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Home > Data > Computer Vision > OpenCV with Python By Example
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 2. Detecting Edges and Applying Image Filters

In this chapter, we are going to see how to apply cool visual effects to images. We will learn how to use fundamental image processing operators. We are going to discuss edge detection and how we can use image filters to apply various effects on photos.

By the end of this chapter, you will know:

  • What is 2D convolution and how to use it
  • How to blur an image
  • How to detect edges in an image
  • How to apply motion blur to an image
  • How to sharpen and emboss an image
  • How to erode and dilate an image
  • How to create a vignette filter
  • How to enhance image contrast

    Tip

    Downloading the example code

    You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

2D convolution

Convolution is a fundamental operation in image processing. We basically apply a mathematical operator to each pixel and change its value in some way. To apply this mathematical operator, we use another matrix called a kernel. The kernel is usually much smaller in size than the input image. For each pixel in the image, we take the kernel and place it on top such that the center of the kernel coincides with the pixel under consideration. We then multiply each value in the kernel matrix with the corresponding values in the image, and then sum it up. This is the new value that will be substituted in this position in the output image.

Here, the kernel is called the "image filter" and the process of applying this kernel to the given image is called "image filtering". The output obtained after applying the kernel to the image is called the filtered image. Depending on the values in the kernel, it performs different functions like blurring, detecting edges, and...

Blurring

Blurring refers to averaging the pixel values within a neighborhood. This is also called a low pass filter. A low pass filter is a filter that allows low frequencies and blocks higher frequencies. Now, the next question that comes to our mind is—What does "frequency" mean in an image? Well, in this context, frequency refers to the rate of change of pixel values. So we can say that the sharp edges would be high frequency content because the pixel values change rapidly in that region. Going by that logic, plain areas would be low frequency content. Going by this definition, a low pass filter would try to smoothen the edges.

A simple way to build a low pass filter is by uniformly averaging the values in the neighborhood of a pixel. We can choose the size of the kernel depending on how much we want to smoothen the image, and it will correspondingly have different effects. If you choose a bigger size, then you will be averaging over a larger area. This tends to increase...

Edge detection

The process of edge detection involves detecting sharp edges in the image and producing a binary image as the output. Typically, we draw white lines on a black background to indicate those edges. We can think of edge detection as a high pass filtering operation. A high pass filter allows high frequency content to pass through and blocks the low frequency content. As we discussed earlier, edges are high frequency content. In edge detection, we want to retain these edges and discard everything else. Hence, we should build a kernel that is the equivalent of a high pass filter.

Let's start with a simple edge detection filter known as the Sobel filter. Since edges can occur in both horizontal and vertical directions, the Sobel filter is composed of the following two kernels:

Edge detection

The kernel on the left detects horizontal edges and the kernel on the right detects vertical edges. OpenCV provides a function to directly apply the Sobel filter to a given image. Here is the code to use...

Motion blur

When we apply the motion blurring effect, it will look like you captured the picture while moving in a particular direction. For example, you can make an image look like it was captured from a moving car.

The input and output images will look like the following ones:

Motion blur

Following is the code to achieve this motion blurring effect:

import cv2
import numpy as np

img = cv2.imread('input.jpg')
cv2.imshow('Original', img)

size = 15

# generating the kernel
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size

# applying the kernel to the input image
output = cv2.filter2D(img, -1, kernel_motion_blur)

cv2.imshow('Motion Blur', output)
cv2.waitKey(0)

Under the hood

We are reading the image as usual. We are then constructing a motion blur kernel. A motion blur kernel averages the pixel values in a particular direction. It's like a directional low pass filter. A 3x3...

Sharpening

Applying the sharpening filter will sharpen the edges in the image. This filter is very useful when we want to enhance the edges in an image that's not crisp. Here are some images to give you an idea of what the image sharpening process looks like:

Sharpening

As you can see in the preceding figure, the level of sharpening depends on the type of kernel we use. We have a lot of freedom to customize the kernel here, and each kernel will give you a different kind of sharpening. To just sharpen an image, like we are doing in the top right image in the preceding picture, we would use a kernel like this:

Sharpening

If we want to do excessive sharpening, like in the bottom left image, we would use the following kernel:

Sharpening

But the problem with these two kernels is that the output image looks artificially enhanced. If we want our images to look more natural, we would use an Edge Enhancement filter. The underlying concept remains the same, but we use an approximate Gaussian kernel to build this filter. It will...

2D convolution

Convolution is a fundamental operation in image processing. We basically apply a mathematical operator to each pixel and change its value in some way. To apply this mathematical operator, we use another matrix called a kernel. The kernel is usually much smaller in size than the input image. For each pixel in the image, we take the kernel and place it on top such that the center of the kernel coincides with the pixel under consideration. We then multiply each value in the kernel matrix with the corresponding values in the image, and then sum it up. This is the new value that will be substituted in this position in the output image.

Here, the kernel is called the "image filter" and the process of applying this kernel to the given image is called "image filtering". The output obtained after applying the kernel to the image is called the filtered image. Depending on the values in the kernel, it performs different functions like blurring, detecting edges, and so on. The following...

Blurring

Blurring refers to averaging the pixel values within a neighborhood. This is also called a low pass filter. A low pass filter is a filter that allows low frequencies and blocks higher frequencies. Now, the next question that comes to our mind is—What does "frequency" mean in an image? Well, in this context, frequency refers to the rate of change of pixel values. So we can say that the sharp edges would be high frequency content because the pixel values change rapidly in that region. Going by that logic, plain areas would be low frequency content. Going by this definition, a low pass filter would try to smoothen the edges.

A simple way to build a low pass filter is by uniformly averaging the values in the neighborhood of a pixel. We can choose the size of the kernel depending on how much we want to smoothen the image, and it will correspondingly have different effects. If you choose a bigger size, then you will be averaging over a larger area. This tends to increase the smoothening...

Edge detection

The process of edge detection involves detecting sharp edges in the image and producing a binary image as the output. Typically, we draw white lines on a black background to indicate those edges. We can think of edge detection as a high pass filtering operation. A high pass filter allows high frequency content to pass through and blocks the low frequency content. As we discussed earlier, edges are high frequency content. In edge detection, we want to retain these edges and discard everything else. Hence, we should build a kernel that is the equivalent of a high pass filter.

Let's start with a simple edge detection filter known as the Sobel filter. Since edges can occur in both horizontal and vertical directions, the Sobel filter is composed of the following two kernels:

The kernel on the left detects horizontal edges and the kernel on the right detects vertical edges. OpenCV provides a function to directly apply the Sobel filter to a given image. Here is the code to use Sobel...

Motion blur

When we apply the motion blurring effect, it will look like you captured the picture while moving in a particular direction. For example, you can make an image look like it was captured from a moving car.

The input and output images will look like the following ones:

Following is the code to achieve this motion blurring effect:

import cv2
import numpy as np

img = cv2.imread('input.jpg')
cv2.imshow('Original', img)

size = 15

# generating the kernel
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size

# applying the kernel to the input image
output = cv2.filter2D(img, -1, kernel_motion_blur)

cv2.imshow('Motion Blur', output)
cv2.waitKey(0)

Under the hood

We are reading the image as usual. We are then constructing a motion blur kernel. A motion blur kernel averages the pixel values in a particular direction. It's like a directional low pass filter. A 3x3 horizontal motion-blurring kernel...

Sharpening

Applying the sharpening filter will sharpen the edges in the image. This filter is very useful when we want to enhance the edges in an image that's not crisp. Here are some images to give you an idea of what the image sharpening process looks like:

As you can see in the preceding figure, the level of sharpening depends on the type of kernel we use. We have a lot of freedom to customize the kernel here, and each kernel will give you a different kind of sharpening. To just sharpen an image, like we are doing in the top right image in the preceding picture, we would use a kernel like this:

If we want to do excessive sharpening, like in the bottom left image, we would use the following kernel:

But the problem with these two kernels is that the output image looks artificially enhanced. If we want our images to look more natural, we would use an Edge Enhancement filter. The underlying concept remains the same, but we use an approximate Gaussian kernel to build this filter. It will help...

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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, Length, Edition, Language, ISBN-13
Publication date : Sep 22, 2015
Length: 296 pages
Edition : 1st
Language : English
ISBN-13 : 9781785283932
Category :
Data
Languages :
Python
Concepts :
Computer Vision
Tools :
OpenCV

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Product Details

Publication date : Sep 22, 2015
Length: 296 pages
Edition : 1st
Language : English
ISBN-13 : 9781785283932
Category :
Data
Languages :
Python
Concepts :
Computer Vision
Tools :
OpenCV

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Table of Contents

13 Chapters
1. Applying Geometric Transformations to Images Chevron down icon Chevron up icon
1. Applying Geometric Transformations to Images
Installing OpenCV-Python
Reading, displaying, and saving images
Image color spaces
Image translation
Image rotation
Image scaling
Affine transformations
Projective transformations
Image warping
Summary
2. Detecting Edges and Applying Image Filters Chevron down icon Chevron up icon
2. Detecting Edges and Applying Image Filters
2D convolution
Blurring
Edge detection
Motion blur
Sharpening
Embossing
Erosion and dilation
Creating a vignette filter
Enhancing the contrast in an image
Summary
3. Cartoonizing an Image Chevron down icon Chevron up icon
3. Cartoonizing an Image
Accessing the webcam
Keyboard inputs
Mouse inputs
Interacting with a live video stream
Cartoonizing an image
Summary
4. Detecting and Tracking Different Body Parts Chevron down icon Chevron up icon
4. Detecting and Tracking Different Body Parts
Using Haar cascades to detect things
What are integral images?
Detecting and tracking faces
Fun with faces
Detecting eyes
Fun with eyes
Detecting ears
Detecting a mouth
It's time for a moustache
Detecting a nose
Detecting pupils
Summary
5. Extracting Features from an Image Chevron down icon Chevron up icon
5. Extracting Features from an Image
Why do we care about keypoints?
What are keypoints?
Detecting the corners
Good Features To Track
Scale Invariant Feature Transform (SIFT)
Speeded Up Robust Features (SURF)
Features from Accelerated Segment Test (FAST)
Binary Robust Independent Elementary Features (BRIEF)
Oriented FAST and Rotated BRIEF (ORB)
Summary
6. Creating a Panoramic Image Chevron down icon Chevron up icon
6. Creating a Panoramic Image
Matching keypoint descriptors
Creating the panoramic image
What if the images are at an angle to each other?
Summary
7. Seam Carving Chevron down icon Chevron up icon
7. Seam Carving
Why do we care about seam carving?
How does it work?
How do we define "interesting"?
How do we compute the seams?
Can we expand an image?
Can we remove an object completely?
Summary
8. Detecting Shapes and Segmenting an Image Chevron down icon Chevron up icon
8. Detecting Shapes and Segmenting an Image
Contour analysis and shape matching
Approximating a contour
Identifying the pizza with the slice taken out
How to censor a shape?
What is image segmentation?
Watershed algorithm
Summary
9. Object Tracking Chevron down icon Chevron up icon
9. Object Tracking
Frame differencing
Colorspace based tracking
Building an interactive object tracker
Feature based tracking
Background subtraction
Summary
10. Object Recognition Chevron down icon Chevron up icon
10. Object Recognition
Object detection versus object recognition
What is a dense feature detector?
What is a visual dictionary?
What is supervised and unsupervised learning?
What are Support Vector Machines?
How do we actually implement this?
Summary
11. Stereo Vision and 3D Reconstruction Chevron down icon Chevron up icon
11. Stereo Vision and 3D Reconstruction
What is stereo correspondence?
What is epipolar geometry?
Building the 3D map
Summary
12. Augmented Reality Chevron down icon Chevron up icon
12. Augmented Reality
What is the premise of augmented reality?
What does an augmented reality system look like?
Geometric transformations for augmented reality
What is pose estimation?
How to track planar objects?
How to augment our reality?
Let's add some movements
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Top Reviews
  • Top Reviews
  • Most Recent
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.
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Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
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Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
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Machine Learning Engineering with Python
Machine Learning Engineering with Python
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Aug 2023 462 pages
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About the author

Profile icon Joshi
Joshi
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Vijay Joshi is a full stack web developer having more than a decade of experience in working with PHP and JavaScript.
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