# !/usr/bin/env python
# -*- encoding: utf-8 -*-
# @File: PostProcess.py
# @Time: 2021/01/19 14:12:35
# @Author: Max
import cv2
import os
import imghdr
import numpy as np


class BaseRecLabelDecode(object):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 character_type='ch',
                 use_space_char=False):
        self.character_str = ""
        assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
        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")
                self.character_str += line
        if use_space_char:
            self.character_str += " "
        dict_character = list(self.character_str)

        self.character_type = character_type
        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character

    def add_special_char(self, dict_character):
        return dict_character

    def decode(self, text_index, text_prob=None, is_remove_duplicate=True):
        """ convert text-index into text-label. """
        result_list = []
        ignored_tokens = self.get_ignored_tokens()
        batch_size = len(text_index)
        for batch_idx in range(batch_size):
            char_list = []
            conf_list = []
            for idx in range(len(text_index[batch_idx])):
                if text_index[batch_idx][idx] in ignored_tokens:
                    continue
                if is_remove_duplicate:
                    # only for predict
                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
                            batch_idx][idx]:
                        continue
                char_list.append(self.character[int(text_index[batch_idx][
                    idx])])
                if text_prob is not None:
                    conf_list.append(text_prob[batch_idx][idx])
                else:
                    conf_list.append(1)
            text = ''.join(char_list)
            result_list.append((text, np.mean(conf_list)))
        return result_list

    def get_ignored_tokens(self):
        return [0]  # for ctc blank


class CTCLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 character_type='ch',
                 use_space_char=False,
                 **kwargs):
        super(CTCLabelDecode, self).__init__(character_dict_path,
                                             character_type, use_space_char)

    def __call__(self, preds, label=None, *args, **kwargs):
        preds_idx = preds.argmax(axis=2)
        preds_prob = preds.max(axis=2)
        text = self.decode(preds_idx, preds_prob)
        if label is None:
            return text
        label = self.decode(label, is_remove_duplicate=False)
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character
        return dict_character


def get_image_file_list(img_file):
    imgs_lists = []
    if img_file is None or not os.path.exists(img_file):
        raise Exception("not found any img file in {}".format(img_file))

    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
        imgs_lists.append(img_file)
    elif os.path.isdir(img_file):
        for single_file in os.listdir(img_file):
            file_path = os.path.join(img_file, single_file)
            if imghdr.what(file_path) in img_end:
                imgs_lists.append(file_path)
    if len(imgs_lists) == 0:
        raise Exception("not found any img file in {}".format(img_file))
    return imgs_lists


def check_and_read_gif(img_path):
    if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
        gif = cv2.VideoCapture(img_path)
        ret, frame = gif.read()
        if not ret:
            print("Cannot read {}. This gif image maybe corrupted.")
            return None, False
        if len(frame.shape) == 2 or frame.shape[-1] == 1:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
        imgvalue = frame[:, :, ::-1]
        return imgvalue, True
    return None, False