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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
Languages
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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

Handling user input from a keyboard

OpenCV has simple and clear way to handle input from a keyboard. This functionality is organically built into the cv2.waitKey function. Let's see how we can use it.

Getting ready

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

How to do it...

You will need to perform the following steps for this recipe:

  1. As done previously, open an image and get its width and height. Also, make a copy of the original image and define a simple function that returns a random point with coordinates inside our image:
import cv2, numpy as np, random

image = cv2.imread('../data/Lena.png')
w, h = image.shape[1], image.shape[0]
image_to_show = np.copy(image)

def rand_pt():
return (random.randrange(w),
random.randrange(h))
  1. Now when the user presses P, L, R, E, or T draw points, lines, rectangles, ellipses, or text, respectively. Also, we will clear an image when the user hits C and closes the application when the Esc key is pushed:
finish = False
while not finish:
cv2.imshow("result", image_to_show)
key = cv2.waitKey(0)
if key == ord('p'):
for pt in [rand_pt() for _ in range(10)]:
cv2.circle(image_to_show, pt, 3, (255, 0, 0), -1)
elif key == ord('l'):
cv2.line(image_to_show, rand_pt(), rand_pt(), (0, 255, 0), 3)
elif key == ord('r'):
cv2.rectangle(image_to_show, rand_pt(), rand_pt(), (0, 0, 255), 3)
elif key == ord('e'):
cv2.ellipse(image_to_show, rand_pt(), rand_pt(), random.randrange(360), 0, 360, (255, 255, 0), 3)
elif key == ord('t'):
cv2.putText(image_to_show, 'OpenCV', rand_pt(), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
elif key == ord('c'):
image_to_show = np.copy(image)
elif key == 27:
finish = True

How it works...

As you can see, we just analyze the waitKey() return value. If we set a duration and no key is pressed, waitKey() would return -1.

After launching the code and pressing the P, L, R, E, and T keys a few times, you will get an image close to the following:

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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