PaddleOCR/ppocr/data/imaug/iaa_augment.py

102 lines
3.2 KiB
Python

# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import imgaug
import imgaug.augmenters as iaa
class AugmenterBuilder(object):
def __init__(self):
pass
def build(self, args, root=True):
if args is None or len(args) == 0:
return None
elif isinstance(args, list):
if root:
sequence = [self.build(value, root=False) for value in args]
return iaa.Sequential(sequence)
else:
return getattr(iaa, args[0])(
*[self.to_tuple_if_list(a) for a in args[1:]])
elif isinstance(args, dict):
cls = getattr(iaa, args['type'])
return cls(**{
k: self.to_tuple_if_list(v)
for k, v in args['args'].items()
})
else:
raise RuntimeError('unknown augmenter arg: ' + str(args))
def to_tuple_if_list(self, obj):
if isinstance(obj, list):
return tuple(obj)
return obj
class IaaAugment():
def __init__(self, augmenter_args=None, **kwargs):
if augmenter_args is None:
augmenter_args = [{
'type': 'Fliplr',
'args': {
'p': 0.5
}
}, {
'type': 'Affine',
'args': {
'rotate': [-10, 10]
}
}, {
'type': 'Resize',
'args': {
'size': [0.5, 3]
}
}]
self.augmenter = AugmenterBuilder().build(augmenter_args)
def __call__(self, data):
image = data['image']
shape = image.shape
if self.augmenter:
aug = self.augmenter.to_deterministic()
data['image'] = aug.augment_image(image)
data = self.may_augment_annotation(aug, data, shape)
return data
def may_augment_annotation(self, aug, data, shape):
if aug is None:
return data
line_polys = []
for poly in data['polys']:
new_poly = self.may_augment_poly(aug, shape, poly)
line_polys.append(new_poly)
data['polys'] = np.array(line_polys)
return data
def may_augment_poly(self, aug, img_shape, poly):
keypoints = [imgaug.Keypoint(p[0], p[1]) for p in poly]
keypoints = aug.augment_keypoints(
[imgaug.KeypointsOnImage(
keypoints, shape=img_shape)])[0].keypoints
poly = [(p.x, p.y) for p in keypoints]
return poly