🖼️ Bilderrahmen | The Smart Digital Photo Frame

Bilderrahmen is a high-performance, responsive digital photo frame application built with Java. It transforms any display—from a minimalist Raspberry Pi setup to a high-resolution 4K monitor—into a sleek, interactive dashboard that blends personal memories with real-time smart home data.

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🎯 Project Overview

The project is designed to be "set and forget": it handles resolution scaling automatically, detects home presence, and dims itself during night hours, all while maintaining a buttery-smooth 60fps during UI transitions without taxing the system CPU.

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✨ Key Features

📡 Smart Home & IoT Integration

The application features a modular widget system. Each widget is designed to be unobtrusive while providing high-value information at a glance:

• 🕒 Clock Widget: The core display showing current time and date in a bold, readable format.
  • Data Source: Local system time, updated once per minute.
• 🌦️ Weather Widget: Displays the current temperature and a context-aware weather icon.
  • Data Source: Open-Meteo API. To maintain high performance, data is cached and updated in the background only at specific intervals, ensuring no network dependency on the main UI thread.
• ☀️ Solar Widget: A minimalist energy dashboard for solar power producers. It shows current power production (W), today's total yield (kWh), and a sparkline chart of the last 8 hours.
  • Data Source: Home Assistant REST API. Historical data is fetched asynchronously to populate the chart without blocking the rendering of the next photo.
• 👥 Presence Widget (Network): Shows which family members or roommates are currently "at home."
  • Data Source: Background ARP Scanning. The application utilizes an ExecutorService to parallelize pings to known device IPs, filling the system's ARP cache (/proc/net/arp). This multi-threaded approach ensures that local network latency never impacts the smoothness of the slideshow.

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🎨 Premium User Experience

• Responsive Engine: A custom-built scaling utility (util.Scale) ensures the UI remains perfectly proportioned whether running on a 7-inch touchscreen or a full-sized television.
• Smooth Transitions: A decoupled RenderLoop architecture allows for resource-efficient performance (0.2 FPS during idle) while ramping up to 60 FPS for fluid cross-fading and UI animations.
• Gesture Control: Supports touch swipes for manual photo navigation and intuitive click-interactions for widget overlays.

🛠️ Technical Robustness

• Persistent Configuration: A JSON-based configuration management system that remembers user preferences, API keys, and image directories.
• Metadata Awareness: Built-in EXIF support to respect image orientation and provide technical insights into the displayed photography.
• Modern Aesthetics: Styled with a customized FlatLaf Dark Theme, featuring clean typography and a minimalist widget layout.

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🏗️ Technical Architecture

Adaptive Render Loop

To balance visual fidelity with low power consumption (specifically for ARM-based systems like the Raspberry Pi), the architecture utilizes a state-based loop:

• IDLE Mode: Drops refresh rates to ~0.2 FPS during stagnant photo display. This drastically reduces CPU overhead and heat generation compared to traditional 60Hz rendering.
• TRANSITION Mode: Automatically ramps up to 60 FPS only during cross-fading and UI animations for a premium, fluid feel.
• MENU Mode: High-priority interaction mode ensuring sub-16ms touch/click response.

Thread-Safe Data Pipeline

A major performance hurdle in Java Swing is the blocking of the Event Dispatch Thread (EDT). This project solves this by offloading all IO-bound tasks:

1. Image Loading: High-resolution photos are pre-loaded and decoded in a background thread to prevent "stuttering" during transitions.
2. API Decoupling: All IoT data (Weather/Solar) is fetched via a non-blocking HTTP client and stored in thread-safe caches.
3. Parallel Scans: The Network presence detection handles up to 254 pings simultaneously using a managed thread pool, keeping the system responsive regardless of network size.

Resolution Independence

Instead of relying on standard OS scaling—which often results in blurred Swing components on HiDPI displays—the project features a custom-built rendering engine. By treating 1080p as a reference height and mathematically recalculating every coordinate, font size, and padding in real-time, the UI remains perfectly sharp and proportioned across any output resolution.

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💻 Tech Stack

• Language: Java 17+
• UI Framework: Java Swing + FlatLaf (Modern Look & Feel)
• Integration: Home Assistant REST API, OpenWeatherMap API
• Data Handling: JSON for configuration and state persistence
• Libraries:
  • com.formdev.flatlaf for advanced styling.
  • Custom implementation for gesture detection and image transition shaders.

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🚀 How to Run

Prerequisites

• JDK 17 or higher
• (Optional) Home Assistant instance for full IoT features

Quick Start

1. Clone the repository:

   git clone https://github.com/yourusername/Bilderrahmen.git

2. Build the project (using your IDE or Maven/Gradle if implemented).
3. Run the application:

   java -jar Bilderrahmen.jar /path/to/your/photos