OpenCV 4 with Python Blueprints demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. and allow you to understand the foundations and approaches used in modern computer vision.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Chapter 1: Fun with Filters
  • Getting started
  • Planning the app
  • Creating a black-and-white pencil sketch
    • Understanding approaches for using dodging and burning techniques
    • Implementing a Gaussian blur with two-dimensional convolution
    • Applying pencil sketch transformation
      • Using an optimized version of a Gaussian blur
  • Generating a warming and cooling filter
    • Using color manipulation via curve shifting
    • Implementing a curve filter using lookup tables
    • Designing the warming and cooling effect
  • Cartoonizing an image
    • Using a bilateral filter for edge-aware smoothing
    • Detecting and emphasizing prominent edges
    • Combining colors and outlines to produce a cartoon
  • Putting it all together
    • Running the app
    • Mapping the GUI base class
      • Understanding the GUI constructor
        • Learning about a basic GUI layout
        • Handling video streams
    • Drafting a custom filter layout
  • Summary
  • Attributions
• Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
  • Getting started
  • Planning the app
  • Setting up the app
    • Accessing the Kinect 3D sensor
    • Utilizing OpenNI-compatible sensors
    • Running the app and main function routine
  • Tracking hand gestures in real time
  • Understanding hand region segmentation
    • Finding the most prominent depth of the image center region
    • Applying morphological closing for smoothening
    • Finding connected components in a segmentation mask
  • Performing hand shape analysis
    • Determining the contour of the segmented hand region
    • Finding the convex hull of a contour area
    • Finding the convexity defects of a convex hull
  • Performing hand gesture recognition
    • Distinguishing between different causes of convexity defects
    • Classifying hand gestures based on the number of extended fingers
  • Summary
• Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
  • Getting started
  • Listing the tasks performed by the app
  • Planning the app
  • Setting up the app
    • Running the app – the main() function routine
    • Displaying results
  • Understanding the process flow
  • Learning feature extraction
    • Looking at feature detection
    • Detecting features in an image with SURF
    • Obtaining feature descriptors with SURF
  • Understanding feature matching
    • Matching features across images with FLANN
    • Testing the ratio for outlier removal
    • Visualizing feature matches
    • Mapping homography estimation
    • Warping the image
  • Learning feature tracking
    • Understanding early outlier detection and rejection
  • Seeing the algorithm in action
  • Summary
  • Attributions
• Chapter 4: 3D Scene Reconstruction Using Structure from Motion
  • Getting started
  • Planning the app
  • Learning about camera calibration
    • Understanding the pinhole camera model
    • Estimating the intrinsic camera parameters
      • Defining the camera calibration GUI
      • Initializing the algorithm
      • Collecting image and object points
      • Finding the camera matrix
  • Setting up the app
    • Understanding the main routine function
    • Implementing the SceneReconstruction3D class
  • Estimating the camera motion from a pair of images
    • Applying point matching with rich feature descriptors
    • Using point matching with optic flow
    • Finding the camera matrices
    • Applying image rectification
  • Reconstructing the scene
  • Understanding 3D point cloud visualization
  • Learning about structure from motion
  • Summary
• Chapter 5: Using Computational Photography with OpenCV
  • Getting started
  • Planning the app
  • Understanding the 8-bit problem
    • Learning about RAW images
    • Using gamma correction
  • Understanding high-dynamic-range imaging
    • Exploring ways to vary exposure
      • Shutter speed
      • Aperture
      • ISO speed
    • Generating HDR images using multiple exposure images
      • Extracting exposure strength from images
      • Estimating the camera response function
    • Writing an HDR script using OpenCV
    • Displaying HDR images
  • Understanding panorama stitching
    • Writing script arguments and filtering images
    • Figuring out relative positions and the final picture size
      • Finding camera parameters
      • Creating the canvas for the panorama
      • Blending the images together
    • Improving panorama stitching
  • Summary
  • Further reading
  • Attributions
• Chapter 6: Tracking Visually Salient Objects
  • Getting started
  • Understanding visual saliency
  • Planning the app
  • Setting up the app
    • Implementing the main function 
    • Understanding the MultiObjectTracker class
  • Mapping visual saliency
    • Learning about Fourier analysis
    • Understanding the natural scene statistics
    • Generating a saliency map with the spectral residual approach
    • Detecting proto-objects in a scene
  • Understanding mean-shift tracking
    • Automatically tracking all players on a soccer field
  • ​Learning about the OpenCV Tracking API 
  • Putting it all together
  • Summary
  • Dataset attribution
• Chapter 7: Learning to Recognize Traffic Signs
  • Getting started
  • Planning the app
  • Briefing on supervised learning concepts
    • The training procedure
    • The testing procedure
  • Understanding the GTSRB dataset
    • Parsing the dataset
  • Learning about dataset feature extraction
    • Understanding common preprocessing
    • Learning about grayscale features
    • Understanding color spaces
    • Using SURF descriptor
    • Mapping HOG descriptor
  • Learning about SVMs
    • Using SVMs for multiclass classification
    • Training the SVM
    • Testing the SVM
      • Accuracy
      • Confusion matrix
      • Precision
      • Recall
  • Putting it all together
  • Improving results with neural networks
  • Summary
  • Dataset attribution
• Chapter 8: Learning to Recognize Facial Emotions
  • Getting started
  • Planning the app
  • Learning about face detection
    • Learning about Haar-based cascade classifiers
    • Understanding pre-trained cascade classifiers
    • Using a pre-trained cascade classifier
    • Understanding the FaceDetector class
      • Detecting faces in grayscale images
      • Preprocessing detected faces
        • Detecting the eyes
        • Transforming the face
  • Collecting data
    • Assembling a training dataset
      • Running the application
      • Implementing the data collector GUI
        • Augmenting the basic layout
        • Processing the current frame
        • Storing the data
  • Understanding facial emotion recognition
    • Processing the dataset
      • Learning about PCA
    • Understanding MLPs
      • Understanding a perceptron
      • Knowing about deep architectures
    • Crafting an MLP for facial expression recognition
      • Training the MLP
      • Testing the MLP
      • Running the script
  • Putting it all together
  • Summary
  • Further reading
  • Attributions
• Chapter 9: Learning to Classify and Localize Objects
  • Getting started
  • Planning the app
  • Preparing an inference script
  • Preparing the dataset
    • Downloading and parsing the dataset
    • Creating a TensorFlow dataset 
  • Classifying with CNNs
    • Understanding CNNs
    • Learning about transfer learning
    • Preparing the pet type and breed classifier
    • Training and evaluating the classifier
  • Localizing with CNNs
    • Preparing the model
    • Understanding backpropagation
    • Training the model
  • Seeing inference in action
  • Summary
  • Dataset attribution
• Chapter 10: Learning to Detect and Track Objects
  • Getting started
  • Planning the app
  • Preparing the main script 
    • Detecting objects with SSD
    • Using other detectors
    • Understanding object detectors
      • The single-object detector
      • The sliding-window approach
      • Single-pass detectors
      • Learning about Intersection over Union
    • Training SSD- and YOLO-like networks 
    • Tracking detected objects
  • Implementing a Sort tracker
    • Understanding the Kalman filter
    • Using a box tracker with the Kalman filter
      • Converting boundary boxes to observations
      • Implementing a Kalman filter
    • Associating detections with trackers
    • Defining the main class of the tracker
  • Seeing the app in action
  • Summary
• Appendix A: Profiling and Accelerating Your Apps
  • Accelerating with Numba
    • Accelerating with the CPU
    • Understanding Numba, CUDA, and GPU acceleration
• Appendix B: Setting Up a Docker Container
  • Defining a Dockerfile
    • Working with a GPU
• Other Books You May Enjoy
• Index