TIR_PROJ/herding/perception/sheep_tracker.py

"""Multi-target tracker for LiDAR-detected sheep.

Greedy nearest-neighbour data association across frames, with a wider
re-acquisition gate for stale tracks (sheep flee during occlusion and
reappear off-position), plus memory of last-seen positions for sheep
out of FOV. Output is ``{name: (x, y)}`` — Strömbom / Sequential
consume it directly.

When **predictive mode** is enabled (the default), tracks carry a
constant-velocity state ``(vx, vy)`` estimated from the last two
observations. While a track is occluded its position is extrapolated
using this velocity for up to ``PREDICT_STEPS`` frames, keeping the
teacher's CoM estimate stable during brief losses. After prediction
expires, the track falls back to its last-seen position (static memory)
until ``FORGET_STEPS`` deletes it entirely.

A track is marked penned once its estimated position crosses the gate
plane south (``is_penned_position``). Penned tracks are excluded from
``get_positions`` and kept indefinitely.
"""

from __future__ import annotations

import math
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from herding.config import TrackerConfig

from herding.world.geometry import MAX_SHEEP, in_pen, is_penned_position


GATE_M = 2.5              # m — primary NN gate (recently observed tracks)
REACQUIRE_GATE_M = 4.5    # m — wider gate for re-binding stale tracks
REACQUIRE_MIN_AGE = 20    # steps — track must be this stale to use the wider gate
PENNED_GATE_M = 4.0       # m — gate for matching detections to existing penned tracks
FORGET_STEPS = 200        # ~3.2 s — delete stale active tracks (penned ones kept forever)
MAX_ACTIVE_TRACKS = MAX_SHEEP

# Predictive tracking constants.
PREDICT_STEPS = 120       # ~1.9 s — extrapolate velocity this many frames
VELOCITY_CLAMP = 1.0      # m/s — max predicted speed (sheep max is ~0.78 m/s)


class Track:
    """Single track with position, velocity, and age."""

    __slots__ = ("x", "y", "vx", "vy", "last_seen", "penned")

    def __init__(self, x: float, y: float, step: int, penned: bool = False):
        self.x = x
        self.y = y
        self.vx = 0.0
        self.vy = 0.0
        self.last_seen = step
        self.penned = penned

    @property
    def age(self) -> int:
        """Not-a-property in the hot loop — callers pass current step."""
        raise NotImplementedError

    def predicted_position(
        self,
        current_step: int,
        predict_steps: int = PREDICT_STEPS,
        velocity_clamp: float = VELOCITY_CLAMP,
    ) -> tuple[float, float]:
        """Extrapolated position using constant velocity, clamped."""
        dt = current_step - self.last_seen
        if dt <= 0 or dt > predict_steps:
            return self.x, self.y
        speed = math.hypot(self.vx, self.vy)
        if speed < 1e-4:
            return self.x, self.y
        # Clamp extrapolation distance.
        max_d = velocity_clamp * dt * 0.016  # steps → seconds
        d = min(speed * dt * 0.016, max_d)
        return (
            self.x + d * (self.vx / speed),
            self.y + d * (self.vy / speed),
        )

    def update(self, x: float, y: float, step: int) -> None:
        """Absorb a new detection and re-estimate velocity."""
        dt = step - self.last_seen
        if dt > 0:
            dt_s = dt * 0.016  # steps → seconds
            new_vx = (x - self.x) / dt_s
            new_vy = (y - self.y) / dt_s
            # Exponential smoothing on velocity.
            alpha = 0.6
            self.vx = alpha * new_vx + (1.0 - alpha) * self.vx
            self.vy = alpha * new_vy + (1.0 - alpha) * self.vy
        self.x = x
        self.y = y
        self.last_seen = step


class SheepTracker:
    """Online tracker with NN association, prediction, and forgetful memory.

    Each track is a :class:`Track` with position, velocity estimate,
    last-seen step, and penned flag.

    Pass a :class:`~herding.config.TrackerConfig` to override any
    module-level defaults without changing this file.
    """

    def __init__(
        self,
        gate: float = GATE_M,
        tracker_cfg: "TrackerConfig | None" = None,
    ):
        if tracker_cfg is not None:
            self.gate = tracker_cfg.gate_m
            self._reacquire_gate = tracker_cfg.reacquire_gate_m
            self._reacquire_min_age = tracker_cfg.reacquire_min_age
            self._penned_gate = tracker_cfg.penned_gate_m
            self._forget_steps = tracker_cfg.forget_steps
            self._predict_steps = tracker_cfg.predict_steps
            self._velocity_clamp = tracker_cfg.velocity_clamp
            self._max_new_per_step = tracker_cfg.max_new_tracks_per_step
            self._pen_latch_depth = tracker_cfg.pen_latch_depth
        else:
            self.gate = gate
            self._reacquire_gate = REACQUIRE_GATE_M
            self._reacquire_min_age = REACQUIRE_MIN_AGE
            self._penned_gate = PENNED_GATE_M
            self._forget_steps = FORGET_STEPS
            self._predict_steps = PREDICT_STEPS
            self._velocity_clamp = VELOCITY_CLAMP
            self._max_new_per_step = MAX_ACTIVE_TRACKS
            self._pen_latch_depth = 0.0
        self._tracks: dict[int, Track] = {}
        self._next_id = 0
        self.step = 0

    def reset(self) -> None:
        self._tracks.clear()
        self._next_id = 0
        self.step = 0

    def update(self, detections: list[tuple[float, float]]) -> dict[str, tuple[float, float]]:
        """Fold a new set of detections in and return active positions."""
        self.step += 1

        det_used: set[int] = set()
        updated_tids: set[int] = set()

        # Pass 1 — match active tracks within the primary gate.
        # Use predicted positions for matching, oldest-first.
        active_tids = [tid for tid, t in self._tracks.items() if not t.penned]
        active_tids.sort(key=lambda tid: self._tracks[tid].last_seen)
        for tid in active_tids:
            track = self._tracks[tid]
            tx, ty = track.predicted_position(
                self.step, self._predict_steps, self._velocity_clamp)
            best_j, best_d = -1, self.gate
            for j, (dx, dy) in enumerate(detections):
                if j in det_used:
                    continue
                d = math.hypot(dx - tx, dy - ty)
                if d < best_d:
                    best_d = d
                    best_j = j
            if best_j >= 0:
                dx, dy = detections[best_j]
                track.update(dx, dy, self.step)
                det_used.add(best_j)
                updated_tids.add(tid)

        # Pass 1b — re-acquisition with wider gate for stale tracks.
        for tid in active_tids:
            if tid in updated_tids:
                continue
            track = self._tracks[tid]
            if (self.step - track.last_seen) < self._reacquire_min_age:
                continue
            tx, ty = track.predicted_position(
                self.step, self._predict_steps, self._velocity_clamp)
            best_j, best_d = -1, self._reacquire_gate
            for j, (dx, dy) in enumerate(detections):
                if j in det_used:
                    continue
                d = math.hypot(dx - tx, dy - ty)
                if d < best_d:
                    best_d = d
                    best_j = j
            if best_j >= 0:
                dx, dy = detections[best_j]
                track.update(dx, dy, self.step)
                det_used.add(best_j)
                updated_tids.add(tid)

        # Pass 2 — match remaining detections to penned tracks.
        penned_tids = [tid for tid, t in self._tracks.items() if t.penned]
        for tid in penned_tids:
            track = self._tracks[tid]
            best_j, best_d = -1, self._penned_gate
            for j, (dx, dy) in enumerate(detections):
                if j in det_used:
                    continue
                d = math.hypot(dx - track.x, dy - track.y)
                if d < best_d:
                    best_d = d
                    best_j = j
            if best_j >= 0:
                dx, dy = detections[best_j]
                track.update(dx, dy, self.step)
                det_used.add(best_j)

        # Spawn new tracks for unmatched detections — rate-capped.
        spawned = 0
        for j, (dx, dy) in enumerate(detections):
            if j in det_used:
                continue
            if spawned >= self._max_new_per_step:
                break
            penned = self._is_penned(dx, dy)
            self._tracks[self._next_id] = Track(dx, dy, self.step, penned)
            self._next_id += 1
            spawned += 1

        # Promote active tracks whose current estimate crosses the gate.
        for track in self._tracks.values():
            if track.penned:
                continue
            px, py = track.predicted_position(
                self.step, self._predict_steps, self._velocity_clamp)
            if self._is_penned(px, py):
                track.penned = True

        # Forget stale active tracks; penned tracks decay too but at a
        # longer horizon (real penned sheep are still observed occasionally
        # when the dog faces south; pure FPs at gate posts stop being
        # detected once the dog drives away).
        penned_forget = self._forget_steps * 8
        stale = [tid for tid, t in self._tracks.items()
                 if (not t.penned and (self.step - t.last_seen) > self._forget_steps)
                 or (t.penned and (self.step - t.last_seen) > penned_forget)]
        for tid in stale:
            del self._tracks[tid]

        # Hard cap on the active set — drop the oldest-seen overflow.
        active = [(tid, t.last_seen) for tid, t in self._tracks.items()
                  if not t.penned]
        if len(active) > MAX_ACTIVE_TRACKS:
            active.sort(key=lambda kv: kv[1])
            for tid, _ in active[: len(active) - MAX_ACTIVE_TRACKS]:
                del self._tracks[tid]

        return self.get_positions()

    def _is_penned(self, x: float, y: float) -> bool:
        """Check whether a position should be considered penned.

        Uses ``pen_latch_depth`` to require the position to be that many
        metres past the gate line before latching.  Increasing the depth
        prevents gate-area LiDAR false positives (gate hardware reflections
        at y ≈ -15) from being permanently latched as penned tracks.
        """
        from herding.world.geometry import GATE_Y
        # Apply depth threshold to both in_pen and is_penned_position so
        # that any position in the gate column must clear GATE_Y - depth.
        threshold = GATE_Y - self._pen_latch_depth
        return (in_pen(x, y) or is_penned_position(x, y)) and y <= threshold

    def get_positions(self, min_freshness: int | None = None) -> dict[str, tuple[float, float]]:
        """Active (not-penned) tracks as a ``{name: (x, y)}`` dict.

        For tracks currently being predicted (occluded but within
        predict_steps), returns the extrapolated position so the teacher
        sees a smooth estimate.

        ``min_freshness`` (optional, deploy-only): drop tracks whose
        last_seen is older than ``step - min_freshness``. Real sheep in
        FOV are detected nearly every step; phantom tracks from sporadic
        Webots FPs stop being re-observed and decay. Default ``None``
        preserves training behaviour (extrapolated tracks visible).
        """
        result = {}
        for tid, track in self._tracks.items():
            if track.penned:
                continue
            if (min_freshness is not None
                    and self.step - track.last_seen > min_freshness):
                continue
            px, py = track.predicted_position(
                self.step, self._predict_steps, self._velocity_clamp)
            result[f"t{tid}"] = (px, py)
        return result

    def get_penned_set(self) -> set[str]:
        return {f"t{tid}" for tid, t in self._tracks.items() if t.penned}

    def n_active(self) -> int:
        return sum(1 for t in self._tracks.values() if not t.penned)

    def n_penned(self) -> int:
        return sum(1 for t in self._tracks.values() if t.penned)

    def n_predicted(self) -> int:
        """Number of active tracks currently being extrapolated (not directly observed)."""
        return sum(1 for t in self._tracks.values()
                   if not t.penned and (self.step - t.last_seen) > 0
                   and (self.step - t.last_seen) <= self._predict_steps)