source/PaddleOCR/ppocr/data/imaug/pg_process.py

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
import cv2
import numpy as np
from skimage.morphology._skeletonize import thin
from ppocr.utils.e2e_utils.extract_textpoint_fast import (
    sort_and_expand_with_direction_v2,
)

__all__ = ["PGProcessTrain"]


class PGProcessTrain(object):
    def __init__(
        self,
        character_dict_path,
        max_text_length,
        max_text_nums,
        tcl_len,
        batch_size=14,
        use_resize=True,
        use_random_crop=False,
        min_crop_size=24,
        min_text_size=4,
        max_text_size=512,
        point_gather_mode=None,
        **kwargs,
    ):
        self.tcl_len = tcl_len
        self.max_text_length = max_text_length
        self.max_text_nums = max_text_nums
        self.batch_size = batch_size
        if use_random_crop is True:
            self.min_crop_size = min_crop_size
            self.use_random_crop = use_random_crop
        self.min_text_size = min_text_size
        self.max_text_size = max_text_size
        self.use_resize = use_resize
        self.point_gather_mode = point_gather_mode
        self.Lexicon_Table = self.get_dict(character_dict_path)
        self.pad_num = len(self.Lexicon_Table)
        self.img_id = 0

    def get_dict(self, character_dict_path):
        character_str = ""
        with open(character_dict_path, "rb") as fin:
            lines = fin.readlines()
            for line in lines:
                line = line.decode("utf-8").strip("\n").strip("\r\n")
                character_str += line
            dict_character = list(character_str)
        return dict_character

    def quad_area(self, poly):
        """
        compute area of a polygon
        :param poly:
        :return:
        """
        edge = [
            (poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
            (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
            (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
            (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1]),
        ]
        return np.sum(edge) / 2.0

    def gen_quad_from_poly(self, poly):
        """
        Generate min area quad from poly.
        """
        point_num = poly.shape[0]
        min_area_quad = np.zeros((4, 2), dtype=np.float32)
        rect = cv2.minAreaRect(
            poly.astype(np.int32)
        )  # (center (x,y), (width, height), angle of rotation)
        box = np.array(cv2.boxPoints(rect))

        first_point_idx = 0
        min_dist = 1e4
        for i in range(4):
            dist = (
                np.linalg.norm(box[(i + 0) % 4] - poly[0])
                + np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1])
                + np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2])
                + np.linalg.norm(box[(i + 3) % 4] - poly[-1])
            )
            if dist < min_dist:
                min_dist = dist
                first_point_idx = i
        for i in range(4):
            min_area_quad[i] = box[(first_point_idx + i) % 4]

        return min_area_quad

    def check_and_validate_polys(self, polys, tags, im_size):
        """
        check so that the text poly is in the same direction,
        and also filter some invalid polygons
        :param polys:
        :param tags:
        :return:
        """
        (h, w) = im_size
        if polys.shape[0] == 0:
            return polys, np.array([]), np.array([])
        polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
        polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1)

        validated_polys = []
        validated_tags = []
        hv_tags = []
        for poly, tag in zip(polys, tags):
            quad = self.gen_quad_from_poly(poly)
            p_area = self.quad_area(quad)
            if abs(p_area) < 1:
                print("invalid poly")
                continue
            if p_area > 0:
                if tag == False:
                    print("poly in wrong direction")
                    tag = True  # reversed cases should be ignore
                poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1), :]
                quad = quad[(0, 3, 2, 1), :]

            len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(
                quad[3] - quad[2]
            )
            len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(
                quad[1] - quad[2]
            )
            hv_tag = 1

            if len_w * 2.0 < len_h:
                hv_tag = 0

            validated_polys.append(poly)
            validated_tags.append(tag)
            hv_tags.append(hv_tag)
        return np.array(validated_polys), np.array(validated_tags), np.array(hv_tags)

    def crop_area(
        self, im, polys, tags, hv_tags, txts, crop_background=False, max_tries=25
    ):
        """
        make random crop from the input image
        :param im:
        :param polys:  [b,4,2]
        :param tags:
        :param crop_background:
        :param max_tries: 50 -> 25
        :return:
        """
        h, w, _ = im.shape
        pad_h = h // 10
        pad_w = w // 10
        h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
        w_array = np.zeros((w + pad_w * 2), dtype=np.int32)
        for poly in polys:
            poly = np.round(poly, decimals=0).astype(np.int32)
            minx = np.min(poly[:, 0])
            maxx = np.max(poly[:, 0])
            w_array[minx + pad_w : maxx + pad_w] = 1
            miny = np.min(poly[:, 1])
            maxy = np.max(poly[:, 1])
            h_array[miny + pad_h : maxy + pad_h] = 1
        # ensure the cropped area not across a text
        h_axis = np.where(h_array == 0)[0]
        w_axis = np.where(w_array == 0)[0]
        if len(h_axis) == 0 or len(w_axis) == 0:
            return im, polys, tags, hv_tags, txts
        for i in range(max_tries):
            xx = np.random.choice(w_axis, size=2)
            xmin = np.min(xx) - pad_w
            xmax = np.max(xx) - pad_w
            xmin = np.clip(xmin, 0, w - 1)
            xmax = np.clip(xmax, 0, w - 1)
            yy = np.random.choice(h_axis, size=2)
            ymin = np.min(yy) - pad_h
            ymax = np.max(yy) - pad_h
            ymin = np.clip(ymin, 0, h - 1)
            ymax = np.clip(ymax, 0, h - 1)
            if xmax - xmin < self.min_crop_size or ymax - ymin < self.min_crop_size:
                continue
            if polys.shape[0] != 0:
                poly_axis_in_area = (
                    (polys[:, :, 0] >= xmin)
                    & (polys[:, :, 0] <= xmax)
                    & (polys[:, :, 1] >= ymin)
                    & (polys[:, :, 1] <= ymax)
                )
                selected_polys = np.where(np.sum(poly_axis_in_area, axis=1) == 4)[0]
            else:
                selected_polys = []
            if len(selected_polys) == 0:
                # no text in this area
                if crop_background:
                    txts_tmp = []
                    for selected_poly in selected_polys:
                        txts_tmp.append(txts[selected_poly])
                    txts = txts_tmp
                    return (
                        im[ymin : ymax + 1, xmin : xmax + 1, :],
                        polys[selected_polys],
                        tags[selected_polys],
                        hv_tags[selected_polys],
                        txts,
                    )
                else:
                    continue
            im = im[ymin : ymax + 1, xmin : xmax + 1, :]
            polys = polys[selected_polys]
            tags = tags[selected_polys]
            hv_tags = hv_tags[selected_polys]
            txts_tmp = []
            for selected_poly in selected_polys:
                txts_tmp.append(txts[selected_poly])
            txts = txts_tmp
            polys[:, :, 0] -= xmin
            polys[:, :, 1] -= ymin
            return im, polys, tags, hv_tags, txts

        return im, polys, tags, hv_tags, txts

    def fit_and_gather_tcl_points_v2(
        self,
        min_area_quad,
        poly,
        max_h,
        max_w,
        fixed_point_num=64,
        img_id=0,
        reference_height=3,
    ):
        """
        Find the center point of poly as key_points, then fit and gather.
        """
        key_point_xys = []
        point_num = poly.shape[0]
        for idx in range(point_num // 2):
            center_point = (poly[idx] + poly[point_num - 1 - idx]) / 2.0
            key_point_xys.append(center_point)

        tmp_image = np.zeros(
            shape=(
                max_h,
                max_w,
            ),
            dtype="float32",
        )
        cv2.polylines(tmp_image, [np.array(key_point_xys).astype("int32")], False, 1.0)
        ys, xs = np.where(tmp_image > 0)
        xy_text = np.array(list(zip(xs, ys)), dtype="float32")

        left_center_pt = ((min_area_quad[0] + min_area_quad[3]) / 2.0).reshape(1, 2)
        right_center_pt = ((min_area_quad[1] + min_area_quad[2]) / 2.0).reshape(1, 2)
        proj_unit_vec = (right_center_pt - left_center_pt) / (
            np.linalg.norm(right_center_pt - left_center_pt) + 1e-6
        )
        proj_unit_vec_tile = np.tile(proj_unit_vec, (xy_text.shape[0], 1))  # (n, 2)
        left_center_pt_tile = np.tile(left_center_pt, (xy_text.shape[0], 1))  # (n, 2)
        xy_text_to_left_center = xy_text - left_center_pt_tile
        proj_value = np.sum(xy_text_to_left_center * proj_unit_vec_tile, axis=1)
        xy_text = xy_text[np.argsort(proj_value)]

        # convert to np and keep the num of point not greater then fixed_point_num
        pos_info = np.array(xy_text).reshape(-1, 2)[:, ::-1]  # xy-> yx
        point_num = len(pos_info)
        if point_num > fixed_point_num:
            keep_ids = [
                int((point_num * 1.0 / fixed_point_num) * x)
                for x in range(fixed_point_num)
            ]
            pos_info = pos_info[keep_ids, :]

        keep = int(min(len(pos_info), fixed_point_num))
        if np.random.rand() < 0.2 and reference_height >= 3:
            dl = (np.random.rand(keep) - 0.5) * reference_height * 0.3
            random_float = np.array([1, 0]).reshape([1, 2]) * dl.reshape([keep, 1])
            pos_info += random_float
            pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
            pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)

        # padding to fixed length
        pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
        pos_l[:, 0] = np.ones((self.tcl_len,)) * img_id
        pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
        pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
        pos_m[:keep] = 1.0
        return pos_l, pos_m

    def fit_and_gather_tcl_points_v3(
        self,
        min_area_quad,
        poly,
        max_h,
        max_w,
        fixed_point_num=64,
        img_id=0,
        reference_height=3,
    ):
        """
        Find the center point of poly as key_points, then fit and gather.
        """
        det_mask = np.zeros(
            (int(max_h / self.ds_ratio), int(max_w / self.ds_ratio))
        ).astype(np.float32)

        # score_big_map
        cv2.fillPoly(det_mask, np.round(poly / self.ds_ratio).astype(np.int32), 1.0)
        det_mask = cv2.resize(det_mask, dsize=None, fx=self.ds_ratio, fy=self.ds_ratio)
        det_mask = np.array(det_mask > 1e-3, dtype="float32")

        f_direction = self.f_direction
        skeleton_map = thin(det_mask.astype(np.uint8))
        instance_count, instance_label_map = cv2.connectedComponents(
            skeleton_map.astype(np.uint8), connectivity=8
        )

        ys, xs = np.where(instance_label_map == 1)
        pos_list = list(zip(ys, xs))
        if len(pos_list) < 3:
            return None
        pos_list_sorted = sort_and_expand_with_direction_v2(
            pos_list, f_direction, det_mask
        )

        pos_list_sorted = np.array(pos_list_sorted)
        length = len(pos_list_sorted) - 1
        insert_num = 0
        for index in range(length):
            stride_y = np.abs(
                pos_list_sorted[index + insert_num][0]
                - pos_list_sorted[index + 1 + insert_num][0]
            )
            stride_x = np.abs(
                pos_list_sorted[index + insert_num][1]
                - pos_list_sorted[index + 1 + insert_num][1]
            )
            max_points = int(max(stride_x, stride_y))

            stride = (
                pos_list_sorted[index + insert_num]
                - pos_list_sorted[index + 1 + insert_num]
            ) / (max_points)
            insert_num_temp = max_points - 1

            for i in range(int(insert_num_temp)):
                insert_value = pos_list_sorted[index + insert_num] - (i + 1) * stride
                insert_index = index + i + 1 + insert_num
                pos_list_sorted = np.insert(
                    pos_list_sorted, insert_index, insert_value, axis=0
                )
            insert_num += insert_num_temp

        pos_info = (
            np.array(pos_list_sorted).reshape(-1, 2).astype(np.float32)
        )  # xy-> yx

        point_num = len(pos_info)
        if point_num > fixed_point_num:
            keep_ids = [
                int((point_num * 1.0 / fixed_point_num) * x)
                for x in range(fixed_point_num)
            ]
            pos_info = pos_info[keep_ids, :]

        keep = int(min(len(pos_info), fixed_point_num))
        reference_width = (
            np.abs(poly[0, 0, 0] - poly[-1, 1, 0])
            + np.abs(poly[0, 3, 0] - poly[-1, 2, 0])
        ) // 2
        if np.random.rand() < 1:
            dh = (np.random.rand(keep) - 0.5) * reference_height
            offset = np.random.rand() - 0.5
            dw = np.array([[0, offset * reference_width * 0.2]])
            random_float_h = np.array([1, 0]).reshape([1, 2]) * dh.reshape([keep, 1])
            random_float_w = dw.repeat(keep, axis=0)
            pos_info += random_float_h
            pos_info += random_float_w
            pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
            pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)

        # padding to fixed length
        pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
        pos_l[:, 0] = np.ones((self.tcl_len,)) * img_id
        pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
        pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
        pos_m[:keep] = 1.0
        return pos_l, pos_m

    def generate_direction_map(self, poly_quads, n_char, direction_map):
        """ """
        width_list = []
        height_list = []
        for quad in poly_quads:
            quad_w = (
                np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])
            ) / 2.0
            quad_h = (
                np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])
            ) / 2.0
            width_list.append(quad_w)
            height_list.append(quad_h)
        norm_width = max(sum(width_list) / n_char, 1.0)
        average_height = max(sum(height_list) / len(height_list), 1.0)
        k = 1
        for quad in poly_quads:
            direct_vector_full = ((quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0
            direct_vector = (
                direct_vector_full
                / (np.linalg.norm(direct_vector_full) + 1e-6)
                * norm_width
            )
            direction_label = tuple(
                map(float, [direct_vector[0], direct_vector[1], 1.0 / average_height])
            )
            cv2.fillPoly(
                direction_map,
                quad.round().astype(np.int32)[np.newaxis, :, :],
                direction_label,
            )
            k += 1
        return direction_map

    def calculate_average_height(self, poly_quads):
        """ """
        height_list = []
        for quad in poly_quads:
            quad_h = (
                np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])
            ) / 2.0
            height_list.append(quad_h)
        average_height = max(sum(height_list) / len(height_list), 1.0)
        return average_height

    def generate_tcl_ctc_label(
        self,
        h,
        w,
        polys,
        tags,
        text_strs,
        ds_ratio,
        tcl_ratio=0.3,
        shrink_ratio_of_width=0.15,
    ):
        """
        Generate polygon.
        """
        self.ds_ratio = ds_ratio
        score_map_big = np.zeros(
            (
                h,
                w,
            ),
            dtype=np.float32,
        )
        h, w = int(h * ds_ratio), int(w * ds_ratio)
        polys = polys * ds_ratio

        score_map = np.zeros(
            (
                h,
                w,
            ),
            dtype=np.float32,
        )
        score_label_map = np.zeros(
            (
                h,
                w,
            ),
            dtype=np.float32,
        )
        tbo_map = np.zeros((h, w, 5), dtype=np.float32)
        training_mask = np.ones(
            (
                h,
                w,
            ),
            dtype=np.float32,
        )
        direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape(
            [1, 1, 3]
        ).astype(np.float32)

        label_idx = 0
        score_label_map_text_label_list = []
        pos_list, pos_mask, label_list = [], [], []
        for poly_idx, poly_tag in enumerate(zip(polys, tags)):
            poly = poly_tag[0]
            tag = poly_tag[1]

            # generate min_area_quad
            min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly)
            min_area_quad_h = 0.5 * (
                np.linalg.norm(min_area_quad[0] - min_area_quad[3])
                + np.linalg.norm(min_area_quad[1] - min_area_quad[2])
            )
            min_area_quad_w = 0.5 * (
                np.linalg.norm(min_area_quad[0] - min_area_quad[1])
                + np.linalg.norm(min_area_quad[2] - min_area_quad[3])
            )

            if (
                min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio
                or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio
            ):
                continue

            if tag:
                cv2.fillPoly(
                    training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0.15
                )
            else:
                text_label = text_strs[poly_idx]
                text_label = self.prepare_text_label(text_label, self.Lexicon_Table)
                text_label_index_list = [
                    [self.Lexicon_Table.index(c_)]
                    for c_ in text_label
                    if c_ in self.Lexicon_Table
                ]
                if len(text_label_index_list) < 1:
                    continue

                tcl_poly = self.poly2tcl(poly, tcl_ratio)
                tcl_quads = self.poly2quads(tcl_poly)
                poly_quads = self.poly2quads(poly)

                stcl_quads, quad_index = self.shrink_poly_along_width(
                    tcl_quads,
                    shrink_ratio_of_width=shrink_ratio_of_width,
                    expand_height_ratio=1.0 / tcl_ratio,
                )

                cv2.fillPoly(score_map, np.round(stcl_quads).astype(np.int32), 1.0)
                cv2.fillPoly(
                    score_map_big, np.round(stcl_quads / ds_ratio).astype(np.int32), 1.0
                )

                for idx, quad in enumerate(stcl_quads):
                    quad_mask = np.zeros((h, w), dtype=np.float32)
                    quad_mask = cv2.fillPoly(
                        quad_mask,
                        np.round(quad[np.newaxis, :, :]).astype(np.int32),
                        1.0,
                    )
                    tbo_map = self.gen_quad_tbo(
                        poly_quads[quad_index[idx]], quad_mask, tbo_map
                    )

                # score label map and score_label_map_text_label_list for refine
                if label_idx == 0:
                    text_pos_list_ = [
                        [len(self.Lexicon_Table)],
                    ]
                    score_label_map_text_label_list.append(text_pos_list_)

                label_idx += 1
                cv2.fillPoly(
                    score_label_map, np.round(poly_quads).astype(np.int32), label_idx
                )
                score_label_map_text_label_list.append(text_label_index_list)

                # direction info, fix-me
                n_char = len(text_label_index_list)
                direction_map = self.generate_direction_map(
                    poly_quads, n_char, direction_map
                )

                # pos info
                average_shrink_height = self.calculate_average_height(stcl_quads)

                if self.point_gather_mode == "align":
                    self.f_direction = direction_map[:, :, :-1].copy()
                    pos_res = self.fit_and_gather_tcl_points_v3(
                        min_area_quad,
                        stcl_quads,
                        max_h=h,
                        max_w=w,
                        fixed_point_num=64,
                        img_id=self.img_id,
                        reference_height=average_shrink_height,
                    )
                    if pos_res is None:
                        continue
                    pos_l, pos_m = pos_res[0], pos_res[1]

                else:
                    pos_l, pos_m = self.fit_and_gather_tcl_points_v2(
                        min_area_quad,
                        poly,
                        max_h=h,
                        max_w=w,
                        fixed_point_num=64,
                        img_id=self.img_id,
                        reference_height=average_shrink_height,
                    )

                label_l = text_label_index_list
                if len(text_label_index_list) < 2:
                    continue

                pos_list.append(pos_l)
                pos_mask.append(pos_m)
                label_list.append(label_l)

        # use big score_map for smooth tcl lines
        score_map_big_resized = cv2.resize(
            score_map_big, dsize=None, fx=ds_ratio, fy=ds_ratio
        )
        score_map = np.array(score_map_big_resized > 1e-3, dtype="float32")

        return (
            score_map,
            score_label_map,
            tbo_map,
            direction_map,
            training_mask,
            pos_list,
            pos_mask,
            label_list,
            score_label_map_text_label_list,
        )

    def adjust_point(self, poly):
        """
        adjust point order.
        """
        point_num = poly.shape[0]
        if point_num == 4:
            len_1 = np.linalg.norm(poly[0] - poly[1])
            len_2 = np.linalg.norm(poly[1] - poly[2])
            len_3 = np.linalg.norm(poly[2] - poly[3])
            len_4 = np.linalg.norm(poly[3] - poly[0])

            if (len_1 + len_3) * 1.5 < (len_2 + len_4):
                poly = poly[[1, 2, 3, 0], :]

        elif point_num > 4:
            vector_1 = poly[0] - poly[1]
            vector_2 = poly[1] - poly[2]
            cos_theta = np.dot(vector_1, vector_2) / (
                np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6
            )
            theta = np.arccos(np.round(cos_theta, decimals=4))

            if abs(theta) > (70 / 180 * math.pi):
                index = list(range(1, point_num)) + [0]
                poly = poly[np.array(index), :]
        return poly

    def gen_min_area_quad_from_poly(self, poly):
        """
        Generate min area quad from poly.
        """
        point_num = poly.shape[0]
        min_area_quad = np.zeros((4, 2), dtype=np.float32)
        if point_num == 4:
            min_area_quad = poly
            center_point = np.sum(poly, axis=0) / 4
        else:
            rect = cv2.minAreaRect(
                poly.astype(np.int32)
            )  # (center (x,y), (width, height), angle of rotation)
            center_point = rect[0]
            box = np.array(cv2.boxPoints(rect))

            first_point_idx = 0
            min_dist = 1e4
            for i in range(4):
                dist = (
                    np.linalg.norm(box[(i + 0) % 4] - poly[0])
                    + np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1])
                    + np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2])
                    + np.linalg.norm(box[(i + 3) % 4] - poly[-1])
                )
                if dist < min_dist:
                    min_dist = dist
                    first_point_idx = i

            for i in range(4):
                min_area_quad[i] = box[(first_point_idx + i) % 4]

        return min_area_quad, center_point

    def shrink_quad_along_width(self, quad, begin_width_ratio=0.0, end_width_ratio=1.0):
        """
        Generate shrink_quad_along_width.
        """
        ratio_pair = np.array(
            [[begin_width_ratio], [end_width_ratio]], dtype=np.float32
        )
        p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
        p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
        return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])

    def shrink_poly_along_width(
        self, quads, shrink_ratio_of_width, expand_height_ratio=1.0
    ):
        """
        shrink poly with given length.
        """
        upper_edge_list = []

        def get_cut_info(edge_len_list, cut_len):
            for idx, edge_len in enumerate(edge_len_list):
                cut_len -= edge_len
                if cut_len <= 0.000001:
                    ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx]
                    return idx, ratio

        for quad in quads:
            upper_edge_len = np.linalg.norm(quad[0] - quad[1])
            upper_edge_list.append(upper_edge_len)

        # length of left edge and right edge.
        left_length = np.linalg.norm(quads[0][0] - quads[0][3]) * expand_height_ratio
        right_length = np.linalg.norm(quads[-1][1] - quads[-1][2]) * expand_height_ratio

        shrink_length = (
            min(left_length, right_length, sum(upper_edge_list)) * shrink_ratio_of_width
        )
        # shrinking length
        upper_len_left = shrink_length
        upper_len_right = sum(upper_edge_list) - shrink_length

        left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left)
        left_quad = self.shrink_quad_along_width(
            quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1
        )
        right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right)
        right_quad = self.shrink_quad_along_width(
            quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio
        )

        out_quad_list = []
        if left_idx == right_idx:
            out_quad_list.append(
                [left_quad[0], right_quad[1], right_quad[2], left_quad[3]]
            )
        else:
            out_quad_list.append(left_quad)
            for idx in range(left_idx + 1, right_idx):
                out_quad_list.append(quads[idx])
            out_quad_list.append(right_quad)

        return np.array(out_quad_list), list(range(left_idx, right_idx + 1))

    def prepare_text_label(self, label_str, Lexicon_Table):
        """
        Prepare text label by given Lexicon_Table.
        """
        if len(Lexicon_Table) == 36:
            return label_str.lower()
        else:
            return label_str

    def vector_angle(self, A, B):
        """
        Calculate the angle between vector AB and x-axis positive direction.
        """
        AB = np.array([B[1] - A[1], B[0] - A[0]])
        return np.arctan2(*AB)

    def theta_line_cross_point(self, theta, point):
        """
        Calculate the line through given point and angle in ax + by + c =0 form.
        """
        x, y = point
        cos = np.cos(theta)
        sin = np.sin(theta)
        return [sin, -cos, cos * y - sin * x]

    def line_cross_two_point(self, A, B):
        """
        Calculate the line through given point A and B in ax + by + c =0 form.
        """
        angle = self.vector_angle(A, B)
        return self.theta_line_cross_point(angle, A)

    def average_angle(self, poly):
        """
        Calculate the average angle between left and right edge in given poly.
        """
        p0, p1, p2, p3 = poly
        angle30 = self.vector_angle(p3, p0)
        angle21 = self.vector_angle(p2, p1)
        return (angle30 + angle21) / 2

    def line_cross_point(self, line1, line2):
        """
        line1 and line2 in  0=ax+by+c form, compute the cross point of line1 and line2
        """
        a1, b1, c1 = line1
        a2, b2, c2 = line2
        d = a1 * b2 - a2 * b1

        if d == 0:
            print("Cross point does not exist")
            return np.array([0, 0], dtype=np.float32)
        else:
            x = (b1 * c2 - b2 * c1) / d
            y = (a2 * c1 - a1 * c2) / d

        return np.array([x, y], dtype=np.float32)

    def quad2tcl(self, poly, ratio):
        """
        Generate center line by poly clock-wise point. (4, 2)
        """
        ratio_pair = np.array([[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
        p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair
        p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair
        return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]])

    def poly2tcl(self, poly, ratio):
        """
        Generate center line by poly clock-wise point.
        """
        ratio_pair = np.array([[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
        tcl_poly = np.zeros_like(poly)
        point_num = poly.shape[0]

        for idx in range(point_num // 2):
            point_pair = (
                poly[idx] + (poly[point_num - 1 - idx] - poly[idx]) * ratio_pair
            )
            tcl_poly[idx] = point_pair[0]
            tcl_poly[point_num - 1 - idx] = point_pair[1]
        return tcl_poly

    def gen_quad_tbo(self, quad, tcl_mask, tbo_map):
        """
        Generate tbo_map for give quad.
        """
        # upper and lower line function: ax + by + c = 0;
        up_line = self.line_cross_two_point(quad[0], quad[1])
        lower_line = self.line_cross_two_point(quad[3], quad[2])

        quad_h = 0.5 * (
            np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])
        )
        quad_w = 0.5 * (
            np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])
        )

        # average angle of left and right line.
        angle = self.average_angle(quad)

        xy_in_poly = np.argwhere(tcl_mask == 1)
        for y, x in xy_in_poly:
            point = (x, y)
            line = self.theta_line_cross_point(angle, point)
            cross_point_upper = self.line_cross_point(up_line, line)
            cross_point_lower = self.line_cross_point(lower_line, line)
            ##FIX, offset reverse
            upper_offset_x, upper_offset_y = cross_point_upper - point
            lower_offset_x, lower_offset_y = cross_point_lower - point
            tbo_map[y, x, 0] = upper_offset_y
            tbo_map[y, x, 1] = upper_offset_x
            tbo_map[y, x, 2] = lower_offset_y
            tbo_map[y, x, 3] = lower_offset_x
            tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2
        return tbo_map

    def poly2quads(self, poly):
        """
        Split poly into quads.
        """
        quad_list = []
        point_num = poly.shape[0]

        # point pair
        point_pair_list = []
        for idx in range(point_num // 2):
            point_pair = [poly[idx], poly[point_num - 1 - idx]]
            point_pair_list.append(point_pair)

        quad_num = point_num // 2 - 1
        for idx in range(quad_num):
            # reshape and adjust to clock-wise
            quad_list.append(
                (np.array(point_pair_list)[[idx, idx + 1]]).reshape(4, 2)[[0, 2, 3, 1]]
            )

        return np.array(quad_list)

    def rotate_im_poly(self, im, text_polys):
        """
        rotate image with 90 / 180 / 270 degre
        """
        im_w, im_h = im.shape[1], im.shape[0]
        dst_im = im.copy()
        dst_polys = []
        rand_degree_ratio = np.random.rand()
        rand_degree_cnt = 1
        if rand_degree_ratio > 0.5:
            rand_degree_cnt = 3
        for i in range(rand_degree_cnt):
            dst_im = np.rot90(dst_im)
        rot_degree = -90 * rand_degree_cnt
        rot_angle = rot_degree * math.pi / 180.0
        n_poly = text_polys.shape[0]
        cx, cy = 0.5 * im_w, 0.5 * im_h
        ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0]
        for i in range(n_poly):
            wordBB = text_polys[i]
            poly = []
            for j in range(4):  # 16->4
                sx, sy = wordBB[j][0], wordBB[j][1]
                dx = (
                    math.cos(rot_angle) * (sx - cx)
                    - math.sin(rot_angle) * (sy - cy)
                    + ncx
                )
                dy = (
                    math.sin(rot_angle) * (sx - cx)
                    + math.cos(rot_angle) * (sy - cy)
                    + ncy
                )
                poly.append([dx, dy])
            dst_polys.append(poly)
        return dst_im, np.array(dst_polys, dtype=np.float32)

    def __call__(self, data):
        input_size = 512
        im = data["image"]
        text_polys = data["polys"]
        text_tags = data["ignore_tags"]
        text_strs = data["texts"]
        h, w, _ = im.shape
        text_polys, text_tags, hv_tags = self.check_and_validate_polys(
            text_polys, text_tags, (h, w)
        )
        if text_polys.shape[0] <= 0:
            return None
        # set aspect ratio and keep area fix
        asp_scales = np.arange(1.0, 1.55, 0.1)
        asp_scale = np.random.choice(asp_scales)
        if np.random.rand() < 0.5:
            asp_scale = 1.0 / asp_scale
        asp_scale = math.sqrt(asp_scale)

        asp_wx = asp_scale
        asp_hy = 1.0 / asp_scale
        im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy)
        text_polys[:, :, 0] *= asp_wx
        text_polys[:, :, 1] *= asp_hy

        if self.use_resize is True:
            ori_h, ori_w, _ = im.shape
            if max(ori_h, ori_w) < 200:
                ratio = 200 / max(ori_h, ori_w)
                im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio)))
                text_polys[:, :, 0] *= ratio
                text_polys[:, :, 1] *= ratio

            if max(ori_h, ori_w) > 512:
                ratio = 512 / max(ori_h, ori_w)
                im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio)))
                text_polys[:, :, 0] *= ratio
                text_polys[:, :, 1] *= ratio
        elif self.use_random_crop is True:
            h, w, _ = im.shape
            if max(h, w) > 2048:
                rd_scale = 2048.0 / max(h, w)
                im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale)
                text_polys *= rd_scale
            h, w, _ = im.shape
            if min(h, w) < 16:
                return None

            # no background
            im, text_polys, text_tags, hv_tags, text_strs = self.crop_area(
                im, text_polys, text_tags, hv_tags, text_strs, crop_background=False
            )

        if text_polys.shape[0] == 0:
            return None
        # continue for all ignore case
        if np.sum((text_tags * 1.0)) >= text_tags.size:
            return None
        new_h, new_w, _ = im.shape
        if (new_h is None) or (new_w is None):
            return None
        # resize image
        std_ratio = float(input_size) / max(new_w, new_h)
        rand_scales = np.array(
            [0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0]
        )
        rz_scale = std_ratio * np.random.choice(rand_scales)
        im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale)
        text_polys[:, :, 0] *= rz_scale
        text_polys[:, :, 1] *= rz_scale

        # add gaussian blur
        if np.random.rand() < 0.1 * 0.5:
            ks = np.random.permutation(5)[0] + 1
            ks = int(ks / 2) * 2 + 1
            im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0)
        # add brighter
        if np.random.rand() < 0.1 * 0.5:
            im = im * (1.0 + np.random.rand() * 0.5)
            im = np.clip(im, 0.0, 255.0)
        # add darker
        if np.random.rand() < 0.1 * 0.5:
            im = im * (1.0 - np.random.rand() * 0.5)
            im = np.clip(im, 0.0, 255.0)

        # Padding the im to [input_size, input_size]
        new_h, new_w, _ = im.shape
        if min(new_w, new_h) < input_size * 0.5:
            return None
        im_padded = np.ones((input_size, input_size, 3), dtype=np.float32)
        im_padded[:, :, 2] = 0.485 * 255
        im_padded[:, :, 1] = 0.456 * 255
        im_padded[:, :, 0] = 0.406 * 255

        # Random the start position
        del_h = input_size - new_h
        del_w = input_size - new_w
        sh, sw = 0, 0
        if del_h > 1:
            sh = int(np.random.rand() * del_h)
        if del_w > 1:
            sw = int(np.random.rand() * del_w)

        # Padding
        im_padded[sh : sh + new_h, sw : sw + new_w, :] = im.copy()
        text_polys[:, :, 0] += sw
        text_polys[:, :, 1] += sh

        (
            score_map,
            score_label_map,
            border_map,
            direction_map,
            training_mask,
            pos_list,
            pos_mask,
            label_list,
            score_label_map_text_label,
        ) = self.generate_tcl_ctc_label(
            input_size, input_size, text_polys, text_tags, text_strs, 0.25
        )
        if len(label_list) <= 0:  # eliminate negative samples
            return None
        pos_list_temp = np.zeros([64, 3])
        pos_mask_temp = np.zeros([64, 1])
        label_list_temp = np.zeros([self.max_text_length, 1]) + self.pad_num

        for i, label in enumerate(label_list):
            n = len(label)
            if n > self.max_text_length:
                label_list[i] = label[: self.max_text_length]
                continue
            while n < self.max_text_length:
                label.append([self.pad_num])
                n += 1

        for i in range(len(label_list)):
            label_list[i] = np.array(label_list[i])

        if len(pos_list) <= 0 or len(pos_list) > self.max_text_nums:
            return None
        for __ in range(self.max_text_nums - len(pos_list), 0, -1):
            pos_list.append(pos_list_temp)
            pos_mask.append(pos_mask_temp)
            label_list.append(label_list_temp)

        if self.img_id == self.batch_size - 1:
            self.img_id = 0
        else:
            self.img_id += 1

        im_padded[:, :, 2] -= 0.485 * 255
        im_padded[:, :, 1] -= 0.456 * 255
        im_padded[:, :, 0] -= 0.406 * 255
        im_padded[:, :, 2] /= 255.0 * 0.229
        im_padded[:, :, 1] /= 255.0 * 0.224
        im_padded[:, :, 0] /= 255.0 * 0.225
        im_padded = im_padded.transpose((2, 0, 1))
        images = im_padded[::-1, :, :]
        tcl_maps = score_map[np.newaxis, :, :]
        tcl_label_maps = score_label_map[np.newaxis, :, :]
        border_maps = border_map.transpose((2, 0, 1))
        direction_maps = direction_map.transpose((2, 0, 1))
        training_masks = training_mask[np.newaxis, :, :]
        pos_list = np.array(pos_list)
        pos_mask = np.array(pos_mask)
        label_list = np.array(label_list)
        data["images"] = images
        data["tcl_maps"] = tcl_maps
        data["tcl_label_maps"] = tcl_label_maps
        data["border_maps"] = border_maps
        data["direction_maps"] = direction_maps
        data["training_masks"] = training_masks
        data["label_list"] = label_list
        data["pos_list"] = pos_list
        data["pos_mask"] = pos_mask
        return data