| import numpy as np |
| import cv2 |
| from shapely.geometry import Polygon |
| import pyclipper |
|
|
| from concern.config import State |
| from .data_process import DataProcess |
|
|
|
|
| class MakeSegDetectionData(DataProcess): |
| r''' |
| Making binary mask from detection data with ICDAR format. |
| Typically following the process of class `MakeICDARData`. |
| ''' |
| min_text_size = State(default=8) |
| shrink_ratio = State(default=0.4) |
|
|
| def __init__(self, **kwargs): |
| self.load_all(**kwargs) |
|
|
| def process(self, data): |
| ''' |
| requied keys: |
| image, polygons, ignore_tags, filename |
| adding keys: |
| mask |
| ''' |
| image = data['image'] |
| polygons = data['polygons'] |
| ignore_tags = data['ignore_tags'] |
| image = data['image'] |
| filename = data['filename'] |
|
|
| h, w = image.shape[:2] |
| if data['is_training']: |
| 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(len(polygons)): |
| polygon = polygons[i] |
| height = max(polygon[:, 1]) - min(polygon[:, 1]) |
| width = max(polygon[:, 0]) - min(polygon[:, 0]) |
| |
| |
| |
| |
| 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 len(polygons) == 0: |
| return polygons, ignore_tags |
| assert len(polygons) == len(ignore_tags) |
| for polygon in polygons: |
| polygon[:, 0] = np.clip(polygon[:, 0], 0, w - 1) |
| polygon[:, 1] = np.clip(polygon[:, 1], 0, h - 1) |
|
|
| for i in range(len(polygons)): |
| area = self.polygon_area(polygons[i]) |
| if abs(area) < 1: |
| ignore_tags[i] = True |
| if area > 0: |
| polygons[i] = polygons[i][::-1, :] |
| return polygons, ignore_tags |
|
|
| def polygon_area(self, polygon): |
| edge = 0 |
| for i in range(polygon.shape[0]): |
| next_index = (i + 1) % polygon.shape[0] |
| edge += (polygon[next_index, 0] - polygon[i, 0]) * (polygon[next_index, 1] + polygon[i, 1]) |
|
|
| return edge / 2. |
|
|
|
|
