Open-source alternatives to Sewar

ImageHash

Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w

Tiler is an image mosaic generator and tiling engine designed to assemble large composite images by arranging a collection of smaller tiles to match a target visual. It functions as a tool for algorithmic art composition, mapping source image fragments to target pixel data. The system includes a mosaic tile library generator that produces multiple colored versions and rotations of a source image. This process expands the available tile set to increase the accuracy of the final composite visual. The project handles the technical process of mosaic creation through grid-based spatial partitioni

SAHI is a sliced inference framework and computer vision pipeline designed to detect small objects in high-resolution images. It provides a system for dividing large images into overlapping patches to prevent the detail loss that typically occurs during standard model downscaling, alongside an image tiling utility and a COCO dataset toolkit. The project distinguishes itself by offering a model-agnostic prediction wrapper that standardizes different machine learning frameworks into a unified interface. This allows it to implement sliced inference and object detection across various model backe

A noGDAL tool for reading geotiff files

This project is a Python wrapper for the OpenCV computer vision library, providing a bridge that exposes high-performance C++ functions to the Python programming language. It serves as a collection of tools for real-time image processing, object detection, and machine learning on visual data. The project provides precompiled binary distributions, allowing for the integration of vision capabilities into Python applications without requiring a local C++ compiler. It offers multi-variant package distributions, including headless versions designed for server or cloud environments where a graphica

xbatcher: Batch Generation from Xarray Datasets

Satellite imagery for dummies.

A module to programmatically create geotiff images which can be used for unit tests.

Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr

Neuraltalk2 is a deep learning vision system designed for automatic image captioning. Built with PyTorch, it utilizes a hybrid architecture that combines a convolutional neural network encoder with a recurrent neural network decoder to generate textual descriptions from visual input. The project features a GPU-accelerated training pipeline capable of distributing workloads across multiple graphics processing units through multi-process distribution. It supports the generation of descriptions for both static image files and real-time video streams. The framework includes capabilities for enco

This project serves as a comprehensive educational resource and curriculum for mastering machine learning and deep learning within the Python data science ecosystem. It provides a structured collection of tutorials and code examples designed to guide users through the end-to-end process of building, training, and deploying predictive models. The material focuses on practical implementation, covering the construction of machine learning pipelines that integrate data processing, feature engineering, and model training. It distinguishes itself by offering hands-on guidance for complex domains, i

This is an open-source autonomous driving perception pipeline that processes camera and lidar sensor data to detect, track, and fuse objects in real-world driving environments. The project integrates an end-to-end perception workflow combining sensor calibration, deep learning object detection, Kalman filter tracking, and sensor fusion for robust scene understanding. The pipeline includes camera calibration tools to remove lens distortion from raw images, deep learning model training for object classification and detection, and multi-object tracking using Kalman filters with data association

NOTE: PLEASE CHANGE ALL import gdal imports to ` from osgeo import gdal ` in all python programs and notebooks

Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc

AugLy is a multimodal data augmentation library and machine learning dataset augmentor. It provides a system for generating synthetic variations of training data across audio, image, text, and video datasets to increase sample diversity and improve model robustness. The library functions as a multimedia noise simulator, specifically designed to mimic real-world user captures by overlaying social media templates and internet artifacts onto media. It includes a data provenance tracker to record the specific transformations and intensity levels applied to each piece of augmented data. The tool

Geo Data Analytics tool for VSCode IDE with kepler.gl support to generate and view maps 🗺️ without any Python 🐍, IPyWidgets ⚙️, pandas 🐼, Jupyter notebooks 📚, or ReactJS ⚛️ app code.

TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.

Temporal fusion of raster image time-Series. R interface for the imagefusion framework, which provides implementation of the FITFC, ESTARFM and STARFM algorithms in C++.

A Python tool to perform deep learning experiments on various hyperspectral datasets.

This repository holds both the frontend web-application and backend server that make up our "Land Cover Mapping" tool.

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

Ning Zhang, Lin Zhang*, and Zaixi Cheng