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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

Simple image transformations—resizing and flipping

Now we're able to load an image, it's time to do some simple image processing. The operations we're going to review—resize and flip—are basic and usually used as preliminary steps of complex computer vision algorithms.

Getting ready

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

How to do it...

For this recipe, we need the following steps to be executed:

  1. Load an image and print its original size:
img = cv2.imread('../data/Lena.png')
print('original image shape:', img.shape)
  1. OpenCV offers several ways of using the cv2.resize function. We can set the target size (width, height) in pixels as the second parameter:
width, height = 128, 256
resized_img = cv2.resize(img, (width, height))
print('resized to 128x256 image shape:', resized_img.shape)
  1. Resize by setting multipliers of the image's original width and height:
w_mult, h_mult = 0.25, 0.5
resized_img = cv2.resize(img, (0, 0), resized_img, w_mult, h_mult)
print('image shape:', resized_img.shape)
  1. Resize using nearest-neighbor interpolation instead of the default one:
w_mult, h_mult = 2, 4
resized_img = cv2.resize(img, (0, 0), resized_img, w_mult, h_mult, cv2.INTER_NEAREST)
print('half sized image shape:', resized_img.shape)
  1. Reflect the image along its horizontal x-axis. To do this, we should pass 0 as the last argument of the cv2.flip function:
img_flip_along_x = cv2.flip(img, 0)
  1. Of course, it's possible to flip the image along its vertical y-axis—just pass any value greater than 0:
img_flip_along_y = cv2.flip(img, 1)
  1. We can flip both x and y simultaneously by passing any negative value to the function:

img_flipped_xy = cv2.flip(img, -1)

How it works...

We can play with interpolation mode in cv2.resize—it defines how values between pixels are computed. There are quite a few types of interpolation, each with a different outcome. This argument can be passed as the last one and doesn't influence the result's size—only the quality and smoothness of the output.

By default, bilinear interpolation (cv2.INTER_LINEAR) is used. But in some situations, it may be necessary to apply other, more complicated options.

The cv2.flip function is used for mirroring images. It doesn't change the size of an image, but rather swaps the pixels.

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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