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| 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): | |
| # Landmark indices | |
| 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] | |
| HEAD_POSE_LANDMARKS = [1, 152, 263, 33, 287, 57] | |
| 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)) | |
| # Caching and state variables | |
| self.frame_counter = 0 | |
| self.last_indicators = {"drowsiness_level": "Initializing...", "lighting": "Good", "details": {}} | |
| self.last_landmarks = None | |
| self.last_drawn_frame = None | |
| self.counters = { "eye_closure": 0, "yawning": 0, "head_nod": 0, "looking_away": 0 } | |
| self.zeros_4x1 = np.zeros((4, 1), np.float32) | |
| # --- FIX: Combined process_frame and analyse_frame into one method --- | |
| def process_frame(self, frame, draw_visuals=True): | |
| self.frame_counter += 1 | |
| # Adaptive frame skipping logic | |
| 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: | |
| # If we are drawing visuals and have a cached frame, return it. | |
| if draw_visuals and self.last_drawn_frame is not None: | |
| return self.last_drawn_frame, self.last_indicators | |
| # If we are not drawing, we can just return the last indicators. | |
| elif not draw_visuals: | |
| return None, self.last_indicators | |
| # Fallback for the first frame | |
| else: | |
| return frame.copy(), self.last_indicators | |
| # --- Core Frame Analysis (This part runs for both modes) --- | |
| original_frame = frame.copy() | |
| h_orig, w_orig, _ = original_frame.shape | |
| 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 | |
| gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY) | |
| brightness = np.mean(gray) | |
| drowsiness_indicators = {"drowsiness_level": "Awake", "lighting": "Good", "details": {}} | |
| face_landmarks_data = None | |
| if brightness < self.settings['low_light_thresh']: | |
| drowsiness_indicators["lighting"] = "Low" | |
| else: | |
| img_rgb = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB) | |
| img_rgb.flags.writeable = False | |
| results = self.face_mesh.process(img_rgb) | |
| img_rgb.flags.writeable = True | |
| if results.multi_face_landmarks: | |
| face_landmarks_data = results.multi_face_landmarks[0] | |
| landmarks = face_landmarks_data.landmark | |
| score = 0 | |
| weights = self.settings['indicator_weights'] | |
| # --- 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'] | |
| # --- Head Pose Estimation --- | |
| face_3d_model = 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_points = np.array([(landmarks[i].x*w,landmarks[i].y*h) for i in self.HEAD_POSE_LANDMARKS],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_model, face_2d_points, 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 | |
| self.last_indicators = drowsiness_indicators | |
| self.last_landmarks = face_landmarks_data | |
| # --- FIX: Conditional Drawing --- | |
| if draw_visuals: | |
| processed_frame = self.draw_visuals(original_frame, drowsiness_indicators, face_landmarks_data) | |
| self.last_drawn_frame = processed_frame | |
| return processed_frame, drowsiness_indicators | |
| else: | |
| # For analysis-only, return None for the frame to satisfy the unpack | |
| return None, drowsiness_indicators | |
| def draw_visuals(self, frame, indicators, landmarks_data=None): | |
| h, w, _ = frame.shape | |
| level = indicators['drowsiness_level'] | |
| score_val = indicators.get("details", {}).get("Score", 0) | |
| color = (0, 255, 0) | |
| if indicators['lighting'] == "Low": | |
| color = (0, 165, 255) | |
| 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) | |
| elif level == "Very Drowsy": color = (0, 0, 255) | |
| if landmarks_data: | |
| landmarks = landmarks_data.landmark | |
| eye_mouth_landmarks_indices = self.L_EYE + self.R_EYE + self.MOUTH | |
| for idx in eye_mouth_landmarks_indices: | |
| lm = landmarks[idx] | |
| 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 - 1, h - 1), 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 | |