mhp.py 4.6 KB

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  1. dataset_info = dict(
  2. dataset_name='mhp',
  3. paper_info=dict(
  4. author='Zhao, Jian and Li, Jianshu and Cheng, Yu and '
  5. 'Sim, Terence and Yan, Shuicheng and Feng, Jiashi',
  6. title='Understanding humans in crowded scenes: '
  7. 'Deep nested adversarial learning and a '
  8. 'new benchmark for multi-human parsing',
  9. container='Proceedings of the 26th ACM '
  10. 'international conference on Multimedia',
  11. year='2018',
  12. homepage='https://lv-mhp.github.io/dataset',
  13. ),
  14. keypoint_info={
  15. 0:
  16. dict(
  17. name='right_ankle',
  18. id=0,
  19. color=[255, 128, 0],
  20. type='lower',
  21. swap='left_ankle'),
  22. 1:
  23. dict(
  24. name='right_knee',
  25. id=1,
  26. color=[255, 128, 0],
  27. type='lower',
  28. swap='left_knee'),
  29. 2:
  30. dict(
  31. name='right_hip',
  32. id=2,
  33. color=[255, 128, 0],
  34. type='lower',
  35. swap='left_hip'),
  36. 3:
  37. dict(
  38. name='left_hip',
  39. id=3,
  40. color=[0, 255, 0],
  41. type='lower',
  42. swap='right_hip'),
  43. 4:
  44. dict(
  45. name='left_knee',
  46. id=4,
  47. color=[0, 255, 0],
  48. type='lower',
  49. swap='right_knee'),
  50. 5:
  51. dict(
  52. name='left_ankle',
  53. id=5,
  54. color=[0, 255, 0],
  55. type='lower',
  56. swap='right_ankle'),
  57. 6:
  58. dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''),
  59. 7:
  60. dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''),
  61. 8:
  62. dict(
  63. name='upper_neck',
  64. id=8,
  65. color=[51, 153, 255],
  66. type='upper',
  67. swap=''),
  68. 9:
  69. dict(
  70. name='head_top', id=9, color=[51, 153, 255], type='upper',
  71. swap=''),
  72. 10:
  73. dict(
  74. name='right_wrist',
  75. id=10,
  76. color=[255, 128, 0],
  77. type='upper',
  78. swap='left_wrist'),
  79. 11:
  80. dict(
  81. name='right_elbow',
  82. id=11,
  83. color=[255, 128, 0],
  84. type='upper',
  85. swap='left_elbow'),
  86. 12:
  87. dict(
  88. name='right_shoulder',
  89. id=12,
  90. color=[255, 128, 0],
  91. type='upper',
  92. swap='left_shoulder'),
  93. 13:
  94. dict(
  95. name='left_shoulder',
  96. id=13,
  97. color=[0, 255, 0],
  98. type='upper',
  99. swap='right_shoulder'),
  100. 14:
  101. dict(
  102. name='left_elbow',
  103. id=14,
  104. color=[0, 255, 0],
  105. type='upper',
  106. swap='right_elbow'),
  107. 15:
  108. dict(
  109. name='left_wrist',
  110. id=15,
  111. color=[0, 255, 0],
  112. type='upper',
  113. swap='right_wrist')
  114. },
  115. skeleton_info={
  116. 0:
  117. dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
  118. 1:
  119. dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
  120. 2:
  121. dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]),
  122. 3:
  123. dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]),
  124. 4:
  125. dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
  126. 5:
  127. dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
  128. 6:
  129. dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]),
  130. 7:
  131. dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]),
  132. 8:
  133. dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]),
  134. 9:
  135. dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]),
  136. 10:
  137. dict(
  138. link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128,
  139. 0]),
  140. 11:
  141. dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
  142. 12:
  143. dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]),
  144. 13:
  145. dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]),
  146. 14:
  147. dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0])
  148. },
  149. joint_weights=[
  150. 1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5
  151. ],
  152. # Adapted from COCO dataset.
  153. sigmas=[
  154. 0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026,
  155. 0.062, 0.072, 0.179, 0.179, 0.072, 0.062
  156. ])