Book Image

Mastering OpenCV 4 with Python

By : Alberto Fernández Villán
5 (1)
Book Image

Mastering OpenCV 4 with Python

5 (1)
By: Alberto Fernández Villán

Overview of this book

OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language. In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction to OpenCV 4 and Python
Section 1: Introduction to OpenCV 4 and Python
2
Setting Up OpenCV
Setting Up OpenCV
Technical requirements
Understanding Python
A theoretical introduction to the OpenCV library
Installing OpenCV, Python, and other packages
Installing Python, OpenCV, and other packages with virtualenv
Python IDEs to create virtual environments with virtualenv
Anaconda/Miniconda distributions and conda package–and environment-management system
Packages for scientific computing, data science, machine learning, deep learning, and computer vision
Jupyter Notebook
The OpenCV and Python project structure
Our first Python and OpenCV project
Summary
Questions
Further reading
3
Image Basics in OpenCV
Image Basics in OpenCV
Technical requirements
A theoretical introduction to image basics
Concepts of pixels, colors, channels, images, and color spaces
The coordinate system in OpenCV
Accessing and manipulating pixels in OpenCV
BGR order in OpenCV
Summary
Questions
Further reading
4
Handling Files and Images
Handling Files and Images
Technical requirements
An introduction to handling files and images
Reading and writing images
Reading camera frames and video files
Writing a video file
Playing with video capture properties
Summary
Questions
Further reading
5
Constructing Basic Shapes in OpenCV
Constructing Basic Shapes in OpenCV
Technical requirements
A theoretical introduction to drawing in OpenCV
Drawing shapes
Writing text
Dynamic drawing with mouse events
Advanced drawing
Summary
Questions
Further reading
6
Section 2: Image Processing in OpenCV
Section 2: Image Processing in OpenCV
7
Image Processing Techniques
Image Processing Techniques
Technical requirements
Splitting and merging channels in OpenCV
Geometric transformations of images
Image filtering
Arithmetic with images
Morphological transformations
Color spaces
Color maps
Summary
Questions
Further reading
8
Constructing and Building Histograms
Constructing and Building Histograms
Technical requirements
A theoretical introduction to histograms
Grayscale histograms
Color histograms
Custom visualizations of histograms
Comparing OpenCV, NumPy, and Matplotlib histograms
Histogram equalization
Contrast Limited Adaptive Histogram Equalization
Comparing CLAHE and histogram equalization
Histogram comparison
Summary
Questions
Further reading
9
Thresholding Techniques
Thresholding Techniques
Technical requirements
Introducing thresholding techniques
Simple thresholding
Adaptive thresholding
Otsu's thresholding algorithm
The triangle binarization algorithm
Thresholding color images
Thresholding algorithms using scikit-image
Summary
Questions
Further reading
10
Contour Detection, Filtering, and Drawing
Contour Detection, Filtering, and Drawing
Technical requirements
An introduction to contours
Compressing contours
Image moments
More functionality related to contours
Filtering contours
Recognizing contours
Matching contours
Summary
Questions
Further reading
11
Augmented Reality
Augmented Reality
Technical requirements
An introduction to augmented reality
Markerless-based augmented reality
Marker-based augmented reality
Snapchat-based augmented reality
QR code detection
Summary
Questions
Further reading
12
Section 3: Machine Learning and Deep Learning in OpenCV
Section 3: Machine Learning and Deep Learning in OpenCV
13
Machine Learning with OpenCV
Machine Learning with OpenCV
Technical requirements
An introduction to machine learning
k-means clustering
k-nearest neighbor
Support vector machine
Summary
Questions
Further reading
14
Face Detection, Tracking, and Recognition
Face Detection, Tracking, and Recognition
Technical requirements
Face processing introduction
Face detection
Detecting facial landmarks
Face tracking
Face recognition
Summary
Questions
Further reading
15
Introduction to Deep Learning
Introduction to Deep Learning
Technical requirements
Deep learning overview for computer vision tasks
Deep learning in OpenCV
The TensorFlow library
The Keras library
Summary
Questions
Further reading
16
Section 4: Mobile and Web Computer Vision
Section 4: Mobile and Web Computer Vision
17
Mobile and Web Computer Vision with Python and OpenCV
Mobile and Web Computer Vision with Python and OpenCV
Technical requirements
Introduction to Python web frameworks
Introduction to Flask
Web computer vision applications using OpenCV and Flask
Deep learning API using Keras and Flask
Deploying a Flask application to the cloud
Summary
Questions
Further reading
18
Assessments
Assessments
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
19
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Chapter 8

  1. The cv2.findContours() function finds contours in a binary image (for example, the resulting image after a thresholding operation).
  2. The four flags OpenCV provides for compressing contours are as follows:
  • cv2.CHAIN_APPROX_NONE
  • cv2.CHAIN_APPROX_SIMPLE
  • cv2.CHAIN_APPROX_TC89_KCOS
  • cv2.CHAIN_APPROX_TC89_L1
  1. The cv2.moments() function calculates all of the moments up to the third order of a polygon or rasterized shape.
  2. The moment, m00, gives the area of the contour.
  3. OpenCV provides the cv2.HuMoments() function to calculate the seven Hu moment invariants.
  4. The cv2.approxPolyDP() function returns a contour approximation of the given contour based on the given precision. This function uses the Douglas-Peucker algorithm. The epsilon parameter specifies the precision for establishing the maximum distance between the original curve and its approximation.
  5. The extreme_points() function...
Previous Section
End of Section 9
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