# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'

import cv2
import numpy as np
import time
import json

import tools.infer.utility as utility
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppocr.utils.visual import draw_rectangle
from ppstructure.utility import parse_args

logger = get_logger()


def build_pre_process_list(args):
    resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }}
    pad_op = {
        'PaddingTableImage': {
            'size': [args.table_max_len, args.table_max_len]
        }
    }
    normalize_op = {
        'NormalizeImage': {
            'std': [0.229, 0.224, 0.225] if
            args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5],
            'mean': [0.485, 0.456, 0.406] if
            args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5],
            'scale': '1./255.',
            'order': 'hwc'
        }
    }
    to_chw_op = {'ToCHWImage': None}
    keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}}
    if args.table_algorithm not in ['TableMaster']:
        pre_process_list = [
            resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op
        ]
    else:
        pre_process_list = [
            resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op
        ]
    return pre_process_list


class TableStructurer(object):
    def __init__(self, args):
        self.use_onnx = args.use_onnx
        pre_process_list = build_pre_process_list(args)
        if args.table_algorithm not in ['TableMaster']:
            postprocess_params = {
                'name': 'TableLabelDecode',
                "character_dict_path": args.table_char_dict_path,
                'merge_no_span_structure': args.merge_no_span_structure
            }
        else:
            postprocess_params = {
                'name': 'TableMasterLabelDecode',
                "character_dict_path": args.table_char_dict_path,
                'box_shape': 'pad',
                'merge_no_span_structure': args.merge_no_span_structure
            }

        self.preprocess_op = create_operators(pre_process_list)
        self.postprocess_op = build_post_process(postprocess_params)
        self.predictor, self.input_tensor, self.output_tensors, self.config = \
            utility.create_predictor(args, 'table', logger)

    def __call__(self, img):
        starttime = time.time()
        ori_im = img.copy()
        data = {'image': img}
        data = transform(data, self.preprocess_op)
        img = data[0]
        if img is None:
            return None, 0
        img = np.expand_dims(img, axis=0)
        img = img.copy()
        if self.use_onnx:
            input_dict = {}
            input_dict[self.input_tensor.name] = img
            outputs = self.predictor.run(self.output_tensors, input_dict)
        else:
            self.input_tensor.copy_from_cpu(img)
            self.predictor.run()
            outputs = []
            for output_tensor in self.output_tensors:
                output = output_tensor.copy_to_cpu()
                outputs.append(output)

        preds = {}
        preds['structure_probs'] = outputs[1]
        preds['loc_preds'] = outputs[0]

        shape_list = np.expand_dims(data[-1], axis=0)
        post_result = self.postprocess_op(preds, [shape_list])

        structure_str_list = post_result['structure_batch_list'][0]
        bbox_list = post_result['bbox_batch_list'][0]
        structure_str_list = structure_str_list[0]
        structure_str_list = [
            '<html>', '<body>', '<table>'
        ] + structure_str_list + ['</table>', '</body>', '</html>']
        elapse = time.time() - starttime
        return (structure_str_list, bbox_list), elapse


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    table_structurer = TableStructurer(args)
    count = 0
    total_time = 0
    os.makedirs(args.output, exist_ok=True)
    with open(
            os.path.join(args.output, 'infer.txt'), mode='w',
            encoding='utf-8') as f_w:
        for image_file in image_file_list:
            img, flag, _ = check_and_read(image_file)
            if not flag:
                img = cv2.imread(image_file)
            if img is None:
                logger.info("error in loading image:{}".format(image_file))
                continue
            structure_res, elapse = table_structurer(img)
            structure_str_list, bbox_list = structure_res
            bbox_list_str = json.dumps(bbox_list.tolist())
            logger.info("result: {}, {}".format(structure_str_list,
                                                bbox_list_str))
            f_w.write("result: {}, {}\n".format(structure_str_list,
                                                bbox_list_str))

            if len(bbox_list) > 0 and len(bbox_list[0]) == 4:
                img = draw_rectangle(image_file, bbox_list)
            else:
                img = utility.draw_boxes(img, bbox_list)
            img_save_path = os.path.join(args.output,
                                         os.path.basename(image_file))
            cv2.imwrite(img_save_path, img)
            logger.info("save vis result to {}".format(img_save_path))
            if count > 0:
                total_time += elapse
            count += 1
            logger.info("Predict time of {}: {}".format(image_file, elapse))


if __name__ == "__main__":
    main(parse_args())