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Index
Cover Table of Contents OpenCV: Computer Vision Projects with Python OpenCV: Computer Vision Projects with Python OpenCV: Computer Vision Projects with Python Credits Preface What you need for this learning path Who this learning path is for Reader feedback Customer support 1. Module 1 1. Setting up OpenCV Running samples Finding documentation, help, and updates Summary 2. Handling Files, Cameras, and GUIs Project concept An object-oriented design Summary 3. Filtering Images Channel mixing – seeing in Technicolor Curves – bending color space Highlighting edges Custom kernels – getting convoluted Modifying the application Summary 4. Tracking Faces with 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 5. Detecting Foreground/Background Regions and Depth Capturing frames from a depth camera Creating a mask from a disparity map Masking a copy operation Modifying the application Summary A. Integrating with Pygame Documentation and tutorials Subclassing managers.WindowManager Modifying the application Further uses of Pygame Summary B. Generating Haar Cascades for Custom Targets Finding the training executables Creating the training sets and cascade Testing and improving <cascade> Summary 2. Module 2 1. Detecting Edges and Applying Image Filters Blurring Edge detection Motion blur Sharpening Embossing Erosion and dilation Creating a vignette filter Enhancing the contrast in an image Summary 2. Cartoonizing an Image Keyboard inputs Mouse inputs Interacting with a live video stream Cartoonizing an image Summary 3. Detecting and Tracking Different Body Parts 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 4. Extracting Features from an Image 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 5. Creating a Panoramic Image Creating the panoramic image What if the images are at an angle to each other? Summary 6. 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 7. Detecting Shapes and Segmenting an Image Approximating a contour Identifying the pizza with the slice taken out How to censor a shape? What is image segmentation? Watershed algorithm Summary 8. Object Tracking Colorspace based tracking Building an interactive object tracker Feature based tracking Background subtraction Summary 9. 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 10. Stereo Vision and 3D Reconstruction What is epipolar geometry? Building the 3D map Summary 11. 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 3. Module 3 1. Fun with Filters Creating a black-and-white pencil sketch Generating a warming/cooling filter Cartoonizing an image Putting it all together Summary 2. Hand Gesture Recognition Using a Kinect Depth Sensor Setting up the app Tracking hand gestures in real time Hand region segmentation Hand shape analysis Hand gesture recognition Summary 3. Finding Objects via Feature Matching and Perspective Transforms Planning the app Setting up the app The process flow Feature extraction Feature matching Feature tracking Seeing the algorithm in action Summary 4. 3D Scene Reconstruction Using Structure from Motion Camera calibration Setting up the app Estimating the camera motion from a pair of images Reconstructing the scene 3D point cloud visualization Summary 5. Tracking Visually Salient Objects Setting up the app Visual saliency Mean-shift tracking Putting it all together Summary 6. Learning to Recognize Traffic Signs Supervised learning The GTSRB dataset Feature extraction Support Vector Machine Putting it all together Summary 7. Learning to Recognize Emotions on Faces Face detection Facial expression recognition Putting it all together Summary A. Bibliography Index
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