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[Feture] kie visualizer () 

* kie visualizer

* add  textspotting visualizer

* Fix

* fix

* Some fixes

Co-authored-by: gaotongxiao <gaotongxiao@gmail.com>
pull/1348/head
liukuikun 2022-08-29 20:13:06 +08:00 committed by GitHub
parent 56179fe1a9
commit bf042f8267
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8 changed files with 889 additions and 1 deletions

3
configs/kie/_base_/default_runtime.py
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@ -28,3 +28,6 @@ log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True)
load_from = None
resume = False
visualizer = dict(
type='KIELocalVisualizer', name='visualizer', is_openset=False)

3
configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset.py
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@ -65,3 +65,6 @@ val_evaluator = [
num_classes=edge_num_classes)
]
test_evaluator = val_evaluator
visualizer = dict(
type='KIELocalVisualizer', name='visualizer', is_openset=True)

7
mmocr/visualization/__init__.py
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@ -1,5 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .kie_visualizer import KIELocalVisualizer
from .textdet_visualizer import TextDetLocalVisualizer
from .textrecog_visualizer import TextRecogLocalVisualizer
from .textspotting_visualizer import TextSpottingLocalVisualizer
__all__ = ['TextDetLocalVisualizer', 'TextRecogLocalVisualizer']
__all__ = [
'KIELocalVisualizer', 'TextDetLocalVisualizer', 'TextRecogLocalVisualizer',
'TextSpottingLocalVisualizer'
]

150
mmocr/visualization/base_visualizer.py 100644
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@ -0,0 +1,150 @@
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Union
import numpy as np
import torch
from mmengine.visualization import Visualizer
from mmocr.registry import VISUALIZERS
@VISUALIZERS.register_module()
class BaseLocalVisualizer(Visualizer):
"""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.
vis_backends (list, optional): Visual backend config list.
Default 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.
fig_save_cfg (dict): Keyword parameters of figure for saving.
Defaults to empty dict.
fig_show_cfg (dict): Keyword parameters of figure for showing.
Defaults to empty dict.
is_openset (bool, optional): Whether the visualizer is used in
OpenSet. Defaults to False.
"""
PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
(0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
(110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
(255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
(0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
(78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
(134, 134, 103), (145, 148, 174), (255, 208, 186),
(197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
(151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
(166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
(179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
(147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
(119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
(95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
(219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
(127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
(95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
(191, 162, 208)]
@staticmethod
def _draw_labels(visualizer: Visualizer,
image: np.ndarray,
labels: Union[np.ndarray, torch.Tensor],
bboxes: Union[np.ndarray, torch.Tensor],
colors: Union[str, Sequence[str]] = 'k',
font_size: Union[int, float] = 10,
auto_font_size: bool = False) -> np.ndarray:
"""Draw labels on image.
Args:
image (np.ndarray): The origin image to draw. The format
should be RGB.
labels (Union[np.ndarray, torch.Tensor]): The labels to draw.
bboxes (Union[np.ndarray, torch.Tensor]): The bboxes to draw.
colors (Union[str, Sequence[str]]): The colors of labels.
``colors`` can have the same length with labels or just single
value. If ``colors`` is single value, all the labels will have
the same colors. Refer to `matplotlib.colors` for full list of
formats that are accepted. Defaults to 'k'.
font_size (Union[int, float]): The font size of labels. Defaults
to 10.
auto_font_size (bool): Whether to automatically adjust font size.
Defaults to False.
"""
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(labels) / len(colors))
colors = (colors * size)[:len(labels)]
if auto_font_size:
assert font_size is not None and isinstance(
font_size, (int, float))
font_size = (bboxes[:, 2:] - bboxes[:, :2]).min(-1) * font_size
font_size = font_size.tolist()
visualizer.set_image(image)
visualizer.draw_texts(
labels, (bboxes[:, :2] + bboxes[:, 2:]) / 2,
vertical_alignments='center',
horizontal_alignments='center',
colors='k',
font_sizes=font_size)
return visualizer.get_image()
@staticmethod
def _draw_polygons(visualizer: Visualizer,
image: np.ndarray,
polygons: Sequence[np.ndarray],
colors: Union[str, Sequence[str]] = 'g',
filling: bool = False,
line_width: Union[int, float] = 0.5,
alpha: float = 0.5) -> np.ndarray:
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(polygons) / len(colors))
colors = (colors * size)[:len(polygons)]
visualizer.set_image(image)
if filling:
visualizer.draw_polygons(
polygons,
face_colors=colors,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
else:
visualizer.draw_polygons(
polygons,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
return visualizer.get_image()
@staticmethod
def _draw_bboxes(visualizer: Visualizer,
image: np.ndarray,
bboxes: Union[np.ndarray, torch.Tensor],
colors: Union[str, Sequence[str]] = 'g',
filling: bool = False,
line_width: Union[int, float] = 0.5,
alpha: float = 0.5) -> np.ndarray:
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(bboxes) / len(colors))
colors = (colors * size)[:len(bboxes)]
visualizer.set_image(image)
if filling:
visualizer.draw_bboxes(
bboxes,
face_colors=colors,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
else:
visualizer.draw_bboxes(
bboxes,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
return visualizer.get_image()
def _draw_instances(self) -> np.ndarray:
raise NotImplementedError

505
mmocr/visualization/kie_visualizer.py 100644
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@ -0,0 +1,505 @@
# Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
from typing import Dict, List, Optional, Sequence, Union
import mmcv
import numpy as np
import torch
from matplotlib.collections import PatchCollection
from matplotlib.patches import FancyArrow
from mmengine.visualization import Visualizer
from mmengine.visualization.utils import (check_type, check_type_and_length,
color_val_matplotlib, tensor2ndarray,
value2list)
from mmocr.registry import VISUALIZERS
from mmocr.structures import KIEDataSample
PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70), (0, 0, 192),
(250, 170, 30), (100, 170, 30), (220, 220, 0), (175, 116, 175),
(250, 0, 30), (165, 42, 42), (255, 77, 255), (0, 226, 252),
(182, 182, 255), (0, 82, 0), (120, 166, 157), (110, 76, 0),
(174, 57, 255), (199, 100, 0), (72, 0, 118), (255, 179, 240),
(0, 125, 92), (209, 0, 151), (188, 208, 182), (0, 220, 176),
(255, 99, 164), (92, 0, 73), (133, 129, 255), (78, 180, 255),
(0, 228, 0), (174, 255, 243), (45, 89, 255), (134, 134, 103),
(145, 148, 174), (255, 208, 186), (197, 226, 255), (171, 134, 1),
(109, 63, 54), (207, 138, 255), (151, 0, 95), (9, 80, 61),
(84, 105, 51), (74, 65, 105), (166, 196, 102), (208, 195, 210),
(255, 109, 65), (0, 143, 149), (179, 0, 194), (209, 99, 106),
(5, 121, 0), (227, 255, 205), (147, 186, 208), (153, 69, 1),
(3, 95, 161), (163, 255, 0), (119, 0, 170), (0, 182, 199),
(0, 165, 120), (183, 130, 88), (95, 32, 0), (130, 114, 135),
(110, 129, 133), (166, 74, 118), (219, 142, 185), (79, 210, 114),
(178, 90, 62), (65, 70, 15), (127, 167, 115), (59, 105, 106),
(142, 108, 45), (196, 172, 0), (95, 54, 80), (128, 76, 255),
(201, 57, 1), (246, 0, 122), (191, 162, 208)]
@VISUALIZERS.register_module()
class KIELocalVisualizer(Visualizer):
"""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.
vis_backends (list, optional): Visual backend config list.
Default 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.
fig_save_cfg (dict): Keyword parameters of figure for saving.
Defaults to empty dict.
fig_show_cfg (dict): Keyword parameters of figure for showing.
Defaults to empty dict.
is_openset (bool, optional): Whether the visualizer is used in
OpenSet. Defaults to False.
"""
def __init__(self,
name='kie_visualizer',
is_openset: bool = False,
**kwargs) -> None:
super().__init__(name=name, **kwargs)
self.is_openset = is_openset
@staticmethod
def _draw_labels(visualizer: Visualizer,
image: np.ndarray,
labels: Union[np.ndarray, torch.Tensor],
bboxes: Union[np.ndarray, torch.Tensor],
colors: Union[str, Sequence[str]] = 'k',
font_size: Union[int, float] = 10,
auto_font_size: bool = False) -> np.ndarray:
"""Draw labels on image.
Args:
image (np.ndarray): The origin image to draw. The format
should be RGB.
labels (Union[np.ndarray, torch.Tensor]): The labels to draw.
bboxes (Union[np.ndarray, torch.Tensor]): The bboxes to draw.
colors (Union[str, Sequence[str]]): The colors of labels.
``colors`` can have the same length with labels or just single
value. If ``colors`` is single value, all the labels will have
the same colors. Refer to `matplotlib.colors` for full list of
formats that are accepted. Defaults to 'k'.
font_size (Union[int, float]): The font size of labels. Defaults
to 10.
auto_font_size (bool): Whether to automatically adjust font size.
Defaults to False.
"""
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(labels) / len(colors))
colors = (colors * size)[:len(labels)]
if auto_font_size:
assert font_size is not None and isinstance(
font_size, (int, float))
font_size = (bboxes[:, 2:] - bboxes[:, :2]).min(-1) * font_size
font_size = font_size.tolist()
visualizer.set_image(image)
visualizer.draw_texts(
labels, (bboxes[:, :2] + bboxes[:, 2:]) / 2,
vertical_alignments='center',
horizontal_alignments='center',
colors='k',
font_sizes=font_size)
return visualizer.get_image()
@staticmethod
def _draw_polygons(visualizer: Visualizer,
image: np.ndarray,
polygons: Sequence[np.ndarray],
colors: Union[str, Sequence[str]] = 'g',
filling: bool = False,
line_width: Union[int, float] = 0.5,
alpha: float = 0.5) -> np.ndarray:
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(polygons) / len(colors))
colors = (colors * size)[:len(polygons)]
visualizer.set_image(image)
if filling:
visualizer.draw_polygons(
polygons,
face_colors=colors,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
else:
visualizer.draw_polygons(
polygons,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
return visualizer.get_image()
@staticmethod
def _draw_bboxes(visualizer: Visualizer,
image: np.ndarray,
bboxes: Union[np.ndarray, torch.Tensor],
colors: Union[str, Sequence[str]] = 'g',
filling: bool = False,
line_width: Union[int, float] = 0.5,
alpha: float = 0.5) -> np.ndarray:
if colors is not None and isinstance(colors, Sequence):
size = math.ceil(len(bboxes) / len(colors))
colors = (colors * size)[:len(bboxes)]
visualizer.set_image(image)
if filling:
visualizer.draw_bboxes(
bboxes,
face_colors=colors,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
else:
visualizer.draw_bboxes(
bboxes,
edge_colors=colors,
line_widths=line_width,
alpha=alpha)
return visualizer.get_image()
def _draw_edge_label(self,
image: np.ndarray,
edge_labels: Union[np.ndarray, torch.Tensor],
bboxes: Union[np.ndarray, torch.Tensor],
texts: Sequence[str],
arrow_colors: str = 'g') -> np.ndarray:
"""Draw edge labels on image.
Args:
image (np.ndarray): The origin image to draw. The format
should be RGB.
edge_labels (np.ndarray or torch.Tensor): The edge labels to draw.
The shape of edge_labels should be (N, N), where N is the
number of texts.
bboxes (np.ndarray or torch.Tensor): The bboxes to draw. The shape
of bboxes should be (N, 4), where N is the number of texts.
texts (Sequence[str]): The texts to draw. The length of texts
should be the same as the number of bboxes.
arrow_colors (str, optional): The colors of arrows. Refer to
`matplotlib.colors` for full list of formats that are accepted.
Defaults to 'g'.
"""
pairs = np.where(edge_labels > 0)
key_bboxes = bboxes[pairs[0]]
value_bboxes = bboxes[pairs[1]]
x_data = np.stack([(key_bboxes[:, 2] + key_bboxes[:, 0]) / 2,
(value_bboxes[:, 0] + value_bboxes[:, 2]) / 2],
axis=-1)
y_data = np.stack([(key_bboxes[:, 1] + key_bboxes[:, 3]) / 2,
(value_bboxes[:, 1] + value_bboxes[:, 3]) / 2],
axis=-1)
key_index = np.array(list(set(pairs[0])))
val_index = np.array(list(set(pairs[1])))
key_texts = [texts[i] for i in key_index]
val_texts = [texts[i] for i in val_index]
self.set_image(image)
if key_texts:
self.draw_texts(
key_texts, (bboxes[key_index, :2] + bboxes[key_index, 2:]) / 2,
colors='k',
horizontal_alignments='center',
vertical_alignments='center')
if val_texts:
self.draw_texts(
val_texts, (bboxes[val_index, :2] + bboxes[val_index, 2:]) / 2,
colors='k',
horizontal_alignments='center',
vertical_alignments='center')
self.draw_arrows(
x_data,
y_data,
colors=arrow_colors,
line_widths=0.3,
arrow_tail_widths=0.05,
arrow_head_widths=5,
overhangs=1,
arrow_shapes='full')
return self.get_image()
def _draw_instances(
self,
image: np.ndarray,
bbox_labels: Union[np.ndarray, torch.Tensor],
bboxes: Union[np.ndarray, torch.Tensor],
polygons: Sequence[np.ndarray],
edge_labels: Union[np.ndarray, torch.Tensor],
texts: Sequence[str],
class_names: Dict,
is_openset: bool = False,
arrow_colors: str = 'g',
) -> np.ndarray:
"""Draw instances on image.
Args:
image (np.ndarray): The origin image to draw. The format
should be RGB.
bbox_labels (np.ndarray or torch.Tensor): The bbox labels to draw.
The shape of bbox_labels should be (N,), where N is the
number of texts.
bboxes (np.ndarray or torch.Tensor): The bboxes to draw. The shape
of bboxes should be (N, 4), where N is the number of texts.
polygons (Sequence[np.ndarray]): The polygons to draw. The length
of polygons should be the same as the number of bboxes.
edge_labels (np.ndarray or torch.Tensor): The edge labels to draw.
The shape of edge_labels should be (N, N), where N is the
number of texts.
texts (Sequence[str]): The texts to draw. The length of texts
should be the same as the number of bboxes.
class_names (dict): The class names for bbox labels.
is_openset (bool): Whether the dataset is openset. Defaults to
False.
"""
img_shape = image.shape[:2]
empty_shape = (img_shape[0], img_shape[1], 3)
if polygons:
polygons = [polygon.reshape(-1, 2) for polygon in polygons]
if polygons:
image = self._draw_polygons(
self, image, polygons, filling=True, colors=PALETTE)
else:
image = self._draw_bboxes(
self, image, bboxes, filling=True, colors=PALETTE)
text_image = np.full(empty_shape, 255, dtype=np.uint8)
text_image = self._draw_labels(self, text_image, texts, bboxes)
if polygons:
text_image = self._draw_polygons(
self, text_image, polygons, colors=PALETTE)
else:
text_image = self._draw_bboxes(
self, text_image, bboxes, colors=PALETTE)
classes_image = np.full(empty_shape, 255, dtype=np.uint8)
bbox_classes = [class_names[int(i)]['name'] for i in bbox_labels]
classes_image = self._draw_labels(self, classes_image, bbox_classes,
bboxes)
if polygons:
classes_image = self._draw_polygons(
self, classes_image, polygons, colors=PALETTE)
else:
classes_image = self._draw_bboxes(
self, classes_image, bboxes, colors=PALETTE)
edge_image = None
if is_openset:
edge_image = np.full(empty_shape, 255, dtype=np.uint8)
edge_image = self._draw_edge_label(edge_image, edge_labels, bboxes,
texts, arrow_colors)
cat_image = []
for i in [image, text_image, classes_image, edge_image]:
if i is not None:
cat_image.append(i)
return np.concatenate(cat_image, axis=1)
def add_datasample(self,
name: str,
image: np.ndarray,
data_sample: Optional['KIEDataSample'] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: int = 0,
pred_score_thr: float = None,
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``. 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:`KIEDataSample`, optional):
KIEDataSample which contains gt and prediction. Defaults
to None.
draw_gt (bool): Whether to draw GT KIEDataSample.
Defaults to True.
draw_pred (bool): Whether to draw Predicted KIEDataSample.
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.
pred_score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
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:
gt_bboxes = data_sample.gt_instances.bboxes
gt_labels = data_sample.gt_instances.labels
gt_texts = data_sample.gt_instances.texts
gt_polygons = data_sample.gt_instances.get('polygons', None)
gt_edge_labels = data_sample.gt_instances.get('edge_labels', None)
gt_img_data = self._draw_instances(image, gt_labels, gt_bboxes,
gt_polygons, gt_edge_labels,
gt_texts,
self.dataset_meta['category'],
self.is_openset, 'g')
if draw_pred:
gt_bboxes = data_sample.gt_instances.bboxes
pred_labels = data_sample.pred_instances.labels
gt_texts = data_sample.gt_instances.texts
gt_polygons = data_sample.gt_instances.get('polygons', None)
pred_edge_labels = data_sample.pred_instances.get(
'edge_labels', None)
pred_img_data = self._draw_instances(image, pred_labels, gt_bboxes,
gt_polygons, pred_edge_labels,
gt_texts,
self.dataset_meta['category'],
self.is_openset, 'r')
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=0)
elif gt_img_data is not None:
drawn_img = gt_img_data
elif pred_img_data is not None:
drawn_img = pred_img_data
else:
drawn_img = image
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)
def draw_arrows(self,
x_data: Union[np.ndarray, torch.Tensor],
y_data: Union[np.ndarray, torch.Tensor],
colors: Union[str, tuple, List[str], List[tuple]] = 'C1',
line_widths: Union[Union[int, float],
List[Union[int, float]]] = 1,
line_styles: Union[str, List[str]] = '-',
arrow_tail_widths: Union[Union[int, float],
List[Union[int, float]]] = 0.001,
arrow_head_widths: Union[Union[int, float],
List[Union[int, float]]] = None,
arrow_head_lengths: Union[Union[int, float],
List[Union[int, float]]] = None,
arrow_shapes: Union[str, List[str]] = 'full',
overhangs: Union[int, List[int]] = 0) -> 'Visualizer':
"""Draw single or multiple arrows.
Args:
x_data (np.ndarray or torch.Tensor): The x coordinate of
each line' start and end points.
y_data (np.ndarray, torch.Tensor): The y coordinate of
each line' start and end points.
colors (str or tuple or list[str or tuple]): The colors of
lines. ``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. Reference to
https://matplotlib.org/stable/gallery/color/named_colors.html
for more details. Defaults to 'g'.
line_widths (int or float or list[int or float]):
The linewidth of lines. ``line_widths`` can have
the same length with lines or just single value.
If ``line_widths`` is single value, all the lines will
have the same linewidth. Defaults to 2.
line_styles (str or list[str]]): The linestyle of lines.
``line_styles`` can have the same length with lines or just
single value. If ``line_styles`` is single value, all the
lines will have the same linestyle. Defaults to '-'.
arrow_tail_widths (int or float or list[int, float]):
The width of arrow tails. ``arrow_tail_widths`` can have
the same length with lines or just single value. If
``arrow_tail_widths`` is single value, all the lines will
have the same width. Defaults to 0.001.
arrow_head_widths (int or float or list[int, float]):
The width of arrow heads. ``arrow_head_widths`` can have
the same length with lines or just single value. If
``arrow_head_widths`` is single value, all the lines will
have the same width. Defaults to None.
arrow_head_lengths (int or float or list[int, float]):
The length of arrow heads. ``arrow_head_lengths`` can have
the same length with lines or just single value. If
``arrow_head_lengths`` is single value, all the lines will
have the same length. Defaults to None.
arrow_shapes (str or list[str]]): The shapes of arrow heads.
``arrow_shapes`` can have the same length with lines or just
single value. If ``arrow_shapes`` is single value, all the
lines will have the same shape. Defaults to 'full'.
overhangs (int or list[int]]): The overhangs of arrow heads.
``overhangs`` can have the same length with lines or just
single value. If ``overhangs`` is single value, all the lines
will have the same overhangs. Defaults to 0.
"""
check_type('x_data', x_data, (np.ndarray, torch.Tensor))
x_data = tensor2ndarray(x_data)
check_type('y_data', y_data, (np.ndarray, torch.Tensor))
y_data = tensor2ndarray(y_data)
assert x_data.shape == y_data.shape, (
'`x_data` and `y_data` should have the same shape')
assert x_data.shape[-1] == 2, (
f'The shape of `x_data` should be (N, 2), but got {x_data.shape}')
if len(x_data.shape) == 1:
x_data = x_data[None]
y_data = y_data[None]
number_arrow = x_data.shape[0]
check_type_and_length('colors', colors, (str, tuple, list),
number_arrow)
colors = value2list(colors, (str, tuple), number_arrow)
colors = color_val_matplotlib(colors) # type: ignore
check_type_and_length('line_widths', line_widths, (int, float),
number_arrow)
line_widths = value2list(line_widths, (int, float), number_arrow)
check_type_and_length('arrow_tail_widths', arrow_tail_widths,
(int, float), number_arrow)
check_type_and_length('line_styles', line_styles, str, number_arrow)
line_styles = value2list(line_styles, str, number_arrow)
arrow_tail_widths = value2list(arrow_tail_widths, (int, float),
number_arrow)
check_type_and_length('arrow_head_widths', arrow_head_widths,
(int, float, type(None)), number_arrow)
arrow_head_widths = value2list(arrow_head_widths,
(int, float, type(None)), number_arrow)
check_type_and_length('arrow_head_lengths', arrow_head_lengths,
(int, float, type(None)), number_arrow)
arrow_head_lengths = value2list(arrow_head_lengths,
(int, float, type(None)), number_arrow)
check_type_and_length('arrow_shapes', arrow_shapes, (str, list),
number_arrow)
arrow_shapes = value2list(arrow_shapes, (str, list), number_arrow)
check_type('overhang', overhangs, int)
overhangs = value2list(overhangs, int, number_arrow)
lines = np.concatenate(
(x_data.reshape(-1, 2, 1), y_data.reshape(-1, 2, 1)), axis=-1)
if not self._is_posion_valid(lines):
warnings.warn(
'Warning: The line is out of bounds,'
' the drawn line may not be in the image', UserWarning)
arrows = []
for i in range(number_arrow):
arrows.append(
FancyArrow(
*tuple(lines[i, 0]),
*tuple(lines[i, 1] - lines[i, 0]),
linestyle=line_styles[i],
color=colors[i],
length_includes_head=True,
width=arrow_tail_widths[i],
head_width=arrow_head_widths[i],
head_length=arrow_head_lengths[i],
overhang=overhangs[i],
shape=arrow_shapes[i],
linewidth=line_widths[i]))
p = PatchCollection(arrows, match_original=True)
self.ax_save.add_collection(p)
return self

110
mmocr/visualization/textspotting_visualizer.py 100644
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@ -0,0 +1,110 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
import mmcv
import numpy as np
import torch
from mmocr.registry import VISUALIZERS
from mmocr.structures import TextDetDataSample
from mmocr.utils.polygon_utils import poly2bbox
from .base_visualizer import BaseLocalVisualizer
@VISUALIZERS.register_module()
class TextSpottingLocalVisualizer(BaseLocalVisualizer):
def _draw_instances(
self,
image: np.ndarray,
bboxes: Union[np.ndarray, torch.Tensor],
polygons: Sequence[np.ndarray],
texts: Sequence[str],
) -> np.ndarray:
"""Draw instances on image.
Args:
image (np.ndarray): The origin image to draw. The format
should be RGB.
bboxes (np.ndarray, torch.Tensor): The bboxes to draw. The shape of
bboxes should be (N, 4), where N is the number of texts.
polygons (Sequence[np.ndarray]): The polygons to draw. The length
of polygons should be the same as the number of bboxes.
edge_labels (np.ndarray, torch.Tensor): The edge labels to draw.
The shape of edge_labels should be (N, N), where N is the
number of texts.
texts (Sequence[str]): The texts to draw. The length of texts
should be the same as the number of bboxes.
class_names (dict): The class names for bbox labels.
is_openset (bool): Whether the dataset is openset. Default: False.
"""
img_shape = image.shape[:2]
empty_shape = (img_shape[0], img_shape[1], 3)
if polygons:
polygons = [polygon.reshape(-1, 2) for polygon in polygons]
if polygons:
image = self._draw_polygons(
self, image, polygons, filling=True, colors=self.PALETTE)
else:
image = self._draw_bboxes(
self, image, bboxes, filling=True, colors=self.PALETTE)
text_image = np.full(empty_shape, 255, dtype=np.uint8)
text_image = self._draw_labels(self, text_image, texts, bboxes)
if polygons:
text_image = self._draw_polygons(
self, text_image, polygons, colors=self.PALETTE)
else:
text_image = self._draw_bboxes(
self, text_image, bboxes, colors=self.PALETTE)
return np.concatenate([image, text_image], axis=1)
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,
pred_score_thr: float = None,
out_file: Optional[str] = None,
step: int = 0) -> None:
gt_img_data = None
pred_img_data = None
if draw_gt:
gt_bboxes = data_sample.gt_instances.bboxes
gt_texts = data_sample.gt_instances.texts
gt_polygons = data_sample.gt_instances.polygons
gt_img_data = self._draw_instances(image, gt_bboxes, gt_polygons,
gt_texts)
if draw_pred:
pred_instances = data_sample.pred_instances
pred_instances = pred_instances[
pred_instances.scores > pred_score_thr].cpu().numpy()
pred_bboxes = pred_instances.get('bboxes', None)
pred_texts = pred_instances.texts
pred_polygons = pred_instances.polygons
if pred_bboxes is None:
pred_bboxes = [poly2bbox(poly) for poly in pred_polygons]
pred_bboxes = np.array(pred_bboxes)
pred_img_data = self._draw_instances(image, pred_bboxes,
pred_polygons, pred_texts)
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=0)
elif gt_img_data is not None:
drawn_img = gt_img_data
elif pred_img_data is not None:
drawn_img = pred_img_data
else:
drawn_img = image
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)

111
tests/test_visualization/test_kie_visualizer.py 100644
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@ -0,0 +1,111 @@
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import unittest
import cv2
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmocr.structures import KIEDataSample
from mmocr.utils import bbox2poly
from mmocr.visualization import KIELocalVisualizer
class TestTextKIELocalVisualizer(unittest.TestCase):
def setUp(self):
h, w = 12, 10
self.image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
edge_labels = torch.rand((5, 5)) > 0.5
# gt_instances
data_sample = KIEDataSample()
gt_instances_data = dict(
bboxes=self._rand_bboxes(5, h, w),
polygons=self._rand_polys(5, h, w),
labels=torch.zeros(5, ),
texts=['text1', 'text2', 'text3', 'text4', 'text5'],
edge_labels=edge_labels)
gt_instances = InstanceData(**gt_instances_data)
data_sample.gt_instances = gt_instances
pred_instances_data = dict(
bboxes=self._rand_bboxes(5, h, w),
labels=torch.zeros(5, ),
scores=torch.rand((5, )),
texts=['text1', 'text2', 'text3', 'text4', 'text5'],
edge_labels=edge_labels)
pred_instances = InstanceData(**pred_instances_data)
data_sample.pred_instances = pred_instances
data_sample = data_sample.numpy()
self.data_sample = data_sample
@staticmethod
def _rand_bboxes(num_boxes, h, w):
cx, cy, bw, bh = torch.rand(num_boxes, 4).T
tl_x = ((cx * w) - (w * bw / 2)).clamp(0, w).unsqueeze(0)
tl_y = ((cy * h) - (h * bh / 2)).clamp(0, h).unsqueeze(0)
br_x = ((cx * w) + (w * bw / 2)).clamp(0, w).unsqueeze(0)
br_y = ((cy * h) + (h * bh / 2)).clamp(0, h).unsqueeze(0)
bboxes = torch.cat([tl_x, tl_y, br_x, br_y], dim=0).T
return bboxes
def _rand_polys(self, num_bboxes, h, w):
bboxes = self._rand_bboxes(num_bboxes, h, w)
bboxes = bboxes.tolist()
polys = [bbox2poly(bbox) for bbox in bboxes]
return polys
def test_add_datasample(self):
image = self.image
h, w, c = image.shape
visualizer = KIELocalVisualizer(is_openset=True)
visualizer.dataset_meta = dict(category=[
dict(id=0, name='bg'),
dict(id=1, name='key'),
dict(id=2, name='value'),
dict(id=3, name='other')
])
visualizer.add_datasample('image', image, self.data_sample)
with tempfile.TemporaryDirectory() as tmp_dir:
# test out
out_file = osp.join(tmp_dir, 'out_file.jpg')
visualizer.add_datasample(
'image',
image,
self.data_sample,
out_file=out_file,
draw_gt=False,
draw_pred=False)
self._assert_image_and_shape(out_file, (h, w, c))
visualizer.add_datasample(
'image', image, self.data_sample, out_file=out_file)
self._assert_image_and_shape(out_file, (h * 2, w * 4, c))
visualizer.add_datasample(
'image',
image,
self.data_sample,
draw_gt=False,
out_file=out_file)
self._assert_image_and_shape(out_file, (h, w * 4, c))
visualizer.add_datasample(
'image',
image,
self.data_sample,
draw_pred=False,
out_file=out_file)
self._assert_image_and_shape(out_file, (h, w * 4, c))
def _assert_image_and_shape(self, out_file, out_shape):
self.assertTrue(osp.exists(out_file))
drawn_img = cv2.imread(out_file)
self.assertTrue(drawn_img.shape == out_shape)

1
tools/analysis_tools/browse_dataset.py
View File

@ -50,6 +50,7 @@ def main():
dataset = DATASETS.build(cfg.train_dataloader.dataset)
visualizer = VISUALIZERS.build(cfg.visualizer)
visualizer.dataset_meta = dataset.metainfo
progress_bar = mmengine.ProgressBar(len(dataset))
for item in dataset:
img = item['inputs'].permute(1, 2, 0).numpy()