# DB/data/processes/random_crop_data.py

import numpy as np
import math
import cv2
# import imgaug
# import imgaug.augmenters as iaa

# from .data_process import DataProcess
# from concern.config import Configurable, State

class State:
    def __init__(self, autoload=True, default=None):
        self.autoload = autoload
        self.default = default

# random crop algorithm similar to https://github.com/argman/EAST
class RandomCropData():
    size = (512, 512)
    max_tries = 50
    min_crop_side_ratio = 0.1
    require_original_image = False

    def __init__(self, **kwargs):
        pass

    def process(self, data):
        img = data['image']
        ori_img = img
        ori_lines = data['polys']

        all_care_polys = [line['points']
                          for line in data['polys'] if not line['ignore']]
        crop_x, crop_y, crop_w, crop_h = self.crop_area(img, all_care_polys)
        scale_w = self.size[0] / crop_w
        scale_h = self.size[1] / crop_h
        scale = min(scale_w, scale_h)
        h = int(crop_h * scale)
        w = int(crop_w * scale)
        padimg = np.zeros(
            (self.size[1], self.size[0], img.shape[2]), img.dtype)
        padimg[:h, :w] = cv2.resize(
            img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
        img = padimg

        lines = []
        for line in data['polys']:
            poly = ((np.array(line['points']) -
                     (crop_x, crop_y)) * scale).tolist()
            if not self.is_poly_outside_rect(poly, 0, 0, w, h):
                lines.append({**line, 'points': poly})
        data['polys'] = lines

        if self.require_original_image:
            data['image'] = ori_img
        else:
            data['image'] = img
        data['lines'] = ori_lines
        data['scale_w'] = scale
        data['scale_h'] = scale

        return data

    def is_poly_in_rect(self, poly, x, y, w, h):
        poly = np.array(poly)
        if poly[:, 0].min() < x or poly[:, 0].max() > x + w:
            return False
        if poly[:, 1].min() < y or poly[:, 1].max() > y + h:
            return False
        return True

    def is_poly_outside_rect(self, poly, x, y, w, h):
        poly = np.array(poly)
        if poly[:, 0].max() < x or poly[:, 0].min() > x + w:
            return True
        if poly[:, 1].max() < y or poly[:, 1].min() > y + h:
            return True
        return False

    def split_regions(self, axis):
        regions = []
        min_axis = 0
        for i in range(1, axis.shape[0]):
            if axis[i] != axis[i-1] + 1:
                region = axis[min_axis:i]
                min_axis = i
                regions.append(region)
        return regions

    def random_select(self, axis, max_size):
        xx = np.random.choice(axis, size=2)
        xmin = np.min(xx)
        xmax = np.max(xx)
        xmin = np.clip(xmin, 0, max_size - 1)
        xmax = np.clip(xmax, 0, max_size - 1)
        return xmin, xmax

    def region_wise_random_select(self, regions, max_size):
        selected_index = list(np.random.choice(len(regions), 2))
        selected_values = []
        for index in selected_index:
            axis = regions[index]
            xx = int(np.random.choice(axis, size=1))
            selected_values.append(xx)
        xmin = min(selected_values)
        xmax = max(selected_values)
        return xmin, xmax

    def crop_area(self, img, polys):
        h, w, _ = img.shape
        h_array = np.zeros(h, dtype=np.int32)
        w_array = np.zeros(w, dtype=np.int32)
        for points in polys:
            points = np.round(points, decimals=0).astype(np.int32)
            minx = np.min(points[:, 0])
            maxx = np.max(points[:, 0])
            w_array[minx:maxx] = 1
            miny = np.min(points[:, 1])
            maxy = np.max(points[:, 1])
            h_array[miny:maxy] = 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 0, 0, w, h

        h_regions = self.split_regions(h_axis)
        w_regions = self.split_regions(w_axis)

        for i in range(self.max_tries):
            if len(w_regions) > 1:
                xmin, xmax = self.region_wise_random_select(w_regions, w)
            else:
                xmin, xmax = self.random_select(w_axis, w)
            if len(h_regions) > 1:
                ymin, ymax = self.region_wise_random_select(h_regions, h)
            else:
                ymin, ymax = self.random_select(h_axis, h)

            if xmax - xmin < self.min_crop_side_ratio * w or ymax - ymin < self.min_crop_side_ratio * h:
                # area too small
                continue
            num_poly_in_rect = 0
            for poly in polys:
                if not self.is_poly_outside_rect(poly, xmin, ymin, xmax - xmin, ymax - ymin):
                    num_poly_in_rect += 1
                    break

            if num_poly_in_rect > 0:
                return xmin, ymin, xmax - xmin, ymax - ymin

        return 0, 0, w, h


if __name__ == "__main__":
    
    im = './datasets/icdar2015/train_images/img_1.jpg'
    gt = './datasets/icdar2015/train_gts/gt_img_1.txt'
    
    items = []
    reader = open(gt, 'r').readlines()
    for line in reader:
        item = {}
        parts = line.strip().split(',')
        label = parts[-1]
        if 'TD' in gt and label == '1':
            label = '###'
        line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in parts]
        if 'icdar' in gt:
            poly = np.array(list(map(float, line[:8]))).reshape(
                (-1, 2)).tolist()
        else:
            num_points = math.floor((len(line) - 1) / 2) * 2
            poly = np.array(list(map(float, line[:num_points]))).reshape(
                (-1, 2)).tolist()
        item['points'] = poly  # 多边形是用一个个的点表示的，起点连接第二个点，第二个连接第三个 ... 最后一点连接起点，构成一个闭合的区域
        item['text'] = label
        item['ignore'] = True if label == '###' else False  # 此标记表示文字模糊不可辨认，文本框的标记是不可靠的
        items.append( item )

    img = cv2.imdecode(np.fromfile(im, dtype=np.uint8), -1)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img_shape = img.shape

    data = dict(
        image = img,
        polys = items,
    )

    crop = RandomCropData()
    crop.process(data)