Moar re structure

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: MAMware

README.md

@@ -113,20 +113,40 @@ web/
 
 ### [Contributing](docs/CONTRIBUTING.md)
 
-- Please follow the link above for the detailed contributing guidelines, branching strategy and examples.
-
-### [To-Do List](docs/TO_DO.md)
+>We welcome contributors! 
 
 - At this document linked above, you will find the list for our current TO TO list, now from milestone 5 (v0.5.2)
 
 ### [Code flow diagrams](docs/DIAGRAMS.md) 
 
 Diagrams covering the Turnk Based Development approach (v0.2). 
 
-Reflecting:  
   - Process Frame Flow
   - Audio Generation Flow
   - Motion Detection such as oscillator logic.
+```mermaid
+
+graph TD
+        A[dispatcher.js] -->|routes| B[core/handlers/]
+        B --> C[video-handlers.js]
+        B --> D[audio-handlers.js]
+        B --> E[ui-handlers.js]
+        B --> F[settings-handlers.js]
+        B --> G[grid-handlers.js]
+        B --> H[debug-handlers.js]
+        C -->|calls| I[video/frame-processor.js]
+        D -->|calls| J[audio/audio-processor.js]
+        E -->|updates| K[ui/ui-settings.js]
+        F -->|uses| L[utils/utils.js]
+        A -->|state| M[state.js]
+        A -->|logs| N[utils/logging.js]
+        B -->|future| O[ml-handlers.js]
+```
+
+### [Changelog](docs/CHANGELOG.md)
+
+- Current "stable" version from "present" is v0.4.7, the link above logs the history and details past milestones achieved.
+- Current "future" version in development starts from v0.6 
 
 ### [FAQ](docs/FAQ.md)
 

docs/TO_DO.md

@@ -18,91 +18,3 @@ Sorted by priority
 - New capabilities like screen/video capture to sound engine.
 - Android/iOS app development if considerable performance gain can be achieved.
 
-**6/16/2025**
-## Considering adding OpenCV support
- Adapting OpenCV for AcoustSee
-To meet the 66ms constraint and aid a blind user via audio cues, here’s how OpenCV can be tailored for AcoustSee:
-Pipeline Design
-Camera Input:
-Capture frames at 15–30 FPS (66–33.3 ms) at 480p or 720p to reduce processing load.
-
-Use browser-based APIs (e.g., getUserMedia) for WebAssembly compatibility.
-
-Object Detection:
-Use a lightweight model like MobileNet-SSD or YOLO-Tiny (pre-trained for common objects: sidewalk, wall, car, swing).
-
-Processing time: ~15–30 ms on mid-range devices for 720p.
-
-Output: Bounding boxes and labels for objects (e.g., “sidewalk: center, wall: left”).
-
-Feature Extraction:
-Analyze color and texture within bounding boxes using OpenCV’s image processing (e.g., HSV color histograms, edge detection).
-
-Example: Sidewalk = smooth, gray (low-frequency hum); wall = flat, textured (mid-frequency tone).
-
-Processing time: ~5–10 ms.
-
-Depth Estimation:
-Use a lightweight monocular depth model (e.g., MiDaS Small, optimized for TFLite) to estimate object distances.
-
-Example: Swing at 2m = loud, broad sound; at 5m = quiet, narrow sound.
-
-Processing time: ~20–30 ms on flagships, ~30–50 ms on mid-range.
-
-Alternative: Use motion cues (e.g., optical flow) for faster processing (~10–20 ms).
-
-Audio Mapping:
-Map visual features to audio cues using Web Audio API:
-Position: Stereo panning (left-right based on bounding box x-coordinate).
-
-Depth: Volume (louder for closer) and spectral complexity (broader for closer).
-
-Object type: Unique spectral signatures (e.g., hum for sidewalk, tone for wall, broadband for swing).
-
-Generate 3–6 cues per 66ms frame, each ~5–10 ms, to align with auditory resolution.
-
-Total Latency:
-Example: MobileNet-SSD (20 ms) + feature extraction (10 ms) + depth estimation (~30 ms) = ~60 ms on a flagship device for 720p.
-
-Optimizations (e.g., 480p, quantized models, GPU) can reduce this to ~40–50 ms, fitting within 66ms.
-
-Optimizations for Real-Time
-Lower resolution: Use 480p (640×480) instead of 720p/1080p to cut processing time by ~30–50%.
-
-Lightweight models: Use quantized TFLite models (e.g., MobileNet-SSD, MiDaS Small) for 2–3x speedup.
-
-Frame skipping: Process every other frame (effective 15 FPS) if needed, while interpolating audio cues.
-
-GPU/Neural acceleration: Leverage OpenCV’s DNN module with OpenCL or mobile neural engines.
-
-Asynchronous processing: Run image processing in parallel with audio synthesis to reduce perceived latency.
-
-Audio Cue Design
-Number of cues: Limit to 3–6 per 66ms frame (e.g., sidewalk, wall, swing) to ensure auditory clarity, based on the 5–10 ms per cue limit from our previous discussion.
-
-Spectral signatures:
-Sidewalk: Low-pass noise (100–200 Hz), center-panned, steady.
-
-Wall: Sine wave (500–1000 Hz), left-panned, constant.
-
-Swing: Sawtooth wave (200–2000 Hz), center-panned, dynamic volume/filter.
-
-Car: Bandpass noise (500–5000 Hz), panned based on position.
-
-Dynamic updates: Adjust volume and filter cutoff every 66ms based on depth/motion (e.g., swing closer = louder, broader spectrum).
-
-
-
-
-
-Example of UI Templates:
-
-- Bezel type template: The top trapezoid is (should be) where the setting toggle is, this toggle shifts the function of the lateral trapezoid a the left (dayNight toggle without shift) and right (languaje selectror for speech synthesis) for a cursor for options navigation such as grid and synth engine both versioned selector.
-
-The confirmation is done by pressing the center vertical rectangular square, that also works as webcam feed preview/canvas
-
-The start and stop of the navigation is done by pressing the buttom trapezoid.
-
-- A reintroduction of a frames per seconds (FPS) toggle that is usefull if your device stutters or generates artifacts due to processing issues, likely by a cpu processor limitation will be reconsidered as a configuration option, among the grid and synth engine selector.
-
-A console log live view and a copy feature is being considered too.