The 12 Most Popular Computer Vision Tools in 2021

computer vision tools and software
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The tech industry has seen a massive increase in the adoption of computer vision tools and software in healthcare, agriculture, smart city, security, automotive, and more. This has been accompanied by the development of numerous toolkits, frameworks, and software libraries related to this field in recent years.

If you are looking for the most popular computer vision tools for developing your next project, this article is perfect for you. Below are some of the best tools you can use for creating an effective computer vision application.

List of the Most Popular Computer Vision Tools in 2021
  • Tool #1: OpenCV
  • Tool #2: TensorFlow
  • Tool #3: CUDA
  • Tool #4: Viso Suite
  • Tool #5: MATLAB
  • Tool #6: Keras
  • Tool #7: SimpleCV
  • Tool #8: BoofCV
  • Tool #9: CAFFE
  • Tool #10: OpenVINO
  • Tool #11: DeepFace
  • Tool #12: YOLO

1. OpenCV – Real-Time Computer Vision Library

OpenCV is an open-source machine learning and computer vision software library. Created with a view of providing a common infrastructure for computer vision applications, OpenCV allows access to 2,500-plus classic and state-of-the-art algorithms.

These algorithms are useful for several tasks, including face detection and recognition, red-eye removal, object identification, extraction of 3D models of objects, tracking moving objects, and stitching multiple frames together into a high-resolution image.

OpenCV has multiple interfaces like C++, Python, Java, and MATLAB, and it supports most operating systems, including Windows, Android, Linux, and Mac.

Pros:

  • Usage is free, and it is open-source
  • Large community support
  • Offers access to more than 2,500 algorithms
  • Allows you to tweak the code to serve specific purposes

Cons:

  • It is not as easy to use as other tools like MATLAB

2. TensorFlow – Software Library for Machine Learning

TensorFlow is among the most popular end-to-end open-source machine learning platforms with a comprehensive set of tools, resources, and libraries. TensorFlow is especially useful for building and deploying applications related to computer vision that are powered by machine learning.

TensorFlow is one of the easiest computer vision tools and allows users to develop computer vision-related machine learning models for tasks like facial recognition, image classification, object detection, and more. Tensorflow, like OpenCV, also supports various languages like Python, C, C++, Java, or JavaScript.

For real-world computer vision projects, the TensorFlow Lite is a lightweight implementation for on-device machine learning with edge devices. As part of TensorFlow, TF Lite greatly accelerates edge ML implementations with reduced model size and high accuracy at much higher efficiency, making it possible to run ML everywhere.

Pros:

  • It is an open-source platform
  • The platform is compatible with multiple languages
  • It provides constant updates for more features and improvements

Cons:

  • It is an extremely resource-hungry toolkit

3. CUDA – Parallel Computing and Programming

CUDA (short for Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model developed by NVIDIA. It allows developers to use the power of GPUs (Graphics Processing Units) to make processing-intensive applications faster.

The toolkit includes the NVIDIA Performance Primitives (NPP) library that provides GPU-accelerated image, video, and signal processing functions for multiple domains, including computer vision. In addition, the CUDA architecture is useful for a wide range of tasks like face recognition, image manipulation, rendition of 3D graphics, and others. Real-time image processing with Nvidia CUDA is supported for Edge AI implementations, to run on-device AI inference on edge devices such as the Jetson TX2.

It supports various programming languages, including C, C++, Python, Fortran, or MATLAB, and is also compatible with most operating systems.

Pros:

  • The NPP library comes with 5000-plus primitives for image and signal processing
  • It includes multiple language support
  • It is fast and effective

Cons:

  • Its power consumption is quite high

4. Viso Suite – No-Code Computer Vision Platform for Businesses

Viso Suite is an end-to-end computer vision platform for businesses to build, deploy and monitor real-world computer vision applications. The Viso platform allows businesses to build their own computer vision solution faster to market. It provides a robust infrastructure to deploy an AI model to a fleet of edge devices. The tools are optimized to build and monitor a large-scale computer vision system (for industrial automation, visual inspection, remote monitoring, and more).

It provides a workspace that comes with a large library of AI models, application templates, edge device management, visual editor, dashboards, and more.

Pros:

  • End-to-end platform to automate tasks with computer vision.
  • Visual programming saves a lot of development time and costs.
  • Cross-platform migration: Build once, deploy anywhere.
  • Brick-safe and robust deployment to edge devices.
  • Re-use cameras for deep learning applications.
  • Enterprise-grade security and access management.

Cons:

  • The platform isn’t free to use.

For more information about Viso Suite, check out the Features. To learn about Computer Vision platforms, I recommend reading the article What is a Computer Vision Platform?.

5. MATLAB – Programming Platform for Engineers and Scientists

MATLAB is a programming platform that is useful for a range of different applications such as machine learning, deep learning, and image, video, and signal processing.

It comes with a computer vision toolbox that has multiple functions, apps, and algorithms to help you design solutions for tasks related to computer vision.

Pros:

  • It is easy to use and learn; there are many free resources on MATLAB
  • Since it is a programming language, writing code is easier
  • MATLAB allows very fast coding, is excellent for fast prototyping
  • It has a convenient automatic debugging process
  • Considered to be the best tool for researchers
  • All the functions are very well documented

Cons:

  • The tool isn’t free to use
  • It’s rather slow for many tasks
  • Not easy to integrate with third-party tools

6. Keras – The Python Deep Learning API

Keras is a Python-based open-source software library that acts as an interface for the machine learning platform TensorFlow. It is especially suited for beginners as it allows one to build a neural network model quickly while providing backend support.

Pros:

  • Easy-to-use Python library, user-friendly and fast
  • Provides multiple backend support
  • It comes with great community support

Cons:

  • Features can be improved
  • Debugging can be somewhat difficult

7. SimpleCV – Open Source Framework for Machine Vision

SimpleCV is an open-source collection of libraries and software that allows you to develop machine vision applications easily. Through its framework, you gain access to several high-powered computer vision libraries such as OpenCV without the need of possessing in-depth knowledge about complex concepts like bit depths, color spaces, buffer management, or file formats.

SimpleCV is written in Python and is compatible with multiple operating systems such as Mac, Windows, and Linux.

Pros:

  • It is free to use
  • Most of the algorithms are optimized to a great extent
  • Involves good documentation

Cons:

  • It does not support any programming languages except Python

8. BoofCV – Computer Vision Library for Real-Time Applications

BoofCV is a Java-based computer vision software that is specially written for real-time computer vision solutions. It is open-source and is released under an Apache 2.0 license that makes it free to use for academic and commercial purposes.

It is a complete library with all the basic and advanced features that one may require to develop a computer vision application.

Pros:

  • It has a user-friendly interface
  • Provides multiple language support

Cons:

  • Is slower in low-level operations

9. CAFFE – A Fast Open Framework for Deep Learning

CAFFE or Convolutional Architecture for Fast Feature Embedding is a deep learning and computer vision framework developed at the University of California, Berkeley.

This framework is written in the C++ programming language and supports multiple deep learning architectures related to image classification and segmentation. It is especially useful for research purposes and industrial implementation due to its excellent speed and image processing capabilities.

Pros:

  • It is open-source
  • Fast and easy to use
  • Supports multiple languages

Cons:

  • The documentation could be enhanced
  • Provides only partial support for multi-GPU training

10. OpenVINO – Free Toolkit for Deep Learning Models on Intel Hardware

OpenVINO (Open Visual Inference and Neural Network Optimization) is a set of comprehensive computer vision tools that are useful for developing applications emulating human vision. Developed by Intel, it is a free-to-use cross-platform toolkit.

The OpenVINO toolkit comes with models for several tasks like object detection, face recognition, colorization, movement recognition, and more. To learn more about this tool, I recommend you to read the article What is OpenVINO? The Ultimate Overview.

Pros:

  • It is a free and efficient toolkit
  • Supports multiple deep learning frameworks
  • It is compatible with Windows, Mac, and Linux operating systems

Cons:

  • Only a few examples using Python

11. DeepFace – Free Deep Learning Library for Face Recognition

DeepFace is currently the most popular open-source computer vision library for facial recognition with deep learning. The library offers an easy way to perform face recognition-based computer vision with Python.

If you are looking for image processing tools to perform face recognition, face verification, or real-time facial attribute analysis, DeepFace is a great way to use the best performing deep learning recognition models (Google FaceNet, VGG-Face, OpenFace, Facebook DeepFace, and more).

Pros:

  • It is free and open-source, even for commercial use
  • Lightweight and easy to install
  • Supports the popular models and detectors
  • Optimized to perform real-time on-device inference (Edge AI)

Cons:

  • No Cloud API is available

To get more information, check out our article about how to use the DeepFace library.

12. YOLO – Real-Time Object Detection

You Only Look Once, or YOLO is among the fastest computer vision tools you can opt for in 2021. Developed by Joseph Redmon and Ali Farhadi in 2016, it was specifically made for real-time object detection.

Faster than all other object detection tools out there, YOLO owes its speed to the application of a neural network to the complete image, which then partitions the image into grids. The software then simultaneously predicts the probabilities of each grid.

To learn more about YOLO, check out the articles YOLOv3: Real-Time Object Detection Algorithm (What’s New?) and YOLOv5 Is Here! Is It Real or a Fake?

Pros:

  • It is exceptionally fast
  • The tool is highly accurate, with minimal background errors
  • The algorithm has top-notch learning capabilities

Cons:

  • It is not as effective in detecting small objects
  • There is limited community support

For more in-depth information about YOLO, we suggest you read some of the other articles we’ve written discussing the nuances between the various versions of YOLO:

  1. YOLOv3: Real-Time Object Detection Algorithm (What’s New?)
  2. YOLOv5 Is Here! Is It Real or a Fake?

What’s Next?

We hope this article helped you to find the best computer vision tools and software available right now. These are sure to assist you in developing the most powerful and effective computer vision-related solutions you need.

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