ui/pipeline.py

import collections
import glob
import json
import math
import os
import pathlib
import sys

import numpy as np
import joblib

_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if _PROJECT_ROOT not in sys.path:
    sys.path.insert(0, _PROJECT_ROOT)

from models.face_mesh import FaceMeshDetector
from models.head_pose import HeadPoseEstimator
from models.eye_scorer import EyeBehaviourScorer, compute_mar, MAR_YAWN_THRESHOLD
from models.eye_crop import extract_eye_crops
from models.eye_classifier import load_eye_classifier, GeometricOnlyClassifier
from models.collect_features import FEATURE_NAMES, TemporalTracker, extract_features

_FEAT_IDX = {name: i for i, name in enumerate(FEATURE_NAMES)}


def _clip_features(vec):
    """Clip raw features to the same ranges used during training."""
    out = vec.copy()
    _i = _FEAT_IDX

    out[_i["yaw"]] = np.clip(out[_i["yaw"]], -45, 45)
    out[_i["pitch"]] = np.clip(out[_i["pitch"]], -30, 30)
    out[_i["roll"]] = np.clip(out[_i["roll"]], -30, 30)

    out[_i["head_deviation"]] = math.sqrt(
        float(out[_i["yaw"]]) ** 2 + float(out[_i["pitch"]]) ** 2
    )

    for f in ("ear_left", "ear_right", "ear_avg"):
        out[_i[f]] = np.clip(out[_i[f]], 0, 0.85)

    out[_i["mar"]] = np.clip(out[_i["mar"]], 0, 1.0)
    out[_i["gaze_offset"]] = np.clip(out[_i["gaze_offset"]], 0, 0.50)
    out[_i["perclos"]] = np.clip(out[_i["perclos"]], 0, 0.80)
    out[_i["blink_rate"]] = np.clip(out[_i["blink_rate"]], 0, 30.0)
    out[_i["closure_duration"]] = np.clip(out[_i["closure_duration"]], 0, 10.0)
    out[_i["yawn_duration"]] = np.clip(out[_i["yawn_duration"]], 0, 10.0)

    return out


class _OutputSmoother:
    # Asymmetric EMA: rises fast (recognise focus), falls slower (avoid flicker).
    # Grace period holds score steady for a few frames when face is lost.

    def __init__(self, alpha_up=0.55, alpha_down=0.45, grace_frames=10):
        self._alpha_up = alpha_up
        self._alpha_down = alpha_down
        self._grace = grace_frames
        self._score = 0.5
        self._no_face = 0

    def reset(self):
        self._score = 0.5
        self._no_face = 0

    def update(self, raw_score, face_detected):
        if face_detected:
            self._no_face = 0
            alpha = self._alpha_up if raw_score > self._score else self._alpha_down
            self._score += alpha * (raw_score - self._score)
        else:
            self._no_face += 1
            if self._no_face > self._grace:
                self._score *= 0.80
        return self._score


DEFAULT_HYBRID_CONFIG = {
    "w_mlp": 0.7,
    "w_geo": 0.3,
    "threshold": 0.55,
    "use_yawn_veto": True,
    "geo_face_weight": 0.4,
    "geo_eye_weight": 0.6,
    "mar_yawn_threshold": float(MAR_YAWN_THRESHOLD),
}


class _RuntimeFeatureEngine:
    """Runtime feature engineering (magnitudes, velocities, variances) with EMA baselines."""

    _MAG_FEATURES = ["pitch", "yaw", "head_deviation", "gaze_offset", "v_gaze", "h_gaze"]
    _VEL_FEATURES = ["pitch", "yaw", "h_gaze", "v_gaze", "head_deviation", "gaze_offset"]
    _VAR_FEATURES = ["h_gaze", "v_gaze", "pitch"]
    _VAR_WINDOW = 30
    _WARMUP = 15

    def __init__(self, base_feature_names, norm_features=None):
        self._base_names = list(base_feature_names)
        self._norm_features = list(norm_features) if norm_features else []

        tracked = set(self._MAG_FEATURES) | set(self._norm_features)
        self._ema_mean = {f: 0.0 for f in tracked}
        self._ema_var = {f: 1.0 for f in tracked}
        self._n = 0
        self._prev = None
        self._var_bufs = {
            f: collections.deque(maxlen=self._VAR_WINDOW) for f in self._VAR_FEATURES
        }

        self._ext_names = (
            list(self._base_names)
            + [f"{f}_mag" for f in self._MAG_FEATURES]
            + [f"{f}_vel" for f in self._VEL_FEATURES]
            + [f"{f}_var" for f in self._VAR_FEATURES]
        )

    @property
    def extended_names(self):
        return list(self._ext_names)

    def transform(self, base_vec):
        self._n += 1
        raw = {name: float(base_vec[i]) for i, name in enumerate(self._base_names)}

        alpha = 2.0 / (min(self._n, 120) + 1)
        for feat in self._ema_mean:
            if feat not in raw:
                continue
            v = raw[feat]
            if self._n == 1:
                self._ema_mean[feat] = v
                self._ema_var[feat] = 0.0
            else:
                self._ema_mean[feat] += alpha * (v - self._ema_mean[feat])
                self._ema_var[feat] += alpha * (
                    (v - self._ema_mean[feat]) ** 2 - self._ema_var[feat]
                )

        out = base_vec.copy().astype(np.float32)
        if self._n > self._WARMUP:
            for feat in self._norm_features:
                if feat in raw:
                    idx = self._base_names.index(feat)
                    std = max(math.sqrt(self._ema_var[feat]), 1e-6)
                    out[idx] = (raw[feat] - self._ema_mean[feat]) / std

        mag = np.zeros(len(self._MAG_FEATURES), dtype=np.float32)
        for i, feat in enumerate(self._MAG_FEATURES):
            if feat in raw:
                mag[i] = abs(raw[feat] - self._ema_mean.get(feat, raw[feat]))

        vel = np.zeros(len(self._VEL_FEATURES), dtype=np.float32)
        if self._prev is not None:
            for i, feat in enumerate(self._VEL_FEATURES):
                if feat in raw and feat in self._prev:
                    vel[i] = abs(raw[feat] - self._prev[feat])
        self._prev = dict(raw)

        for feat in self._VAR_FEATURES:
            if feat in raw:
                self._var_bufs[feat].append(raw[feat])
        var = np.zeros(len(self._VAR_FEATURES), dtype=np.float32)
        for i, feat in enumerate(self._VAR_FEATURES):
            buf = self._var_bufs[feat]
            if len(buf) >= 2:
                arr = np.array(buf)
                var[i] = float(arr.var())

        return np.concatenate([out, mag, vel, var])


class FaceMeshPipeline:
    def __init__(
        self,
        max_angle: float = 22.0,
        alpha: float = 0.4,
        beta: float = 0.6,
        threshold: float = 0.55,
        eye_model_path: str | None = None,
        eye_backend: str = "yolo",
        eye_blend: float = 0.5,
        detector=None,
    ):
        self.detector = detector or FaceMeshDetector()
        self._owns_detector = detector is None
        self.head_pose = HeadPoseEstimator(max_angle=max_angle)
        self.eye_scorer = EyeBehaviourScorer()
        self.alpha = alpha
        self.beta = beta
        self.threshold = threshold
        self.eye_blend = eye_blend

        self.eye_classifier = load_eye_classifier(
            path=eye_model_path if eye_model_path and os.path.exists(eye_model_path) else None,
            backend=eye_backend,
            device="cpu",
        )
        self._has_eye_model = not isinstance(self.eye_classifier, GeometricOnlyClassifier)
        if self._has_eye_model:
            print(f"[PIPELINE] Eye model: {self.eye_classifier.name}")
        self._smoother = _OutputSmoother()

    def process_frame(self, bgr_frame: np.ndarray) -> dict:
        landmarks = self.detector.process(bgr_frame)
        h, w = bgr_frame.shape[:2]

        out = {
            "landmarks": landmarks,
            "s_face": 0.0,
            "s_eye": 0.0,
            "raw_score": 0.0,
            "is_focused": False,
            "yaw": None,
            "pitch": None,
            "roll": None,
            "mar": None,
            "is_yawning": False,
            "left_bbox": None,
            "right_bbox": None,
        }

        if landmarks is None:
            smoothed = self._smoother.update(0.0, False)
            out["raw_score"] = smoothed
            out["is_focused"] = smoothed >= self.threshold
            return out

        angles = self.head_pose.estimate(landmarks, w, h)
        if angles is not None:
            out["yaw"], out["pitch"], out["roll"] = angles
        out["s_face"] = self.head_pose.score(landmarks, w, h)

        s_eye_geo = self.eye_scorer.score(landmarks)
        if self._has_eye_model:
            left_crop, right_crop, left_bbox, right_bbox = extract_eye_crops(bgr_frame, landmarks)
            out["left_bbox"] = left_bbox
            out["right_bbox"] = right_bbox
            s_eye_model = self.eye_classifier.predict_score([left_crop, right_crop])
            out["s_eye"] = (1.0 - self.eye_blend) * s_eye_geo + self.eye_blend * s_eye_model
        else:
            out["s_eye"] = s_eye_geo

        out["mar"] = compute_mar(landmarks)
        out["is_yawning"] = out["mar"] > MAR_YAWN_THRESHOLD

        raw = self.alpha * out["s_face"] + self.beta * out["s_eye"]
        if out["is_yawning"]:
            raw = 0.0
        out["raw_score"] = self._smoother.update(raw, True)
        out["is_focused"] = out["raw_score"] >= self.threshold

        return out

    @property
    def has_eye_model(self) -> bool:
        return self._has_eye_model

    def reset_session(self):
        self._smoother.reset()

    def close(self):
        if self._owns_detector:
            self.detector.close()

    def __enter__(self):
        return self

    def __exit__(self, *args):
        self.close()


def _latest_model_artifacts(model_dir):
    model_files = sorted(glob.glob(os.path.join(model_dir, "model_*.joblib")))
    if not model_files:
        model_files = sorted(glob.glob(os.path.join(model_dir, "mlp_*.joblib")))
    if not model_files:
        return None, None, None
    basename = os.path.basename(model_files[-1])
    tag = ""
    for prefix in ("model_", "mlp_"):
        if basename.startswith(prefix):
            tag = basename[len(prefix) :].replace(".joblib", "")
            break
    scaler_path = os.path.join(model_dir, f"scaler_{tag}.joblib")
    meta_path = os.path.join(model_dir, f"meta_{tag}.npz")
    if not os.path.isfile(scaler_path) or not os.path.isfile(meta_path):
        return None, None, None
    return model_files[-1], scaler_path, meta_path


def _load_hybrid_config(model_dir: str, config_path: str | None = None):
    cfg = dict(DEFAULT_HYBRID_CONFIG)
    resolved = config_path or os.path.join(model_dir, "hybrid_focus_config.json")
    if not os.path.isfile(resolved):
        print(f"[HYBRID] No config found at {resolved}; using defaults")
        return cfg, None

    with open(resolved, "r", encoding="utf-8") as f:
        file_cfg = json.load(f)

    for key in DEFAULT_HYBRID_CONFIG:
        if key in file_cfg:
            cfg[key] = file_cfg[key]

    cfg["w_mlp"] = float(cfg["w_mlp"])
    cfg["w_geo"] = float(cfg["w_geo"])
    weight_sum = cfg["w_mlp"] + cfg["w_geo"]
    if weight_sum <= 0:
        raise ValueError("[HYBRID] Invalid config: w_mlp + w_geo must be > 0")
    cfg["w_mlp"] /= weight_sum
    cfg["w_geo"] /= weight_sum
    cfg["threshold"] = float(cfg["threshold"])
    cfg["use_yawn_veto"] = bool(cfg["use_yawn_veto"])
    cfg["geo_face_weight"] = float(cfg["geo_face_weight"])
    cfg["geo_eye_weight"] = float(cfg["geo_eye_weight"])
    cfg["mar_yawn_threshold"] = float(cfg["mar_yawn_threshold"])

    print(f"[HYBRID] Loaded config: {resolved}")
    return cfg, resolved


class MLPPipeline:
    def __init__(self, model_dir=None, detector=None, threshold=0.5):
        if model_dir is None:
            # Check primary location
            model_dir = os.path.join(_PROJECT_ROOT, "MLP", "models")
            if not os.path.exists(model_dir):
                model_dir = os.path.join(_PROJECT_ROOT, "checkpoints")

        mlp_path, scaler_path, meta_path = _latest_model_artifacts(model_dir)
        if mlp_path is None:
            raise FileNotFoundError(f"No MLP artifacts in {model_dir}")
        self._mlp = joblib.load(mlp_path)
        self._scaler = joblib.load(scaler_path)
        meta = np.load(meta_path, allow_pickle=True)
        self._feature_names = list(meta["feature_names"])

        norm_feats = list(meta["norm_features"]) if "norm_features" in meta else []
        self._engine = _RuntimeFeatureEngine(FEATURE_NAMES, norm_features=norm_feats)
        ext_names = self._engine.extended_names
        self._indices = [ext_names.index(n) for n in self._feature_names]

        self._detector = detector or FaceMeshDetector()
        self._owns_detector = detector is None
        self._head_pose = HeadPoseEstimator()
        self.head_pose = self._head_pose
        self._eye_scorer = EyeBehaviourScorer()
        self._temporal = TemporalTracker()
        self._smoother = _OutputSmoother()
        self._threshold = threshold
        print(f"[MLP] Loaded {mlp_path} | {len(self._feature_names)} features | threshold={threshold}")

    def process_frame(self, bgr_frame):
        landmarks = self._detector.process(bgr_frame)
        h, w = bgr_frame.shape[:2]
        out = {
            "landmarks": landmarks,
            "is_focused": False,
            "s_face": 0.0,
            "s_eye": 0.0,
            "raw_score": 0.0,
            "mlp_prob": 0.0,
            "mar": None,
            "yaw": None,
            "pitch": None,
            "roll": None,
        }
        if landmarks is None:
            smoothed = self._smoother.update(0.0, False)
            out["raw_score"] = smoothed
            out["is_focused"] = smoothed >= self._threshold
            return out
        vec = extract_features(landmarks, w, h, self._head_pose, self._eye_scorer, self._temporal)
        vec = _clip_features(vec)

        out["yaw"] = float(vec[_FEAT_IDX["yaw"]])
        out["pitch"] = float(vec[_FEAT_IDX["pitch"]])
        out["roll"] = float(vec[_FEAT_IDX["roll"]])
        out["s_face"] = float(vec[_FEAT_IDX["s_face"]])
        out["s_eye"] = float(vec[_FEAT_IDX["s_eye"]])
        out["mar"] = float(vec[_FEAT_IDX["mar"]])

        ext_vec = self._engine.transform(vec)
        X = ext_vec[self._indices].reshape(1, -1).astype(np.float64)
        X_sc = self._scaler.transform(X)
        if hasattr(self._mlp, "predict_proba"):
            mlp_prob = float(self._mlp.predict_proba(X_sc)[0, 1])
        else:
            mlp_prob = float(self._mlp.predict(X_sc)[0] == 1)
        out["mlp_prob"] = float(np.clip(mlp_prob, 0.0, 1.0))
        out["raw_score"] = self._smoother.update(out["mlp_prob"], True)
        out["is_focused"] = out["raw_score"] >= self._threshold
        return out

    def reset_session(self):
        self._temporal = TemporalTracker()
        self._smoother.reset()

    def close(self):
        if self._owns_detector:
            self._detector.close()

    def __enter__(self):
        return self

    def __exit__(self, *args):
        self.close()


class HybridFocusPipeline:
    def __init__(
        self,
        model_dir=None,
        config_path: str | None = None,
        eye_model_path: str | None = None,
        eye_backend: str = "yolo",
        eye_blend: float = 0.5,
        max_angle: float = 22.0,
        detector=None,
    ):
        if model_dir is None:
            model_dir = os.path.join(_PROJECT_ROOT, "checkpoints")
        mlp_path, scaler_path, meta_path = _latest_model_artifacts(model_dir)
        if mlp_path is None:
            raise FileNotFoundError(f"No MLP artifacts in {model_dir}")

        self._mlp = joblib.load(mlp_path)
        self._scaler = joblib.load(scaler_path)
        meta = np.load(meta_path, allow_pickle=True)
        self._feature_names = list(meta["feature_names"])

        norm_feats = list(meta["norm_features"]) if "norm_features" in meta else []
        self._engine = _RuntimeFeatureEngine(FEATURE_NAMES, norm_features=norm_feats)
        ext_names = self._engine.extended_names
        self._indices = [ext_names.index(n) for n in self._feature_names]

        self._cfg, self._cfg_path = _load_hybrid_config(model_dir=model_dir, config_path=config_path)

        self._detector = detector or FaceMeshDetector()
        self._owns_detector = detector is None
        self._head_pose = HeadPoseEstimator(max_angle=max_angle)
        self._eye_scorer = EyeBehaviourScorer()
        self._temporal = TemporalTracker()
        self._eye_blend = eye_blend
        self.eye_classifier = load_eye_classifier(
            path=eye_model_path if eye_model_path and os.path.exists(eye_model_path) else None,
            backend=eye_backend,
            device="cpu",
        )
        self._has_eye_model = not isinstance(self.eye_classifier, GeometricOnlyClassifier)
        if self._has_eye_model:
            print(f"[HYBRID] Eye model: {self.eye_classifier.name}")

        self.head_pose = self._head_pose
        self._smoother = _OutputSmoother()

        print(
            f"[HYBRID] Loaded {mlp_path} | {len(self._feature_names)} features | "
            f"w_mlp={self._cfg['w_mlp']:.2f}, w_geo={self._cfg['w_geo']:.2f}, "
            f"threshold={self._cfg['threshold']:.2f}"
        )

    @property
    def has_eye_model(self) -> bool:
        return self._has_eye_model

    @property
    def config(self) -> dict:
        return dict(self._cfg)

    def process_frame(self, bgr_frame: np.ndarray) -> dict:
        landmarks = self._detector.process(bgr_frame)
        h, w = bgr_frame.shape[:2]
        out = {
            "landmarks": landmarks,
            "is_focused": False,
            "focus_score": 0.0,
            "mlp_prob": 0.0,
            "geo_score": 0.0,
            "raw_score": 0.0,
            "s_face": 0.0,
            "s_eye": 0.0,
            "mar": None,
            "is_yawning": False,
            "yaw": None,
            "pitch": None,
            "roll": None,
            "left_bbox": None,
            "right_bbox": None,
        }
        if landmarks is None:
            smoothed = self._smoother.update(0.0, False)
            out["focus_score"] = smoothed
            out["raw_score"] = smoothed
            out["is_focused"] = smoothed >= self._cfg["threshold"]
            return out

        angles = self._head_pose.estimate(landmarks, w, h)
        if angles is not None:
            out["yaw"], out["pitch"], out["roll"] = angles

        out["s_face"] = self._head_pose.score(landmarks, w, h)
        s_eye_geo = self._eye_scorer.score(landmarks)
        if self._has_eye_model:
            left_crop, right_crop, left_bbox, right_bbox = extract_eye_crops(bgr_frame, landmarks)
            out["left_bbox"] = left_bbox
            out["right_bbox"] = right_bbox
            s_eye_model = self.eye_classifier.predict_score([left_crop, right_crop])
            out["s_eye"] = (1.0 - self._eye_blend) * s_eye_geo + self._eye_blend * s_eye_model
        else:
            out["s_eye"] = s_eye_geo

        geo_score = (
            self._cfg["geo_face_weight"] * out["s_face"] +
            self._cfg["geo_eye_weight"] * out["s_eye"]
        )
        geo_score = float(np.clip(geo_score, 0.0, 1.0))

        out["mar"] = compute_mar(landmarks)
        out["is_yawning"] = out["mar"] > self._cfg["mar_yawn_threshold"]
        if self._cfg["use_yawn_veto"] and out["is_yawning"]:
            geo_score = 0.0
        out["geo_score"] = geo_score

        pre = {
            "angles": angles,
            "s_face": out["s_face"],
            "s_eye": s_eye_geo,
            "mar": out["mar"],
        }
        vec = extract_features(landmarks, w, h, self._head_pose, self._eye_scorer, self._temporal, _pre=pre)
        vec = _clip_features(vec)
        ext_vec = self._engine.transform(vec)
        X = ext_vec[self._indices].reshape(1, -1).astype(np.float64)
        X_sc = self._scaler.transform(X)
        if hasattr(self._mlp, "predict_proba"):
            mlp_prob = float(self._mlp.predict_proba(X_sc)[0, 1])
        else:
            mlp_prob = float(self._mlp.predict(X_sc)[0] == 1)
        out["mlp_prob"] = float(np.clip(mlp_prob, 0.0, 1.0))

        focus_score = self._cfg["w_mlp"] * out["mlp_prob"] + self._cfg["w_geo"] * out["geo_score"]
        out["focus_score"] = self._smoother.update(float(np.clip(focus_score, 0.0, 1.0)), True)
        out["raw_score"] = out["focus_score"]
        out["is_focused"] = out["focus_score"] >= self._cfg["threshold"]
        return out

    def reset_session(self):
        self._temporal = TemporalTracker()
        self._smoother.reset()

    def close(self):
        if self._owns_detector:
            self._detector.close()

    def __enter__(self):
        return self

    def __exit__(self, *args):
        self.close()


class XGBoostPipeline:
    """Real-time XGBoost inference pipeline using the same feature extraction as MLPPipeline."""

    # Same 10 features used during training (data_preparation.prepare_dataset.SELECTED_FEATURES)
    SELECTED = [
        'head_deviation', 's_face', 's_eye', 'h_gaze', 'pitch',
        'ear_left', 'ear_avg', 'ear_right', 'gaze_offset', 'perclos',
    ]

    def __init__(self, model_path=None, threshold=0.5):
        from xgboost import XGBClassifier

        if model_path is None:
            model_path = os.path.join(_PROJECT_ROOT, "models", "xgboost", "checkpoints", "face_orientation_best.json")
            if not os.path.isfile(model_path):
                # Fallback to legacy path
                model_path = os.path.join(_PROJECT_ROOT, "checkpoints", "xgboost_face_orientation_best.json")
        if not os.path.isfile(model_path):
            raise FileNotFoundError(f"No XGBoost checkpoint at {model_path}")

        self._model = XGBClassifier()
        self._model.load_model(model_path)
        self._threshold = threshold

        self._detector = FaceMeshDetector()
        self._head_pose = HeadPoseEstimator()
        self.head_pose = self._head_pose
        self._eye_scorer = EyeBehaviourScorer()
        self._temporal = TemporalTracker()
        self._smoother = _OutputSmoother()

        self._indices = [FEATURE_NAMES.index(n) for n in self.SELECTED]
        print(f"[XGB] Loaded {model_path} | {len(self.SELECTED)} features, threshold={threshold}")

    def process_frame(self, bgr_frame):
        landmarks = self._detector.process(bgr_frame)
        h, w = bgr_frame.shape[:2]
        out = {
            "landmarks": landmarks,
            "is_focused": False,
            "s_face": 0.0,
            "s_eye": 0.0,
            "raw_score": 0.0,
            "mar": None,
            "yaw": None,
            "pitch": None,
            "roll": None,
        }
        if landmarks is None:
            smoothed = self._smoother.update(0.0, False)
            out["raw_score"] = smoothed
            out["is_focused"] = smoothed >= self._threshold
            return out

        vec = extract_features(landmarks, w, h, self._head_pose, self._eye_scorer, self._temporal)
        vec = _clip_features(vec)

        out["yaw"] = float(vec[_FEAT_IDX["yaw"]])
        out["pitch"] = float(vec[_FEAT_IDX["pitch"]])
        out["roll"] = float(vec[_FEAT_IDX["roll"]])
        out["s_face"] = float(vec[_FEAT_IDX["s_face"]])
        out["s_eye"] = float(vec[_FEAT_IDX["s_eye"]])
        out["mar"] = float(vec[_FEAT_IDX["mar"]])

        X = vec[self._indices].reshape(1, -1).astype(np.float32)
        prob = self._model.predict_proba(X)[0]  # [prob_unfocused, prob_focused]
        out["raw_score"] = self._smoother.update(float(prob[1]), True)
        out["is_focused"] = out["raw_score"] >= self._threshold
        return out

    def reset_session(self):
        self._temporal = TemporalTracker()
        self._smoother.reset()

    def close(self):
        self._detector.close()

    def __enter__(self):
        return self

    def __exit__(self, *args):
        self.close()


def _resolve_l2cs_weights():
    for p in [
        os.path.join(_PROJECT_ROOT, "models", "L2CS-Net", "models", "L2CSNet_gaze360.pkl"),
        os.path.join(_PROJECT_ROOT, "models", "L2CSNet_gaze360.pkl"),
        os.path.join(_PROJECT_ROOT, "checkpoints", "L2CSNet_gaze360.pkl"),
    ]:
        if os.path.isfile(p):
            return p
    return None


def is_l2cs_weights_available():
    return _resolve_l2cs_weights() is not None


class L2CSPipeline:
    # Uses in-tree l2cs.Pipeline (RetinaFace + ResNet50) for gaze estimation
    # and MediaPipe for head pose, EAR, MAR, and roll de-rotation.

    YAW_THRESHOLD = 22.0
    PITCH_THRESHOLD = 20.0

    def __init__(self, weights_path=None, arch="ResNet50", device="cpu",
                 threshold=0.52, detector=None):
        resolved = weights_path or _resolve_l2cs_weights()
        if resolved is None or not os.path.isfile(resolved):
            raise FileNotFoundError(
                "L2CS weights not found. Place L2CSNet_gaze360.pkl in "
                "models/L2CS-Net/models/ or checkpoints/"
            )

        # add in-tree L2CS-Net to import path
        l2cs_root = os.path.join(_PROJECT_ROOT, "models", "L2CS-Net")
        if l2cs_root not in sys.path:
            sys.path.insert(0, l2cs_root)
        from l2cs import Pipeline as _L2CSPipeline

        import torch
        # bypass upstream select_device bug by constructing torch.device directly
        self._pipeline = _L2CSPipeline(
            weights=pathlib.Path(resolved), arch=arch, device=torch.device(device),
        )

        self._detector = detector or FaceMeshDetector()
        self._owns_detector = detector is None
        self._head_pose = HeadPoseEstimator()
        self.head_pose = self._head_pose
        self._eye_scorer = EyeBehaviourScorer()
        self._threshold = threshold
        self._smoother = _OutputSmoother()

        print(
            f"[L2CS] Loaded {resolved} | arch={arch} device={device} "
            f"yaw_thresh={self.YAW_THRESHOLD} pitch_thresh={self.PITCH_THRESHOLD} "
            f"threshold={threshold}"
        )

    @staticmethod
    def _derotate_gaze(pitch_rad, yaw_rad, roll_deg):
        # remove head roll so tilted-but-looking-at-screen reads as (0,0)
        roll_rad = -math.radians(roll_deg)
        cos_r, sin_r = math.cos(roll_rad), math.sin(roll_rad)
        return (yaw_rad * sin_r + pitch_rad * cos_r,
                yaw_rad * cos_r - pitch_rad * sin_r)

    def process_frame(self, bgr_frame):
        landmarks = self._detector.process(bgr_frame)
        h, w = bgr_frame.shape[:2]

        out = {
            "landmarks": landmarks, "is_focused": False, "raw_score": 0.0,
            "s_face": 0.0, "s_eye": 0.0, "gaze_pitch": None, "gaze_yaw": None,
            "yaw": None, "pitch": None, "roll": None, "mar": None, "is_yawning": False,
        }

        # MediaPipe: head pose, eye/mouth scores
        roll_deg = 0.0
        if landmarks is not None:
            angles = self._head_pose.estimate(landmarks, w, h)
            if angles is not None:
                out["yaw"], out["pitch"], out["roll"] = angles
                roll_deg = angles[2]
            out["s_face"] = self._head_pose.score(landmarks, w, h)
            out["s_eye"] = self._eye_scorer.score(landmarks)
            out["mar"] = compute_mar(landmarks)
            out["is_yawning"] = out["mar"] > MAR_YAWN_THRESHOLD

        # L2CS gaze (uses its own RetinaFace detector internally)
        results = self._pipeline.step(bgr_frame)

        if results is None or results.pitch.shape[0] == 0:
            smoothed = self._smoother.update(0.0, landmarks is not None)
            out["raw_score"] = smoothed
            out["is_focused"] = smoothed >= self._threshold
            return out

        pitch_rad = float(results.pitch[0])
        yaw_rad = float(results.yaw[0])

        pitch_rad, yaw_rad = self._derotate_gaze(pitch_rad, yaw_rad, roll_deg)
        out["gaze_pitch"] = pitch_rad
        out["gaze_yaw"] = yaw_rad

        yaw_deg = abs(math.degrees(yaw_rad))
        pitch_deg = abs(math.degrees(pitch_rad))

        # fall back to L2CS angles if MediaPipe didn't produce head pose
        out["yaw"] = out.get("yaw") or math.degrees(yaw_rad)
        out["pitch"] = out.get("pitch") or math.degrees(pitch_rad)

        # cosine scoring: 1.0 at centre, 0.0 at threshold
        yaw_t = min(yaw_deg / self.YAW_THRESHOLD, 1.0)
        pitch_t = min(pitch_deg / self.PITCH_THRESHOLD, 1.0)
        yaw_score = 0.5 * (1.0 + math.cos(math.pi * yaw_t))
        pitch_score = 0.5 * (1.0 + math.cos(math.pi * pitch_t))
        gaze_score = 0.55 * yaw_score + 0.45 * pitch_score

        if out["is_yawning"]:
            gaze_score = 0.0

        out["raw_score"] = self._smoother.update(float(gaze_score), True)
        out["is_focused"] = out["raw_score"] >= self._threshold
        return out

    def reset_session(self):
        self._smoother.reset()

    def close(self):
        if self._owns_detector:
            self._detector.close()

    def __enter__(self):
        return self

    def __exit__(self, *args):
        self.close()