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| # drive_paddy/detection/strategies/geometric.py | |
| import cv2 | |
| import mediapipe as mp | |
| import numpy as np | |
| import math | |
| from ..base_processor import BaseProcessor | |
| # --- Helper Functions (No changes here) --- | |
| 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): | |
| def __init__(self, config): | |
| self.settings = config['geometric_settings'] | |
| self.face_mesh = mp.solutions.face_mesh.FaceMesh(max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5) | |
| self.counters = { "eye_closure": 0, "yawning": 0, "head_nod": 0, "looking_away": 0 } | |
| self.L_EYE = [362, 385, 387, 263, 373, 380]; self.R_EYE = [33, 160, 158, 133, 153, 144] | |
| self.MOUTH = [61, 291, 39, 181, 0, 17, 84, 178] | |
| def process_frame(self, frame): | |
| h, w, _ = frame.shape | |
| gray = cv2.cvtColor(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: | |
| img_rgb = cv2.cvtColor(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'] | |
| # --- Draw Facial Landmarks (Logic Added Back) --- | |
| # This will draw the green dots for eyes and mouth. | |
| eye_mouth_landmarks = self.L_EYE + self.R_EYE + self.MOUTH | |
| for idx in eye_mouth_landmarks: | |
| lm = landmarks[idx] | |
| x, y = int(lm.x * w), int(lm.y * h) | |
| cv2.circle(frame, (x, y), 1, (0, 255, 0), -1) | |
| # --- Drowsiness Calculations --- | |
| 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'] | |
| 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.float64) | |
| 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.float64) | |
| cam_matrix = np.array([[w,0,w/2],[0,w,h/2],[0,0,1]],dtype=np.float64) | |
| _, rot_vec, _ = cv2.solvePnP(face_3d, face_2d, cam_matrix, np.zeros((4,1),dtype=np.float64)) | |
| rmat, _ = cv2.Rodrigues(rot_vec); 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: # is_low_light is True | |
| drowsiness_indicators["lighting"] = "Low" | |
| # --- Visualization on Video Frame --- | |
| level = drowsiness_indicators['drowsiness_level'] | |
| score_val = drowsiness_indicators.get("details", {}).get("Score", 0) | |
| color = (0, 255, 0) # Green for Awake | |
| if drowsiness_indicators['lighting'] == "Low": | |
| color = (0, 165, 255) # Orange for low light | |
| 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 | |
| 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, drowsiness_indicators, face_landmarks | |