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# Copyright 2021 Huawei Technologies Co., Ltd
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# ============================================================================
import copy
import cv2
import os
import torch.utils.data as data
import scipy.io as io
import numpy as np
from PIL import Image
from util.config import config as cfg
from skimage.draw import polygon as drawpoly
from util.misc import find_bottom, find_long_edges, split_edge_seqence, \
    norm2, vector_cos, vector_sin


def pil_load_img(path):
    image = Image.open(path)
    image = np.array(image)
    return image


class TextInstance(object):

    def __init__(self, points, orient, text):
        self.orient = orient
        self.text = text

        remove_points = []

        if len(points) > 4:
            # remove point if area is almost unchanged after removing it
            ori_area = cv2.contourArea(points)
            for p in range(len(points)):
                # attempt to remove p
                index = list(range(len(points)))
                index.remove(p)
                area = cv2.contourArea(points[index])
                if np.abs(ori_area - area) / ori_area < 0.017 and len(points) - len(remove_points) > 4:
                    remove_points.append(p)
            self.points = np.array([point for i, point in enumerate(points) if i not in remove_points])
        else:
            self.points = np.array(points)

    def find_bottom_and_sideline(self):
        self.bottoms = find_bottom(self.points)  # find two bottoms of this Text
        self.e1, self.e2 = find_long_edges(self.points, self.bottoms)  # find two long edge sequence

    def disk_cover(self, n_disk=15):
        """
        cover text region with several disks
        :param n_disk: number of disks
        :return:
        """
        inner_points1 = split_edge_seqence(self.points, self.e1, n_disk)
        inner_points2 = split_edge_seqence(self.points, self.e2, n_disk)
        inner_points2 = inner_points2[::-1]  # innverse one of long edge

        center_points = (inner_points1 + inner_points2) / 2  # disk center
        radii = norm2(inner_points1 - center_points, axis=1)  # disk radius

        return inner_points1, inner_points2, center_points, radii

    def __repr__(self):
        return str(self.__dict__)

    def __getitem__(self, item):
        return getattr(self, item)


class TextDataset(data.Dataset):

    def __init__(self, transform):
        super().__init__()

        self.transform = transform

    def parse_mat(self, mat_path):
        """
        .mat file parser
        :param mat_path: (str), mat file path
        :return: (list), TextInstance
        """
        annot = io.loadmat(mat_path)
        polygon = []
        for cell in annot['polygt']:
            x = cell[1][0]
            y = cell[3][0]
            text = cell[4][0]
            if len(x) < 4: # too few points
                continue
            try:
                ori = cell[5][0]
            except:
                ori = 'c'
            pts = np.stack([x, y]).T.astype(np.int32)
            polygon.append(TextInstance(pts, ori, text))
        return polygon

    def make_text_region(self, image, polygons):

        tr_mask = np.zeros(image.shape[:2], np.uint8)
        train_mask = np.ones(image.shape[:2], np.uint8)

        for polygon in polygons:
            cv2.fillPoly(tr_mask, [polygon.points.astype(np.int32)], color=(1,))
            if polygon.text == '#':
                cv2.fillPoly(train_mask, [polygon.points.astype(np.int32)], color=(0,))
        return tr_mask, train_mask

    def fill_polygon(self, mask, polygon, value):
        """
        fill polygon in the mask with value
        :param mask: input mask
        :param polygon: polygon to draw
        :param value: fill value
        """
        rr, cc = drawpoly(polygon[:, 1], polygon[:, 0], shape=(cfg.input_size, cfg.input_size))
        mask[rr, cc] = value

    def make_text_center_line(self, sideline1, sideline2, center_line, radius, \
                              tcl_mask, radius_map, sin_map, cos_map, expand=0.3, shrink=1):

        # TODO: shrink 1/2 * radius at two line end
        for i in range(shrink, len(center_line) - 1 - shrink):

            c1 = center_line[i]
            c2 = center_line[i + 1]
            top1 = sideline1[i]
            top2 = sideline1[i + 1]
            bottom1 = sideline2[i]
            bottom2 = sideline2[i + 1]

            sin_theta = vector_sin(c2 - c1)
            cos_theta = vector_cos(c2 - c1)

            p1 = c1 + (top1 - c1) * expand
            p2 = c1 + (bottom1 - c1) * expand
            p3 = c2 + (bottom2 - c2) * expand
            p4 = c2 + (top2 - c2) * expand
            polygon = np.stack([p1, p2, p3, p4])

            self.fill_polygon(tcl_mask, polygon, value=1)
            self.fill_polygon(radius_map, polygon, value=radius[i])
            self.fill_polygon(sin_map, polygon, value=sin_theta)
            self.fill_polygon(cos_map, polygon, value=cos_theta)

    def get_training_data(self, image, polygons, image_id, image_path):

        H, W, _ = image.shape

        for i, polygon in enumerate(polygons):
            if polygon.text != '#':
                polygon.find_bottom_and_sideline()

        if self.transform:
            image, polygons = self.transform(image, copy.copy(polygons))

        tcl_mask = np.zeros(image.shape[:2], np.uint8)
        radius_map = np.zeros(image.shape[:2], np.float32)
        sin_map = np.zeros(image.shape[:2], np.float32)
        cos_map = np.zeros(image.shape[:2], np.float32)

        for i, polygon in enumerate(polygons):
            if polygon.text != '#':
                sideline1, sideline2, center_points, radius = polygon.disk_cover(n_disk=cfg.n_disk)
                self.make_text_center_line(sideline1, sideline2, center_points, radius, tcl_mask, radius_map, sin_map, cos_map)
        tr_mask, train_mask = self.make_text_region(image, polygons)

        # to pytorch channel sequence
        image = image.transpose(2, 0, 1)

        points = np.zeros((cfg.max_annotation, cfg.max_points, 2))
        length = np.zeros(cfg.max_annotation, dtype=int)

        for i, polygon in enumerate(polygons):
            pts = polygon.points
            points[i, :pts.shape[0]] = polygon.points
            length[i] = pts.shape[0]

        meta = {
            'image_id': image_id,
            'image_path': image_path,
            'annotation': points,
            'n_annotation': length,
            'Height': H,
            'Width': W
        }
        return image, train_mask, tr_mask, tcl_mask, radius_map, sin_map, cos_map, meta

    def get_test_data(self, image, image_id, image_path):
        H, W, _ = image.shape

        if self.transform:
            image, polygons = self.transform(image)

        # to pytorch channel sequence
        image = image.transpose(2, 0, 1)

        meta = {
            'image_id': image_id,
            'image_path': image_path,
            'Height': H,
            'Width': W
        }
        return image, meta

    def __len__(self):
        raise NotImplementedError()