Be at your A game in building Intelligent systems by leveraging Computer vision and Machine Learning.
Key Features
● Step-by-step instructions and code snippets for real world ML projects.
● Covers entire spectrum from basics to advanced concepts such as deep learning, transfer learning, and model optimization
● Loaded with practical tips and best practices for implementing machine learning with OpenCV for optimising your workflow.
Book Description
This book is an in-depth guide that merges machine learning techniques with OpenCV, the most popular computer vision library, using Python. The book introduces fundamental concepts in machine learning and computer vision, progressing to practical implementation with OpenCV. Concepts related to image preprocessing, contour and thresholding techniques, motion detection and tracking are explained in a step-by-step manner using code and output snippets.
Hands-on projects with real-world datasets will offer you an invaluable experience in solving OpenCV challenges with machine learning. It’s an ultimate guide to explore areas like deep learning, transfer learning, and model optimization, empowering readers to tackle complex tasks. Every chapter offers practical tips and tricks to build effective ML models.
By the end, you would have mastered and applied ML concepts confidently to real-world computer vision problems and will be able to develop robust and accurate machine-learning models for diverse applications.
Whether you are new to machine learning or seeking to enhance your computer vision skills, This book is an invaluable resource for mastering the integration of machine learning and computer vision using OpenCV and Python.
What you will learn
● Learn how to work with images and perform basic image processing tasks using OpenCV.
● Implement machine learning techniques to computer vision tasks such as image classification, object detection, and image segmentation.
● Work on real-world projects and datasets to gain hands-on experience in applying machine learning techniques with OpenCV.
● Explore the concepts of deep learning using Tensorflow and Keras and how it can be used for computer vision tasks.
● Understand the concept of transfer learning and how pre-trained models can be leveraged for new tasks.
● Utilize techniques for model optimization and deployment in resource-constrained environments.
Who is this book for?
This book is for everyone with a basic understanding of programming and who wants to apply machine learning in computer vision using OpenCV and Python. Whether you're a student, researcher, or developer, this book will equip you with practical skills for machine learning projects. Some familiarity with Python and machine learning concepts is assumed. Beginners too will find this book valuable as it offers clear examples and explanations for every concept.
Table of Contents
Chapter 1: Getting Started With OpenCV
Chapter 2: Basic Image & Video Analytics in OpenCV
Chapter 3: Image Processing 1 using OpenCV
Chapter 4: Image Processing 2 using OpenCV
Chapter 5: Thresholding and Contour Techniques Using OpenCV
Chapter 6: Detect Corners and Road Lane using OpenCV
Chapter 7: Object And Motion Detection Using Opencv
Chapter 8: Image Segmentation and Detecting Faces Using OpenCV
Chapter 9: Introduction to Deep Learning with OpenCV
Chapter 10: Advance Deep Learning Projects with OpenCV
Chapter 11: Deployment of OpenCV projects
About the Author
This is Mugesh S. I am working as a Data Scientist at Infosys, with a passion for leveraging data-driven insights to tackle complex challenges and drive business success. I am an engineering graduate who completed the PG program in Data Science and Engineering as well as a Master’s in Mathematics and Data Science, to deepen my understanding of the intricacies of data analytics.
Author(s): Mugesh S.;
Publisher: Orange Education PVT Ltd
Year: 2023
Language: English
Pages: 463
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
Technical Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Getting Started With OpenCV
Introduction
Structure
Introduction to Computer Vision
Introduction to OpenCV
Benefits of Learning OpenCV
OpenCV Real-time Applications in Computer Vision
OpenCV Architecture and Explanation
Features of OpenCV Library
Python Code Editors for OpenCV
Downloading and Installing OpenCV for Windows
Downloading and Installing OpenCV for MacOS
Google Colab for OpenCV
Conclusion
Points to Remember
References
Questions/MCQs
2. Basic Image and Video Analytics in OpenCV
Introduction
Structure
Read, Write, and Show Images in OpenCV
Covert Color in Images Using OpenCV
Read, Write, and Show Videos from a Camera in OpenCV
Covert color in Video Using OpenCV
Draw Geometric Ahapes on Images Using OpenCV
Setting Camera Parameters in OpenCV
Show the Date and Time on Videos Using OpenCV
Show Text on Videos Using OpenCV
Basic Mouse Events Using OpenCV
Conclusion
Points to Remember
References
3. Image Processing 1 Using OpenCV
Introduction
Structure
Basic Image Processing Techniques
Image wait function
Image cropping
Image resizing
Image rotation
Grayscaling
Image split
Merging image
Adding two images
Blend two images with different weights
Region of interest (ROI)
Background Removal
Reshaping the Video Frame
Pausing the Video Frame
More Mouse Event Examples
Extract the color of a pixel on the image using the mouse
Extract the X, and Y values and pixel color on the image using the left and right mouse buttons, respectively
Draw the rectangle and curve using the left-click button mouse
Bitwise Operations
Binding a Trackbar
Image Trackbar
Conclusion
Points to Remember
References
4. Image Processing 2 using OpenCV
Introduction
Structure
Matplotlib with OpenCV
Morphological Transformations Using OpenCV
Smoothing and Blurring Images Using OpenCV
Image Gradients Using OpenCV
Image Pyramids with OpenCV
Image Blending Using OpenCV
Edge Detection Using OpenCV
Sobel Operator Using OpenCV
Laplacian of Gaussian (LoG) Filter Using OpenCV
Canny Edge Detection Using OpenCV
Conclusion
Points to Remember
References
5. Thresholding and Contour Techniques Using OpenCV
Introduction
Structure
Image Thresholding using OpenCV
Simple thresholding
Adaptive thresholding
Otsu’s thresholding
Binary thresholding
Inverted thresholding
Finding and Drawing Contours with OpenCV
Detecting Simple Geometric Shapes Using OpenCV
Understanding Image Histograms Using OpenCV
Template Matching Using OpenCV
Hough Line Transform Theory in OpenCV
Standard Hough line transform using OpenCV
Probabilistic Hough Transform Using OpenCV
Circle Detection Using OpenCV Hough Circle Transform
Camera Calibration Using OpenCV
Conclusion
Points to Remember
References
6. Detect Corners and Road Lane Using OpenCV
Introduction
Structure
Road Lane Line Detection Using OpenCV
Detecting Corners in OpenCV
Types of Detect Corners in OpenCV
Harris Corner Detector
Shi Tomasi Corner Detector
FAST corner detection
Blob Detection
Scale-invariant feature transform
Feature Matching with FLANN
Background Subtraction Methods in OpenCV
Types of Background Subtraction Methods in OpenCV
BackgroundSubtractorMOG2
BackgroundSubtractorKNN
Conclusion
Points to Remember
References
7. Object And Motion Detection Using Opencv
Introduction
Structure
HSV Color Space
Object Detection Using HSV Color Space
Object Tracking Using HSV Color Space
Motion Detection and Tracking Using OpenCV
Mean Shift Object Tracking Using OpenCV
Camshift Object Tracking Method Using OpenCV
Augmented Reality in OpenCV
Conclusion
Points to Remember
Questions
References
8. Image Segmentation and Detecting Faces Using OpenCV
Introduction
Structure
Image Segmentation Using OpenCV
Introduction to Haar Cascade Classifiers
Face Detection Using Haar Cascade Classifiers
Eye Detection Haar Feature-based Cascade Classifiers
Smile Detection Haar Feature-based Cascade Classifiers
QR Code Detection Using OpenCV
Optical Character Recognition Using OpenCV
Conclusion
Points to Remember
References
9. Introduction to Deep Learning with OpenCV
Introduction
Structure
Introduction to Machine Learning
Types of machine learning
Introduction to Deep Learning
Artificial Neural Networks
Types of neural networks
Neural network architecture
Activation functions
Neural networks optimization techniques
Steps for training neural networks
Deep learning frameworks
Deep learning applications
Introduction to Deep Learning in OpenCV
Neural networks in the image and video analytics
Image classification with deep neural networks
Object detection with neural networks
Face detection and recognition with neural networks
Semantic segmentation in neural networks
Generative adversarial networks
Integration of OpenCV with Robotics
Iris Dataset in TensorFlow
Fashion-MNIST in TensorFlow
Digit Recognition Training Using TensorFlow
Testing Digit Recognition Model Using OpenCV
Dog Versus Cat Classification in TensorFlow with OpenCV
Dog versus cat classification with OpenCV
Conclusion
Points to Remember
References
10. Advance Deep Learning Projects with OpenCV
Introduction
Structure
Introduction to YOLO
YOLO Versions
YOLO v3 Object Detection Using TensorFlow
YOLO v5 and Custom Dataset Using TensorFlow
Face Recognition Using TensorFlow with OpenCV
FaceNet Architecture
Real-time Age Prediction Using TensorFlow and RESNET 50_CNN
RESNET 50_CNN
Facial Expression Recognition Using TensorFlow
Emotion detection methods
Content-based Image Retrieval Using TensorFlow
Conclusion
Points to Remember
References
11. Deployment of OpenCV Projects
Introduction
Structure
Introduction to Deploying OpenCV Projects
Deploying OpenCV projects in Azure
Deploying OpenCV projects in Azure
Integrating OpenCV with web applications
Integrating dog vs. cat classification project and flask
Conclusion
Points to Remember
References
Index
