Merge pull request #6 from k23172173/main

Update Dev to newest version, correct wrong branch mistakes

.gitignore

@@ -1,4 +1,3 @@
-# Python
 __pycache__/
 *.py[cod]
 *$py.class
@@ -12,25 +11,10 @@ env/
 .eggs/
 dist/
 build/
-
-# IDE
 .idea/
 .vscode/
 *.swp
 *.swo
-
-# Data and outputs (optional: uncomment if you don’t want to track large files)
-# data_preparation/raw/
-# data_preparation/processed/*.npy
-# evaluation/logs/
-# evaluation/results/
-
-# Model checkpoints (uncomment to ignore .pt files)
-# *.pt
-
-# Project
 docs/
-
-# OS
 .DS_Store
 Thumbs.db

models/face_orientation_model/best_model.pt → MLP/models/meta_20260224_024200.npz

@@ -1,3 +1,3 @@
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MLP/models/mlp_20260224_024200.joblib

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MLP/models/scaler_20260224_024200.joblib

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README.md

@@ -1,10 +1,10 @@
-# GAP — FocusGuard
+# FocusGuard
 
-Real-time focus estimation from webcam (head pose + eye behaviour). 
+Webcam-based focus detection: face mesh, head pose, eye (geometry or YOLO), plus an MLP trained on collected features.
 
-## Layout
+- **data_preparation/** — collect data, notebooks, processed/collected files
+- **models/** — face mesh, head pose, eye scorer, YOLO classifier, MLP training, attention feature collection
+- **evaluation/** — metrics and run logs
+- **ui/** — live demo (geometry+YOLO or MLP-only)
 
-- **data_preparation/** — Dataset team (raw data, processed, scripts)
-- **models/** — Face orientation, eye behaviour, fusion, landmarks. Training entry: `models/train.py`
-- **evaluation/** — Metrics, runs, results
-- **ui/** — Live demo + session view
+Run from here: `pip install -r requirements.txt` then `python ui/live_demo.py` or `python ui/live_demo.py --mlp`.

data_preparation/CNN/eye_crops/val/open/.gitkeep

@@ -0,0 +1 @@
+

data_preparation/README.md

@@ -1,3 +1,41 @@
-# data_preparation
+# Data Preparation
 
-Dataset team owns layout and scripts here.
+## Folder Structure
+
+### collected/
+Contains raw session files in `.npz` format.  
+Generated using:
+
+python -m models.attention.collect_features
+
+Each session includes:
+- 17-dimensional feature vectors
+- Corresponding labels
+
+---
+
+
+### MLP/
+Contains notebooks for:
+- Exploring collected data
+- Training the sklearn MLP model (10 features)
+
+Trained models are saved to:
+../MLP/models/
+
+---
+
+### CNN/
+Eye crop directory structure for CNN training (YOLO).
+
+---
+
+## Collecting Data
+
+**Step-by-step**
+
+1. From repo root Install deps: `pip install -r requirements.txt`.
+3. Run: `python -m models.attention.collect_features --name yourname`.
+4. Webcam opens. Look at the camera; press **1** when focused, **0** when unfocused. Switch every 10–30 sec so you get both labels.
+5. Press **p** to pause/resume.
+6. Press **q** when done. One `.npz` is saved to `data_preparation/collected/` (17 features + labels).

data_preparation/collected_Abdelrahman/abdelrahman_20260306_023035.npz

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data_preparation/collected_Ayten/ayten_session_1.npz

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data_preparation/collected_Jarek/Jarek_20260225_012931.npz

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data_preparation/collected_Junhao/Junhao_20260303_113554.npz

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data_preparation/collected_Kexin/kexin2_20260305_180229.npz

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data_preparation/collected_Kexin/kexin_20260224_151043.npz

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data_preparation/collected_Langyuan/Langyuan_20260303_153145.npz

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data_preparation/collected_Mohamed/session_20260224_010131.npz

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data_preparation/collected_Saba/saba_20260306_230710.npz

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data_preparation/collected_Yingtao/Yingtao_20260306_023937.npz

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evaluation/README.md

@@ -1,3 +1,3 @@
 # evaluation
 
-Metrics, experiment configs, and results live here.
+Place metrics scripts, run configs, and results here. Logs dir is used by `models.mlp.train` for training logs.

models/README.md

@@ -1,8 +1,10 @@
 # models
 
-- `face_orientation_model/` — S_face
-- `eye_behaviour_model/` — S_eye
-- `attention_score_fusion/` — fusion + smoothing
-- `face_landmarks_pretrained/` — MediaPipe FaceMesh (no training)
+- **cnn/eye_attention/** — YOLO open/closed eye classifier, crop helper, train stub
+- **mlp/** — PyTorch MLP on feature vectors (face_orientation / eye_behaviour); checkpoints under `mlp/face_orientation_model/`, `mlp/eye_behaviour_model/`
+- **geometric/face_orientation/** — head pose (solvePnP). **geometric/eye_behaviour/** — EAR, gaze, MAR
+- **pretrained/face_mesh/** — MediaPipe face landmarks (no training)
+- **attention/** — webcam feature collection (17-d), stubs for train/classifier/fusion
+- **prepare_dataset.py** — loads from `data_preparation/processed/` or synthetic; used by `mlp/train.py`
 
-`train.py` trains the MLP on feature vectors; `prepare_dataset.py` loads from `data_preparation/processed/` or synthetic.
+Run legacy MLP training: `python -m models.mlp.train`. The sklearn MLP used in the live demo is trained in `data_preparation/MLP/train_mlp.ipynb` and saved under `../MLP/models/`.

models/attention/__init__.py

@@ -0,0 +1 @@
+

models/attention/collect_features.py

@@ -0,0 +1,349 @@
+# Usage: python -m models.attention.collect_features [--name alice] [--duration 600]
+
+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.dirname(os.path.abspath(__file__))))
+if _PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, _PROJECT_ROOT)
+
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+from models.geometric.face_orientation.head_pose import HeadPoseEstimator
+from models.geometric.eye_behaviour.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 = 0.04
+    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):
+    from models.geometric.eye_behaviour.eye_scorer import _LEFT_EYE_EAR, _RIGHT_EYE_EAR, compute_ear
+
+    ear_left = compute_ear(landmarks, _LEFT_EYE_EAR)
+    ear_right = compute_ear(landmarks, _RIGHT_EYE_EAR)
+    ear_avg = (ear_left + ear_right) / 2.0
+    h_gaze, v_gaze = compute_gaze_ratio(landmarks)
+    mar = compute_mar(landmarks)
+
+    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 = head_pose.score(landmarks, w, h)
+    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)
+
+    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_preparation", "collected"),
+                        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
+
+            # record if labeling + face visible
+            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)
+
+                # count transitions
+                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()

models/cnn/CNN_MODEL/.claude/settings.local.json

@@ -0,0 +1,7 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(# Check Dataset_subset counts echo \"\"=== Dataset_subset/train/open ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/train/open/ | wc -l && echo \"\"=== Dataset_subset/train/closed ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/train/closed/ | wc -l && echo \"\"=== Dataset_subset/val/open ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/val/open/ | wc -l && echo \"\"=== Dataset_subset/val/closed ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/val/closed/)"
+    ]
+  }
+}

models/cnn/CNN_MODEL/.gitattributes

@@ -0,0 +1 @@
+DATA/** filter=lfs diff=lfs merge=lfs -text

models/cnn/CNN_MODEL/.gitignore

@@ -0,0 +1,4 @@
+Dataset/train/
+Dataset/val/
+Dataset/test/
+.DS_Store

models/cnn/CNN_MODEL/README.md

@@ -0,0 +1,74 @@
+# Eye Open / Closed Classifier (YOLOv11-CLS)
+
+
+Binary classifier: **open** vs **closed** eyes.  
+Used as a baseline for eye-tracking, drowsiness, or focus detection.
+
+---
+
+## Model team task
+
+- **Train** the YOLOv11s-cls eye classifier in a **separate notebook** (data split, epochs, GPU, export `best.pt`).
+- Provide **trained weights** (`best.pt`) for this repo’s evaluation and inference scripts.
+
+
+
+---
+
+## Repo contents
+
+- **notebooks/eye_classifier_colab.ipynb** — Data download (Kaggle), clean, split, undersample, **evaluate** (needs `best.pt` from model team), export.
+- **scripts/predict_image.py** — Run classifier on single images (needs `best.pt`).
+- **scripts/webcam_live.py** — Live webcam open/closed (needs `best.pt` + optional `weights/face_landmarker.task`).
+- **scripts/video_infer.py** — Run on video files.
+- **scripts/focus_infer.py** — Focus/attention inference.
+- **weights/** — Put `best.pt` here; `face_landmarker.task` is downloaded on first webcam run if missing.
+- **docs/** — Extra docs (e.g. UNNECESSARY_FILES.md if present).
+
+---
+
+## Dataset
+
+- **Source:** [Kaggle — open/closed eyes](https://www.kaggle.com/datasets/sehriyarmemmedli/open-closed-eyes-dataset)
+- The Colab notebook downloads it via `kagglehub`; no local copy in repo.
+
+---
+
+## Weights
+
+- Put **best.pt** from the model team in **weights/best.pt** (or `runs/classify/runs_cls/eye_open_closed_cpu/weights/best.pt`).
+- For webcam: **face_landmarker.task** is downloaded into **weights/** on first run if missing.
+
+---
+
+## Local setup
+
+```bash
+pip install ultralytics opencv-python mediapipe "numpy<2"
+```
+
+Optional: use a venv. From repo root:
+- `python scripts/predict_image.py <image.png>`
+- `python scripts/webcam_live.py`
+- `python scripts/video_infer.py` (expects 1.mp4 / 2.mp4 in repo root or set `VIDEOS` env)
+- `python scripts/focus_infer.py`
+
+---
+
+## Project structure
+
+```
+├── notebooks/
+│   └── eye_classifier_colab.ipynb   # Data + eval (no training)
+├── scripts/
+│   ├── predict_image.py
+│   ├── webcam_live.py
+│   ├── video_infer.py
+│   └── focus_infer.py
+├── weights/                     # best.pt, face_landmarker.task
+├── docs/                        # extra docs
+├── README.md
+└── venv/                        # optional
+```
+
+Training and weight generation: **model team, separate notebook.**

models/cnn/CNN_MODEL/scripts/focus_infer.py

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+from __future__ import annotations
+
+from pathlib import Path
+import os
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+
+def list_images(folder: Path):
+    exts = {".png", ".jpg", ".jpeg", ".bmp", ".webp"}
+    return sorted([p for p in folder.iterdir() if p.suffix.lower() in exts])
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def detect_eyelid_boundary(gray: np.ndarray) -> np.ndarray | None:
+    """
+    Returns an ellipse fit to the largest contour near the eye boundary.
+    Output format: (center(x,y), (axis1, axis2), angle) or None.
+    """
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = cv2.Canny(blur, 40, 120)
+    edges = cv2.dilate(edges, np.ones((3, 3), np.uint8), iterations=1)
+    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)
+    for c in contours:
+        if len(c) >= 5 and cv2.contourArea(c) > 50:
+            return cv2.fitEllipse(c)
+    return None
+
+
+def detect_pupil_center(gray: np.ndarray) -> tuple[int, int] | None:
+    """
+    More robust pupil detection:
+    - enhance contrast (CLAHE)
+    - find dark blobs
+    - score by circularity and proximity to center
+    """
+    h, w = gray.shape
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    eq = clahe.apply(gray)
+    blur = cv2.GaussianBlur(eq, (7, 7), 0)
+
+    # Focus on the central region to avoid eyelashes/edges
+    cx, cy = w // 2, h // 2
+    rx, ry = int(w * 0.3), int(h * 0.3)
+    x0, x1 = max(cx - rx, 0), min(cx + rx, w)
+    y0, y1 = max(cy - ry, 0), min(cy + ry, h)
+    roi = blur[y0:y1, x0:x1]
+
+    # Inverted threshold to capture dark pupil
+    _, thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8), iterations=2)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8), iterations=1)
+
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+
+    best = None
+    best_score = -1.0
+    for c in contours:
+        area = cv2.contourArea(c)
+        if area < 15:
+            continue
+        perimeter = cv2.arcLength(c, True)
+        if perimeter <= 0:
+            continue
+        circularity = 4 * np.pi * (area / (perimeter * perimeter))
+        if circularity < 0.3:
+            continue
+        m = cv2.moments(c)
+        if m["m00"] == 0:
+            continue
+        px = int(m["m10"] / m["m00"]) + x0
+        py = int(m["m01"] / m["m00"]) + y0
+
+        # Score by circularity and distance to center
+        dist = np.hypot(px - cx, py - cy) / max(w, h)
+        score = circularity - dist
+        if score > best_score:
+            best_score = score
+            best = (px, py)
+
+    return best
+
+
+def is_focused(pupil_center: tuple[int, int], img_shape: tuple[int, int]) -> bool:
+    """
+    Decide focus based on pupil offset from image center.
+    """
+    h, w = img_shape
+    cx, cy = w // 2, h // 2
+    px, py = pupil_center
+    dx = abs(px - cx) / max(w, 1)
+    dy = abs(py - cy) / max(h, 1)
+    return (dx < 0.12) and (dy < 0.12)
+
+
+def annotate(img_bgr: np.ndarray, ellipse, pupil_center, focused: bool, cls_label: str, conf: float):
+    out = img_bgr.copy()
+    if ellipse is not None:
+        cv2.ellipse(out, ellipse, (0, 255, 255), 2)
+    if pupil_center is not None:
+        cv2.circle(out, pupil_center, 4, (0, 0, 255), -1)
+    label = f"{cls_label} ({conf:.2f}) | focused={int(focused)}"
+    cv2.putText(out, label, (8, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+    return out
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    data_dir = project_root / "Dataset"
+    alt_data_dir = project_root / "DATA"
+    out_dir = project_root / "runs_focus"
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    weights = find_weights(project_root)
+    if weights is None:
+        print("Weights not found. Train first.")
+        return
+
+    # Support both Dataset/test/{open,closed} and Dataset/{open,closed}
+    def resolve_test_dirs(root: Path):
+        test_open = root / "test" / "open"
+        test_closed = root / "test" / "closed"
+        if test_open.exists() and test_closed.exists():
+            return test_open, test_closed
+        test_open = root / "open"
+        test_closed = root / "closed"
+        if test_open.exists() and test_closed.exists():
+            return test_open, test_closed
+        alt_closed = root / "close"
+        if test_open.exists() and alt_closed.exists():
+            return test_open, alt_closed
+        return None, None
+
+    test_open, test_closed = resolve_test_dirs(data_dir)
+    if (test_open is None or test_closed is None) and alt_data_dir.exists():
+        test_open, test_closed = resolve_test_dirs(alt_data_dir)
+
+    if not test_open.exists() or not test_closed.exists():
+        print("Test folders missing. Expected:")
+        print(test_open)
+        print(test_closed)
+        return
+
+    test_files = list_images(test_open) + list_images(test_closed)
+    print("Total test images:", len(test_files))
+    max_images = int(os.getenv("MAX_IMAGES", "0"))
+    if max_images > 0:
+        test_files = test_files[:max_images]
+        print("Limiting to MAX_IMAGES:", max_images)
+
+    model = YOLO(str(weights))
+    results = model.predict(test_files, imgsz=224, device="cpu", verbose=False)
+
+    names = model.names
+    for r in results:
+        probs = r.probs
+        top_idx = int(probs.top1)
+        top_conf = float(probs.top1conf)
+        pred_label = names[top_idx]
+
+        img = cv2.imread(r.path)
+        if img is None:
+            continue
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        ellipse = detect_eyelid_boundary(gray)
+        pupil_center = detect_pupil_center(gray)
+        focused = False
+        if pred_label.lower() == "open" and pupil_center is not None:
+            focused = is_focused(pupil_center, gray.shape)
+
+        annotated = annotate(img, ellipse, pupil_center, focused, pred_label, top_conf)
+        out_path = out_dir / (Path(r.path).stem + "_annotated.jpg")
+        cv2.imwrite(str(out_path), annotated)
+
+        print(f"{Path(r.path).name}: pred={pred_label} conf={top_conf:.3f} focused={focused}")
+
+    print(f"\nAnnotated outputs saved to: {out_dir}")
+
+
+if __name__ == "__main__":
+    main()

models/cnn/CNN_MODEL/scripts/predict_image.py

@@ -0,0 +1,49 @@
+"""Run the eye open/closed model on one or more images."""
+import sys
+from pathlib import Path
+
+from ultralytics import YOLO
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weight_candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    weights = next((p for p in weight_candidates if p.is_file()), None)
+    if weights is None:
+        print("Weights not found. Put best.pt in weights/ or runs/.../weights/ (from model team).")
+        sys.exit(1)
+
+    if len(sys.argv) < 2:
+        print("Usage: python scripts/predict_image.py <image1> [image2 ...]")
+        print("Example: python scripts/predict_image.py path/to/image.png")
+        sys.exit(0)
+
+    model = YOLO(str(weights))
+    names = model.names
+
+    for path in sys.argv[1:]:
+        p = Path(path)
+        if not p.is_file():
+            print(p, "- file not found")
+            continue
+        try:
+            results = model.predict(str(p), imgsz=224, device="cpu", verbose=False)
+        except Exception as e:
+            print(p, "- error:", e)
+            continue
+        if not results:
+            print(p, "- no result")
+            continue
+        r = results[0]
+        top_idx = int(r.probs.top1)
+        conf = float(r.probs.top1conf)
+        label = names[top_idx]
+        print(f"{p.name}: {label} ({conf:.2%})")
+
+
+if __name__ == "__main__":
+    main()

models/cnn/CNN_MODEL/scripts/video_infer.py

@@ -0,0 +1,281 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+try:
+    import mediapipe as mp
+except Exception:  # pragma: no cover
+    mp = None
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def detect_pupil_center(gray: np.ndarray) -> tuple[int, int] | None:
+    h, w = gray.shape
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    eq = clahe.apply(gray)
+    blur = cv2.GaussianBlur(eq, (7, 7), 0)
+
+    cx, cy = w // 2, h // 2
+    rx, ry = int(w * 0.3), int(h * 0.3)
+    x0, x1 = max(cx - rx, 0), min(cx + rx, w)
+    y0, y1 = max(cy - ry, 0), min(cy + ry, h)
+    roi = blur[y0:y1, x0:x1]
+
+    _, thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8), iterations=2)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8), iterations=1)
+
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+
+    best = None
+    best_score = -1.0
+    for c in contours:
+        area = cv2.contourArea(c)
+        if area < 15:
+            continue
+        perimeter = cv2.arcLength(c, True)
+        if perimeter <= 0:
+            continue
+        circularity = 4 * np.pi * (area / (perimeter * perimeter))
+        if circularity < 0.3:
+            continue
+        m = cv2.moments(c)
+        if m["m00"] == 0:
+            continue
+        px = int(m["m10"] / m["m00"]) + x0
+        py = int(m["m01"] / m["m00"]) + y0
+
+        dist = np.hypot(px - cx, py - cy) / max(w, h)
+        score = circularity - dist
+        if score > best_score:
+            best_score = score
+            best = (px, py)
+
+    return best
+
+
+def is_focused(pupil_center: tuple[int, int], img_shape: tuple[int, int]) -> bool:
+    h, w = img_shape
+    cx = w // 2
+    px, _ = pupil_center
+    dx = abs(px - cx) / max(w, 1)
+    return dx < 0.12
+
+
+def classify_frame(model: YOLO, frame: np.ndarray) -> tuple[str, float]:
+    # Use classifier directly on frame (assumes frame is eye crop)
+    results = model.predict(frame, imgsz=224, device="cpu", verbose=False)
+    r = results[0]
+    probs = r.probs
+    top_idx = int(probs.top1)
+    top_conf = float(probs.top1conf)
+    pred_label = model.names[top_idx]
+    return pred_label, top_conf
+
+
+def annotate_frame(frame: np.ndarray, label: str, focused: bool, conf: float, time_sec: float):
+    out = frame.copy()
+    text = f"{label} | focused={int(focused)} | conf={conf:.2f} | t={time_sec:.2f}s"
+    cv2.putText(out, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+    return out
+
+
+def write_segments(path: Path, segments: list[tuple[float, float, str]]):
+    with path.open("w") as f:
+        for start, end, label in segments:
+            f.write(f"{start:.2f},{end:.2f},{label}\n")
+
+
+def process_video(video_path: Path, model: YOLO | None):
+    cap = cv2.VideoCapture(str(video_path))
+    if not cap.isOpened():
+        print(f"Failed to open {video_path}")
+        return
+
+    fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    out_path = video_path.with_name(video_path.stem + "_pred.mp4")
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    writer = cv2.VideoWriter(str(out_path), fourcc, fps, (width, height))
+
+    csv_path = video_path.with_name(video_path.stem + "_predictions.csv")
+    seg_path = video_path.with_name(video_path.stem + "_segments.txt")
+
+    frame_idx = 0
+    last_label = None
+    seg_start = 0.0
+    segments: list[tuple[float, float, str]] = []
+
+    with csv_path.open("w") as fcsv:
+        fcsv.write("time_sec,label,focused,conf\n")
+        if mp is None:
+            print("mediapipe is not installed. Falling back to classifier-only mode.")
+        use_mp = mp is not None
+        if use_mp:
+            mp_face_mesh = mp.solutions.face_mesh
+            face_mesh = mp_face_mesh.FaceMesh(
+                static_image_mode=False,
+                max_num_faces=1,
+                refine_landmarks=True,
+                min_detection_confidence=0.5,
+                min_tracking_confidence=0.5,
+            )
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            time_sec = frame_idx / fps
+            conf = 0.0
+            pred_label = "open"
+            focused = False
+
+            if use_mp:
+                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                res = face_mesh.process(rgb)
+                if res.multi_face_landmarks:
+                    lm = res.multi_face_landmarks[0].landmark
+                    h, w = frame.shape[:2]
+
+                    # Eye landmarks (MediaPipe FaceMesh)
+                    left_eye = [33, 160, 158, 133, 153, 144]
+                    right_eye = [362, 385, 387, 263, 373, 380]
+                    left_iris = [468, 469, 470, 471]
+                    right_iris = [473, 474, 475, 476]
+
+                    def pts(idxs):
+                        return np.array([(int(lm[i].x * w), int(lm[i].y * h)) for i in idxs])
+
+                    def ear(eye_pts):
+                        # EAR using 6 points
+                        p1, p2, p3, p4, p5, p6 = eye_pts
+                        v1 = np.linalg.norm(p2 - p6)
+                        v2 = np.linalg.norm(p3 - p5)
+                        h1 = np.linalg.norm(p1 - p4)
+                        return (v1 + v2) / (2.0 * h1 + 1e-6)
+
+                    le = pts(left_eye)
+                    re = pts(right_eye)
+                    le_ear = ear(le)
+                    re_ear = ear(re)
+                    ear_avg = (le_ear + re_ear) / 2.0
+
+                    # openness threshold
+                    pred_label = "open" if ear_avg > 0.22 else "closed"
+
+                    # iris centers
+                    li = pts(left_iris)
+                    ri = pts(right_iris)
+                    li_c = li.mean(axis=0).astype(int)
+                    ri_c = ri.mean(axis=0).astype(int)
+
+                    # eye centers (midpoint of corners)
+                    le_c = ((le[0] + le[3]) / 2).astype(int)
+                    re_c = ((re[0] + re[3]) / 2).astype(int)
+
+                    # focus = iris close to eye center horizontally for both eyes
+                    le_dx = abs(li_c[0] - le_c[0]) / max(np.linalg.norm(le[0] - le[3]), 1)
+                    re_dx = abs(ri_c[0] - re_c[0]) / max(np.linalg.norm(re[0] - re[3]), 1)
+                    focused = (pred_label == "open") and (le_dx < 0.18) and (re_dx < 0.18)
+
+                    # draw eye boundaries
+                    cv2.polylines(frame, [le], True, (0, 255, 255), 1)
+                    cv2.polylines(frame, [re], True, (0, 255, 255), 1)
+                    # draw iris centers
+                    cv2.circle(frame, tuple(li_c), 3, (0, 0, 255), -1)
+                    cv2.circle(frame, tuple(ri_c), 3, (0, 0, 255), -1)
+                else:
+                    pred_label = "closed"
+                    focused = False
+            else:
+                if model is not None:
+                    pred_label, conf = classify_frame(model, frame)
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                pupil_center = detect_pupil_center(gray) if pred_label.lower() == "open" else None
+                focused = False
+                if pred_label.lower() == "open" and pupil_center is not None:
+                    focused = is_focused(pupil_center, gray.shape)
+
+            if pred_label.lower() != "open":
+                focused = False
+
+            label = "open_focused" if (pred_label.lower() == "open" and focused) else "open_not_focused"
+            if pred_label.lower() != "open":
+                label = "closed_not_focused"
+
+            fcsv.write(f"{time_sec:.2f},{label},{int(focused)},{conf:.4f}\n")
+
+            if last_label is None:
+                last_label = label
+                seg_start = time_sec
+            elif label != last_label:
+                segments.append((seg_start, time_sec, last_label))
+                seg_start = time_sec
+                last_label = label
+
+            annotated = annotate_frame(frame, label, focused, conf, time_sec)
+            writer.write(annotated)
+            frame_idx += 1
+
+    if last_label is not None:
+        end_time = frame_idx / fps
+        segments.append((seg_start, end_time, last_label))
+    write_segments(seg_path, segments)
+
+    cap.release()
+    writer.release()
+    print(f"Saved: {out_path}")
+    print(f"CSV: {csv_path}")
+    print(f"Segments: {seg_path}")
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weights = find_weights(project_root)
+    model = YOLO(str(weights)) if weights is not None else None
+
+    # Default to 1.mp4 and 2.mp4 in project root
+    videos = []
+    for name in ["1.mp4", "2.mp4"]:
+        p = project_root / name
+        if p.exists():
+            videos.append(p)
+
+    # Also allow passing paths via env var
+    extra = os.getenv("VIDEOS", "")
+    for v in [x.strip() for x in extra.split(",") if x.strip()]:
+        vp = Path(v)
+        if not vp.is_absolute():
+            vp = project_root / vp
+        if vp.exists():
+            videos.append(vp)
+
+    if not videos:
+        print("No videos found. Expected 1.mp4 / 2.mp4 in project root.")
+        return
+
+    for v in videos:
+        process_video(v, model)
+
+
+if __name__ == "__main__":
+    main()

models/cnn/CNN_MODEL/scripts/webcam_live.py

@@ -0,0 +1,184 @@
+"""
+Live webcam: detect face, crop each eye, run open/closed classifier, show on screen.
+Requires: opencv-python, ultralytics, mediapipe (pip install mediapipe).
+Press 'q' to quit.
+"""
+import urllib.request
+from pathlib import Path
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+try:
+    import mediapipe as mp
+    _mp_has_solutions = hasattr(mp, "solutions")
+except ImportError:
+    mp = None
+    _mp_has_solutions = False
+
+# New MediaPipe Tasks API (Face Landmarker) eye indices
+LEFT_EYE_INDICES_NEW = [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398]
+RIGHT_EYE_INDICES_NEW = [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173]
+# Old Face Mesh (solutions) indices
+LEFT_EYE_INDICES_OLD = [33, 160, 158, 133, 153, 144]
+RIGHT_EYE_INDICES_OLD = [362, 385, 387, 263, 373, 380]
+EYE_PADDING = 0.35
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def get_eye_roi(frame: np.ndarray, landmarks, indices: list[int]) -> np.ndarray | None:
+    h, w = frame.shape[:2]
+    pts = np.array([(int(landmarks[i].x * w), int(landmarks[i].y * h)) for i in indices])
+    x_min, y_min = pts.min(axis=0)
+    x_max, y_max = pts.max(axis=0)
+    dx = max(int((x_max - x_min) * EYE_PADDING), 8)
+    dy = max(int((y_max - y_min) * EYE_PADDING), 8)
+    x0 = max(0, x_min - dx)
+    y0 = max(0, y_min - dy)
+    x1 = min(w, x_max + dx)
+    y1 = min(h, y_max + dy)
+    if x1 <= x0 or y1 <= y0:
+        return None
+    return frame[y0:y1, x0:x1].copy()
+
+
+def _run_with_solutions(mp, model, cap):
+    face_mesh = mp.solutions.face_mesh.FaceMesh(
+        static_image_mode=False,
+        max_num_faces=1,
+        refine_landmarks=True,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,
+    )
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = face_mesh.process(rgb)
+        left_label, left_conf = "—", 0.0
+        right_label, right_conf = "—", 0.0
+        if results.multi_face_landmarks:
+            lm = results.multi_face_landmarks[0].landmark
+            for roi, indices, side in [
+                (get_eye_roi(frame, lm, LEFT_EYE_INDICES_OLD), LEFT_EYE_INDICES_OLD, "left"),
+                (get_eye_roi(frame, lm, RIGHT_EYE_INDICES_OLD), RIGHT_EYE_INDICES_OLD, "right"),
+            ]:
+                if roi is not None and roi.size > 0:
+                    try:
+                        pred = model.predict(roi, imgsz=224, device="cpu", verbose=False)
+                        if pred:
+                            r = pred[0]
+                            label = model.names[int(r.probs.top1)]
+                            conf = float(r.probs.top1conf)
+                            if side == "left":
+                                left_label, left_conf = label, conf
+                            else:
+                                right_label, right_conf = label, conf
+                    except Exception:
+                        pass
+        cv2.putText(frame, f"L: {left_label} ({left_conf:.0%})", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.putText(frame, f"R: {right_label} ({right_conf:.0%})", (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.imshow("Eye open/closed (q to quit)", frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+
+def _run_with_tasks(project_root: Path, model, cap):
+    from mediapipe.tasks.python import BaseOptions
+    from mediapipe.tasks.python.vision import FaceLandmarker, FaceLandmarkerOptions
+    from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode
+    from mediapipe.tasks.python.vision.core import image as image_lib
+
+    model_path = project_root / "weights" / "face_landmarker.task"
+    if not model_path.is_file():
+        print("Downloading face_landmarker.task ...")
+        url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task"
+        urllib.request.urlretrieve(url, model_path)
+        print("Done.")
+
+    options = FaceLandmarkerOptions(
+        base_options=BaseOptions(model_asset_path=str(model_path)),
+        running_mode=running_mode.VisionTaskRunningMode.IMAGE,
+        num_faces=1,
+    )
+    face_landmarker = FaceLandmarker.create_from_options(options)
+    ImageFormat = image_lib.ImageFormat
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        left_label, left_conf = "—", 0.0
+        right_label, right_conf = "—", 0.0
+
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        rgb_contiguous = np.ascontiguousarray(rgb)
+        mp_image = image_lib.Image(ImageFormat.SRGB, rgb_contiguous)
+        result = face_landmarker.detect(mp_image)
+
+        if result.face_landmarks:
+            lm = result.face_landmarks[0]
+            for roi, side in [
+                (get_eye_roi(frame, lm, LEFT_EYE_INDICES_NEW), "left"),
+                (get_eye_roi(frame, lm, RIGHT_EYE_INDICES_NEW), "right"),
+            ]:
+                if roi is not None and roi.size > 0:
+                    try:
+                        pred = model.predict(roi, imgsz=224, device="cpu", verbose=False)
+                        if pred:
+                            r = pred[0]
+                            label = model.names[int(r.probs.top1)]
+                            conf = float(r.probs.top1conf)
+                            if side == "left":
+                                left_label, left_conf = label, conf
+                            else:
+                                right_label, right_conf = label, conf
+                    except Exception:
+                        pass
+
+        cv2.putText(frame, f"L: {left_label} ({left_conf:.0%})", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.putText(frame, f"R: {right_label} ({right_conf:.0%})", (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.imshow("Eye open/closed (q to quit)", frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weights = find_weights(project_root)
+    if weights is None:
+        print("Weights not found. Put best.pt in weights/ or runs/.../weights/ (from model team).")
+        return
+    if mp is None:
+        print("MediaPipe required. Install: pip install mediapipe")
+        return
+
+    model = YOLO(str(weights))
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print("Could not open webcam.")
+        return
+
+    print("Live eye open/closed on your face. Press 'q' to quit.")
+    try:
+        if _mp_has_solutions:
+            _run_with_solutions(mp, model, cap)
+        else:
+            _run_with_tasks(project_root, model, cap)
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()

models/cnn/CNN_MODEL/weights/yolo11s-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2b605d1c8c212b434a75a32759a6f7adf1d2b29c35f76bdccd4c794cb653cf2
+size 13630112

models/cnn/eye_attention/__init__.py

@@ -0,0 +1 @@
+

models/cnn/eye_attention/classifier.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+
+class EyeClassifier(ABC):
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        pass
+
+    @abstractmethod
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        pass
+
+
+class GeometricOnlyClassifier(EyeClassifier):
+    @property
+    def name(self) -> str:
+        return "geometric"
+
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        return 1.0
+
+
+class YOLOv11Classifier(EyeClassifier):
+    def __init__(self, checkpoint_path: str, device: str = "cpu"):
+        from ultralytics import YOLO
+
+        self._model = YOLO(checkpoint_path)
+        self._device = device
+
+        names = self._model.names
+        self._attentive_idx = None
+        for idx, cls_name in names.items():
+            if cls_name in ("open", "attentive"):
+                self._attentive_idx = idx
+                break
+        if self._attentive_idx is None:
+            self._attentive_idx = max(names.keys())
+        print(f"[YOLO] Classes: {names}, attentive_idx={self._attentive_idx}")
+
+    @property
+    def name(self) -> str:
+        return "yolo"
+
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        if not crops_bgr:
+            return 1.0
+        results = self._model.predict(crops_bgr, device=self._device, verbose=False)
+        scores = [float(r.probs.data[self._attentive_idx]) for r in results]
+        return sum(scores) / len(scores) if scores else 1.0
+
+
+def load_eye_classifier(
+    path: str | None = None,
+    backend: str = "yolo",
+    device: str = "cpu",
+) -> EyeClassifier:
+    if path is None or backend == "geometric":
+        return GeometricOnlyClassifier()
+
+    try:
+        return YOLOv11Classifier(path, device=device)
+    except ImportError:
+        print("[CLASSIFIER] ultralytics required for YOLO. pip install ultralytics")
+        raise

models/cnn/eye_attention/crop.py

@@ -0,0 +1,70 @@
+import cv2
+import numpy as np
+
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+
+LEFT_EYE_CONTOUR = FaceMeshDetector.LEFT_EYE_INDICES
+RIGHT_EYE_CONTOUR = FaceMeshDetector.RIGHT_EYE_INDICES
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+CROP_SIZE = 96
+
+
+def _bbox_from_landmarks(
+    landmarks: np.ndarray,
+    indices: list[int],
+    frame_w: int,
+    frame_h: int,
+    expand: float = 0.4,
+) -> tuple[int, int, int, int]:
+    pts = landmarks[indices, :2]
+    px = pts[:, 0] * frame_w
+    py = pts[:, 1] * frame_h
+
+    x_min, x_max = px.min(), px.max()
+    y_min, y_max = py.min(), py.max()
+    w = x_max - x_min
+    h = y_max - y_min
+    cx = (x_min + x_max) / 2
+    cy = (y_min + y_max) / 2
+
+    size = max(w, h) * (1 + expand)
+    half = size / 2
+
+    x1 = int(max(cx - half, 0))
+    y1 = int(max(cy - half, 0))
+    x2 = int(min(cx + half, frame_w))
+    y2 = int(min(cy + half, frame_h))
+
+    return x1, y1, x2, y2
+
+
+def extract_eye_crops(
+    frame: np.ndarray,
+    landmarks: np.ndarray,
+    expand: float = 0.4,
+    crop_size: int = CROP_SIZE,
+) -> tuple[np.ndarray, np.ndarray, tuple, tuple]:
+    h, w = frame.shape[:2]
+
+    left_bbox = _bbox_from_landmarks(landmarks, LEFT_EYE_CONTOUR, w, h, expand)
+    right_bbox = _bbox_from_landmarks(landmarks, RIGHT_EYE_CONTOUR, w, h, expand)
+
+    left_crop = frame[left_bbox[1] : left_bbox[3], left_bbox[0] : left_bbox[2]]
+    right_crop = frame[right_bbox[1] : right_bbox[3], right_bbox[0] : right_bbox[2]]
+
+    left_crop = cv2.resize(left_crop, (crop_size, crop_size), interpolation=cv2.INTER_AREA)
+    right_crop = cv2.resize(right_crop, (crop_size, crop_size), interpolation=cv2.INTER_AREA)
+
+    return left_crop, right_crop, left_bbox, right_bbox
+
+
+def crop_to_tensor(crop_bgr: np.ndarray):
+    import torch
+
+    rgb = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+    for c in range(3):
+        rgb[:, :, c] = (rgb[:, :, c] - IMAGENET_MEAN[c]) / IMAGENET_STD[c]
+    return torch.from_numpy(rgb.transpose(2, 0, 1))

models/geometric/eye_behaviour/eye_scorer.py

@@ -0,0 +1,164 @@
+import math
+
+import numpy as np
+
+_LEFT_EYE_EAR = [33, 160, 158, 133, 153, 145]
+_RIGHT_EYE_EAR = [362, 385, 387, 263, 373, 380]
+
+_LEFT_IRIS_CENTER = 468
+_RIGHT_IRIS_CENTER = 473
+
+_LEFT_EYE_INNER = 133
+_LEFT_EYE_OUTER = 33
+_RIGHT_EYE_INNER = 362
+_RIGHT_EYE_OUTER = 263
+
+_LEFT_EYE_TOP = 159
+_LEFT_EYE_BOTTOM = 145
+_RIGHT_EYE_TOP = 386
+_RIGHT_EYE_BOTTOM = 374
+
+_MOUTH_TOP = 13
+_MOUTH_BOTTOM = 14
+_MOUTH_LEFT = 78
+_MOUTH_RIGHT = 308
+_MOUTH_UPPER_1 = 82
+_MOUTH_UPPER_2 = 312
+_MOUTH_LOWER_1 = 87
+_MOUTH_LOWER_2 = 317
+
+MAR_YAWN_THRESHOLD = 0.55
+
+
+def _distance(p1: np.ndarray, p2: np.ndarray) -> float:
+    return float(np.linalg.norm(p1 - p2))
+
+
+def compute_ear(landmarks: np.ndarray, eye_indices: list[int]) -> float:
+    p1 = landmarks[eye_indices[0], :2]
+    p2 = landmarks[eye_indices[1], :2]
+    p3 = landmarks[eye_indices[2], :2]
+    p4 = landmarks[eye_indices[3], :2]
+    p5 = landmarks[eye_indices[4], :2]
+    p6 = landmarks[eye_indices[5], :2]
+
+    vertical1 = _distance(p2, p6)
+    vertical2 = _distance(p3, p5)
+    horizontal = _distance(p1, p4)
+
+    if horizontal < 1e-6:
+        return 0.0
+
+    return (vertical1 + vertical2) / (2.0 * horizontal)
+
+
+def compute_avg_ear(landmarks: np.ndarray) -> float:
+    left_ear = compute_ear(landmarks, _LEFT_EYE_EAR)
+    right_ear = compute_ear(landmarks, _RIGHT_EYE_EAR)
+    return (left_ear + right_ear) / 2.0
+
+
+def compute_gaze_ratio(landmarks: np.ndarray) -> tuple[float, float]:
+    left_iris = landmarks[_LEFT_IRIS_CENTER, :2]
+    left_inner = landmarks[_LEFT_EYE_INNER, :2]
+    left_outer = landmarks[_LEFT_EYE_OUTER, :2]
+    left_top = landmarks[_LEFT_EYE_TOP, :2]
+    left_bottom = landmarks[_LEFT_EYE_BOTTOM, :2]
+
+    right_iris = landmarks[_RIGHT_IRIS_CENTER, :2]
+    right_inner = landmarks[_RIGHT_EYE_INNER, :2]
+    right_outer = landmarks[_RIGHT_EYE_OUTER, :2]
+    right_top = landmarks[_RIGHT_EYE_TOP, :2]
+    right_bottom = landmarks[_RIGHT_EYE_BOTTOM, :2]
+
+    left_h_total = _distance(left_inner, left_outer)
+    right_h_total = _distance(right_inner, right_outer)
+
+    if left_h_total < 1e-6 or right_h_total < 1e-6:
+        return 0.5, 0.5
+
+    left_h_ratio = _distance(left_outer, left_iris) / left_h_total
+    right_h_ratio = _distance(right_outer, right_iris) / right_h_total
+    h_ratio = (left_h_ratio + right_h_ratio) / 2.0
+
+    left_v_total = _distance(left_top, left_bottom)
+    right_v_total = _distance(right_top, right_bottom)
+
+    if left_v_total < 1e-6 or right_v_total < 1e-6:
+        return h_ratio, 0.5
+
+    left_v_ratio = _distance(left_top, left_iris) / left_v_total
+    right_v_ratio = _distance(right_top, right_iris) / right_v_total
+    v_ratio = (left_v_ratio + right_v_ratio) / 2.0
+
+    return float(np.clip(h_ratio, 0, 1)), float(np.clip(v_ratio, 0, 1))
+
+
+def compute_mar(landmarks: np.ndarray) -> float:
+    # Mouth aspect ratio: high = mouth open (yawning / sleepy)
+    top = landmarks[_MOUTH_TOP, :2]
+    bottom = landmarks[_MOUTH_BOTTOM, :2]
+    left = landmarks[_MOUTH_LEFT, :2]
+    right = landmarks[_MOUTH_RIGHT, :2]
+    upper1 = landmarks[_MOUTH_UPPER_1, :2]
+    lower1 = landmarks[_MOUTH_LOWER_1, :2]
+    upper2 = landmarks[_MOUTH_UPPER_2, :2]
+    lower2 = landmarks[_MOUTH_LOWER_2, :2]
+
+    horizontal = _distance(left, right)
+    if horizontal < 1e-6:
+        return 0.0
+    v1 = _distance(upper1, lower1)
+    v2 = _distance(top, bottom)
+    v3 = _distance(upper2, lower2)
+    return (v1 + v2 + v3) / (2.0 * horizontal)
+
+
+class EyeBehaviourScorer:
+    def __init__(
+        self,
+        ear_open: float = 0.30,
+        ear_closed: float = 0.16,
+        gaze_max_offset: float = 0.28,
+    ):
+        self.ear_open = ear_open
+        self.ear_closed = ear_closed
+        self.gaze_max_offset = gaze_max_offset
+
+    def _ear_score(self, ear: float) -> float:
+        if ear >= self.ear_open:
+            return 1.0
+        if ear <= self.ear_closed:
+            return 0.0
+        return (ear - self.ear_closed) / (self.ear_open - self.ear_closed)
+
+    def _gaze_score(self, h_ratio: float, v_ratio: float) -> float:
+        h_offset = abs(h_ratio - 0.5)
+        v_offset = abs(v_ratio - 0.5)
+        offset = math.sqrt(h_offset**2 + v_offset**2)
+        t = min(offset / self.gaze_max_offset, 1.0)
+        return 0.5 * (1.0 + math.cos(math.pi * t))
+
+    def score(self, landmarks: np.ndarray) -> float:
+        ear = compute_avg_ear(landmarks)
+        ear_s = self._ear_score(ear)
+        if ear_s < 0.3:
+            return ear_s
+        h_ratio, v_ratio = compute_gaze_ratio(landmarks)
+        gaze_s = self._gaze_score(h_ratio, v_ratio)
+        return ear_s * gaze_s
+
+    def detailed_score(self, landmarks: np.ndarray) -> dict:
+        ear = compute_avg_ear(landmarks)
+        ear_s = self._ear_score(ear)
+        h_ratio, v_ratio = compute_gaze_ratio(landmarks)
+        gaze_s = self._gaze_score(h_ratio, v_ratio)
+        s_eye = ear_s if ear_s < 0.3 else ear_s * gaze_s
+        return {
+            "ear": round(ear, 4),
+            "ear_score": round(ear_s, 4),
+            "h_gaze": round(h_ratio, 4),
+            "v_gaze": round(v_ratio, 4),
+            "gaze_score": round(gaze_s, 4),
+            "s_eye": round(s_eye, 4),
+        }

models/geometric/face_orientation/__init__.py

@@ -0,0 +1 @@
+

models/geometric/face_orientation/head_pose.py

@@ -0,0 +1,112 @@
+import math
+
+import cv2
+import numpy as np
+
+_LANDMARK_INDICES = [1, 152, 33, 263, 61, 291]
+
+_MODEL_POINTS = np.array(
+    [
+        [0.0, 0.0, 0.0],
+        [0.0, -330.0, -65.0],
+        [-225.0, 170.0, -135.0],
+        [225.0, 170.0, -135.0],
+        [-150.0, -150.0, -125.0],
+        [150.0, -150.0, -125.0],
+    ],
+    dtype=np.float64,
+)
+
+
+class HeadPoseEstimator:
+    def __init__(self, max_angle: float = 30.0, roll_weight: float = 0.5):
+        self.max_angle = max_angle
+        self.roll_weight = roll_weight
+        self._camera_matrix = None
+        self._frame_size = None
+        self._dist_coeffs = np.zeros((4, 1), dtype=np.float64)
+
+    def _get_camera_matrix(self, frame_w: int, frame_h: int) -> np.ndarray:
+        if self._camera_matrix is not None and self._frame_size == (frame_w, frame_h):
+            return self._camera_matrix
+        focal_length = float(frame_w)
+        cx, cy = frame_w / 2.0, frame_h / 2.0
+        self._camera_matrix = np.array(
+            [[focal_length, 0, cx], [0, focal_length, cy], [0, 0, 1]],
+            dtype=np.float64,
+        )
+        self._frame_size = (frame_w, frame_h)
+        return self._camera_matrix
+
+    def _solve(self, landmarks: np.ndarray, frame_w: int, frame_h: int):
+        image_points = np.array(
+            [
+                [landmarks[i, 0] * frame_w, landmarks[i, 1] * frame_h]
+                for i in _LANDMARK_INDICES
+            ],
+            dtype=np.float64,
+        )
+        camera_matrix = self._get_camera_matrix(frame_w, frame_h)
+        success, rvec, tvec = cv2.solvePnP(
+            _MODEL_POINTS,
+            image_points,
+            camera_matrix,
+            self._dist_coeffs,
+            flags=cv2.SOLVEPNP_ITERATIVE,
+        )
+        return success, rvec, tvec, image_points
+
+    def estimate(
+        self, landmarks: np.ndarray, frame_w: int, frame_h: int
+    ) -> tuple[float, float, float] | None:
+        success, rvec, tvec, _ = self._solve(landmarks, frame_w, frame_h)
+        if not success:
+            return None
+
+        rmat, _ = cv2.Rodrigues(rvec)
+        nose_dir = rmat @ np.array([0.0, 0.0, 1.0])
+        face_up = rmat @ np.array([0.0, 1.0, 0.0])
+
+        yaw = math.degrees(math.atan2(nose_dir[0], -nose_dir[2]))
+        pitch = math.degrees(math.asin(np.clip(-nose_dir[1], -1.0, 1.0)))
+        roll = math.degrees(math.atan2(face_up[0], -face_up[1]))
+
+        return (yaw, pitch, roll)
+
+    def score(self, landmarks: np.ndarray, frame_w: int, frame_h: int) -> float:
+        angles = self.estimate(landmarks, frame_w, frame_h)
+        if angles is None:
+            return 0.0
+
+        yaw, pitch, roll = angles
+        deviation = math.sqrt(yaw**2 + pitch**2 + (self.roll_weight * roll) ** 2)
+        t = min(deviation / self.max_angle, 1.0)
+        return 0.5 * (1.0 + math.cos(math.pi * t))
+
+    def draw_axes(
+        self,
+        frame: np.ndarray,
+        landmarks: np.ndarray,
+        axis_length: float = 50.0,
+    ) -> np.ndarray:
+        h, w = frame.shape[:2]
+        success, rvec, tvec, image_points = self._solve(landmarks, w, h)
+        if not success:
+            return frame
+
+        camera_matrix = self._get_camera_matrix(w, h)
+        nose = tuple(image_points[0].astype(int))
+
+        axes_3d = np.float64(
+            [[axis_length, 0, 0], [0, axis_length, 0], [0, 0, axis_length]]
+        )
+        projected, _ = cv2.projectPoints(
+            axes_3d, rvec, tvec, camera_matrix, self._dist_coeffs
+        )
+
+        colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
+        for i, color in enumerate(colors):
+            pt = tuple(projected[i].ravel().astype(int))
+            cv2.line(frame, nose, pt, color, 2)
+
+        return frame

models/{train.py → mlp/train.py}

@@ -1,18 +1,18 @@
-# Run from repo root: python -m models.train (or cd models && python train.py)
-
 import json
-import os
+import os, sys
 import random
 
-import numpy as np as np
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-from prepare_dataset import get_dataloaders
+from clearml import Task
+
+from models.prepare_dataset import get_dataloaders
 
 CFG = {
-    "model_name": "face_orientation",  # "face_orientation" or "eye_behaviour"
+    "model_name": "face_orientation",
     "epochs": 30,
     "batch_size": 32,
     "lr": 1e-3,
@@ -22,10 +22,25 @@ CFG = {
         "face_orientation": os.path.join(os.path.dirname(__file__), "face_orientation_model"),
         "eye_behaviour": os.path.join(os.path.dirname(__file__), "eye_behaviour_model"),
     },
-    "logs_dir": os.path.join(os.path.dirname(__file__), "..", "evaluation", "logs"),
+    "logs_dir": os.path.join(os.path.dirname(__file__), "..", "..", "evaluation", "logs"),
 }
 
 
+# ==== ClearML Initialisation =============================================
+task = Task.init(
+    project_name="Focus Guard",
+    task_name=f"MLP Model Training",
+    tags=["training", "mlp_model"]
+)
+
+prefix = 'checkpoints/'+task.name+'_'+task.id+'/'
+os.makedirs(prefix, exist_ok=True)
+
+task.connect(CFG)   
+
+
+
+# ==== Model =============================================
 def set_seed(seed: int):
     random.seed(seed)
     np.random.seed(seed)
@@ -154,6 +169,16 @@ def main():
         history["val_loss"].append(round(val_loss, 4))
         history["val_acc"].append(round(val_acc, 4))
 
+
+        # Log scalars to ClearML
+        current_lr = optimizer.param_groups[0]['lr']
+        task.logger.report_scalar("Loss", "Train", float(train_loss), iteration=epoch)
+        task.logger.report_scalar("Accuracy", "Train", float(train_acc), iteration=epoch)    
+        task.logger.report_scalar("Loss", "Val", float(val_loss), iteration=epoch)
+        task.logger.report_scalar("Accuracy", "Val", float(val_acc), iteration=epoch)
+        task.logger.report_scalar("Learning Rate", "LR", float(current_lr), iteration=epoch)
+        task.logger.flush()
+
         marker = ""
         if val_acc > best_val_acc:
             best_val_acc = val_acc