Awesome GitHub RepositoriesComputer Vision Preprocessing

Real-ESRGAN is a deep learning restoration pipeline designed to enhance low-resolution media and improve the visual quality of damaged photographs. It functions as a generative image upscaler that reconstructs high-resolution details from source inputs by utilizing neural networks trained to fill in missing information and remove noise. The project distinguishes itself as a blind super-resolution tool, meaning it improves image sharpness and fidelity without requiring prior knowledge of the specific degradation applied to the source. It employs high-order degradation modeling to address compl

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

Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

Caire is a command-line image processing engine designed for content-aware resizing and batch manipulation. It utilizes seam carving algorithms to adjust image dimensions by identifying and removing low-energy pixels, allowing for the rescaling of images while preserving primary visual subjects and maintaining aspect ratios. The tool distinguishes itself through its ability to protect specific visual elements, such as human faces, from distortion during the resizing process. Users can apply custom binary masks to define regions for protection or forced removal, and the engine provides real-ti

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

MiDaS 是一个 PyTorch 计算机视觉库和单目深度估计模型，旨在从单张图像中预测场景深度。它作为一种场景深度预测器，通过计算距离图来确定物体与摄像机的距离。 该项目支持零样本深度迁移，允许在无需额外训练数据的情况下将模型应用于新的数据集或环境。它专注于相对深度回归，以预测尺度不变的深度图。 该库包含一个用于捕获实时摄像机馈送并显示相应深度图的实时深度可视化工具。它还提供计算机视觉预处理功能，为其他机器学习模型生成结构化场景数据。

Augmentor 是一个 Python 图像增强库和框架，旨在扩展机器学习数据集。它既是一个生成合成图像变体以增加数据多样性的预处理工具，也是一个训练数据流处理器，可将增强后的图像和标签直接馈送到神经网络循环中，而无需中间磁盘存储。 该框架保持图像与其对应掩码之间的空间对齐，这是语义分割训练所必需的。它支持多种几何和像素级变换，包括弹性形变、通过倾斜和扭曲进行的透视变换、旋转、剪切以及随机区域擦除。 该系统包含针对类别处理的策略以解决数据不平衡问题，并使用多线程来加速增强数据集的并行生成。它还提供了在预处理阶段清理和标准化原始图像文件的实用程序。

imutils 是一个计算机视觉实用工具包和图像处理库，旨在简化使用 OpenCV 进行的常见操作任务。它作为一个图像分析助手和几何变换工具，用于自动化视觉数据处理。 该工具包提供了在变换过程中保持图像完整性的专用功能，例如在保持纵横比的同时调整图像大小，以及在不裁剪边角的情况下旋转图像。它还包括用于创建俯视图的四点透视变换工具，以及从二值图像中提取拓扑骨架的功能。 该库涵盖了广泛的预处理和分析函数，包括自动边缘检测阈值计算、空间轮廓排序以及 RGB 和 BGR 之间的色彩空间映射。此外，它还包括数据集管理实用程序，如用于图像发现的递归文件系统扫描和 Web 图像下载器。

这是一个关于使用 PyTorch 构建神经网络的综合教学资源和课程。它涵盖了深度学习的基本构建块，包括张量操作、自动微分以及模块化神经网络组件的构建。 该仓库是多个专业领域的参考指南。它提供了计算机视觉任务（如图像分类、目标检测和语义分割）的实现细节，以及涉及 Transformer、循环网络和生成模型的自然语言处理工作流。此外，它还包括生成式 AI 的参考资料，专门关注通过扩散模型和对抗网络进行图像合成。 材料延伸至模型优化和部署流水线。它涵盖了通过量化和将模型导出为 ONNX 和 TensorRT 等格式来减小模型大小并提高推理速度的技术。其他能力领域包括用于并行加载的数据工程、使用自定义指标的模型评估，以及开源大语言模型的部署。 该项目主要以一系列 Jupyter Notebook 的形式提供。

ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,

Bild 是一个用 Go 编程语言实现的图像处理库。它提供了一套用于图像操作的算法引擎，包括用于过滤的卷积核引擎、用于图层合成的图像混合工具，以及用于创建合成纹理的程序化噪声生成器。 该项目以其程序化生成能力而著称，实现了 Perlin、高斯、二进制和均匀噪声算法，以产生随机像素分布和有机图案。它还具有一个命令行界面，允许用户在不编写自定义代码的情况下对图像文件应用视觉效果、颜色调整和几何变换。 该库涵盖了广泛的图像处理功能，包括旋转、剪切和缩放等几何变换，以及颜色操作和分布分析。它提供用于图像分析和分割、形态学过滤的工具，并支持读取和写入 PNG、JPEG、BMP 和 WebP 格式的图像数据。