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標題: 计算機毕設選题 基于深度學習的疲劳駕驶檢测 [打印本頁]
作者: admin    時間: 2024-4-24 18:08
標題: 计算機毕設選题 基于深度學習的疲劳駕驶檢测
  1. import cv2
  2. import dlib
  3. import numpy as np
  4. from imutils import face_utils
  5. """
  6. 思绪：
  7. 第一步：2D人脸关頭點檢测；第二步：3D人脸模子匹配；
  8. 第三步：求解3D點和對應2D點的轉换瓜葛；第四步：按照扭轉矩阵求解欧拉角。
  9. """

  11. # 加载人脸檢测和姿式估量模子（dlib）
  12. face_landmark_path = 'D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat'

  14. """
  15. 只要晓得世界坐標系内點的位置、像素坐標位置和相機参数便可以搞定扭轉和平移矩阵(OpenCV自带函数solvePnp())
  16. """

  18. # 世界坐標系(UVW)：填写3D参考點，该模子参考
  19. object_pts = np.float32([[6.825897, 6.760612, 4.402142],  #33左眉左上角
  20. [1.330353, 7.122144, 6.903745],  #29左眉右角
  21. [-1.330353, 7.122144, 6.903745], #34右眉左角
  22. [-6.825897, 6.760612, 4.402142], #38右眉右上角
  23. [5.311432, 5.485328, 3.987654],  #13左眼左上角
  24. [1.789930, 5.393625, 4.413414],  #17左眼右上角
  25. [-1.789930, 5.393625, 4.413414], #25右眼左上角
  26. [-5.311432, 5.485328, 3.987654], #21右眼右上角
  27. [2.005628瘦臉,, 1.409845, 6.165652],  #55鼻子左上角
  28. [-2.005628, 1.409845, 6.165652], #49鼻子右上角
  29. [2.774015, -2.080775, 5.048531], #43嘴左上角
  30. [-2.774015, -2.080775, 5.048531],#39嘴右上角
  31. [0.000000, -3.116408, 6.097667], #45嘴中心下角
  32. [0.000000, -7.415691, 4.070434]])#6下巴角

  34. # 相機坐標系(XYZ)：添加相機内参
  35. K = [6.5308391993466671e+002, 0.0, 3.1950000000000000e+002,
  36. 0.0, 6.5308391993466671e+002, 2.3950000000000000e+002,
  37. 0.0, 0.0, 1.0]# 等價于矩阵[fx, 0, cx; 0, fy, cy; 0, 0, 1]
  38. # 图象中間坐標系(uv)：相機畸變参数[k1, k2, p1, p2, k3]
  39. D = [7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e+000]

  41. # 像素坐標系(xy)：填写凸轮的本征和畸變系数
  42. cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)
  43. dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)

  45. # 從新投影3D點的世界坐標轴以驗證成果姿式
  46. reprojectsrc = np.float32([[10.0, 10.0, 10.0],
  47. [10.0, 10.0, -10.0],
  48. [10.0, -10.0, -10.0],
  49. [10.0, -10.0, 10.0],
  50. [-10.0, 10.0, 10.0],
  51. [-10.0, 10.0, -10.0],
  52. [-10.0, -10.0, -10.0],
  53. [-10.0, -10.0, 10.0]])
  54. # 绘制正方體12轴
  55. line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],
  56. [4, 5], [5, 6], [6, 7], [7, 4],
  57. [0, 4], [1, 5], [2, 6], [3, 7]]

  59. def get_head_pose(shape):
  60. # 填写2D参考點，注释遵守
  61. """
  62. 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/
  63. 4減肥茶,5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嘴中心下角/8下巴角
  64. """
  65. # 像素坐標调集
  66. image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
  67. shape[39], shape[42], shape[45], shape[31], shape[35],
  68. shape[48], shape[54], shape[57], shape[8]])
  69. """
  70. 用solvepnp或sovlepnpRansac，输入3d點、2d點、相機内参、相機畸變，输出r、t以後
  71. 用projectPoints，输入3d點、相機内参、相機畸變、r、t，输出重投影2d點
  72. 计较原2d點和重投影2d點的間隔作為重投影偏差
  73. """
  74. # solvePnP计较姿式——求解扭轉和平移矩阵：
  75. # rotation_vec暗示扭轉矩阵，translation_vec暗示平移矩阵，cam_matrix與K矩阵對應，dist_coeffs與D矩阵對應。
  76. _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
  77. # projectPoints從新投影偏差
  78. reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,dist_coeffs)

  80. reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))# 以8行2列显示

  82. # 计较欧拉角calc euler angle
  83. # 参考
  84. rotation_mat, _ = cv2.Rodrigues(rotation_vec)#罗德里格斯公式（将扭轉矩阵轉换為扭轉向量）
  85. pose_mat = cv2.hconcat((rotation_mat, translation_vec))# 程度拼接，vconcat垂直拼接
  86. # eulerAngles –可選的三元素矢量，包括三個以度為单元的欧拉扭轉角度
  87. _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)# 将投影矩阵分化為扭轉矩阵和相機矩阵

  89. return reprojectdst, euler_angle

  91. def main():
  92. # return
  93. cap = cv2.VideoCapture(0)
  94. if not cap.isOpened():
  95. print("Unable to connect to came山楂茶,ra.")
  96. return
  97. # 檢测人脸
  98. detector = dlib.get_frontal_face_detector()
  99. # 檢测第一小我脸的关頭點
  100. predictor = dlib.shape_predictor(face_landmark_path)

  102. while cap.isOpened()腰酸背痛止痛,:
  103. ret, frame = cap.read()
  104. if ret:
  105. face_rects = detector(frame, 0)

  107. if len(face_rects) > 0:
  108. # 轮回面部位置信息，利用predictor(gray, rect)得到面部特性位置的信息
  109. shape = predictor(frame, face_rects[0])
  110. # 将面部特性信息轉换為数组array的格局
  111. 瘦肚子方法,  shape = face_utils.shape_to_np(shape)
  112. # 获得頭部姿态
  113. reprojectdst, euler_ang日貨百貨推薦,le = get_head_pose(shape)
  114. pitch = format(euler_angle[0, 0])
  115. yaw = format(euler_angle[1, 0])
  116. roll = format(euler_angle[2, 0])
  117. print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))

  119. # 標出68個特性點
  120. for (x, y) in shape:
  121. cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)

  123. # 绘制正方體12轴
  124. for start, end in line_pairs:
  125. cv2.line(frame, reprojectdst[start], reprojectdst[end], (0, 0, 255))
  126. # 显示角度成果
  127. cv2.putText(frame, "X: " + "{:7.2f}".format(euler_angle[0, 0]), (20, 20), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)
  128. cv2.putText(frame, "Y: " + "{:7.2f}".format(euler_angle[1, 0]), (20, 50), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)
  129. cv2.putText(frame, "Z: " + "{:7.2f}".format(euler_angle[2, 0]), (20, 80), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)

  131. # 按q退出提醒
  132. cv2.putText(frame, "Press 'q': Quit", (20, 450),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2)
  133. # 窗口显示 show with opencv
  134. cv2.imshow("Head_Posture", frame)

  136. if cv2.waitKey(1) & 0xFF == ord('q'):
  137. break
  138. # 開释摄像頭 release camera
  139. cap.release()
  140. # do a bit of cleanup
  141. cv2.destroyAllWindows()

  143. if __name__ == '__main__':
  144. main()
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