Source code for rastervision.pytorch_learner.dataset.visualizer.semantic_segmentation_visualizer

from typing import (Sequence, Optional, Union)

import torch
import numpy as np
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches

from rastervision.pytorch_learner.dataset.visualizer import Visualizer  # NOQA
from rastervision.pytorch_learner.utils import (
    color_to_triple, plot_channel_groups, channel_groups_to_imgs)

[docs]class SemanticSegmentationVisualizer(Visualizer): """Plots samples from semantic segmentation Datasets."""
[docs] def plot_xyz(self, axs: Sequence, x: torch.Tensor, y: Union[torch.Tensor, np.ndarray], z: Optional[torch.Tensor] = None) -> None: channel_groups = self.get_channel_display_groups(x.shape[1]) img_axes = axs[:len(channel_groups)] label_ax = axs[len(channel_groups)] # plot image imgs = channel_groups_to_imgs(x, channel_groups) plot_channel_groups(img_axes, imgs, channel_groups) # plot labels class_colors = self.class_colors colors = [ color_to_triple(c) if isinstance(c, str) else c for c in class_colors ] colors = np.array(colors) / 255. cmap = mcolors.ListedColormap(colors) label_ax.imshow( y, vmin=0, vmax=len(colors), cmap=cmap, interpolation='none') label_ax.set_title(f'Ground truth') label_ax.set_xticks([]) label_ax.set_yticks([]) # plot predictions if z is not None: pred_ax = axs[-1] preds = z.argmax(dim=0) pred_ax.imshow( preds, vmin=0, vmax=len(colors), cmap=cmap, interpolation='none') pred_ax.set_title(f'Predicted labels') pred_ax.set_xticks([]) pred_ax.set_yticks([]) # add a legend to the rightmost subplot class_names = self.class_names if class_names: legend_items = [ mpatches.Patch(facecolor=col, edgecolor='black', label=name) for col, name in zip(colors, class_names) ] axs[-1].legend( handles=legend_items, loc='center right', bbox_to_anchor=(1.8, 0.5))
[docs] def get_plot_ncols(self, **kwargs) -> int: x = kwargs['x'] nb_img_channels = x.shape[1] ncols = len(self.get_channel_display_groups(nb_img_channels)) + 1 z = kwargs.get('z') if z is not None: ncols += 1 return ncols