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38 Repos

Awesome GitHub RepositoriesPixel Normalizers

Utilities for scaling pixel intensity values to specific ranges for neural network input.

Distinct from Image Preprocessing Utilities: Focuses on pixel-level intensity scaling rather than general image dimension standardization.

Explore 38 awesome GitHub repositories matching data & databases · Pixel Normalizers. Refine with filters or upvote what's useful.

Awesome Pixel Normalizers GitHub Repositories

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  • d2l-ai/d2l-enAvatar von d2l-ai

    d2l-ai/d2l-en

    29,001Auf GitHub ansehen↗

    This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

    Scales pixel values to specific ranges to ensure compatibility with activation functions during training.

    Pythonbookcomputer-visiondata-science
    Auf GitHub ansehen↗29,001
  • yaronn/blessed-contribAvatar von yaronn

    yaronn/blessed-contrib

    15,745Auf GitHub ansehen↗

    blessed-contrib is a terminal user interface framework and a Node.js console widget library designed for building data-driven command line interfaces. It serves as an ASCII data visualization toolkit and a dashboard framework for organizing grid-based layouts and interactive elements within a console. The project provides a collection of reusable terminal components, including a command line image renderer and tools for text-based graphic rendering. It specifically enables the creation of terminal dashboards through a system for positioning multiple widgets across rows and columns and a mecha

    Converts image pixel data into characters to enable graphical image display within a text-only terminal.

    JavaScript
    Auf GitHub ansehen↗15,745
  • imagemagick/imagemagickAvatar von ImageMagick

    ImageMagick/ImageMagick

    15,742Auf GitHub ansehen↗

    ImageMagick is a comprehensive software suite for the creation, editing, composition, and conversion of digital images. It functions as both a command-line utility for batch processing and automation, and as a programming library that allows developers to integrate advanced image manipulation capabilities into external applications. The project is distinguished by its modular architecture, which supports hundreds of image formats through a pluggable coder system and external delegate libraries. It is designed for high-performance environments, utilizing memory-mapped pixel caching, stream-ori

    Forces pixels below or above a specific value to black or white to create high-contrast masks.

    Ccommand-line-image-tooldigital-image-editingimage-conversion
    Auf GitHub ansehen↗15,742
  • oliver-moran/jimpAvatar von oliver-moran

    oliver-moran/jimp

    14,621Auf GitHub ansehen↗

    Jimp is a JavaScript image processing library and Node.js manipulation tool designed to perform image transformations and edits entirely within a JavaScript environment. It is a zero-dependency image library that operates without requiring native binaries or external system software dependencies. The project provides a programmatic interface for automated image transformations, including resizing, cropping, and filtering. It supports the creation of custom image pipelines and server-side image editing by processing data without relying on native system tools.

    Provides a direct interface for reading and writing raw pixel data via a coordinate-based grid.

    TypeScript
    Auf GitHub ansehen↗14,621
  • hammerspoon/hammerspoonAvatar von Hammerspoon

    Hammerspoon/hammerspoon

    14,497Auf GitHub ansehen↗

    Hammerspoon is a programmable automation engine for macOS that enables deep system-level control through a Lua scripting environment. By bridging high-level scripts with native Objective-C APIs, it allows users to interact with the operating system's accessibility tree, intercept hardware input streams, and manage the lifecycle of running applications. The project distinguishes itself through an event-driven architecture that registers asynchronous hooks for system notifications and hardware events. This allows for real-time automation, such as remapping keyboard and mouse inputs, managing wi

    Reads pixel colors and converts image data for programmatic inspection.

    Objective-Cautomationhammerspoonirc
    Auf GitHub ansehen↗14,497
  • paddlepaddle/paddledetectionAvatar von PaddlePaddle

    PaddlePaddle/PaddleDetection

    14,243Auf GitHub ansehen↗

    PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

    Standardizes image pixel values and bounding box coordinates to ensure consistent model input.

    Pythonblazefacedeepsortdetr
    Auf GitHub ansehen↗14,243
  • rivo/tviewAvatar von rivo

    rivo/tview

    13,928Auf GitHub ansehen↗

    tview is a library for building interactive text-based user interfaces in Go. It functions as a toolkit for managing event loops, user input, and screen rendering, providing a framework of pre-built widgets and an integrated layout engine for creating command-line applications. The project distinguishes itself through a comprehensive layout system that uses grid and flexbox models to create responsive designs. It also supports UI layer stacking to manage multiple screens and modal overlays. The framework includes a diverse suite of interactive components for data display, such as tables and

    A process for displaying images in the terminal by approximating pixels with graphical characters and applying dithering.

    Gogolangterminal-baseduser-interface
    Auf GitHub ansehen↗13,928
  • wangshub/wechat_jump_gameAvatar von wangshub

    wangshub/wechat_jump_game

    13,833Auf GitHub ansehen↗

    This project is a Python-based game automation bot and computer vision assistant designed to automate gameplay on Android devices. It functions as a controller that identifies game elements via pixel color scanning and simulates touch inputs to execute gameplay without manual intervention. The system distinguishes itself through the use of anti-detection measures, implementing interaction coordinate management and timing offsets to avoid being flagged by security systems. It also employs resolution-dependent scaling coefficients to maintain jump accuracy across different device screen sizes.

    Analyzes pixel-level color data to identify and locate game objects on the screen.

    Pythonadbpythonwechat-app
    Auf GitHub ansehen↗13,833
  • python-pillow/pillowAvatar von python-pillow

    python-pillow/Pillow

    13,637Auf GitHub ansehen↗

    Pillow is a Python image processing library and digital image manipulation toolkit used for opening, manipulating, and saving various image file formats. It serves as a multi-format image codec wrapper that enables the reading and writing of diverse standards such as JPEG, PNG, TIFF, and BMP. The library provides tools for programmatic image manipulation, including resizing, cropping, rotating, and transforming visual content through direct pixel data modification. It supports pixel data analysis to extract and modify raw information for custom visual processing and data transformations. The

    Provides interfaces for extracting and modifying raw pixel information for custom visual processing.

    Pythonccross-platformimage
    Auf GitHub ansehen↗13,637
  • octalmage/robotjsAvatar von octalmage

    octalmage/robotjs

    12,740Auf GitHub ansehen↗

    Robotjs is a native Node.js automation library and desktop input simulator. It uses C++ bindings to provide low-level access to operating system functions, allowing for the programmatic control of the mouse and keyboard and the analysis of screen pixels. The library functions as a toolkit for automating user interfaces and desktop workflows, including those within Electron applications. It enables the simulation of key presses and mouse movements to automate interactions with desktop software and perform automated data entry. Its capabilities extend to screen pixel analysis, where it capture

    Inspects pixel-level color data to monitor and detect visual changes on the display.

    C
    Auf GitHub ansehen↗12,740
  • marcotcr/limeAvatar von marcotcr

    marcotcr/lime

    12,142Auf GitHub ansehen↗

    This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

    Groups pixels into contiguous regions to treat blocks of pixels as single features during model analysis.

    JavaScript
    Auf GitHub ansehen↗12,142
  • soumith/ganhacksAvatar von soumith

    soumith/ganhacks

    11,619Auf GitHub ansehen↗

    This project is a PyTorch-based generative framework and implementation template for building Generative Adversarial Networks. It provides a collection of foundational toolkits and architectural patterns designed to synthesize high-quality artificial data while focusing on the stability of adversarial neural networks. The framework distinguishes itself through a specialized toolkit for conditional image generation, which integrates discrete labels and auxiliary classification into the training process. It utilizes specific mechanisms to guide the generative process toward target classes by co

    Provides utilities to scale input data and apply activation functions for consistent distribution.

    Auf GitHub ansehen↗11,619
  • android/ndk-samplesAvatar von android

    android/ndk-samples

    10,513Auf GitHub ansehen↗

    The Android NDK samples provide a comprehensive collection of code examples demonstrating how to integrate C and C++ native code into Android applications. This repository serves as a practical guide for developers utilizing the Android Native Development Kit to implement performance-critical application components that require direct hardware access and low-level system interaction. The project highlights the use of the Java Native Interface to bridge managed code with native modules, enabling cross-language function calls and efficient data exchange. It demonstrates how to manage native act

    Retrieves and modifies raw pixel data from image objects to perform custom image processing.

    C++
    Auf GitHub ansehen↗10,513
  • openvinotoolkit/openvinoAvatar von openvinotoolkit

    openvinotoolkit/openvino

    10,414Auf GitHub ansehen↗

    OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

    Subtracts mean values and divides by standard deviations to normalize tensors for model inference.

    C++aicomputer-visiondeep-learning
    Auf GitHub ansehen↗10,414
  • cadene/pretrained-models.pytorchAvatar von Cadene

    Cadene/pretrained-models.pytorch

    9,102Auf GitHub ansehen↗

    This project is a pretrained model library for PyTorch, providing a collection of convolutional neural network architectures and weights. It serves as a computer vision model zoo for image classification and feature extraction, offering a framework for transfer learning where pretrained networks are adapted for custom image recognition tasks. The library focuses on transforming images into high-level numerical representations and calculating class probability scores. It includes utilities for downloading and initializing standard architectures such as ResNet, Inception, and Xception. Capabil

    Includes utilities to normalize image pixels using mean and standard deviation values specific to each architecture.

    Pythonimagenetinceptionpretrained
    Auf GitHub ansehen↗9,102
  • bishopfox/unredacterAvatar von BishopFox

    BishopFox/unredacter

    8,351Auf GitHub ansehen↗

    Unredacter ist ein Computer-Vision-Tool zur Textrekonstruktion und Bildforensik, das darauf ausgelegt ist, versteckte Zeichen aus verpixelten Bildern wiederherzustellen. Es fungiert als Werkzeug zur Umkehrung der Verpixelung, um Text innerhalb verdeckter visueller Blöcke zu identifizieren. Das System verwendet einen Prozess, bei dem verpixelte Bildblöcke mit gerenderten Kandidatenzeichen verglichen werden, die den typografischen Stilen des Zieltextes entsprechen. Dies ermöglicht die Rekonstruktion verdeckter Informationen durch automatisierte visuelle Analyse. Das Projekt deckt Funktionen für digitale forensische Analysen, Tests zur Bildschwärzung und Bewertungen von Informationslecks ab, um die Wirksamkeit bildbasierter Maskierungstechniken zu überprüfen.

    Maps pixelated image data back to characters by comparing blocks against rendered templates.

    TypeScript
    Auf GitHub ansehen↗8,351
  • vietnh1009/ascii-generatorAvatar von vietnh1009

    vietnh1009/ASCII-generator

    8,270Auf GitHub ansehen↗

    ASCII-generator is a tool for converting images and videos into text-based ASCII art. It functions as an image-to-ASCII converter and a video-to-ASCII processor that maps pixel intensity and color to specific alphanumeric characters. The system generates stylized visual representations by transforming visual files into grayscale or colored ASCII art text files. It can render static images into text art or process video files into a sequence of ASCII art frames for animation. The rendering process involves translating image pixels into text grids and mapping brightness values to characters ba

    Maps pixel brightness values to a predefined set of ASCII characters based on visual density.

    Pythonasciiascii-artascii-generator
    Auf GitHub ansehen↗8,270
  • mic-dkfz/nnunetAvatar von MIC-DKFZ

    MIC-DKFZ/nnUNet

    8,041Auf GitHub ansehen↗

    nnU-Net is a PyTorch-based deep learning framework for the supervised semantic segmentation of 2D and 3D biomedical images. It functions as an automated medical imaging pipeline that generates predicted masks and labels from clinical images. The system distinguishes itself by using dataset-driven auto-configuration to automatically select the optimal network architecture, preprocessing steps, and training hyperparameters based on the specific properties of the input medical dataset. The framework covers a broad range of capabilities including medical dataset preparation, intensity normalizat

    Provides utilities for scaling pixel intensity values using dataset-specific statistics to ensure consistent distributions across modalities.

    Pythonsegmentation
    Auf GitHub ansehen↗8,041
  • tingsongyu/pytorch_tutorialAvatar von TingsongYu

    TingsongYu/PyTorch_Tutorial

    8,018Auf GitHub ansehen↗

    This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

    Standardizes image pixel values and coordinates to ensure they meet required normalization standards.

    Python
    Auf GitHub ansehen↗8,018
  • sixlabors/imagesharpAvatar von SixLabors

    SixLabors/ImageSharp

    7,954Auf GitHub ansehen↗

    ImageSharp is a .NET image processing library and manipulation framework used for decoding, encoding, and modifying digital images. It functions as a comprehensive toolkit for resizing, cropping, and applying pixel-level filters while managing color profiles and pixel data across various file formats. The project integrates a 2D vector graphics engine and a typography rendering engine to draw geometric shapes, paths, and complex stylized text onto images. It also includes a geometry boolean operation library for calculating intersections, unions, and differences between complex polygons and c

    Provides direct read and write access to raw pixel data via indexers and row-based buffers.

    C#bmpc-sharpdrawing
    Auf GitHub ansehen↗7,954
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  3. Image Preprocessing Utilities
  4. Pixel Normalizers

Unter-Tags erkunden

  • Histogram-Based ScalersScales pixel values to a 0-1 range using frequency-based histogram binning. **Distinct from Pixel Normalizers:** Distinct from Pixel Normalizers: uses histogram binning rather than min-max or z-score normalization.
  • Input Normalizers3 Sub-TagsUtilities for standardizing image pixel values and bounding box coordinates for model input. **Distinct from Pixel Normalizers:** Distinct from Pixel Normalizers: focuses on both pixel-level and coordinate-level normalization for computer vision models.
  • Intensity Thresholders1 Sub-TagUtilities for binarizing pixel intensity values to create high-contrast masks. **Distinct from Pixel Normalizers:** Distinct from Pixel Normalizers: focuses on thresholding rather than range normalization.
  • Pixel Analyzers3 Sub-TagsTools for inspecting pixel-level color data and converting image content into text-based representations. **Distinct from Pixel Normalizers:** Distinct from Pixel Normalizers: focuses on inspection and conversion rather than intensity scaling for neural networks.
  • Pixel Removal UtilitiesTools for deleting interior rows or columns and shifting pixels to close gaps. **Distinct from Pixel Normalizers:** Distinct from pixel normalizers: focuses on structural pixel removal.