# Copyright (c) 2021 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.
from paddle_serving_server.web_service import WebService, Op

import logging
import numpy as np
import cv2
import base64
# from paddle_serving_app.reader import OCRReader
from ocr_reader import OCRReader, DetResizeForTest
from paddle_serving_app.reader import Sequential, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes

import yaml
from argparse import ArgumentParser,RawDescriptionHelpFormatter

_LOGGER = logging.getLogger()

class ArgsParser(ArgumentParser):
    def __init__(self):
        super(ArgsParser, self).__init__(
            formatter_class=RawDescriptionHelpFormatter)
        self.add_argument("-c", "--config", help="configuration file to use")
        self.add_argument(
            "-o", "--opt", nargs='+', help="set configuration options")

    def parse_args(self, argv=None):
        args = super(ArgsParser, self).parse_args(argv)
        assert args.config is not None, \
            "Please specify --config=configure_file_path."
        args.conf_dict = self._parse_opt(args.opt, args.config)
        return args

    def _parse_helper(self, v):
        if v.isnumeric():
            if "." in v:
                v = float(v)
            else:
                v = int(v)
        elif v == "True" or v == "False":
            v = (v == "True")
        return v

    def _parse_opt(self, opts, conf_path):
        f = open(conf_path)
        config = yaml.load(f, Loader=yaml.Loader)
        if not opts:
            return config
        for s in opts:
            s = s.strip()
            k, v = s.split('=')
            v = self._parse_helper(v)
            print(k,v, type(v))
            cur = config
            parent = cur
            for kk in k.split("."):
                if kk not in cur:
                     cur[kk] = {}
                     parent = cur
                     cur = cur[kk]
                else:
                     parent = cur
                     cur = cur[kk]
            parent[k.split(".")[-1]] = v
        return config

class DetOp(Op):
    def init_op(self):
        self.det_preprocess = Sequential([
            DetResizeForTest(), Div(255),
            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
                (2, 0, 1))
        ])
        self.filter_func = FilterBoxes(10, 10)
        self.post_func = DBPostProcess({
            "thresh": 0.3,
            "box_thresh": 0.5,
            "max_candidates": 1000,
            "unclip_ratio": 1.5,
            "min_size": 3
        })

    def preprocess(self, input_dicts, data_id, log_id):
        (_, input_dict), = input_dicts.items()
        data = base64.b64decode(input_dict["image"].encode('utf8'))
        self.raw_im = data
        data = np.fromstring(data, np.uint8)
        # Note: class variables(self.var) can only be used in process op mode
        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
        self.ori_h, self.ori_w, _ = im.shape
        det_img = self.det_preprocess(im)
        _, self.new_h, self.new_w = det_img.shape
        return {"x": det_img[np.newaxis, :].copy()}, False, None, ""

    def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
        det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
        ratio_list = [
            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
        ]
        dt_boxes_list = self.post_func(det_out, [ratio_list])
        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
        out_dict = {"dt_boxes": str(dt_boxes)}

        return out_dict, None, ""


class OcrService(WebService):
    def get_pipeline_response(self, read_op):
        det_op = DetOp(name="det", input_ops=[read_op])
        return det_op


uci_service = OcrService(name="ocr")
FLAGS = ArgsParser().parse_args()
uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
uci_service.run_service()