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OpenCV with Python By Example
OpenCV with Python By Example

OpenCV with Python By Example: Build real-world computer vision applications and develop cool demos using OpenCV for Python

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OpenCV with Python By Example

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 discuss how to install the necessary tools and packages as well.

By the end of this chapter, you will know:

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

Installing OpenCV-Python

Let's see how to install OpenCV with Python support on multiple platforms.

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 any error. 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.

Mac OS X

To install OpenCV-Python, we will be using Homebrew. Homebrew is a great package manager for Mac OS X and it will come in handy when you are installing various libraries and utilities on OS X. If you don't have Homebrew, you can install it by running the following command on 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 pip 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 package NumPy 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/2.4.9/. 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:

$ cd /Library/Python/2.7/site-packages/
$ ln -s /usr/local/Cellar/opencv/2.4.9/lib/python2.7/site-packages/cv.py cv.py
$ ln -s /usr/local/Cellar/opencv/2.4.9/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 message. You are now ready to use OpenCV in Python.

Linux (for Ubuntu)

Before we start, we need to install some dependencies. Let's install them using the package manager as shown below:

$ sudo apt-get -y install libopencv-dev build-essential cmake libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff4-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils python-scipy python-pip python-virtualenv

Now that you have installed the necessary packages, let's go ahead and build OpenCV with Python support:

$ wget "https://github.com/Itseez/opencv/archive/2.4.9.tar.gz" -O ./opencv/opencv.tar.gz
$ cd opencv
$ tar xvzf opencv.tar.gz -C .
$ mkdir release
$ cd release
$ sudo apt-get –y install cmake
$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -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 ../ 
$ make –j4
$ sudo make install

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.

If you have any other Linux distribution, please refer to the OpenCV downloads page (http://opencv.org/downloads.html) for installation details.

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 0 as the argument, this function will wait 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 flag cv2.IMREAD_GRAYSCALE to load the image in grayscale mode. You can see that from the image being displayed in the new window. Next, is the input image:

Loading and saving an image

Following is the corresponding grayscale image:

Loading and saving an image

We can save this image into a file as well:

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

This will save the grayscale image into 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.

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: It's 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 of the 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 between 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 into any other color space. Let's see how we can convert a color image into a grayscale image:

import cv2
img = cv2.imread('./images/input.jpg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale image', gray_img)
cv2.waitKey()

What just happened?

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

What just happened?

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

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 array, 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 whose dimensions are NUM_ROWS x NUM_COLUMNS x NUM_CHANNELS. So once you run the preceding piece of code, you will see three different images. Following is the Y channel:

What just happened?

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

What just happened?

And lastly, the V channel:

What just happened?

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

Let's convert to HSV and see what happens:

hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cv2.imshow('HSV image', hsv_img)
What just happened?

Again, let's separate the channels:

cv2.imshow('H channel', hsv_img[:, :, 0])
cv2.imshow('S channel', hsv_img[:, :, 1])
cv2.imshow('V channel', hsv_img[:, :, 2])
cv2.waitKey()

If you run the preceding piece of code, you will see three different images. Take a look at the H channel:

What just happened?

Next is the S channel:

What just happened?

Following is the V channel:

What just happened?

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 about 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.imshow('Translation', img_translation)
cv2.waitKey()

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

Image translation

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 shown as follows:

What just happened?

Here, the tx and ty values are the X and Y translation values, that is, the image will be moved by X units towards the right, and by Y units downwards. So once we create a matrix like this, we can use the function, warpAffine, to apply to our image. The third argument in warpAffine refers to the number of rows and columns in the resulting image. 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 because 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:

What just happened?

Let's say you want to move the image in 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:

What just happened?
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Key benefits

  • Learn how to apply complex visual effects to images using geometric transformations and image filters
  • Extract features from an image and use them to develop advanced applications
  • Build algorithms to understand the image content and recognize objects in an image

Description

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 are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.

Who is this book for?

This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

What you will learn

  • Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
  • Detect and track various body parts such as the face, nose, eyes, ears, and mouth
  • Stitch multiple images of a scene together to create a panoramic image
  • Make an object disappear from an image
  • Identify different shapes, segment an image, and track an object in a live video
  • Recognize objects in an image and understand the content
  • Reconstruct a 3D map from images
  • Build an augmented reality application

Product Details

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Publication date : Sep 22, 2015
Length: 296 pages
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Language : English
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Table of Contents

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

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(11 Ratings)
5 star 45.5%
4 star 9.1%
3 star 9.1%
2 star 0%
1 star 36.4%
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Prashant Bhardwaj Dec 28, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Really good for starting off in Opencv and creating applications.
Amazon Verified review Amazon
Sridhar D Dec 18, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent introduction to OpenCV and Computer Vision.The chapters are written in a clear and concise manner. Even though I'm a beginner in Python, I could understand what's going on. I enjoyed reading the sections after code snippets where the author explains what's going on. Some of the really technical topics are explained very nicely, which helps beginners like myself. It was fun to understand how those image transformations actually work. Overall, I would say it was a great experience! You'll need to be familiar with Python if you want to fully appreciate the book. I don't know that much Python, but the code was pretty easy to follow.
Amazon Verified review Amazon
Gopal Lingsur Dec 23, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am a huge fan of Prateek Joshi's blog and I think he is excellent at explaining things. I really like his writing style, so I was looking forward to getting this book. It is very well written and the explanations are clear. Another thing I liked about it is the abundance of images and code samples. The code is well commented and it helped me visualize the results at each step.I was happy to see different chapters dedicated to various aspects of computer vision. At the end of each chapter, you get to see something working. There's not a single chapter that's "dull". It was really fun when I saw the output of the augmented reality code on my own laptop. I would highly recommend this book to people who want to get started with computer vision and OpenCV.
Amazon Verified review Amazon
Pramod kulkarni Dec 18, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book to get started on open CV . This is an excellent book for beginners like me. I really liked how the author breaks down each topic to explain it in simple terms. Computer vision algorithm are very mathematical but the author has done a great job of communicating them. I especially liked the chapters on panoramic imaging and seam carving . It feels good to understand the underlying concepts without going through all the equations. The book also provides relevant links wherever required so that we can follow up in more details
Amazon Verified review Amazon
Spoorthi V. Dec 16, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very easy to follow! The examples are friendly and I liked the explanations. I'm not very familiar with OpenCV, so I wanted to try it out. I enjoyed playing around with projects like augmented reality and object recognition. You need to know a little bit of Python to understand what's going on. The best thing about this book is the focus on intuitive explanations and the associated real-life examples.I liked how the author starts each topic with by explaining the problem. It's easy to follow the flow of the topic when we know why we are solving it in the first place. I liked the clear explanations in some of the more involved topics like object tracking and 3D reconstruction. I would definitely recommend this to people who want to get started with computer vision.
Amazon Verified review Amazon
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Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

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Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.