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OpenCV 3 Computer Vision with Python Cookbook

You're reading from   OpenCV 3 Computer Vision with Python Cookbook Leverage the power of OpenCV 3 and Python to build computer vision applications

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788474443
Length 306 pages
Edition 1st Edition
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Authors (2):
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Aleksandr Rybnikov Aleksandr Rybnikov
Author Profile Icon Aleksandr Rybnikov
Aleksandr Rybnikov
Aleksei Spizhevoi Aleksei Spizhevoi
Author Profile Icon Aleksei Spizhevoi
Aleksei Spizhevoi
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Toc

Table of Contents (11) Chapters Close

Preface 1. I/O and GUI FREE CHAPTER 2. Matrices, Colors, and Filters 3. Contours and Segmentation 4. Object Detection and Machine Learning 5. Deep Learning 6. Linear Algebra 7. Detectors and Descriptors 8. Image and Video Processing 9. Multiple View Geometry 10. Other Books You May Enjoy

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:

You have been reading a chapter from
OpenCV 3 Computer Vision with Python Cookbook
Published in: Mar 2018
Publisher: Packt
ISBN-13: 9781788474443
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