人脸裁剪

 

 

使用检测网络检测出人脸之后，在下一步比对或者训练之前，要对人脸进行对齐，我觉得对齐这个词不够精确，我称之为摆正。通过调研，我使用的方法是使用两只眼睛的坐标作为摆正的标准（当然，这需要检测网路能够输出眼睛坐标），摆正之后要对人脸进行重新裁剪，这需要利用眼睛、鼻子和嘴角的信息。归纳起来就是：

1、得出两只眼睛形成的直线的夹角，按照该夹角的反方向进行图像的旋转；旋转之后关键的一步是根据旋转公式，获得旋转之后的特征点的坐标

2、摆正之后，根据眼睛、鼻子、嘴巴纵向距离，估测要裁剪的人脸的高度；

3、确定高度之后，设定高宽比（如1:1，或者1.16:1）等等，确定要裁出的人脸的宽度；

4、根据鼻子和两只眼睛横向的距离比，计算左右两边的具体坐标

 

1.                 std::vector points;//points(vector)元素是人脸两只眼睛所在的点,应该是为了拟合直线以进行人脸旋转

2.  

3.                 cv::Point eye0 = cv::Point(res[max_index].points_x[0], res[max_index].points_y[0]);

4.                 cv::Point eye1 = cv::Point(res[max_index].points_x[1], res[max_index].points_y[1]);

5.                 cv::Point mouth_0 = cv::Point(res[max_index].points_x[3], res[max_index].points_y[3]);

6.                 cv::Point mouth_1 = cv::Point(res[max_index].points_x[4], res[max_index].points_y[4]);

7.                 cv::Point nose = cv::Point(res[max_index].points_x[2], res[max_index].points_y[2]);

8.  

9.                 cv::line(image, eye0, eye1, (255, 0, 255), 3);

10.                 cv::line(image, mouth_0, mouth_1, (0, 0, 255), 3);

11.                 cv::imshow("1", image);

12.  

13.                 points.push_back(eye0);

14.                 points.push_back(eye1);

15. cv::Vec4f line;//vec4f类型的line是拟合的结果，前两个元素表示方向，后两个元素表示直线上一点。

16. cv::fitLine(points,

17.          line,

18. CV_DIST_HUBER,

19. 0,

20. 0.01,

21. 0.01);

22.  

23. double cos_theta = line[0];

24. double sin_theta = line[1];

25. double x0 = line[2], y0 = line[3];

26.  

27. double phi = atan2(sin_theta, cos_theta) + 3.1416 / 2.0;

28. double rho = y0 * cos_theta - x0 * sin_theta;

29.  

30. /*std::cout << "phi = " << phi / 3.1416 * 180 << std::endl;

31. std::cout << "rho = " << rho << std::endl;*/

32. //DrawLine(image, phi, rho, cv::Scalar(255));

33. double k = sin_theta / cos_theta;

34. double b = y0 - k * x0;

35. double x = 0;

36. double y = k * x + b;

37. /*std::cout << k << std::endl;

38. std::cout << b << std::endl;*/

39.  

40. cv::Mat input_img = image.clone();

41. cv::Mat temp_img;

42. // 将图像按照相应的角度旋转

43. float angle = phi / 3.1416 * 180 - 90; //角度

44. float radian = (float)(angle / 180.0 * CV_PI); //弧度

45.  

46. // 填充图像使其符合旋转要求

47. int uniSize = (int)(max(input_img.cols, input_img.rows)* 1.414); //以宽、高较大者的根号2倍，作为啥呢？

48. int dx = (int)(uniSize - input_img.cols) / 2;//原来是作为扩充宽度

49. int dy = (int)(uniSize - input_img.rows) / 2;

50. copyMakeBorder(input_img, temp_img, dy, dy, dx, dx, cv::BORDER_CONSTANT);//以周边恒定值0为扩充值进行扩充

51. //copyMakeBorder(temp_mat, temp_mat, dy, dy, dx, dx, cv::BORDER_CONSTANT);

52. // 旋转中心

53. //cv::Point2f center((float)(temp_img.cols / 2), (float)(temp_img.rows / 2));//看旋转中心的值就知道是以图片中作为旋转中心的

54. cv::Point2f center((float)(image.cols / 2), (float)(image.rows / 2));

55. cv::Mat affine_matrix = getRotationMatrix2D(center, angle, 1.0);

56.  
57.  
58.  

59. // 旋转

60. warpAffine(temp_img, temp_img, affine_matrix, temp_img.size());

61. //warpAffine(temp_mat, temp_mat, affine_matrix, temp_img.size());

62. warpAffine(temp_mat, temp_mat, affine_matrix, image.size());

63.  

64. radian = -radian;

65.  

66. /*float eye0_new_x = (eye0.x - center.x)*cos(radian) - (eye0.y - center.y)*sin(radian) + center.x; //以center为坐标原点

67. float eye0_new_y = (eye0.x - center.x)*sin(radian) + (eye0.y - center.y)*cos(radian) + center.y;

68.  

69. float eye1_new_x = (eye1.x - center.x)*cos(radian) - (eye1.y - center.y)*sin(radian) + center.x;

70. float eye1_new_y = (eye1.x - center.x)*sin(radian) + (eye1.y - center.y)*cos(radian) + center.y;

71.  

72. cv::Point2f eye0_new(eye0_new_x, eye0_new_y);

73. cv::Point2f eye1_new(eye1_new_x, eye1_new_y);*/

74.  

75. cv::Point2f eye0_new = get_new_point(eye0, center, radian);

76. cv::Point2f eye1_new = get_new_point(eye1, center, radian);

77. cv::Point2f mouth0_new = get_new_point(mouth_0, center, radian);

78. cv::Point2f mouth1_new = get_new_point(mouth_1, center, radian);

79. cv::Point2f nose_new = get_new_point(nose, center, radian);

80.  

81. cout << eye0_new.x << " "<< eye1_new.x << " " << nose_new.x << endl;

82.  

83. //cv::line(temp_mat, eye0_new, eye1_new, (255, 255, 255), 2);

84. //cv::line(temp_mat, eye0_new, nose_new, (0, 0, 255), 2);

85. //cv::line(temp_mat, mouth0_new, mouth1_new, (255, 255, 255), 5);

86.  

87. cv::imshow("test", temp_mat);

88.  

89. float delta_W = eye1_new.x - eye0_new.x;

90. float delta_H = (mouth0_new.y + mouth1_new.y) / 2 - eye0_new.y; //眼睛和嘴唇之间高度差

91.  

92. cout << "两眼间距：" << delta_W << endl;

93. cout << "眼睛、嘴角高度差：" << delta_H << endl;

94.  

95. //确定要截出的顶端和底端

96. float y_top = eye0_new.y - delta_H / 2.14;

97. float y_bottom = (mouth0_new.y + mouth1_new.y) / 2 + delta_H / 3.33;

98. cout << y_bottom - y_top << endl;

99.  

100. float width = (float)(y_bottom - y_top);

101.  

102. float both_sides = width - delta_W;

103. float h_distance_eye0_nose = abs(nose.x - eye0_new.x);

104. float h_distance_eye1_nose = abs(eye1_new.x - nose.x);

105. float h_ratio_0 = h_distance_eye0_nose /( h_distance_eye0_nose + h_distance_eye1_nose);

106.  

107. float x_left = 0.0;

108. float x_right = 0.0;

109.  

110. if (h_ratio_0 >= 0.5)

111. {

112. x_left = min(eye0_new.x, nose.x) - both_sides*h_ratio_0;

113. x_right = width + x_left;

114. }

115. else

116. {

117. x_right = max(nose.x, eye1_new.x) + both_sides*(1-h_ratio_0);

118. x_left = x_right - width;

119. }

120.  

121. rect = cv::Rect(cv::Point(x_left, y_top), cv::Point(x_right, y_bottom));

122.  

123. if (rect.x < 0) { rect.x = 0; }

124. if (rect.y < 0) { rect.y = 0; }

125. if (rect.x > temp_mat.cols){ rect.x = temp_mat.cols; }

126. if (rect.y > temp_mat.rows){ rect.y = temp_mat.rows; }

127. if (rect.x + rect.width > temp_mat.cols){ rect.width = temp_mat.cols - rect.x; }

128. if (rect.y + rect.height > temp_mat.rows){ rect.height = temp_mat.rows - rect.y; }

129.  

130. cv::Mat face_cut(temp_mat, rect);