Book Image

OpenCV 3 Computer Vision with Python Cookbook

By : Aleksei Spizhevoi, Aleksandr Rybnikov
Book Image

OpenCV 3 Computer Vision with Python Cookbook

By: Aleksei Spizhevoi, Aleksandr Rybnikov

Overview of this book

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.
Table of Contents (11 chapters)

Computing gradients using Sobel operator

In this recipe, you will learn how to compute the approximation of an image's gradient using Sobel filters.

Getting ready

Install the OpenCV 3.x Python API package and the matplotlib package.

How to do it...

Perform the following steps:

  1. Import the packages:
import cv2
import numpy as np
import matplotlib.pyplot as plt
  1. Read the image as grayscale:
image = cv2.imread('../data/Lena.png', 0)
  1. Compute the gradient approximations using the Sobel operator:
dx = cv2.Sobel(image, cv2.CV_32F, 1, 0)
dy = cv2.Sobel(image, cv2...