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Hands-On ML Projects with OpenCV : Master Computer Vision and Machine Learning Using OpenCV and Python.
Title:
Hands-On ML Projects with OpenCV : Master Computer Vision and Machine Learning Using OpenCV and Python.
Author:
S., Mugesh.
ISBN:
9789388590877
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (355 pages)
Contents:
Intro -- 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.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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