#### Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Title Page
Contributors
Packt Upsell
Preface
Free Chapter
Applying Geometric Transformations to Images
Detecting Edges and Applying Image Filters
Cartoonizing an Image
Detecting and Tracking Different Body Parts
Extracting Features from an Image
Seam Carving
Detecting Shapes and Segmenting an Image
Object Tracking
Machine Learning by an Artificial Neural Network
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## Image scaling

In this section, we will discuss 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:

```import cv2
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 area-based interpolation. Cubic interpolation is computationally more complex, and hence slower than linear interpolation. However, 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 xÂ 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:

Here is the corresponding cubic interpolation:

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: