Search Results for "3d data visual"
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Showing 205 open source projects for "3d data visual"
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Deploy Red Hat Ansible Automation Platform on Microsoft Azure for a strategic automation solution that allows you to orchestrate, govern and operationalize your Azure environment.
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This means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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Blender GIS
Blender addons to make the bridge between Blender and geographic data
Import in Blender most commons GIS data format, Shapefile vector, raster image, geotiff DEM, OpenStreetMap XML. There are a lot of possibilities to create a 3D terrain from geographic data with BlenderGIS, check the Flowchart to have an overview. Display dynamics web maps inside Blender 3d view, requests for OpenStreetMap data (buildings, roads, etc.), get true elevation data from the NASA SRTM mission. Manage georeferencing information of a scene, compute a terrain mesh by Delaunay... -
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Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot -
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PyVista
3D plotting and mesh analysis through a streamlined interface
3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). PyVista is a helper module for the Visualization Toolkit (VTK) that takes a different approach on interfacing with VTK through NumPy and direct array access. This package provides a Pythonic, well-documented interface exposing VTK’s powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets. This module can be used... -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
... be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video. -
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seaborn
Statistical data visualization in Python
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements... -
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Pix2Text
Open-Source Python3 tool for recognizing layouts, tables, and math
An Open-Source Python3 tool for recognizing layouts, tables, math formulas, and text in images, converting them into Markdown format. A free alternative to Mathpix, empowering seamless conversion of visual content into text-based representations. 80+ languages are supported. Pix2Text (P2T) aims to be a free and open-source Python alternative to Mathpix, and it can already accomplish Mathpix's core functionality. Pix2Text (P2T) can recognize layouts, tables, images, text, and mathematical... -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP... -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed... -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload... -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
..., the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. If you use the PaddleX visualization client for model training, the data set division function is integrated in the client, and you do not need to use command division by yourself. -
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Vedo
A python module for scientific analysis of 3D data
A lightweight and powerful python module for scientific analysis and visualization of 3d objects. Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII... -
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DocArray
The data structure for multimodal data
DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data... -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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XLabel
XLabel: An Explainable Data Labeling Assistant
XLabel is an open-source Streamlit app that takes an explainable machine-learning approach to visual-interactive data labeling. Predict the most probable labels using Explainable Boosting Machine (EBM). Show the contributions of each feature towards the predicted labels. Provide an option to write the labels directly into the data file (use XLabel.py) or save them in a separate file (use XLabelDL.py) Support data with multiple labels and multiple classes. Support data with missing values... -
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tslearn
The machine learning toolkit for time series analysis in Python
The machine learning toolkit for time series analysis in Python. tslearn expects a time series dataset to be formatted as a 3D numpy array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (n_ts, max_sz, d). In order to get the data in the right format. -
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Orchest
Build data pipelines, the easy way
Code, run and monitor your data pipelines all from your browser! From idea to scheduled pipeline in hours, not days. Interactively build your data science pipelines in our visual pipeline editor. Versioned as a JSON file. Run scripts or Jupyter notebooks as steps in a pipeline. Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container images... -
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Segments.ai
Segments.ai Python SDK
Multi-sensor labeling platform for robotics and autonomous vehicles. The platform for fast and accurate multi-sensor data annotation. Label in-house or with an external workforce. Intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Obtain segmentation labels, vector labels, and more. Our labeling interfaces are set up to label fast and precise. Powerful ML assistance lets you label faster and reduce costs. Integrate data labeling into your existing ML... -
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TorchIO
Medical imaging toolkit for deep learning
TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of 3D medical images in deep learning, following the design of PyTorch. It includes multiple intensity and spatial transforms for data augmentation and preprocessing. These transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity (bias... -
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solo-learn
Library of self-supervised methods for visual representation
A library of self-supervised methods for visual representation learning powered by Pytorch Lightning. A library of self-supervised methods for unsupervised visual representation learning powered by PyTorch Lightning. We aim at providing SOTA self-supervised methods in a comparable environment while, at the same time, implementing training tricks. The library is self-contained, but it is possible to use the models outside of solo-learn. -
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missingno
Missing data visualization module for Python
Messy datasets? Missing values? missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion... -
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towhee
Framework that is dedicated to making neural data processing
Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model... -
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BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance...