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e4a653e
downsample output
Browse files- __pycache__/fusion.cpython-37.pyc +0 -0
- app.py +7 -6
- helpers.py +1 -1
- images/08/000295.jpg +0 -0
- images/08/001385.jpg +0 -0
- monoscene/__pycache__/CRP3D.cpython-37.pyc +0 -0
- monoscene/__pycache__/DDR.cpython-37.pyc +0 -0
- monoscene/__pycache__/__init__.cpython-37.pyc +0 -0
- monoscene/__pycache__/config.cpython-37.pyc +0 -0
- monoscene/__pycache__/flosp.cpython-37.pyc +0 -0
- monoscene/__pycache__/modules.cpython-37.pyc +0 -0
- monoscene/__pycache__/monoscene.cpython-37.pyc +0 -0
- monoscene/__pycache__/monoscene_model.cpython-37.pyc +0 -0
- monoscene/__pycache__/unet2d.cpython-37.pyc +0 -0
- monoscene/__pycache__/unet3d_kitti.cpython-37.pyc +0 -0
- monoscene/__pycache__/unet3d_nyu.cpython-37.pyc +0 -0
- monoscene/monoscene.py +4 -4
__pycache__/fusion.cpython-37.pyc
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app.py
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@@ -46,18 +46,17 @@ def predict(img):
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pred = model(batch).squeeze()
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# print(pred.shape)
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-
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fig = draw(pred, batch['
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return fig
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# The output is <b>downsampled by 2</b> to be able to be rendered in browsers.
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description = """
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MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
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Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
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<b>Darker</b> colors represent the <b>scenery outside the Field of View</b>, i.e. not visible on the image.
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<center>
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<a href="https://cv-rits.github.io/MonoScene/">
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<img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
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@@ -74,7 +73,9 @@ article="""
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"""
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examples = [
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'images/08/
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'images/08/000085.jpg',
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'images/08/000290.jpg',
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'images/08/000465.jpg',
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@@ -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',
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'images/08/001122.jpg',
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'images/08/003533.jpg',
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'images/08/003365.jpg',
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pred = model(batch).squeeze()
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# print(pred.shape)
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pred = majority_pooling(pred, k_size=2)
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fig = draw(pred, batch['fov_mask_2'])
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return fig
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description = """
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MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
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Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
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+
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.
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<center>
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<a href="https://cv-rits.github.io/MonoScene/">
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<img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
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"""
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examples = [
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'images/08/001385.jpg',
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'images/08/000295.jpg',
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'images/08/002505.jpg',
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'images/08/000085.jpg',
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'images/08/000290.jpg',
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'images/08/000465.jpg',
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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/08/004059.jpg',
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'images/08/003149.jpg',
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'images/08/001446.jpg',
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'images/08/000010.jpg',
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'images/08/001122.jpg',
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'images/08/003533.jpg',
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'images/08/003365.jpg',
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helpers.py
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@@ -188,7 +188,7 @@ def draw(
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fov_mask,
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# img_size,
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# f,
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voxel_size=0.
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# d=7, # 7m - determine the size of the mesh representing the camera
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):
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fov_mask,
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# img_size,
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# f,
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voxel_size=0.4,
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# d=7, # 7m - determine the size of the mesh representing the camera
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):
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images/08/000295.jpg
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images/08/001385.jpg
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monoscene/__pycache__/CRP3D.cpython-37.pyc
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monoscene/__pycache__/DDR.cpython-37.pyc
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monoscene/__pycache__/__init__.cpython-37.pyc
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monoscene/__pycache__/config.cpython-37.pyc
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monoscene/__pycache__/flosp.cpython-37.pyc
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monoscene/__pycache__/modules.cpython-37.pyc
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monoscene/__pycache__/monoscene.cpython-37.pyc
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monoscene/__pycache__/monoscene_model.cpython-37.pyc
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monoscene/__pycache__/unet2d.cpython-37.pyc
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monoscene/__pycache__/unet3d_kitti.cpython-37.pyc
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monoscene/__pycache__/unet3d_nyu.cpython-37.pyc
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monoscene/monoscene.py
CHANGED
@@ -96,15 +96,15 @@ class MonoScene(pl.LightningModule):
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if x3d is None:
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x3d = self.projects[str(scale_2d)](
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x_rgb["1_" + str(scale_2d)][i],
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torch.div(projected_pix, scale_2d, rounding_mode='floor'),
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-
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fov_mask,
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)
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else:
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x3d += self.projects[str(scale_2d)](
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x_rgb["1_" + str(scale_2d)][i],
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torch.div(projected_pix, scale_2d, rounding_mode='floor'),
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fov_mask,
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)
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x3ds.append(x3d)
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if x3d is None:
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x3d = self.projects[str(scale_2d)](
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x_rgb["1_" + str(scale_2d)][i],
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# torch.div(projected_pix, scale_2d, rounding_mode='floor'),
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projected_pix // scale_2d,
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fov_mask,
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)
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else:
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x3d += self.projects[str(scale_2d)](
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x_rgb["1_" + str(scale_2d)][i],
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# torch.div(projected_pix, scale_2d, rounding_mode='floor'),
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projected_pix // scale_2d,
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fov_mask,
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)
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x3ds.append(x3d)
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