15 Best Computer Vision Packages for Python

In recent years, the field of best Computer Vision (CV) has undergone remarkable evolution, propelled by advancements in hardware, software, and a growing community. Python’s supportive libraries, often powered by C/C++, have made Computer Vision (CV) more accessible and efficient, establishing Python as a critical language in this field.

Starting with Python for Computer Vision

Initiating your journey in Python based computer vision requires establishing the appropriate environment. Python 3.10 and essential packages can be installed in a virtual environment, providing a solid foundation for CV projects​​.

To start this pre-configured Python project, you’ll first need to set up a complimentary account on the ActiveState Platform. You can quickly register using either your GitHub credentials or an email address. This simple signup process not only grants you access to this project but also opens up a range of dependency management features offered by the platform.

You can opt for the State tool CLI to set up the Computer Vision Python runtime environment:

  • Windows users should execute the following command in a CMD window. It will seamlessly download and install the necessary Computer Vision Python runtime and project code into a virtual environment:
powershell -Command "& $([scriptblock]::Create((New-Object Net.WebClient).DownloadString('https://platform.activestate.com/dl/cli/911674306.1670279101_pdli01/install.ps1')))" -c'state activate --default Pizza-Team/Computer-Vision'
  • For users on Linux systems, execute the following command to seamlessly download and set up the Python runtime for Computer Vision, along with the project code, in a virtual environment:
sh <(curl -q https://platform.activestate.com/dl/cli/911674306.1670279101_pdli01/install.sh) -c'state activate --default Pizza-Team/Computer-Vision'

15 Best Computer Vision Packages for Python

These are some essential Computer Vision packages for Python:

Best Computer Vision Packages for Python

OpenCV

OpenCV, which stands for Open Source Computer Vision Library, is a powerful tool used in Python for processing and analyzing images and videos. These are overviews in simple terms:

What is OpenCV?

OpenCV is a library of programming functions mainly aimed at real-time computer vision. It’s open-source, free to use and distribute, and anyone can modify its code.

opencv python install

Uses of OpenCV

  • Image Processing: You can use OpenCV to edit or transform images, like changing color, size, or orientation.
  • Face Recognition: It can detect faces in images or videos, proper in security systems or photo tagging in social media.
  • Object Detection: OpenCV can identify objects in images or videos, like recognizing a car in a street video.
  • Motion Tracking: It’s used to track movements, like a person walking in a video.

Features of OpenCV

  • It has more than 2500 optimized algorithms.
  • Can process images and videos to identify objects, faces, or handwriting.
  • It works with different programming languages, but it’s popular with Python because of its easy syntax and large community.

Applications

  • Robotics
  • Automobiles for self driving cars
  • Medical for analyzing images
  • Security systems.

Most suitable for

  • Real-time image processing
  • Face recognition

Advantages

  • Open source 
  • Large community 
  • Extensive utilities

Limitations

  • Documentation can be sparse or overly technical.

TensorFlow

TensorFlow is a powerful library used in Python for a wide range of tasks in computer vision, a field of artificial intelligence that trains computers to interpret and understand the visual world. These are some key aspects of TensorFlow in simple terms:

conda install tensorflow

Applications 

  • Image classification 
  • Object detection
  • Image generation
  • Automated image labeling 
  • Facial recognition 
  • Medical imaging

Most suitable for 

  • We are deploying models on heterogeneous devices, image, and video processing.

Advantages 

  • Supports several algorithms 
  • Awesome documentation 
  • Large community

Limitations 

  • It cannot be evident for beginners due to multiple programming models.

PyTorch

PyTorch is a popular open-source machine learning library for Python, known for its flexibility and ease of use, especially in computer vision. These are some key details about PyTorch and its use in computer vision:

pytorch install

Most suitable for

  •  Deep learning models
  • Image processing

Advantages

  • Flexible computation model
  • Native GPU acceleration
  • Large community

Limitations

  • Steep learning curve
  • Limited model execution portability

Scikit Image

Scikit-image is a popular open-source library in Python designed for image processing. It’s part of the larger SciPy ecosystem, a collection of open-source software for scientific computing in Python. Here are some key details about scikit-image, explained in simple terms:

Scikit Image

Applications

  • Medical image analysis
  • Machine learning
  • Robotics
  • Scikit image

Most suitable for 

  • I am learning and experimenting with CV concepts/algorithms.

Advantages 

  • Familiar scikit-learn API
  • Compatible with OpenCV.

Limitations 

  • No built-in object detection 
  • Video processing processing

SimpleCV

SimpleCV is a framework for building computer vision applications in Python. It’s designed to be easy to use and understand, especially for those new to computer vision. These are some key points about SimpleCV:

python simplecv

Most suitable for

  • Application development
  • Beginners in CV

Advantages 

  • Simplified image tasks
  • Processing
  • Easy learning

Limitations

  • Smaller community compared to OpenCV.

DeepFace

DeepFace is a powerful and versatile tool in computer vision, particularly for Python users. These a simple breakdown of what it is and what it does:

deepfaces

Most suitable for

  • Face recognition 
  • Attribute analysis

Advantages

  • State-of-the-art models 
  • Strong in real-time video analysis

Limitations

  • No GPU acceleration
  • Small community 
  • Limited scope

YOLO (You Only Look Once)

DeepFace is a powerful and versatile tool in computer vision, particularly for Python users. Known for its object detection and image segmentation prowess, YOLO is user-friendly and capable of real-time video processing. However, it has a more limited scope compared to some other libraries.

YOLO (You Only Look Once)

Most suitable for

  • Object detection
  • Image segmentation

Advantages

  • Model size segmentation
  • Easy to use
  • Real-time support for video

Limitations

  • Limited scope 
  • Small community

Detectron2

Detectron2 is a popular software framework used for computer vision, a field of artificial intelligence that teaches computers to interpret and understand the visual world. Developed by Facebook AI Research (FAIR), Detectron2 is written in Python and is used primarily for object detection and segmentation. These a breakdown of its key features in simple terms:

detectron2 install

Most suitable for 

  • Pose prediction
  • Object detection

Advantages 

  • Specialized models
  • Data augmentation capabilities

Limitations

  • Scarce documentation
  • Small community

OpenVINO

OpenVINO is a software toolkit designed to optimize and accelerate computer vision and deep learning applications. It’s part of the broader set of tools developed by Intel for machine learning and AI applications. These are some critical details about OpenVINO, explained in simple terms:

Most suitable for 

  • Emulating human vision

Advantages

  • Compatibility with major frameworks
  • Model security schema
  • Pre-trained models

Limitations

  • Sparse documentation 
  • Small community

Albumentations

Albumentations is a powerful and flexible library for image augmentation in Python, particularly useful in computer vision. A library dedicated to image augmentation, Albumentations enriches datasets for classification, segmentation, and detection tasks, integrating seamlessly with PyTorch and Keras.

Albumentations

Most suitable for

  • Image augmentation

Advantages

  • Supports key points 
  • Multiple targets augmentation

Limitations

  • Scarce documentation 
  • Small community

Pillow

A fork of the Python Imaging Library (PIL), Pillow offers extensive file format support and straightforward image processing capabilities, making it ideal for basic image manipulation tasks.

Most suitable for

  • Basic image manipulation tasks

Advantages

  • Extensive file format support 
  • Straightforward usage

Limitations

  • Limited advanced features and image processing capabilities

Mahotas

Mahotas is a computer vision and image processing library for Python. It’s designed to be fast and efficient, making it suitable for handling large images and data sets. This library provides fast computer vision algorithms implemented in C++, accessible from Python. It’s beneficial for tasks involving image segmentation and feature extraction.

mahotas

Most suitable for

  • Image segmentation
  • Feature extraction

Advantages 

  • Fast algorithms 
  • Accessible from Python

Limitations 

It may require additional integration for complete CV applications.

ImageAI

 A powerful tool for applying state-of-the-art image recognition models with minimal code, ImageAI is user-friendly and highly suitable for beginners.

imageai python

Applications

  • Security (for face recognition)
  •  Retail (to track customer behavior) 
  • Healthcare (for analyzing medical images)

Most suitable for 

  • Easy image recognition

Advantages

  • User-friendly 
  • Minimal code

Limitations 

  • Limited advanced features

Pycocotools

Pycocotools is a collection of tools used in computer vision, specifically designed to work with the COCO (Common Objects in Context) dataset, a large image dataset famous for object detection, segmentation, and image captioning tasks. This tool is essential for working with the COCO dataset and is widely used in object detection, segmentation, and captioning tasks. It provides utilities for dataset analysis and result evaluation.

Pycocotools python

Most suitable for

  • COCO dataset tasks

Advantages

  • Dataset analysis 
  • Result evaluation

Limitations 

  • Limited to the COCO dataset

Dlib

Dlib is a versatile library in Python, widely used in computer vision. Known for its machine learning and computer vision capabilities, Dlib is particularly strong in face detection and recognition, offering a range of utilities for these purposes.

Dlib python

Most suitable for

  • Face detection
  • Recognition

Advantages 

  • Strong in face-related tasks

Limitations

  • Limited to face-related tasks

Conclusion

These open-source tools have helped computer vision models do many things. They work on standard computers and can be used for many different tasks. This makes computer vision easy to use and always getting better.

To summarize, using Python for computer vision, deep learning, open-source tools, and many Python libraries is a growing area. Python is easy to use and has many helpful tools, making it great for new and experienced people in computer vision.

Next steps:

Download the Computer Vision Python and try the packages in this post for yourself. 

Hey, I'm Faheem Bhatti An AI Powered Digital Marketing Expert, passionate writer, and expert in the fields of technology, gaming, artificial intelligence, and robotics.

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