Python for Computer Vision Practice Exam

Python for Computer Vision Practice Exam

About Python for Computer Vision Exam

This exam is designed to assess your proficiency in applying Python programming skills to solve real-world problems in computer vision. It evaluates the practical application of various libraries and tools, such as OpenCV, TensorFlow, Keras, and PyTorch, to handle tasks like image processing, object detection, and face recognition. It covers key concepts in machine learning and deep learning, with an emphasis on their implementation in computer vision projects.

Skills Required

• A strong understanding of Python programming is essential, especially in terms of its syntax, libraries, and data structures.
• Familiarity with basic image processing tasks such as filtering, edge detection, and image transformations.
• Hands-on experience with libraries like OpenCV for image manipulation and TensorFlow, PyTorch, or Keras for machine learning and deep learning models.
• Understanding of core machine learning concepts, particularly in relation to image classification, object detection, and segmentation.
• Ability to evaluate and optimize models based on accuracy, precision, recall, and other performance metrics.
• Skills in preparing and cleaning data for training, including image augmentation and normalization techniques.

Who should take the Exam?

This exam is ideal for professionals and enthusiasts in the field of artificial intelligence, computer vision, and machine learning who want to showcase their ability to use Python for solving vision-related tasks. It is suitable for:

• Software Developers
• Data Scientists
• Machine Learning Engineers
• Students and Researchers
• AI Enthusiasts

Course Outline

The Python for Computer Vision Exam covers the following topics - 

Domain 1 - Course Overview and Instructor Introduction

• Course Introduction
• Instructor Introduction
• Overview of AI Sciences
• Applications of Computer Vision

Domain 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

Domain 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

Domain 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

Domain 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

Domain 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

Domain 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

Domain 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

Domain 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

Domain 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

Domain 11 - Corner Detection

• Understanding Corners in Image Analysis
• Methods for Measuring Corners
• Implementing Moravec Corner Detector
• Harris Corner Detection and Optimization Techniques

Domain 12 - Automatic Panorama with SIFT

• Introduction to Point Correspondences in SIFT
• SIFT-based Scale and Orientation Alignment
• Matching Points in SIFT and HOG

Domain 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

Domain 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

Domain 15 - Motion Analysis

• Optical Flow Theory and Computation

Domain 16 - Object Tracking

• Tracking by Detection and Motion Models
• KLT and TLD for Single and Multiple Object Tracking

Domain 17 - 3D Reconstruction

• Introduction to 3D Reconstruction Concepts
• Motion Capture and Camera Calibration for 3D Imaging

Domain 18 - Smart CCTV Project

• Project Overview and Data Introduction
• Implementing Change Detection in Video Files
• Tracking and Saving Segments with Object Detection