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import warnings
import numpy as np
import cv2
from shapely.geometry import Polygon
import pyclipper
from concern.config import State
from .data_process import DataProcess
class MakeCenterDistanceMap(DataProcess):
r'''
Making the border map from detection data with ICDAR format.
Typically following the process of class `MakeICDARData`.
'''
expansion_ratio = State(default=0.1)
def __init__(self, cmd={}, *args, **kwargs):
self.load_all(cmd=cmd, **kwargs)
warnings.simplefilter("ignore")
def process(self, data, *args, **kwargs):
r'''
required keys:
image.
lines: Instace of `TextLines`, which is defined in data/text_lines.py
adding keys:
distance_map
'''
image = data['image']
lines = data['lines']
h, w = image.shape[:2]
canvas = np.zeros(image.shape[:2], dtype=np.float32)
mask = np.zeros(image.shape[:2], dtype=np.float32)
for _, quad in lines:
padded = self.expand_quad(quad)
center_x = padded[:, 0].mean()
center_y = padded[:, 1].mean()
index_x, index_y = np.meshgrid(np.arange(w), np.arange(h))
self.render_distance_map(canvas, center_x, center_y, index_x, index_y)
self.render_constant(mask, quad, 1)
canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min
data['thresh_map'] = canvas
return data
def expand_quad(self, polygon):
polygon = np.array(polygon)
assert polygon.ndim == 2
assert polygon.shape[1] == 2
polygon_shape = Polygon(polygon)
distance = polygon_shape.area * \
(1 - np.power(self.expansion_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])
return padded_polygon
cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)
def distance(self, xs, ys, point):
'''
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):
ex_point_1 = (int(round(point_1[0] + (point_1[0] - point_2[0]) * (1 + self.shrink_ratio))),
int(round(point_1[1] + (point_1[1] - point_2[1]) * (1 + self.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 + self.shrink_ratio))),
int(round(point_2[1] + (point_2[1] - point_1[1]) * (1 + self.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
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