Source code for torch_geometric.transforms.to_superpixels

import torch
from torch_scatter import scatter_mean
from import Data

    from skimage.segmentation import slic
except ImportError:
    slic = None

[docs]class ToSLIC(object): r"""Converts an image to a superpixel representation using the :meth:`skimage.segmentation.slic` algorithm, resulting in a :obj:`` object holding the centroids of superpixels in :obj:`pos` and their mean color in :obj:`x`. This transform can be used with any :obj:`torchvision` dataset. Example:: from torchvision.datasets import MNIST import torchvision.transforms as T from torch_geometric.transforms import ToSLIC transform = T.Compose([T.ToTensor(), ToSLIC(n_segments=75)]) dataset = MNIST('/tmp/MNIST', download=True, transform=transform) Args: add_seg (bool, optional): If set to `True`, will add the segmentation result to the data object. (default: :obj:`False`) add_img (bool, optional): If set to `True`, will add the input image to the data object. (default: :obj:`False`) **kwargs (optional): Arguments to adjust the output of the SLIC algorithm. See the `SLIC documentation < #skimage.segmentation.slic>`_ for an overview. """ def __init__(self, add_seg=False, add_img=False, **kwargs): if slic is None: raise ImportError('`ToSlic` requires `scikit-image`.') self.add_seg = add_seg self.add_img = add_img self.kwargs = kwargs def __call__(self, img): img = img.permute(1, 2, 0) h, w, c = img.size() seg = slic(, **self.kwargs) seg = torch.from_numpy(seg) x = scatter_mean(img.view(h * w, c), seg.view(h * w), dim=0) pos_y = torch.arange(h, dtype=torch.float) pos_y = pos_y.view(-1, 1).repeat(1, w).view(h * w) pos_x = torch.arange(w, dtype=torch.float) pos_x = pos_x.view(1, -1).repeat(h, 1).view(h * w) pos = torch.stack([pos_x, pos_y], dim=-1) pos = scatter_mean(pos, seg.view(h * w), dim=0) data = Data(x=x, pos=pos) if self.add_seg: data.seg = seg.view(1, h, w) if self.add_img: data.img = img.permute(2, 0, 1).view(1, c, h, w) return data