Tag: Open Source

Top 20 Computer Vision Libraries

by rajeshkumarUncategorizedLeave a Comment on Top 20 Computer Vision Libraries

Computer vision libraries are essential tools for developing applications that analyze and understand visual data. Here are the top 20 computer vision libraries widely used by developers:

1. OpenCV (Open Source Computer Vision Library):

One of the most popular and comprehensive computer vision libraries, providing a wide range of algorithms and functions for image and video processing.

Key features:

  • Image and Video Processing: OpenCV provides a comprehensive set of functions for image and video processing, including manipulation, enhancement, filtering, and transformation.
  • Object Detection and Tracking: OpenCV includes algorithms for object detection and tracking, such as Haar cascades, HOG (Histogram of Oriented Gradients), and deep learning-based methods like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector).
  • Feature Detection and Extraction: OpenCV offers various feature detection and extraction algorithms, such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), and more.

2. TensorFlow:

An open-source machine learning framework developed by Google, TensorFlow offers a powerful set of tools for computer vision tasks, including image recognition and object detection.

Key features:

  • Deep Learning Framework: TensorFlow is a popular open-source deep learning framework that provides a flexible and scalable environment for building and training deep neural networks.
  • Neural Network Models: TensorFlow offers a wide range of pre-built neural network models, including popular architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. These models can be used for various tasks such as image classification, object detection, language translation, and more.
  • Automatic Differentiation: TensorFlow provides automatic differentiation capabilities, which enable efficient calculation of gradients for training neural networks using backpropagation. This makes it easier to optimize models and update the network weights during the training process.

3. PyTorch:

Another popular deep learning framework, PyTorch provides extensive support for computer vision tasks, including image classification, segmentation, and object detection.

Key features:

  • Dynamic Computation Graph: PyTorch utilizes a dynamic computation graph, which allows for flexible and dynamic neural network architectures. It enables intuitive model building and debugging by executing operations on the fly.
  • Neural Network Models: PyTorch provides a rich set of pre-built neural network modules and architectures that can be easily combined to create complex models. It supports popular network types such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and more.
  • Automatic Differentiation: PyTorch offers automatic differentiation, enabling efficient computation of gradients. This feature allows for easy implementation of backpropagation and makes it convenient to train neural networks by optimizing model parameters.

4. Caffe:

A deep learning framework specifically designed for convolutional neural networks (CNNs), Caffe is widely used for image classification and object detection.

Key features:

  • Modularity: Caffe provides a modular architecture that allows easy experimentation and prototyping. It consists of different layers such as convolutional, pooling, fully connected, and activation layers, which can be combined to build complex neural networks.
  • Expressive Architecture: Caffe supports a wide range of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and combinations of both. It allows users to define and train complex models for various tasks such as image classification, object detection, and segmentation.
  • GPU Acceleration: Caffe is designed to efficiently utilize GPUs for training and inference. It leverages GPU parallelism to speed up computations and improve overall performance, making it suitable for large-scale deep-learning tasks.

5. scikit-image:

Built on top of NumPy, sci-kit-image offers a collection of algorithms for image preprocessing, filtering, segmentation, and feature extraction.

Key features:

  • Comprehensive Image Processing Library: Scikit-image offers a comprehensive set of image processing algorithms and functions for tasks such as filtering, morphology, segmentation, feature extraction, and more. It provides a wide range of tools for manipulating and analyzing images.
  • NumPy Integration: Scikit-image is built on top of NumPy, a fundamental library for numerical computing in Python. This integration allows seamless interoperability between scikit-image and other scientific Python libraries, enabling efficient data manipulation and processing.
  • Easy-to-Use API: Scikit-image provides a user-friendly API that simplifies the process of performing complex image processing tasks. The functions and algorithms are designed to be intuitive and easy to understand, making it accessible to both beginners and experienced users.

6. Dlib:

A C++ library with Python bindings, Dlib provides tools for face detection, facial landmark detection, and deep learning-based face recognition.

Key features:

  • Facial Landmark Detection: Dlib includes a powerful facial landmark detection algorithm that can accurately localize facial landmarks, such as the eyes, nose, and mouth. This feature is useful for tasks like face recognition, facial expression analysis, and facial feature tracking.
  • Object Detection and Tracking: Dlib offers object detection algorithms based on the Histogram of Oriented Gradients (HOG) and Support Vector Machines (SVM). It enables the detection and tracking of objects in images and video streams, making it suitable for applications like pedestrian detection, vehicle detection, and motion analysis.
  • Machine Learning Tools: Dlib provides a set of machine learning tools, including classifiers, regression algorithms, and clustering algorithms. It offers implementations of popular machine learning algorithms like SVM, k-nearest neighbors, and deep neural networks. These tools enable tasks such as classification, regression, and clustering.

7. MXNet:

A deep learning framework supported by Apache, MXNet offers efficient implementations of various computer vision algorithms and models.

Key Features:

  • Multi-language support: MXNet provides APIs for multiple programming languages, including Python, R, Scala, Julia, and C++. This allows developers to work with MXNet using their preferred language.
  • Dynamic and static computational graphs: MXNet supports both dynamic and static computational graphs. In the dynamic mode, the graph is defined and evaluated dynamically, which is useful for models with varying input shapes or sizes. In the static mode, the graph is defined upfront and optimized for efficiency, which is beneficial for models with fixed input shapes.
  • Efficient execution: MXNet is designed for efficient execution on various hardware architectures, including CPUs, GPUs, and distributed systems. It optimizes performance by leveraging parallelism, asynchronous execution, and memory optimization techniques.

8. Keras:

A high-level neural networks library, Keras simplifies the process of building and training deep learning models for computer vision applications.

Key features:

  • User-friendly API: Keras offers a simple and intuitive API that makes it easy to build, configure, and train deep learning models. It provides a higher-level abstraction, allowing users to focus more on model design and less on implementation details.
  • Modularity: Keras follows a modular design, enabling users to create models by stacking layers together. It provides a wide range of pre-built layers, including dense (fully connected), convolutional, recurrent, normalization, and activation layers. Users can easily combine and configure these layers to construct complex neural network architectures.
  • Support for multiple backends: Keras can run on top of various deep learning backends, including TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). This allows users to choose the backend that best suits their needs, without having to modify their Keras code.

9. Theano:

A Python library specializing in deep learning and symbolic mathematics, Theano enables efficient computation and optimization of mathematical expressions.

Key Features:

  • Symbolic mathematical expressions: Theano allows users to define mathematical operations as symbolic expressions. This symbolic representation enables automatic differentiation, which is crucial for efficient gradient computations used in training neural networks.
  • Efficient computation backend: Theano is designed to efficiently perform numerical computations, especially on GPUs. It can take advantage of GPU acceleration to speed up the execution of deep learning models. Additionally, Theano also supports multi-core CPU computation.
  • Automatic differentiation: Theano provides automatic differentiation capabilities, which allow users to compute gradients automatically. This feature is essential for backpropagation, which is used to update the model parameters during the training process.

10. Mahotas:

A computer vision and image processing library for Python, Mahotas includes algorithms for feature extraction, filtering, and analysis.

Key Features:

  • Image processing operations: Mahotas offers a comprehensive set of image processing operations, including filtering, morphology, thresholding, feature extraction, and geometric transformations. These operations allow users to enhance, segment, and analyze images for various computer vision tasks.
  • Efficient and memory-friendly: Mahotas is designed for efficiency and memory optimization. It provides optimized algorithms and data structures that enable fast image processing operations even on large images. Mahotas is implemented in C++, with a Python interface, which contributes to its performance.
  • Numerical and scientific computing: Mahotas is built on top of NumPy, a popular numerical computing library in Python. It seamlessly integrates with NumPy arrays, allowing users to perform efficient and vectorized operations on images. Mahotas takes advantage of the computational power of NumPy for fast and accurate computations.

11. TorchVision:

Part of the PyTorch ecosystem, TorchVision provides datasets, models, and utilities for computer vision tasks, including object detection and image segmentation.

Key Features:

  • Image processing operations: Mahotas offers a comprehensive set of image processing operations, including filtering, morphology, thresholding, feature extraction, and geometric transformations. These operations allow users to enhance, segment, and analyze images for various computer vision tasks.
  • Efficient and memory-friendly: Mahotas is designed for efficiency and memory optimization. It provides optimized algorithms and data structures that enable fast image processing operations even on large images. Mahotas is implemented in C++, with a Python interface, which contributes to its performance.
  • Numerical and scientific computing: Mahotas is built on top of NumPy, a popular numerical computing library in Python. It seamlessly integrates with NumPy arrays, allowing users to perform efficient and vectorized operations on images. Mahotas takes advantage of the computational power of NumPy for fast and accurate computations.

12. SimpleCV:

A user-friendly computer vision library for Python, SimpleCV simplifies the process of working with visual data, offering a high-level API.

Key Features:

  • Easy image acquisition: SimpleCV simplifies image acquisition by providing easy-to-use functions for capturing images from webcams, video files, or image streams. It abstracts the complexities of acquiring images, allowing users to focus on image processing and analysis.
  • Image manipulation and enhancement: SimpleCV provides a variety of functions for manipulating and enhancing images. These functions include resizing, cropping, rotating, flipping, adjusting brightness/contrast, applying filters, and more. These operations can be performed effortlessly to preprocess images before analysis.
  • Object detection and tracking: SimpleCV includes built-in methods for object detection and tracking. It offers various techniques, such as color tracking, feature detection (using SIFT or SURF), and motion detection. These features enable users to detect and track objects of interest in images or video streams.

13. VLFeat:

A popular computer vision library, VLFeat includes implementations of various algorithms, such as SIFT and HOG, for feature extraction and matching.

Key features:

  • Feature extraction and matching: VLFeat offers a comprehensive set of algorithms for feature extraction and matching, including popular techniques like SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), and MSER (Maximally Stable Extremal Regions). These algorithms allow users to detect and describe key points in images, enabling tasks such as image registration, object recognition, and image retrieval.
  • Image filtering and enhancement: VLFeat provides a wide range of image filtering and enhancement algorithms, such as Gaussian and median filtering, histogram equalization, and image resizing. These operations enable users to preprocess and enhance images before further analysis or visualization.
  • Spatial pyramid matching: VLFeat includes algorithms for spatial pyramid matching, which is a technique commonly used in image classification and object recognition. It allows users to efficiently handle images at different scales and levels of detail, capturing both local and global information for improved accuracy.

14. BoofCV:

A Java-based computer vision library, BoofCV offers a wide range of algorithms for image processing, feature detection, and visual odometry.

Key Features:

  • Efficient Java implementation: BoofCV is implemented in Java, which makes it suitable for Java developers and allows for easy integration with Java-based projects. The library is designed to be efficient and optimized for performance.
  • Extensive algorithm collection: BoofCV offers a wide range of computer vision algorithms for tasks such as feature detection and matching, image filtering, camera calibration, image segmentation, object tracking, and more. It covers both classical computer vision algorithms and modern techniques.
  • Modular architecture: BoofCV has a modular architecture that allows users to easily combine and configure different algorithms to create custom computer vision pipelines. The modular design promotes code reusability and flexibility in implementing complex vision systems.

15. Accord.NET:

A comprehensive framework for scientific computing and machine learning in .NET, Accord.NET includes modules for computer vision tasks, such as object detection and image classification.

Key Features:

  • Efficient Java implementation: BoofCV is implemented in Java, which makes it suitable for Java developers and allows for easy integration with Java-based projects. The library is designed to be efficient and optimized for performance.
  • Extensive algorithm collection: BoofCV offers a wide range of computer vision algorithms for tasks such as feature detection and matching, image filtering, camera calibration, image segmentation, object tracking, and more. It covers both classical computer vision algorithms and modern techniques.
  • Modular architecture: BoofCV has a modular architecture that allows users to easily combine and configure different algorithms to create custom computer vision pipelines. The modular design promotes code reusability and flexibility in implementing complex vision systems.

16. Halide:

A programming language and compiler for image processing pipelines, Halide provides high-performance optimizations for computer vision algorithms.

Key features:

  • Expressive and concise DSL: Halide provides a high-level, functional programming language specifically designed for image and array computations. The DSL allows users to express complex image processing algorithms in a concise and readable manner. It abstracts away low-level details, enabling users to focus on the algorithmic aspects of their code.
  • Compiler-driven optimization: Halide incorporates a sophisticated compiler that performs automatic optimizations on image processing pipelines. It analyzes the code and applies a range of optimizations, including loop fusion, loop unrolling, memory layout optimizations, and specialized scheduling strategies. These optimizations aim to maximize performance by exploiting parallelism, memory locality, and vectorization.
  • Algorithm introspection and scheduling: Halide provides facilities for introspecting and manipulating the scheduled representation of the computation. Users can experiment with different scheduling strategies to optimize performance and resource utilization. The ability to schedule computations manually or semi-automatically allows fine-grained control over optimizations.

17. ImageJ:

A powerful image processing and analysis tool, ImageJ offers a wide range of functions and plugins for scientific and biomedical image analysis.

Key features:

  • Image visualization and manipulation: ImageJ allows users to open, display, and interact with various types of digital images, including 2D and 3D images. It provides tools for adjusting brightness, contrast, and color balance, as well as functions for cropping, rotating, and resizing images.
  • Image analysis and measurement: ImageJ offers a range of image analysis and measurement tools. It includes functions for thresholding, particle analysis, morphological operations, image segmentation, and more. These tools enable users to extract quantitative information from images and perform measurements such as area, intensity, distance, and shape characteristics.
  • Plugins and extensibility: ImageJ has a plugin architecture that allows users to extend its capabilities. A wide variety of plugins are available, including those for specialized image processing algorithms, analysis techniques, and visualization methods. Users can also develop their own plugins to customize and enhance ImageJ according to their specific needs.

18. cv2 (OpenCV for Python):

The Python bindings for OpenCV, cv2 allow developers to access OpenCV’s functionality and algorithms from Python scripts.

Key features:

  • Image and video I/O: OpenCV allows users to read, write, and manipulate images and videos in various formats. It provides functions for loading images from files or cameras, saving processed images, and working with video streams. It supports common image and video file formats such as JPEG, PNG, BMP, and MP4.
  • Image processing and filtering: OpenCV offers a comprehensive set of image processing functions. It includes operations such as resizing, cropping, rotating, flipping, and color space conversions. It also provides various image filtering functions, including smoothing filters (e.g., Gaussian blur), sharpening filters, thresholding, and morphological operations (e.g., erosion and dilation).
  • Feature detection and extraction: OpenCV provides algorithms for detecting and extracting features from images. It includes methods for detecting corners (e.g., Harris corner detection), blob detection, edge detection (e.g., Canny edge detection), and more. These features are useful for tasks such as image registration, object detection, and tracking.

19. skimage (scikit-image for Python):

The Python interface for scikit-image, skimage provides a simple and intuitive API for performing various image processing tasks.

Key Features:

  • Image preprocessing and manipulation: skimage offers a variety of functions for image preprocessing and manipulation. It includes operations such as resizing, cropping, rotating, flipping, and color space conversions. It also provides filters for smoothing, sharpening, denoising, and enhancing images.
  • Image filtering and enhancement: skimage provides a collection of filters for image enhancement and noise reduction. It includes standard filters such as Gaussian, median, and bilateral filters, as well as more specialized filters like Sobel, Laplacian, and Hessian filters. These filters can be used to enhance image details, remove noise, and detect edges or other features.
  • Image segmentation and object detection: skimage offers algorithms and functions for image segmentation and object detection. It includes techniques like thresholding, region growing, and watershed segmentation. These tools assist in separating objects or regions of interest in images and can be used for tasks such as image analysis and object recognition.

20. VisionLib:

A commercial computer vision library, VisionLib offers tools for 3D object tracking, pose estimation, and augmented reality applications.

Key features:

  • Marker-based and markerless tracking: VisionLib offers robust marker-based and markerless tracking capabilities. It supports the detection and tracking of fiducial markers, such as ARToolkit-compatible markers, as well as markerless tracking of objects and scenes using natural feature detection and tracking algorithms.
  • Pose estimation and tracking: VisionLib enables accurate pose estimation and tracking of objects in real-time. It provides algorithms for estimating the 3D position and orientation (pose) of objects, allowing them to be accurately aligned with the real world. This feature is essential for placing virtual objects in AR scenes and aligning them with the physical environment.
  • Object recognition and tracking: VisionLib includes object recognition and tracking capabilities. It allows users to define and train custom object recognition models for identifying and tracking specific objects or patterns in real time. This feature is useful for applications that require precise detection and tracking of specific objects or markers.
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What is Prometheus, Grafana, and How to learn Online?

by narayan kPrometheusLeave a Comment on What is Prometheus, Grafana, and How to learn Online?

Prometheus is an open-source system monitoring and alerting toolkit originally built at SoundCloud. Many companies and organizations have adopted Prometheus and have a very active developer and user community in the project. It is now a standalone open-source project and is maintained independently of any company.

Features of Prometheus:

  • A multi-dimensional data model with time series data identified by metric name and key/value pairs.
  • PromQL, a flexible query language to leverage this dimensionality.
  • No reliance on distributed storage; single server nodes are autonomous.
  • Time series collection happens via a pull model over HTTP.

What is Grafana?

Grafana is an open-source solution for running data analytics, understanding large amounts of data, and monitoring our apps with the help of cool customizable dashboards.

What are the benefits of Grafana?

  • Customizable dashboards are feature-rich and can be configured to display data from a wide range of databases using visualization tools such as heatmaps, histograms, and charts.
  • The platform is flexible and easy to use.
  • Native support of a broad range of databases.

Grafana Features:

  • Dashboard templating – This is a Grafana feature that is really useful. It allows users to create a dashboard setup to suit their every need.
  • Provisioning – It can be simple enough to set up a single dashboard with a few clicks, dragging and dropping, but some users need even more ingenuity this way.
  • Annotations – This Grafana feature lets you mark graphs, which is especially helpful when you need to correlate data in case something misbehaves.
  • Custom plugins – You can extend the functionality of Grafana with plugins that provide additional tools, visualizations, and more.
  • Teams and permissions – Where an organization has an instance of Grafana and multiple teams, they usually prefer to have the option of implementing some dashboard isolation.

Pre-requisites to learn Prometheus and Grafana:

  • Basic experience with Linux/Unix system administration.
  • Familiarity with common shell commands, such as ls, cd, curl, etc.
  • Some knowledge and/or development experience in Go and Python.
  • Some experience working with Kubernetes.

How Long Does it Take to Learn Prometheus and Grafana?

Typically, Prometheus and Grafana training by DevOpsSchool will let you master the concepts in about 2 months if you devote 2 to 4 hours per week. The training takes place in such a way that you become proficient in almost all the concepts of Prometheus and Grafana can easily add certification to your resume.

How to Learn Prometheus and Grafana?

The first thing you should do before learning Prometheus and Grafana is to ask yourself how you learn best.

Do you prefer online tutorials, or do you prefer online courses? Do you want to work on projects as part of a team, or are you comfortable learning at your own pace? By considering this question in advance, you will be able to better understand what kind of material you should be looking for.

If you are planning to learn Prometheus and Grafana, go with DevOpsSchool institute. Will provide you online and classroom training and certification course Program by an expert. To get in-depth knowledge of Prometheus and Grafana along with its various applications, check out our interactive, live-online training.

Here you can see the Agenda of Prometheus and Grafana Course:

This course covers the basics of Prometheus and Grafana its main features. You will develop a critical understanding of why Prometheus and Grafana are useful and how they can be combined with other web development frameworks.
You will learn about the basics of Prometheus and Grafana its advantages. Then, you’ll cover more advanced topics like integrating Prometheus and Grafana with other frameworks.

Hopefully, I think this information is helpful for you.

However, if you want to find out more details, be sure to check out our Prometheus and Grafana course to learn more about this technology.

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What is Zabbix?

by Manish KumarZabbixLeave a Comment on What is Zabbix?

Today, applications have evolved from standalone to the client-server model and ultimately to the cloud-based elastic application. Application performance will directly have an effect on a business, symptom the revenue as client struggle to finish the method. It’s continually been troublesome to examine what’s occurring within the system.

And doing thus has become a necessary half. Having a decent watching tool can facilitate us to grasp the system standing and verify after they area unit behaving unremarkably and after they area unit behaving abnormally.

Once one thing goes wrong, the tool alerts quickly and so we are able to act on that and fix it while not touching customers. Zabbix may be a well-known tool to try to that for you.

What is Zabbix?

Zabbix is an open source application, and additionally a network observance tool. It offers observance of thousands of metrics collected from physical machines or virtual machines.

Zabbix work on a web-based management interface that is centralized through a info. Also visualization of your data is available in the form of graphs, screens, maps and overviews with the help of Zabbix.

With Zabbix it’s very much possible to collect virtually types of data from the network. Along with storing the data, visualization features are available (overviews, maps, graphs, screens, etc.), as well as very flexible ways of analysing the data for the purpose of alerting.

Benefits of Zabbix

 The main advantages of Zabbix square measure its complimentary platform, reliable options like Zabbix agent, notification and redress module, and easy-to-use and sturdy user interface. Here square measure a lot of details:

  • Money Saver

Zabbix gives businesses and individuals the freedom to utilize an open-source platform with source code that can be accessed for free and without vendor lock-in. The software also enables a seamless and fast set-up and configuration.

  • Zabbix Agent

Zabbix Agent is one amongst the platform’s noteworthy modules. It options infrastructure management capabilities and provides dependable tools for network observance that enables users to strategize their capability enlargement.

  • Notification & redress Module

This practicality allows an across-the-board communication flow and supports proactive notifications further as automatic actions. With this module, users will receive notifications.  Users will like better to receive it either via email, SMS, Jabber, and Ez Texting.

  • Robust and Easy-to-use user interface

Zabbix, with its easy-to-use Graphic programme (GUI), are often simply navigated even while not abundant technical information. The platform supports vivid mental image with huge-scale configuration functionalities. Users will have access and see a visible summary of the complete IT setting in various ways that like a widget-based dashboard, graphs, slideshows further as drill-down reports.

Why Choose Zabbix?

  • Open source– It is an open source application and also with free features.
  • All time Monitoring– It offers features that you can easily monitor servers, applications and any network devices and performance information
  • Enterprise Ready – Zabbix has been designed to scale from tiny environments to giant environments
  • Potential Planning – With the data collected by Zabbix, you can easily investigate your infrastructure and set up the capability consequently.

Zabbix Architecture

Zabbix is distributed observation tool with a central web interface. The easy design of Zabbix will be composed of 3 servers.

  • Web Server
  • RDBMS Server
  • Zabbix server

This setup is sweet enough to handle large environment successfully. It’s better to use the dedicated server for everyone in all these parts.

Zabbix uses client-server design Architecture and uses an agent within the servers that ought to be monitored. This agent gathers all required info and standing from the system and sends to Zabbix server. Zabbix version three and on top of support encrypted communication between server and shoppers.

Its proxies are used to monitor remote servers. It will collect performance and handiness knowledge behalf of Zabbix server and put it aside in a very buffer domestically and so send it to Zabbix server. In this fashion proxy servers can take on itself some of the load and offload the Zabbix servers.

Let us look into a number of the terminologies of Zabbix.

  • Frontend – net interface given Zabbix
  • Zabbix Server – Central server to gather the info and method it.
  • Zabbix agent – A method deployed on consumer servers to observe domestically
  • Host – A networked device that ought to be monitored
  • Host cluster – A logical cluster of hosts. It’s used once distribution access rights.
  • Template – a group of entities (items, triggers, applications, low-level discovery rules, graphs, screens, net scenarios) able to be applied to 1 or many hosts
  • Item – a knowledge that you simply wish to receive from a number
  • Trigger – it’s a logical expression that defines a threshold
  • Event – prevalence of one thing that deserves attention
  • Action – Predefined means of reacting to an occurrence.

Conclusion

Zabbix is an open source application, and additionally a network observance tool. With Zabbix it’s very much possible to collect virtually types of data from the network.

I Hope you like this particular blog on Zabbix.

If you want know more about Zabbix or are you planning to learn Zabbix, then I would suggest you DevOpsSchool, one the top institute for Zabbix Certified Specialist (ZCS) Training. We provide you the best instructors who are highly qualified professionals, have more than 10 years of working and teaching experience.  

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Social networking software and ratings

by scmgalaxy KWebsitesLeave a Comment on Social networking software and ratings

Best Open-Source Social Networking Software | Best Open Source Tools to Create Scalable Online Social Networking Platforms |

  1. Elgg
  2. Oxwall
  3. Phpfox
  4. WordPress
  5. Jcow
  6. Buddypress
  7. Dolphin
  8. Drupal
  9. Pligg
  10. Socialengine
  11. Jomsocial
  12. XOOPS
  13. Anahita
  14. Mahara
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Best Open Source eCommerce Software

by scmgalaxy KWebsitesLeave a Comment on Best Open Source eCommerce Software

nopCommerce
nopCommerce is an open source e-commerce solution that contains both a catalog frontend and an administration tool backend. nopCommerce is a fully customizable shopping cart. It’s stable and highly usable.

osCommerce
osCommerce is the leading Open Source online shop e-commerce solution. It offers shopping cart functionality that allows store owners to setup, run, and maintain their online stores

Pinnacle Cart
Premium ApplicationPinnacle Cart is an extremely powerful php shopping cart and website builder application. Designed from marketing perspective, this eCommerce application is feature-packed, robust, scalable and easy to use

ShopSite
Award winning ShopSite© is the easiest-to-use shopping cart software for small to medium-sized businesses. With ShopSite you can have a store online in as little as 15 minutes. ShopSite’s flexible templates allow you to create a site with the look and feel that you want.

Zen Cart
Zen Cartâ„¢ truly is the art of e-commerce; a free, user-friendly, open source shopping cart system. The software is being developed by group of like-minded shop owners, programmers, designers, and consultants that think e-commerce could be and should be done differently.

Magento
If your business is serious about eCommerce success, we can help. Magento offers flexible, feature-rich solutions that address a wide range of business sizes, needs and budgets.

Prestashop
Fast, efficient and easy to use, PrestaShop’s free e-commerce solution provides everything you need to open, operate and maintain a successful online store.

X-Cart – Paid
Like proud parents, we can’t help but brag about all the great features that make X-Cart the trusted ecommerce platform of thousands of successful businesses around the world.

VirtueMart
VirtueMart – a free, easy to use and up-to-date e-commerce solution. Fully integrated into a free, but professional Content Management System

BigCommerce
Bigcommerce gives you a plethora of securely hosted e-commerce tools: Website, Domain name, Secure shopping cart, Product catalog, Payment gateway, CRM, Email accounts, Marketing tools, Reporting, Mobile-optimized store

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Top 5 Open Source Code Management tools

by scmgalaxy KSource Code ManagementLeave a Comment on Top 5 Open Source Code Management tools
top-5-open-source-code-management-tools
The old way of software development where the release engineers ran from one mates to another mates to keep track on Developers to know which module they are working and when they changed something in the code or which bugs has been tracked or fixed. No doubt, that process was pathetic, stressful, filled with issues and errors.
But, now things have changed. This is the era of DevOps where everyone works as a Team and collaborate with each other and use various tools in the Software Development Lifecycle. So, today we are going to discuss on one of the most important tool called by name Code Management Tools. In this article we will see top 5 open source code management tools which are mostly used by DevOps professionals these days.
But before going further lets see what is source code management?
Code management is actually a process of handling or managing changes to source code over time with the help of softwares or tools. These softwares keeps track of every changes and modifications in the code which is made by developers frequently. If there is some kind of mistakes made by developers they can simply go back compare it with previously written code and fix the mistakes easily. With the help of code management tools Developers team can work on different features and on bug fixing simultaneously by changing code with out disrupting team members. In other words you can say It give developers the ability to work concurrently on files (in branches that may or may not converge) without conflicting each other and also give developers the ability to merge changes with other developers’ changes, to track and audit changes that were requested and made, to track bug-fix status and to perform releases.
So now the question is which code management tools to use. When you search for the options you will get hundred of tools in your search results. To make this job easy for you we have done some research and make this list of Top 5 Open Source Code Management tools.

1. Subversion

Subversion

Key Features
  • Open Source
  • Merge tracking
  • Renamed/copied/moved/removed files retain full revision history.
  • Google Code even uses SVN
  • Path-based authorization
  • Language bindings for C#, PHP, Python, Perl, Ruby, and Java
  • Changelists to organize commits into commit groups

2. GIT

GIT

Key Features
  • open source
  • cheap local branching
  • fast performance
  • multiple workflows
  • convenient staging areas
3. Mercurial

Mercurial
Key Features
  • open source
  • fast and powerful
  • easy to learn
  • functions are less complicated
  • integrated web-interface

4. Github

Github

Key Features
  • Open Source
  • Code review
  • Issue tracking with labels, milestones
  • largest host of source code in the world
  • Commits history
  • Graphs: pulse, contributors, commits, code frequency, punch card, network, members
  • Unified and split diffs

5. CVS

CVS

Key Features
  • Open Source
  • Easy to learn
  • Google still hosts the original Usenet post that announced CVS
  • Maintains a central repository of the most recent repository
  • CVS allows to rollback any commit in the repository, even if this may require some time
  • CVS uses a client–server architecture
  • CVS can also maintain different “branches” of a project
  • Several developers may work on the same project concurrently
Code Management these days plays an important role in development process and especially when team (large / small) works on single application. So, choose it wisely. Now , its your turn if you think this list should contain some other tools instead of this than write below in the comment box.
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Top 5 Open Source Release Management Tools | List of Best Release Management Tools

by scmgalaxy KCruise Control / Release ConceptLeave a Comment on Top 5 Open Source Release Management Tools | List of Best Release Management Tools
top-5-open-source-release-management-tool
Few days ago, I published an article in which i covered the topic Top five release management tools which are high in trend these days. Today, I am going to write on the same topic but there is one difference, today we will discuss about open source release management tools.
In this article I am going to share top 5 open source tools for Release Management. But, if you want to know about Release management and it’s benefits than you can check out the previously written article here.
So, Without any further ado in this article, let’s directly move to the main section.
Let’s check out open source release management tools.
1. CruiseControl
 CruiseControl
CruiseControl basically known as continuous integration tool. It can be used in Release management process. Cruisecontrol is an open source tool which is written in java and supports cross platform and it was developed by CruiseControl development team and initially released in the year 2001. You can extend this tool with the help of plugins and extensions.

2. Luntbuild
Luntbuild
LuntBuild is known as Build management and continuous integration tool. This one is another open source release management tool which is written in java ans supports cross platform. This tool is developed by PMEase. This tool is easy to install and configure. It has a clean web interface.

3. Maven
Maven
Maven is also known as build tool but it’s also an open source release management tool which is written in java and supports cross platform. Maven was initially released in the year 2004 and developed by Apache Software Foundation. Maven can also be used to build and manage projects written in C#, Ruby, Scala, and other languages.

4. Jenkins
Jenkins
Jenkins is one of the most popular tool for Continuous integration but it can be used as a release management software. This software is written in java and it was initially released in the year 2011. It was developed as a Hudson project as Sun Microsystems which was later take over by Oracle corporation. The great thing about this tool is its functionality can be extended with plugins.
5. Subversion (SVN)

Subversion (SVN)

Subversion or SVN is a versioning and revision control system but it also can be used as Release management software . You can say this one is the leading release management tool in the open source category. This tool is written in C and it supports cross platform was developed by  CollabNet Inc in the year 2000.

So, That’s it. Now it’s your turn , share your views in this article and if you think this list should contain some other tools instead of this than fee free to share with us the in the comment section below.

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Top 5 Virtualization tools for Developers | List of Virtualization tools | scmGalaxy

by scmgalaxy KVirtualizationLeave a Comment on Top 5 Virtualization tools for Developers | List of Virtualization tools | scmGalaxy

top-5-virtualization-tools

Software developers are busier now than ever before. They want to do more work in less time with quality. But, as we all know it’s not possible without right set of tools in your arsenal. So, it’s always good to find tools that can make work faster and make you more productive. Today we are going to talk on Virtualization tools. In this article I’m going to share a list of top 5 virtualization tool which will definitely help developers to improve their productivity and allow them to build better applications faster.

But, before that let’s have Quick introduction to Virtualization

Virtualization is a process in which, with the help of tools you can create a virtual version of resources like memory, operating system , server or networks instead of deploying the actual resource. In simple words you can understand it like this way: Virtualization let you run or operate a single computer into multiple ones. It let you run a Mac OS on Linux or Windows system in a single computer simultaneously. The Windows, Mac, Linux, or other system running in the virtual machine acts exactly as if it were running on real hardware. The same thing apply with storage, server or networks.

Basically Virtualization tools are used for server consolidation. But they are useful for software developers for writing or testing software.

If we look on to the benefits than these are the major benefits of Virtualization.

  1. Lessen the number of Hardware Resources
  2. 80% server use possible
  3. Cut IT costs
  4. consolidated hardware to achieve higher productivity from fewer servers
  5. multiple operating systems and applications on a single computer
  6. simple IT infrastructure with low upkeep
  7. Quickly deployment of new applications

So, Now without any further ado lets check out the Top 5 tools and their features.

1. Vagrant

Vagrant is an open source virtualization tool which developed by Hashicorp and written in Ruby. But, it’s support projects written in other languages too, like as Java, javascript, C#, Python and PHP. It’s work on platforms Linux, FreeBSD, macOS, and Microsoft Windows.

Features:

  • Open Source
  • Cross Platform: Linux, FreeBSD, macOS, and Microsoft Windows essential
  • Natively supports Docker containers
  • Can work with Puppet & Chef
  • Users can share running environment via Internet

 

2. VirtualBox

VirtualBox is also an open source virtualization tool which is developed by originally by Innotek GmbH and now being developed by Oracle Corporation. This virtualization tool is written in C, C++, x86 Assembly. It is one of the oldest tool in the industry which is used by developers.
Features:
  • Open Source
  • Virtualization Type: Hardware Assisted Virtualization & Paravirtualization
  • Cross Platform: Windows, macOS, Linux and Solaris
  • Easy to install and use-friendly
  • Video capture support
  • VirtualBox adds support for touchscreens

3. VMware

 

VMware is a veteran in the software industry which is developed by VMware Inc. and comes in 3 different packages by name VMware Workstation, VMware Fusion and VMware workstation Player in which player comes free for users who are looking to run virtual machines on their Windows or Linux systems. The first one is good option which is designed for professionals and the second one is for home users who simply wants to run windows on MAC. VMWare products are mostly written in c, C++ and assembly but different different languages are used in various other bits.
Features:
  • Free and Paid
  • Cross Platform: Windows, Linux and Mac OSX
  • Comes in three packages
  • Virtualization Type: Full Virtualization, Hardware Assisted Virtualization & Paravirtualization
  • Easy to use
  • Quick Installation

4. Xen

 


Xen is initially developed by University of Cambridge but now it’s belongs to Linux Foundation. Xen runs on Linux and Unix. Xen is included with most popular Linux platforms like Fedora, RHEL, CentOS, Ubuntu, and Debian.
Features:
  • Open Source
  • Platform: Linux/Unix
  • Virtualization Type: Paravirtualization & Hardware Assisted Virtualization
  • Guest Os: Windows Server 2003, Windows XP and Windows 2000
  • Also supports Red Hat, SUSE and Debian Linux guest OS.

5. KVM

Kernel-based Virtual Machine (KVM) is also an open source intended for personal and small-medium business solutions. It’s originally developed by Qumranet and now belongs to Red Hat. KVM is written in C and it;s suitable for Linux. If you need or looking for command line interface virtualization tool which comes with updated solutions, boundless use mode and without extra features and permitting charges than this is the right tool you can have.
Features:
  • Open Source
  • Platform: Linux
  • Virtualization type: Full Virtualization, Hardware Assisted Virtualization and Paravirtualization
  • Powerful command line interface
So, This is my list of Top 5 virtualization tools. Now, what you think about my list or if you are not agree with my selected tools or if you think this list should contain some other Virtualization tools instead of this, than write in comment section below.
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Top 4 open source alternatives to Slack for team collaboration and Chat

by scmgalaxy KGeneralLeave a Comment on Top 4 open source alternatives to Slack for team collaboration and Chat
Off course Slack is market leader in terms team collaboration and Chat but here are the list of 4 open source alternatives to Slack for team collaboration and Chat.
Slack 
Slack offers a lot of IRC-like features: persistent chat rooms (channels) organized by topic, as well as private groups and direct messaging (again, historically based on IRC).[14] All content inside Slack is searchable, including files, conversations, and people. Slack integrates with a large number of third-party services and supports community-built integrations. Major integrations include services such as Google Drive, Trello, Dropbox, Box, Heroku, Crashlytics, GitHub, Runscope and Zendesk. In December 2015, Slack announced their app directory, consisting of over 150 integrations that users can install.[21] Users can add emoji buttons to their messages, which other users can then click on to express their reactions to messages.
More info – https://slack.com/
IRC
Internet Relay Chat, or IRC, is a protocol which dates back to the late 1980s. Since it’s been around so long, there are numerous open source implementations on both the client and the server side.
Coming with its age, however, are numerous drawbacks. It lacks many features one might expect in a modern chat client, from security to identity management to even just being able to easily transmit non-text components, like images, files, or emoticons (the latter might be seen as a plus to some, however). Some features have been implemented after-the-fact through bot services, including nickname management, logging, and other features, but these vary from server to server.
IRC does still have some things going for it, though. It’s nearly universal, and clients are available for basically every platform out there. Though the command-driven interface isn’t necessarily intuitive for beginners, many clients re-implement commands through a GUI. And if you’re doing upstream open source development, there’s a good chance you’re already hanging out in IRC anyway, so adding a team server might be a path of least resistance.
Let’s Chat
Let’s Chat is a persistent messaging application that runs on Node.js and MongoDB. It’s designed to be easily deployable and fits well with small, intimate teams.
It’s free (MIT licensed) and ships with killer features such as LDAP/Kerberos authentication, a REST-like API and XMPP support.
Let’s Chat is a side-project of the development team at Security Compass. (A real life 10% time project!)
More –
Mattermost
As an alternative to proprietary SaaS messaging, Mattermost brings all your team communication into one place, making it searchable and accessible anywhere. It’s written in Golang and React and runs as a production-ready Linux binary under an MIT license with either MySQL or Postgres.
Rocket.chat
Rocket.Chat is an incredible product because we have an incredible developer community.
Over 200 contributors have made our platform a dynamic and innovative toolkit, from group messages and video calls to helpdesk killer features.
Our contributors are the reason we’re the best cross-platform open source chat solution available today.
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Best Open Source Scanning Software

by scmgalaxy KGeneralLeave a Comment on Best Open Source Scanning Software

Open Source Scanning Software

  • Palamida

  • openlogic

  • Blackduck

  • fisheye

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