python疲勞駕駛困倦低頭檢測功能的實現(xiàn)

更新時間：2022年04月04日 10:10:25 作者：babyai996

這篇文章主要介紹了python疲勞駕駛困倦低頭檢測,該系統(tǒng)可以檢測一個人在開車時是否困倦，及時提醒，做到安全隱患排查，對實現(xiàn)代碼感興趣的朋友一起看看吧

python疲勞駕駛困倦低頭檢測，代碼如下所示：

def get_head_pose(shape):  # 頭部姿態(tài)估計
    # （像素坐標集合）填寫2D參考點
    # 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計算姿勢——求解旋轉(zhuǎn)和平移矩陣：
    # rotation_vec表示旋轉(zhuǎn)矩陣，translation_vec表示平移矩陣，cam_matrix與K矩陣對應，dist_coeffs與D矩陣對應。
    _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
    # projectPoints重新投影誤差：原2d點和重投影2d點的距離（輸入3d點、相機內(nèi)參、相機畸變、r、t，輸出重投影2d點）
    reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix, dist_coeffs)
    reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))  # 以8行2列顯示
 
    # 計算歐拉角calc euler angle
    rotation_mat, _ = cv2.Rodrigues(rotation_vec)  # 羅德里格斯公式（將旋轉(zhuǎn)矩陣轉(zhuǎn)換為旋轉(zhuǎn)向量）
    pose_mat = cv2.hconcat((rotation_mat, translation_vec))  # 水平拼接，vconcat垂直拼接
    # decomposeProjectionMatrix將投影矩陣分解為旋轉(zhuǎn)矩陣和相機矩陣
    _, _, _, _, _, _, 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  # 投影誤差，歐拉角
def eye_aspect_ratio(eye):
    # 垂直眼標志（X，Y）坐標
    A = dist.euclidean(eye[1], eye[5])  # 計算兩個集合之間的歐式距離
    B = dist.euclidean(eye[2], eye[4])
    # 計算水平之間的歐幾里得距離
    # 水平眼標志（X，Y）坐標
    C = dist.euclidean(eye[0], eye[3])
    # 眼睛長寬比的計算
    ear = (A + B) / (2.0 * C)
    # 返回眼睛的長寬比
    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