threshold map图gt标签

红色框为gt标签，蓝色框为膨胀(dilate)图，绿色框为缩小(shrink), 膨胀距离和缩小距离均为D,计算膨胀区域内所有的点与gt的距离，并做归一化，绿色框与蓝色框之间的区域取值处于[0,1]之间，绿色框区域内的取值大于1。
膨胀和缩小距离的计算公式如下:

在实践中，r设置为0.4
代码部分

class MakeBorderMap():
    def __init__(self, shrink_ratio=0.4, thresh_min=0.3, thresh_max=0.7):
        self.shrink_ratio = shrink_ratio
        self.thresh_min = thresh_min
        self.thresh_max = thresh_max

    def __call__(self, data: dict) -> dict:
        """
        从scales中随机选择一个尺度，对图片和文本框进行缩放
        :param data: {'img':,'text_polys':,'texts':,'ignore_tags':}
        :return:
        """
        im = data['img']
        text_polys = data['text_polys']
        ignore_tags = data['ignore_tags']

        canvas = np.zeros(im.shape[:2], dtype=np.float32)
        mask = np.zeros(im.shape[:2], dtype=np.float32)

        for i in range(len(text_polys)):
            if ignore_tags[i]:
                continue
            self.draw_border_map(text_polys[i], canvas, mask=mask)
        # 计算后的值介于0-1之间，rescale到0.3到0.7
        canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min

        data['threshold_map'] = canvas
        data['threshold_mask'] = mask
        return data

    def draw_border_map(self, polygon, canvas, mask):
        polygon = np.array(polygon)
        assert polygon.ndim == 2
        assert polygon.shape[1] == 2

        polygon_shape = Polygon(polygon)
        if polygon_shape.area <= 0:
            return
        distance = polygon_shape.area * (1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length
        subject = [tuple(l) for l in polygon]
        padding = pyclipper.PyclipperOffset()
        padding.AddPath(subject, pyclipper.JT_ROUND,
                        pyclipper.ET_CLOSEDPOLYGON)

        padded_polygon = np.array(padding.Execute(distance)[0])
        cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)

        xmin = padded_polygon[:, 0].min()
        xmax = padded_polygon[:, 0].max()
        ymin = padded_polygon[:, 1].min()
        ymax = padded_polygon[:, 1].max()
        width = xmax - xmin + 1
        height = ymax - ymin + 1

        polygon[:, 0] = polygon[:, 0] - xmin
        polygon[:, 1] = polygon[:, 1] - ymin

        xs = np.broadcast_to(
            np.linspace(0, width - 1, num=width).reshape(1, width), (height, width))
        ys = np.broadcast_to(
            np.linspace(0, height - 1, num=height).reshape(height, 1), (height, width))

        distance_map = np.zeros(
            (polygon.shape[0], height, width), dtype=np.float32)
        for i in range(polygon.shape[0]):
            j = (i + 1) % polygon.shape[0]
            absolute_distance = self.distance(xs, ys, polygon[i], polygon[j])
            distance_map[i] = np.clip(absolute_distance / distance, 0, 1)
        distance_map = distance_map.min(axis=0)

        # 保证在canvas宽高范围内
        xmin_valid = min(max(0, xmin), canvas.shape[1] - 1)
        xmax_valid = min(max(0, xmax), canvas.shape[1] - 1)
        ymin_valid = min(max(0, ymin), canvas.shape[0] - 1)
        ymax_valid = min(max(0, ymax), canvas.shape[0] - 1)
        canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax(
            1 - distance_map[
                ymin_valid - ymin:ymax_valid - ymax + height,
                xmin_valid - xmin:xmax_valid - xmax + width],
            canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1])

    def distance(self, xs, ys, point_1, point_2):
        '''
        使用正、余弦定理求点到直线的距离
        compute the distance from point to a line
        ys: coordinates in the first axis
        xs: coordinates in the second axis
        point_1, point_2: (x, y), the end of the line
        '''
        height, width = xs.shape[:2]
        square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[1])
        square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[1])
        square_distance = np.square(point_1[0] - point_2[0]) + np.square(point_1[1] - point_2[1])

        cosin = (square_distance - square_distance_1 - square_distance_2) / (2 * np.sqrt(square_distance_1 * square_distance_2))
        square_sin = 1 - np.square(cosin)
        square_sin = np.nan_to_num(square_sin)

        result = np.sqrt(square_distance_1 * square_distance_2 * square_sin / square_distance)
        # 对于夹角是锐角的，使用最短边作为距离
        result[cosin < 0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin < 0]
        # self.extend_line(point_1, point_2, result)
        return result

    def extend_line(self, point_1, point_2, result):
        ex_point_1 = (int(round(point_1[0] + (point_1[0] - point_2[0]) * (1 + self.shrink_ratio))),
                      int(round(point_1[1] + (point_1[1] - point_2[1]) * (1 + self.shrink_ratio))))
        cv2.line(result, tuple(ex_point_1), tuple(point_1), 4096.0, 1, lineType=cv2.LINE_AA, shift=0)
        ex_point_2 = (int(round(point_2[0] + (point_2[0] - point_1[0]) * (1 + self.shrink_ratio))),
                      int(round(point_2[1] + (point_2[1] - point_1[1]) * (1 + self.shrink_ratio))))
        cv2.line(result, tuple(ex_point_2), tuple(point_2), 4096.0, 1, lineType=cv2.LINE_AA, shift=0)
        return ex_point_1, ex_point_2

if __name__ == '__main__':
    import numpy as np
    make_border_map = MakeBorderMap()
    img = cv2.imread("../../datasets/train/img/img_41.jpg")

    points = np.array([[[533, 134], [562, 133], [561, 145], [532, 146]],
    [[564, 131], [617, 129], [617, 145], [564, 146]],
    [[620, 126], [657, 127], [656, 143], [618, 143]],
    [[153, 150], [209, 144], [210, 159], [154, 165]]])

    draw_img = img.copy()
    for pt in points:
        cv2.polylines(draw_img,[pt], True, color=(0, 255, 0))

    print(draw_img)
    texts = ['EW15', 'Tanjong', 'Pagar', 'CAUTION']
    ignore_tags = [False, False, False, False]


    data = {"img":img, "img_41":"img_41","text_polys":points,"texts":texts, "ignore_tags":ignore_tags}
    data = make_border_map(data)
    print(data)

probability map图gt

代码部分

def shrink_polygon_pyclipper(polygon, shrink_ratio):
    from shapely.geometry import Polygon
    import pyclipper
    polygon_shape = Polygon(polygon)
    distance = polygon_shape.area * (1 - np.power(shrink_ratio, 2)) / polygon_shape.length
    subject = [tuple(l) for l in polygon]
    padding = pyclipper.PyclipperOffset()
    padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
    shrinked = padding.Execute(-distance)
    if shrinked == []:
        shrinked = np.array(shrinked)
    else:
        shrinked = np.array(shrinked[0]).reshape(-1, 2)
    return shrinked


class MakeShrinkMap():
    r'''
    Making binary mask from detection data with ICDAR format.
    Typically following the process of class `MakeICDARData`.
    '''

    def __init__(self, min_text_size=8, shrink_ratio=0.4, shrink_type='pyclipper'):
        shrink_func_dict = {'py': shrink_polygon_py, 'pyclipper': shrink_polygon_pyclipper}
        self.shrink_func = shrink_func_dict[shrink_type]
        self.min_text_size = min_text_size
        self.shrink_ratio = shrink_ratio

    def __call__(self, data: dict) -> dict:
        """
        从scales中随机选择一个尺度，对图片和文本框进行缩放
        :param data: {'img':,'text_polys':,'texts':,'ignore_tags':}
        :return:
        """
        image = data['img']
        text_polys = data['text_polys']
        ignore_tags = data['ignore_tags']

        h, w = image.shape[:2]
        text_polys, ignore_tags = self.validate_polygons(text_polys, ignore_tags, h, w)
        gt = np.zeros((h, w), dtype=np.float32)
        mask = np.ones((h, w), dtype=np.float32)
        for i in range(len(text_polys)):
            polygon = text_polys[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:
                shrinked = self.shrink_func(polygon, self.shrink_ratio)
                if shrinked.size == 0:
                    cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0)
                    ignore_tags[i] = True
                    continue
                cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)

        data['shrink_map'] = gt
        data['shrink_mask'] = mask
        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):
        return cv2.contourArea(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.


if __name__ == '__main__':

    import numpy as np

    make_shrink_map = MakeShrinkMap()
    img = cv2.imread("../../datasets/train/img/img_41.jpg")

    points = np.array([[[533, 134], [562, 133], [561, 145], [532, 146]],
                       [[564, 131], [617, 129], [617, 145], [564, 146]],
                       [[620, 126], [657, 127], [656, 143], [618, 143]],
                       [[153, 150], [209, 144], [210, 159], [154, 165]]])

    draw_img = img.copy()
    for pt in points:
        cv2.polylines(draw_img, [pt], True, color=(0, 255, 0))

    print(draw_img)
    texts = ['EW15', 'Tanjong', 'Pagar', 'CAUTION']
    ignore_tags = [False, False, False, False]

    data = {"img": img, "img_41": "img_41", "text_polys": points, "texts": texts, "ignore_tags": ignore_tags}
    data = make_shrink_map(data)
    print(data)

概率和阈值图的计算