MediaPipe is a cross-platform framework for building multimodal machine learning pipelines, primarily targeting on-device inference. It provides a graph-based execution model where modular 'calculators' process typed 'packets' through directed acyclic graphs. The framework includes a rich library of pre-built calculators for image processing, tensor manipulation, audio analysis, and ML inference, as well as high-level task APIs for vision (face/hand/pose landmarking, image classification, segmentation), text (classification, language detection), audio (classification), and generative AI (LLM inference). The architecture is layered: a core C++ framework (mediapipe/framework/) defines the graph runtime, stream handlers, and packet infrastructure; a calculator library (mediapipe/calculators/) provides reusable processing nodes; and task-specific APIs (mediapipe/tasks/) offer consistent interfaces across C++, Java, Python, iOS, Web, and C. The system relies on TensorFlow Lite (LiteRT) for model execution, OpenCV for image operations, protobuf for configuration, and Abseil for utilities. Key entry points include the Python package (mediapipe/__init__.py), the C++ task APIs (mediapipe/tasks/cc/), and the web TypeScript modules (mediapipe/web/). The codebase is large and actively developed, with ongoing migration from older API patterns to newer ones (api2/api3), and some deprecated components (legacy Python solutions, older calculator interfaces).
Apollo is a comprehensive autonomous driving platform developed by Baidu, providing a full software stack for self-driving vehicles. The repository contains a modular architecture built on Cyber RT, a high-performance runtime framework optimized for autonomous driving scenarios with low latency and high throughput. Key modules include perception (camera, LiDAR, radar, sensor fusion), planning (scenario-based with multiple planners), control (PID, LQR, MPC controllers), localization (RTK, NDT, MSF), prediction, routing, and V2X communication. The system integrates with hardware through extensive driver support for various sensors (Velodyne, Hesai, Continental radar, Mobileye, etc.) and vehicle platforms (Lincoln, Lexus, Geely, etc.). Dreamview provides a web-based visualization and monitoring interface. The codebase demonstrates strong engineering practices with clear module boundaries, extensive configuration management, comprehensive testing infrastructure, and support for both x86_64 and ARM64 (Orin) platforms. Recent versions introduce package management for easier deployment, Dreamview Plus for enhanced developer experience, and cloud-based model training services (Apollo Fuel).