OpenCV Programming with Python
Duration: 5 Days
Course Overview
OpenCV 3 is a native cross-platform library that can be used for computer vision, machine learning, and image processing application development. OpenCV provides high-level APIs that provide access to powerful image processing algorithms and data structures. The code in OpenCV can make use of multicore and GPU processing technologies.
This course is for image processing based application developers who already have a sound basic knowledge of Python programming as well as a basic working knowledge of mathematical geometry and a basic understanding of Signal Processing. The topics included in this course are
- Image manipulation at the pixel level
- Image analysis using histograms
- Image segmentation and feature extraction
- Camera calibration and multiple-view analysis
- Introduction to object detection and recognition and scene monitoring
- Processing of video from files or cameras
- Detection and tracking of moving objects.
- Overview of deep learning, object classification, and neural networks as applied to image processing.
Course Contents
- Setting Up OpenCV - Python
- Introduction to GUI features of OpenCV
- Loading, Displaying and Saving Images
- Playing Videos, Capturing Videos from Camera, Saving Videos
- OpenCV Drawing functions, Using the Mouse as a Paintbrush
- Basic Manipulation of Images
- Reading and Editing of Pixels
- Region of Interest (ROI)
- Mathematical Operations on Images
- OpenCV Image Processing
- Colourspaces
- Geometric Image Transformations
- Image Thresholding
- Image Smoothing
- Morphological Transformations - Erosion, Dilation, Opening ...
- Image Gradients
- Canny Edge Detection
- Image Pyramids and Image Blending
- Contours
- Histograms
- Image Transforms - Fourier, Cosine ...
- Template Matching
- Hough Line and Circle Transforms
- Watershed Algorithm - Image Segmentation
- GrabCut Algorithms - Foreground Extraction
- Feature Detection
- What are features?
- Harris and Shi-Tomasi Corner Detectors
- SIFT - Scale Invariant Feature Transform
- SURF - Speeded Up Robust Features
- FAST Algorithm for Corner Detection
- BRIEF - Binary Robust Independent Elementary Features
- ORB - Oriented FAST and Rotated BRIEF
- Feature Matching and Homography
- Video Analysis
- Meanshift and Camshift
- Optical Flow
- Camera Calibration and 3D Reconstruction
- Camera Calibration
- Pose Reconstruction
- Epipolar Geometry
- Depth Map from Stereo Images
- Overview of Machine Learning and Image Processing
- Overview of Computational Photography
- Case study - Face Detection using Haar Cascades
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