MMSearch is a framework for evaluating large multimodal models (LMMs) as multi-modal search engines. It provides a pipeline (MMSearch-Engine) that enables any LMM to perform multimodal search by generating queries, retrieving web content, reranking results, and summarizing answers. The repository includes evaluation scripts for four tasks (end2end, requery, rerank, summarization) and a demo for running the engine on custom queries. The system relies on external dependencies like DuckDuckGo search, Playwright for web scraping, and Hugging Face datasets. The architecture is modular with separate components for model loading, prompt templating, web retrieval, content processing, and scoring.
This repository provides the `lidar` Python package, a specialized library for hydrological depression analysis of high-resolution topographic data, particularly LiDAR-derived DEMs. Its core functionality includes DEM smoothing (mean, median, Gaussian filters), sink extraction, and a novel level-set algorithm for delineating nested depression hierarchy, yielding topological and geometric properties (size, volume, depth, perimeter). The package is designed as a reusable library with a CLI (`lidar` command), a PySimpleGUI-based graphical interface, and integration with ArcGIS via a dedicated toolbox. It also includes Jupyter notebooks for tutorial and research workflows, demonstrating the complete analysis pipeline. The project is research-grade, backed by peer-reviewed publications, and follows a modular architecture with separate modules for filtering, filling, slicing, and mounts. However, some dependencies (GDAL, arcpy) and Python version constraints introduce moderate technical drift risk.
face_recognition is a Python library and CLI toolkit that wraps dlib's deep-learning-based face recognition models behind a simple, high-level API. It provides functions to detect face locations (HOG or CNN-based), extract 68-point or 5-point facial landmarks, compute 128-dimensional face encodings, and compare faces via Euclidean distance. The package ships two Click-based CLI entry points â `face_recognition` for batch identification against a known-people folder and `face_detection` for locating faces in images â both supporting multiprocessing and tunable tolerance/upsample parameters. The core logic lives in `face_recognition/api.py`, which is essentially a thin, well-abstracted wrapper around dlib detectors, shape predictors, and the ResNet face encoder, with coordinate conversion helpers to keep the public API in (top, right, bottom, left) tuple form. The repo also includes a large examples directory (webcam recognition, KNN/SVM classifiers, blink detection, batch GPU inference, Flask web service, Raspberry Pi integration) and multi-stage Docker builds for CPU and GPU deployment. Last tagged release is 1.4.0 (2020); the final commit on master dates to mid-2026 but the project shows little active development and many open issues, placing it in 'stale' maintenance status. Technical drift is medium: the codebase still references Python 2.7 in places, pins older dlib versions in some Dockerfiles, and has CI only through 3.10, while the ecosystem has moved to Python 3.12+ and dlib 19.24+. The API wrapper itself is clean and reusable, but the CLI modules and examples are too tightly coupled to the library and filesystem assumptions to be extracted as standalone components.
SnapX is a cross-platform screenshot and screen recording tool, forked from ShareX, with extensive upload capabilities to dozens of services (image hosts, file storage, pastebins, URL shorteners). It features image effects (adjustments, filters, manipulations), OCR via PaddleOCR, QR code generation/scanning, hotkey automation, and clipboard monitoring. The application is built with Avalonia UI and .NET 10, targeting Linux, Windows, macOS, and FreeBSD. It includes a CLI, a native messaging host for browser integration, and a comprehensive settings system. The codebase is in early access with many features ported from ShareX but several stubs and incomplete implementations remain, particularly in screen recording, image annotation, and some uploaders. The architecture follows a core library (SnapX.Core) with separate UI (SnapX.Avalonia) and CLI projects, using MVVM pattern and dependency injection. While functional, the project exhibits significant technical debt and incomplete feature parity with its predecessor.
The GoPro CineForm SDK is a production-grade, wavelet-based video codec library (SMPTE VC-5 super-set) implementing the full CineForm compression pipeline: recursive spatial/temporal wavelet transforms, quantization with rate control, run-length + Huffman entropy coding, and tag/value bitstream framing. It exposes a layered architecture: a C core codec engine (Codec/), high-level EncoderSDK (with asynchronous encoder pools) and DecoderSDK (with thumbnail fast-paths and output format negotiation), plus supporting libraries for color space conversion and scaling (ConvertLib/), geometric mesh warping for GoPro lens correction (WarpLib/), and an Active Metadata engine handling 3D LUTs, CDL color corrections, and timecode. Both encoder and decoder C APIs (CFHD_OpenEncoder/CFHD_EncodeSample, CFHD_OpenDecoder/CFHD_DecodeSample) are the primary integration points, with TestCFHD and WaveletDemo as example tools. The codec is cross-platform (Windows, macOS, Linux) and built with CMake, with a GitHub Actions CI workflow. The codebase is mature and battle-tested in commercial video production, with heavy SIMD (MMX/SSE2) hand-optimization in the wavelet and color conversion paths. However, it carries significant technical debt: large monolithic translation units (e.g., ImageScaler.cpp, DemoasicFrames.cpp with thousands of lines), extensive #ifdef platform conditionals, large blocks of disabled #if 0 code, hardcoded debug paths, and dated SIMD (Pentium 3 MMX) that underutilizes modern AVX-512 hardware. Several latent bugs exist (e.g., a copy-paste index error in ScaleB64ARow), and the FSM/Huffman entropy tables (Common/table17.inc, table18.inc) are checked-in generated artifacts that may silently drift from their generator. Overall it is a strong, working codec with cleanly separable infrastructure components, but the surrounding code style shows its commercial origins and accumulated debt.
This repository is a comprehensive frontend visual effects collection and demonstration platform. It contains over 100 self-contained demos and tools covering CSS animations, Canvas 2D/WebGL effects, Three.js 3D scenes, ECharts data visualizations, AI/ML integrations (TensorFlow.js for pose detection and body segmentation, MediaPipe, Tesseract.js OCR), and client-side media processing (image, video, PDF). The architecture is a React 18 single-page application (SPA) using TypeScript, Webpack 5, Ant Design, and react-intl for internationalization. All processing occurs entirely in the browser; there is no backend or persistent data store. The project serves as both a personal portfolio and a learning resource for advanced frontend techniques, but it is not a single-purpose production application. The code quality varies widely: some components demonstrate high craftsmanship (e.g., custom PNG encoder, image manipulation utilities, cross-page communication hook), while many others are boilerplate-heavy duplicates or exploratory demos. No automated tests are present, and the build output includes large minified chunks alongside source code, creating maintenance overhead.
The exifLooter is a command-line application written in Go that extracts EXIF metadata, particularly GPS coordinates, from image files. It relies on the external exiftool command-line utility to perform the actual metadata extraction and provides a user-friendly interface for analyzing single images, directories of images, or images downloaded from URLs via stdin. The tool also offers functionality to remove all metadata from images and to generate OpenStreetMap URLs from GPS coordinates found in images. The architecture is a standard CLI tool built using the Cobra library for command parsing and the fatih/color library for colored terminal output. The main entry point is in main.go, which delegates to the command defined in cmd/root.go. The core functionality involves invoking exiftool via os/exec, parsing its output to detect GPS-related tags, and presenting the results with color-coded warnings when geolocation data is detected. Additional features include downloading images from URLs (with validation of content type) for batch processing in a pipe mode. While the tool fulfills its intended purpose, the codebase exhibits some areas for improvement: the pipe mode's image validation step is inefficient (rescanning the entire directory for each download), the OpenStreetMap URL generation function incorrectly handles southern and western hemisphere coordinates, and there is a lack of automated tests. Despite these issues, the project is actively maintained with recent commits and uses dependency management via Go modules.
PDFQFZ is a Chinese-language Windows Forms desktop application that overlays seal/stamp images onto existing PDFs, including cross-page seam stamps (éŠįžįŦ ) split across page edges. It supports file/folder batch processing, configurable stamp size/opacity/rotation/placement, white-background transparency removal, PDF password encryption, and optional digital signatures using self-generated or imported PFX certificates. A "merge" mode rasterizes PDF pages at 300 DPI and reconstructs them to flatten content. The app accepts PDF files as command-line arguments and supports drag-and-drop input. The architecture is a single large Form1.cs WinForms monolith for UI and orchestration, with a small Library/ folder of extracted helper classes: a generic PageCache<T> for lazy page preview loading, pure stamp policy predicates, layout calculators, numeric input sanitizers, an async request gate for stale preview cancellation, and an image transparency helper. A separate xUnit test project targets net9.0-windows and compiles linked Library sources to test these helpers, but the main project targets .NET Framework 4.7.2 and is not included in the solution file. The data flow is straightforward: users select source PDFs and a stamp image; pdfGz() iterates files, opens each PDF with iTextSharp PdfReader/PdfStamper, splits the stamp into segments via subImages(), and places them according to seam-stamp mode and position settings. The preview path renders pages lazily through PDFRender4NET at low DPI and uses PageCache plus PreviewOverlayRequestGate to avoid UI hangs on large PDFs. Maintenance is recent and tests exist, but the main application still depends on abandoned/legacy libraries, references an external DLL via an out-of-repo Downloads path, includes committed probe artifacts, and contains a fair amount of monolith-style code debt.
This repository implements a wide range of image processing algorithms in C++ (with some Python components for license plate recognition). It provides functionalities including contrast enhancement, color space conversion, filtering (median, Gaussian, bilateral), feature detection (edges, corners), image interpolation, haze removal, and artistic effects similar to Photoshop. The architecture is modular with algorithms grouped by category in separate directories. Many files include main() functions allowing them to be compiled and run as standalone demonstrations. The primary external dependency is OpenCV, with some files using OpenMP for parallel processing and Intel intrinsics for AVX optimizations. The license plate recognition system integrates C++ preprocessing with TensorFlow/Keras models in Python for character recognition.
This repository contains a proof-of-concept CLI tool for recovering text from pixelated screenshots, specifically targeting password recovery. The core algorithm identifies same-color rectangles in the pixelated image, computes their average color (with optional gamma correction), and matches them against a search image containing a De Bruijn sequence of expected characters. Geometric relationships between matched blocks are used to resolve ambiguous matches. The tool is implemented as a single Python script (depix.py) with a supporting library (depixlib) for image loading, rectangle detection, and matching logic. Additional utility scripts generate pixelated images and visualize detected boxes. The code is functional but lacks tests, dependency pinning, and modern packaging; it is a proof-of-concept rather than a production-ready application. The README indicates the project has migrated to Codeberg, suggesting the GitHub repository is no longer actively maintained.