Controling Data Visualization Layouts with Blueprints скачать в хорошем качестве

Controling Data Visualization Layouts with Blueprints

Learn how to programmatically control the Rerun viewer layout using blueprints to create customized, reusable visualization configurations for robotics and computer vision workflows. This tutorial demonstrates how blueprints give you templated control over panels, views, and layouts—essential for debugging autonomous systems and displaying multimodal sensor data. What You'll Build: Build multi-panel visualization layouts that combine 2D scenes, 3D point clouds, geospatial maps, time series data, and text logs in a single configurable interface. You'll learn how to create blueprints programmatically with Python, save and load custom layouts, and apply them across different sessions using application IDs. By the end, you'll be able to template visualization layouts for specific debugging scenarios—like perception failures or autonomy issues—and automatically apply the right view configuration when certain conditions occur. Technical Concepts Covered: Blueprint structure and composition (containers, views, panels) Application ID system for blueprint persistence Programmatic vs. manual blueprint creation View types: Spatial2D, Spatial3D, MapView, TextLog, TimeSeries, TextDocument Container types: Grid, Horizontal, Vertical, Tab Blueprint properties: visual bounds, background colors, zoom levels, panel visibility Code & Resources: 📦 Rerun Documentation: Getting Started: https://rerun.io/docs/getting-started... Blueprint API Reference: https://rerun.io/docs Example Gallery: https://rerun.io/examples 💻 Try It Yourself: GitHub Repository: https://github.com/rerun-io/rerun Installation: pip install rerun-sdk Get the code we used in this example at: https://rerun.io/docs/getting-started... Integration & Compatibility: Rerun blueprints work seamlessly with ROS2, PyTorch, TensorFlow, and other robotics/ML frameworks. Use blueprints to visualize MCAP files, LIDAR point clouds, camera streams, sensor fusion outputs, and neural network predictions in synchronized multi-view layouts. Perfect for Physical AI applications, autonomous vehicle development, and robotics debugging workflows. Developer CTA: Try this blueprint example with your own robotics or computer vision data. Star the repo if you find this useful, and join our Discord community to share your custom layouts and get help with advanced blueprint configurations. 🔗 Connect with Rerun: Discord:   / discord   Twitter/X: https://x.com/rerundotio LinkedIn:   / rerun-io   Website: https://rerun.io #Rerun #ComputerVision #Robotics #PhysicalAI #DataVisualization #MultimodalData #ROS2 #DeveloperTools #AutonomousSystems #MLOps