Learning OpenCV 3 computer vision with Python unleash the power of computer vision with Python using OpenCV
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing
2015
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| Series: | Community experience distilled
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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)
- 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
- Detecting features
- corners
- 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
- 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