Home Data OpenCV 3.x with Python By Example - Second Edition

OpenCV 3.x with Python By Example - Second Edition

By Gabriel Garrido Calvo , Prateek Joshi
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  1. Free Chapter
    Applying Geometric Transformations to Images
About 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.
Publication date:
January 2018
Publisher
Packt
Pages
268
ISBN
9781788396905

 

Chapter 1. Applying Geometric Transformations to Images

In this chapter, we are going to learn how to apply cool geometric effects to images. Before we get started, we need to install OpenCV-Python. We will explain how to compile and install the necessary libraries to follow every example in this book.

By the end of this chapter, you will know:

  • How to install OpenCV-Python
  • How to read, display, and save images
  • How to convert to multiple color spaces
  • How to apply geometric transformations such as translation, rotation, and scaling
  • How to use affine and projective transformations to apply funny geometric effects to photos
 

Installing OpenCV-Python


In this section, we explain how to install OpenCV 3.X with Python 2.7 on multiple platforms. If you desire it, OpenCV 3.X also supports the use of Python 3.X and it will be fully compatible with the examples in this book. Linux is recommended as the examples in this book were tested on that OS.

Windows

In order to get OpenCV-Python up and running, we need to perform the following steps:

  1. Install Python: Make sure you have Python 2.7.x installed on your machine. If you don't have it, you can install it from: https://www.python.org/downloads/windows/.
  2. Install NumPy: NumPy is a great package to do numerical computing in Python. It is very powerful and has a wide variety of functions. OpenCV-Python plays nicely with NumPy, and we will be using this package a lot during the course of this book. You can install the latest version from: http://sourceforge.net/projects/numpy/files/NumPy/.

We need to install all these packages in their default locations. Once we install Python and NumPy, we need to ensure that they're working fine. Open up the Python shell and type the following:

 >>> import numpy 

If the installation has gone well, this shouldn't throw up any errors. Once you confirm it, you can go ahead and download the latest OpenCV version from: http://opencv.org/downloads.html.

Once you finish downloading it, double-click to install it. We need to make a couple of changes, as follows:

  1. Navigate to opencv/build/python/2.7/.
  2. You will see a file named cv2.pyd. Copy this file to C:/Python27/lib/site-packages.

You're all set! Let's make sure that OpenCV is working. Open up the Python shell and type the following:

 >>> import cv2

If you don't see any errors, then you are good to go! You are now ready to use OpenCV-Python.

 

macOS X

To install OpenCV-Python, we will be using Homebrew. Homebrew is a great package manager for macOS X and it will come in handy when you are installing various libraries and utilities on macOS X. If you don't have Homebrew, you can install it by running the following command in your terminal:

$ ruby -e "$(curl -fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)"

Even though OS X comes with inbuilt Python, we need to install Python using Homebrew to make our lives easier. This version is called brewed Python. Once you install Homebrew, the next step is to install brewed Python. Open up the terminal, and type the following:

$ brew install python

This will automatically install it as well. Pip is a package management tool to install packages in Python, and we will be using it to install other packages. Let's make sure the brewed Python is working correctly. Go to your terminal and type the following:

$ which python

You should see /usr/local/bin/python printed on the terminal. This means that we are using the brewed Python, and not the inbuilt system Python. Now that we have installed brewed Python, we can go ahead and add the repository, homebrew/science, which is where OpenCV is located. Open the terminal and run the following command:

$ brew tap homebrew/science

Make sure the NumPy package is installed. If not, run the following in your terminal:

$ pip install numpy

Now, we are ready to install OpenCV. Go ahead and run the following command from your terminal:

$ brew install opencv --with-tbb --with-opengl

OpenCV is now installed on your machine, and you can find it at /usr/local/Cellar/opencv/3.1.0/. You can't use it just yet. We need to tell Python where to find our OpenCV packages. Let's go ahead and do that by symlinking the OpenCV files. Run the following commands from your terminal (please, double check that you are actually using the right versions, as they might be slightly different):

$ cd /Library/Python/2.7/site-packages/
$ ln -s /usr/local/Cellar/opencv/3.1.0/lib/python2.7/site-packages/cv.py
cv.py
$ ln -s /usr/local/Cellar/opencv/3.1.0/lib/python2.7/site-packages/cv2.so
cv2.so

You're all set! Let's see if it's installed properly. Open up the Python shell and type the following:

> import cv2

If the installation went well, you will not see any error messages. You are now ready to use OpenCV in Python.

If you want to use OpenCV within a virtual environment, you could follow the instructions in the Virtual environments section, applying small changes to each of the commands for macOS X.

Linux (for Ubuntu)

First, we need to install the OS requirements:

[compiler] $ sudo apt-get install build-essential
[required] $ sudo apt-get install cmake git libgtk2.0-dev pkg-config 
           libavcodec-dev libavformat-dev libswscale-dev git 
           libgstreamer0.10-dev libv4l-dev
[optional] $ sudo apt-get install python-dev python-numpy libtbb2 
           libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev 
           libdc1394-22-dev

Once the OS requirements are installed, we need to download and compile the latest version of OpenCV along with several supported flags to let us implement the following code samples. Here we are going to install Version 3.3.0:

$ mkdir ~/opencv
$ git clone -b 3.3.0 https://github.com/opencv/opencv.git opencv
$ cd opencv
$ git clone https://github.com/opencv/opencv_contrib.git opencv_contrib
$ mkdir release
$ cd release
$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local  -D INSTALL_PYTHON_EXAMPLES=ON   -D INSTALL_C_EXAMPLES=OFF -D OPENCV_EXTRA_MODULES_PATH=~/opencv/opencv_contrib/modules -D BUILD_PYTHON_SUPPORT=ON -D WITH_XINE=ON -D WITH_OPENGL=ON -D WITH_TBB=ON -D WITH_EIGEN=ON -D BUILD_EXAMPLES=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D BUILD_EXAMPLES=ON ../
$ make -j4 ; echo 'Running in 4 jobs'
$ sudo make install

If you are using Python 3, place -D + flags together, as you see in the following command:

cmake -DCMAKE_BUILD_TYPE=RELEASE....

Virtual environments

If you are using virtual environments to keep your test environment completely separate from the rest of your OS, you could install a tool called virtualenvwrapper by following this tutorial: https://virtualenvwrapper.readthedocs.io/en/latest/.

To get OpenCV running on this virtualenv, we need to install the NumPy package:

$(virtual_env) pip install numpy

Following all the previous steps, just add the following three flags on compilation by cmake (pay attention that flag CMAKE_INSTALL_PREFIX is being redefined):

$(<env_name>) > cmake ...
-D CMAKE_INSTALL_PREFIX=~/.virtualenvs/<env_name> \ 
-D PYTHON_EXECUTABLE=~/.virtualenvs/<env_name>/bin/python 
-D PYTHON_PACKAGES_PATH=~/.virtualenvs/<env_name>/lib/python<version>/site-packages ...

Let's make sure that it's installed correctly. Open up the Python shell and type the following:

> import cv2

If you don't see any errors, you are good to go.

Troubleshooting

If the cv2 library was not found, identify where the library was compiled. It should be located at /usr/local/lib/python2.7/site-packages/cv2.so. If that is the case, make sure your Python version matches the one package that has been stored, otherwise just move it into the according site-packages folder of Python, including same for virtualenvs.

During cmake command execution, try to join -DMAKE... and the rest of the -D lines. Moreover, if execution fails during the compiling process, some libraries might be missing from the OS initial requirements. Make sure you installed them all.

You can find an official tutorial about how to install the latest version of OpenCV on Linux at the following website: http://docs.opencv.org/trunk/d7/d9f/tutorial_linux_install.html.

If you are trying to compile using Python 3, and cv2.so is not installed, make sure you installed OS dependency Python 3 and NumPy.

OpenCV documentation

OpenCV official documentation is at http://docs.opencv.org/. There are three documentation categories: Doxygen, Sphinx, and Javadoc.

In order to obtain a better understanding of how to use each of the functions used during this book, we encourage you to open one of those doc pages and research the different uses of each OpenCV library method used in our examples. As a suggestion, Doxygen documentation has more accurate and extended information about the use of OpenCV.

 

Reading, displaying, and saving images


Let's see how we can load an image in OpenCV-Python. Create a file named first_program.py and open it in your favorite code editor. Create a folder named images in the current folder, and make sure that you have an image named input.jpg in that folder.

Once you do that, add the following lines to that Python file:

import cv2
img = cv2.imread('./images/input.jpg')
cv2.imshow('Input image', img)
cv2.waitKey()

If you run the preceding program, you will see an image being displayed in a new window.

What just happened?

Let's understand the previous piece of code, line by line. In the first line, we are importing the OpenCV library. We need this for all the functions we will be using in the code. In the second line, we are reading the image and storing it in a variable. OpenCV uses NumPy data structures to store the images. You can learn more about NumPy at http://www.numpy.org.

So if you open up the Python shell and type the following, you will see the datatype printed on the terminal:

> import cv2
> img = cv2.imread('./images/input.jpg')
> type(img)
<type 'numpy.ndarray'>

In the next line, we display the image in a new window. The first argument in cv2.imshow is the name of the window. The second argument is the image you want to display.

You must be wondering why we have the last line here. The function, cv2.waitKey(), is used in OpenCV for keyboard binding. It takes a number as an argument, and that number indicates the time in milliseconds. Basically, we use this function to wait for a specified duration, until we encounter a keyboard event. The program stops at this point, and waits for you to press any key to continue. If we don't pass any argument, or if we pass as the argument, this function will wait for a keyboard event indefinitely.

The last statement, cv2.waitKey(n), performs the rendering of the image loaded in the step before. It takes a number that indicates the time in milliseconds of rendering. Basically, we use this function to wait for a specified duration until we encounter a keyboard event. The program stops at this point, and waits for you to press any key to continue. If we don't pass any argument, or if we pass 0 as the argument, this function waits for a keyboard event indefinitely.

 

Loading and saving an image


OpenCV provides multiple ways of loading an image. Let's say we want to load a color image in grayscale mode, we can do that using the following piece of code:

import cv2
gray_img = cv2.imread('images/input.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('Grayscale', gray_img)
cv2.waitKey()

Here, we are using the ImreadFlag, as cv2.IMREAD_GRAYSCALE, and loading the image in grayscale mode, although you may find more read modes in the official documentation.

You can see the image displayed in the new window. Here is the input image:

Following is the corresponding grayscale image:

We can save this image as a file as well:

cv2.imwrite('images/output.jpg', gray_img)

This will save the grayscale image as an output file named output.jpg. Make sure you get comfortable with reading, displaying, and saving images in OpenCV, because we will be doing this quite a bit during the course of this book.

Changing image format

We can save this image as a file as well, and change the original image format to PNG:

import cv2
img = cv2.imread('images/input.jpg')
cv2.imwrite('images/output.png', img, [cv2.IMWRITE_PNG_COMPRESSION])

The imwrite() method will save the grayscale image as an output file named output.png. This is done using PNG compression with the help of ImwriteFlag and cv2.IMWRITE_PNG_COMPRESSION. The ImwriteFlag allows the output image to change the format, or even the image quality.

 

Image color spaces


In computer vision and image processing, color space refers to a specific way of organizing colors. A color space is actually a combination of two things, a color model and a mapping function. The reason we want color models is because it helps us in representing pixel values using tuples. The mapping function maps the color model to the set of all possible colors that can be represented.

There are many different color spaces that are useful. Some of the more popular color spaces are RGB, YUV, HSV, Lab, and so on. Different color spaces provide different advantages. We just need to pick the color space that's right for the given problem. Let's take a couple of color spaces and see what information they provide:

  • RGB: Probably the most popular color space. It stands for Red, Green, and Blue. In this color space, each color is represented as a weighted combination of red, green, and blue. So every pixel value is represented as a tuple of three numbers corresponding to red, green, and blue. Each value ranges between 0 and 255.
  • YUV: Even though RGB is good for many purposes, it tends to be very limited for many real-life applications. People started thinking about different methods to separate the intensity information from the color information. Hence, they came up with the YUV color space. Y refers to the luminance or intensity, and U/V channels represent color information. This works well in many applications because the human visual system perceives intensity information very differently from color information.
  • HSV: As it turned out, even YUV was still not good enough for some applications. So people started thinking about how humans perceive color, and they came up with the HSV color space. HSV stands for Hue, Saturation, and Value. This is a cylindrical system where we separate three of the most primary properties of colors and represent them using different channels. This is closely related to how the human visual system understands color. This gives us a lot of flexibility as to how we can handle images.

Converting color spaces

Considering all the color spaces, there are around 190 conversion options available in OpenCV. If you want to see a list of all available flags, go to the Python shell and type the following:

import cv2
print([x for x in dir(cv2) if x.startswith('COLOR_')])

You will see a list of options available in OpenCV for converting from one color space to another. We can pretty much convert any color space to any other color space. Let's see how we can convert a color image to a grayscale image:

import cv2
img = cv2.imread('./images/input.jpg', cv2.IMREAD_COLOR)
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imshow('Grayscale image', gray_img)
cv2.waitKey()

What just happened?

We use the cvtColor function to convert color spaces. The first argument is the input image and the second argument specifies the color space conversion.

Splitting image channels

You can convert to YUV by using the following flag:

yuv_img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)

The image will look something like the following one:

This may look like a deteriorated version of the original image, but it's not. Let's separate out the three channels:

# Alternative 1
y,u,v = cv2.split(yuv_img)
cv2.imshow('Y channel', y)
cv2.imshow('U channel', u)
cv2.imshow('V channel', v)
cv2.waitKey()
# Alternative 2 (Faster)
cv2.imshow('Y channel', yuv_img[:, :, 0])
cv2.imshow('U channel', yuv_img[:, :, 1])
cv2.imshow('V channel', yuv_img[:, :, 2])
cv2.waitKey()

Since yuv_img is a NumPy (which provides dimensional selection operators), we can separate out the three channels by slicing it. If you look at yuv_img.shape, you will see that it is a 3D array. So once you run the preceding piece of code, you will see three different images. Following is the Ychannel:

The channel is basically the grayscale image. Next is the U channel:

And lastly, the V channel:

As we can see here, the channel is the same as the grayscale image. It represents the intensity values, and channels represent the color information.

Merging image channels

Now we are going to read an image, split it into separate channels, and merge them to see how different effects can be obtained out of different combinations:

img = cv2.imread('./images/input.jpg', cv2.IMREAD_COLOR)
g,b,r = cv2.split(img)
gbr_img = cv2.merge((g,b,r))
rbr_img = cv2.merge((r,b,r))
cv2.imshow('Original', img)
cv2.imshow('GRB', gbr_img)
cv2.imshow('RBR', rbr_img)
cv2.waitKey()

Here we can see how channels can be recombined to obtain different color intensities:

In this one, the red channel is used twice so the reds are more intense:

This should give you a basic idea of how to convert between color spaces. You can play around with more color spaces to see what the images look like. We will discuss the relevant color spaces as and when we encounter them during subsequent chapters.

 

Image translation


In this section, we will discuss shifting an image. Let's say we want to move the image within our frame of reference. In computer vision terminology, this is referred to as translation. Let's go ahead and see how we can do that:

import cv2
import numpy as np
img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]
translation_matrix = np.float32([ [1,0,70], [0,1,110] ])
img_translation = cv2.warpAffine(img, translation_matrix, (num_cols, num_rows), cv2.INTER_LINEAR)
cv2.imshow('Translation', img_translation)
cv2.waitKey()

If you run the preceding code, you will see something like the following:

What just happened?

To understand the preceding code, we need to understand how warping works. Translation basically means that we are shifting the image by adding/subtracting the x and y coordinates. In order to do this, we need to create a transformation matrix, as follows:

Here, the tx and ty values are the x and y translation values; that is, the image will be moved by x units to the right, and by y units downwards. So once we create a matrix like this, we can use the function, warpAffine, to apply it to our image. The third argument in warpAffine refers to the number of rows and columns in the resulting image. As follows, it passes InterpolationFlags which defines combination of interpolation methods.

Since the number of rows and columns is the same as the original image, the resultant image is going to get cropped. The reason for this is we didn't have enough space in the output when we applied the translation matrix. To avoid cropping, we can do something like this:

img_translation = cv2.warpAffine(img, translation_matrix,
 (num_cols + 70, num_rows + 110))

If you replace the corresponding line in our program with the preceding line, you will see the following image:

Let's say you want to move the image to the middle of a bigger image frame; we can do something like this by carrying out the following:

import cv2
import numpy as np
img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]
translation_matrix = np.float32([ [1,0,70], [0,1,110] ])
img_translation = cv2.warpAffine(img, translation_matrix, (num_cols + 70, num_rows + 110))
translation_matrix = np.float32([ [1,0,-30], [0,1,-50] ])
img_translation = cv2.warpAffine(img_translation, translation_matrix, (num_cols + 70 + 30, num_rows + 110 + 50))
cv2.imshow('Translation', img_translation)
cv2.waitKey()

If you run the preceding code, you will see an image like the following:

Moreover, there are two more arguments, borderMode and borderValue, that allow you to fill up the empty borders of the translation with a pixel extrapolation method:

import cv2
import numpy as np
img = cv2.imread('./images/input.jpg')
num_rows, num_cols = img.shape[:2]
translation_matrix = np.float32([ [1,0,70], [0,1,110] ])
img_translation = cv2.warpAffine(img, translation_matrix, (num_cols, num_rows), cv2.INTER_LINEAR, cv2.BORDER_WRAP, 1)
cv2.imshow('Translation', img_translation)
cv2.waitKey()
About the Authors
  • Gabriel Garrido Calvo

    Gabriel Garrido is a multifaceted and versatile software engineer with more than 7 years of experience in developing web applications for companies such as Telefonica, Trivago, and Base7Booking. He has a degree in computer science from the University of Granada, Spain. He is passionate about coding, focusing on its quality and spending hours working on personal projects based on technologies such as computer vision, artificial intelligence, and augmented reality. Taking part in hackathons is one of his hobbies. He has won a couple of prizes for implementing beta software for a Google Cardboard hackathon and another for a travel assistant at a TNOOZ hackathon.

    Browse publications by this author
  • Prateek Joshi

    Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world.

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