mmocr.visualization.textdet_visualizer 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
import torch
from mmocr.registry import VISUALIZERS
from mmocr.structures import TextDetDataSample
from .base_visualizer import BaseLocalVisualizer
[文档]@VISUALIZERS.register_module()
class TextDetLocalVisualizer(BaseLocalVisualizer):
"""The MMOCR Text Detection 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.
with_poly (bool): Whether to draw polygons. Defaults to True.
with_bbox (bool): Whether to draw bboxes. Defaults to False.
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.
gt_color (Union[str, tuple, list[str], list[tuple]]): The
colors of GT polygons and bboxes. ``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 'g'.
gt_ignored_color (Union[str, tuple, list[str], list[tuple]]): The
colors of ignored GT polygons and bboxes. ``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 'b'.
pred_color (Union[str, tuple, list[str], list[tuple]]): The
colors of pred polygons and bboxes. ``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'.
line_width (int, float): The linewidth of lines. Defaults to 2.
alpha (float): The transparency of bboxes or polygons. Defaults to 0.8.
"""
def __init__(self,
name: str = 'visualizer',
image: Optional[np.ndarray] = None,
with_poly: bool = True,
with_bbox: bool = False,
vis_backends: Optional[Dict] = None,
save_dir: Optional[str] = None,
gt_color: Union[str, Tuple, List[str], List[Tuple]] = 'g',
gt_ignored_color: Union[str, Tuple, List[str],
List[Tuple]] = 'b',
pred_color: Union[str, Tuple, List[str], List[Tuple]] = 'r',
line_width: Union[int, float] = 2,
alpha: float = 0.8) -> None:
super().__init__(
name=name,
image=image,
vis_backends=vis_backends,
save_dir=save_dir)
self.with_poly = with_poly
self.with_bbox = with_bbox
self.gt_color = gt_color
self.gt_ignored_color = gt_ignored_color
self.pred_color = pred_color
self.line_width = line_width
self.alpha = alpha
def _draw_instances(
self,
image: np.ndarray,
bboxes: Union[np.ndarray, torch.Tensor],
polygons: Sequence[np.ndarray],
color: Union[str, Tuple, List[str], List[Tuple]] = 'g',
) -> np.ndarray:
"""Draw bboxes and polygons on image.
Args:
image (np.ndarray): The origin image to draw.
bboxes (Union[np.ndarray, torch.Tensor]): The bboxes to draw.
polygons (Sequence[np.ndarray]): The polygons to draw.
color (Union[str, tuple, list[str], list[tuple]]): The
colors of polygons and bboxes. ``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 'g'.
Returns:
np.ndarray: The image with bboxes and polygons drawn.
"""
if polygons is not None and self.with_poly:
polygons = [polygon.reshape(-1, 2) for polygon in polygons]
image = self.get_polygons_image(
image, polygons, filling=True, colors=color, alpha=self.alpha)
if bboxes is not None and self.with_bbox:
image = self.get_bboxes_image(
image,
bboxes,
colors=color,
line_width=self.line_width,
alpha=self.alpha)
return image
[文档] def add_datasample(self,
name: str,
image: np.ndarray,
data_sample: Optional['TextDetDataSample'] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: int = 0,
out_file: Optional[str] = None,
pred_score_thr: float = 0.3,
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``. This is usually used when the display
is not available.
Args:
name (str): The image identifier.
image (np.ndarray): The image to draw.
data_sample (:obj:`TextDetDataSample`, optional):
TextDetDataSample which contains gt and prediction. Defaults
to None.
draw_gt (bool): Whether to draw GT TextDetDataSample.
Defaults to True.
draw_pred (bool): Whether to draw Predicted TextDetDataSample.
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.
pred_score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
step (int): Global step value to record. Defaults to 0.
"""
cat_images = []
if data_sample is not None:
if draw_gt and 'gt_instances' in data_sample:
gt_instances = data_sample.gt_instances
gt_img_data = image.copy()
if gt_instances.get('ignored', None) is not None:
ignore_flags = gt_instances.ignored
gt_ignored_instances = gt_instances[ignore_flags]
gt_ignored_polygons = gt_ignored_instances.get(
'polygons', None)
gt_ignored_bboxes = gt_ignored_instances.get(
'bboxes', None)
gt_img_data = self._draw_instances(gt_img_data,
gt_ignored_bboxes,
gt_ignored_polygons,
self.gt_ignored_color)
gt_instances = gt_instances[~ignore_flags]
gt_polygons = gt_instances.get('polygons', None)
gt_bboxes = gt_instances.get('bboxes', None)
gt_img_data = self._draw_instances(gt_img_data, gt_bboxes,
gt_polygons, self.gt_color)
cat_images.append(gt_img_data)
if draw_pred and 'pred_instances' in data_sample:
pred_instances = data_sample.pred_instances
pred_instances = pred_instances[
pred_instances.scores > pred_score_thr].cpu()
pred_polygons = pred_instances.get('polygons', None)
pred_bboxes = pred_instances.get('bboxes', None)
pred_img_data = self._draw_instances(image.copy(), pred_bboxes,
pred_polygons,
self.pred_color)
cat_images.append(pred_img_data)
cat_images = self._cat_image(cat_images, axis=1)
if cat_images is None:
cat_images = image
if show:
self.show(cat_images, win_name=name, wait_time=wait_time)
else:
self.add_image(name, cat_images, step)
if out_file is not None:
mmcv.imwrite(cat_images[..., ::-1], out_file)
self.set_image(cat_images)
return self.get_image()