#### 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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## Projective transformations

Affine transformations are nice, but they impose certain restrictions. A projective transformation, on the other hand, gives us more freedom. In order to understand projective transformations, we need to understand how projective geometry works. We basically describe what happens to an image when the point of view is changed. For example, if you are standing right in front of a sheet of paper with a square drawn on it, it will look like a square.

Now, if you start tilting that sheet of paper, the square will start looking more and more like a trapezoid. Projective transformations allow us to capture this dynamic in a nice mathematical way. These transformations preserve neither sizes nor angles, but they do preserve incidence and cross-ratio.

### Note

You can read more about incidence and cross-ratio at http://en.wikipedia.org/wiki/Incidence_(geometry) and http://en.wikipedia.org/wiki/Cross-ratio.

Now that we know what projective transformations are, let's see if we can extract more information here. We can say that any two images on a given plane are related by a homography. As long as they are in the same plane, we can transform anything into anything else. This has many practical applications such as augmented reality, image rectification, image registration, or the computation of camera motion between two images. Once the camera rotation and translation have been extracted from an estimated homography matrix, this information may be used for navigation, or to insert models of 3D objects into an image or video. This way, they are rendered with the correct perspective, and it will look like they were part of the original scene.

Let's go ahead and see how to do this:

```import cv2
import numpy as np
rows, cols = img.shape[:2]
src_points = np.float32([[0,0], [cols-1,0], [0,rows-1], [cols-1,rows-1]])
dst_points = np.float32([[0,0], [cols-1,0], [int(0.33*cols),rows-1], [int(0.66*cols),rows-1]])
projective_matrix = cv2.getPerspectiveTransform(src_points, dst_points)
img_output = cv2.warpPerspective(img, projective_matrix, (cols,rows))
cv2.imshow('Input', img)
cv2.imshow('Output', img_output)
cv2.waitKey()```

If you run the preceding code you will see the funny-looking output, such as the following screenshot:

### What just happened?

We can choose four control points in the source image and map them to the destination image. Parallel lines will not remain parallel lines after the transformation. We use a function calledÂ `getPerspectiveTransform` to get the transformation matrix.

Let's apply a couple of fun effects using projective transformation, and see what they look like. All we need to do is change the control points to get different effects.

Here's an example:

The control points are as follows:

```src_points = np.float32([[0,0], [0,rows-1], [cols/2,0],[cols/2,rows-1]])
dst_points = np.float32([[0,100], [0,rows-101], [cols/2,0],[cols/2,rows-1]])```

As an exercise, you should map the preceding points on a plane, and see how the points are mapped (just like we did earlier, while discussing affine transformations). You will get a good understanding about the mapping system, and you can create your own control points. If we want to obtain the same effect on theÂ y axis we could apply the previous transformation.