Análisis de la app EdgeTigator para 9 de enero

EdgeTigator is a real-time computer vision tool that transforms your device’s camera feed into a structured, edge-based visualisation, with optional SLS-style body tracking and motion-focused analysis. Unlike traditional camera filters, EdgeTigator performs live image analysis on every frame of the camera feed, extracting structural edges, motion-defined shapes, and human pose landmarks using advanced on-device computer vision techniques. The result is a high-contrast, wireframe-style view of the environment that responds dynamically to changes within the scene. 🔍 Core Features • Live Edge Detection View Highlights the structural outlines of objects and environments in real time. • Movement-Triggered Edge Mode (Optional) When enabled, edges are displayed only where movement is detected. Static objects fade into the background, allowing motion, changes, and shifting forms to stand out clearly. This mode is ideal for reducing visual clutter and focusing attention on activity within the scene. • SLS-Style Body Tracking (Optional) Detects and tracks body movement using pose landmark analysis, rendering a skeletal overlay with joint markers and head representation that responds naturally to motion and position. • Adjustable Vision Controls Fine-tune edge sensitivity, blur, dilation, and colour output using live sliders. • Multiple Visual Modes Change edge and skeleton colours for improved visibility in different environments and lighting conditions. • Screen Recording with Audio Record your session directly to the device gallery, including microphone audio. • Optimised for Real-Time Performance Built using efficient, on-device processing for smooth, low-latency visualisation. 🧠 How It Works EdgeTigator uses real-time computer vision algorithms to analyse contrast, gradients, and motion within the camera feed. Structural edges are extracted from visual data, while optional motion analysis allows the system to emphasise only areas of change within the scene. Human body tracking is performed using pose landmark estimation rather than simple overlays or filters, allowing the skeletal model to respond naturally to real movement. All processing is done entirely on-device — no cloud processing, no data upload. 📱 Use Cases • Experimental visual analysis • Creative and abstract video recording • Motion and movement visualisation • Research and educational demonstrations • Paranormal-style investigations and exploration 🔒 Privacy • EdgeTigator does not collect, store, or transmit personal data. • Camera and microphone access are used solely for live processing and optional recording. ⚠️ Notes • Performance may vary depending on device hardware and lighting conditions • Not all devices support the same frame rates or resolutions