| from collections import OrderedDict |
|
|
| import numpy as np |
| import cv2 |
| from shapely.geometry import Polygon |
| import pyclipper |
|
|
| from concern.config import Configurable, State |
|
|
|
|
| class MakeSegDetectorData(Configurable): |
| min_text_size = State(default=8) |
| shrink_ratio = State(default=0.4) |
|
|
| def __init__(self, **kwargs): |
| self.load_all(**kwargs) |
|
|
| def __call__(self, data, *args, **kwargs): |
| ''' |
| data: a dict typically returned from `MakeICDARData`, |
| where the following keys are contrains: |
| image*, polygons*, ignore_tags*, shape, filename |
| * means required. |
| ''' |
| image = data['image'] |
| polygons = data['polygons'] |
| ignore_tags = data['ignore_tags'] |
| image = data['image'] |
| filename = data['filename'] |
|
|
| h, w = image.shape[:2] |
| polygons, ignore_tags = self.validate_polygons( |
| polygons, ignore_tags, h, w) |
| gt = np.zeros((1, h, w), dtype=np.float32) |
| mask = np.ones((h, w), dtype=np.float32) |
| for i in range(polygons.shape[0]): |
| polygon = polygons[i] |
| height = min(np.linalg.norm(polygon[0] - polygon[3]), |
| np.linalg.norm(polygon[1] - polygon[2])) |
| width = min(np.linalg.norm(polygon[0] - polygon[1]), |
| np.linalg.norm(polygon[2] - polygon[3])) |
| if ignore_tags[i] or min(height, width) < self.min_text_size: |
| cv2.fillPoly(mask, polygon.astype( |
| np.int32)[np.newaxis, :, :], 0) |
| ignore_tags[i] = True |
| else: |
| polygon_shape = Polygon(polygon) |
| distance = polygon_shape.area * \ |
| (1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length |
| subject = [tuple(l) for l in polygons[i]] |
| padding = pyclipper.PyclipperOffset() |
| padding.AddPath(subject, pyclipper.JT_ROUND, |
| pyclipper.ET_CLOSEDPOLYGON) |
| shrinked = padding.Execute(-distance) |
| if shrinked == []: |
| cv2.fillPoly(mask, polygon.astype( |
| np.int32)[np.newaxis, :, :], 0) |
| ignore_tags[i] = True |
| continue |
| shrinked = np.array(shrinked[0]).reshape(-1, 2) |
| cv2.fillPoly(gt[0], [shrinked.astype(np.int32)], 1) |
|
|
| if filename is None: |
| filename = '' |
| data.update(image=image, |
| polygons=polygons, |
| gt=gt, mask=mask, filename=filename) |
| return data |
|
|
| def validate_polygons(self, polygons, ignore_tags, h, w): |
| ''' |
| polygons (numpy.array, required): of shape (num_instances, num_points, 2) |
| ''' |
| if polygons.shape[0] == 0: |
| return polygons, ignore_tags |
| assert polygons.shape[0] == len(ignore_tags) |
|
|
| polygons[:, :, 0] = np.clip(polygons[:, :, 0], 0, w - 1) |
| polygons[:, :, 1] = np.clip(polygons[:, :, 1], 0, h - 1) |
|
|
| for i in range(polygons.shape[0]): |
| area = self.polygon_area(polygons[i]) |
| if abs(area) < 1: |
| ignore_tags[i] = True |
| if area > 0: |
| polygons[i] = polygons[i][(0, 3, 2, 1), :] |
| return polygons, ignore_tags |
|
|
| def polygon_area(self, polygon): |
| edge = [ |
| (polygon[1][0] - polygon[0][0]) * (polygon[1][1] + polygon[0][1]), |
| (polygon[2][0] - polygon[1][0]) * (polygon[2][1] + polygon[1][1]), |
| (polygon[3][0] - polygon[2][0]) * (polygon[3][1] + polygon[2][1]), |
| (polygon[0][0] - polygon[3][0]) * (polygon[0][1] + polygon[3][1]) |
| ] |
| return np.sum(edge) / 2. |
|
|
