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6fe98c6b00
CI build oscope-of / build-check (push) Has been cancelled
Add gesture_quality() pure helper (hand_display.py) that maps a slot's plausibility, facing, and proximity to a score in [0,1]. Renderer uses it to modulate panel frame brightness (0.25+0.75*q) and stroke count (1..3 passes at q≥0, ≥0.5, ≥0.85 / status==3). Status still drives hue (pid 7/8/9). New gesture_slot_quality field in State written alongside gesture_slot_status each tick. Add gauge_segments() for X/Y position gauges around each panel: a horizontal rail below and a vertical rail on the outer side, each with a bold two-tick notch at the hand's normalised cx/cy (mirror-aware for X). Dim rails (conf 0.25) when slot absent. Renderer reads hand_feats L/R directly from state. 11 new tests (6 gesture_quality + 5 gauge_segments); suite 430 passed.
282 lines
12 KiB
Python
282 lines
12 KiB
Python
"""Thread-safe state container for the Metal visualizer.
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Le listener OSC ecrit ; le renderer Metal lit a 60 fps. Tous les acces
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sont proteges par un Lock — la contention est negligeable (lectures
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courtes, ecritures rares).
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"""
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from __future__ import annotations
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import threading
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import time
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from dataclasses import dataclass, field
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import numpy as np
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@dataclass
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class PoseKp:
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x: float = 0.0
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y: float = 0.0
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z: float = 0.0 # profondeur (mediapipe world_landmarks, metres ; 0 par defaut)
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c: float = 0.0
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@dataclass
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class Kp3D:
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"""3D keypoint in metric coordinates relative to hip-center.
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Used for MediaPipe pose_world_landmarks (xyz in meters)."""
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x: float = 0.0
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y: float = 0.0
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z: float = 0.0
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c: float = 0.0
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@dataclass
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class SMPLXPerson:
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"""Resultats Multi-HMR pour une personne : params SMPL-X + vertices
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decodes en metres. Vertices en repere camera (z > 0 devant)."""
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pid: int = -1
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vertices_3d: np.ndarray = field(default_factory=lambda: np.empty((0, 3), dtype=np.float32)) # (10475, 3)
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translation: np.ndarray = field(default_factory=lambda: np.zeros(3, dtype=np.float32)) # (3,)
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confidence: float = 0.0
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betas: np.ndarray = field(default_factory=lambda: np.zeros(10, dtype=np.float32)) # (10,)
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expression: np.ndarray = field(default_factory=lambda: np.zeros(10, dtype=np.float32)) # (10,)
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@dataclass
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class NLFPerson:
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"""Resultats NLF pour une personne : vertices 3D SMPL (6890) en metres,
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coordonnees camera (z > 0 devant). Le path nonparametrique fournit les
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vertices directement sans decodage SMPL explicite."""
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pid: int = -1
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vertices_3d: tuple = field(default_factory=tuple) # ((x,y,z),) x 6890
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joints_3d: tuple = field(default_factory=tuple) # ((x,y,z),) x 24 (SMPL)
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translation: tuple = (0.0, 0.0, 0.0)
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confidence: float = 0.0
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@dataclass
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class State:
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# Audio sync
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bpm: float = 120.0
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beat: int = 0
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rms: float = 0.0
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amps: dict[str, float] = field(default_factory=dict)
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album: str = ""
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# Data feeds
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bridge_alive: bool = False
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last_heartbeat: float = 0.0
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swpc_kp: float = 2.0
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swpc_flare_norm: float = 0.0
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swpc_wind_speed: float = 400.0
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swpc_bz: float = 0.0
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netz_dev: float = 0.0
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lightning_rate_min: float = 0.0
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last_lightning: tuple[float, float, float] = (0.0, 0.0, 999.0) # lat, lon, age
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last_lightning_t: float = 0.0
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usgs_last_mag: float = 0.0
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usgs_last_mag_t: float = 0.0
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aviation_count: int = 0
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social_rate: float = 0.0
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pose_count: int = 0
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pose_kp: list[PoseKp] = field(default_factory=lambda: [PoseKp() for _ in range(17)]) # YOLO COCO legacy
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pose_last_t: float = 0.0
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# MediaPipe : compat single-person (holistic legacy, fallback)
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body_kp: list[PoseKp] = field(
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default_factory=lambda: [PoseKp() for _ in range(33)])
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face_kp: list[PoseKp] = field(
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default_factory=lambda: [PoseKp() for _ in range(478)])
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left_hand_kp: list[PoseKp] = field(
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default_factory=lambda: [PoseKp() for _ in range(21)])
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right_hand_kp: list[PoseKp] = field(
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default_factory=lambda: [PoseKp() for _ in range(21)])
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body_present: bool = False
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face_present: bool = False
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hands_present: bool = False
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# MediaPipe multi-personne : 3 workers paralleles, jusqu'a 4 sujets.
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# Chaque entree = liste de landmarks d'UNE personne. Les listes sont
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# independantes (pas d'association inter-personne — assemblees par
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# proximite si besoin dans le renderer).
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persons_body: list[list[PoseKp]] = field(default_factory=list)
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persons_face: list[list[PoseKp]] = field(default_factory=list)
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persons_hands: list[list[PoseKp]] = field(default_factory=list)
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# iPhone Vision hands (on-device, 21 kp MediaPipe order, .right upright,
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# y already top-left/down). Stored separately from MediaPipe persons_hands
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# so the air-piano can prefer this stabler, rotation-invariant source.
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persons_hands_iphone: list[list[PoseKp]] = field(default_factory=list)
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persons_hands_iphone_t: float = 0.0
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# Chirality for each entry in persons_hands_iphone: 0=left, 1=right.
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# Aligned 1:1 with persons_hands_iphone (same length after each TAG_HANDS update).
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# Not valid for MediaPipe-written persons_hands (stays empty on that path).
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persons_hands_chirality: list[int] = field(default_factory=list)
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hand_feats: dict | None = None
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# MediaPipe pose_world_landmarks per person : 33 keypoints in meters,
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# relative to the hip-center. Optional companion of persons_body
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# (image-space xy). Empty if no detection or backend doesn't emit it.
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persons_body3d: list[list[Kp3D]] = field(default_factory=list)
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# IDs persistants entre frames (ByteTrack-like via Hungarian IoU).
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# Couleur du skeleton dans le shader Metal = ID % palette_size.
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persons_body_ids: list[int] = field(default_factory=list)
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persons_face_ids: list[int] = field(default_factory=list)
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persons_hands_ids: list[int] = field(default_factory=list)
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# NLF (SMPL 6890 verts x N personnes, path nonparametrique)
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persons_nlf: list = field(default_factory=list) # list[NLFPerson]
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nlf_last_t: float = 0.0
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# Multi-HMR (SMPL-X 10475 verts x N personnes)
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persons_smplx: list = field(default_factory=list) # list[SMPLXPerson]
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smplx_last_t: float = 0.0
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# SMPL-X joint positions (127 joints incl. body + jaw + eyes + hands)
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# per pid, shape (127, 3) float32, camera coords (z>0 forward).
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# Indices 25-39 = left hand 15 finger joints, 40-54 = right hand.
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persons_smplx_joints: dict = field(default_factory=dict)
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# HaMeR MANO hand meshes (v1.2 task #26-28). Keyed by pid -> side
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# (0=left, 1=right) -> ndarray shape (778, 3) in camera-space metres.
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# Companion arrays per pid/side:
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# persons_hands_mesh_t : last_update timestamp (perf_counter)
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# persons_hands_mesh_cam_t : (3,) translation of the hand mesh root.
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persons_hands_mesh: dict = field(default_factory=dict)
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persons_hands_mesh_cam_t: dict = field(default_factory=dict)
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persons_hands_mesh_last_t: float = 0.0
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# ARKit body tracking (iOS ARBodyTracker app) : 91 joints world
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# space per pid. Same units as MediaPipe pose_world_landmarks
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# (metres, hip-centered). Fresh = updated within < 1 s.
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persons_arkit_joints: dict = field(default_factory=dict)
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persons_arkit_last_t: dict = field(default_factory=dict)
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# ARKit 2D projected skeleton (Task 3): 91×2 float32 arrays (normalized
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# screen coords 0..1) per pid. Updated by IphoneUSBSource on TAG_SKELETON2D.
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persons_arkit_2d: dict[int, "np.ndarray"] = field(default_factory=dict)
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persons_arkit_2d_t: dict[int, float] = field(default_factory=dict)
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persons_arkit_2d_valid: dict = field(default_factory=dict)
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arkit_joint_names: list = field(default_factory=list)
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arkit_parents: list = field(default_factory=list)
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# iPhone video is mirrored (CONCERT_MIRROR); renderer mirrors overlays to match.
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mirror_2d: bool = False
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# ---- LiDAR / ICP mesh fusion (Task 8 - 2026-05-14) ----
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# Set by the LidarTCPReader poller; consumed by FusionWorker.run_once.
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# The mesh-level fusion is complementary to the ARKit *joint* fusion
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# above: joints are sparse + 60 Hz, LiDAR is dense + 5-10 Hz.
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lidar_points: object = None # np.ndarray (N, 3) float32 ARKit world; None if no frame
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lidar_timestamp_ns: int = 0
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icp_metadata: object = None # FusionMetadata from icp_fusion or None
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# v1.3: centralised webcam source. WebcamSource owns the single
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# cv2.VideoCapture on the host and writes BGR frames here so all
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# consumers (MediaPipe Multi, Apple Vision, Multi-HMR worker,
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# HaMeR) read from one shared buffer instead of fighting over the
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# camera device. ``latest_bgr_id`` is a monotonic counter so a
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# consumer can detect new frames vs. re-reads.
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latest_bgr: object = None # np.ndarray (H, W, 3) BGR uint8
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latest_bgr_id: int = 0
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latest_bgr_t: float = 0.0
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# Gesture slot status per hand slot (written by action_head_pub, read by renderer):
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# 0=absent, 1=detected(plausible+established not armed), 2=armed(near+facing), 3=pinch engaged
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gesture_slot_status: list[int] = field(default_factory=lambda: [0, 0])
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# Continuous quality score per slot ∈ [0, 1] (written alongside gesture_slot_status).
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# Drives panel frame brightness (0.25+0.75*q) and stroke thickness.
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gesture_slot_quality: list[float] = field(default_factory=lambda: [0.0, 0.0])
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# Renderer
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width: int = 1280
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height: int = 720
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start_t: float = field(default_factory=time.monotonic)
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# Mode visuel 0..7 (cf scene.metal::bg_fragment dispatcher)
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viz_mode: int = 0
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viz_mode_names: tuple = (
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"storm", "tunnel", "plasma", "kaleido",
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"voronoi", "metaballs", "starfield", "bars",
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"hands3d", # mode 8 : voyage 3D pilote par les mains
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"openpos", # mode 9 : skeleton multi-personne sur fond minimal
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)
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# Preset open-data actif (USGS, Blitz, Wind, Kp/Bz, X-ray, OpenSky,
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# Bsky, Pose, Cosmos) — affiche dans le HUD.
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active_preset: str = ""
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# Scene audio active (envoyee par le clavier qsdfghjklm).
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active_scene: str = ""
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# Derniere frame webcam au format JPEG bytes (pour NSImageView overlay).
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# Le pose worker la met a jour ; le HUD timer lit et l'affiche.
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last_webcam_jpeg: bytes | None = None
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# Last full RGB frame fed to Multi-HMR (uint8 HxWx3, typ. 672x672).
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# Updated by multi_hmr_worker right before inference. Read by
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# MeshRigger for DINOv2-based person re-id. None when absent.
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last_frame_rgb: np.ndarray | None = None
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last_frame_rgb_t: float = 0.0
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_lock: threading.RLock = field(default_factory=threading.RLock, repr=False)
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def elapsed(self) -> float:
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return time.monotonic() - self.start_t
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def lock(self):
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return self._lock
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def pose_alive(self, timeout: float = 1.5) -> bool:
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return (time.monotonic() - self.pose_last_t) < timeout
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# Mappings clavier AZERTY pour les 3 dimensions :
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# azertyuiop = video (viz mode, 8 + 2 libres)
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# qsdfghjklm = audio (scene SC)
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# wxcvbn = data source (focus HUD + signal a SC)
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KEYMAP_VIDEO: tuple[tuple[str, str], ...] = (
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("a", "storm"),
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("z", "tunnel"),
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("e", "plasma"),
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("r", "kaleido"),
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("t", "voronoi"),
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("y", "metaballs"),
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("u", "starfield"),
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("i", "bars"),
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("o", "hands3d"), # voyage 3D pilote par les mains MediaPipe
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("p", "openpos"), # skeleton multi-personne 3D-stylise
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)
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KEYMAP_AUDIO: tuple[tuple[str, str], ...] = (
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("q", "cavity"),
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("s", "geo"),
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("d", "body"),
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("f", "weather"),
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("g", "flight"),
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("h", "pulse"),
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("j", "quiet"),
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("k", "all"),
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("l", "full"),
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("m", "stop"),
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)
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# Bundle preset = (source, scene SC, viz mode Metal).
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# Selectionner une source applique les 3 dimensions d'un coup : focus HUD,
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# scene audio dediee, mode visuel correspondant.
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SourceBundle = tuple[str, str, str, str] # (key, source, scene, viz)
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KEYMAP_SOURCE: tuple[SourceBundle, ...] = (
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("w", "USGS", "geo", "voronoi"),
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("x", "Blitz", "pulse", "storm"),
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("c", "SWPC", "weather", "tunnel"),
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("v", "OpenSky", "flight", "kaleido"),
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("b", "Bsky", "pulse", "bars"),
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("n", "Pose", "body", "metaballs"),
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)
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# 10 sources distinctes via touches 0-9 (granularite fine sur SWPC).
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KEYMAP_SOURCE_NUM: tuple[SourceBundle, ...] = (
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("0", "Cosmos", "full", "starfield"), # toutes sources
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("1", "USGS", "geo", "voronoi"), # earthquakes
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("2", "Blitz", "pulse", "storm"), # lightning
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("3", "Wind", "weather", "tunnel"), # SWPC solar wind speed
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("4", "Kp/Bz", "geo", "plasma"), # SWPC geomagnetic
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("5", "X-ray", "weather", "bars"), # SWPC solar flare
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("6", "OpenSky", "flight", "kaleido"), # aviation
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("7", "Bsky", "pulse", "bars"), # social firehose
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("8", "Pose", "body", "metaballs"), # body YOLO
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("9", "Grid", "weather", "plasma"), # netzfrequenz (futur)
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)
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