imutils is a computer vision utility toolkit and image processing library designed to simplify common manipulation tasks using OpenCV. It serves as an image analysis helper and geometry transformation tool for automating visual data processing.
The main features of jrosebr1/imutils are: Image Processing, Aspect Ratio Preserving Resizing, Computer Vision Preprocessing, Computer Vision Utilities, Classical Edge Detection, Aspect Ratio Preservation, Image Geometric Transformations, Image Analysis Helpers.
Open-source alternatives to jrosebr1/imutils include: scikit-image/scikit-image — scikit-image is a Python image processing library and scientific image analysis toolkit. It provides a framework for… open-mmlab/mmcv — mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing… anthonynsimon/bild — Bild is an image processing library implemented in the Go programming language. It provides a collection of… pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection… roboflow/supervision — Supervision is a computer vision toolset for normalizing model outputs, managing datasets, and visualizing… mdbloice/augmentor — Augmentor is a Python image augmentation library and framework designed to expand machine learning datasets. It…
scikit-image is a Python image processing library and scientific image analysis toolkit. It provides a framework for digital image processing and computer vision, utilizing numerical arrays for pixel-level manipulations. The library enables the quantification of image properties and the detection of visual features, such as edges and blobs. It includes tools for image segmentation and the extraction of textures and patterns to characterize objects within visual data. Capabilities cover image manipulation through color space conversion, geometric transformations, and digital restoration. It a
mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad
Bild is an image processing library implemented in the Go programming language. It provides a collection of algorithmic engines for image manipulation, including a convolution kernel engine for filtering, an image blending tool for layer composition, and a procedural noise generator for creating synthetic textures. The project is distinguished by its procedural generation capabilities, implementing Perlin, Gaussian, binary, and uniform noise algorithms to produce random pixel distributions and organic patterns. It also features a command-line interface that allows users to apply visual effect
This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management