Role of Python Libraries in Image Processing

Python is a powerful programming language. It is quite easy to pick up and has a strong community worldwide. That’s why it has a huge set of user-generated and maintained libraries for all kinds of stuff. 

Today, we are going to exclusively discuss Python libraries in image processing, a topic every Python developer should be familiar with. Image processing has a lot of uses in the real worldfor example, OCR, designing, and forensics all of them use it in some capacity.

Whether your image processing tasks involve enhancing image quality, object detection, or automating visual tasks, Python’s ecosystem provides a library to do it. So, let’s check them out.

Importance of Image Processing

Image processing is one of those technologies that is used every day, but we don’t realise it. It has a critical role in various fields such as;

• Medical imaging
• Machine learning
• Augmented reality
• Optical character recognition
• Designing 
• Retail

Etc. For example, traffic cameras that can automatically detect infractions use image processing. MRI and X-ray machines’ results are enhanced with image processing to make them more understandable. Professional photographers use it to enhance their shots and recover details that were lost due to overexposure. 

There are many such examples. Here are a couple more to really show you how widely Python image processing is used across so many fields.

• Self-driving cars use real-time image processing to detect pedestrians, traffic lights, signs, road blocks, and whatnot.
• In retail, inventory management and self-checkout systems use image processing to make sure there are no bogus scans (i.e., swiping an empty box). 
• Security and surveillance systems use real-time image processing on the camera feed to identify motion, faces, and even crowd movement. 

All of these systems use a multitude of Python image processing libraries to perform their functions.

The reason we don’t realise it is because the image processing parts are neatly packaged in the form of user-friendly apps and software, where the process is hidden from us. 

Python libraries is commonly used for the development of such apps because it simplifies the process due to its powerful libraries and well-documented frameworks.

Let’s check out some of the most commonly used image processing libraries in Python. 

Key Python Libraries for Image Processing

These are the popular and most powerful image processing libraries available in Python.

OpenCV 

OpenCV is one of the most widely used libraries. It provides a range of functions for image analysis, face detection, and video capture. It is quite popular because it works fast and has less programming overhead as other libraries.

Pillow (PIL)

Pillow is a user-friendly library for basic image manipulation such as resizing, cropping, and format conversion. It’s ideal for lightweight applications and automation scripts. 

Scikit-image

Scikit-image is a library built on top of NumPy and SciPy. It is designed for advanced image analysis and supports operations such as segmentation, morphology, filtering, and feature extraction.

NumPy

Numpy is a number-handling library.  While not an image processing library per se, NumPy is essential for handling image data as arrays. It provides the computational backbone for most image processing tasks.

TensorFlow and PyTorch

These are deep learning libraries and are invaluable for image recognition, object detection, and neural style transfer. Today, they power many AI-based visual applications.

These libraries are used in the development of various applications, many of which we use today. Budding programmers can easily learn to use these libraries for their own apps and experiment with them. 

Integrating Image Processing with Other Tools

Python libraries can be combined with various web-based tools to provide specific functions or enhance existing ones. For instance, in an image to text converter, an OCR library such as pytesseract is used for the extraction of text from images.

However, Tesseract on its own is not that accurate unless you are using very high-quality images. That’s why many programmers commonly use OpenCV together with pytesseract to extract readable text from images. In this scenario, OpenCV is used to enhance the image quality before OCR is done. This is called image preprocessing.

An example workflow looks like this: 

import cv2
import pytesseract

# simple preprocessing + OCR

img = cv2.imread('scan.jpg', cv2.IMREAD_GRAYSCALE)

_, th = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY)

text = pytesseract.image_to_string(th)

print(text)

In this code, the image is being “binarized,” which is a process in which the color of the image is changed such that text contrasts highly against the background. This makes it easy for pytesseract to recognize the characters and perform its OCR functions with much higher accuracy.

To add more functions, you can read the documentation about OpenCV (or your chosen library) and learn what functions it provides. Then you can add them to your pre-processing pipeline.

The Future of Python Libraries in Image Processing

The good thing about Python is that it is very popular, regularly updated, and has a vast community of hobbyists, pros, and beginners. All of whom love it enough to create libraries, update older ones, create unique forks of existing ones, and whatnot.

So, in the future, Python will still be a major player in image processing, and it will evolve with the times to provide new functionality, improve existing functions, and even create out-of-the-box solutions for various image processing problems.

Here are some ways in which image processing in Python may progress in the future.

• AI Enhancement. Since Python is already used extensively for machine learning, we can expect to see image processing libraries that leverage that capability to do things like improve image quality, restore lost details, and remove artifacts. 

• 3D Image Processing. While current image processing technologies work with 2D images, we can expect, in the future, for Python image processing libraries to handle 3D imagery and scenery as well.

• IoT Integration. Current libraries are too heavy to run on small edge computers like IoT devices. In the future, we can expect these libraries to shrink in size and computation requirements so that they can work on IoT devices as well.

Those are just some of the ways in which Python image processing can advance. 

Conclusion

Python’s extensive libraries make it the go-to programming language for doing image processing tasks. 

Whether you want to make simple edits or do complex computer vision tasks, you can rely on these tools/libraries to do it. If you ever run into a problem, you can consult the vast documentation or pose a question to the huge Python community. 

If you’re exploring projects in image processing, partnering with an experienced python development company can help you integrate these libraries seamlessly into scalable applications.

This versatility and support system of Python ensures its continued dominance in image-based applications across industries.

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