import collections import glob import json import math import os import sys import numpy as np import joblib import torch import torch.nn as nn _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 data_preparation.prepare_dataset import SELECTED_FEATURES 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.collect_features import FEATURE_NAMES, TemporalTracker, extract_features # Same 10 features used for MLP training (prepare_dataset) and inference MLP_FEATURE_NAMES = SELECTED_FEATURES["face_orientation"] _FEAT_IDX = {name: i for i, name in enumerate(FEATURE_NAMES)} def _clip_features(vec): 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: def __init__(self, alpha: float = 0.3, grace_frames: int = 15): self._alpha = alpha 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: float, face_detected: bool) -> float: if face_detected: self._no_face = 0 self._score += self._alpha * (raw_score - self._score) else: self._no_face += 1 if self._no_face > self._grace: self._score *= 0.85 return self._score DEFAULT_HYBRID_CONFIG = { "use_xgb": False, "w_mlp": 0.3, "w_xgb": 0.0, "w_geo": 0.7, "threshold": 0.35, "use_yawn_veto": True, "geo_face_weight": 0.7, "geo_eye_weight": 0.3, "mar_yawn_threshold": float(MAR_YAWN_THRESHOLD), "combiner": None, "combiner_path": None, } class _RuntimeFeatureEngine: _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 = 0.7, beta: float = 0.3, threshold: float = 0.55, 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._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) out["s_eye"] = self.eye_scorer.score(landmarks) 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 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() # PyTorch MLP matching models/mlp/train.py BaseModel (10 -> 64 -> 32 -> 2) class _FocusMLP(nn.Module): def __init__(self, num_features: int, num_classes: int = 2): super().__init__() self.network = nn.Sequential( nn.Linear(num_features, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, num_classes), ) def forward(self, x): return self.network(x) def _mlp_artifacts_available(model_dir: str) -> bool: pt_path = os.path.join(model_dir, "mlp_best.pt") scaler_path = os.path.join(model_dir, "scaler_mlp.joblib") return os.path.isfile(pt_path) and os.path.isfile(scaler_path) def _load_mlp_artifacts(model_dir: str): """Load PyTorch MLP + scaler from checkpoints. Returns (model, scaler, feature_names).""" pt_path = os.path.join(model_dir, "mlp_best.pt") scaler_path = os.path.join(model_dir, "scaler_mlp.joblib") if not os.path.isfile(pt_path): raise FileNotFoundError(f"No MLP checkpoint at {pt_path}") if not os.path.isfile(scaler_path): raise FileNotFoundError(f"No scaler at {scaler_path}") num_features = len(MLP_FEATURE_NAMES) num_classes = 2 model = _FocusMLP(num_features, num_classes) model.load_state_dict(torch.load(pt_path, map_location="cpu", weights_only=True)) model.eval() scaler = joblib.load(scaler_path) return model, scaler, list(MLP_FEATURE_NAMES) 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["use_xgb"] = bool(cfg.get("use_xgb", False)) cfg["w_mlp"] = float(cfg.get("w_mlp", 0.3)) cfg["w_xgb"] = float(cfg.get("w_xgb", 0.0)) cfg["w_geo"] = float(cfg["w_geo"]) if cfg["use_xgb"]: weight_sum = cfg["w_xgb"] + cfg["w_geo"] if weight_sum <= 0: raise ValueError("[HYBRID] Invalid config: w_xgb + w_geo must be > 0") cfg["w_xgb"] /= weight_sum cfg["w_geo"] /= weight_sum else: 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"]) cfg["combiner"] = cfg.get("combiner") or None cfg["combiner_path"] = cfg.get("combiner_path") or None print(f"[HYBRID] Loaded config: {resolved}") return cfg, resolved class MLPPipeline: def __init__(self, model_dir=None, detector=None, threshold=0.23): if model_dir is None: model_dir = os.path.join(_PROJECT_ROOT, "checkpoints") self._mlp, self._scaler, self._feature_names = _load_mlp_artifacts(model_dir) self._indices = [FEATURE_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 PyTorch MLP from {model_dir} | {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"]]) X = vec[self._indices].reshape(1, -1).astype(np.float32) X_sc = self._scaler.transform(X) if self._scaler is not None else X with torch.no_grad(): x_t = torch.from_numpy(X_sc).float() logits = self._mlp(x_t) probs = torch.softmax(logits, dim=1) mlp_prob = float(probs[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() def _resolve_xgb_path(): return os.path.join(_PROJECT_ROOT, "checkpoints", "xgboost_face_orientation_best.json") class HybridFocusPipeline: def __init__( self, model_dir=None, config_path: str | None = None, max_angle: float = 22.0, detector=None, ): if model_dir is None: model_dir = os.path.join(_PROJECT_ROOT, "checkpoints") self._cfg, self._cfg_path = _load_hybrid_config(model_dir=model_dir, config_path=config_path) self._use_xgb = self._cfg["use_xgb"] 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.head_pose = self._head_pose self._smoother = _OutputSmoother() self._combiner = None combiner_path = self._cfg.get("combiner_path") if combiner_path and self._cfg.get("combiner") == "logistic": resolved_combiner = combiner_path if os.path.isabs(combiner_path) else os.path.join(model_dir, combiner_path) if not os.path.isfile(resolved_combiner): resolved_combiner = os.path.join(_PROJECT_ROOT, combiner_path) if os.path.isfile(resolved_combiner): blob = joblib.load(resolved_combiner) self._combiner = blob.get("combiner") if self._combiner is None: self._combiner = blob print(f"[HYBRID] LR combiner loaded from {resolved_combiner}") else: print(f"[HYBRID] combiner_path not found: {resolved_combiner}, using heuristic weights") if self._use_xgb: from xgboost import XGBClassifier xgb_path = _resolve_xgb_path() if not os.path.isfile(xgb_path): raise FileNotFoundError(f"No XGBoost checkpoint at {xgb_path}") self._xgb_model = XGBClassifier() self._xgb_model.load_model(xgb_path) self._xgb_indices = [FEATURE_NAMES.index(n) for n in XGBoostPipeline.SELECTED] self._mlp = None self._scaler = None self._indices = None self._feature_names = list(XGBoostPipeline.SELECTED) mode = "LR combiner" if self._combiner else f"w_xgb={self._cfg['w_xgb']:.2f}, w_geo={self._cfg['w_geo']:.2f}" print(f"[HYBRID] XGBoost+geo | {xgb_path} | {mode}, threshold={self._cfg['threshold']:.2f}") else: self._mlp, self._scaler, self._feature_names = _load_mlp_artifacts(model_dir) self._indices = [FEATURE_NAMES.index(n) for n in self._feature_names] self._xgb_model = None self._xgb_indices = None mode = "LR combiner" if self._combiner else f"w_mlp={self._cfg['w_mlp']:.2f}, w_geo={self._cfg['w_geo']:.2f}" print(f"[HYBRID] MLP+geo | {len(self._feature_names)} features | {mode}, threshold={self._cfg['threshold']:.2f}") @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) out["s_eye"] = self._eye_scorer.score(landmarks) s_eye_geo = out["s_eye"] 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) if self._use_xgb: X = vec[self._xgb_indices].reshape(1, -1).astype(np.float32) prob = self._xgb_model.predict_proba(X)[0] model_prob = float(np.clip(prob[1], 0.0, 1.0)) out["mlp_prob"] = model_prob if self._combiner is not None: meta = np.array([[model_prob, out["geo_score"]]], dtype=np.float32) focus_score = float(self._combiner.predict_proba(meta)[0, 1]) else: focus_score = self._cfg["w_xgb"] * model_prob + self._cfg["w_geo"] * out["geo_score"] else: X = vec[self._indices].reshape(1, -1).astype(np.float32) X_sc = self._scaler.transform(X) if self._scaler is not None else X with torch.no_grad(): x_t = torch.from_numpy(X_sc).float() logits = self._mlp(x_t) probs = torch.softmax(logits, dim=1) mlp_prob = float(probs[0, 1]) out["mlp_prob"] = float(np.clip(mlp_prob, 0.0, 1.0)) if self._combiner is not None: meta = np.array([[out["mlp_prob"], out["geo_score"]]], dtype=np.float32) focus_score = float(self._combiner.predict_proba(meta)[0, 1]) else: 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: 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=0.38): from xgboost import XGBClassifier if model_path is None: 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()