# Copyright (c) OpenMMLab. All rights reserved. from typing import Dict, List, Optional import mmcv import numpy as np from mmengine.dist import master_only from mmengine.structures import PixelData from mmengine.visualization import Visualizer from mmseg.registry import VISUALIZERS from mmseg.structures import SegDataSample from mmseg.utils import get_classes, get_palette @VISUALIZERS.register_module() class SegLocalVisualizer(Visualizer): """Local Visualizer. Args: name (str): Name of the instance. Defaults to 'visualizer'. image (np.ndarray, optional): the origin image to draw. The format should be RGB. Defaults to None. vis_backends (list, optional): Visual backend config list. Defaults to None. save_dir (str, optional): Save file dir for all storage backends. If it is None, the backend storage will not save any data. classes (list, optional): Input classes for result rendering, as the prediction of segmentation model is a segment map with label indices, `classes` is a list which includes items responding to the label indices. If classes is not defined, visualizer will take `cityscapes` classes by default. Defaults to None. palette (list, optional): Input palette for result rendering, which is a list of color palette responding to the classes. Defaults to None. dataset_name (str, optional): `Dataset name or alias `_ visulizer will use the meta information of the dataset i.e. classes and palette, but the `classes` and `palette` have higher priority. Defaults to None. alpha (int, float): The transparency of segmentation mask. Defaults to 0.8. Examples: >>> import numpy as np >>> import torch >>> from mmengine.structures import PixelData >>> from mmseg.data import SegDataSample >>> from mmseg.engine.visualization import SegLocalVisualizer >>> seg_local_visualizer = SegLocalVisualizer() >>> image = np.random.randint(0, 256, ... size=(10, 12, 3)).astype('uint8') >>> gt_sem_seg_data = dict(data=torch.randint(0, 2, (1, 10, 12))) >>> gt_sem_seg = PixelData(**gt_sem_seg_data) >>> gt_seg_data_sample = SegDataSample() >>> gt_seg_data_sample.gt_sem_seg = gt_sem_seg >>> seg_local_visualizer.dataset_meta = dict( >>> classes=('background', 'foreground'), >>> palette=[[120, 120, 120], [6, 230, 230]]) >>> seg_local_visualizer.add_datasample('visualizer_example', ... image, gt_seg_data_sample) >>> seg_local_visualizer.add_datasample( ... 'visualizer_example', image, ... gt_seg_data_sample, show=True) """ # noqa def __init__(self, name: str = 'visualizer', image: Optional[np.ndarray] = None, vis_backends: Optional[Dict] = None, save_dir: Optional[str] = None, classes: Optional[List] = None, palette: Optional[List] = None, dataset_name: Optional[str] = None, alpha: float = 0.8, **kwargs): super().__init__(name, image, vis_backends, save_dir, **kwargs) self.alpha: float = alpha self.set_dataset_meta(palette, classes, dataset_name) def _draw_sem_seg(self, image: np.ndarray, sem_seg: PixelData, classes: Optional[List], palette: Optional[List]) -> np.ndarray: """Draw semantic seg of GT or prediction. Args: image (np.ndarray): The image to draw. sem_seg (:obj:`PixelData`): Data structure for pixel-level annotations or predictions. classes (list, optional): Input classes for result rendering, as the prediction of segmentation model is a segment map with label indices, `classes` is a list which includes items responding to the label indices. If classes is not defined, visualizer will take `cityscapes` classes by default. Defaults to None. palette (list, optional): Input palette for result rendering, which is a list of color palette responding to the classes. Defaults to None. Returns: np.ndarray: the drawn image which channel is RGB. """ num_classes = len(classes) sem_seg = sem_seg.cpu().data ids = np.unique(sem_seg)[::-1] legal_indices = ids < num_classes ids = ids[legal_indices] labels = np.array(ids, dtype=np.int64) colors = [palette[label] for label in labels] self.set_image(image) # draw semantic masks for label, color in zip(labels, colors): self.draw_binary_masks( sem_seg == label, colors=[color], alphas=self.alpha) return self.get_image() def set_dataset_meta(self, classes: Optional[List] = None, palette: Optional[List] = None, dataset_name: Optional[str] = None) -> None: """Set meta information to visualizer. Args: classes (list, optional): Input classes for result rendering, as the prediction of segmentation model is a segment map with label indices, `classes` is a list which includes items responding to the label indices. If classes is not defined, visualizer will take `cityscapes` classes by default. Defaults to None. palette (list, optional): Input palette for result rendering, which is a list of color palette responding to the classes. Defaults to None. dataset_name (str, optional): `Dataset name or alias `_ visulizer will use the meta information of the dataset i.e. classes and palette, but the `classes` and `palette` have higher priority. Defaults to None. """ # noqa # Set default value. When calling # `SegLocalVisualizer().dataset_meta=xxx`, # it will override the default value. if dataset_name is None: dataset_name = 'cityscapes' classes = classes if classes else get_classes(dataset_name) palette = palette if palette else get_palette(dataset_name) assert len(classes) == len( palette), 'The length of classes should be equal to palette' self.dataset_meta: dict = {'classes': classes, 'palette': palette} @master_only def add_datasample( self, name: str, image: np.ndarray, data_sample: Optional[SegDataSample] = None, draw_gt: bool = True, draw_pred: bool = True, show: bool = False, wait_time: float = 0, # TODO: Supported in mmengine's Viusalizer. out_file: Optional[str] = None, step: int = 0) -> None: """Draw datasample and save to all backends. - If GT and prediction are plotted at the same time, they are displayed in a stitched image where the left image is the ground truth and the right image is the prediction. - If ``show`` is True, all storage backends are ignored, and the images will be displayed in a local window. - If ``out_file`` is specified, the drawn image will be saved to ``out_file``. it is usually used when the display is not available. Args: name (str): The image identifier. image (np.ndarray): The image to draw. gt_sample (:obj:`SegDataSample`, optional): GT SegDataSample. Defaults to None. pred_sample (:obj:`SegDataSample`, optional): Prediction SegDataSample. Defaults to None. draw_gt (bool): Whether to draw GT SegDataSample. Default to True. draw_pred (bool): Whether to draw Prediction SegDataSample. Defaults to True. show (bool): Whether to display the drawn image. Default to False. wait_time (float): The interval of show (s). Defaults to 0. out_file (str): Path to output file. Defaults to None. step (int): Global step value to record. Defaults to 0. """ classes = self.dataset_meta.get('classes', None) palette = self.dataset_meta.get('palette', None) gt_img_data = None pred_img_data = None if draw_gt and data_sample is not None and 'gt_sem_seg' in data_sample: gt_img_data = image assert classes is not None, 'class information is ' \ 'not provided when ' \ 'visualizing semantic ' \ 'segmentation results.' gt_img_data = self._draw_sem_seg(gt_img_data, data_sample.gt_sem_seg, classes, palette) if (draw_pred and data_sample is not None and 'pred_sem_seg' in data_sample): pred_img_data = image assert classes is not None, 'class information is ' \ 'not provided when ' \ 'visualizing semantic ' \ 'segmentation results.' pred_img_data = self._draw_sem_seg(pred_img_data, data_sample.pred_sem_seg, classes, palette) if gt_img_data is not None and pred_img_data is not None: drawn_img = np.concatenate((gt_img_data, pred_img_data), axis=1) elif gt_img_data is not None: drawn_img = gt_img_data else: drawn_img = pred_img_data if show: self.show(drawn_img, win_name=name, wait_time=wait_time) if out_file is not None: mmcv.imwrite(mmcv.bgr2rgb(drawn_img), out_file) else: self.add_image(name, drawn_img, step)