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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional
import cv2
import mmcv
import numpy as np
import torch
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.structures import SegDataSample
>>> from mmseg.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 _get_center_loc(self, mask: np.ndarray) -> np.ndarray:
"""Get semantic seg center coordinate.
Args:
mask: np.ndarray: get from sem_seg
"""
loc = np.argwhere(mask == 1)
loc_sort = np.array(
sorted(loc.tolist(), key=lambda row: (row[0], row[1])))
y_list = loc_sort[:, 0]
unique, indices, counts = np.unique(
y_list, return_index=True, return_counts=True)
y_loc = unique[counts.argmax()]
y_most_freq_loc = loc[loc_sort[:, 0] == y_loc]
center_num = len(y_most_freq_loc) // 2
x = y_most_freq_loc[center_num][1]
y = y_most_freq_loc[center_num][0]
return np.array([x, y])
def _draw_sem_seg(self,
image: np.ndarray,
sem_seg: PixelData,
classes: Optional[List],
palette: Optional[List],
with_labels: Optional[bool] = True) -> 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.
with_labels(bool, optional): Add semantic labels in visualization
result, Default to True.
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]
mask = np.zeros_like(image, dtype=np.uint8)
for label, color in zip(labels, colors):
mask[sem_seg[0] == label, :] = color
if with_labels:
font = cv2.FONT_HERSHEY_SIMPLEX
# (0,1] to change the size of the text relative to the image
scale = 0.05
fontScale = min(image.shape[0], image.shape[1]) / (25 / scale)
fontColor = (255, 255, 255)
if image.shape[0] < 300 or image.shape[1] < 300:
thickness = 1
rectangleThickness = 1
else:
thickness = 2
rectangleThickness = 2
lineType = 2
if isinstance(sem_seg[0], torch.Tensor):
masks = sem_seg[0].numpy() == labels[:, None, None]
else:
masks = sem_seg[0] == labels[:, None, None]
masks = masks.astype(np.uint8)
for mask_num in range(len(labels)):
classes_id = labels[mask_num]
classes_color = colors[mask_num]
loc = self._get_center_loc(masks[mask_num])
text = classes[classes_id]
(label_width, label_height), baseline = cv2.getTextSize(
text, font, fontScale, thickness)
mask = cv2.rectangle(mask, loc,
(loc[0] + label_width + baseline,
loc[1] + label_height + baseline),
classes_color, -1)
mask = cv2.rectangle(mask, loc,
(loc[0] + label_width + baseline,
loc[1] + label_height + baseline),
(0, 0, 0), rectangleThickness)
mask = cv2.putText(mask, text, (loc[0], loc[1] + label_height),
font, fontScale, fontColor, thickness,
lineType)
color_seg = (image * (1 - self.alpha) + mask * self.alpha).astype(
np.uint8)
self.set_image(color_seg)
return color_seg
def _draw_depth_map(self, image: np.ndarray,
depth_map: PixelData) -> np.ndarray:
"""Draws a depth map on a given image.
This function takes an image and a depth map as input,
renders the depth map, and concatenates it with the original image.
Finally, it updates the internal image state of the visualizer with
the concatenated result.
Args:
image (np.ndarray): The original image where the depth map will
be drawn. The array should be in the format HxWx3 where H is
the height, W is the width.
depth_map (PixelData): Depth map to be drawn. The depth map
should be in the form of a PixelData object. It will be
converted to a torch tensor if it is a numpy array.
Returns:
np.ndarray: The concatenated image with the depth map drawn.
Example:
>>> depth_map_data = PixelData(data=torch.rand(1, 10, 10))
>>> image = np.random.randint(0, 256,
>>> size=(10, 10, 3)).astype('uint8')
>>> visualizer = SegLocalVisualizer()
>>> visualizer._draw_depth_map(image, depth_map_data)
"""
depth_map = depth_map.cpu().data
if isinstance(depth_map, np.ndarray):
depth_map = torch.from_numpy(depth_map)
if depth_map.ndim == 2:
depth_map = depth_map[None]
depth_map = self.draw_featmap(depth_map, resize_shape=image.shape[:2])
out_image = np.concatenate((image, depth_map), axis=0)
self.set_image(out_image)
return out_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,
with_labels: Optional[bool] = True) -> 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.
with_labels(bool, optional): Add semantic labels in visualization
result, Defaults to True.
"""
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:
if 'gt_sem_seg' in data_sample:
assert classes is not None, 'class information is ' \
'not provided when ' \
'visualizing semantic ' \
'segmentation results.'
gt_img_data = self._draw_sem_seg(image, data_sample.gt_sem_seg,
classes, palette, with_labels)
if 'gt_depth_map' in data_sample:
gt_img_data = gt_img_data if gt_img_data is not None else image
gt_img_data = self._draw_depth_map(gt_img_data,
data_sample.gt_depth_map)
if draw_pred and data_sample is not None:
if 'pred_sem_seg' in data_sample:
assert classes is not None, 'class information is ' \
'not provided when ' \
'visualizing semantic ' \
'segmentation results.'
pred_img_data = self._draw_sem_seg(image,
data_sample.pred_sem_seg,
classes, palette,
with_labels)
if 'pred_depth_map' in data_sample:
pred_img_data = pred_img_data if pred_img_data is not None \
else image
pred_img_data = self._draw_depth_map(
pred_img_data, data_sample.pred_depth_map)
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.rgb2bgr(drawn_img), out_file)
else:
self.add_image(name, drawn_img, step)