| 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] |
| |
| 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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|
