Building a Machine Vision and Image Recognition Engine with Ruby

Building a Machine Vision and Image Recognition Engine with Ruby

by

Hello everyone! Today we are going to explore an interesting and practical topic: Implementing a Machine Vision and Image Recognition Engine Using Ruby. Although Ruby is not specifically designed for machine learning or computer vision, it has powerful libraries and tools, such as ruby-vips and opencv-bindings, that can help us quickly get started with image processing. Even if you are a beginner, don’t worry, I will guide you step by step on how to manipulate image data with Ruby and ultimately achieve a simple image recognition functionality.

Machine vision may seem complex, but its essence boils down to two terms: Image Processing and Pattern Recognition. Our goal today is to understand these core concepts and learn basic operations, such as reading images, processing pixel data, and how to perform simple image classification. Without further ado, let’s get started!

The first step in machine vision is to enable the program to read and display images. In Ruby, we can use the ruby-vips library to handle images. It is fast and powerful, making it ideal for quick implementation.

bash copy

gem install ruby-vips

ruby copy

require 'vips'

image = VipsImage.new_from_file("example.jpg")


image.write_to_file("output.jpg")

Output:

  • The program will load example.jpg and output the image’s width and height.
  • It will also generate a file named output.jpg as a copy of the read image.

Tip: If you do not have the example.jpg file, you can find any image and place it in the working directory, ensuring the file name matches.

The core of image processing lies in manipulating the pixels of the image. Whether it is scaling, cropping, or converting to grayscale, pixel manipulation is essential. Let’s look at some common operations.

We can use the functions provided by ruby-vips to scale or crop images.

ruby copy

require 'vips'

image = VipsImage.new_from_file("example.jpg")

scaled_image = image.resize(0.5)

cropped_image = scaled_image.crop(50, 50, 200, 200)

scaled_image.write_to_file("scaled.jpg")
cropped_image.write_to_file("cropped.jpg")

Output:

  • scaled.jpg is the image reduced to half its original size.
  • cropped.jpg is a 200×200 region cropped from the scaled image.

Grayscale conversion is a fundamental operation in machine vision, allowing color images to be converted to single-channel images, which is very useful in subsequent image analysis.

grayscale_image = image.colourspace(“b-w”)
gray_image.write_to_file(“grayscale.jpg”)

Tip: In machine vision, grayscale images can reduce computational complexity because they only need to process data from a single channel.

To achieve image recognition, we need to use more powerful tools, such as OpenCV. Ruby provides the binding library opencv-bindings that allows us to directly call OpenCV methods.

First, ensure that OpenCV is installed on your system, then install the Ruby binding library:

bash copy

gem install opencv-bindings

We will use OpenCV to detect whether there are specific objects, such as faces, in an image.

ruby copy

require 'opencv'

include OpenCV

image = CvMat.load("example.jpg")

detector = CvHaarClassifierCascade.load("haarcascade_frontalface_default.xml")

faces = detector.detect_objects(image)

faces.each do |rect|
  image.rectangle!(rect.top_left, rect.bottom_right, color: CvColorRed, thickness: 2)
end

image.save_image("faces_detected.jpg")

Output:

  • If there are faces in the image, faces_detected.jpg will contain red rectangles marking them.

Notes:

  1. Make sure to download the haarcascade_frontalface_default.xml file and place it in the working directory.

ruby copy

require 'opencv'
include OpenCV

def classify_image(image_path)
  image = CvMat.load(image_path)

  hist = image.calc_hist([0])

  mean = hist.mean[0]
  if mean > 100
    "Dog"
  else
    "Cat"
  end
end

Output:

The program will output “Cat” or “Dog” based on the color characteristics of the image.

Today we learned:

  1. How to use ruby-vips for basic image processing (reading, scaling, cropping, and grayscale conversion).
  2. How to use OpenCV for simple image recognition (face detection).
  3. How to implement a simple image classifier using color histograms.

Exercises:

  1. Try implementing a blur filter using ruby-vips.
  2. Modify the face detection program to try detecting other objects (such as eyes or vehicles).
  3. Optimize the classifier’s logic to improve its accuracy.

Image processing and machine vision are fascinating fields, and I encourage everyone to practice hands-on and experiment with different image operations. If you have any questions, feel free to ask!

( )

Leave a Comment