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)

Capturing and showing frames from a camera

In this recipe, you will learn how to connect to a USB camera and capture frames from it live using OpenCV.

Getting ready

You need to have OpenCV 3.x installed with Python API support.

How to do it...

For this recipe, the steps are as follows:

  1. Create a VideoCapture object:
import cv2

capture = cv2.VideoCapture(0)
  1. Read the frames from the camera using the capture.read method, which returns a pair: a read success flag and the frame itself:
while True:
has_frame, frame = capture.read()
if not has_frame:
print('Can\'t get frame')
break

cv2.imshow('frame', frame)
key = cv2.waitKey(3)
if key == 27:
print('Pressed Esc')
break
  1. It's generally recommended that you release the video device (a camera, in our case) and destroy all the windows created:
capture.release()
cv2.destroyAllWindows()

How it works...

Working with cameras in OpenCV is done through the cv2.VideoCapture class. In fact it provides support when working with both cameras and video files. To instantiate an object representing a frame stream coming from a camera, you should just specify its number (zero-based device index). If OpenCV doesn't support your camera out of the box, you can try recompiling OpenCV, turning on optional support of other industrial camera types.