Source code for monai.transforms.post.array

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
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"""
A collection of "vanilla" transforms for the model output tensors
https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
"""

import warnings
from typing import Callable, Iterable, Optional, Sequence, Union

import numpy as np
import torch

from monai.config.type_definitions import NdarrayOrTensor
from monai.networks import one_hot
from monai.networks.layers import GaussianFilter, apply_filter
from monai.transforms.transform import Transform
from monai.transforms.utils import fill_holes, get_largest_connected_component_mask
from monai.transforms.utils_pytorch_numpy_unification import unravel_index
from monai.utils import TransformBackends, convert_data_type, deprecated_arg, ensure_tuple, look_up_option
from monai.utils.type_conversion import convert_to_dst_type

__all__ = [
    "Activations",
    "AsDiscrete",
    "FillHoles",
    "KeepLargestConnectedComponent",
    "LabelFilter",
    "LabelToContour",
    "MeanEnsemble",
    "ProbNMS",
    "VoteEnsemble",
]


[docs]class Activations(Transform): """ Add activation operations to the model output, typically `Sigmoid` or `Softmax`. Args: sigmoid: whether to execute sigmoid function on model output before transform. Defaults to ``False``. softmax: whether to execute softmax function on model output before transform. Defaults to ``False``. other: callable function to execute other activation layers, for example: `other = lambda x: torch.tanh(x)`. Defaults to ``None``. Raises: TypeError: When ``other`` is not an ``Optional[Callable]``. """ backend = [TransformBackends.TORCH] def __init__(self, sigmoid: bool = False, softmax: bool = False, other: Optional[Callable] = None) -> None: self.sigmoid = sigmoid self.softmax = softmax if other is not None and not callable(other): raise TypeError(f"other must be None or callable but is {type(other).__name__}.") self.other = other
[docs] def __call__( self, img: NdarrayOrTensor, sigmoid: Optional[bool] = None, softmax: Optional[bool] = None, other: Optional[Callable] = None, ) -> NdarrayOrTensor: """ Args: sigmoid: whether to execute sigmoid function on model output before transform. Defaults to ``self.sigmoid``. softmax: whether to execute softmax function on model output before transform. Defaults to ``self.softmax``. other: callable function to execute other activation layers, for example: `other = torch.tanh`. Defaults to ``self.other``. Raises: ValueError: When ``sigmoid=True`` and ``softmax=True``. Incompatible values. TypeError: When ``other`` is not an ``Optional[Callable]``. ValueError: When ``self.other=None`` and ``other=None``. Incompatible values. """ if sigmoid and softmax: raise ValueError("Incompatible values: sigmoid=True and softmax=True.") if other is not None and not callable(other): raise TypeError(f"other must be None or callable but is {type(other).__name__}.") # convert to float as activation must operate on float tensor img_t: torch.Tensor img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float) # type: ignore if sigmoid or self.sigmoid: img_t = torch.sigmoid(img_t) if softmax or self.softmax: img_t = torch.softmax(img_t, dim=0) act_func = self.other if other is None else other if act_func is not None: img_t = act_func(img_t) out, *_ = convert_to_dst_type(img_t, img) return out
[docs]class AsDiscrete(Transform): """ Execute after model forward to transform model output to discrete values. It can complete below operations: - execute `argmax` for input logits values. - threshold input value to 0.0 or 1.0. - convert input value to One-Hot format. - round the value to the closest integer. Args: argmax: whether to execute argmax function on input data before transform. Defaults to ``False``. to_onehot: if not None, convert input data into the one-hot format with specified number of classes. Defaults to ``None``. threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold. Defaults to ``None``. rounding: if not None, round the data according to the specified option, available options: ["torchrounding"]. Example: >>> transform = AsDiscrete(argmax=True) >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]]))) # [[[1.0, 1.0]]] >>> transform = AsDiscrete(threshold=0.6) >>> print(transform(np.array([[[0.0, 0.5], [0.8, 3.0]]]))) # [[[0.0, 0.0], [1.0, 1.0]]] >>> transform = AsDiscrete(argmax=True, to_onehot=2, threshold=0.5) >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]]))) # [[[0.0, 0.0]], [[1.0, 1.0]]] .. deprecated:: 0.6.0 ``n_classes`` is deprecated, use ``to_onehot`` instead. .. deprecated:: 0.7.0 ``num_classes`` is deprecated, use ``to_onehot`` instead. ``logit_thresh`` is deprecated, use ``threshold`` instead. ``threshold_values`` is deprecated, use ``threshold`` instead. """ backend = [TransformBackends.TORCH] @deprecated_arg(name="n_classes", new_name="num_classes", since="0.6", msg_suffix="please use `to_onehot` instead.") @deprecated_arg("num_classes", since="0.7", msg_suffix="please use `to_onehot` instead.") @deprecated_arg("logit_thresh", since="0.7", msg_suffix="please use `threshold` instead.") @deprecated_arg( name="threshold_values", new_name="threshold", since="0.7", msg_suffix="please use `threshold` instead." ) def __init__( self, argmax: bool = False, to_onehot: Optional[int] = None, threshold: Optional[float] = None, rounding: Optional[str] = None, n_classes: Optional[int] = None, # deprecated num_classes: Optional[int] = None, # deprecated logit_thresh: float = 0.5, # deprecated threshold_values: Optional[bool] = False, # deprecated ) -> None: self.argmax = argmax if isinstance(to_onehot, bool): # for backward compatibility warnings.warn("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.") to_onehot = num_classes if to_onehot else None self.to_onehot = to_onehot if isinstance(threshold, bool): # for backward compatibility warnings.warn("`threshold_values=True/False` is deprecated, please use `threshold=value` instead.") threshold = logit_thresh if threshold else None self.threshold = threshold self.rounding = rounding
[docs] @deprecated_arg(name="n_classes", new_name="num_classes", since="0.6", msg_suffix="please use `to_onehot` instead.") @deprecated_arg("num_classes", since="0.7", msg_suffix="please use `to_onehot` instead.") @deprecated_arg("logit_thresh", since="0.7", msg_suffix="please use `threshold` instead.") @deprecated_arg( name="threshold_values", new_name="threshold", since="0.7", msg_suffix="please use `threshold` instead." ) def __call__( self, img: NdarrayOrTensor, argmax: Optional[bool] = None, to_onehot: Optional[int] = None, threshold: Optional[float] = None, rounding: Optional[str] = None, n_classes: Optional[int] = None, # deprecated num_classes: Optional[int] = None, # deprecated logit_thresh: Optional[float] = None, # deprecated threshold_values: Optional[bool] = None, # deprecated ) -> NdarrayOrTensor: """ Args: img: the input tensor data to convert, if no channel dimension when converting to `One-Hot`, will automatically add it. argmax: whether to execute argmax function on input data before transform. Defaults to ``self.argmax``. to_onehot: if not None, convert input data into the one-hot format with specified number of classes. Defaults to ``self.to_onehot``. threshold: if not None, threshold the float values to int number 0 or 1 with specified threshold value. Defaults to ``self.threshold``. rounding: if not None, round the data according to the specified option, available options: ["torchrounding"]. .. deprecated:: 0.6.0 ``n_classes`` is deprecated, use ``to_onehot`` instead. .. deprecated:: 0.7.0 ``num_classes`` is deprecated, use ``to_onehot`` instead. ``logit_thresh`` is deprecated, use ``threshold`` instead. ``threshold_values`` is deprecated, use ``threshold`` instead. """ if isinstance(to_onehot, bool): warnings.warn("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.") to_onehot = num_classes if to_onehot else None if isinstance(threshold, bool): warnings.warn("`threshold_values=True/False` is deprecated, please use `threshold=value` instead.") threshold = logit_thresh if threshold else None img_t: torch.Tensor img_t, *_ = convert_data_type(img, torch.Tensor) # type: ignore if argmax or self.argmax: img_t = torch.argmax(img_t, dim=0, keepdim=True) to_onehot = self.to_onehot if to_onehot is None else to_onehot if to_onehot is not None: if not isinstance(to_onehot, int): raise AssertionError("the number of classes for One-Hot must be an integer.") img_t = one_hot(img_t, num_classes=to_onehot, dim=0) threshold = self.threshold if threshold is None else threshold if threshold is not None: img_t = img_t >= threshold rounding = self.rounding if rounding is None else rounding if rounding is not None: look_up_option(rounding, ["torchrounding"]) img_t = torch.round(img_t) img, *_ = convert_to_dst_type(img_t, img, dtype=torch.float) return img
[docs]class KeepLargestConnectedComponent(Transform): """ Keeps only the largest connected component in the image. This transform can be used as a post-processing step to clean up over-segment areas in model output. The input is assumed to be a channel-first PyTorch Tensor: 1) For not OneHot format data, the values correspond to expected labels, 0 will be treated as background and the over-segment pixels will be set to 0. 2) For OneHot format data, the values should be 0, 1 on each labels, the over-segment pixels will be set to 0 in its channel. For example: Use with applied_labels=[1], is_onehot=False, connectivity=1:: [1, 0, 0] [0, 0, 0] [0, 1, 1] => [0, 1 ,1] [0, 1, 1] [0, 1, 1] Use with applied_labels=[1, 2], is_onehot=False, independent=False, connectivity=1:: [0, 0, 1, 0 ,0] [0, 0, 1, 0 ,0] [0, 2, 1, 1 ,1] [0, 2, 1, 1 ,1] [1, 2, 1, 0 ,0] => [1, 2, 1, 0 ,0] [1, 2, 0, 1 ,0] [1, 2, 0, 0 ,0] [2, 2, 0, 0 ,2] [2, 2, 0, 0 ,0] Use with applied_labels=[1, 2], is_onehot=False, independent=True, connectivity=1:: [0, 0, 1, 0 ,0] [0, 0, 1, 0 ,0] [0, 2, 1, 1 ,1] [0, 2, 1, 1 ,1] [1, 2, 1, 0 ,0] => [0, 2, 1, 0 ,0] [1, 2, 0, 1 ,0] [0, 2, 0, 0 ,0] [2, 2, 0, 0 ,2] [2, 2, 0, 0 ,0] Use with applied_labels=[1, 2], is_onehot=False, independent=False, connectivity=2:: [0, 0, 1, 0 ,0] [0, 0, 1, 0 ,0] [0, 2, 1, 1 ,1] [0, 2, 1, 1 ,1] [1, 2, 1, 0 ,0] => [1, 2, 1, 0 ,0] [1, 2, 0, 1 ,0] [1, 2, 0, 1 ,0] [2, 2, 0, 0 ,2] [2, 2, 0, 0 ,2] """ backend = [TransformBackends.NUMPY]
[docs] def __init__( self, applied_labels: Union[Sequence[int], int], is_onehot: Optional[bool] = None, independent: bool = True, connectivity: Optional[int] = None, ) -> None: """ Args: applied_labels: Labels for applying the connected component analysis on. If not OneHot. The pixel whose value is in this list will be analyzed. If the data is in OneHot format, this is used to determine which channels to apply. is_onehot: if `True`, treat the input data as OneHot format data, otherwise, not OneHot format data. default to None, which treats multi-channel data as OneHot and single channel data as not OneHot. independent: whether to treat ``applied_labels`` as a union of foreground labels. If ``True``, the connected component analysis will be performed on each foreground label independently and return the intersection of the largest components. If ``False``, the analysis will be performed on the union of foreground labels. default is `True`. connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If ``None``, a full connectivity of ``input.ndim`` is used. for more details: https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label. """ super().__init__() self.applied_labels = ensure_tuple(applied_labels) self.is_onehot = is_onehot self.independent = independent self.connectivity = connectivity
[docs] def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Args: img: shape must be (C, spatial_dim1[, spatial_dim2, ...]). Returns: An array with shape (C, spatial_dim1[, spatial_dim2, ...]). """ is_onehot = img.shape[0] > 1 if self.is_onehot is None else self.is_onehot if self.independent: for i in self.applied_labels: foreground = img[i] > 0 if is_onehot else img[0] == i mask = get_largest_connected_component_mask(foreground, self.connectivity) if is_onehot: img[i][foreground != mask] = 0 else: img[0][foreground != mask] = 0 return img if not is_onehot: # not one-hot, union of labels labels, *_ = convert_to_dst_type(self.applied_labels, dst=img, wrap_sequence=True) foreground = (img[..., None] == labels).any(-1)[0] mask = get_largest_connected_component_mask(foreground, self.connectivity) img[0][foreground != mask] = 0 return img # one-hot, union of labels foreground = (img[self.applied_labels, ...] == 1).any(0) mask = get_largest_connected_component_mask(foreground, self.connectivity) for i in self.applied_labels: img[i][foreground != mask] = 0 return img
[docs]class LabelFilter: """ This transform filters out labels and can be used as a processing step to view only certain labels. The list of applied labels defines which labels will be kept. Note: All labels which do not match the `applied_labels` are set to the background label (0). For example: Use LabelFilter with applied_labels=[1, 5, 9]:: [1, 2, 3] [1, 0, 0] [4, 5, 6] => [0, 5 ,0] [7, 8, 9] [0, 0, 9] """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs] def __init__(self, applied_labels: Union[Iterable[int], int]) -> None: """ Initialize the LabelFilter class with the labels to filter on. Args: applied_labels: Label(s) to filter on. """ self.applied_labels = ensure_tuple(applied_labels)
[docs] def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Filter the image on the `applied_labels`. Args: img: Pytorch tensor or numpy array of any shape. Raises: NotImplementedError: The provided image was not a Pytorch Tensor or numpy array. Returns: Pytorch tensor or numpy array of the same shape as the input. """ if not isinstance(img, (np.ndarray, torch.Tensor)): raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") if isinstance(img, torch.Tensor): if hasattr(torch, "isin"): # `isin` is new in torch 1.10.0 appl_lbls = torch.as_tensor(self.applied_labels, device=img.device) return torch.where(torch.isin(img, appl_lbls), img, torch.tensor(0.0).to(img)) else: out = self(img.detach().cpu().numpy()) out, *_ = convert_to_dst_type(out, img) return out return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0))
[docs]class FillHoles(Transform): r""" This transform fills holes in the image and can be used to remove artifacts inside segments. An enclosed hole is defined as a background pixel/voxel which is only enclosed by a single class. The definition of enclosed can be defined with the connectivity parameter:: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ] [ ] [ ] | \ | / | <- hop 2 [ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ] | / | \ hop 1 [ ] [ ] [ ] [ ] It is possible to define for which labels the hole filling should be applied. The input image is assumed to be a PyTorch Tensor or numpy array with shape [C, spatial_dim1[, spatial_dim2, ...]]. If C = 1, then the values correspond to expected labels. If C > 1, then a one-hot-encoding is expected where the index of C matches the label indexing. Note: The label 0 will be treated as background and the enclosed holes will be set to the neighboring class label. The performance of this method heavily depends on the number of labels. It is a bit faster if the list of `applied_labels` is provided. Limiting the number of `applied_labels` results in a big decrease in processing time. For example: Use FillHoles with default parameters:: [1, 1, 1, 2, 2, 2, 3, 3] [1, 1, 1, 2, 2, 2, 3, 3] [1, 0, 1, 2, 0, 0, 3, 0] => [1, 1 ,1, 2, 0, 0, 3, 0] [1, 1, 1, 2, 2, 2, 3, 3] [1, 1, 1, 2, 2, 2, 3, 3] The hole in label 1 is fully enclosed and therefore filled with label 1. The background label near label 2 and 3 is not fully enclosed and therefore not filled. """ backend = [TransformBackends.NUMPY]
[docs] def __init__( self, applied_labels: Optional[Union[Iterable[int], int]] = None, connectivity: Optional[int] = None ) -> None: """ Initialize the connectivity and limit the labels for which holes are filled. Args: applied_labels: Labels for which to fill holes. Defaults to None, that is filling holes for all labels. connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. Defaults to a full connectivity of ``input.ndim``. """ super().__init__() self.applied_labels = ensure_tuple(applied_labels) if applied_labels else None self.connectivity = connectivity
[docs] def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Fill the holes in the provided image. Note: The value 0 is assumed as background label. Args: img: Pytorch Tensor or numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]]. Raises: NotImplementedError: The provided image was not a Pytorch Tensor or numpy array. Returns: Pytorch Tensor or numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]]. """ if not isinstance(img, (np.ndarray, torch.Tensor)): raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") img_np: np.ndarray img_np, *_ = convert_data_type(img, np.ndarray) # type: ignore out_np: np.ndarray = fill_holes(img_np, self.applied_labels, self.connectivity) out, *_ = convert_to_dst_type(out_np, img) return out
[docs]class LabelToContour(Transform): """ Return the contour of binary input images that only compose of 0 and 1, with Laplacian kernel set as default for edge detection. Typical usage is to plot the edge of label or segmentation output. Args: kernel_type: the method applied to do edge detection, default is "Laplace". Raises: NotImplementedError: When ``kernel_type`` is not "Laplace". """ backend = [TransformBackends.TORCH] def __init__(self, kernel_type: str = "Laplace") -> None: if kernel_type != "Laplace": raise NotImplementedError('Currently only kernel_type="Laplace" is supported.') self.kernel_type = kernel_type
[docs] def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Args: img: torch tensor data to extract the contour, with shape: [channels, height, width[, depth]] Raises: ValueError: When ``image`` ndim is not one of [3, 4]. Returns: A torch tensor with the same shape as img, note: 1. it's the binary classification result of whether a pixel is edge or not. 2. in order to keep the original shape of mask image, we use padding as default. 3. the edge detection is just approximate because it defects inherent to Laplace kernel, ideally the edge should be thin enough, but now it has a thickness. """ img_: torch.Tensor = convert_data_type(img, torch.Tensor)[0] # type: ignore spatial_dims = len(img_.shape) - 1 img_ = img_.unsqueeze(0) # adds a batch dim if spatial_dims == 2: kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32) elif spatial_dims == 3: kernel = -1.0 * torch.ones(3, 3, 3, dtype=torch.float32) kernel[1, 1, 1] = 26.0 else: raise ValueError(f"{self.__class__} can only handle 2D or 3D images.") contour_img = apply_filter(img_, kernel) contour_img.clamp_(min=0.0, max=1.0) output, *_ = convert_to_dst_type(contour_img.squeeze(0), img) return output
class Ensemble: @staticmethod def get_stacked_torch(img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> torch.Tensor: """Get either a sequence or single instance of np.ndarray/torch.Tensor. Return single torch.Tensor.""" if isinstance(img, Sequence) and isinstance(img[0], np.ndarray): img = [torch.as_tensor(i) for i in img] elif isinstance(img, np.ndarray): img = torch.as_tensor(img) out: torch.Tensor = torch.stack(img) if isinstance(img, Sequence) else img # type: ignore return out @staticmethod def post_convert(img: torch.Tensor, orig_img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor: orig_img_ = orig_img[0] if isinstance(orig_img, Sequence) else orig_img out, *_ = convert_to_dst_type(img, orig_img_) return out
[docs]class MeanEnsemble(Ensemble, Transform): """ Execute mean ensemble on the input data. The input data can be a list or tuple of PyTorch Tensor with shape: [C[, H, W, D]], Or a single PyTorch Tensor with shape: [E, C[, H, W, D]], the `E` dimension represents the output data from different models. Typically, the input data is model output of segmentation task or classification task. And it also can support to add `weights` for the input data. Args: weights: can be a list or tuple of numbers for input data with shape: [E, C, H, W[, D]]. or a Numpy ndarray or a PyTorch Tensor data. the `weights` will be added to input data from highest dimension, for example: 1. if the `weights` only has 1 dimension, it will be added to the `E` dimension of input data. 2. if the `weights` has 2 dimensions, it will be added to `E` and `C` dimensions. it's a typical practice to add weights for different classes: to ensemble 3 segmentation model outputs, every output has 4 channels(classes), so the input data shape can be: [3, 4, H, W, D]. and add different `weights` for different classes, so the `weights` shape can be: [3, 4]. for example: `weights = [[1, 2, 3, 4], [4, 3, 2, 1], [1, 1, 1, 1]]`. """ backend = [TransformBackends.TORCH] def __init__(self, weights: Optional[Union[Sequence[float], NdarrayOrTensor]] = None) -> None: self.weights = torch.as_tensor(weights, dtype=torch.float) if weights is not None else None
[docs] def __call__(self, img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor: img_ = self.get_stacked_torch(img) if self.weights is not None: self.weights = self.weights.to(img_.device) shape = tuple(self.weights.shape) for _ in range(img_.ndimension() - self.weights.ndimension()): shape += (1,) weights = self.weights.reshape(*shape) img_ = img_ * weights / weights.mean(dim=0, keepdim=True) out_pt = torch.mean(img_, dim=0) return self.post_convert(out_pt, img)
[docs]class VoteEnsemble(Ensemble, Transform): """ Execute vote ensemble on the input data. The input data can be a list or tuple of PyTorch Tensor with shape: [C[, H, W, D]], Or a single PyTorch Tensor with shape: [E[, C, H, W, D]], the `E` dimension represents the output data from different models. Typically, the input data is model output of segmentation task or classification task. Note: This vote transform expects the input data is discrete values. It can be multiple channels data in One-Hot format or single channel data. It will vote to select the most common data between items. The output data has the same shape as every item of the input data. Args: num_classes: if the input is single channel data instead of One-Hot, we can't get class number from channel, need to explicitly specify the number of classes to vote. """ backend = [TransformBackends.TORCH] def __init__(self, num_classes: Optional[int] = None) -> None: self.num_classes = num_classes
[docs] def __call__(self, img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor: img_ = self.get_stacked_torch(img) if self.num_classes is not None: has_ch_dim = True if img_.ndimension() > 1 and img_.shape[1] > 1: warnings.warn("no need to specify num_classes for One-Hot format data.") else: if img_.ndimension() == 1: # if no channel dim, need to remove channel dim after voting has_ch_dim = False img_ = one_hot(img_, self.num_classes, dim=1) img_ = torch.mean(img_.float(), dim=0) if self.num_classes is not None: # if not One-Hot, use "argmax" to vote the most common class out_pt = torch.argmax(img_, dim=0, keepdim=has_ch_dim) else: # for One-Hot data, round the float number to 0 or 1 out_pt = torch.round(img_) return self.post_convert(out_pt, img)
[docs]class ProbNMS(Transform): """ Performs probability based non-maximum suppression (NMS) on the probabilities map via iteratively selecting the coordinate with highest probability and then move it as well as its surrounding values. The remove range is determined by the parameter `box_size`. If multiple coordinates have the same highest probability, only one of them will be selected. Args: spatial_dims: number of spatial dimensions of the input probabilities map. Defaults to 2. sigma: the standard deviation for gaussian filter. It could be a single value, or `spatial_dims` number of values. Defaults to 0.0. prob_threshold: the probability threshold, the function will stop searching if the highest probability is no larger than the threshold. The value should be no less than 0.0. Defaults to 0.5. box_size: the box size (in pixel) to be removed around the the pixel with the maximum probability. It can be an integer that defines the size of a square or cube, or a list containing different values for each dimensions. Defaults to 48. Return: a list of selected lists, where inner lists contain probability and coordinates. For example, for 3D input, the inner lists are in the form of [probability, x, y, z]. Raises: ValueError: When ``prob_threshold`` is less than 0.0. ValueError: When ``box_size`` is a list or tuple, and its length is not equal to `spatial_dims`. ValueError: When ``box_size`` has a less than 1 value. """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY] def __init__( self, spatial_dims: int = 2, sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0, prob_threshold: float = 0.5, box_size: Union[int, Sequence[int]] = 48, ) -> None: self.sigma = sigma self.spatial_dims = spatial_dims if self.sigma != 0: self.filter = GaussianFilter(spatial_dims=spatial_dims, sigma=sigma) if prob_threshold < 0: raise ValueError("prob_threshold should be no less than 0.0.") self.prob_threshold = prob_threshold if isinstance(box_size, int): self.box_size = np.asarray([box_size] * spatial_dims) elif len(box_size) != spatial_dims: raise ValueError("the sequence length of box_size should be the same as spatial_dims.") else: self.box_size = np.asarray(box_size) if self.box_size.min() <= 0: raise ValueError("box_size should be larger than 0.") self.box_lower_bd = self.box_size // 2 self.box_upper_bd = self.box_size - self.box_lower_bd def __call__(self, prob_map: NdarrayOrTensor): """ prob_map: the input probabilities map, it must have shape (H[, W, ...]). """ if self.sigma != 0: if not isinstance(prob_map, torch.Tensor): prob_map = torch.as_tensor(prob_map, dtype=torch.float) self.filter.to(prob_map) prob_map = self.filter(prob_map) prob_map_shape = prob_map.shape outputs = [] while prob_map.max() > self.prob_threshold: max_idx = unravel_index(prob_map.argmax(), prob_map_shape) prob_max = prob_map[tuple(max_idx)] max_idx = max_idx.cpu().numpy() if isinstance(max_idx, torch.Tensor) else max_idx prob_max = prob_max.item() if isinstance(prob_max, torch.Tensor) else prob_max outputs.append([prob_max] + list(max_idx)) idx_min_range = (max_idx - self.box_lower_bd).clip(0, None) idx_max_range = (max_idx + self.box_upper_bd).clip(None, prob_map_shape) # for each dimension, set values during index ranges to 0 slices = tuple(slice(idx_min_range[i], idx_max_range[i]) for i in range(self.spatial_dims)) prob_map[slices] = 0 return outputs