Computer vision is booming! How many of you know that the computer vision market could surpass more than $48 billion by 2026? This tech allows computers to see and understand images — just like we do. Python is instrumental in this process as it is the primary language for many individuals entering into this domain. In this article, we will walk through the best Python libraries for computer vision. You can get to know what they do and which suits your project best.

OpenCV: The Great Grandfather of Computer Vision Libraries

In the field of computer vision libraries, OpenCV is a big deal. Well, it’s been on the market for some time now, and still many people use it. Let’s explore it.

Why Use OpenCV and What is It?

OpenCV is the elder statesman of computer vision libraries. It was the beginning of a long, arduous, and very powerful journey. It’s a free library that provides utilities for image and video-oriented jobs. Its strengths? ✔ It has multioperational compatibility so that it can be used by many. But if you are just getting started, it may take some time to get used to.

Main Functionality of OpenCV

OpenCV packs a punch! It manages the image and video processing. If it sounds like it could also recognize objects, extract features from images, or create 3D models.

Now, this is very basic image processing example:

import cv2

Read an image

image = cv2. imread(‘image. jpg’)

Convert to grayscale

gray_image = cv2. cvtColor(image, cv2. COLOR_BGR2GRAY)

Display the image

cv2. imshow(‘GrayScale Image’, gray_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

OpenCV Applications in the Real World

OpenCV is Everywhere! That’s everything from robotics to security systems to medical imaging. Driverless cars use it to “see” the road as well. OpenCV, for example, enables robots to recognize objects or analyze medical scans.

TensorFlow: The Engine of Deep Learning for Computer Vision

TensorFlow is a heavyweight in the deep learning domain. It’s a powerful option for computer vision tasks that require that level of juice. Let’s learn how.

Shivam Asher · FollowPublished in TensorFlow for Image Recognition and Classification · 6 min read·Jan 12 ·Updated 10 min ago

Its major use is in recognizing and segregating images, where TensorFlow excels. It uses Convolutional Neural Networks (CNNs) specifically designed to process visual data. CNNs are capable of this, as they learn patterns and predict with high accuracy.

TensorFlow Image classification example†

import tensorflow as tf

Load a pre-trained model

model = tf. keras. applications. MobileNetV2(weights=’imagenet’, # We will use the ImageNet pretrained weights.

 Loading and preprocessing image

image = tf. keras. preprocessing. image. load_img(‘image. jpg’, target_size=(224, 224))

input_arr = tf. keras. preprocessing. image. img_to_array(image)

input_arr = np. # Reshape your single image to make a batch of “1” images input_arr = input_arr.reshape((1,)+input_arr.shape)

Make predictions

predictions = model. predict(input_arr)

decoded_predictions = tf. keras. applications. mobilenet_v2. decode_predictions(predictions)

print(decoded_predictions[0])

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The tensorflow object detection api is very helpful. It aids in the image to object locator. You can use pre-trained models, or you can modify them to better suit your specific requirements.

[TensorFlow and Real-time Computer Vision]

TensorFlow allows real-time operation. However, you have to optimize your models to give them speed. That means applying models compression techniques to make the model smaller and simpler.

PyTorch — Computer Vision Research Favorite Deep Learning Framework

What makes PyTorch to be out as an example is its flexibility. It works well for computer vision research. It allows researchers to experiment with little background noise.

Image Segmentation in PyTorch

One can use PyTorch for Image segmentation task. That’s when a picture breaks down into multiple chunks. Things like semantic segmentation, which labels every pixel, and instance segmentation, which recognizes individual objects, can fall under this umbrella.

Image Segmentation using PyTorch

import torch

import torchvision

Load a pre-trained model

model = torchvision. models. segmentation. fcn_resnet50(pretrained=True)

model.eval()

Read and process an image

image = Image. open(‘image. jpg’)

preprocess = torchvision. transforms. Compose([

torchvision. transforms. ToTensor(),

torchvision. transforms. Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]

])

input_tensor=preprocess(image)

input_batch = input_tensor. unsqueeze(0) # create a mini-batch as expected by the model

 If we have access to a GPU move the input and model there for speed

if torch.cuda.is_available():

input_batch = input_batch. to(‘cuda’)

model.to(‘cuda’)

with torch.no_grad():

output = model(input_batch)’out’

output_predictions = output. argmax(0)

PyTorch for Generative Models

Generative models also work with PyTorch. Imagination (like GANs (Generative Adversarial Networks)); These can produce images of people and things that are overall realistic-looking.

TensorFlow for Custom Computer Vision Solutions

This gives you the freedom to develop your own layer and build your own model in PyTorch. How flexible is it, and does that help you if you have a specific vision task? In addition, PyTorch has an active community, supplying a wealth of resources.

Scikit image: An image Processing library in Python

Scikit-image is mainly oriented toward the scientific side of image analysis. It works well with NumPy. This combined use is helpful for Data Manipulation.

Image Processing with Scikit-image: Filtering and Enhancement

This two-dimensional Fourier Transform magnitude will helps us to detect the most common filter of scikit-image. They are capable of smoothing images, sharpening edges, and more. Which is supplemented with morphological operations to change the shape of objects in the image.

Perhaps the simplest filtering example is as follows:

from skimage import io, filters

Read an image

image = io. imread(‘image. jpg’)

Apply a Gaussian filter

blurred_image = filters. gaussian(image, sigma=1)

Display the image

io.imshow(blurred_image)

io.show()

About Finding More About Image Segmentation and Measurement with Scikit-image

Segmentation algorithms with scikit-image. It can also measure properties of objects in an image.

Biomedical Image Analysis with SciKit-Image

Scikit-image has been used in the analysis of biomedical images. Such as counting cells and analyzing tissue. It gives researchers some important understandings.

Which library should you use for your project?

Choosing the right library can be tricky. Let’s break down the factors.

Factors to Consider

Consider what you want to accomplish. What functionalities do you will need? How fast does it need to run? Also, think about how much assistance is accessible on the internet and the difficulty level of learning it.

Comparison Chart: At a Glance Key Features

Feature OpenCV TensorFlow PyTorch Scikit-image

Usability Moderate Moderate Moderate Easy

Performance High High High Med

Community Support Extensive Extensive Extensive Decent

Object Detection Yes Yes Yes   No

Machine Learning Non-Supervised No Yes No

Image Analysis Basic Basic Basic Advanced

Tips and Tricks You Can Try Now

You can find their official tutorials for OpenCV. TensorFlow has excellent documentation. There are several online courses of PyTorch. Scikit-image is simple to use, with a lot of examples.

Conclusion

Computer vision libraries provide distinctive features. OpenCV is old and versatile. TensorFlow is best at deep learning. PyTorch indeed is easier to use and more flexible. If you need to perform analysis on scientific images, scikit-image is the best. Pick the tech right for your project objectives! Get started building awesome computer vision applications today!