Source code for mmselfsup.visualization.selfsup_visualizer
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Union
import mmcv
import numpy as np
from mmengine.dist import master_only
from mmengine.structures import InstanceData
from mmengine.visualization import Visualizer
from mmselfsup.registry import VISUALIZERS
from mmselfsup.structures import SelfSupDataSample
[docs]@VISUALIZERS.register_module()
class SelfSupVisualizer(Visualizer):
"""MMSelfSup 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.
line_width (int, float): The linewidth of lines.
Defaults to 3.
alpha (int, float): The transparency of boxes or mask.
Defaults to 0.8.
Examples:
>>> import numpy as np
>>> import torch
>>> from mmengine.structures import InstanceData
>>> from mmselfsup.structures import SelfSupDataSample
>>> from mmselfsup.visualization import SelfSupVisualizer
>>> selfsup_visualizer = SelfSupVisualizer()
>>> image = np.random.randint(0, 256,
... size=(10, 12, 3)).astype('uint8')
>>> pseudo_label = InstanceData()
>>> pseudo_label.patch_box = torch.Tensor([[1, 2, 2, 5]])
>>> gt_selfsup_data_sample = SelfSupDataSample()
>>> gt_selfsup_data_sample.pseudo_label = pseudo_label
>>> selfsup_visualizer.add_datasample('image', image,
... gt_selfsup_data_sample)
>>> selfsup_visualizer.add_datasample(
... 'image', image, gt_selfsup_data_sample,
... out_file='out_file.jpg')
>>> selfsup_visualizer.add_datasample(
... 'image', image, gt_selfsup_data_sample,
... show=True)
>>> pseudo_label = InstanceData()
>>> pseudo_label.patch_box = torch.Tensor([[1, 2, 2, 5]])
>>> pred_selfsup_data_sample = SelfSupDataSample()
>>> pred_selfsup_data_sample.pseudo_label = pseudo_label
>>> selfsup_visualizer.add_datasample('image', image,
... gt_selfsup_data_sample,
... pred_selfsup_data_sample)
"""
def __init__(self,
name: str = 'visualizer',
image: Optional[np.ndarray] = None,
vis_backends: Optional[List[Dict]] = None,
save_dir: Optional[str] = None,
line_width: Union[int, float] = 3,
alpha: Union[int, float] = 0.8):
super().__init__(
name=name,
image=image,
vis_backends=vis_backends,
save_dir=save_dir)
self.line_width = line_width
self.alpha = alpha
# Set default value. When calling
# `SelfSupVisualizer().dataset_meta=xxx`,
# it will override the default value.
self.dataset_meta = {}
def _draw_boxes(
self,
image: np.ndarray,
boxes: InstanceData,
edge_colors: Union[str, tuple, List[str], List[tuple]] = 'r'
) -> np.ndarray:
"""Draw instance with boxes.
Args:
image (np.ndarray): The image to draw.
boxes (:obj:`InstanceData`): Data structure for
instance-level box annotations.
edge_colors (Union[str, tuple, List[str], List[tuple]]): The
colors of boxes. ``colors`` can have the same length with
lines or just single value. If ``colors`` is single value, all
the lines will have the same colors. Refer to `matplotlib.
colors` for full list of formats that are accepted.
Defaults to 'r'.
Returns:
np.ndarray: the drawn image which channel is RGB.
"""
self.set_image(image.copy())
self.draw_bboxes(
boxes,
edge_colors=edge_colors,
alpha=self.alpha,
line_widths=self.line_width)
return self.get_image()
def _draw_mask(
self,
image: np.ndarray,
mask: InstanceData,
colors: Union[str, tuple, List[str],
List[tuple]] = 'k') -> np.ndarray:
"""Draw instance with binary mask.
Args:
image (np.ndarray): The image to draw.
mask (:obj:`InstanceData`): Data structure for
pixel-level annotations.
colors (Union[str, tuple, List[str], List[tuple]]): The colors
which binary_masks will convert to. ``colors`` can have
the same length with binary_masks or just single value.
If ``colors`` is single value, all the binary_masks will
convert to the same colors. The colors format is RGB.
Defaults to np.array([0, 0, 0]).
Returns:
np.ndarray: the drawn image which channel is RGB.
"""
self.set_image(image.copy())
if 'value' in mask:
mask = mask.value
mask_ = np.zeros((image.shape[0], image.shape[1]))
num_mask = [
image.shape[0] // mask.shape[0],
image.shape[1] // mask.shape[1]
]
for i in range(image.shape[0]):
for j in range(image.shape[1]):
mask_[i][j] = mask[i // num_mask[0]][j // num_mask[1]]
self.draw_binary_masks(
mask_.astype(np.bool_), colors=colors, alphas=self.alpha)
return self.get_image()
[docs] @master_only
def add_datasample(self,
name: str,
image: np.ndarray,
gt_sample: Optional[SelfSupDataSample] = None,
pred_sample: Optional[SelfSupDataSample] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: float = 0,
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``. t 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:`SelfSupDataSample`, optional): GT
SelfSupDataSample. Defaults to None.
pred_sample (:obj:`SelfSupDataSample`, optional): Prediction
SelfSupDataSample. Defaults to None.
draw_gt (bool): Whether to draw GT SelfSupDataSample.
Default to True.
draw_pred (bool): Whether to draw Prediction SelfSupDataSample.
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.
"""
gt_img_data = None
pred_img_data = None
if draw_gt and gt_sample is not None:
gt_img_data = image
if 'pseudo_label' in gt_sample:
if ('patch_box' in gt_sample.pseudo_label) and \
('unpatched_img' in gt_sample.pseudo_label):
gt_img_data = self._draw_boxes(
gt_sample.pseudo_label.unpatched_img[0, ::].numpy()[
..., [2, 1, 0]],
gt_sample.pseudo_label.patch_box[0, ::].numpy())
if 'mask' in gt_sample:
gt_img_data = self._draw_mask(gt_img_data,
gt_sample.mask.numpy())
if draw_pred and pred_sample is not None:
pred_img_data = image
if 'pseudo_label' in gt_sample:
if ('patch_box' in gt_sample.pseudo_label) and \
('unpatched_img' in gt_sample.pseudo_label):
pred_img_data = self._draw_boxes(
pred_sample.pseudo_label.unpatched_img[0, ::].numpy(),
pred_sample.pseudo_label.patch_box[0, ::].numpy())
if 'mask' in pred_sample:
pred_img_data = self._draw_mask(pred_img_data,
pred_sample.mask.numpy())
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)
else:
self.add_image(name, drawn_img, step)
if out_file is not None:
mmcv.imwrite(drawn_img[..., ::-1], out_file)