Learning OpenCV 3 Computer Vision with Python - Second Edition

Table of contents

1. Learning OpenCV 3 Computer Vision with Python Second Edition
   1. Table of Contents
   2. Learning OpenCV 3 Computer Vision with Python Second Edition
   3. Credits
   4. About the Authors
   5. About the Reviewers
   6. www.PacktPub.com
      1. Support files, eBooks, discount offers, and more
         1. Why subscribe?
         2. Free access for Packt account holders
   7. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
         1. Downloading the example code
         2. Errata
         3. Piracy
         4. Questions
   8. 1. Setting Up OpenCV
      1. Choosing and using the right setup tools
         1. Installation on Windows
            1. Using binary installers (no support for depth cameras)
            2. Using CMake and compilers
         2. Installing on OS X
            1. Using MacPorts with ready-made packages
            2. Using MacPorts with your own custom packages
            3. Using Homebrew with ready-made packages (no support for depth cameras)
            4. Using Homebrew with your own custom packages
         3. Installation on Ubuntu and its derivatives
            1. Using the Ubuntu repository (no support for depth cameras)
            2. Building OpenCV from a source
         4. Installation on other Unix-like systems
      2. Installing the Contrib modules
      3. Running samples
      4. Finding documentation, help, and updates
      5. Summary
   9. 2. Handling Files, Cameras, and GUIs
      1. Basic I/O scripts
         1. Reading/writing an image file
         2. Converting between an image and raw bytes
         3. Accessing image data with numpy.array
         4. Reading/writing a video file
         5. Capturing camera frames
         6. Displaying images in a window
         7. Displaying camera frames in a window
      2. Project Cameo (face tracking and image manipulation)
      3. Cameo – an object-oriented design
         1. Abstracting a video stream with managers.CaptureManager
         2. Abstracting a window and keyboard with managers.WindowManager
         3. Applying everything with cameo.Cameo
      4. Summary
   10. 3. Processing Images with OpenCV 3
       1. Converting between different color spaces
          1. A quick note on BGR
       2. The Fourier Transform
          1. High pass filter
          2. Low pass filter
       3. Creating modules
       4. Edge detection
       5. Custom kernels – getting convoluted
       6. Modifying the application
       7. Edge detection with Canny
       8. Contour detection
       9. Contours – bounding box, minimum area rectangle, and minimum enclosing circle
       10. Contours – convex contours and the Douglas-Peucker algorithm
       11. Line and circle detection
           1. Line detection
           2. Circle detection
       12. Detecting shapes
       13. Summary
   11. 4. Depth Estimation and Segmentation
       1. Creating modules
       2. Capturing frames from a depth camera
       3. Creating a mask from a disparity map
       4. Masking a copy operation
       5. Depth estimation with a normal camera
       6. Object segmentation using the Watershed and GrabCut algorithms
          1. Example of foreground detection with GrabCut
          2. Image segmentation with the Watershed algorithm
       7. Summary
   12. 5. Detecting and Recognizing Faces
       1. Conceptualizing Haar cascades
       2. Getting Haar cascade data
       3. Using OpenCV to perform face detection
          1. Performing face detection on a still image
          2. Performing face detection on a video
          3. Performing face recognition
             1. Generating the data for face recognition
             2. Recognizing faces
             3. Preparing the training data
             4. Loading the data and recognizing faces
             5. Performing an Eigenfaces recognition
             6. Performing face recognition with Fisherfaces
             7. Performing face recognition with LBPH
             8. Discarding results with confidence score
       4. Summary
   13. 6. Retrieving Images and Searching Using Image Descriptors
       1. Feature detection algorithms
          1. Defining features
             1. Detecting features – corners
          2. Feature extraction and description using DoG and SIFT
             1. Anatomy of a keypoint
          3. Feature extraction and detection using Fast Hessian and SURF
          4. ORB feature detection and feature matching
             1. FAST
             2. BRIEF
             3. Brute-Force matching
          5. Feature matching with ORB
          6. Using K-Nearest Neighbors matching
          7. FLANN-based matching
          8. FLANN matching with homography
          9. A sample application – tattoo forensics
             1. Saving image descriptors to file
             2. Scanning for matches
       2. Summary
   14. 7. Detecting and Recognizing Objects
       1. Object detection and recognition techniques
          1. HOG descriptors
             1. The scale issue
             2. The location issue
                1. Image pyramid
                2. Sliding windows
             3. Non-maximum (or non-maxima) suppression
             4. Support vector machines
          2. People detection
          3. Creating and training an object detector
             1. Bag-of-words
             2. BOW in computer vision
                1. The k-means clustering
       2. Detecting cars
          1. What did we just do?
          2. SVM and sliding windows
             1. Example – car detection in a scene
                1. Examining detector.py
                2. Associating training data with classes
             2. Dude, where's my car?
       3. Summary
   15. 8. Tracking Objects
       1. Detecting moving objects
          1. Basic motion detection
       2. Background subtractors – KNN, MOG2, and GMG
          1. Meanshift and CAMShift
          2. Color histograms
             1. The calcHist function
             2. The calcBackProject function
             3. In summary
          3. Back to the code
       3. CAMShift
       4. The Kalman filter
          1. Predict and update
          2. An example
          3. A real-life example – tracking pedestrians
             1. The application workflow
             2. A brief digression – functional versus object-oriented programming
          4. The Pedestrian class
          5. The main program
             1. Where do we go from here?
       5. Summary
   16. 9. Neural Networks with OpenCV – an Introduction
       1. Artificial neural networks
          1. Neurons and perceptrons
       2. The structure of an ANN
          1. Network layers by example
             1. The input layer
             2. The output layer
             3. The hidden layer
                1. The learning algorithms
       3. ANNs in OpenCV
          1. ANN-imal classification
          2. Training epochs
       4. Handwritten digit recognition with ANNs
          1. MNIST – the handwritten digit database
          2. Customized training data
          3. The initial parameters
             1. The input layer
             2. The hidden layer
             3. The output layer
          4. Training epochs
          5. Other parameters
          6. Mini-libraries
          7. The main file
       5. Possible improvements and potential applications
          1. Improvements
             1. Potential applications
       6. Summary
          1. To boldly go…
   17. Index