# 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.
from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
import sys
import cv2
from paddle_serving_app.reader import *
import base64

class PPYoloMbvOp(Op):
    def init_op(self):
        self.img_preprocess = Sequential([
            BGR2RGB(), Div(255.0),
            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
            Resize((320, 320)), Transpose((2, 0, 1))
        ])
        self.img_postprocess = RCNNPostprocess("label_list.txt", "output")

    def generate_scale(self, im):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
        Returns:
            im_scale_x: the resize ratio of X
            im_scale_y: the resize ratio of Y
        """
        target_size = [320, 320]
        origin_shape = im.shape[:2]
        resize_h, resize_w = target_size
        im_scale_y = resize_h / float(origin_shape[0])
        im_scale_x = resize_w / float(origin_shape[1])
        return im_scale_y, im_scale_x
        
    def preprocess(self, input_dicts, data_id, log_id):
        (_, input_dict), = input_dicts.items()
        imgs = []
        for key in input_dict.keys():
            data = base64.b64decode(input_dict[key].encode('utf8'))
            data = np.fromstring(data, np.uint8)
            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
            im_scale_y, im_scale_x = self.generate_scale(im)
            im = self.img_preprocess(im)
            imgs.append({
              "image": im[np.newaxis,:],
              "im_shape": np.array(list(im.shape[1:])).reshape(-1)[np.newaxis,:],
              "scale_factor": np.array([im_scale_y, im_scale_x]).astype('float32'),
            })

        feed_dict = {
            "image": np.concatenate([x["image"] for x in imgs], axis=0),
            "im_shape": np.concatenate([x["im_shape"] for x in imgs], axis=0),
            "scale_factor": np.concatenate([x["scale_factor"] for x in imgs], axis=0)
        }
        for key in feed_dict.keys():
            print(key, feed_dict[key].shape)
        return feed_dict, False, None, ""

    def postprocess(self, input_dicts, fetch_dict, log_id):
        #print(fetch_dict)
        res_dict = {"bbox_result": str(self.img_postprocess(fetch_dict, visualize=False))}
        return res_dict, None, ""


class PPYoloMbv(WebService):
    def get_pipeline_response(self, read_op):
        ppyolo_mbv3_op = PPYoloMbvOp(name="ppyolo_mbv3", input_ops=[read_op])
        return ppyolo_mbv3_op


ppyolo_mbv3_service = PPYoloMbv(name="ppyolo_mbv3")
ppyolo_mbv3_service.prepare_pipeline_config("config.yml")
ppyolo_mbv3_service.run_service()