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# copyright (c) 2021 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 cv2
import numpy as np
import pyclipper
from shapely.geometry import Polygon
__all__ = ['MakePseGt']
class MakePseGt(object):
def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs):
self.kernel_num = kernel_num
self.min_shrink_ratio = min_shrink_ratio
self.size = size
def __call__(self, data):
image = data['image']
text_polys = data['polys']
ignore_tags = data['ignore_tags']
h, w, _ = image.shape
short_edge = min(h, w)
if short_edge < self.size:
# keep short_size >= self.size
scale = self.size / short_edge
image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
text_polys *= scale
gt_kernels = []
for i in range(1, self.kernel_num + 1):
# s1->sn, from big to small
rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1
) * i
text_kernel, ignore_tags = self.generate_kernel(
image.shape[0:2], rate, text_polys, ignore_tags)
gt_kernels.append(text_kernel)
training_mask = np.ones(image.shape[0:2], dtype='uint8')
for i in range(text_polys.shape[0]):
if ignore_tags[i]:
cv2.fillPoly(training_mask,
text_polys[i].astype(np.int32)[np.newaxis, :, :],
0)
gt_kernels = np.array(gt_kernels)
gt_kernels[gt_kernels > 0] = 1
data['image'] = image
data['polys'] = text_polys
data['gt_kernels'] = gt_kernels[0:]
data['gt_text'] = gt_kernels[0]
data['mask'] = training_mask.astype('float32')
return data
def generate_kernel(self,
img_size,
shrink_ratio,
text_polys,
ignore_tags=None):
"""
Refer to part of the code:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py
"""
h, w = img_size
text_kernel = np.zeros((h, w), dtype=np.float32)
for i, poly in enumerate(text_polys):
polygon = Polygon(poly)
distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (
polygon.length + 1e-6)
subject = [tuple(l) for l in poly]
pco = pyclipper.PyclipperOffset()
pco.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
shrinked = np.array(pco.Execute(-distance))
if len(shrinked) == 0 or shrinked.size == 0:
if ignore_tags is not None:
ignore_tags[i] = True
continue
try:
shrinked = np.array(shrinked[0]).reshape(-1, 2)
except:
if ignore_tags is not None:
ignore_tags[i] = True
continue
cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1)
return text_kernel, ignore_tags
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