Python Image Processing Made Easy: A Beginner's Tutorial

Introduction

Image processing is changing the game in many fields, making things faster, safer, and smarter. From helping doctors see more clearly in medical images to making cars drive themselves, it’s everywhere. Python, with its easy-to-learn style and powerful tools, is making image processing more accessible to everyone. Whether you’re new to this or looking to sharpen your skills, Python is the perfect place to start your image processing journey. Let’s dive in and see how it’s making a difference and why it’s the go-to choice for so many.

Comprehensive Overview

What is Image Processing?

Image processing is like giving a computer a pair of smart glasses to help it understand and work with pictures. It involves taking an image and manipulating it to improve its look, find specific parts, or even get important information from it. This can mean making the colors pop, sharpening the edges, or identifying where an object is in the picture.

‍

Applications and Benefits

Image processing is used in tons of areas, making a big impact:

Healthcare: Doctors use it to get clearer images from scans and X-rays, helping them spot diseases early.
Security: It’s used in face recognition and surveillance cameras to keep places safe.
Automotive: Self-driving cars rely on image processing to “see” the road and make safe driving decisions.
Entertainment: In movies and video games, it helps create stunning visual effects.
‍

The benefits are huge. Image processing can save time, make things more accurate, and even save lives in medical settings.

‍

Why Python?

Python is a top pick for image processing because it’s easy to learn and has a massive community behind it. This means lots of guides, tutorials, and tools are available to help you get started. Python’s libraries, like OpenCV, provide ready-made functions for a wide range of image processing tasks. This makes it quicker and easier to get results, even if you’re not a programming pro. Plus, Python works well with other tools and systems, making it a flexible choice for all kinds of projects.

In short, image processing is a powerful tool with applications in many fields, and Python is the ideal language to explore this exciting area, thanks to its simplicity, strong libraries, and supportive community.

‍

Image Processing Libraries in Python

OpenCV

Description: OpenCV is one of the most popular and comprehensive libraries for image processing. It provides a wide range of tools for image manipulation, computer vision, and even machine learning.


pip install opencv-python

Image blurring example:


import cv2
import urllib.request
import numpy as np

req = urllib.request.urlopen('https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png')
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1)

blur_img = cv2.blur(img, (5,5))

cv2.imshow('Image blurring', blur_img)
cv2.waitKey(0)

Pros:‍

Versatile: Offers a huge set of functions covering various aspects of image processing and computer vision.

Performance: Highly optimized for real-time applications, making it a go-to for video analysis and robotics.
Community: Has a large and active community, ensuring plenty of resources and support.

‍

‍Cons:

Complexity: Due to its extensive functionality, it can be overwhelming for beginners.
Learning Curve: Some of its advanced features require a good understanding of image processing concepts.

‍

Scikit-image

Description: Scikit-image is a collection of algorithms for image processing that is built on top of SciPy. It is designed to be easy to use while still providing advanced image processing capabilities.


pip install scikit-image

Image Blurring example:


from skimage import io, filters

img = io.imread("https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png")
blur_img = filters.gaussian(img, sigma=5, channel_axis=-1)

io.imshow(blur_img)
io.show()

‍

Pros:

Integration: Seamlessly works with other SciPy libraries, making it a great choice for scientific and research applications.
User-Friendly: Designed to be easy to use, promoting good practices in image processing.

‍

Cons:

Speed: Some operations might be slower compared to OpenCV, especially for real-time applications.
Less Features: While it covers a wide range of image processing tasks, it might not have as many advanced features as OpenCV.
‍

PIL/Pillow

Description: Pillow is an easy-to-use library for opening, manipulating, and saving many different image file formats. It is a fork of the original PIL (Python Imaging Library), adding some user-friendly features and compatibility with modern Python versions.


pip install pillow

Image blurring example:


import urllib.request
from PIL import Image, ImageFilter

urllib.request.urlretrieve("https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png", "img_dog.png")
img = Image.open("img_dog.png")

blur_img = img.filter(ImageFilter.BLUR)

blur_img.show()

Pros:

Simplicity: Very straightforward to use, making it great for basic image processing tasks.
Documentation: Well-documented, helping new users get started quickly.

‍

Cons:

Limited Advanced Features: Not as feature-rich as OpenCV, especially for complex image processing tasks.
Performance: Might not be as optimized for performance as OpenCV.
‍

NumPy

Description: While not a dedicated image processing library, NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.


pip install numpy

Image blurring example:


from skimage import io
import numpy as np

img = io.imread("https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png")

kernel = np.array([1.0,4.0,6.0,4.0,1.0])/16.0
blur_img = np.apply_along_axis(lambda x: np.convolve(x, kernel, mode='same'), 0, img/255.0)
blur_img= np.apply_along_axis(lambda x: np.convolve(x, kernel, mode='same'), 1, blur_img)

io.imshow(blur_img)
io.show()

‍

Pros:‍

Flexibility: Can be used for a wide range of applications, not limited to image processing.

Performance: Offers high-performance operations, especially for array manipulations.

‍

Cons:

Not Specific to Image Processing: Lacks many of the specialized image processing functions found in libraries like OpenCV.
Learning Curve: Requires a good understanding of array manipulations and mathematical operations.
‍

Each of these libraries has its own strengths and weaknesses, making them suitable for different types of image processing tasks. Choosing the right one depends on your specific needs, your level of expertise, and the nature of your project.

Introduction to Ikomia

Now that we’ve explored various image processing libraries in Python, let’s take a closer look at Ikomia, a platform designed to make image processing even more accessible and powerful. Ikomia provides a set of tools that work seamlessly with Python, helping you to streamline your image processing workflows and achieve better results with less effort. Here’s a breakdown of what Ikomia has to offer:
‍

Ikomia HUB

Description: Ikomia HUB is like a big library filled with open source image processing algorithms. It’s a place where you can find, share, and use different image processing solutions created by experts from around the world.

‍

Pros:

Variety: Offers a wide range of open source algorithms, covering various image processing tasks.
Community-Driven: You can contribute your own solutions and benefit from the knowledge of others.

‍

Cons:

New Platform: As a newer platform, it might not have as many contributions as more established communities yet.
‍

Ikomia STUDIO

Description: Ikomia STUDIO provides a user-friendly interface for designing and executing image processing workflows. It’s like having a personal assistant to help you visualize and manage your image processing tasks.

‍

Pros:

Ease of Use: Designed to be intuitive, making it easier to create and run image processing workflows.
Visual Interface: Helps you see what’s happening at each step of your workflow.

‍

Cons:

Learning New Interface: If you’re used to coding everything by hand, it might take some time to get used to a visual interface.
‍

Ikomia API

Description: The Ikomia API allows you to integrate Ikomia’s capabilities directly into your Python programs. It’s like having a set of superpowers that you can add to your image processing toolkit.

‍

Pros:

Flexibility: Use Ikomia’s advanced features directly in your own Python scripts and applications.
Integration: Easily integrates with existing Python libraries and tools.

‍

Cons:

Requires Python Knowledge: To make the most of the API, you’ll need a good understanding of Python.
‍

Ikomia is all about making image processing easier, faster, and more efficient. Whether you’re looking for ready-made solutions, a visual way to manage your workflows, or a way to supercharge your Python scripts, Ikomia has something to offer. So why not give it a try and see how it can enhance your image processing projects?

‍

-> How to get started with Ikomia STUDIO

‍

-> How to get started with Ikomia API

‍