python疲劳驾驶检测
babyai996 人气:0python疲劳驾驶困倦低头检测,代码如下所示:
def get_head_pose(shape): # 头部姿态估计 # (像素坐标集合)填写2D参考点 # 17左眉左上角/21左眉右角/22右眉左上角/26右眉右上角/36左眼左上角/39左眼右上角/42右眼左上角/ # 45右眼右上角/31鼻子左上角/35鼻子右上角/48左上角/54嘴右上角/57嘴中央下角/8下巴角 image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36], shape[39], shape[42], shape[45], shape[31], shape[35], shape[48], shape[54], shape[57], shape[8]]) # solvePnP计算姿势——求解旋转和平移矩阵: # rotation_vec表示旋转矩阵,translation_vec表示平移矩阵,cam_matrix与K矩阵对应,dist_coeffs与D矩阵对应。 _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs) # projectPoints重新投影误差:原2d点和重投影2d点的距离(输入3d点、相机内参、相机畸变、r、t,输出重投影2d点) reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix, dist_coeffs) reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2))) # 以8行2列显示 # 计算欧拉角calc euler angle rotation_mat, _ = cv2.Rodrigues(rotation_vec) # 罗德里格斯公式(将旋转矩阵转换为旋转向量) pose_mat = cv2.hconcat((rotation_mat, translation_vec)) # 水平拼接,vconcat垂直拼接 # decomposeProjectionMatrix将投影矩阵分解为旋转矩阵和相机矩阵 _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat) pitch, yaw, roll = [math.radians(_) for _ in euler_angle] pitch = math.degrees(math.asin(math.sin(pitch))) roll = -math.degrees(math.asin(math.sin(roll))) yaw = math.degrees(math.asin(math.sin(yaw))) print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll)) return reprojectdst, euler_angle # 投影误差,欧拉角 def eye_aspect_ratio(eye): # 垂直眼标志(X,Y)坐标 A = dist.euclidean(eye[1], eye[5]) # 计算两个集合之间的欧式距离 B = dist.euclidean(eye[2], eye[4]) # 计算水平之间的欧几里得距离 # 水平眼标志(X,Y)坐标 C = dist.euclidean(eye[0], eye[3]) # 眼睛长宽比的计算 ear = (A + B) / (2.0 * C) # 返回眼睛的长宽比 return ear def mouth_aspect_ratio(mouth): # 嘴部 A = np.linalg.norm(mouth[2] - mouth[9]) # 51, 59 B = np.linalg.norm(mouth[4] - mouth[7]) # 53, 57 C = np.linalg.norm(mouth[0] - mouth[6]) # 49, 55 mar = (A + B) / (2.0 * C) return mar
python疲劳驾驶困倦低头检测
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