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)

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