#### 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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## Affine transformations

In this section, we will discuss the various generalized geometrical transformations of 2D images. We have been using the function `warpAffine` quite a bit over the last couple of sections, it's about time we understood what's happening underneath.

Before talking about affine transformations, let's learn what Euclidean transformations are. Euclidean transformations are a type of geometric transformation that preserve length and angle measures. If we take a geometric shape and apply Euclidean transformation to it, the shape will remain unchanged. It might look rotated, shifted, and so on, but the basic structure will not change. So technically, lines will remain lines, planes will remain planes, squares will remain squares, and circles will remain circles.

Coming back to affine transformations, we can say that they are generalizations of Euclidean transformations. Under the realm of affine transformations, lines will remain lines, but squares might become rectangles or parallelograms. Basically, affine transformations don't preserve lengths and angles.

In order to build a general affine transformation matrix, we need to define the control points. Once we have these control points, we need to decide where we want them to be mapped. In this particular situation, all we need are three points in the source image, and three points in the output image. Let's see how we can convert an image into a parallelogram-like image:

```import cv2
import numpy as np
rows, cols = img.shape[:2]
src_points = np.float32([[0,0], [cols-1,0], [0,rows-1]])
dst_points = np.float32([[0,0], [int(0.6*(cols-1)),0], [int(0.4*(cols-1)),rows-1]])
affine_matrix = cv2.getAffineTransform(src_points, dst_points)
img_output = cv2.warpAffine(img, affine_matrix, (cols,rows))
cv2.imshow('Input', img)
cv2.imshow('Output', img_output)
cv2.waitKey()```

### What just happened?

As we discussed earlier, we are defining control points. We just need three points to get the affine transformation matrix. We want the three points in `src_points` to be mapped to the corresponding points in `dst_points`. We are mapping the points as shown in the following image:

To get the transformation matrix, we have a function called in OpenCV. Once we have the affine transformation matrix, we use the function to apply this matrix to the input image.

Following is the input image:

If you run the preceding code, the output will look something like this:

We can also get the mirror image of the input image. We just need to change the control points in the following way:

```src_points = np.float32([[0,0], [cols-1,0], [0,rows-1]])
dst_points = np.float32([[cols-1,0], [0,0], [cols-1,rows-1]])```

Here, the mapping looks something like this:

If you replace the corresponding lines in our affine transformation code with these two lines, you will get the following result: