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import warnings |
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import numpy as np |
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import cv2 |
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from shapely.geometry import Polygon |
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import pyclipper |
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from concern.config import State |
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from .data_process import DataProcess |
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class MakeCenterDistanceMap(DataProcess): |
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r''' |
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Making the border map from detection data with ICDAR format. |
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Typically following the process of class `MakeICDARData`. |
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''' |
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expansion_ratio = State(default=0.1) |
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def __init__(self, cmd={}, *args, **kwargs): |
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self.load_all(cmd=cmd, **kwargs) |
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warnings.simplefilter("ignore") |
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def process(self, data, *args, **kwargs): |
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r''' |
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required keys: |
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image. |
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lines: Instace of `TextLines`, which is defined in data/text_lines.py |
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adding keys: |
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distance_map |
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''' |
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image = data['image'] |
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lines = data['lines'] |
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h, w = image.shape[:2] |
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canvas = np.zeros(image.shape[:2], dtype=np.float32) |
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mask = np.zeros(image.shape[:2], dtype=np.float32) |
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for _, quad in lines: |
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padded = self.expand_quad(quad) |
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center_x = padded[:, 0].mean() |
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center_y = padded[:, 1].mean() |
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index_x, index_y = np.meshgrid(np.arange(w), np.arange(h)) |
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self.render_distance_map(canvas, center_x, center_y, index_x, index_y) |
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self.render_constant(mask, quad, 1) |
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canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min |
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data['thresh_map'] = canvas |
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return data |
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def expand_quad(self, polygon): |
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polygon = np.array(polygon) |
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assert polygon.ndim == 2 |
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assert polygon.shape[1] == 2 |
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polygon_shape = Polygon(polygon) |
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distance = polygon_shape.area * \ |
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(1 - np.power(self.expansion_ratio, 2)) / polygon_shape.length |
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subject = [tuple(l) for l in polygon] |
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padding = pyclipper.PyclipperOffset() |
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padding.AddPath(subject, pyclipper.JT_ROUND, |
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pyclipper.ET_CLOSEDPOLYGON) |
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padded_polygon = np.array(padding.Execute(distance)[0]) |
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return padded_polygon |
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cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0) |
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def distance(self, xs, ys, point): |
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''' |
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compute the distance from point to a line |
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ys: coordinates in the first axis |
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xs: coordinates in the second axis |
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point_1, point_2: (x, y), the end of the line |
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''' |
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height, width = xs.shape[:2] |
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square_distance_1 = np.square( |
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xs - point_1[0]) + np.square(ys - point_1[1]) |
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square_distance_2 = np.square( |
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xs - point_2[0]) + np.square(ys - point_2[1]) |
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square_distance = np.square( |
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point_1[0] - point_2[0]) + np.square(point_1[1] - point_2[1]) |
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cosin = (square_distance - square_distance_1 - square_distance_2) / \ |
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(2 * np.sqrt(square_distance_1 * square_distance_2)) |
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square_sin = 1 - np.square(cosin) |
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square_sin = np.nan_to_num(square_sin) |
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result = np.sqrt(square_distance_1 * square_distance_2 * |
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square_sin / square_distance) |
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result[cosin < 0] = np.sqrt(np.fmin( |
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square_distance_1, square_distance_2))[cosin < 0] |
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return result |
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def extend_line(self, point_1, point_2, result): |
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ex_point_1 = (int(round(point_1[0] + (point_1[0] - point_2[0]) * (1 + self.shrink_ratio))), |
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int(round(point_1[1] + (point_1[1] - point_2[1]) * (1 + self.shrink_ratio)))) |
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cv2.line(result, tuple(ex_point_1), tuple(point_1), |
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4096.0, 1, lineType=cv2.LINE_AA, shift=0) |
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ex_point_2 = (int(round(point_2[0] + (point_2[0] - point_1[0]) * (1 + self.shrink_ratio))), |
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int(round(point_2[1] + (point_2[1] - point_1[1]) * (1 + self.shrink_ratio)))) |
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cv2.line(result, tuple(ex_point_2), tuple(point_2), |
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4096.0, 1, lineType=cv2.LINE_AA, shift=0) |
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return ex_point_1, ex_point_2 |
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