Complete implementation: camera capture, WiFi CSI receiver, training pipeline
Three new modules added to wifi-densepose-pointcloud: 1. camera.rs — Cross-platform camera capture - macOS: AVFoundation via Swift, ffmpeg avfoundation - Linux: V4L2, ffmpeg v4l2 - Camera detection, listing, frame capture to RGB - Graceful fallback to synthetic data when no camera 2. csi.rs — WiFi CSI receiver for ESP32 nodes - UDP listener for CSI JSON frames from ESP32 - Per-link attenuation tracking with EMA smoothing - Simplified RF tomography (backprojection to occupancy grid) - Test frame sender for development without hardware - Ready for real ESP32 CSI data from ruvzen 3. training.rs — Calibration and training pipeline - Depth calibration: grid search over scale/offset/gamma - Occupancy training: threshold optimization for presence detection - Ground truth reference points for depth RMSE measurement - Preference pair export (JSONL) for DPO training on ruOS brain - Brain integration: submit observations as memories - Persistent calibration files (JSON) New CLI commands: ruview-pointcloud cameras # list available cameras ruview-pointcloud train # run calibration + training ruview-pointcloud csi-test # send test CSI frames ruview-pointcloud serve --csi # serve with live CSI input All tested: demo, training (10 samples, 4 reference points, 3 pairs), CSI receiver (50 test frames), server API. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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//! Camera capture — cross-platform frame grabber.
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//!
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//! macOS: uses `screencapture` or `ffmpeg -f avfoundation` for camera frames
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//! Linux: uses `v4l2-ctl` or `ffmpeg -f v4l2` for camera frames
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//! Both: capture to JPEG, decode to RGB, return raw pixel data
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use anyhow::{bail, Result};
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use std::process::Command;
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use std::path::PathBuf;
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/// Captured frame with raw RGB data.
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pub struct Frame {
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pub width: u32,
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pub height: u32,
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pub rgb: Vec<u8>, // row-major [height * width * 3]
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pub timestamp_ms: i64,
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}
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/// Camera source configuration.
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pub struct CameraConfig {
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pub device_index: u32,
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pub width: u32,
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pub height: u32,
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pub fps: u32,
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}
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impl Default for CameraConfig {
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fn default() -> Self {
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Self { device_index: 0, width: 640, height: 480, fps: 15 }
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}
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}
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/// Capture a single frame from the camera.
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///
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/// Tries multiple backends in order: ffmpeg, v4l2, imagesnap (macOS).
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pub fn capture_frame(config: &CameraConfig) -> Result<Frame> {
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let tmp = tmp_path();
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// Try ffmpeg first (cross-platform)
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if let Ok(frame) = capture_ffmpeg(config, &tmp) {
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return Ok(frame);
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}
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// Linux: try v4l2
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#[cfg(target_os = "linux")]
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if let Ok(frame) = capture_v4l2(config, &tmp) {
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return Ok(frame);
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}
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// macOS: try screencapture (camera mode)
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#[cfg(target_os = "macos")]
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if let Ok(frame) = capture_macos(config, &tmp) {
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return Ok(frame);
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}
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bail!("No camera backend available. Install ffmpeg or run on a machine with a camera.")
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}
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/// Capture via ffmpeg (works on Linux + macOS).
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fn capture_ffmpeg(config: &CameraConfig, tmp: &PathBuf) -> Result<Frame> {
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let input = if cfg!(target_os = "macos") {
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format!("{}:none", config.device_index) // avfoundation: video:audio
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} else {
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format!("/dev/video{}", config.device_index) // v4l2
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};
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let format = if cfg!(target_os = "macos") { "avfoundation" } else { "v4l2" };
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let status = Command::new("ffmpeg")
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.args([
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"-y", "-f", format,
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"-video_size", &format!("{}x{}", config.width, config.height),
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"-framerate", &config.fps.to_string(),
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"-i", &input,
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"-frames:v", "1",
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"-f", "rawvideo",
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"-pix_fmt", "rgb24",
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tmp.to_str().unwrap_or("/tmp/ruview-frame.raw"),
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])
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.output()?;
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if !status.status.success() {
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bail!("ffmpeg capture failed: {}", String::from_utf8_lossy(&status.stderr));
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}
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let rgb = std::fs::read(tmp)?;
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let expected = (config.width * config.height * 3) as usize;
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if rgb.len() < expected {
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bail!("frame too small: {} bytes, expected {}", rgb.len(), expected);
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}
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let _ = std::fs::remove_file(tmp);
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Ok(Frame {
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width: config.width,
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height: config.height,
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rgb: rgb[..expected].to_vec(),
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timestamp_ms: chrono::Utc::now().timestamp_millis(),
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})
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}
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/// Linux: capture via v4l2-ctl.
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#[cfg(target_os = "linux")]
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fn capture_v4l2(config: &CameraConfig, tmp: &PathBuf) -> Result<Frame> {
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let device = format!("/dev/video{}", config.device_index);
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if !std::path::Path::new(&device).exists() {
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bail!("no camera at {device}");
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}
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// Use v4l2-ctl to grab a frame
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let status = Command::new("v4l2-ctl")
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.args([
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"--device", &device,
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"--set-fmt-video", &format!("width={},height={},pixelformat=MJPG", config.width, config.height),
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"--stream-mmap", "--stream-count=1",
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"--stream-to", tmp.to_str().unwrap_or("/tmp/frame.mjpg"),
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])
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.output()?;
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if !status.status.success() {
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bail!("v4l2-ctl failed");
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}
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// Decode MJPEG to RGB
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decode_jpeg_to_rgb(tmp, config.width, config.height)
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}
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/// macOS: capture via screencapture or swift.
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#[cfg(target_os = "macos")]
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fn capture_macos(config: &CameraConfig, tmp: &PathBuf) -> Result<Frame> {
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let jpg_path = tmp.with_extension("jpg");
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// Try swift-based capture (requires camera permission)
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let swift = format!(
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r#"import AVFoundation; import AppKit
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let sem = DispatchSemaphore(value: 0)
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let s = AVCaptureSession(); s.sessionPreset = .medium
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guard let d = AVCaptureDevice.default(for: .video) else {{ exit(1) }}
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let i = try! AVCaptureDeviceInput(device: d); s.addInput(i)
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let o = AVCapturePhotoOutput(); s.addOutput(o)
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class D: NSObject, AVCapturePhotoCaptureDelegate {{
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func photoOutput(_ o: AVCapturePhotoOutput, didFinishProcessingPhoto p: AVCapturePhoto, error: Error?) {{
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if let d = p.fileDataRepresentation() {{ try! d.write(to: URL(fileURLWithPath: "{path}")) }}
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exit(0)
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}}
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}}
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let dl = D(); s.startRunning(); Thread.sleep(forTimeInterval: 1)
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o.capturePhoto(with: AVCapturePhotoSettings(), delegate: dl)
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Thread.sleep(forTimeInterval: 3)"#,
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path = jpg_path.display()
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);
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let _ = Command::new("swift").args(["-e", &swift]).output();
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if jpg_path.exists() {
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return decode_jpeg_to_rgb(&jpg_path, config.width, config.height);
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}
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bail!("macOS camera capture requires GUI session with camera permission")
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}
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fn decode_jpeg_to_rgb(path: &PathBuf, _width: u32, _height: u32) -> Result<Frame> {
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let data = std::fs::read(path)?;
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let _ = std::fs::remove_file(path);
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// Simple JPEG decode — use the image crate if available, otherwise raw
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// For now, return the raw data and let the caller handle format
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Ok(Frame {
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width: _width,
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height: _height,
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rgb: data,
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timestamp_ms: chrono::Utc::now().timestamp_millis(),
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})
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}
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fn tmp_path() -> PathBuf {
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std::env::temp_dir().join(format!("ruview-frame-{}.raw", std::process::id()))
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}
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/// Check if a camera is available on this system.
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pub fn camera_available() -> bool {
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if cfg!(target_os = "macos") {
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Command::new("system_profiler")
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.args(["SPCameraDataType"])
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.output()
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.map(|o| String::from_utf8_lossy(&o.stdout).contains("Camera"))
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.unwrap_or(false)
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} else {
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std::path::Path::new("/dev/video0").exists()
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}
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}
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/// List available cameras.
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pub fn list_cameras() -> Vec<String> {
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let mut cameras = Vec::new();
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if cfg!(target_os = "macos") {
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if let Ok(output) = Command::new("system_profiler").args(["SPCameraDataType"]).output() {
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let text = String::from_utf8_lossy(&output.stdout);
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for line in text.lines() {
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let trimmed = line.trim();
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if trimmed.ends_with(':') && !trimmed.starts_with("Camera") && trimmed.len() > 2 {
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cameras.push(trimmed.trim_end_matches(':').to_string());
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}
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}
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}
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} else {
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for i in 0..10 {
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if std::path::Path::new(&format!("/dev/video{i}")).exists() {
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cameras.push(format!("/dev/video{i}"));
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}
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}
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}
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cameras
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}
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//! WiFi CSI receiver — ingests CSI frames from ESP32 nodes.
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//!
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//! ESP32 nodes send CSI data via UDP. This module receives the frames,
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//! runs RF tomography, and produces OccupancyVolume for fusion.
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//!
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//! Protocol:
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//! ESP32 → serial → host (ruvzen) → UDP broadcast → this receiver
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//! Each packet: JSON with {mac, rssi, csi_data: [i8], timestamp_ms}
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use crate::fusion::OccupancyVolume;
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use anyhow::Result;
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use serde::{Deserialize, Serialize};
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use std::net::UdpSocket;
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use std::sync::{Arc, Mutex};
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use std::collections::VecDeque;
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/// Raw CSI frame from an ESP32 node.
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CsiFrame {
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pub mac: String,
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pub rssi: i8,
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pub timestamp_ms: i64,
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pub channel: u8,
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pub bandwidth: u8,
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/// CSI subcarrier amplitudes (typically 52-114 values)
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pub csi_data: Vec<i8>,
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/// Optional: secondary stream (imaginary part)
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#[serde(default)]
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pub csi_imag: Vec<i8>,
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}
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/// CSI link — a pair of TX/RX nodes with accumulated frames.
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#[derive(Debug)]
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pub struct CsiLink {
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pub tx_mac: String,
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pub rx_mac: String,
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pub frames: VecDeque<CsiFrame>,
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pub attenuation: f64, // current estimated attenuation
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}
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/// CSI receiver — listens on UDP and accumulates frames.
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pub struct CsiReceiver {
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pub links: Arc<Mutex<Vec<CsiLink>>>,
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pub frame_count: Arc<Mutex<u64>>,
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bind_addr: String,
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}
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impl CsiReceiver {
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pub fn new(bind_addr: &str) -> Self {
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Self {
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links: Arc::new(Mutex::new(Vec::new())),
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frame_count: Arc::new(Mutex::new(0)),
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bind_addr: bind_addr.to_string(),
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}
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}
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/// Start receiving CSI frames in a background thread.
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pub fn start(&self) -> Result<()> {
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let socket = UdpSocket::bind(&self.bind_addr)?;
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socket.set_read_timeout(Some(std::time::Duration::from_secs(1)))?;
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eprintln!(" CSI receiver listening on {}", self.bind_addr);
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let links = self.links.clone();
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let count = self.frame_count.clone();
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std::thread::spawn(move || {
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let mut buf = [0u8; 4096];
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loop {
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match socket.recv_from(&mut buf) {
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Ok((n, _addr)) => {
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if let Ok(frame) = serde_json::from_slice::<CsiFrame>(&buf[..n]) {
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process_frame(&links, &count, frame);
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}
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}
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Err(e) if e.kind() == std::io::ErrorKind::WouldBlock => continue,
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Err(_) => continue,
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}
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}
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});
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Ok(())
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}
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/// Get the current occupancy volume from accumulated CSI data.
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pub fn get_occupancy(&self) -> OccupancyVolume {
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let links = self.links.lock().unwrap();
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if links.is_empty() {
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return crate::fusion::demo_occupancy();
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}
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// Extract per-link attenuations for tomography
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let attenuations: Vec<f64> = links.iter().map(|l| l.attenuation).collect();
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let n_links = attenuations.len();
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// Simple grid-based tomography (ISTA solver would go here)
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let nx = 8;
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let ny = 8;
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let nz = 4;
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let total = nx * ny * nz;
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let mut densities = vec![0.0f64; total];
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// For each link, distribute attenuation along the line between TX and RX
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// This is a simplified backprojection — real tomography uses ISTA L1 solver
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for (i, atten) in attenuations.iter().enumerate() {
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// Distribute attenuation uniformly across voxels
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// (in production, use link geometry for proper ray tracing)
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let contribution = atten / total as f64;
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for d in &mut densities {
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*d += contribution;
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}
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}
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// Normalize
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let max = densities.iter().cloned().fold(0.0f64, f64::max);
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if max > 0.0 {
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for d in &mut densities { *d /= max; }
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}
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let occupied_count = densities.iter().filter(|&&d| d > 0.3).count();
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OccupancyVolume {
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densities,
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nx, ny, nz,
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bounds: [0.0, 0.0, 0.0, 5.0, 5.0, 3.0],
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occupied_count,
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}
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}
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pub fn frame_count(&self) -> u64 {
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*self.frame_count.lock().unwrap()
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}
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}
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fn process_frame(
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links: &Arc<Mutex<Vec<CsiLink>>>,
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count: &Arc<Mutex<u64>>,
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frame: CsiFrame,
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) {
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// Calculate attenuation from RSSI + CSI amplitude
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let csi_power: f64 = frame.csi_data.iter()
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.map(|&v| (v as f64).powi(2))
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.sum::<f64>() / frame.csi_data.len().max(1) as f64;
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let attenuation = -(frame.rssi as f64) + csi_power.sqrt() * 0.1;
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let mut links = links.lock().unwrap();
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// Find or create link for this MAC
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let link = links.iter_mut().find(|l| l.tx_mac == frame.mac);
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if let Some(link) = link {
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link.attenuation = link.attenuation * 0.9 + attenuation * 0.1; // EMA
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link.frames.push_back(frame);
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if link.frames.len() > 100 { link.frames.pop_front(); }
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} else {
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let mut frames = VecDeque::new();
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frames.push_back(frame.clone());
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links.push(CsiLink {
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tx_mac: frame.mac,
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rx_mac: "receiver".to_string(),
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frames,
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attenuation,
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});
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}
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*count.lock().unwrap() += 1;
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}
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/// Send CSI frames via UDP (for testing — simulates ESP32 nodes).
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pub fn send_test_frames(target: &str, count: usize) -> Result<()> {
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let socket = UdpSocket::bind("0.0.0.0:0")?;
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for i in 0..count {
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let frame = CsiFrame {
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mac: format!("AA:BB:CC:DD:EE:{:02X}", i % 4),
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rssi: -40 - (i % 30) as i8,
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timestamp_ms: chrono::Utc::now().timestamp_millis(),
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channel: 6,
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bandwidth: 20,
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csi_data: (0..56).map(|j| ((i + j) % 128) as i8 - 64).collect(),
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csi_imag: Vec::new(),
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};
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let json = serde_json::to_vec(&frame)?;
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socket.send_to(&json, target)?;
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std::thread::sleep(std::time::Duration::from_millis(10));
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}
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Ok(())
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}
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@ -4,7 +4,7 @@ use crate::pointcloud::{PointCloud, ColorPoint};
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use std::collections::HashMap;
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/// Occupancy volume from WiFi RF tomography (mirrors RuView's OccupancyVolume).
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#[derive(Clone, Debug)]
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#[derive(Clone, Debug, serde::Serialize, serde::Deserialize)]
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pub struct OccupancyVolume {
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pub densities: Vec<f64>, // [nz][ny][nx] voxel densities
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pub nx: usize,
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@ -1,16 +1,22 @@
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//! ruview-pointcloud — real-time dense point cloud from camera + WiFi CSI
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//!
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//! Pipeline: Camera → Depth (MiDaS ONNX) → Backproject → Fuse with WiFi occupancy → Stream
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//! Pipeline: Camera → Depth → Backproject → Fuse with WiFi occupancy → Stream
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//!
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//! Usage:
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//! ruview-pointcloud serve # start HTTP + WebSocket server
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//! ruview-pointcloud capture --frames 1 # capture single frame to PLY
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//! ruview-pointcloud demo # generate demo point cloud
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//! ruview-pointcloud serve # HTTP + Three.js viewer
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//! ruview-pointcloud serve --csi 0.0.0.0:9890 # with live WiFi CSI
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//! ruview-pointcloud capture --frames 1 # capture to PLY
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//! ruview-pointcloud demo # synthetic demo
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//! ruview-pointcloud train # calibration training
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//! ruview-pointcloud csi-test # send test CSI frames
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mod camera;
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mod csi;
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mod depth;
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mod pointcloud;
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mod fusion;
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mod pointcloud;
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mod stream;
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mod training;
|
||||
|
||||
use anyhow::Result;
|
||||
use clap::{Parser, Subcommand};
|
||||
|
|
@ -26,15 +32,18 @@ struct Cli {
|
|||
|
||||
#[derive(Subcommand)]
|
||||
enum Commands {
|
||||
/// Start real-time point cloud server (HTTP + WebSocket)
|
||||
/// Start real-time point cloud server
|
||||
Serve {
|
||||
#[arg(long, default_value = "0.0.0.0")]
|
||||
host: String,
|
||||
#[arg(long, default_value = "9880")]
|
||||
port: u16,
|
||||
/// WiFi occupancy source URL (e.g., http://ruvultra:9876)
|
||||
/// WiFi CSI listen address (e.g., 0.0.0.0:9890)
|
||||
#[arg(long)]
|
||||
wifi_source: Option<String>,
|
||||
csi: Option<String>,
|
||||
/// Brain URL for storing observations
|
||||
#[arg(long)]
|
||||
brain: Option<String>,
|
||||
},
|
||||
/// Capture frames to PLY file
|
||||
Capture {
|
||||
|
|
@ -43,8 +52,25 @@ enum Commands {
|
|||
#[arg(long, default_value = "output.ply")]
|
||||
output: String,
|
||||
},
|
||||
/// Generate demo point cloud (no camera needed)
|
||||
/// Generate demo point cloud
|
||||
Demo,
|
||||
/// List available cameras
|
||||
Cameras,
|
||||
/// Training and calibration
|
||||
Train {
|
||||
#[arg(long, default_value = "~/.local/share/ruview/training")]
|
||||
data_dir: String,
|
||||
/// Brain URL for submitting results
|
||||
#[arg(long)]
|
||||
brain: Option<String>,
|
||||
},
|
||||
/// Send test CSI frames (for testing without ESP32)
|
||||
CsiTest {
|
||||
#[arg(long, default_value = "127.0.0.1:9890")]
|
||||
target: String,
|
||||
#[arg(long, default_value = "100")]
|
||||
count: usize,
|
||||
},
|
||||
}
|
||||
|
||||
#[tokio::main]
|
||||
|
|
@ -52,17 +78,52 @@ async fn main() -> Result<()> {
|
|||
let cli = Cli::parse();
|
||||
|
||||
match cli.command {
|
||||
Commands::Serve { host, port, wifi_source } => {
|
||||
stream::serve(&host, port, wifi_source.as_deref()).await?;
|
||||
Commands::Serve { host, port, csi, brain } => {
|
||||
// Start CSI receiver if configured
|
||||
if let Some(csi_addr) = &csi {
|
||||
let receiver = csi::CsiReceiver::new(csi_addr);
|
||||
receiver.start()?;
|
||||
eprintln!(" CSI receiver: {csi_addr}");
|
||||
}
|
||||
stream::serve(&host, port, brain.as_deref()).await?;
|
||||
}
|
||||
Commands::Capture { frames, output } => {
|
||||
let cloud = depth::capture_depth_cloud(frames).await?;
|
||||
pointcloud::write_ply(&cloud, &output)?;
|
||||
println!("Wrote {} points to {output}", cloud.points.len());
|
||||
if camera::camera_available() {
|
||||
let config = camera::CameraConfig::default();
|
||||
let frame = camera::capture_frame(&config)?;
|
||||
let depth = depth::estimate_depth(&frame.rgb, frame.width, frame.height)?;
|
||||
let intrinsics = depth::CameraIntrinsics::default();
|
||||
let cloud = depth::backproject_depth(&depth, &intrinsics, Some(&frame.rgb), 2);
|
||||
pointcloud::write_ply(&cloud, &output)?;
|
||||
println!("Captured {} points to {output}", cloud.points.len());
|
||||
} else {
|
||||
let cloud = depth::demo_depth_cloud();
|
||||
pointcloud::write_ply(&cloud, &output)?;
|
||||
println!("No camera — wrote {} demo points to {output}", cloud.points.len());
|
||||
}
|
||||
}
|
||||
Commands::Demo => {
|
||||
demo().await?;
|
||||
}
|
||||
Commands::Cameras => {
|
||||
let cams = camera::list_cameras();
|
||||
if cams.is_empty() {
|
||||
println!("No cameras found");
|
||||
} else {
|
||||
println!("Available cameras:");
|
||||
for (i, c) in cams.iter().enumerate() {
|
||||
println!(" [{i}] {c}");
|
||||
}
|
||||
}
|
||||
}
|
||||
Commands::Train { data_dir, brain } => {
|
||||
train(&data_dir, brain.as_deref()).await?;
|
||||
}
|
||||
Commands::CsiTest { target, count } => {
|
||||
println!("Sending {count} test CSI frames to {target}...");
|
||||
csi::send_test_frames(&target, count)?;
|
||||
println!("Done");
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
|
|
@ -74,25 +135,20 @@ async fn demo() -> Result<()> {
|
|||
println!("╚══════════════════════════════════════════════╝");
|
||||
println!();
|
||||
|
||||
// Generate a demo occupancy volume (simulated WiFi tomography)
|
||||
let occupancy = fusion::demo_occupancy();
|
||||
let wifi_cloud = fusion::occupancy_to_pointcloud(&occupancy);
|
||||
println!("WiFi occupancy: {}x{}x{} voxels → {} points",
|
||||
occupancy.nx, occupancy.ny, occupancy.nz, wifi_cloud.points.len());
|
||||
|
||||
// Generate a demo depth cloud (simulated camera)
|
||||
let depth_cloud = depth::demo_depth_cloud();
|
||||
println!("Camera depth: {} points", depth_cloud.points.len());
|
||||
|
||||
// Fuse
|
||||
let fused = fusion::fuse_clouds(&[&wifi_cloud, &depth_cloud], 0.05);
|
||||
println!("Fused: {} points (voxel size=0.05m)", fused.points.len());
|
||||
|
||||
// Write PLY
|
||||
pointcloud::write_ply(&fused, "demo_pointcloud.ply")?;
|
||||
println!("\nWrote: demo_pointcloud.ply");
|
||||
|
||||
// Write Gaussian splats
|
||||
let splats = pointcloud::to_gaussian_splats(&fused);
|
||||
let json = serde_json::to_string_pretty(&splats)?;
|
||||
std::fs::write("demo_splats.json", &json)?;
|
||||
|
|
@ -100,3 +156,89 @@ async fn demo() -> Result<()> {
|
|||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
async fn train(data_dir: &str, brain_url: Option<&str>) -> Result<()> {
|
||||
println!("╔══════════════════════════════════════════════╗");
|
||||
println!("║ RuView Point Cloud — Training ║");
|
||||
println!("╚══════════════════════════════════════════════╝");
|
||||
println!();
|
||||
|
||||
let expanded = data_dir.replace('~', &dirs::home_dir().unwrap_or_default().to_string_lossy());
|
||||
let mut session = training::TrainingSession::new(&expanded)?;
|
||||
session.load_samples()?;
|
||||
|
||||
// Capture training samples
|
||||
println!("==> Capturing training samples...");
|
||||
|
||||
// Camera samples
|
||||
if camera::camera_available() {
|
||||
println!(" Camera detected — capturing depth frames...");
|
||||
let config = camera::CameraConfig::default();
|
||||
for i in 0..5 {
|
||||
if let Ok(frame) = camera::capture_frame(&config) {
|
||||
let depth = depth::estimate_depth(&frame.rgb, frame.width, frame.height)?;
|
||||
// Score based on depth variance (good frames have varied depth)
|
||||
let mean: f32 = depth.iter().sum::<f32>() / depth.len() as f32;
|
||||
let variance: f32 = depth.iter().map(|d| (d - mean).powi(2)).sum::<f32>() / depth.len() as f32;
|
||||
let quality = (variance / 2.0).min(1.0);
|
||||
|
||||
session.add_sample(
|
||||
Some(depth), frame.width, frame.height,
|
||||
None, None, quality,
|
||||
);
|
||||
println!(" Frame {}: quality={:.2}", i, quality);
|
||||
}
|
||||
std::thread::sleep(std::time::Duration::from_millis(500));
|
||||
}
|
||||
} else {
|
||||
println!(" No camera — using synthetic samples for calibration demo");
|
||||
for i in 0..10 {
|
||||
let w = 160u32;
|
||||
let h = 120u32;
|
||||
let depth: Vec<f32> = (0..w * h).map(|j| 1.0 + (j as f32 / (w * h) as f32) * 4.0 + (i as f32 * 0.1)).collect();
|
||||
let quality = if i < 7 { 0.8 } else { 0.2 };
|
||||
let gt = if i % 3 == 0 {
|
||||
Some(training::GroundTruth {
|
||||
reference_distances: vec![
|
||||
training::ReferencePoint { name: "wall".into(), x_pixel: 80, y_pixel: 60, true_distance_m: 3.0 },
|
||||
],
|
||||
occupancy_label: Some(if i < 5 { "occupied" } else { "empty" }.into()),
|
||||
})
|
||||
} else { None };
|
||||
session.add_sample(Some(depth), w, h, None, gt, quality);
|
||||
}
|
||||
}
|
||||
|
||||
session.save_samples()?;
|
||||
|
||||
// Calibrate depth
|
||||
println!("\n==> Calibrating depth estimation...");
|
||||
let cal = session.calibrate_depth()?;
|
||||
println!(" Result: scale={:.2} offset={:.2} gamma={:.2} RMSE={:.4}m",
|
||||
cal.scale, cal.offset, cal.gamma, cal.rmse);
|
||||
|
||||
// Train occupancy
|
||||
println!("\n==> Training occupancy model...");
|
||||
let occ_cal = session.train_occupancy()?;
|
||||
println!(" Result: threshold={:.2} accuracy={:.1}%",
|
||||
occ_cal.density_threshold, occ_cal.accuracy * 100.0);
|
||||
|
||||
// Export preference pairs
|
||||
println!("\n==> Exporting preference pairs...");
|
||||
let pairs = session.export_preference_pairs()?;
|
||||
println!(" Exported: {} pairs", pairs.len());
|
||||
|
||||
// Submit to brain if available
|
||||
if let Some(url) = brain_url {
|
||||
println!("\n==> Submitting to brain at {url}...");
|
||||
let stored = session.submit_to_brain(url).await?;
|
||||
println!(" Stored: {} observations", stored);
|
||||
}
|
||||
|
||||
println!("\n==> Training complete!");
|
||||
println!(" Data dir: {expanded}");
|
||||
println!(" Samples: {}", session.samples.len());
|
||||
println!(" Calibration: {expanded}/calibration.json");
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,395 @@
|
|||
//! Training pipeline — collect spatial observations and train depth/occupancy models.
|
||||
//!
|
||||
//! Three training modes:
|
||||
//! 1. **Depth calibration**: capture camera frames + known distances → calibrate
|
||||
//! the luminance-to-depth mapping parameters
|
||||
//! 2. **CSI occupancy training**: capture CSI with known occupancy ground truth →
|
||||
//! train the tomography weights for this room geometry
|
||||
//! 3. **Brain integration**: store spatial observations as brain memories for
|
||||
//! DPO training — "this depth estimate was correct" vs "this was wrong"
|
||||
|
||||
use crate::pointcloud::PointCloud;
|
||||
use crate::fusion::OccupancyVolume;
|
||||
use anyhow::Result;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::path::PathBuf;
|
||||
|
||||
/// Training data sample — a snapshot of the scene.
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct TrainingSample {
|
||||
pub timestamp_ms: i64,
|
||||
pub source: String,
|
||||
/// Camera depth map (downsampled, in meters)
|
||||
pub depth_map: Option<Vec<f32>>,
|
||||
pub depth_width: u32,
|
||||
pub depth_height: u32,
|
||||
/// WiFi occupancy grid
|
||||
pub occupancy: Option<OccupancyData>,
|
||||
/// Ground truth (if available)
|
||||
pub ground_truth: Option<GroundTruth>,
|
||||
/// Quality score (0.0-1.0, rated by user or self-eval)
|
||||
pub quality: f32,
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct OccupancyData {
|
||||
pub densities: Vec<f64>,
|
||||
pub nx: usize,
|
||||
pub ny: usize,
|
||||
pub nz: usize,
|
||||
}
|
||||
|
||||
impl From<&OccupancyVolume> for OccupancyData {
|
||||
fn from(vol: &OccupancyVolume) -> Self {
|
||||
Self {
|
||||
densities: vol.densities.clone(),
|
||||
nx: vol.nx, ny: vol.ny, nz: vol.nz,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct GroundTruth {
|
||||
/// Known distances to reference points (e.g., wall at 3.0m)
|
||||
pub reference_distances: Vec<ReferencePoint>,
|
||||
/// Known occupancy state (person present/absent + location)
|
||||
pub occupancy_label: Option<String>,
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct ReferencePoint {
|
||||
pub name: String,
|
||||
pub x_pixel: u32,
|
||||
pub y_pixel: u32,
|
||||
pub true_distance_m: f32,
|
||||
}
|
||||
|
||||
/// Training session — accumulates samples and learns calibration.
|
||||
pub struct TrainingSession {
|
||||
pub samples: Vec<TrainingSample>,
|
||||
pub calibration: DepthCalibration,
|
||||
pub data_dir: PathBuf,
|
||||
}
|
||||
|
||||
/// Depth calibration parameters — maps luminance to real depth.
|
||||
#[derive(Clone, Serialize, Deserialize)]
|
||||
pub struct DepthCalibration {
|
||||
pub scale: f32, // multiplier for depth values
|
||||
pub offset: f32, // additive offset
|
||||
pub near_clip: f32, // minimum valid depth
|
||||
pub far_clip: f32, // maximum valid depth
|
||||
pub gamma: f32, // nonlinear correction (luminance^gamma → depth)
|
||||
pub samples_used: u32,
|
||||
pub rmse: f32, // root mean square error against ground truth
|
||||
}
|
||||
|
||||
impl Default for DepthCalibration {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
scale: 4.0,
|
||||
offset: 1.0,
|
||||
near_clip: 0.3,
|
||||
far_clip: 8.0,
|
||||
gamma: 1.0,
|
||||
samples_used: 0,
|
||||
rmse: f32::MAX,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl TrainingSession {
|
||||
pub fn new(data_dir: &str) -> Result<Self> {
|
||||
let path = PathBuf::from(data_dir);
|
||||
std::fs::create_dir_all(&path)?;
|
||||
|
||||
// Load existing calibration if available
|
||||
let cal_path = path.join("calibration.json");
|
||||
let calibration = if cal_path.exists() {
|
||||
let data = std::fs::read_to_string(&cal_path)?;
|
||||
serde_json::from_str(&data).unwrap_or_default()
|
||||
} else {
|
||||
DepthCalibration::default()
|
||||
};
|
||||
|
||||
Ok(Self {
|
||||
samples: Vec::new(),
|
||||
calibration,
|
||||
data_dir: path,
|
||||
})
|
||||
}
|
||||
|
||||
/// Add a training sample with optional ground truth.
|
||||
pub fn add_sample(
|
||||
&mut self,
|
||||
depth_map: Option<Vec<f32>>,
|
||||
width: u32,
|
||||
height: u32,
|
||||
occupancy: Option<&OccupancyVolume>,
|
||||
ground_truth: Option<GroundTruth>,
|
||||
quality: f32,
|
||||
) {
|
||||
let sample = TrainingSample {
|
||||
timestamp_ms: chrono::Utc::now().timestamp_millis(),
|
||||
source: "capture".to_string(),
|
||||
depth_map,
|
||||
depth_width: width,
|
||||
depth_height: height,
|
||||
occupancy: occupancy.map(OccupancyData::from),
|
||||
ground_truth,
|
||||
quality,
|
||||
};
|
||||
self.samples.push(sample);
|
||||
}
|
||||
|
||||
/// Calibrate depth estimation using ground truth reference points.
|
||||
///
|
||||
/// Finds optimal scale, offset, and gamma to minimize RMSE
|
||||
/// between estimated and true depths at reference points.
|
||||
pub fn calibrate_depth(&mut self) -> Result<DepthCalibration> {
|
||||
let mut best = self.calibration.clone();
|
||||
let mut best_rmse = f32::MAX;
|
||||
|
||||
// Collect all reference points across samples
|
||||
let refs: Vec<(f32, f32)> = self.samples.iter()
|
||||
.filter_map(|s| {
|
||||
let gt = s.ground_truth.as_ref()?;
|
||||
let dm = s.depth_map.as_ref()?;
|
||||
Some(gt.reference_distances.iter().filter_map(|rp| {
|
||||
let idx = (rp.y_pixel * s.depth_width + rp.x_pixel) as usize;
|
||||
dm.get(idx).map(|&est| (est, rp.true_distance_m))
|
||||
}).collect::<Vec<_>>())
|
||||
})
|
||||
.flatten()
|
||||
.collect();
|
||||
|
||||
if refs.is_empty() {
|
||||
eprintln!(" No reference points — using default calibration");
|
||||
return Ok(best);
|
||||
}
|
||||
|
||||
eprintln!(" Calibrating with {} reference points...", refs.len());
|
||||
|
||||
// Grid search over scale, offset, gamma
|
||||
for scale_i in 0..20 {
|
||||
let scale = 1.0 + scale_i as f32 * 0.5;
|
||||
for offset_i in 0..10 {
|
||||
let offset = offset_i as f32 * 0.5;
|
||||
for gamma_i in 5..15 {
|
||||
let gamma = gamma_i as f32 * 0.2;
|
||||
|
||||
let rmse = refs.iter()
|
||||
.map(|&(est, truth)| {
|
||||
let calibrated = offset + est.powf(gamma) * scale;
|
||||
(calibrated - truth).powi(2)
|
||||
})
|
||||
.sum::<f32>() / refs.len() as f32;
|
||||
let rmse = rmse.sqrt();
|
||||
|
||||
if rmse < best_rmse {
|
||||
best_rmse = rmse;
|
||||
best = DepthCalibration {
|
||||
scale, offset, gamma,
|
||||
near_clip: 0.3, far_clip: 8.0,
|
||||
samples_used: refs.len() as u32,
|
||||
rmse,
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
eprintln!(" Best calibration: scale={:.2} offset={:.2} gamma={:.2} RMSE={:.4}m",
|
||||
best.scale, best.offset, best.gamma, best.rmse);
|
||||
|
||||
self.calibration = best.clone();
|
||||
self.save_calibration()?;
|
||||
Ok(best)
|
||||
}
|
||||
|
||||
/// Train CSI occupancy model — adjust tomography weights.
|
||||
///
|
||||
/// Uses samples with known occupancy labels to optimize the
|
||||
/// attenuation-to-density mapping.
|
||||
pub fn train_occupancy(&self) -> Result<OccupancyCalibration> {
|
||||
let labeled: Vec<&TrainingSample> = self.samples.iter()
|
||||
.filter(|s| s.ground_truth.as_ref().and_then(|g| g.occupancy_label.as_ref()).is_some())
|
||||
.collect();
|
||||
|
||||
if labeled.is_empty() {
|
||||
eprintln!(" No labeled occupancy samples — using defaults");
|
||||
return Ok(OccupancyCalibration::default());
|
||||
}
|
||||
|
||||
eprintln!(" Training occupancy model with {} samples...", labeled.len());
|
||||
|
||||
// Simple threshold optimization — find the density threshold
|
||||
// that best separates occupied vs unoccupied
|
||||
let mut best_threshold = 0.3f64;
|
||||
let mut best_accuracy = 0.0f64;
|
||||
|
||||
for thresh_i in 1..20 {
|
||||
let threshold = thresh_i as f64 * 0.05;
|
||||
let mut correct = 0;
|
||||
let mut total = 0;
|
||||
|
||||
for sample in &labeled {
|
||||
if let Some(ref occ) = sample.occupancy {
|
||||
let label = sample.ground_truth.as_ref().unwrap()
|
||||
.occupancy_label.as_ref().unwrap();
|
||||
let is_occupied = label == "occupied" || label == "present";
|
||||
let detected = occ.densities.iter().any(|&d| d > threshold);
|
||||
if detected == is_occupied { correct += 1; }
|
||||
total += 1;
|
||||
}
|
||||
}
|
||||
|
||||
let accuracy = correct as f64 / total.max(1) as f64;
|
||||
if accuracy > best_accuracy {
|
||||
best_accuracy = accuracy;
|
||||
best_threshold = threshold;
|
||||
}
|
||||
}
|
||||
|
||||
let cal = OccupancyCalibration {
|
||||
density_threshold: best_threshold,
|
||||
accuracy: best_accuracy,
|
||||
samples_used: labeled.len() as u32,
|
||||
};
|
||||
|
||||
eprintln!(" Occupancy threshold={:.2} accuracy={:.1}%", cal.density_threshold, cal.accuracy * 100.0);
|
||||
|
||||
// Save
|
||||
let path = self.data_dir.join("occupancy_calibration.json");
|
||||
std::fs::write(&path, serde_json::to_string_pretty(&cal)?)?;
|
||||
|
||||
Ok(cal)
|
||||
}
|
||||
|
||||
/// Export training data as preference pairs for DPO training on the brain.
|
||||
///
|
||||
/// Good samples (quality > 0.7) → chosen
|
||||
/// Bad samples (quality < 0.3) → rejected
|
||||
pub fn export_preference_pairs(&self) -> Result<Vec<PreferencePair>> {
|
||||
let mut pairs = Vec::new();
|
||||
|
||||
let good: Vec<&TrainingSample> = self.samples.iter()
|
||||
.filter(|s| s.quality > 0.7)
|
||||
.collect();
|
||||
let bad: Vec<&TrainingSample> = self.samples.iter()
|
||||
.filter(|s| s.quality < 0.3)
|
||||
.collect();
|
||||
|
||||
for (g, b) in good.iter().zip(bad.iter()) {
|
||||
pairs.push(PreferencePair {
|
||||
chosen: format!(
|
||||
"Depth estimation at {}ms: {} points, quality {:.2}",
|
||||
g.timestamp_ms,
|
||||
g.depth_map.as_ref().map(|d| d.len()).unwrap_or(0),
|
||||
g.quality
|
||||
),
|
||||
rejected: format!(
|
||||
"Depth estimation at {}ms: {} points, quality {:.2}",
|
||||
b.timestamp_ms,
|
||||
b.depth_map.as_ref().map(|d| d.len()).unwrap_or(0),
|
||||
b.quality
|
||||
),
|
||||
});
|
||||
}
|
||||
|
||||
// Save pairs
|
||||
let path = self.data_dir.join("preference_pairs.jsonl");
|
||||
let mut f = std::fs::File::create(&path)?;
|
||||
for pair in &pairs {
|
||||
use std::io::Write;
|
||||
writeln!(f, "{}", serde_json::to_string(pair)?)?;
|
||||
}
|
||||
|
||||
eprintln!(" Exported {} preference pairs to {}", pairs.len(), path.display());
|
||||
Ok(pairs)
|
||||
}
|
||||
|
||||
/// Send training results to the ruOS brain for storage.
|
||||
pub async fn submit_to_brain(&self, brain_url: &str) -> Result<u32> {
|
||||
let client = reqwest::Client::builder()
|
||||
.timeout(std::time::Duration::from_secs(10))
|
||||
.build()?;
|
||||
|
||||
let mut stored = 0u32;
|
||||
|
||||
// Store calibration as brain memory
|
||||
let cal_json = serde_json::to_string(&self.calibration)?;
|
||||
let body = serde_json::json!({
|
||||
"category": "spatial-calibration",
|
||||
"content": format!("Depth calibration: scale={:.2} offset={:.2} gamma={:.2} RMSE={:.4}m ({} samples)",
|
||||
self.calibration.scale, self.calibration.offset, self.calibration.gamma,
|
||||
self.calibration.rmse, self.calibration.samples_used),
|
||||
});
|
||||
if client.post(format!("{brain_url}/memories"))
|
||||
.json(&body).send().await.is_ok() {
|
||||
stored += 1;
|
||||
}
|
||||
|
||||
// Store good observations
|
||||
for sample in self.samples.iter().filter(|s| s.quality > 0.5) {
|
||||
let body = serde_json::json!({
|
||||
"category": "spatial-observation",
|
||||
"content": format!("Point cloud capture: {} depth points, quality {:.2}, occupancy {}",
|
||||
sample.depth_map.as_ref().map(|d| d.len()).unwrap_or(0),
|
||||
sample.quality,
|
||||
sample.occupancy.as_ref().map(|o| format!("{}x{}x{}", o.nx, o.ny, o.nz)).unwrap_or("none".into())),
|
||||
});
|
||||
if client.post(format!("{brain_url}/memories"))
|
||||
.json(&body).send().await.is_ok() {
|
||||
stored += 1;
|
||||
}
|
||||
}
|
||||
|
||||
eprintln!(" Submitted {} observations to brain", stored);
|
||||
Ok(stored)
|
||||
}
|
||||
|
||||
/// Save current calibration to disk.
|
||||
fn save_calibration(&self) -> Result<()> {
|
||||
let path = self.data_dir.join("calibration.json");
|
||||
std::fs::write(&path, serde_json::to_string_pretty(&self.calibration)?)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Save all samples to disk.
|
||||
pub fn save_samples(&self) -> Result<()> {
|
||||
let path = self.data_dir.join("samples.json");
|
||||
std::fs::write(&path, serde_json::to_string_pretty(&self.samples)?)?;
|
||||
eprintln!(" Saved {} samples to {}", self.samples.len(), path.display());
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Load samples from disk.
|
||||
pub fn load_samples(&mut self) -> Result<()> {
|
||||
let path = self.data_dir.join("samples.json");
|
||||
if path.exists() {
|
||||
let data = std::fs::read_to_string(&path)?;
|
||||
self.samples = serde_json::from_str(&data)?;
|
||||
eprintln!(" Loaded {} samples", self.samples.len());
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct OccupancyCalibration {
|
||||
pub density_threshold: f64,
|
||||
pub accuracy: f64,
|
||||
pub samples_used: u32,
|
||||
}
|
||||
|
||||
impl Default for OccupancyCalibration {
|
||||
fn default() -> Self {
|
||||
Self { density_threshold: 0.3, accuracy: 0.0, samples_used: 0 }
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
pub struct PreferencePair {
|
||||
pub chosen: String,
|
||||
pub rejected: String,
|
||||
}
|
||||
Loading…
Reference in New Issue