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Checkpoint 8

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Johnny Fernandes
2026-05-12 22:41:03 +01:00
parent a01a5c9cef
commit 5c2ee4bba5
31 changed files with 3189 additions and 380 deletions
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herding/perception/sheep_tracker.py
+118 -42
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@@ -6,6 +6,14 @@ 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.
@@ -25,16 +33,71 @@ PENNED_GATE_M = 4.0 # m — gate for matching detections to existing penne
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) -> 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 and forgetful memory.
"""Online tracker with NN association, prediction, and forgetful memory.
Each track stores ``(x, y, last_seen_step, penned)``.
Each track is a :class:`Track` with position, velocity estimate,
last-seen step, and penned flag.
"""
def __init__(self, gate: float = GATE_M):
self.gate = gate
self._tracks: dict[int, tuple[float, float, int, bool]] = {}
self._tracks: dict[int, Track] = {}
self._next_id = 0
self.step = 0
@@ -50,13 +113,14 @@ class SheepTracker:
det_used: set[int] = set()
updated_tids: set[int] = set()
# Pass 1 — match active tracks within the primary gate. Oldest-
# seen tracks bind first so a re-emerging long-lost sheep keeps
# its old ID instead of being grabbed by a fresh neighbour.
active_tids = [tid for tid, t in self._tracks.items() if not t[3]]
active_tids.sort(key=lambda tid: self._tracks[tid][2])
# 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:
tx, ty, _, _ = self._tracks[tid]
track = self._tracks[tid]
# Use predicted position for matching.
tx, ty = track.predicted_position(self.step)
best_j, best_d = -1, self.gate
for j, (dx, dy) in enumerate(detections):
if j in det_used:
@@ -67,20 +131,18 @@ class SheepTracker:
best_j = j
if best_j >= 0:
dx, dy = detections[best_j]
self._tracks[tid] = (dx, dy, self.step, False)
track.update(dx, dy, self.step)
det_used.add(best_j)
updated_tids.add(tid)
# Pass 1b — re-acquisition. Sheep flee at ~0.6 m/s, so over a
# 1–2 s occlusion the same sheep may reappear outside the primary
# gate. Allow rebinding within a wider gate for stale-enough
# tracks; otherwise phantom tracks accumulate and corrupt CoM.
# Pass 1b — re-acquisition with wider gate for stale tracks.
for tid in active_tids:
if tid in updated_tids:
continue
tx, ty, last, _ = self._tracks[tid]
if (self.step - last) < REACQUIRE_MIN_AGE:
track = self._tracks[tid]
if (self.step - track.last_seen) < REACQUIRE_MIN_AGE:
continue
tx, ty = track.predicted_position(self.step)
best_j, best_d = -1, REACQUIRE_GATE_M
for j, (dx, dy) in enumerate(detections):
if j in det_used:
@@ -91,53 +153,52 @@ class SheepTracker:
best_j = j
if best_j >= 0:
dx, dy = detections[best_j]
self._tracks[tid] = (dx, dy, self.step, False)
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[3]]
penned_tids = [tid for tid, t in self._tracks.items() if t.penned]
for tid in penned_tids:
tx, ty, _, _ = self._tracks[tid]
track = self._tracks[tid]
best_j, best_d = -1, PENNED_GATE_M
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
d = math.hypot(dx - tx, dy - ty)
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]
self._tracks[tid] = (dx, dy, self.step, True)
track.update(dx, dy, self.step)
det_used.add(best_j)
# Spawn new tracks for unmatched detections. Born "penned" if
# the detection already sits inside the pen geometry.
# Spawn new tracks for unmatched detections.
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
penned = in_pen(dx, dy) or is_penned_position(dx, dy)
self._tracks[self._next_id] = (dx, dy, self.step, penned)
self._tracks[self._next_id] = Track(dx, dy, self.step, penned)
self._next_id += 1
# Promote active tracks whose current estimate crosses the gate.
for tid, (tx, ty, last, penned) in list(self._tracks.items()):
if penned:
for track in self._tracks.values():
if track.penned:
continue
if is_penned_position(tx, ty):
self._tracks[tid] = (tx, ty, last, True)
px, py = track.predicted_position(self.step)
if is_penned_position(px, py):
track.penned = True
# Forget stale active tracks; penned tracks live forever.
for tid, (tx, ty, last, penned) in list(self._tracks.items()):
if penned:
continue
if (self.step - last) > FORGET_STEPS:
del self._tracks[tid]
stale = [tid for tid, t in self._tracks.items()
if not t.penned and (self.step - t.last_seen) > FORGET_STEPS]
for tid in stale:
del self._tracks[tid]
# Hard cap on the active set — drop the oldest-seen overflow.
active = [(tid, last) for tid, (_, _, last, p) in self._tracks.items()
if not p]
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]:
@@ -146,16 +207,31 @@ class SheepTracker:
return self.get_positions()
def get_positions(self) -> dict[str, tuple[float, float]]:
"""Active (not-penned) tracks as a ``{name: (x, y)}`` dict."""
return {f"t{tid}": (x, y)
for tid, (x, y, _, penned) in self._tracks.items()
if not penned}
"""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.
"""
result = {}
for tid, track in self._tracks.items():
if track.penned:
continue
px, py = track.predicted_position(self.step)
result[f"t{tid}"] = (px, py)
return result
def get_penned_set(self) -> set[str]:
return {f"t{tid}" for tid, (_, _, _, penned) in self._tracks.items() if penned}
return {f"t{tid}" for tid, t in self._tracks.items() if t.penned}
def n_active(self) -> int:
return sum(1 for _, _, _, penned in self._tracks.values() if not penned)
return sum(1 for t in self._tracks.values() if not t.penned)
def n_penned(self) -> int:
return sum(1 for _, _, _, penned in self._tracks.values() if penned)
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) <= PREDICT_STEPS)