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import numpy as np
import torch
from typing import Dict
from torch import Tensor
from .utility import chance
def remap_intesities(
image: Tensor,
bins: int = 256) -> Tensor:
"""
Remap image intensities to a random distribution.
Parameters
----------
image : torch.Tensor
Image to be remapped
bins : int, optional
The number of intensity bins to use when remapping the image.
The default value of 256 is appropriate for 8-bit images.
Returns
-------
torch.Tensor
Remapped image
"""
device = image.device
image = image.type(torch.float32) - image.min()
image /= image.max()
image *= (bins - 1)
image = image.type(torch.int64)
# these were somewhat arbitrarily chosen based on
# testing a few values
samples = 2
radians = 1
# generate noise
noise = torch.ones(bins, device=device, dtype=torch.float32)
for i in range(samples):
low = np.random.uniform(-radians, 0)
high = np.random.uniform(0, radians)
noise *= torch.sin(torch.linspace(low, high, bins,
device=device, dtype=torch.float32))
noise -= noise.min()
noise /= noise.max()
return noise[image]
def densities_to_image(densities: Tensor) -> Tensor:
"""
Replace density values with random signal between 0 and 1.
Parameters
----------
densities : torch.Tensor
Multi-channel image of class density (probability) values
Returns
-------
torch.Tensor
Synthetic image with the same geometry as the input `densities` tensor.
"""
dims = [1] * (densities.ndim - 1)
intensities = torch.rand(densities.shape[0], *dims)
return torch.sum(densities * intensities, axis=0).unsqueeze(0)
def labels_to_image(
labels: Tensor,
intensity_ranges: Dict = None) -> Tensor:
"""
Replace segmentation labels with random signal.
Parameters
----------
labels : torch.Tensor
Image of integer segmentation labels
intensity_ranges : dict, optional
A dictionary mapping label values to intensity range tuples (low, high).
Intensity values will be randomly generated within the specified range
for each label. If not provided, a random intensity will be chosen for
each label.
Returns
-------
torch.Tensor
Synthetic image with the same shape as the input `labels` tensor.
"""
labels = labels.type(torch.int64)
max_label = labels.max() + 1
if intensity_ranges is not None:
mapping = torch.zeros(max_label, device=labels.device, dtype=torch.float32)
for k, (low, high) in intensity_ranges.items():
mapping[k] = np.random.uniform(low, high)
else:
mapping = torch.rand(max_label, device=labels.device, dtype=torch.float32)
image = mapping[labels]
return image
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