| | 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. |
| |
|
| |
|
