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9b6010ea0d
* changes * pair pixel sampling with a mask * reverting changes * revert changes * remove line in erode * support for patch pixel samplerr masks * fix --------- Co-authored-by: machenmusik <machenmusik@users.noreply.github.com>
66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Pixel sampling utils such as eroding of valid masks that we sample from. """
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import torch
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from torch import Tensor
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from jaxtyping import Float
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def dilate(tensor: Float[Tensor, "bs 1 H W"], kernel_size=3) -> Float[Tensor, "bs 1 H W"]:
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"""Dilate a tensor with 0s and 1s. 0s will be be expanded based on the kernel size.
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Args:
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kernel_size: Size of the pooling region. Dilates/contracts 1 pixel if kernel_size is 3.
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"""
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unique_vals = torch.unique(tensor)
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if any(val not in (0, 1) for val in unique_vals) or tensor.dtype != torch.float32:
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raise ValueError("Input tensor should contain only values 0 and 1, and should have dtype torch.float32.")
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return torch.nn.functional.max_pool2d(tensor, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2)
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def erode(tensor: Float[Tensor, "bs 1 H W"], kernel_size=3) -> Float[Tensor, "bs 1 H W"]:
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"""Erode a tensor with 0s and 1s. 1s will be expanded based on the kernel size.
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Args:
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kernel_size: Size of the pooling region. Erodes/expands 1 pixel if kernel_size is 3.
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"""
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unique_vals = torch.unique(tensor)
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if any(val not in (0, 1) for val in unique_vals) or tensor.dtype != torch.float32:
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raise ValueError("Input tensor should contain only values 0 and 1, and should have dtype torch.float32.")
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x = 1 - dilate(1 - tensor, kernel_size=kernel_size)
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# set edge pixels to 0
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p = (kernel_size - 1) // 2
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x[:, :, :p, :] *= 0
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x[:, :, :, :p] *= 0
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x[:, :, -p:, :] *= 0
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x[:, :, :, -p:] *= 0
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return x
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def erode_mask(tensor: Float[Tensor, "bs 1 H W"], pixel_radius: int = 1):
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"""Erode a mask. Expands 1 values to nearby pixels with a max pooling operation.
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A pixel radius of 1 will also extend the 1s along the diagonal.
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Args:
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pixel_radius: The number of pixels away from valid pixels (1s) that we may sample.
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"""
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kernel_size = 1 + 2 * pixel_radius
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return erode(tensor, kernel_size=kernel_size)
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