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OpenCV 4 with Python Blueprints

You're reading from   OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801811
Length 366 pages
Edition 2nd Edition
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Authors (4):
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Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Dr. Menua Gevorgyan Dr. Menua Gevorgyan
Author Profile Icon Dr. Menua Gevorgyan
Dr. Menua Gevorgyan
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Arsen Mamikonyan Arsen Mamikonyan
Author Profile Icon Arsen Mamikonyan
Arsen Mamikonyan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Fun with Filters 2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER 3. Finding Objects via Feature Matching and Perspective Transforms 4. 3D Scene Reconstruction Using Structure from Motion 5. Using Computational Photography with OpenCV 6. Tracking Visually Salient Objects 7. Learning to Recognize Traffic Signs 8. Learning to Recognize Facial Emotions 9. Learning to Classify and Localize Objects 10. Learning to Detect and Track Objects 11. Profiling and Accelerating Your Apps 12. Setting Up a Docker Container 13. Other Books You May Enjoy

Setting up the app

Before we can get down to the nitty-gritty of our feature-matching algorithm, we need to make sure that we can access the webcam and display the video stream.

Let's learn how to run the application in the next section.

Running the app – the main() function routine

To run our app, we will need to execute the main() function routine. The following steps show us the execution of main() routine:

  1. The function first accesses the webcam with the VideoCapture method by passing 0 as an argument, which is a reference to the default webcam. If it can not access the webcam, the app will be terminated:
import cv2 as cv
from feature_matching import FeatureMatching

def main():
capture = cv.VideoCapture...
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