摘要：人脸疲劳检测：一种通过分析人脸特征来判断一个人是否处于疲劳状态的技术。其原理主要基于计算机视觉和机器学习方法。当人疲劳时，面部会出现一些特征变化，如眼睛闭合程度增加、眨眼频率变慢、打哈欠、头部姿态改变等。例如，通过检测眼睛的状态来判断疲劳程度是一个关键部分。正
本篇源自：优秀创作者 lulugl
本文将介绍基于米尔电子MYD-LR3576开发板（米尔基于瑞芯微 RK3576开发板）的人脸疲劳检测方案测试。
米尔基于RK3576核心板/开发板
【前言】
人脸疲劳检测：一种通过分析人脸特征来判断一个人是否处于疲劳状态的技术。其原理主要基于计算机视觉和机器学习方法。当人疲劳时，面部会出现一些特征变化，如眼睛闭合程度增加、眨眼频率变慢、打哈欠、头部姿态改变等。
例如，通过检测眼睛的状态来判断疲劳程度是一个关键部分。正常情况下，人的眨眼频率相对稳定，而当疲劳时，眨眼频率会降低，并且每次眨眼时眼睛闭合的时间可能会延长。同时，头部可能会不自觉地下垂或者摇晃，这些特征都可以作为疲劳检测的依据。米尔MYC-LR3576采用8核CPU+搭载6 TOPS的NPU加速器，3D GPU，能够非常轻松的实现这个功能，下面就如何实现这一功能分享如下：
【硬件】
1、米尔MYC-LR3576开发板
2、USB摄像头
【软件】
1、v4l2
2、openCV
3、dlib库：dlib 是一个现代化的 C++ 工具包，它包含了许多用于机器学习、图像处理、数值计算等多种任务的算法和工具。它的设计目标是提供高性能、易于使用的库，并且在开源社区中被广泛应用。
【实现步骤】
1、安装python-opencv
2、安装dlib库
3、安装v4l2库
【代码实现】
1、引入cv2、dlib以及线程等：
import cv2import dlibimport numpy as npimport timefrom concurrent.futures import ThreadPoolExecutorimport threading2、初始化dlib的面部检测器和特征点预测器
detector = dlib.get_frontal_face_detectorpredictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')3、定义计算眼睛纵横比的函数
def eye_aspect_ratio(eye): A = np.linalg.norm(np.array(eye[1]) - np.array(eye[5])) B = np.linalg.norm(np.array(eye[2]) - np.array(eye[4])) C = np.linalg.norm(np.array(eye[0]) - np.array(eye[3])) ear = (A + B) / (2.0 * C) return ear4、定义计算头部姿势的函数
def get_head_pose(shape): # 定义面部特征点的三维坐标 object_points = np.array([ (0.0, 0.0, 0.0), # 鼻尖 (0.0, -330.0, -65.0), # 下巴 (-225.0, 170.0, -135.0), # 左眼左眼角 (225.0, 170.0, -135.0), # 右眼右眼角 (-150.0, -150.0, -125.0), # 左嘴角 (150.0, -150.0, -125.0) # 右嘴角 ], dtype=np.float32) image_pts = np.float32([shape[i] for i in [30, 8, 36, 45, 48, 54]]) size = frame.shape focal_length = size[1] center = (size[1] // 2, size[0] // 2) camera_matrix = np.array( [[focal_length, 0, center[0]], [0, focal_length, center[1]], [0, 0, 1]], dtype="double" ) dist_coeffs = np.zeros((4, 1)) (success, rotation_vector, translation_vector) = cv2.solvePnP( object_points, image_pts, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE ) rmat, _ = cv2.Rodrigues(rotation_vector) angles, _, _, _, _, _ = cv2.RQDecomp3x3(rmat) return angles5、定义眼睛纵横比阈值和连续帧数阈值
EYE_AR_THRESH = 0.3EYE_AR_CONSEC_FRAMES = 486、打开摄像头
我们先使用v4l2-ctl --list-devices来例出接在开发板上的列表信息：
在代码中填入60为摄像头的编号：
cap = cv2.VideoCapture(60)cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480) # 降低分辨率cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 320)7、创建多线程处理函数，实现采集与分析分离：
# 多线程处理函数def process_frame(frame): global COUNTER, TOTAL gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = detector(gray, 0) # 第二个参数为0，表示不使用upsampling for face in faces: landmarks = predictor(gray, face) shape = [(landmarks.part(i).x, landmarks.part(i).y) for i in range(68)] left_eye = shape[36:42] right_eye = shape[42:48] left_ear = eye_aspect_ratio(left_eye) right_ear = eye_aspect_ratio(right_eye) ear = (left_ear + right_ear) / 2.0 if ear = EYE_AR_CONSEC_FRAMES: TOTAL += 1 COUNTER = 0 # 绘制68个特征点 for n in range(0, 68): x, y = shape[n] cv2.circle(frame, (x, y), 2, (0, 255, 0), -1) cv2.putText(frame, f"Eye AR: {ear:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Blink Count: {TOTAL}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) # 计算头部姿势 angles = get_head_pose(shape) pitch, yaw, roll = angles cv2.putText(frame, f"Pitch: {pitch:.2f}", (10, 120), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Yaw: {yaw:.2f}", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Roll: {roll:.2f}", (10, 180), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) # 判断疲劳状态 if COUNTER >= EYE_AR_CONSEC_FRAMES or abs(pitch) > 30 or abs(yaw) > 30 or abs(roll) > 30: cv2.putText(frame, "Fatigue Detected!", (10, 210), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) return frame8、创建图像显示线程：
with ThreadPoolExecutor(max_workers=2) as executor: future_to_frame = {} while True: ret, frame = cap.read if not ret: break # 提交当前帧到线程池 future = executor.submit(process_frame, frame.copy) future_to_frame[future] = frame # 获取已完成的任务结果 for future in list(future_to_frame.keys): if future.done: processed_frame = future.result cv2.imshow("Frame", processed_frame) del future_to_frame[future] break # 计算帧数 fps_counter += 1 elapsed_time = time.time - start_time if elapsed_time > 1.0: fps = fps_counter / elapsed_time fps_counter = 0 start_time = time.time cv2.putText(processed_frame, f"FPS: {fps:.2f}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) if cv2.waitKey(1) & 0xFF == ord('q'):整体代码如下：import cv2import dlibimport numpy as npimport timefrom concurrent.futures import ThreadPoolExecutorimport threading# 初始化dlib的面部检测器和特征点预测器detector = dlib.get_frontal_face_detectorpredictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')# 修改字体大小font_scale = 0.5 # 原来的字体大小是0.7，现在改为0.5# 定义计算眼睛纵横比的函数def eye_aspect_ratio(eye): A = np.linalg.norm(np.array(eye[1]) - np.array(eye[5])) B = np.linalg.norm(np.array(eye[2]) - np.array(eye[4])) C = np.linalg.norm(np.array(eye[0]) - np.array(eye[3])) ear = (A + B) / (2.0 * C) return ear# 定义计算头部姿势的函数def get_head_pose(shape): # 定义面部特征点的三维坐标 object_points = np.array([ (0.0, 0.0, 0.0), # 鼻尖 (0.0, -330.0, -65.0), # 下巴 (-225.0, 170.0, -135.0), # 左眼左眼角 (225.0, 170.0, -135.0), # 右眼右眼角 (-150.0, -150.0, -125.0), # 左嘴角 (150.0, -150.0, -125.0) # 右嘴角 ], dtype=np.float32) image_pts = np.float32([shape[i] for i in [30, 8, 36, 45, 48, 54]]) size = frame.shape focal_length = size[1] center = (size[1] // 2, size[0] // 2) camera_matrix = np.array( [[focal_length, 0, center[0]], [0, focal_length, center[1]], [0, 0, 1]], dtype="double" ) dist_coeffs = np.zeros((4, 1)) (success, rotation_vector, translation_vector) = cv2.solvePnP( object_points, image_pts, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE ) rmat, _ = cv2.Rodrigues(rotation_vector) angles, _, _, _, _, _ = cv2.RQDecomp3x3(rmat) return angles# 定义眼睛纵横比阈值和连续帧数阈值EYE_AR_THRESH = 0.3EYE_AR_CONSEC_FRAMES = 48# 初始化计数器COUNTER = 0TOTAL = 0# 创建锁对象lock = threading.Lock# 打开摄像头cap = cv2.VideoCapture(60)cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480) # 降低分辨率cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 320)# 初始化帧计数器和时间戳fps_counter = 0start_time = time.time# 多线程处理函数def process_frame(frame): global COUNTER, TOTAL gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = detector(gray, 0) # 第二个参数为0，表示不使用upsampling for face in faces: landmarks = predictor(gray, face) shape = [(landmarks.part(i).x, landmarks.part(i).y) for i in range(68)] left_eye = shape[36:42] right_eye = shape[42:48] left_ear = eye_aspect_ratio(left_eye) right_ear = eye_aspect_ratio(right_eye) ear = (left_ear + right_ear) / 2.0 if ear = EYE_AR_CONSEC_FRAMES: TOTAL += 1 COUNTER = 0 # 绘制68个特征点 for n in range(0, 68): x, y = shape[n] cv2.circle(frame, (x, y), 2, (0, 255, 0), -1) cv2.putText(frame, f"Eye AR: {ear:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Blink Count: {TOTAL}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) # 计算头部姿势 angles = get_head_pose(shape) pitch, yaw, roll = angles cv2.putText(frame, f"Pitch: {pitch:.2f}", (10, 120), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Yaw: {yaw:.2f}", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) cv2.putText(frame, f"Roll: {roll:.2f}", (10, 180), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) # 判断疲劳状态 if COUNTER >= EYE_AR_CONSEC_FRAMES or abs(pitch) > 30 or abs(yaw) > 30 or abs(roll) > 30: cv2.putText(frame, "Fatigue Detected!", (10, 210), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), 2) return framewith ThreadPoolExecutor(max_workers=2) as executor: future_to_frame = {} while True: ret, frame = cap.read if not ret: break # 提交当前帧到线程池 future = executor.submit(process_frame, frame.copy) future_to_frame[future] = frame # 获取已完成的任务结果 for future in list(future_to_frame.keys): if future.done: processed_frame = future.result cv2.imshow("Frame", processed_frame) del future_to_frame[future] break # 计算帧数 fps_counter += 1 elapsed_time = time.time - start_time if elapsed_time > 1.0: fps = fps_counter / elapsed_time fps_counter = 0 start_time = time.time cv2.putText(processed_frame, f"FPS: {fps:.2f}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) if cv2.waitKey(1) & 0xFF == ord('q'): break# 释放摄像头并关闭所有窗口cap.releasecv2.destroyAllWindows
【总结】
来源：吕军军侃万事
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