# encoding:utf8

import numpy as np
from PIL import Image, ImageFont, ImageDraw, ImageEnhance, ImageFilter
import cv2
import glob
import re


class TextGenerator(object):
    def __init__(self, font_flag):
        self.fontPath = None
        self.chars = None
        self.textSize = None
        if font_flag == 0:
            self.fontPath = "font\\fangzheng-zhongdengxian-GBK.ttf"
            self.chars = list(
                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            # 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            self.textSize = 11
        elif font_flag == 1:
            self.fontPath = "font\\OCR-B-10-BT.ttf"
            self.chars = list('0123456789X')
            self.textSize = 18
        self.width = 280
        self.height = 32
        self.channel = 1

        self.bg_list = [Image.open(path).convert("L") for path in glob.glob("background\\*.*")]

    def get_next_batch(self, batch_size=30):
        texts = np.random.choice(len(self.chars), (batch_size, self.textSize))  # 随机生成批量文本下标，字符数量都为10
        batch_bg = [self.bg_list[np.random.choice(len(self.bg_list), 1)[0]].copy() for i in
                    range(batch_size)]  # 随机的选择batch_size个背景图像

        for i in range(batch_size):
            font = ImageFont.truetype(font=self.fontPath, size=np.random.randint(23, 25))  # 随机的字体大小
            draw = ImageDraw.Draw(batch_bg[i])

            advance = np.random.randint(0, 5)  # 随机的文本行左上角的x起始坐标
            descent = np.random.randint(5, 7)  # 随机的文本行左上角的y起始坐标
            draw.text([advance, descent], "".join([self.chars[texts[i][j]] for j in range(self.textSize)]), 0, font)

            # 对image进行亮度、模糊、倾斜、透视、拉伸、阴影、描边、加框、随机噪声等变化
            batch_bg[i] = batch_bg[i].filter(ImageFilter.GaussianBlur(np.random.uniform(0, 1)))  # 随机模糊
            batch_bg[i] = ImageEnhance.Brightness(batch_bg[i]).enhance(np.random.uniform(0.8, 1.6))  # 随机亮度
            batch_bg[i] = batch_bg[i].rotate(np.random.uniform(-1, 1), Image.BICUBIC)  # 随机旋转
            batch_bg[i] = np.array(batch_bg[i])

        batch_bg = np.array(batch_bg)
        batch_bg = batch_bg.reshape([batch_size, self.height, self.width, self.channel])

        indices = []
        for i in range(batch_size):
            for j in range(self.textSize):
                indices.append([i, j])
        values = texts.reshape([batch_size * self.textSize])  # np.zeros([batch_size * self.textSize])
        dense_shape = [batch_size, self.textSize]
        sparse_targets = (indices, values, dense_shape)

        seq_len = np.ones([batch_size], dtype=np.int32)
        return batch_bg, sparse_targets, seq_len


def char2index(chars):
    for i in range(chars.shape[0]):
        for j in range(chars.shape[1]):
            if chars[i][j] == '0':
                chars[i][j] = 0
            elif chars[i][j] == '1':
                chars[i][j] = 1
            elif chars[i][j] == '2':
                chars[i][j] = 2
            elif chars[i][j] == '3':
                chars[i][j] = 3
            elif chars[i][j] == '4':
                chars[i][j] = 4
            elif chars[i][j] == '5':
                chars[i][j] = 5
            elif chars[i][j] == '6':
                chars[i][j] = 6
            elif chars[i][j] == '7':
                chars[i][j] = 7
            elif chars[i][j] == '8':
                chars[i][j] = 8
            elif chars[i][j] == '9':
                chars[i][j] = 9
            elif chars[i][j] == 'X':
                chars[i][j] = 10
    return chars


def get_batches_from_file():
    paths = glob.glob("images\\*.*")
    max_len = max([len(path) for path in paths])
    batches_size = len(paths)
    height = 32
    # width = 128
    channel = 1

    images = [np.array(Image.open(path).convert("L")) for path in paths]
    images = np.array(images)
    images = images.reshape([batches_size, height, -1, channel])

    texts = [np.array(list(re.split(r"[\\.]", path)[-2])) for path in paths]
    texts = np.array(texts)
    # print(texts)
    # texts = char2index(texts)
    texts = texts.astype(np.int32)
    # print(texts)

    indices = []
    for i in range(batches_size):
        for j in range(len(texts[i])):
            indices.append([i, j])
    values = texts.reshape([-1])  # np.zeros([batch_size * self.textSize])
    dense_shape = [batches_size, max_len]
    sparse_targets = (indices, values, dense_shape)

    seq_len = np.ones([batches_size], dtype=np.int32)
    return images, sparse_targets, seq_len


if __name__ == '__main__':
    get_batches_from_file()

    textGen = TextGenerator(0)
    batch_size = 100

    import time

    start = time.time()
    img, sparse_targets, seq_len = textGen.get_next_batch(batch_size)
    interval = time.time() - start
    print("产生{}个数据耗时：{}".format(batch_size, interval))
    # print(img.shape, sparse_targets, seq_len)
    # img = img.astype(np.uint8)
    # temp = img[0].reshape([64, 360])
    # print(img)
    # cv2.imwrite("1.bmp",img[0],[int( cv2.IMWRITE_JPEG_QUALITY), 95])
    for i in range(batch_size):
        cv2.imshow('test', img[i])  # 对于浮点数类型的img显示的是01二值图，对于uint8类型的才会显示真正的灰度图
        # for j in range(10):
        #     print(textGen.chars[sparse_targets[1][i*10+j]])
        cv2.waitKey(0)
