107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle_serving_client import Client
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import cv2
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import sys
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import numpy as np
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import os
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import time
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import re
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import base64
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from tools.infer.predict_det import TextDetector
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from params import read_params
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global_args = read_params()
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if global_args.use_gpu:
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from paddle_serving_server_gpu.web_service import WebService
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else:
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from paddle_serving_server.web_service import WebService
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class TextDetectorHelper(TextDetector):
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def __init__(self, args):
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super(TextDetectorHelper, self).__init__(args)
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if self.det_algorithm == "SAST":
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self.fetch = [
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"bn_f_border4.output.tmp_2", "bn_f_tco4.output.tmp_2",
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"bn_f_tvo4.output.tmp_2", "sigmoid_0.tmp_0"
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]
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elif self.det_algorithm == "EAST":
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self.fetch = ["sigmoid_0.tmp_0", "tmp_2"]
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elif self.det_algorithm == "DB":
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self.fetch = ["sigmoid_0.tmp_0"]
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def preprocess(self, img):
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im, ratio_list = self.preprocess_op(img)
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if im is None:
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return None, 0
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return {
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"image": im[0]
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}, self.fetch, {
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"ratio_list": [ratio_list],
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"ori_im": img
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}
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def postprocess(self, outputs, args):
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outs_dict = {}
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if self.det_algorithm == "EAST":
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outs_dict['f_geo'] = outputs[0]
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outs_dict['f_score'] = outputs[1]
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elif self.det_algorithm == 'SAST':
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outs_dict['f_border'] = outputs[0]
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outs_dict['f_score'] = outputs[1]
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outs_dict['f_tco'] = outputs[2]
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outs_dict['f_tvo'] = outputs[3]
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else:
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outs_dict['maps'] = outputs[0]
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dt_boxes_list = self.postprocess_op(outs_dict, args["ratio_list"])
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dt_boxes = dt_boxes_list[0]
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if self.det_algorithm == "SAST" and self.det_sast_polygon:
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dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes,
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args["ori_im"].shape)
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else:
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dt_boxes = self.filter_tag_det_res(dt_boxes, args["ori_im"].shape)
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return dt_boxes
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class DetService(WebService):
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def init_det(self):
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self.text_detector = TextDetectorHelper(global_args)
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def preprocess(self, feed=[], fetch=[]):
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data = base64.b64decode(feed[0]["image"].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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feed, fetch, self.tmp_args = self.text_detector.preprocess(im)
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return feed, fetch
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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outputs = [fetch_map[x] for x in fetch]
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res = self.text_detector.postprocess(outputs, self.tmp_args)
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return {"boxes": res.tolist()}
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if __name__ == "__main__":
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ocr_service = DetService(name="ocr")
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ocr_service.load_model_config(global_args.det_model_dir)
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ocr_service.init_det()
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if global_args.use_gpu:
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ocr_service.prepare_server(
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workdir="workdir", port=9292, device="gpu", gpuid=0)
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else:
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ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
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ocr_service.run_rpc_service()
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ocr_service.run_web_service()
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