计算機毕設選题 基於深度學習的疲劳駕驶檢測

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1. import cv2
   
2. import dlib
   
3. import numpy as np毛孔清潔棒,
   
4. from imutils import face_utils
   
5. """
   
6. 思绪：
   
7. 第一步：2D人脸關頭點檢測；第二步：3D人脸模子匹配；
   
8. 第三步：求解3D點和對應2D點的转換瓜葛；第四步：按照扭转矩阵求解欧拉角。
   
9. """
   
10. 
    
11. # 加载人脸檢測和姿式估量模子（dlib）
    
12. face_landmark_path = 'D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat'
    
13. 
    
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转和平移矩阵(OpenCV自带函数solvePnp())
    
16. """
    
17. 
    
18. # 世界坐標系(UVW)：填寫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, 塑形背心,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, 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
    
33. 
    
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]# 等价於矩阵[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]
    
40. 
    
41. # 像素坐標系(xy)：填寫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)
    
44. 
    
45. # 從新投影3D點的世界坐標轴以验證成果姿式
    
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泡腳包,, -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]]
    
58. 
    
59. def get_head_pose(shape):
    
60. # 填寫2D参考點，注释遵守
    
61. """
    
62. 17左眉左上角/21左眉右角/檬山楂脂流茶,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. 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. 治療痛風噴劑推薦,   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——求解扭转和平移矩阵：
    
75. # rotation_vec暗示扭转矩阵，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)
    
79. 
    
80. reprojectdst = tuple(map(瘦身食品,tuple, reprojectdst.reshape(8, 2)))# 以8行2列显示
    
81. 
    
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（将扭转矩阵转換為扭转向量）
    
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转角度
    
87. _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)# 将投影矩阵分化為扭转矩阵和相機矩阵
    
88. 
    
89. return reprojectdst, euler_angle
    
90. 
    
91. def main():
    
92. # return
    
93. cap = cv2.VideoCapture(0)
    
94. if not cap.isOpened():
    
95. print("Unable to connect to camera.")
    
96. return
    
97. # 檢測人脸
    
98. detector = dlib.get_frontal_face_detector()
    
99. # 檢測第一小我脸的關頭點
    
100. predictor = dlib.shape_predictor(face_landmark_path)
     
101. 
     
102. while cap.isOpened():
     
103. ret, frame = cap.read()
     
104. if ret:
     
105. face_rects = detector(frame, 0)
     
106. 
     
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. # 将面部特性信息转換為数组array的格局
     
111. shape = face_utils.shape_to_np(shape)
     
112. # 获得頭部姿态
     
113. reprojectdst, euler_angle = 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))
     
118. 
     
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)
     
122. 
     
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)
     
130. 
     
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)
     
135. 
     
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()
     
142. 
     
143. if __name__ == '__main__':
     
144. main()

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