Basics of image processing using Python and practical machine learning programming course: Models and usage from MLP to ViT

Introduction to Image Processing with Python

Image processing is a fundamental skill in the field of computer vision and machine learning.
It involves manipulating and analyzing images to extract valuable information or enhance the image quality.
Python, with its extensive libraries and community support, is one of the most popular languages for image processing tasks.
In this article, we’ll dive into the basics of image processing using Python and explore how it integrates with machine learning models, ranging from Multilayer Perceptrons (MLPs) to Vision Transformers (ViT).

Getting Started with Python Image Processing

Python provides numerous libraries that simplify image processing tasks.
Among the most widely used are OpenCV, PIL (Pillow), and scikit-image.

1. OpenCV

OpenCV (Open Source Computer Vision Library) is a comprehensive library that contains over 2,500 optimized algorithms.
To get started with OpenCV, you can install it using pip:

“`
pip install opencv-python
“`

OpenCV allows you to read, display, and manipulate images effortlessly.
A basic example of reading and displaying an image using OpenCV in Python is as follows:

“`python
import cv2

# Load an image
image = cv2.imread(‘example.jpg’)

# Display the image
cv2.imshow(‘Image’, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

2. PIL/Pillow

PIL (Python Imaging Library) or its modern fork Pillow is another popular library for handling images.
Pillow makes it simple to open, manipulate, and save different image file formats.
To install Pillow, you can use:

“`
pip install pillow
“`

Here’s how you can use Pillow to open and display an image:

“`python
from PIL import Image

# Load an image
image = Image.open(‘example.jpg’)

# Display the image
image.show()
“`

3. scikit-image

scikit-image is a collection of algorithms for image processing based on NumPy arrays.
It’s particularly useful for scientific and advanced image processing tasks.
You can install it with:

“`
pip install scikit-image
“`

A quick example of using scikit-image to read and manipulate images:

“`python
from skimage import io

# Load an image
image = io.imread(‘example.jpg’)

# Manipulate image (convert to grayscale)
gray_image = io.rgb2gray(image)

# Display the image
io.imshow(gray_image)
io.show()
“`

Integrating Image Processing with Machine Learning

Image processing is often the first step in preparing data for machine learning models.
By converting images into numerical representations, we can train models to recognize patterns and make predictions.

Multilayer Perceptrons (MLP)

MLPs are one of the simplest types of neural networks.
They consist of an input layer, hidden layers, and an output layer.
To use MLPs for image processing, images must be flattened into 1D arrays.
While MLPs can handle image data, they are usually less effective than convolutional neural networks for image-specific tasks.

Convolutional Neural Networks (CNN)

CNNs are designed to process visual data and are very effective in image classification and recognition tasks.
They use convolutional layers to detect local patterns and features in images.

Vision Transformers (ViT)

Vision Transformers, a recent advancement in the field, apply the transformer architecture, initially developed for natural language processing, to vision tasks.
They have shown great promise in achieving high accuracy with fewer data and less computational power than traditional CNNs.

Practical Machine Learning Programming

Applying machine learning models to image processing tasks requires a curated dataset and a clear understanding of the problem.

1. Data Preprocessing

Before feeding data into a machine learning model, it’s essential to preprocess it.
This includes resizing images, normalizing pixel values, and augmenting data to improve model robustness.

2. Model Selection

Choosing the right model depends on the complexity of the task and the available data.
For simple classification tasks, an MLP might suffice.
For more complex tasks like object detection, CNNs or ViTs may be required.

3. Training and Evaluation

Training involves feeding the preprocessed images to the model and iteratively updating the model’s parameters.
Evaluating the model’s performance on unseen data helps ensure that it generalizes well.

4. Deployment

Once a model is sufficiently trained and validated, it can be deployed into a production environment.
This might involve integrating the model into an application or using it as a standalone tool for inference.

Conclusion

The combination of Python’s image processing capabilities and the power of machine learning models opens up numerous possibilities in the field of AI.
Whether you’re working with simple datasets or complex visual data, mastering the basics of image processing and machine learning can greatly enhance your projects.
As you continue to explore these tools, you’ll find ways to optimize and innovate in your applications.