Search Results for "processing" - Page 9
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Showing 315 open source projects for "processing"
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pivottablejs
Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook
...I first built PivotTable.js with a plan to build an in-browser data analysis tool, and got as far as one where you could load up a CSV file in the browser for display. Since then, however, the Jupyter project has gathered steam and now provides a browser-based interface to some of the most powerful data processing libraries in the world, so it makes sense to interface with it. -
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react-native-video-processing
Native Video editing/trimming/compressing library
Native Video editing/trimming/compressing library for React-Native. -
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RxJavaSamples
Examples of some of the most common usages of RxJava 2 and Retrofit
Examples of some of the most common uses of RxJava 2 with Retrofit. For the interface that needs to request the token first, then use flatMap() to connect the token request and the actual data request coherently, without having to write a nested Callback structure. For non-disposable tokens (tokens that can be reused), after obtaining the token, save it for repeated use, and use retryWhen() to automatically re-acquire the token when it expires, making the process of token acquisition... -
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OracleClientDAC for Delphi
A feature-rich and high-performance data access components for Oracle
OracleClient Data Access Components (OracleClientDAC) is a feature-rich and high-performance library of components that provides native connectivity to Oracle from Delphi. OracleClientDAC-based applications connect to Oracle directly through the ADO.Net OracleClient, which is an Oracle Data Provider for .NET (ODP.Net). OracleClientDAC aims to assist programmers in developing of fast and native Oracle database applications. OracleClientDAC allows developers to take advantage of advanced... -
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OLEDBDAC for Delphi
Provides data access for data sources exposed through OLE DB.
OLE DB Data Access Components (OLEDBDAC) is a feature-rich and high-performance library of components that provides data access for data sources exposed through OLE DB from Delphi for both 32-bit and 64-bit Windows platforms. OLEDBDAC-based applications connects to any data source exposed through OLE DB using the .NET Framework Data Provider for OLE DB. OLEDBDAC aims to assist programmers in developing of fast and native database applications whose data source is exposed through OLE DB.... -
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SqlClientDAC for Delphi
A feature-rich and high-performance data access components for MS SQL
SqlClient Data Access Components (SqlClientDAC) is a feature-rich and high-performance library of components that provides native connectivity to SQL Server from Delphi. SqlClientDAC-based applications connect to SQL Server directly through the ADO.Net SqlClient, which is a .NET Framework Data Provider for SQL Server. SqlClientDAC is designed to help programmers develop faster and cleaner SQL Server database applications. SqlClientDAC uses its own protocol to communicate with SQL Server.... -
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ADO.Net DAC for Delphi
Fastest and most reliable database connectivity solutions for any DB
ADO.Net Data Access Components (DAC) for Delphi is the fastest and most reliable database connectivity solutions for any database using ADO.Net technology in Delphi. It provides data access to data sources such as SQL Server, Oracle and to data sources exposed through OLE DB and ODBC using ADO.Net data provider framework. ADO.Net DAC for Delphi is an enterprise solution with highest performance and unlimited possibilities. Data-sharing consumer applications can use ADO.NET DAC for Delphi to... -
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ImatiSTL
Fix STL files for 3D printing
ImatiSTL is a C++ library for applications that need to finely post-process raw polygon meshes represented by STL files. It is particularly useful in 3D printing scenarios: the repairing functions provided by ImatiSTL bridge the gap between printable meshes and real-world meshes with diverse potential defects and flaws (e.g. degenerate triangles, self-intersections, surface holes, ...). ImatiSTL provides both functions for local mesh repairing and methods for global remeshing. Local... -
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fast-neural-style
Feedforward style transfer
...It also provides insights into the underlying techniques used in neural style transfer, making it both a practical tool and a learning resource. By combining performance and quality, it enables creative applications in image processing and design. Overall, fast-neural-style showcases how deep learning can be used for real-time artistic transformations. -
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Stringy
A PHP string manipulation library with multibyte support
...It offers a fluent interface for common string operations, including case conversion, trimming, and formatting. Stringy is designed to simplify string handling by providing a consistent and expressive API, making it a valuable tool for text processing in PHP applications. -
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JavaLambdaCompose
Utility methods for composing functional lambda expressions
Provides for composing functional lambda instances to improve run-time efficiency and flexibility. The composition avoids excessive call stacking when processing multiple Predicate or Consumer instances, and can avoid the processing overhead of Collection.stream().filter().forEach() by replacing with Collection.forEach() using a composed Consumer from a Predicate and Consumer. Includes utility methods for composing an Iterable or array of Predicate instances into a single Predicate instance for testing for all, any, or none of the Predicate results. ... -
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Caffe
A fast open framework for deep learning
...It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. ... -
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Swift AI
The Swift machine learning library
...The example projects are another great resource for seeing real-world usage of these tools. Swift AI currently depends on Apple's Accelerate framework for vector/matrix calculations and digital signal processing. -
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aeneas
Automagically synchronize audio and text (aka forced alignment)
aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment). aeneas automatically generates a synchronization map between a list of text fragments and an audio file containing the narration of the text. In computer science this task is known as (automatically computing a) forced alignment. -
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WrapImaJ
Multi-platform API for Image Processing systems in Life Sciences
WrapImaJ purposes to be a multi-platform wrapper for different Image Processing systems for: - using the Java programming language. The purpose of WrapImaJ is not to combine an exhaustive collection of all functionalities of different imaging system, but to offer a simple, concise Application Programming Interface (API) - allowing to develop imaging software, the source code of which is independent from the underlying imaging system on which it relies. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. ... -
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JavaMutex
Provides 100% pure Java mutual exclusion.
Freely redistributable open source 100% pure Java for mutual exclusion. Read-write and read-only reentrant lock, condition variable, atomic wraps for Boolean, Integer, Long, and Reference. Cell pool with optional locking, object exchanger, Intersect. -
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CSSgram
CSS library for Instagram filters
...What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. ... -
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tracking.js
A modern approach for Computer Vision on the web
The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The... -
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Ruby-processing
Bridge between the Processing creative-coding framework and Ruby
Ruby-processing is a bridge between the Processing creative-coding framework (originally Java-based) and the Ruby language, created to let developers and artists write Processing sketches using Ruby syntax. It enables the well-known graphics, animation, interaction and sketching patterns of Processing in a Ruby environment, which can be appealing to those who prefer Ruby’s syntax or already live in the Ruby ecosystem. -
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. -
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deemon
Deemon Scripting Language
A rich and powerful scripting language supporting the c preprocessor, classes, exceptions, lambdas, yield functions, label, switch-statement, pointers, foreign functions, and much more. With a fully packed standard library including support for file-io, threads, atomics, pipes, math, file-system, sockets, randomization, hashing, serialization, a wide selection of emulated c/c++ headers and more, deemon provides a suitable environment for any application. Syntax is easy to read and... -
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CRFasRNN
Semantic image segmentation method described in the ICCV 2015 paper
CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us. ... -
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Sesame
Java RDF Framework
This project is no longer actively maintained. It is succeeded by the Eclipse RDF4J project, which can be found at GitHub and at http://www.rdf4j.org/. Sesame is a de-facto standard framework for processing RDF data. This includes parsing, scalable storage, reasoning and full SPARQL 1.1 query/update support. Sesame offers a fully modular toolkit and an easy-to-use Java API that can be connected to all leading RDF storage solutions. -
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Synder
Atom/RSS/SearchSuggestion/OPML processing framework
Synder is an Atom/RSS/SearchSuggestion/OPML processing framework It is Optimized For Android, but works with Java SE/EE. Synder has a small-footprint, low memory consumption. The only dependency for parsing is a SAX2 implementation.
