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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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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

Obtaining a frame stream properties

In this recipe, you will learn how to get such VideoCapture properties as frame height and width, frame count for video files, and camera frame rate.

Getting ready

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

How to do it...

Execute the following steps:

  1. Let's create an auxiliary function that will take the VideoCapture ID (either what the camera device is or the path to the video), create a VideoCapture object, and request the frame height and width, count, and rate:
import numpy
import cv2

def print_capture_properties(*args):
capture = cv2.VideoCapture(*args)
print('Created capture:', ' '.join(map(str, args)))
print('Frame count:', int(capture.get(cv2.CAP_PROP_FRAME_COUNT)))
print('Frame width:', int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)))
print('Frame height:', int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
print('Frame rate:', capture.get(cv2.CAP_PROP_FPS))
  1. Let's call this function for a video file:
print_capture_properties('../data/drop.avi')
  1. Now let's request properties for the camera capture object:
print_capture_properties(0)

How it works...

As in the earlier recipes, working with cameras and video frame streams is done through the cv2.VideoCapture class. You can get properties using the capture.get function, which takes the property ID and returns its value as a floating-point value.

Note that, depending on the OS and video backend used, not all of the properties being requested can be accessed.

The following output is expected (it might vary depending on the OS and the video backend that OpenCV was compiled with):

Created capture: ../data/drop.avi
Frame count: 182
Frame width: 256
Frame height: 240
Frame rate: 30.0

Created capture: 0
Frame count: -1
Frame width: 640
Frame height: 480
Frame rate: 30.0

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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