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.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Introduction to OpenCV 4 and Python
6
Section 2: Image Processing in OpenCV
12
Section 3: Machine Learning and Deep Learning in OpenCV
16
Section 4: Mobile and Web Computer Vision

Recognizing contours

We have previously introduced cv2.approxPolyDP(), which can be used to approximate one contour with another with fewer points using the Douglas-Peucker algorithm. A key parameter in this function is epsilon, which sets the approximation accuracy. In contours_shape_recognition.py, we will make use of cv2.approxPolyDP() in order to recognize the contours (for example, triangle, square, rectangle, pentagon, or hexagon, among others) based on the number of detected vertices in the decimated contour (the output of cv2.approxPolyDP()). In order to decimate the number of points, given a certain contour, we first compute the perimeter of the contour. Based on the perimeter, the epsilon parameter is established. This way, the decimated contour is invariant to scale. The epsilon parameter is calculated as follows:

epsilon = 0.03 * perimeter

The constant 0.03 is established...