Computer Vision for Beginners

AI Publishing

Description

Computer Vision Textbook for Beginners with 3 Hands-On ProjectsAre you ready to crush your Computer Vision career goals?The recent advances in the field of computer vision have simply been astounding. In less than a decade, the rate of accuracy for object identification and classification has risen from 50 percent to 99 percent. Today's systems are, in fact, more accurate than humans at swiftly detecting and responding to visual inputs.The emergence of deep learning and the advent of very large datasets in recent years have led to an increase in the number of computer vision applications. Against this backdrop, it's worthwhile to add computer vision knowledge to your data science arsenal. Now is the perfect time to enter this dynamic field.Computer Vision with Python for Beginners presents you with a hands-on, straightforward approach to learn computer vision fast. The step-by-step format of this book makes learning computer vision simple, fast, and easy. The exercises at the end of each chapter test your knowledge of the concepts you have covered. They also help you apply what you have learned. This book presents you with: A solid foundation in computer vision. Knowledge of elementary and intermediate topics. Basics of coding in Python. Links to additional content related to the topics you study. Access to external files to train and test all the knowledge you have acquired about a computer vision tool. Three mini-projects in the concluding section of the book that help you to bring together all the theoretical concepts you've learned.You begin with Python installation in the first chapter. Then you have a crash course in Python in the second chapter. Jumping straight to Python quickens your learning and makes it simpler to follow along. Throughout this book, the code is written using Jupyter Notebook. Access to the datasets used in this book is easy. In the final section, you work on three hands-on mini-projects: 1. Detecting Hand Symbols for Rock, Paper, Scissors Game2. Covid-19 Detection from X-Rays3. Detecting Weather from ImagesThe scripts, images, and graphs are clear. They are designed to help you understand the visuals to the text description easily. This book is the perfect option for self-study, even if your proficiency is at the level of an intermediate learner. You can tackle new computer vision problems confidently and develop complete solutions at your workplace. Finally, you can count on this learning by doing book to accomplish your computer vision career goals faster.The topics covered include: Introduction to Computer Vision Environment Setup & Writing Your First Program in Python Python Crash Course Basics of Image Processing Basics of Video Processing Face Detection with OpenCV in Python Introduction to Machine Learning for Computer Vision Introduction to Deep Learning for Computer Vision Transfer Learning for Computer Vision Object Detection with YOLO Introduction to GANSHit BUY NOW and begin your Computer Vision learning journey.

Sujets

• Informatique
• Computer vision

Informations

• Publié par: AI Sciences
• Date de parution: 15 août 2021
• Nombre de lectures: 3
• EAN13: 9781956591088
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

© Copyright 2021 by AI Publishing
All rights reserved.
First Printing, 2021
Edited by AI Publishing
Ebook Converted and Cover by Gazler Studio
Published by AI Publishing LLC
ISBN-13: 978-1-7377085-0-6
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Table of Contents
How to Use This Book?
About the Author
Chapter 1: Introduction
1.1. What Is Computer Vision
1.2. Computer Vision Research Areas
1.2.1. Image Classification
1.2.2. Object Identification
1.2.3. Image Clustering
1.2.4. Image Retouching and Modification
1.2.5. Video Classification
1.2.6. Video Summarization
1.2.7. Moving Object Detection
1.3. Applications of Computer Vision
1.3.1. Healthcare Applications
1.3.2. Applications in Agriculture
1.3.3. Applications in Transportation
1.3.4. Applications in Retail Industry
1.3.5. Sports and Media
1.4. Common Python Libraries for Computer Vision
1.4.1. OpenCV
1.4.2. Pillow Library
1.4.3. Matplotlib
1.4.4. Scikit Learn
1.4.5. TensorFlow
1.4.6. Keras
1.4.7. ImageAI
1.5. Installation and Environment Setup
1.5.1. Windows Setup
1.5.2. Mac Setup
1.5.3. Linux Setup
1.5.4. Using Google Colab Cloud Environment
1.6. Writing Your First Program
1.7. Python Syntax
Chapter 2: Python Crash Course
2.1. Python Variables and Data Types
2.3. Python Operators
2.3. Conditional Statements
2.4. Iteration Statements
2.5. Functions
2.6. Objects and Classes
Exercise 2.1
Exercise 2.2
Chapter 3: Basics of Image Processing
3.1. How Do Computers See Images?
3.2. Image Processing with OpenCV
3.2.1. Opening an Image with OpenCV
3.2.2. Saving an Image in a Different Format
3.2.3. Converting an Image to Grayscale
3.2.4. Cropping an Image
3.2.5. Resizing an Image
3.2.6. Applying Filters
3.2.7. Drawing Shapes
3.3. Image Processing with Python Pillow Library
3.3.1. Opening an Image with Pillow
3.3.2. Saving an Image in a Different Format
3.3.3. Changing Image Color
3.3.4. Changing Image Orientation
3.3.5. Cropping an Image
3.3.6. Modifying an Image Area
3.3.7. Resizing an Image
3.3.8. Applying Filters to Images
Exercise 3.1
Exercise 3.2
Chapter 4: Basics of Video Processing
4.1. How Do Computers Process Videos?
4.2. Capturing and Displaying a Video from a Camera
4.3. Saving a Camera Video
4.4. Importing and Displaying Video from a Video File
4.5. Drawing Shapes on Videos
Exercise 4.1
Exercise 4.2
Chapter 5: Face Detection with OpenCV in Python
5.1. OpenCV for Face Detection
5.2. Installing Library and Importing Images
5.3. Detecting Whole Faces
5.4. Detecting Eyes
5.5. Detecting Smile
5.6. Face Detection from Live Videos
Exercise 5.1
Exercise 5.2
Chapter 6: Introduction to Machine Learning for Computer Vision
6.1. Solving Regression Problems in Machine Learning
6.1.1. Importing and Analyzing the Dataset
6.1.2. Dividing Data into Features and Labels
6.1.3. Converting Categorical Data to Numbers
6.1.4. Divide the Data into Training and Test Sets
6.1.5. Training the Regression Algorithm
6.1.6. Making Predictions and Evaluating the Algorithm
6.2. Solving Classification Problems in Machine Learning
6.2.1. Importing and Analyzing the Dataset
6.2.2. Dividing the Data into Features and Labels
6.2.3. Converting Categorical Data to Numbers
6.2.4. Divide the Data into Training and Test Sets
6.2.5. Training the Classification Algorithm
6.2.6. Making Predictions and Evaluating the Algorithm
6.3. Machine Learning for Computer Vision – Classifying Handwritten Digits
6.3.1. Importing and Visualizing the Dataset
6.3.2. Extracting Features and Labels from the Digits Dataset
6.3.3. Dividing the Data into Training and Test Sets
6.3.4. Training the Classification Algorithm
6.3.5. Making Predictions and Evaluating the Algorithm
6.3.6. Making Predictions on a Single Image
Exercise 6.1
Exercise 6.2
Chapter 7: Introduction to Deep Learning for Computer Vision
7.1. What Is Deep Learning?
7.2. Image Classification with a Densely Connected Neural Network (DNN)
7.2.1. Feed Forward
7.2.2. Backpropagation
7.2.3. Implementing a DNN for Image Classification with TensorFlow Keras
7.3. Image Classification with a Convolutional Neural Network (DNN)
7.3.1. How Do Computers See Images?
7.3.2. The Convolution Operation
7.3.3. The ReLu Operation
7.3.4. The Pooling Operation
7.3.5. Flattening and Fully Connected Layer
7.3.6. Implementing a CNN With TensorFlow Keras
Exercise 7.1
Exercise 7.2
Chapter 8: Transfer Learning for Computer Vision
8.1. Transfer Learning
8.2. Image Classification with a Custom CNN
8.2.1. Importing and Formating the Dataset
8.2.2. Image Augmentation
8.2.3. Training the CNN Model
8.3. Image Classification Using Pretrained VGG 16 Model
8.3.1. What Is the VGG16 Model?
8.4. Image Classification Using Pretrained RESNET Model
8.4.1. What Is a RESNET Model?
8.4.2. Using RESNET50 Model for Image Classification in TensorFlow
Exercise 8.1
Exercise 8.2
Chapter 9: Object Detection with YOLO
9.1. What Is YOLO Algorithm?
9.2. Detecting Objects in Images with YOLO
9.3. Detecting Objects in Videos with YOLO
9.4. Detecting Objects from WebCam Streams with YOLO
Exercise 9.1
Exercise 9.2
Chapter 10: Introduction to GANS
10.1. What Are GANS?
10.2. Handwritten Digit Generation with GANS