Diffgram is a full-stack data annotation platform designed for creating, managing, and reviewing annotations on images, video, audio, text, 3D point clouds, and geospatial data. The system is composed of multiple microservices: a Flask-based REST API (`default`), a background processing service (`walrus`) for media ingestion, export generation, and project migration, an event-driven action runner service (`eventhandlers`) that executes automated workflows (e.g., ML model inference, webhooks, exports), and a Vue 2 + Vuetify single-page application frontend. The backend uses PostgreSQL with SQLAlchemy ORM, RabbitMQ for async messaging, and supports multiple cloud storage backends (AWS S3, GCP, Azure, MinIO). Authentication supports OAuth2/Keycloak, API keys, and session cookies with fine-grained role-based permissions. The platform's core domain includes label schemas, attribute groups, instance templates, task/job management with review workflows, discussions/issues, and a custom query engine for filtering files by labels and attributes. It integrates with external AI services (HuggingFace, Vertex AI, Azure Text Analytics) and labeling vendors (Labelbox, Scale AI). The frontend provides rich annotation tools including 2D canvas (boxes, polygons, keypoints), 3D point cloud annotation with Three.js, audio waveform annotation, text token annotation, and geospatial tile annotation. A command pattern enables undo/redo for annotation actions. The codebase demonstrates solid engineering practices with clear separation of concerns, reusable patterns (strategy, observer, command, policy enforcer), and extensive test coverage (Cypress E2E, Jest unit tests, Python unittest). However, there are signs of technical drift: some outdated dependencies, mixed JavaScript/TypeScript in the frontend, and a few legacy modules marked as deprecated. The open-core model is evident from `OPENCORE - ADD` markers, suggesting proprietary extensions. Overall, Diffgram is a mature, actively maintained annotation platform suitable for teams building training data pipelines.
amazoncaptcha is a pure Python library for solving Amazon's image-based text captchas. It does not rely on external OCR or machine learning models; instead, it uses a custom algorithm that converts the captcha image to monochrome, segments individual letters, and matches each extracted letter against a precomputed library of compressed patterns (stored as zlib-compressed byte strings in JSON files). The library provides a simple API: the `AmazonCaptcha` class can be instantiated with an image file path, URL, or a Selenium WebDriver screenshot, and its `solve()` method returns the predicted captcha text. The architecture is monolithic: a single package with image processing utilities (utils.py), the solving logic (solver.py), optional data collection tools (devtools.py), and embedded training data for letters A–Z. Externally, it depends on Pillow for image manipulation and requests for downloading captchas from URLs. The solver is designed for high accuracy (claimed 99.9% on various tests) and lightness, making it suitable for web automation and scraping tasks that encounter Amazon captchas. While the core algorithm is elegant and self-contained, its effectiveness is tightly coupled to the specific font, distortion, and noise patterns used by Amazon in its captchas. Any change to Amazon's captcha design would likely require retraining or updating the embedded pattern database. The repository also includes a test suite, CI configuration, and documentation for integration with Selenium.