| 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 MakeBorderMap(DataProcess): |
| r''' |
| Making the border map from detection data with ICDAR format. |
| Typically following the process of class `MakeICDARData`. |
| ''' |
| shrink_ratio = State(default=0.4) |
| thresh_min = State(default=0.3) |
| thresh_max = State(default=0.7) |
|
|
| 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, polygons, ignore_tags |
| adding keys: |
| thresh_map, thresh_mask |
| ''' |
| image = data['image'] |
| polygons = data['polygons'] |
| ignore_tags = data['ignore_tags'] |
| canvas = np.zeros(image.shape[:2], dtype=np.float32) |
| mask = np.zeros(image.shape[:2], dtype=np.float32) |
|
|
| for i in range(len(polygons)): |
| if ignore_tags[i]: |
| continue |
| self.draw_border_map(polygons[i], canvas, mask=mask) |
| canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min |
| data['thresh_map'] = canvas |
| data['thresh_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) |
| 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] |
| |
| 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 |
|
|
|
|
