# 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.
"""
This code is refer from:
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/make_border_map.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import cv2
np.seterr(divide='ignore', invalid='ignore')
import pyclipper
from shapely.geometry import Polygon
import sys
import warnings
warnings.simplefilter("ignore")
__all__ = ['MakeBorderMap']
class MakeBorderMap(object):
def __init__(self,
shrink_ratio=0.4,
thresh_min=0.3,
thresh_max=0.7,
**kwargs):
self.shrink_ratio = shrink_ratio
self.thresh_min = thresh_min
self.thresh_max = thresh_max
def __call__(self, data):
img = data['image']
text_polys = data['polys']
ignore_tags = data['ignore_tags']
canvas = np.zeros(img.shape[:2], dtype=np.float32)
mask = np.zeros(img.shape[:2], dtype=np.float32)
for i in range(len(text_polys)):
if ignore_tags[i]:
continue
self.draw_border_map(text_polys[i], canvas, mask=mask)
canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min
data['threshold_map'] = canvas
data['threshold_mask'] = mask
return data
def draw_border_map(self, polygon, canvas, mask):
polygon = np.array(polygon)
assert polygon.ndim == 2
assert polygon.shape[1] == 2
polygon_shape = Polygon(polygon)
if polygon_shape.area <= 0:
return
distance = polygon_shape.area * (
1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length
subject = [tuple(l) for l in polygon]
padding = pyclipper.PyclipperOffset()
padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
padded_polygon = np.array(padding.Execute(distance)[0])
cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)
xmin = padded_polygon[:, 0].min()
xmax = padded_polygon[:, 0].max()
ymin = padded_polygon[:, 1].min()
ymax = padded_polygon[:, 1].max()
width = xmax - xmin + 1
height = ymax - ymin + 1
polygon[:, 0] = polygon[:, 0] - xmin
polygon[:, 1] = polygon[:, 1] - ymin
xs = np.broadcast_to(
np.linspace(
0, width - 1, num=width).reshape(1, width), (height, width))
ys = np.broadcast_to(
np.linspace(
0, height - 1, num=height).reshape(height, 1), (height, width))
distance_map = np.zeros(
(polygon.shape[0], height, width), dtype=np.float32)
for i in range(polygon.shape[0]):
j = (i + 1) % polygon.shape[0]
absolute_distance = self._distance(xs, ys, polygon[i], polygon[j])
distance_map[i] = np.clip(absolute_distance / distance, 0, 1)
distance_map = distance_map.min(axis=0)
xmin_valid = min(max(0, xmin), canvas.shape[1] - 1)
xmax_valid = min(max(0, xmax), canvas.shape[1] - 1)
ymin_valid = min(max(0, ymin), canvas.shape[0] - 1)
ymax_valid = min(max(0, ymax), canvas.shape[0] - 1)
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax(
1 - distance_map[ymin_valid - ymin:ymax_valid - ymax + height,
xmin_valid - xmin:xmax_valid - xmax + width],
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1])
def _distance(self, xs, ys, point_1, point_2):
'''
compute the distance from point to a line
ys: coordinates in the first axis
xs: coordinates in the second axis
point_1, point_2: (x, y), the end of the line
'''
height, width = xs.shape[:2]
square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[
1])
square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[
1])
square_distance = np.square(point_1[0] - point_2[0]) + np.square(
point_1[1] - point_2[1])
cosin = (square_distance - square_distance_1 - square_distance_2) / (
2 * np.sqrt(square_distance_1 * square_distance_2))
square_sin = 1 - np.square(cosin)
square_sin = np.nan_to_num(square_sin)
result = np.sqrt(square_distance_1 * square_distance_2 * square_sin /
square_distance)
result[cosin <
0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin
< 0]
# self.extend_line(point_1, point_2, result)
return result
def extend_line(self, point_1, point_2, result, shrink_ratio):
ex_point_1 = (int(
round(point_1[0] + (point_1[0] - point_2[0]) * (1 + shrink_ratio))),
int(
round(point_1[1] + (point_1[1] - point_2[1]) * (
1 + shrink_ratio))))
cv2.line(
result,
tuple(ex_point_1),
tuple(point_1),
4096.0,
1,
lineType=cv2.LINE_AA,
shift=0)
ex_point_2 = (int(
round(point_2[0] + (point_2[0] - point_1[0]) * (1 + shrink_ratio))),
int(
round(point_2[1] + (point_2[1] - point_1[1]) * (
1 + shrink_ratio))))
cv2.line(
result,
tuple(ex_point_2),
tuple(point_2),
4096.0,
1,
lineType=cv2.LINE_AA,
shift=0)
return ex_point_1, ex_point_2