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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 MakeSegDetectionData(DataProcess): |
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r''' |
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Making binary mask 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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min_text_size = State(default=8) |
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shrink_ratio = State(default=0.4) |
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def __init__(self, **kwargs): |
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self.load_all(**kwargs) |
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def process(self, data): |
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''' |
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requied keys: |
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image, polygons, ignore_tags, filename |
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adding keys: |
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mask |
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''' |
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image = data['image'] |
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polygons = data['polygons'] |
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ignore_tags = data['ignore_tags'] |
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image = data['image'] |
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filename = data['filename'] |
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h, w = image.shape[:2] |
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if data['is_training']: |
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polygons, ignore_tags = self.validate_polygons( |
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polygons, ignore_tags, h, w) |
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gt = np.zeros((1, h, w), dtype=np.float32) |
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mask = np.ones((h, w), dtype=np.float32) |
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for i in range(len(polygons)): |
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polygon = polygons[i] |
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height = max(polygon[:, 1]) - min(polygon[:, 1]) |
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width = max(polygon[:, 0]) - min(polygon[:, 0]) |
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if ignore_tags[i] or min(height, width) < self.min_text_size: |
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cv2.fillPoly(mask, polygon.astype( |
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np.int32)[np.newaxis, :, :], 0) |
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ignore_tags[i] = True |
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else: |
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polygon_shape = Polygon(polygon) |
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distance = polygon_shape.area * \ |
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(1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length |
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subject = [tuple(l) for l in polygons[i]] |
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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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shrinked = padding.Execute(-distance) |
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if shrinked == []: |
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cv2.fillPoly(mask, polygon.astype( |
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np.int32)[np.newaxis, :, :], 0) |
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ignore_tags[i] = True |
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continue |
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shrinked = np.array(shrinked[0]).reshape(-1, 2) |
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cv2.fillPoly(gt[0], [shrinked.astype(np.int32)], 1) |
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if filename is None: |
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filename = '' |
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data.update(image=image, |
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polygons=polygons, |
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gt=gt, mask=mask, filename=filename) |
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return data |
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def validate_polygons(self, polygons, ignore_tags, h, w): |
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''' |
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polygons (numpy.array, required): of shape (num_instances, num_points, 2) |
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''' |
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if len(polygons) == 0: |
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return polygons, ignore_tags |
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assert len(polygons) == len(ignore_tags) |
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for polygon in polygons: |
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polygon[:, 0] = np.clip(polygon[:, 0], 0, w - 1) |
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polygon[:, 1] = np.clip(polygon[:, 1], 0, h - 1) |
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for i in range(len(polygons)): |
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area = self.polygon_area(polygons[i]) |
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if abs(area) < 1: |
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ignore_tags[i] = True |
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if area > 0: |
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polygons[i] = polygons[i][::-1, :] |
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return polygons, ignore_tags |
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def polygon_area(self, polygon): |
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edge = 0 |
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for i in range(polygon.shape[0]): |
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next_index = (i + 1) % polygon.shape[0] |
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edge += (polygon[next_index, 0] - polygon[i, 0]) * (polygon[next_index, 1] + polygon[i, 1]) |
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return edge / 2. |
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