src/detection/strategies/geometric.py

# drive_paddy/detection/strategies/geometric.py
import cv2
import mediapipe as mp
import numpy as np
import math
from src.detection.base_processor import BaseProcessor

# --- Helper Functions (Unchanged) ---
def calculate_ear(eye_landmarks, frame_shape):
    coords = np.array([(lm.x * frame_shape[1], lm.y * frame_shape[0]) for lm in eye_landmarks])
    v1 = np.linalg.norm(coords[1] - coords[5])
    v2 = np.linalg.norm(coords[2] - coords[4])
    h1 = np.linalg.norm(coords[0] - coords[3])
    return (v1 + v2) / (2.0 * h1) if h1 > 0 else 0.0

def calculate_mar(mouth_landmarks, frame_shape):
    coords = np.array([(lm.x * frame_shape[1], lm.y * frame_shape[0]) for lm in mouth_landmarks])
    v1 = np.linalg.norm(coords[1] - coords[7])
    v2 = np.linalg.norm(coords[2] - coords[6])
    v3 = np.linalg.norm(coords[3] - coords[5])
    h1 = np.linalg.norm(coords[0] - coords[4])
    return (v1 + v2 + v3) / (2.0 * h1) if h1 > 0 else 0.0

class GeometricProcessor(BaseProcessor):
    L_EYE = [362, 385, 387, 263, 373, 380]
    R_EYE = [33, 160, 158, 133, 153, 144]
    MOUTH = [61, 291, 39, 181, 0, 17, 84, 178]

    def __init__(self, config):
        self.settings = config['geometric_settings']
        self.face_mesh = mp.solutions.face_mesh.FaceMesh(
            max_num_faces=1,
            refine_landmarks=False,
            min_detection_confidence=0.5,
            min_tracking_confidence=0.5)

        self.downscale_factor = self.settings.get('downscale_factor', 0.35) 
        self.default_skip = max(1, self.settings.get("skip_frames", 2))

        
        # State variables for frame skipping
        self.frame_counter = 0
        self.last_indicators = {"drowsiness_level": "Initializing...", "lighting": "Good", "details": {}}
        self.last_landmarks = None

        # Landmark and counter definitions
        self.counters = { "eye_closure": 0, "yawning": 0, "head_nod": 0, "looking_away": 0 }

        # Pre-allocated buffers
        self.zeros_4x1 = np.zeros((4, 1), np.float32)
        self.landmark_ids = self.L_EYE + self.R_EYE + self.MOUTH

        
    def process_frame(self, frame):
        self.frame_counter += 1
        
        # adaptive skipping: fewer analyses when driver is awake
        last_level = self.last_indicators.get("drowsiness_level", "Awake")
        skip_n = 1 if last_level != "Awake" else self.default_skip

        if self.frame_counter % skip_n != 0:
            drawn = self.draw_visuals(frame.copy(), self.last_indicators, self.last_landmarks)
            return drawn, self.last_indicators, self.last_landmarks

        # --- FRAME PROCESSING ---
        original_frame = frame.copy()
        h_orig, w_orig, _ = original_frame.shape

        # --- OPTIMIZATION 1: DOWNSCALING ---
        # Create a small frame for fast processing
        small_frame = cv2.resize(original_frame, (0, 0), fx=self.downscale_factor, fy=self.downscale_factor, interpolation=cv2.INTER_AREA)
        h, w, _ = small_frame.shape
        
        # --- FIX 1: PROCESS THE SMALL FRAME, NOT THE ORIGINAL ---
        # All processing should be done on the `small_frame` to gain the speed advantage.
        gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
        brightness = np.mean(gray)
        is_low_light = brightness < self.settings['low_light_thresh']

        drowsiness_indicators = {"drowsiness_level": "Awake", "lighting": "Good", "details": {}}
        face_landmarks = None

        if not is_low_light:
            # Convert the SMALL frame to RGB for MediaPipe
            img_rgb = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
            results = self.face_mesh.process(img_rgb)
            face_landmarks = results.multi_face_landmarks

            if face_landmarks:
                landmarks = face_landmarks[0].landmark
                score = 0
                weights = self.settings['indicator_weights']

                # --- Drowsiness Calculations (these correctly use the small frame's 'h' and 'w') ---
                ear = (calculate_ear([landmarks[i] for i in self.L_EYE],(h,w)) + calculate_ear([landmarks[i] for i in self.R_EYE],(h,w)))/2.0
                if ear < self.settings['eye_ar_thresh']: self.counters['eye_closure']+=1
                else: self.counters['eye_closure']=0
                if self.counters['eye_closure'] >= self.settings['eye_ar_consec_frames']: score += weights['eye_closure']
                
                mar = calculate_mar([landmarks[i] for i in self.MOUTH], (h, w))
                if mar > self.settings['yawn_mar_thresh']: self.counters['yawning']+=1
                else: self.counters['yawning']=0
                if self.counters['yawning'] >= self.settings['yawn_consec_frames']: score += weights['yawning']

                # --- Head Pose Estimation (also uses small frame dimensions 'h' and 'w') ---
                face_3d = 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.float32)
                face_2d = np.array([(landmarks[1].x*w,landmarks[1].y*h),(landmarks[152].x*w,landmarks[152].y*h),(landmarks[263].x*w,landmarks[263].y*h),(landmarks[33].x*w,landmarks[33].y*h),(landmarks[287].x*w,landmarks[287].y*h),(landmarks[57].x*w,landmarks[57].y*h)],dtype=np.float32)
                cam_matrix = np.array([[w,0,w/2],[0,w,h/2],[0,0,1]],dtype=np.float32)
                _, rvec, _ = cv2.solvePnP(face_3d, face_2d, cam_matrix, self.zeros_4x1, flags=cv2.SOLVEPNP_EPNP)
                rmat, _ = cv2.Rodrigues(rvec)
                angles, _, _, _, _, _ = cv2.RQDecomp3x3(rmat)
                pitch, yaw = angles[0], angles[1]

                if pitch > self.settings['head_nod_thresh']: 
                    self.counters['head_nod']+=1
                else: 
                    self.counters['head_nod']=0
                if self.counters['head_nod'] >= self.settings['head_pose_consec_frames']: 
                    score += weights['head_nod']

                if abs(yaw) > self.settings['head_look_away_thresh']: 
                    self.counters['looking_away']+=1
                else: 
                    self.counters['looking_away']=0
                if self.counters['looking_away'] >= self.settings['head_pose_consec_frames']: 
                    score += weights['looking_away']
                
                levels = self.settings['drowsiness_levels']
                if score >= levels['very_drowsy_threshold']: 
                    drowsiness_indicators['drowsiness_level'] = "Very Drowsy"
                elif score >= levels['slightly_drowsy_threshold']: drowsiness_indicators['drowsiness_level'] = "Slightly Drowsy"
                
                drowsiness_indicators['details']['Score'] = score

        else:
            drowsiness_indicators["lighting"] = "Low"
        
        # --- Update state for skipped frames ---
        self.last_indicators = drowsiness_indicators
        self.last_landmarks = face_landmarks
        
        # --- Draw visuals on the ORIGINAL frame for a high-quality output ---
        processed_frame = self.draw_visuals(original_frame, drowsiness_indicators, face_landmarks)

        return processed_frame, drowsiness_indicators, face_landmarks

    def draw_visuals(self, frame, indicators, landmarks_data=None):
        """Helper function to draw all visualizations on the frame."""
        # --- FIX 2: USE THE DIMENSIONS OF THE FRAME BEING DRAWN ON ---
        # We get the height and width from the input 'frame' itself, which is the original, full-sized one.
        h, w, _ = frame.shape
        level = indicators['drowsiness_level']
        score_val = indicators.get("details", {}).get("Score", 0)
        color = (0, 255, 0) # Green for Awake

        if indicators['lighting'] == "Low":
            color = (0, 165, 255) # Orange
            cv2.putText(frame, "LOW LIGHT", (w // 2 - 120, h // 2), cv2.FONT_HERSHEY_SIMPLEX, 2, color, 3, cv2.LINE_AA)
        elif level == "Slightly Drowsy": color = (0, 255, 255) # Yellow
        elif level == "Very Drowsy": color = (0, 0, 255) # Red

        # Draw landmarks if they were detected
        if landmarks_data:
            landmarks = landmarks_data[0].landmark
            eye_mouth_landmarks = self.L_EYE + self.R_EYE + self.MOUTH
            for idx in eye_mouth_landmarks:
                lm = landmarks[idx]
                # Scale landmark coordinates to the full-sized frame using its 'w' and 'h'.
                x, y = int(lm.x * w), int(lm.y * h)
                cv2.circle(frame, (x, y), 2, (0, 255, 0), -1)

        cv2.rectangle(frame, (0, 0), (w, h), color, 10)
        status_text = f"Status: {level} (Score: {score_val:.2f})"
        cv2.putText(frame, status_text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

        return frame