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import numpy as np
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import cv2
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def shrink_polygon_py(polygon, shrink_ratio):
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"""
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对框进行缩放，返回去的比例为1/shrink_ratio 即可
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"""
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cx = polygon[:, 0].mean()
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cy = polygon[:, 1].mean()
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polygon[:, 0] = cx + (polygon[:, 0] - cx) * shrink_ratio
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polygon[:, 1] = cy + (polygon[:, 1] - cy) * shrink_ratio
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return polygon
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def shrink_polygon_pyclipper(polygon, shrink_ratio):
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from shapely.geometry import Polygon
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import pyclipper
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polygon_shape = Polygon(polygon)
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distance = (
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polygon_shape.area * (1 - np.power(shrink_ratio, 2)) / polygon_shape.length
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)
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subject = [tuple(l) for l in polygon]
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padding = pyclipper.PyclipperOffset()
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padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
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shrunk = padding.Execute(-distance)
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if shrunk == []:
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shrunk = np.array(shrunk)
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else:
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shrunk = np.array(shrunk[0]).reshape(-1, 2)
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return shrunk
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class MakeShrinkMap:
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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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def __init__(self, min_text_size=8, shrink_ratio=0.4, shrink_type="pyclipper"):
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shrink_func_dict = {
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"py": shrink_polygon_py,
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"pyclipper": shrink_polygon_pyclipper,
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}
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self.shrink_func = shrink_func_dict[shrink_type]
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self.min_text_size = min_text_size
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self.shrink_ratio = shrink_ratio
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def __call__(self, data: dict) -> dict:
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"""
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从scales中随机选择一个尺度，对图片和文本框进行缩放
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:param data: {'img':,'text_polys':,'texts':,'ignore_tags':}
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:return:
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"""
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image = data["img"]
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text_polys = data["text_polys"]
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ignore_tags = data["ignore_tags"]
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h, w = image.shape[:2]
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text_polys, ignore_tags = self.validate_polygons(text_polys, ignore_tags, h, w)
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gt = np.zeros((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(text_polys)):
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polygon = text_polys[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(np.int32)[np.newaxis, :, :], 0)
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ignore_tags[i] = True
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else:
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shrunk = self.shrink_func(polygon, self.shrink_ratio)
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if shrunk.size == 0:
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cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0)
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ignore_tags[i] = True
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continue
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cv2.fillPoly(gt, [shrunk.astype(np.int32)], 1)
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data["shrink_map"] = gt
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data["shrink_mask"] = mask
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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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return cv2.contourArea(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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#
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# return edge / 2.
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if __name__ == "__main__":
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from shapely.geometry import Polygon
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import pyclipper
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polygon = np.array([[0, 0], [100, 10], [100, 100], [10, 90]])
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a = shrink_polygon_py(polygon, 0.4)
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print(a)
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print(shrink_polygon_py(a, 1 / 0.4))
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b = shrink_polygon_pyclipper(polygon, 0.4)
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print(b)
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poly = Polygon(b)
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distance = poly.area * 1.5 / poly.length
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offset = pyclipper.PyclipperOffset()
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offset.AddPath(b, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
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expanded = np.array(offset.Execute(distance))
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bounding_box = cv2.minAreaRect(expanded)
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points = cv2.boxPoints(bounding_box)
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print(points)
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