Learning OpenCV 3 computer vision with Python : unleash the power of computer vision with Python using OpenCV /
Unleash the power of computer vision with Python using OpenCV About This Book Create impressive applications with OpenCV and Python Familiarize yourself with advanced machine learning concepts Harness the power of computer vision with this easy-to-follow guide Who This Book Is For Intended for novic...
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| Format: | Electronic eBook |
| Language: | English |
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Birmingham, UK :
Packt Publishing,
[2015]
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| Edition: | Second edition. |
| Series: | Community experience distilled.
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Setting Up OpenCV; Choosing and using the right setup tools; Installation on Windows; Using binary installers (no support for depth cameras); Using CMake and compilers; Installing on OS X; Using MacPorts with ready-made packages; Using MacPorts with your own custom packages; Using Homebrew with ready-made packages (no support for depth cameras); Using Homebrew with your own custom packages; Installation on Ubuntu and its derivatives
- Using the Ubuntu repository (no support for depth cameras)Building OpenCV from a source; Installation on other Unix-like systems; Installing the Contrib modules; Running samples; Finding documentation, help, and updates; Summary; Chapter 2: Handling Files, Cameras, and GUIs; Basic I/O scripts; Reading/writing an image file; Converting between an image and raw bytes; Accessing image data with numpy.array; Reading/writing a video file; Capturing camera frames; Displaying images in a window; Displaying camera frames in a window; Project Cameo (face tracking and image manipulation)
- Cameo
- an object-oriented designAbstracting a video stream with managers.CaptureManager; Abstracting a window and keyboard with managers.WindowManager; Applying everything with cameo.Cameo; Summary; Chapter 3: Processing Images with OpenCV 3; Converting between different color spaces; A quick note on BGR; The Fourier Transform; High pass filter; Low pass filter; Creating modules; Edge detection; Custom kernels
- getting convoluted; Modifying the application; Edge detection with Canny; Contour detection; Contours
- bounding box, minimum area rectangle, and minimum enclosing circle
- Contours
- convex contours and the Douglas-Peucker algorithmLine and circle detection; Line detection; Circle detection; Detecting shapes; Summary; Chapter 4: Depth Estimation and Segmentation; Creating modules; Capturing frames from a depth camera; Creating a mask from a disparity map; Masking a copy operation; Depth estimation with a normal camera; Object segmentation using the Watershed and GrabCut algorithms; Example of foreground detection with GrabCut; Image segmentation with the Watershed algorithm; Summary; Chapter 5: Detecting and Recognizing Faces; Conceptualizing Haar cascades
- Getting Haar cascade dataUsing OpenCV to perform face detection; Performing face detection on a still image; Performing face detection on a video; Performing face recognition; Generating the data for face recognition; Recognizing faces; Preparing the training data; Loading the data and recognizing faces; Performing an Eigenfaces recognition; Performing face recognition with Fisherfaces; Performing face recognition with LBPH; Discarding results with confidence score; Summary; Chapter 6: Retrieving Images and Searching Using Image Descriptors; Feature detection algorithms; Defining features
- Detecting features
- corners
