Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Blurring an image in OpenCV


Since we are already familiar with the basics, let's jump right into the code. I am skipping the header declarations because they remain the same as we saw in our previous code:

int main() 
{ 
  Mat input_image = imread("lena.png", IMREAD_GRAYSCALE); 
  Mat filtered_image; 
  blur(input_image, filtered_image, Size(3, 3), Point(-1, -1), BORDER_REPLICATE); 
   
  imshow("Original Image", input_image); 
  imshow("Filtered Image", filtered_image); 
  waitKey(0); 
  return 0; 
} 

The first thing that you notice about the blur() function is that the number of arguments is less than its counterpart. Upon a closer inspection, you'll find that the following two arguments are missing:

  • Depth of the output image: According to OpenCV's documentation for blur(), the output image has the same size and type as the source image. Since the equality between the input and output image types is already enforced by the implementation...