models/collect_features.py

import argparse
import collections
import math
import os
import sys
import time

import cv2
import numpy as np

_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_gaze_ratio, compute_mar

FONT = cv2.FONT_HERSHEY_SIMPLEX
GREEN = (0, 255, 0)
RED = (0, 0, 255)
WHITE = (255, 255, 255)
YELLOW = (0, 255, 255)
ORANGE = (0, 165, 255)
GRAY = (120, 120, 120)

FEATURE_NAMES = [
    "ear_left", "ear_right", "ear_avg", "h_gaze", "v_gaze", "mar",
    "yaw", "pitch", "roll", "s_face", "s_eye", "gaze_offset", "head_deviation",
    "perclos", "blink_rate", "closure_duration", "yawn_duration",
]

NUM_FEATURES = len(FEATURE_NAMES)
assert NUM_FEATURES == 17


class TemporalTracker:
    EAR_BLINK_THRESH = 0.21
    MAR_YAWN_THRESH = 0.55
    PERCLOS_WINDOW = 60
    BLINK_WINDOW_SEC = 30.0

    def __init__(self):
        self.ear_history = collections.deque(maxlen=self.PERCLOS_WINDOW)
        self.blink_timestamps = collections.deque()
        self._eyes_closed = False
        self._closure_start = None
        self._yawn_start = None

    def update(self, ear_avg, mar, now=None):
        if now is None:
            now = time.time()

        closed = ear_avg < self.EAR_BLINK_THRESH
        self.ear_history.append(1.0 if closed else 0.0)
        perclos = sum(self.ear_history) / len(self.ear_history) if self.ear_history else 0.0

        if self._eyes_closed and not closed:
            self.blink_timestamps.append(now)
        self._eyes_closed = closed

        cutoff = now - self.BLINK_WINDOW_SEC
        while self.blink_timestamps and self.blink_timestamps[0] < cutoff:
            self.blink_timestamps.popleft()
        blink_rate = len(self.blink_timestamps) * (60.0 / self.BLINK_WINDOW_SEC)

        if closed:
            if self._closure_start is None:
                self._closure_start = now
            closure_dur = now - self._closure_start
        else:
            self._closure_start = None
            closure_dur = 0.0

        yawning = mar > self.MAR_YAWN_THRESH
        if yawning:
            if self._yawn_start is None:
                self._yawn_start = now
            yawn_dur = now - self._yawn_start
        else:
            self._yawn_start = None
            yawn_dur = 0.0

        return perclos, blink_rate, closure_dur, yawn_dur


def extract_features(landmarks, w, h, head_pose, eye_scorer, temporal,
                     *, _pre=None):
    from models.eye_scorer import _LEFT_EYE_EAR, _RIGHT_EYE_EAR, compute_ear

    p = _pre or {}

    ear_left = p.get("ear_left", compute_ear(landmarks, _LEFT_EYE_EAR))
    ear_right = p.get("ear_right", compute_ear(landmarks, _RIGHT_EYE_EAR))
    ear_avg = (ear_left + ear_right) / 2.0

    if "h_gaze" in p and "v_gaze" in p:
        h_gaze, v_gaze = p["h_gaze"], p["v_gaze"]
    else:
        h_gaze, v_gaze = compute_gaze_ratio(landmarks)

    mar = p.get("mar", compute_mar(landmarks))

    angles = p.get("angles")
    if angles is None:
        angles = head_pose.estimate(landmarks, w, h)
    yaw = angles[0] if angles else 0.0
    pitch = angles[1] if angles else 0.0
    roll = angles[2] if angles else 0.0

    s_face = p.get("s_face", head_pose.score(landmarks, w, h))
    s_eye = p.get("s_eye", eye_scorer.score(landmarks))

    gaze_offset = math.sqrt((h_gaze - 0.5) ** 2 + (v_gaze - 0.5) ** 2)
    head_deviation = math.sqrt(yaw ** 2 + pitch ** 2)  # cleaned downstream

    perclos, blink_rate, closure_dur, yawn_dur = temporal.update(ear_avg, mar)

    return np.array([
        ear_left, ear_right, ear_avg,
        h_gaze, v_gaze,
        mar,
        yaw, pitch, roll,
        s_face, s_eye,
        gaze_offset,
        head_deviation,
        perclos, blink_rate, closure_dur, yawn_dur,
    ], dtype=np.float32)


def quality_report(labels):
    n = len(labels)
    n1 = int((labels == 1).sum())
    n0 = n - n1
    transitions = int(np.sum(np.diff(labels) != 0))
    duration_sec = n / 30.0  # approximate at 30fps

    warnings = []

    print(f"\n{'='*50}")
    print(f"  DATA QUALITY REPORT")
    print(f"{'='*50}")
    print(f"  Total samples : {n}")
    print(f"  Focused       : {n1} ({n1/max(n,1)*100:.1f}%)")
    print(f"  Unfocused     : {n0} ({n0/max(n,1)*100:.1f}%)")
    print(f"  Duration      : {duration_sec:.0f}s ({duration_sec/60:.1f} min)")
    print(f"  Transitions   : {transitions}")
    if transitions > 0:
        print(f"  Avg segment   : {n/transitions:.0f} frames ({n/transitions/30:.1f}s)")

    # checks
    if duration_sec < 120:
        warnings.append(f"TOO SHORT: {duration_sec:.0f}s — aim for 5-10 minutes (300-600s)")

    if n < 3000:
        warnings.append(f"LOW SAMPLE COUNT: {n} frames — aim for 9000+ (5 min at 30fps)")

    balance = n1 / max(n, 1)
    if balance < 0.3 or balance > 0.7:
        warnings.append(f"IMBALANCED: {balance:.0%} focused — aim for 35-65% focused")

    if transitions < 10:
        warnings.append(f"TOO FEW TRANSITIONS: {transitions} — switch every 10-30s, aim for 20+")

    if transitions == 1:
        warnings.append("SINGLE BLOCK: you recorded one unfocused + one focused block — "
                         "model will learn temporal position, not focus patterns")

    if warnings:
        print(f"\n  ⚠️  WARNINGS ({len(warnings)}):")
        for w in warnings:
            print(f"    • {w}")
        print(f"\n  Consider re-recording this session.")
    else:
        print(f"\n  ✅ All checks passed!")

    print(f"{'='*50}\n")
    return len(warnings) == 0


# ---------------------------------------------------------------------------
# Main
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--name", type=str, default="session",
                        help="Your name or session ID")
    parser.add_argument("--camera", type=int, default=0,
                        help="Camera index")
    parser.add_argument("--duration", type=int, default=600,
                        help="Max recording time (seconds, default 10 min)")
    parser.add_argument("--output-dir", type=str,
                        default=os.path.join(_PROJECT_ROOT, "data", "collected_data"),
                        help="Where to save .npz files")
    args = parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    detector = FaceMeshDetector()
    head_pose = HeadPoseEstimator()
    eye_scorer = EyeBehaviourScorer()
    temporal = TemporalTracker()

    cap = cv2.VideoCapture(args.camera)
    if not cap.isOpened():
        print("[COLLECT] ERROR: can't open camera")
        return

    print("[COLLECT] Data Collection Tool")
    print(f"[COLLECT] Session: {args.name}, max {args.duration}s")
    print(f"[COLLECT] Features per frame: {NUM_FEATURES}")
    print("[COLLECT] Controls:")
    print("  1 = FOCUSED       (looking at screen normally)")
    print("  0 = NOT FOCUSED   (phone, away, eyes closed, yawning)")
    print("  p = pause")
    print("  q = save & quit")
    print()
    print("[COLLECT] TIPS for good data:")
    print("  • Switch between 1 and 0 every 10-30 seconds")
    print("  • Aim for 20+ transitions total")
    print("  • Act out varied scenarios: reading, phone, talking, drowsy")
    print("  • Record at least 5 minutes")
    print()

    features_list = []
    labels_list = []
    label = None        # None = paused
    transitions = 0     # count label switches
    prev_label = None
    status = "PAUSED -- press 1 (focused) or 0 (not focused)"
    t_start = time.time()
    prev_time = time.time()
    fps = 0.0

    try:
        while True:
            elapsed = time.time() - t_start
            if elapsed > args.duration:
                print(f"[COLLECT] Time limit ({args.duration}s)")
                break

            ret, frame = cap.read()
            if not ret:
                break

            h, w = frame.shape[:2]
            landmarks = detector.process(frame)
            face_ok = landmarks is not None

            if face_ok and label is not None:
                vec = extract_features(landmarks, w, h, head_pose, eye_scorer, temporal)
                features_list.append(vec)
                labels_list.append(label)

                if prev_label is not None and label != prev_label:
                    transitions += 1
                prev_label = label

            now = time.time()
            fps = 0.9 * fps + 0.1 * (1.0 / max(now - prev_time, 1e-6))
            prev_time = now

            # --- draw UI ---
            n = len(labels_list)
            n1 = sum(1 for x in labels_list if x == 1)
            n0 = n - n1
            remaining = max(0, args.duration - elapsed)

            bar_color = GREEN if label == 1 else (RED if label == 0 else (80, 80, 80))
            cv2.rectangle(frame, (0, 0), (w, 70), (0, 0, 0), -1)
            cv2.putText(frame, status, (10, 22), FONT, 0.55, bar_color, 2, cv2.LINE_AA)
            cv2.putText(frame, f"Samples: {n}  (F:{n1}  U:{n0})  Switches: {transitions}",
                        (10, 48), FONT, 0.42, WHITE, 1, cv2.LINE_AA)
            cv2.putText(frame, f"FPS:{fps:.0f}", (w - 80, 22), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
            cv2.putText(frame, f"{int(remaining)}s left", (w - 80, 48), FONT, 0.42, YELLOW, 1, cv2.LINE_AA)

            if n > 0:
                bar_w = min(w - 20, 300)
                bar_x = w - bar_w - 10
                bar_y = 58
                frac = n1 / n
                cv2.rectangle(frame, (bar_x, bar_y), (bar_x + bar_w, bar_y + 8), (40, 40, 40), -1)
                cv2.rectangle(frame, (bar_x, bar_y), (bar_x + int(bar_w * frac), bar_y + 8), GREEN, -1)
                cv2.putText(frame, f"{frac:.0%}F", (bar_x + bar_w + 4, bar_y + 8),
                            FONT, 0.3, GRAY, 1, cv2.LINE_AA)

            if not face_ok:
                cv2.putText(frame, "NO FACE", (w // 2 - 60, h // 2), FONT, 0.7, RED, 2, cv2.LINE_AA)

            # red dot = recording
            if label is not None and face_ok:
                cv2.circle(frame, (w - 20, 80), 8, RED, -1)

            # live warnings
            warn_y = h - 35
            if n > 100 and transitions < 3:
                cv2.putText(frame, "! Switch more often (aim for 20+ transitions)",
                            (10, warn_y), FONT, 0.38, ORANGE, 1, cv2.LINE_AA)
                warn_y -= 18
            if elapsed > 30 and n > 0:
                bal = n1 / n
                if bal < 0.25 or bal > 0.75:
                    cv2.putText(frame, f"! Imbalanced ({bal:.0%} focused) - record more of the other",
                                (10, warn_y), FONT, 0.38, ORANGE, 1, cv2.LINE_AA)
                    warn_y -= 18

            cv2.putText(frame, "1:focused  0:unfocused  p:pause  q:save+quit",
                        (10, h - 10), FONT, 0.38, GRAY, 1, cv2.LINE_AA)

            cv2.imshow("FocusGuard -- Data Collection", frame)

            key = cv2.waitKey(1) & 0xFF
            if key == ord("1"):
                label = 1
                status = "Recording: FOCUSED"
                print(f"[COLLECT] -> FOCUSED (n={n}, transitions={transitions})")
            elif key == ord("0"):
                label = 0
                status = "Recording: NOT FOCUSED"
                print(f"[COLLECT] -> NOT FOCUSED (n={n}, transitions={transitions})")
            elif key == ord("p"):
                label = None
                status = "PAUSED"
                print(f"[COLLECT] paused (n={n})")
            elif key == ord("q"):
                break

    finally:
        cap.release()
        cv2.destroyAllWindows()
        detector.close()

        if len(features_list) > 0:
            feats = np.stack(features_list)
            labs = np.array(labels_list, dtype=np.int64)

            ts = time.strftime("%Y%m%d_%H%M%S")
            fname = f"{args.name}_{ts}.npz"
            fpath = os.path.join(args.output_dir, fname)
            np.savez(fpath,
                     features=feats,
                     labels=labs,
                     feature_names=np.array(FEATURE_NAMES))

            print(f"\n[COLLECT] Saved {len(labs)} samples -> {fpath}")
            print(f"  Shape: {feats.shape}  ({NUM_FEATURES} features)")

            quality_report(labs)
        else:
            print("\n[COLLECT] No data collected")

        print("[COLLECT] Done")


if __name__ == "__main__":
    main()