Python for Computer Vision Online Course
Python for Computer Vision Online Course
The Mastering Computer Vision from the Absolute Beginning Using Python course offers an exceptional opportunity to gain expertise in Computer Vision (CV). It covers core CV concepts, the digital imaging process, and key application areas. The course is designed to be clear, comprehensive, and hands-on, with live coding sessions and up-to-date content. You will engage in coding tasks, quizzes, and assignments after each section to reinforce your understanding. The course culminates in two practical projects: Change Detection in CCTV Cameras (Real-Time) and Smart DVRs (Real-Time), which will enhance your skills and strengthen your portfolio. By the end, you'll be well-equipped to apply CV concepts to real-world projects and advance your career.
Key Benefits
- Bridge the gap between theoretical concepts and real-world applications by understanding how Computer Vision principles can be applied to solve practical problems.
- Gain a comprehensive understanding of both the theoretical foundations and practical implementations of core Computer Vision techniques.
- Develop the skills to create real-time applications that leverage Computer Vision methods, specifically for change detection in live camera feeds, using Python.
Target Audience
This course is designed for data scientists, machine learning professionals, and individuals who are complete beginners with no prior knowledge of Computer Vision. It is also ideal for those who wish to gain hands-on experience in Computer Vision by working with real-world data and implementing these techniques in practical, real-life projects.
Learning Objectives
- Understand the concepts of both colored and black-and-white images, with hands-on practice to reinforce your knowledge.
- Gain a deep understanding of panoramic images, including both the theoretical foundations and practical implementation techniques.
- Learn image filtering techniques and their application through Python implementations.
- Acquire the skills to develop a project from the ground up that requires a strong foundation in Computer Vision principles.
- Master key Computer Vision techniques such as edge detection, shape detection, and corner detection, and apply them to real-world scenarios.
- Build an advanced, intelligent, and efficient DVR system using Python, leveraging the knowledge and skills you have gained throughout the course.
Course Outline
The Python for Computer Vision Exam covers the following topics -
Module 1 - Course Overview and Instructor Introduction
- Course Introduction
- Instructor Introduction
- Overview of AI Sciences
- Applications of Computer Vision
Module 2 - Image Fundamentals
- Introduction to Grayscale Images
- Understanding Grayscale Spectrum
- Manipulating and Saving Grayscale Images with Matplotlib in Python
- Working with Grayscale Images in OpenCV
- Introduction to RGB Images
- RGB Image Handling with Matplotlib and OpenCV
- Theory and Algorithm for RGB to HSV Conversion
- Implementing RGB to HSV Conversion in Python
- Segmenting Red Rose Using HSV in Python
- Hyper-Spectral Images
Module 3 - 2D Scaling Transformations
- Introduction to Geometric Transformations
- Example of Scaling in OpenCV
- Scaling in Physical Space
- Understanding Linear Transformation
- Scaling as a Linear Transformation
- Matrix Multiplication Example for Scaling in Python
- Image Coordinate System
- Image Copying and Vertical Flipping
- Continuous Coordinates in Image Transformation
- Saturation and Holes in Images
- Image Doubling and Hole Handling in Python
- Inverse Scaling and Quiz
- Solution with Nearest Neighbor Interpolation
- Inverse Scaling in Python
- Nearest Neighbor Interpolation Technique
- Weighted vs. Simple Averaging
- Bilinear Interpolation
- Implementing Bilinear Interpolation in Python
- Scaling Transformation with Bilinear Interpolation
- Recap of Scaling Transformation Algorithm
Module 4 - 2D Geometric Transformations
- Introduction to Rotation
- Optional Proof of Rotation as a Linear Transformation
- Challenges with Negative Coordinates in Rotation
- Calculating Width and Height of the Rotated Image
- Index Shifting in Rotation
- Complete Rotation Implementation
- Best Practices for Rotation Implementation
- Introduction to Reflection Transformations
- Implementing Reflection Transformation
- Introduction to Shearing Transformations
- Shearing Transformation Implementation and Quiz
- Challenges of Translation and Nonlinearity
- Homogeneous Coordinates for Transformation Representation
- Matrix Representation of Translation
- Homogeneous Transformation Representations
- Affine Transformation Implementation
- Theory of Rotation About Any Point
- Implementation of Rotation About Any Point
- Reflection About a Line: Theory and Implementation
- Transformation Matrix Properties and Implementation
- Affine Transformation Hierarchy and Optional SVD
- Homography and Projective Transformation
- Implementing Projective Transformations
- Projective Warping Algorithm
Module 5 - Estimation of Geometric Transformations (Panorama Stitching)
- Objective of Panorama Stitching
- Introduction to Affine Transformation Estimation
- Point Correspondences in Affine Transformation
- Marking Correspondence Points in Python
- Minimum Number of Points for Affine Estimation
- Affine Transformation Estimation with Python
- Verification of Affine Estimation with Python
- Estimating Affine Transformations with More Than Three Points
- Implementation of Affine Estimation with More Than Three Points
- Optional Affine Estimation using Least Squares Method
- Introduction to Projective Transformation Estimation
- Projective Transformation Estimation with Bug Fixes
- Addressing Bugs in Projective Transformation Estimation
- Removing Scale Factor in Projective Transformation Estimation
- Direct Linear Transformation (DLT) for Projective Estimation
- DLT Nullspace and Four-Point Requirement for Projective Estimation
- Implementing DLT for Projective Transformation
- Panorama Stitching and Implementation in OpenCV
- Projective Transformation in Panorama Creation
Module 6 - Binary Morphology
- Theory of Binary Images
- Binary Image Processing in Python
- Structuring Element Theory and Sliding Window Concept
- Implementing Structuring Elements in Python
- Theory of Erosion and Its Implementation
- Dilation Process in Morphology
- Opening and Closing Transformations in Morphology
- Morphological Gradient and Its Python Implementation
- Top Hat and Black Hat Operations
Module 7 - Image Filtering
- Basics of Image Blurring
- General Concepts in Image Filtering
- Convolution Techniques for Edge Detection
- Naive Edge Detection Methods
- Image Sharpening Techniques
- Implementation of Blurring, Edge Detection, and Sharpening in Python
- Low Pass, High Pass, and Band Pass Filters
Module 8 - Canny Edge Detection
- Introduction to the Canny Edge Detection Algorithm
- Implementing Canny Edge Detection with OpenCV
- Gaussian Filter and Its Role in Edge Detection
- Gaussian Filter Size and Its Implementation
- Smoothing Images with Gaussian Filter
- Derivative of Gaussian (DoG) for Edge Detection
- Implementation of Derivative of Gaussian Filters
- Gradient Magnitude, Direction, and Non-Maximum Suppression
- Quantization of Gradient Direction
- Implementation of Non-Maxima Suppression (NMS)
- Hysteresis Thresholding and Final Edge Detection
Module 9 - Shape Detection
- Introduction to Shape Detection Techniques
- Why Edge Detection Alone is Insufficient
- Introduction to RANSAC for Line Detection
- Implementing RANSAC for Line Fitting and Consistency Scoring
- RANSAC for Circle Detection and its Application
- RANSAC in Real-World Image Testing
Module 10 - Shape Detection via Hough Transform
- Introduction to Hough Transform
- Hough Transform as a Voting Mechanism
- Polar Representation in Hough Transform and Its Benefits
- Implementation of Hough Transform for Line Detection
- Fast Version of Hough Transform for Circles
Module 11 - Corner Detection
- Understanding Corners in Image Analysis
- Methods for Measuring Corners
- Implementing Moravec Corner Detector
- Harris Corner Detection and Optimization Techniques
Module 12 - Automatic Panorama with SIFT
- Introduction to Point Correspondences in SIFT
- SIFT-based Scale and Orientation Alignment
- Matching Points in SIFT and HOG
Module 13 - Object Detection
- Overview of Object Detection
- Classification Pipeline and Sliding Window Method
- Detection using HOG Features and CNNs
- Implementation of Object Detection and Activity Tracking
Module 14 - YOLO Object Detection
- Introduction to YOLO Algorithm and its Concepts
- YOLO Training Data Generation and Anchor Boxes
- YOLO Implementation for Object Localization and Non-Maxima Suppression
Module 15 - Motion Analysis
- Optical Flow Theory and Computation
Module 16 - Object Tracking
- Tracking by Detection and Motion Models
- KLT and TLD for Single and Multiple Object Tracking
Module 17 - 3D Reconstruction
- Introduction to 3D Reconstruction Concepts
- Motion Capture and Camera Calibration for 3D Imaging
Module 18 - Smart CCTV Project
- Project Overview and Data Introduction
- Implementing Change Detection in Video Files
- Tracking and Saving Segments with Object Detection
