying the application
- Further uses of Pygame
- Summary
- Appendix B: Generating Haar Cascades for Custom Targets
- Gathering positive and negative training images
- Finding the training executables
- Creating the training sets and cascade
- Testing and improving &cascade&
- Summary
- Module 2: OpenCV with Python By Example
- Chapter 1: Detecting Edges and Applying Image Filters
- 2D convolution
- Blurring
- Edge detection
- Motion blur
- Sharpening
- Embossing
- Erosion and dilation
- Creating a vignette filter
- Enhancing the contrast in an image
- Summary
- Chapter 2: Cartoonizing an Image
- Accessing the webcam
- Keyboard inputs
- Mouse inputs
- Interacting with a live video stream
- Cartoonizing an image
- Summary
- Chapter 3: Detecting and Tracking Different Body Parts
- Using Haar cascades to detect things
- What are integral images?
- Detecting and tracking faces
- Fun with faces
- Detecting eyes
- Fun with eyes
- Detecting ears
- Detecting a mouth
- It's time for a moustache
- Detecting a nose
- Detecting pupils
- Summary
- Chapter 4: 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 5: Creating a Panoramic Image
- Matching keypoint descriptors
- Creating the panoramic image
- What if the images are at an angle to each other?
- 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?
- Summary
- Chapter 7: Detecting Shapes and Segmenting an Image
- Contour analysis and shape matching
- Approximating a contour
- Identifying the pizza with the slice taken out
- How to censor a shape?
- What is image segmentation?
- Watershed algorithm
- Summary
- Chapter 8: Object Tracking
- Frame differencing
- Colorspace based tracking
- Building an interactive object tracker
- Feature based tracking
- Background subtraction
- Summary
- Chapter 9: Object Recognition
- Object detection versus object recognition
- What is a dense feature detector?
- What is a visual dictionary?
- What is supervised and unsupervised learning?
- What are Support Vector Machines?
- How do we actually implement this?
- Summary
- Chapter 10: Stereo Vision and 3D Reconstruction
- What is stereo correspondence?
- What is epipolar geometry?
- Building the 3D map
- Summary
- Chapter 11: Augmented Reality
- What is the premise of augmented reality?
- What does an augmented reality system look like?
- Geometric transformations for augmented reality
- What is pose estimation?
- How to track planar objects?
- How to augment our reality?
- Let's add some movements
- Summary
- Module 3: OpenCV with Python Blueprints
- Chapter 1: Fun with Filters
- Planning the app
- Creating a black-and-white pencil sketch
- Generating a warming/cooling filter
- Cartoonizing an image
- Putting it all together
- Summary
- Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
- Planning the app
- Setting up the app
- Tracking hand gestures in real time
- Hand region segmentation
- Hand shape analysis
- Hand gesture recognition
- Summary
- Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
- Tasks performed by the app
- Planning the app
- Setting up the app
- The process flow
- Feature extraction
- Feature matching
- Feature tracking
- Seeing the algorithm in action
- Summary
- Chapter 4: 3D Scene Reconstruction Using Structure from Motion
- Planning the app
- Camera calibration
- Setting up the app
- Estimating the camera motion from a pair of images
- Reconstructing the scene
- 3D point cloud visualization
- Summary
- Chapter 5: Tracking Visually Salient Objects
- Planning the app
- Setting up the app
- Visual saliency
- Mean-shift tracking
- Putting it all together
- Summary
- Chapter 6: Learning to Recognize Traffic Signs
- Planning the app
- Supervised learning
- The GTSRB dataset
- Feature extraction
- Support Vector Machine
- Putting it all together
- Summary
- Chapter 7: Learning to Recognize Emotions on Faces
- Planning the app
- Face detection
- Facial expression recognition
- Putting it all together
- Bibliography
- _GoBack
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This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.