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)

Playing frame stream from video

In this recipe, you will learn how to open an existing video file using OpenCV. You will also learn how to replay frames from the opened video.

Getting ready

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

How to do it...

The following are the steps for this recipe:

  1. Create a VideoCapture object for video file:
import cv2

capture = cv2.VideoCapture('../data/drop.avi')
  1. Replay all the frames in the video:
while True:
has_frame, frame = capture.read()
if not has_frame:
print('Reached the end of the video')
break

cv2.imshow('frame', frame)
key = cv2.waitKey(50)
if key == 27:
print('Pressed Esc')
break

cv2.destroyAllWindows()

How it works...

Working with video files is virtually the same as working with cameras—it's done through the same cv2.VideoCapture class. This time, however, instead of the camera device index, you should specify the path to the video file you want to open. Depending on the OS and video codecs available, OpenCV might not support some of the video formats.

After the video file is opened in a infinite while loop, we acquire frames using the capture.read method. The function returns a pair: a Boolean frame read success flag, and the frame itself. Note that frames are read at the maximum possible rate, meaning if you want to replay video at a certain FPS, you should implement it on your own. In the preceding code, after we call the cv2.imshow function, we wait for 50 milliseconds in the cv2.waitKey function. Assuming the time spent on showing the image and decoding the video is negligible, the video will be replayed at a rate no greater than 20 FPS.

The following frames are expected to be seen: