Spaces:
Runtime error
Runtime error
File size: 10,680 Bytes
2ae34e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
# 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 <https://github.com/open-mmlab/mmsegmentation/blob/main/mmseg/utils/class_names.py#L302-L317>`_
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 <https://github.com/open-mmlab/mmsegmentation/blob/main/mmseg/utils/class_names.py#L302-L317>`_
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)
|