If you're looking for valuable resources for your next computer vision project, you're in the right place.
We, humans, can quickly identify objects due to our biological sensors: eyes. However, computers don't "see" things the way we do. It takes a lot of data and hardware (cameras, sensors) for a computer to recognize a single object. Just like human eyes help us see and react to the world around us, computer vision enables a machine to identify, classify, and respond to the objects it sees.
Today, it's no secret that computer vision has multiple applications across many industries including security, agriculture, medicine, and more. So the demand for quality computer vision tools and libraries increases accordingly. There are various vision libraries, image recognition libraries, and face recognition libraries, that's why we've decided to compose this list of powerful computer vision libraries for you to easily filter and find the ones that fit your needs best.
What is a computer vision library?
A computer vision library is basically a set of pre-written code and data that is used to build or optimize a computer program. The computer vision libraries are numerous and tailored to specific needs or programming languages.
Popular computer vision libraries
In addition to the top 15 computer vision books, we've gathered a list of the most popular computer vision libraries in this article to help you get started. Without further ado, let's dive in.
- Pillow (PIL Fork)
- NVIDIA CUDA-XKerKeras
- NVIDIA Performance Primitives
- Hugging Face
OpenCV is the oldest and by far the most popular open-source computer vision library, which aims at real-time vision. It's a cross-platform library supporting Windows, Linux, Android, and macOS and can be used in different languages, such as Python, Java, C++, etc. OpenCV has a Python Wrapper and uses the CUDA model for GPU. Originally developed by Intel, it is now free to use under the open-source BSD license. It also contains some models that can be converted into TensorFlow models. A few use cases of OpenCV include:
- 2D and 3D feature toolkits
- Facial recognition application
- Gesture recognition
- Motion understanding
- Human-computer interaction
- Object detection
- Segmentation and recognition
Scikit-Image is considered to be the most convenient and natural Python library which is an “extension” of Scikit-Learn. It is one of the most commonly used tools for supervised and unsupervised machine learning. Scikit-Learn is a Python package that is used for image processing and operating natively NumPy arrays as image objects. As it is Naturally Python and uses the Scikit-cuda module, Scikit-Image is free of charge and restrictions. To properly use Scikit-Learn, you just need to pip install it and you're good to go. Its use cases are a variety of:
- Finding exoplanets
- Data classification, identification, and recognition
- Clustering similar data into datasets
- Detecting fraud in credit card transactions
- Interoperability with other libraries
3. Pillow (PIL Fork)
Next on our computer vision library list is Pillow, the friendly PIL fork by Jeffrey A. Clark (Alex) and contributors, an open-source library for the Python programming language. It can be utilized by Windows, Mac OS X, and Linux. The Python Imaging Library provides image processing capabilities to the Python interpreter and its image library is modified for fast data access. It can be used in both C and Python languages and it has a Python Wrapper. Mostly used for reading and saving images of different formats, Pillow also comprises different basic image transformations such as rotation, merging, scaling, etc. Once again, for its usage, you just need to pip install it. The use cases of Pillow are:
- Fast access to stored data
- Saving various image file formats.
- Extensive file format support
- Internal representation
- Image processing capabilities.
As an extension of a PyTorch library, TorchVision contains the most common image transformations for computer vision. It also contains datasets and model architectures for computer vision neural networks. One of the main goals of TorchVision is to provide a natural way of using computer vision image transformations with PyTorch models without converting them into a NumPy array and back. Its package comprises common datasets, model architectures, and regular computer vision image transformations. TorchVision is Naturally Python and it can be used for Python and C++ languages. You can use it with the PyTorch library by pip install.
MMCV is a type of PyTorch extension that provides image/video processing and transformations, image and annotation visualization, and also many CNN architectures. It supports systems such as Linux, Windows, and macOS, and it is one of the most beneficial toolkits for computer vision researchers. It is used for Python, C++, and CUDA and it has a Python Wrapper. You can either pip or mim install it and use it in your jupyter notebook. Some of MMCV's use cases are:
- Universal IO APIs
- Useful utilities (a timer, progress bar, etc)
- PyTorch runner with a hooking mechanism
- Reuse trained models like BERT and Faster R-CNN.
- Find ready-to-deploy models for your AI project.
- Host your models for others to use.
Keras is a Python-based open-source software library that's especially useful for beginners because it allows building neural network models quickly and provides backend support. It is a toolbox of modular building blocks that computer vision engineers can leverage to quickly assemble production-grade, state-of-the-art training, and inference pipelines. With over 400,000 individual users, Keras has strong community support. It uses TensorFlow and you can pip install it. A few use cases of Keras include:
- Image segmentation and classification
- Handwriting recognition
- 3D image classification
- Semantic image clustering
MATLAB is short for Matrix Laboratory and it is a paid programming platform that fits various applications such as machine learning, deep learning, image, video, and signal processing. Users can buy a MATLAB License and install it on your own PC. It comes with a computer vision toolbox that has multiple functions, apps, and algorithms to help with computer vision-related tasks, such as:
- Detecting and tracking objects in video frames
- Recognizing objects
- Calibrating cameras
- Performing stereo vision
- Processing 3D point loads
9. NVIDIA CUDA-X
When it was first introduced, CUDA was an acronym for Compute Unified Device Architecture, but NVIDIA later dropped the common use of the acronym. NVIDIA CUDA-X is the updated version of CUDA. It is a collection of GPU-accelerated libraries and tools to get started with a new application or GPA acceleration. NVIDIA CUDA-X contains:
10. NVIDIA Performance Primitives
The NVIDIA Performance Primitives (NPP) library provides GPU-accelerated image, video, and signal processing functions that perform much faster than CPU-only implementations. This library is designed for engineers, scientists, and researchers working in a range of fields such as computer vision, industrial inspection, robotics, medical imaging, telecommunications, deep learning, and more. The NPP library comes with 5000+ primitives for image and signal processing to perform the following tasks:
- Color conversion
- Image compression
- Filtering, thresholding
- Image manipulation
OpenVINO stands for Open Visual Inference and Neural Network Optimization. It's a set of comprehensive computer vision tools for optimizing applications emulating human vision. To use OpenVINO, you'll need a pre-trained model, given that it's a model optimizing and deployment toolkit. Developed by Intel, it is a free-to-use cross-platform framework with models for several tasks:
- Object detection
- Face recognition
- Movement recognition
PyTorch is an open-source machine learning library for Python developed mainly by Facebook's AI research group. It uses dynamic computation, which allows greater flexibility in building complex architectures. Pytorch uses core Python concepts like classes, structures, and conditional loops and is compatible with C++. You need to pip install timm and you will be all set. PyTorch supports both CPU and GPU computations and is useful for:
- Image estimation models
- Image segmentation
- Image classification
13. Hugging Face
Founded in 2016, Hugging Face was initially a chatbot company that later became an open-source provider of NLP technologies. It is regarded as a big and powerful resource that contains different neural network architectures and pre-trained models. To install Hugging Face, you can pip install datasets. Hugging Face offers many models through its many tools including Hugging Face Hub, diffusers, transformers, etc. Its most common use cases are:
- Sequence classification
- Question answering
- Language modeling
Albumentations is an open-source Python library that provides a large range of image augmentation algorithms. It's free under MIT license and is hosted on github. The library is a part of the PyTorch ecosystem, and it's easily integrable with deep learning frameworks such as PyTorch and Keras. Albumentations supports a wide variety of image transform operations for tasks such as:
- Semantic segmentation
- Instance segmentation
- Object detection
- Pose estimation
CAFFE stands for Convolutional Architecture for Fast Feature Embedding. It's an easy-to-use open-source deep learning and computer vision framework developed at the University of California, Berkeley. It is written in C++ and supports multiple languages and several deep-learning architectures related to image classification and segmentation. Caffe is used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Caffe supports:
- Image segmentation
- Image classification
Detecrton2 is a PyTorch-based modular object detection library by Facebook AI Research (FAIR). It was built to meet the Facebook AI demand and cover the object detection use cases at Facebook. Detectron2 is a refined version of Detection; it includes all the models of the original Detectron, such as Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose. It also features several new models, including Cascade R-CNN, Panoptic FPN, and TensorMask. Detecrton2 is a great fit for:
- Dense pose prediction
- Panoptic segmentation
- Synaptic segmentation
- Object detection
SAM (Segment Anything Model) is the next generation state-of-the Facebook AI Research algorithm that provides high-quality image segmentation. Both Detectron2 and SAM were implemented by using PyTorch. Despite the many shortcomings of SAM, We at SuperAnnotate are enhancing its quality, scalability, and speed with our tool. To learn more about it, we invite you to join our upcoming webinar and see how it looks.
Depending on your skill set, project, and budget, you may need different computer vision programs, toolkits, and libraries. Some of the suggested libraries will need little prior knowledge of deep learning, but they may not be free. On the other hand, there are a bunch of open-source tools and resources that are available for you to use anytime.