What is OpenCV and what are its top alternatives?
OpenCV is an open-source computer vision and machine learning software library that is widely used for various image and video processing tasks. It provides a wide range of functions and algorithms for tasks like object detection, image segmentation, facial recognition, and more. Some key features of OpenCV include support for multiple programming languages, a large community for support and development, and compatibility with various operating systems. However, OpenCV can be complex for beginners to use, and its documentation can sometimes be unclear. 1. Dlib: Dlib is a modern C++ toolkit containing machine learning algorithms and tools for computer vision tasks. It provides implementations of various machine learning algorithms, as well as tools for facial recognition and object detection. Pros: Easy to use, well-documented, efficient implementation of machine learning algorithms. Cons: Limited support for certain image processing tasks, less extensive community compared to OpenCV. 2. SimpleCV: SimpleCV is an open-source framework for building computer vision applications in Python. It provides a high-level interface for common image processing tasks, such as color correction, feature extraction, and object tracking. Pros: easy to learn and use, Python-based for rapid prototyping, good for beginners. Cons: limited advanced features compared to OpenCV, less optimized for performance. 3. VLFeat: VLFeat is an open-source library for computer vision and machine learning algorithms. It offers implementations of popular image processing algorithms, such as SIFT and k-means, as well as tools for feature extraction and matching. Pros: efficient implementations of algorithms, good for feature extraction tasks. Cons: limited support for newer machine learning models, not as user-friendly as OpenCV. 4. TensorFlow: TensorFlow is an open-source machine learning framework created by Google. While primarily focused on deep learning tasks, it also offers tools for image processing and computer vision applications. Pros: powerful deep learning capabilities, extensive community support, good for complex image processing tasks. Cons: more complex than OpenCV for basic image processing, requires knowledge of machine learning concepts. 5. Matplotlib: Matplotlib is a popular Python library for creating static, animated, and interactive visualizations in Python. While not specifically designed for computer vision, it can be used for tasks like image plotting, processing, and displaying. Pros: versatile visualization capabilities, easy integration with other Python libraries, good for data exploration. Cons: not as optimized for performance as OpenCV, limited image processing functionalities. 6. Scikit-Image: Scikit-Image is a collection of algorithms for image processing and computer vision tasks built on top of the SciPy stack. It offers tools for image manipulation, filtering, feature detection, and more. Pros: easy to use, well-documented, good for basic image processing tasks. Cons: not as extensive as OpenCV in terms of algorithms and functionalities, may not be suitable for advanced computer vision projects. 7. Mahotas: Mahotas is an open-source computer vision library for Python built on top of NumPy. It provides tools for image processing, filtering, feature extraction, and object recognition. Pros: easy to install and use, efficient data structures for handling images, good for basic computer vision tasks. Cons: limited advanced features compared to OpenCV, may not be suitable for complex image processing tasks. 8. ImageJ: ImageJ is an open-source image processing program developed by the National Institutes of Health. It offers a wide range of tools for image analysis, visualization, and processing, as well as support for plugins and extensions. Pros: extensive features for image analysis, good for scientific image processing tasks, customizable with plugins. Cons: more focused on scientific research applications, not as versatile as OpenCV for general computer vision tasks. 9. Caffe: Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center. It is primarily focused on deep learning tasks, such as image classification and object detection. Pros: optimized for deep learning tasks, good for neural network training, efficient implementations of deep learning algorithms. Cons: less versatile than OpenCV for general image processing tasks, requires knowledge of deep learning concepts. 10. Tesseract: Tesseract is an open-source optical character recognition (OCR) engine that can be used for text detection and extraction from images. It provides tools for text recognition, language support, and image preprocessing. Pros: accurate text recognition capabilities, good for OCR tasks, supports multiple languages. Cons: limited to text-related tasks, not suitable for general image processing tasks.
Top Alternatives to OpenCV
TensorFlowTensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...
CImgIt mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images. ...
OpenGLIt is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering. ...
PyTorchPyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...
OpenCLIt is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing. ...
MATLABUsing MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...
FFMPEGThe universal multimedia toolkit.
CloudinaryCloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery. ...
OpenCV alternatives & related posts
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
- Documentation not very helpful2
related TensorFlow posts
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
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related CImg posts
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- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
- Lots of code3
- It eats poop1
related PyTorch posts
Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
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MATLAB
- Simulink20
- Model based software development5
- Functions, statements, plots, directory navigation easy5
- S-Functions3
- REPL2
- Simple variabel control1
- Solve invertible matrix1
- Parameter-value pairs syntax to pass arguments clunky2
- Doesn't allow unpacking tuples/arguments lists with *2
- Does not support named function arguments2
related MATLAB posts
- Open Source5
related FFMPEG posts
Hi Team,
Could you please suggest which one need to be used in between OpenCV and FFMPEG.
Thank you in Advance.
I have a situation to convert the H264 streams into MP4 format using FFMPEG/GStreamer.
However Im stuck with the gst-ugly plugin, now trying my luck with ffmeg. How big are the ffmeg libs and licensing complications?
Cloudinary
- Easy setup37
- Fast image delivery31
- Vast array of image manipulation capabilities26
- Free tier21
- Heroku add-on11
- Reduce development costs9
- Amazing support7
- Heroku plugin6
- Great libraries for all languages6
- Virtually limitless scale6
- Easy to integrate with Rails5
- Cheap4
- Shot setup time3
- Very easy setup3
- Solves alot of image problems.2
- Best in the market and includes free plan1
- Extremely generous free pricing tier1
- Fast image delivery, vast array0
- Paid plan is expensive5