mirror of https://github.com/open-mmlab/mmocr.git
401 lines
14 KiB
Python
401 lines
14 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import argparse
|
|
import os.path as osp
|
|
import sys
|
|
from typing import Optional, Tuple
|
|
|
|
import cv2
|
|
import mmcv
|
|
import numpy as np
|
|
from mmengine.config import Config, DictAction
|
|
from mmengine.dataset import Compose
|
|
from mmengine.utils import ProgressBar
|
|
from mmengine.visualization import Visualizer
|
|
|
|
from mmocr.registry import DATASETS, VISUALIZERS
|
|
from mmocr.utils import register_all_modules
|
|
|
|
|
|
# TODO: Support for printing the change in key of results
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description='Browse a dataset')
|
|
parser.add_argument('config', help='Path to model or dataset config.')
|
|
parser.add_argument(
|
|
'--phase',
|
|
'-p',
|
|
default='train',
|
|
type=str,
|
|
help='Phase of dataset to visualize. Use "train", "test" or "val" if '
|
|
"you just want to visualize the default split. It's also possible to "
|
|
'be a dataset variable name, which might be useful when a dataset '
|
|
'split has multiple variants in the config.')
|
|
parser.add_argument(
|
|
'--mode',
|
|
'-m',
|
|
default='transformed',
|
|
type=str,
|
|
choices=['original', 'transformed', 'pipeline'],
|
|
help='Display mode: display original pictures or '
|
|
'transformed pictures or comparison pictures. "original" '
|
|
'only visualizes the original dataset & annotations; '
|
|
'"transformed" shows the resulting images processed through all the '
|
|
'transforms; "pipeline" shows all the intermediate images. '
|
|
'Defaults to "transformed".')
|
|
parser.add_argument(
|
|
'--output-dir',
|
|
'-o',
|
|
default=None,
|
|
type=str,
|
|
help='If there is no display interface, you can save it.')
|
|
parser.add_argument(
|
|
'--task',
|
|
'-t',
|
|
default='auto',
|
|
choices=['auto', 'textdet', 'textrecog'],
|
|
type=str,
|
|
help='Specify the task type of the dataset. If "auto", the task type '
|
|
'will be inferred from the config. If the script is unable to infer '
|
|
'the task type, you need to specify it manually. Defaults to "auto".')
|
|
parser.add_argument('--not-show', default=False, action='store_true')
|
|
parser.add_argument(
|
|
'--show-number',
|
|
'-n',
|
|
type=int,
|
|
default=sys.maxsize,
|
|
help='number of images selected to visualize, '
|
|
'must bigger than 0. if the number is bigger than length '
|
|
'of dataset, show all the images in dataset; '
|
|
'default "sys.maxsize", show all images in dataset')
|
|
parser.add_argument(
|
|
'--show-interval',
|
|
'-i',
|
|
type=float,
|
|
default=3,
|
|
help='the interval of show (s)')
|
|
parser.add_argument(
|
|
'--cfg-options',
|
|
nargs='+',
|
|
action=DictAction,
|
|
help='override some settings in the used config, the key-value pair '
|
|
'in xxx=yyy format will be merged into config file. If the value to '
|
|
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
|
|
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
|
|
'Note that the quotation marks are necessary and that no white space '
|
|
'is allowed.')
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def _get_adaptive_scale(img_shape: Tuple[int, int],
|
|
min_scale: float = 0.3,
|
|
max_scale: float = 3.0) -> float:
|
|
"""Get adaptive scale according to image shape.
|
|
|
|
The target scale depends on the the short edge length of the image. If the
|
|
short edge length equals 224, the output is 1.0. And output linear
|
|
scales according the short edge length. You can also specify the minimum
|
|
scale and the maximum scale to limit the linear scale.
|
|
|
|
Args:
|
|
img_shape (Tuple[int, int]): The shape of the canvas image.
|
|
min_scale (int): The minimum scale. Defaults to 0.3.
|
|
max_scale (int): The maximum scale. Defaults to 3.0.
|
|
|
|
Returns:
|
|
int: The adaptive scale.
|
|
"""
|
|
short_edge_length = min(img_shape)
|
|
scale = short_edge_length / 224.
|
|
return min(max(scale, min_scale), max_scale)
|
|
|
|
|
|
def make_grid(imgs, names):
|
|
"""Concat list of pictures into a single big picture, align height here."""
|
|
visualizer = Visualizer.get_current_instance()
|
|
ori_shapes = [img.shape[:2] for img in imgs]
|
|
max_height = int(max(img.shape[0] for img in imgs) * 1.1)
|
|
min_width = min(img.shape[1] for img in imgs)
|
|
horizontal_gap = min_width // 10
|
|
img_scale = _get_adaptive_scale((max_height, min_width))
|
|
|
|
texts = []
|
|
text_positions = []
|
|
start_x = 0
|
|
for i, img in enumerate(imgs):
|
|
pad_height = (max_height - img.shape[0]) // 2
|
|
pad_width = horizontal_gap // 2
|
|
# make border
|
|
imgs[i] = cv2.copyMakeBorder(
|
|
img,
|
|
pad_height,
|
|
max_height - img.shape[0] - pad_height + int(img_scale * 30 * 2),
|
|
pad_width,
|
|
pad_width,
|
|
cv2.BORDER_CONSTANT,
|
|
value=(255, 255, 255))
|
|
texts.append(f'{"execution: "}{i}\n{names[i]}\n{ori_shapes[i]}')
|
|
text_positions.append(
|
|
[start_x + img.shape[1] // 2 + pad_width, max_height])
|
|
start_x += img.shape[1] + horizontal_gap
|
|
|
|
display_img = np.concatenate(imgs, axis=1)
|
|
visualizer.set_image(display_img)
|
|
img_scale = _get_adaptive_scale(display_img.shape[:2])
|
|
visualizer.draw_texts(
|
|
texts,
|
|
positions=np.array(text_positions),
|
|
font_sizes=img_scale * 7,
|
|
colors='black',
|
|
horizontal_alignments='center',
|
|
font_families='monospace')
|
|
return visualizer.get_image()
|
|
|
|
|
|
class InspectCompose(Compose):
|
|
"""Compose multiple transforms sequentially.
|
|
|
|
And record "img" field of all results in one list.
|
|
"""
|
|
|
|
def __init__(self, transforms, intermediate_imgs):
|
|
super().__init__(transforms=transforms)
|
|
self.intermediate_imgs = intermediate_imgs
|
|
|
|
def __call__(self, data):
|
|
if 'img' in data:
|
|
self.intermediate_imgs.append({
|
|
'name': 'original',
|
|
'img': data['img'].copy()
|
|
})
|
|
self.ptransforms = [
|
|
self.transforms[i] for i in range(len(self.transforms) - 1)
|
|
]
|
|
for t in self.ptransforms:
|
|
data = t(data)
|
|
# Keep the same meta_keys in the PackDetInputs
|
|
self.transforms[-1].meta_keys = [key for key in data]
|
|
data_sample = self.transforms[-1](data)
|
|
if data is None:
|
|
return None
|
|
if 'img' in data:
|
|
self.intermediate_imgs.append({
|
|
'name':
|
|
t.__class__.__name__,
|
|
'dataset_sample':
|
|
data_sample['data_samples']
|
|
})
|
|
return data
|
|
|
|
|
|
def infer_dataset_task(task: str,
|
|
dataset_cfg: Config,
|
|
var_name: Optional[str] = None) -> str:
|
|
"""Try to infer the dataset's task type from the config and the variable
|
|
name."""
|
|
if task != 'auto':
|
|
return task
|
|
|
|
if dataset_cfg.pipeline is not None:
|
|
if dataset_cfg.pipeline[-1].type == 'PackTextDetInputs':
|
|
return 'textdet'
|
|
elif dataset_cfg.pipeline[-1].type == 'PackTextRecogInputs':
|
|
return 'textrecog'
|
|
|
|
if var_name is not None:
|
|
if 'det' in var_name:
|
|
return 'textdet'
|
|
elif 'rec' in var_name:
|
|
return 'textrecog'
|
|
|
|
raise ValueError(
|
|
'Unable to infer the task type from dataset pipeline '
|
|
'or variable name. Please specify the task type with --task argument '
|
|
'explicitly.')
|
|
|
|
|
|
def obtain_dataset_cfg(cfg: Config, phase: str, mode: str, task: str) -> Tuple:
|
|
"""Obtain dataset and visualizer from config. Two modes are supported:
|
|
1. Model Config Mode:
|
|
In this mode, the input config should be a complete model config, which
|
|
includes a dataset within pipeline and a visualizer.
|
|
2. Dataset Config Mode:
|
|
In this mode, the input config should be a complete dataset config,
|
|
which only includes basic dataset information, and it may does not
|
|
contain a visualizer and dataset pipeline.
|
|
|
|
Examples:
|
|
Typically, the model config files are stored in
|
|
`configs/textdet/dbnet/xxx.py` and should look like:
|
|
>>> train_dataloader = dict(
|
|
>>> batch_size=16,
|
|
>>> num_workers=8,
|
|
>>> persistent_workers=True,
|
|
>>> sampler=dict(type='DefaultSampler', shuffle=True),
|
|
>>> dataset=icdar2015_textdet_train)
|
|
|
|
while the dataset config files are stored in
|
|
`configs/textdet/_base_/datasets/xxx.py` and should be like:
|
|
>>> icdar2015_textdet_train = dict(
|
|
>>> type='OCRDataset',
|
|
>>> data_root=ic15_det_data_root,
|
|
>>> ann_file='textdet_train.json',
|
|
>>> filter_cfg=dict(filter_empty_gt=True, min_size=32),
|
|
>>> pipeline=None)
|
|
|
|
Args:
|
|
cfg (Config): Config object.
|
|
phase (str): The dataset phase to visualize.
|
|
mode (str): Script mode.
|
|
task (str): The current task type.
|
|
|
|
Returns:
|
|
Tuple: Tuple of (dataset, visualizer).
|
|
"""
|
|
default_cfgs = dict(
|
|
textdet=dict(
|
|
visualizer=dict(
|
|
type='TextDetLocalVisualizer',
|
|
name='visualizer',
|
|
vis_backends=[dict(type='LocalVisBackend')]),
|
|
pipeline=[
|
|
dict(
|
|
type='LoadImageFromFile',
|
|
file_client_args=dict(backend='disk'),
|
|
color_type='color_ignore_orientation'),
|
|
dict(
|
|
type='LoadOCRAnnotations',
|
|
with_polygon=True,
|
|
with_bbox=True,
|
|
with_label=True,
|
|
),
|
|
dict(
|
|
type='PackTextDetInputs',
|
|
meta_keys=('img_path', 'ori_shape', 'img_shape'))
|
|
]),
|
|
textrecog=dict(
|
|
visualizer=dict(
|
|
type='TextRecogLocalVisualizer',
|
|
name='visualizer',
|
|
vis_backends=[dict(type='LocalVisBackend')]),
|
|
pipeline=[
|
|
dict(
|
|
type='LoadImageFromFile',
|
|
file_client_args=dict(backend='disk'),
|
|
ignore_empty=True,
|
|
min_size=2),
|
|
dict(type='LoadOCRAnnotations', with_text=True),
|
|
dict(
|
|
type='PackTextRecogInputs',
|
|
meta_keys=('img_path', 'ori_shape', 'img_shape',
|
|
'valid_ratio'))
|
|
]),
|
|
)
|
|
|
|
# Model config mode
|
|
dataloader_name = f'{phase}_dataloader'
|
|
if dataloader_name in cfg:
|
|
dataset = cfg.get(dataloader_name).dataset
|
|
visualizer = cfg.visualizer
|
|
|
|
if mode == 'original':
|
|
default_cfg = default_cfgs[infer_dataset_task(task, dataset)]
|
|
dataset.pipeline = default_cfg['pipeline']
|
|
|
|
return dataset, visualizer
|
|
|
|
# Dataset config mode
|
|
|
|
for key in cfg.keys():
|
|
if key.endswith(phase) and cfg[key]['type'].endswith('Dataset'):
|
|
dataset = cfg[key]
|
|
default_cfg = default_cfgs[infer_dataset_task(
|
|
task, dataset, key.lower())]
|
|
visualizer = default_cfg['visualizer']
|
|
dataset['pipeline'] = default_cfg['pipeline'] if dataset[
|
|
'pipeline'] is None else dataset['pipeline']
|
|
|
|
return dataset, visualizer
|
|
|
|
raise ValueError(
|
|
f'Unable to find "{phase}_dataloader" or any dataset variable ending '
|
|
f'with "{phase}". Please check your config file or --phase argument '
|
|
'and try again. More details can be found in the docstring of '
|
|
'obtain_dataset_cfg function. Or, you may visit the documentation via '
|
|
'https://mmocr.readthedocs.io/en/dev-1.x/user_guides/useful_tools.html#dataset-visualization-tool' # noqa: E501
|
|
)
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
cfg = Config.fromfile(args.config)
|
|
if args.cfg_options is not None:
|
|
cfg.merge_from_dict(args.cfg_options)
|
|
|
|
# register all modules in mmyolo into the registries
|
|
register_all_modules()
|
|
|
|
dataset_cfg, visualizer_cfg = obtain_dataset_cfg(cfg, args.phase,
|
|
args.mode, args.task)
|
|
dataset = DATASETS.build(dataset_cfg)
|
|
visualizer = VISUALIZERS.build(visualizer_cfg)
|
|
visualizer.dataset_meta = dataset.metainfo
|
|
|
|
intermediate_imgs = []
|
|
|
|
if dataset_cfg.type == 'ConcatDataset':
|
|
for sub_dataset in dataset.datasets:
|
|
sub_dataset.pipeline = InspectCompose(
|
|
sub_dataset.pipeline.transforms, intermediate_imgs)
|
|
else:
|
|
dataset.pipeline = InspectCompose(dataset.pipeline.transforms,
|
|
intermediate_imgs)
|
|
|
|
# init visualization image number
|
|
assert args.show_number > 0
|
|
display_number = min(args.show_number, len(dataset))
|
|
|
|
progress_bar = ProgressBar(display_number)
|
|
# fetching items from dataset is a must for visualization
|
|
for i, _ in zip(range(display_number), dataset):
|
|
image_i = []
|
|
result_i = [result['dataset_sample'] for result in intermediate_imgs]
|
|
for k, datasample in enumerate(result_i):
|
|
image = datasample.img
|
|
image = image[..., [2, 1, 0]] # bgr to rgb
|
|
image_show = visualizer.add_datasample(
|
|
'result',
|
|
image,
|
|
datasample,
|
|
draw_pred=False,
|
|
draw_gt=True,
|
|
show=False)
|
|
image_i.append(image_show)
|
|
|
|
if args.mode == 'pipeline':
|
|
image = make_grid([result for result in image_i],
|
|
[result['name'] for result in intermediate_imgs])
|
|
else:
|
|
image = image_i[-1]
|
|
|
|
if hasattr(datasample, 'img_path'):
|
|
filename = osp.basename(datasample.img_path)
|
|
else:
|
|
# some dataset have not image path
|
|
filename = f'{i}.jpg'
|
|
out_file = osp.join(args.output_dir,
|
|
filename) if args.output_dir is not None else None
|
|
|
|
if out_file is not None:
|
|
mmcv.imwrite(image[..., ::-1], out_file)
|
|
|
|
if not args.not_show:
|
|
visualizer.show(
|
|
image, win_name=filename, wait_time=args.show_interval)
|
|
|
|
intermediate_imgs.clear()
|
|
progress_bar.update()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|