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OpenCV with Python By Example

You're reading from   OpenCV with Python By Example Build real-world computer vision applications and develop cool demos using OpenCV for Python

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781785283932
Length 296 pages
Edition 1st Edition
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Author (1):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (14) Chapters Close

Preface 1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Creating a Panoramic Image 7. Seam Carving 8. Detecting Shapes and Segmenting an Image 9. Object Tracking 10. Object Recognition 11. Stereo Vision and 3D Reconstruction 12. Augmented Reality Index

Image scaling

In this section, we will discuss about resizing an image. This is one of the most common operations in computer vision. We can resize an image using a scaling factor, or we can resize it to a particular size. Let's see how to do that:

img_scaled = cv2.resize(img,None,fx=1.2, fy=1.2, interpolation = cv2.INTER_LINEAR)
cv2.imshow('Scaling - Linear Interpolation', img_scaled) img_scaled = cv2.resize(img,None,fx=1.2, fy=1.2, interpolation = cv2.INTER_CUBIC)
cv2.imshow('Scaling - Cubic Interpolation', img_scaled) img_scaled = cv2.resize(img,(450, 400), interpolation = cv2.INTER_AREA)
cv2.imshow('Scaling - Skewed Size', img_scaled) cv2.waitKey()

What just happened?

Whenever we resize an image, there are multiple ways to fill in the pixel values. When we are enlarging an image, we need to fill up the pixel values in between pixel locations. When we are shrinking an image, we need to take the best representative value. When we are scaling by a non-integer value, we need to interpolate values appropriately, so that the quality of the image is maintained. There are multiple ways to do interpolation. If we are enlarging an image, it's preferable to use linear or cubic interpolation. If we are shrinking an image, it's preferable to use the area-based interpolation. Cubic interpolation is computationally more complex, and hence slower than linear interpolation. But the quality of the resulting image will be higher.

OpenCV provides a function called resize to achieve image scaling. If you don't specify a size (by using None), then it expects the X and Y scaling factors. In our example, the image will be enlarged by a factor of 1.2. If we do the same enlargement using cubic interpolation, we can see that the quality improves, as seen in the following figure. The following screenshot shows what linear interpolation looks like:

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Here is the corresponding cubic interpolation:

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If we want to resize it to a particular size, we can use the format shown in the last resize instance. We can basically skew the image and resize it to whatever size we want. The output will look something like the following:

What just happened?
You have been reading a chapter from
OpenCV with Python By Example
Published in: Sep 2015
Publisher: Packt
ISBN-13: 9781785283932
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