Computer Vision Projects with OpenCV and Python 3

Book description

Gain a working knowledge of advanced machine learning and explore Python's powerful tools for extracting data from images and videos

Key Features

  • Implement image classification and object detection using machine learning and deep learning
  • Perform image classification, object detection, image segmentation, and other Computer Vision tasks
  • Crisp content with a practical approach to solving real-world problems in Computer Vision

Book Description

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems.

With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google's Tesseract software, and tracking human body poses using DeeperCut within TensorFlow.

By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.

What you will learn

  • Install and run major Computer Vision packages within Python
  • Apply powerful support vector machines for simple digit classification
  • Understand deep learning with TensorFlow
  • Build a deep learning classifier for general images
  • Use LSTMs for automated image captioning
  • Read text from real-world images
  • Extract human pose data from images

Who this book is for

Python programmers and machine learning developers who wish to build exciting Computer Vision projects using the power of machine learning and OpenCV will find this book useful. The only prerequisite for this book is that you should have a sound knowledge of Python programming.

Publisher resources

Download Example Code

Table of contents

  1. Title Page
  2. Copyright and Credits
    1. Computer Vision Projects with OpenCV and Python 3
  3. About Packt
    1. Why subscribe?
  4. Contributors
    1. About the author
    2. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
    4. Get in touch
      1. Reviews
  6. Setting Up an Anaconda Environment
    1. Introducing and installing Python and Anaconda
      1. Installing Anaconda
    2. Installing additional libraries
      1. Installing OpenCV
      2. Installing dlib
      3. Installing Tesseract
      4. Installing TensorFlow
    3. Exploring Jupyter Notebook
    4. Summary
  7. Image Captioning with TensorFlow
    1. Technical requirements
    2. Introduction to image captioning
      1. Difference between image classification and image captioning
      2. Recurrent neural networks with long short-term memory
    3. Google Brain im2txt captioning model
    4. Running the captioning code on Jupyter
      1. Analyzing the result captions
      2. Running the captioning code on Jupyter for multiple images
    5. Retraining the captioning model
    6. Summary
  8. Reading License Plates with OpenCV
    1. Identifying the license plate
    2. Plate utility functions
      1. The gray_thresh_img function and morphological functions
      2. Kernels
      3. The matching character function
      4. The k-nearest neighbors digit classifier
    3. Finding plate characters
      1. Finding matches and groups of characters
    4. Finding and reading license plates with OpenCV
    5. Result analysis
    6. Summary
  9. Human Pose Estimation with TensorFlow
    1. Pose estimation using DeeperCut and ArtTrack
    2. Single-person pose detection
    3. Multi-person pose detection
    4. Retraining the human pose estimation model
    5. Summary
  10. Handwritten Digit Recognition with scikit-learn and TensorFlow
    1. Acquiring and processing MNIST digit data
    2. Creating and training a support vector machine
      1. Applying the support vector machine to new data
    3. Introducing TensorFlow with digit classification
    4. Evaluating the results
    5. Summary
  11. Facial Feature Tracking and Classification with dlib
    1. Introducing dlib
    2. Facial landmarks
    3. Finding 68 facial landmarks in images
    4. Faces in videos
    5. Facial recognition
    6. Summary
  12. Deep Learning Image Classification with TensorFlow
    1. Technical requirements
    2. An introduction to TensorFlow
    3. Using Inception for image classification
    4. Retraining with our own images
    5. Speeding up computation with your GPU
    6. Summary
  13. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think

Product information

  • Title: Computer Vision Projects with OpenCV and Python 3
  • Author(s): Matthew Rever
  • Release date: December 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789954555