Top 5 Image Processing Open Source AI Solutions

Self-driving cars and machines with a human-like vision were a mere figment of the imagination of the fantasy writer a few decades or even years ago. Yet Artificial Intelligence (AI) developments today make it possible for cars to travel safely through crowded streets and machines to view images much as humans do.

On how to apply AI on image processing tasks, developers have made remarkable progress. In addition, AI image processing technologies are commonly used in different sectors, from medicine and law enforcement to online games and retail.

AI and machine learning allow machines to inherit new functionalities through the learning process. Like small children, by processing large quantities of images, machines can learn the ability to acquire and comprehend visual knowledge. Developers use image processing models based on machine learning and deep learning algorithms in order to process too much information faster without losing the final result.

In particular, solutions for image processing and computer vision use deep learning to fulfill such complex tasks as:

• Classifying Image
• Recognizing objects
• Tracking Items
• Generation Picture
• Retrieval of Picture

You will need a wide range of tools and resources to integrate some computer vision features into your product: image datasets, libraries with code samples, frameworks for building and training deep learning models. Fortunately, you can use a lot of open-course resources to make machine learning less of a mystery and ease the development of your own image processing solution based on AI.

Below, we look more closely at the five best solutions for open source image processing that you can apply to your projects:

Let's start now!

OpenCV-Open

One of the most common open source resources for image processing is the Open Source Computer Vision Library (OpenCV). It's a free library of computer vision that you can use to perform different tasks of image processing:

• Acquisition of image
• Compression and decompression of picture
• Enhancement of image and restoration
• Denoising Picture
• Segmentation Image
• Extraction of results, and more

The library contains various algorithms and functions as well as special modules targeted at image processing tasks. There is also a deep learning module for building and training deep learning models, starting from the library version 3.1.

You can use OpenCV with this module to extract features from processed images, and then use one of the supported machine learning frameworks to apply a machine learning model.

As of today, a range of common frameworks are supported by OpenCV, including TensorFlow, PyTorch, and Caffe. Convolutional, deconvolutionary, and recurrent neural networks are included in the list of supported neural network forms.

The library is written in C++ and supports interfaces like C++, Java, Python, and MATLAB.

TensorFlow

TensorFlow is a Google-created open-source machine learning platform. Initially, the project was initiated by the Google Brain team for research purposes. Today, however, both small startups and large businesses such as Dropbox, Intel, and Twitter are commonly utilizing TensorFlow.

You may use TensorFlow to process various types of data, but this framework works best in relation to image processing for:

• Classifying Image
• Recognizing pictures
• Segmentation Image
• Translation Image to Image (pix2pix)

A collection of libraries for building and training custom deep learning models and neural networks is included in TensorFlow. The platform supports notebooks from Jupyter and offers a style guide with readable, clear code writing recommendations.

Many common programming languages, including C++, Python, Java, Rust, and Go, are supported by TensorFlow. However, you can install third-party bindings for other languages, such as Ruby, Scala, or PHP.

Keras-Keras

For building deep learning models, Keras is an open-source Python library. For those who are just beginning to use machine learning algorithms in their projects, it is a perfect solution as it simplifies the development from scratch of a deep learning model. Keras is easy to handle and is ideal for prototyping various forms of neural networks quickly and easily.

The library has been built on TensorFlow and is completely incorporated into the system at present. This means that in Keras, since it has a much more user-friendly interface, you can write your deep learning model and then easily incorporate a particular functionality or feature from TensorFlow in this model.

On top of other popular AI systems, such as Microsoft Cognitive Toolkit and Theano, Keras can also be deployed.

Caffe Caffe

Quick Feature Embedding (Caffe) Convolutionary Architecture is an open-source framework that can also be used for developing and training common types of deep learning architectures. You may use Caffe to accomplish such tasks as image classification, segmentation, and identification.

Caffe is written in C++ but also has an interface with Python. Both CPU and GPU-based accelerated libraries such as NVIDIA cuDNN and Intel MKL are provided by the framework. The framework also has a special database containing a range of pre-trained deep learning models, Caffe Model Zoo. It offers four BAIR-trained models as of today:

• CaffeNet BAIR Guide
• BAIR R-CNN ILSVRC reference-2013
• AlexNet BAIR
• GoogLeNet BAIR

A variety of group models trained by other users of Caffe are also part of the Model Zoo. As for the forms of assisted neural networks, convolutionary neural networks (CNN) and feedforward networks function best with Caffe. At the same time, for training recurrent neural networks, it's not the best option.

Caffe2, an open-source platform for training and deployment of deep learning models, was introduced by Facebook in 2017. And Caffe2 was integrated with another common AI structure, PyTorch, in 2018.

Caffe2 comes with C++ and Python APIs, and all common platforms are supported.

Colaboratory for Google (Colab)

One of the top image processing facilities is Google Colaboratory, or simply Colab. Although it is a cloud service rather than a platform, you can still use Colab from scratch to develop custom deep learning applications. You may perform such image processing duties as image classification, segmentation, and object detection with the aid of Colab.

The use of other common AI-based tools such as OpenCV, TensorFlow, and Keras is made easier by Google Colab. The service uses Jupyter Notebooks to help developers share AI-based applications with their expertise, tips and best practices. Plus, Colab makes free use of both CPU- and GPU-based acceleration, in comparison to other related services.

Machine learning models and algorithms help developers quickly and easily incorporate new image processing functionalities into their products. However, it takes lots of money and a high degree of technology knowledge to create a custom machine learning model or neural network. You can simplify the process of leveraging Artificial Intelligence technologies to your advantage with the aid of the mentioned open source resources, libraries, and frameworks.

This paper is a Marcell Gogan contribution. Marcell, working with Ekran System, is a specialist in digital security technologies, business design and development, virtualization and cloud computing, R&D ventures, software research path establishment and management. He likes to write about data protection and cybersecurity as well.