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anhquancao commited on
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b5be88d
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1 Parent(s): e4a653e

downsample output

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Files changed (17) hide show
  1. __pycache__/fusion.cpython-37.pyc +0 -0
  2. app.py +7 -6
  3. helpers.py +1 -1
  4. images/08/000295.jpg +0 -0
  5. images/08/001385.jpg +0 -0
  6. monoscene/__pycache__/CRP3D.cpython-37.pyc +0 -0
  7. monoscene/__pycache__/DDR.cpython-37.pyc +0 -0
  8. monoscene/__pycache__/__init__.cpython-37.pyc +0 -0
  9. monoscene/__pycache__/config.cpython-37.pyc +0 -0
  10. monoscene/__pycache__/flosp.cpython-37.pyc +0 -0
  11. monoscene/__pycache__/modules.cpython-37.pyc +0 -0
  12. monoscene/__pycache__/monoscene.cpython-37.pyc +0 -0
  13. monoscene/__pycache__/monoscene_model.cpython-37.pyc +0 -0
  14. monoscene/__pycache__/unet2d.cpython-37.pyc +0 -0
  15. monoscene/__pycache__/unet3d_kitti.cpython-37.pyc +0 -0
  16. monoscene/__pycache__/unet3d_nyu.cpython-37.pyc +0 -0
  17. monoscene/monoscene.py +4 -4
__pycache__/fusion.cpython-37.pyc DELETED
Binary file (14.9 kB)
 
app.py CHANGED
@@ -46,18 +46,17 @@ def predict(img):
46
 
47
  pred = model(batch).squeeze()
48
  # print(pred.shape)
49
- # pred = majority_pooling(pred, k_size=2)
50
- fig = draw(pred, batch['fov_mask_1'])
51
 
52
 
53
  return fig
54
 
55
- # The output is <b>downsampled by 2</b> to be able to be rendered in browsers.
56
 
57
  description = """
58
  MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
59
  Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
60
- <b>Darker</b> colors represent the <b>scenery outside the Field of View</b>, i.e. not visible on the image.
61
  <center>
62
  <a href="https://cv-rits.github.io/MonoScene/">
63
  <img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
@@ -74,7 +73,9 @@ article="""
74
  """
75
 
76
  examples = [
77
- 'images/08/000010.jpg',
 
 
78
  'images/08/000085.jpg',
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  'images/08/000290.jpg',
80
  'images/08/000465.jpg',
@@ -83,10 +84,10 @@ examples = [
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  'images/08/001380.jpg',
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  'images/08/001530.jpg',
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  'images/08/002360.jpg',
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- 'images/08/002505.jpg',
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  'images/08/004059.jpg',
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  'images/08/003149.jpg',
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  'images/08/001446.jpg',
 
90
  'images/08/001122.jpg',
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  'images/08/003533.jpg',
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  'images/08/003365.jpg',
 
46
 
47
  pred = model(batch).squeeze()
48
  # print(pred.shape)
49
+ pred = majority_pooling(pred, k_size=2)
50
+ fig = draw(pred, batch['fov_mask_2'])
51
 
52
 
53
  return fig
54
 
 
55
 
56
  description = """
57
  MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
58
  Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
59
+ The output is <b>downsampled by 2</b> for faster rendering. <b>Darker</b> colors represent the <b>scenery outside the Field of View</b>, i.e. not visible on the image.
60
  <center>
61
  <a href="https://cv-rits.github.io/MonoScene/">
62
  <img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
 
73
  """
74
 
75
  examples = [
76
+ 'images/08/001385.jpg',
77
+ 'images/08/000295.jpg',
78
+ 'images/08/002505.jpg',
79
  'images/08/000085.jpg',
80
  'images/08/000290.jpg',
81
  'images/08/000465.jpg',
 
84
  'images/08/001380.jpg',
85
  'images/08/001530.jpg',
86
  'images/08/002360.jpg',
 
87
  'images/08/004059.jpg',
88
  'images/08/003149.jpg',
89
  'images/08/001446.jpg',
90
+ 'images/08/000010.jpg',
91
  'images/08/001122.jpg',
92
  'images/08/003533.jpg',
93
  'images/08/003365.jpg',
helpers.py CHANGED
@@ -188,7 +188,7 @@ def draw(
188
  fov_mask,
189
  # img_size,
190
  # f,
191
- voxel_size=0.2,
192
  # d=7, # 7m - determine the size of the mesh representing the camera
193
  ):
194
 
 
188
  fov_mask,
189
  # img_size,
190
  # f,
191
+ voxel_size=0.4,
192
  # d=7, # 7m - determine the size of the mesh representing the camera
193
  ):
194
 
images/08/000295.jpg ADDED
images/08/001385.jpg ADDED
monoscene/__pycache__/CRP3D.cpython-37.pyc DELETED
Binary file (2.34 kB)
 
monoscene/__pycache__/DDR.cpython-37.pyc DELETED
Binary file (3.07 kB)
 
monoscene/__pycache__/__init__.cpython-37.pyc DELETED
Binary file (144 Bytes)
 
monoscene/__pycache__/config.cpython-37.pyc DELETED
Binary file (1.19 kB)
 
monoscene/__pycache__/flosp.cpython-37.pyc DELETED
Binary file (1.26 kB)
 
monoscene/__pycache__/modules.cpython-37.pyc DELETED
Binary file (6.39 kB)
 
monoscene/__pycache__/monoscene.cpython-37.pyc DELETED
Binary file (2.48 kB)
 
monoscene/__pycache__/monoscene_model.cpython-37.pyc DELETED
Binary file (953 Bytes)
 
monoscene/__pycache__/unet2d.cpython-37.pyc DELETED
Binary file (5.36 kB)
 
monoscene/__pycache__/unet3d_kitti.cpython-37.pyc DELETED
Binary file (2.01 kB)
 
monoscene/__pycache__/unet3d_nyu.cpython-37.pyc DELETED
Binary file (2.14 kB)
 
monoscene/monoscene.py CHANGED
@@ -96,15 +96,15 @@ class MonoScene(pl.LightningModule):
96
  if x3d is None:
97
  x3d = self.projects[str(scale_2d)](
98
  x_rgb["1_" + str(scale_2d)][i],
99
- torch.div(projected_pix, scale_2d, rounding_mode='floor'),
100
- # projected_pix // scale_2d,
101
  fov_mask,
102
  )
103
  else:
104
  x3d += self.projects[str(scale_2d)](
105
  x_rgb["1_" + str(scale_2d)][i],
106
- torch.div(projected_pix, scale_2d, rounding_mode='floor'),
107
- # projected_pix // scale_2d,
108
  fov_mask,
109
  )
110
  x3ds.append(x3d)
 
96
  if x3d is None:
97
  x3d = self.projects[str(scale_2d)](
98
  x_rgb["1_" + str(scale_2d)][i],
99
+ # torch.div(projected_pix, scale_2d, rounding_mode='floor'),
100
+ projected_pix // scale_2d,
101
  fov_mask,
102
  )
103
  else:
104
  x3d += self.projects[str(scale_2d)](
105
  x_rgb["1_" + str(scale_2d)][i],
106
+ # torch.div(projected_pix, scale_2d, rounding_mode='floor'),
107
+ projected_pix // scale_2d,
108
  fov_mask,
109
  )
110
  x3ds.append(x3d)