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https://github.com/PaddlePaddle/PaddleOCR.git
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79 lines
2.8 KiB
Python
79 lines
2.8 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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from paddle_serving_client import Client
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from paddle_serving_app.reader import Sequential, ResizeByFactor
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from paddle_serving_app.reader import Div, Normalize, Transpose
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from paddle_serving_app.reader import DBPostProcess, FilterBoxes
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if sys.argv[1] == 'gpu':
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from paddle_serving_server_gpu.web_service import WebService
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elif sys.argv[1] == 'cpu':
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from paddle_serving_server.web_service import WebService
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det(self):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), Div(255),
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Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
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(2, 0, 1))
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])
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self.filter_func = FilterBoxes(10, 10)
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self.post_func = DBPostProcess({
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"thresh": 0.3,
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"box_thresh": 0.5,
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"max_candidates": 1000,
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"unclip_ratio": 1.5,
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"min_size": 3
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})
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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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self.ori_h, self.ori_w, _ = im.shape
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det_img = self.det_preprocess(im)
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_, self.new_h, self.new_w = det_img.shape
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print(det_img)
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return {"image": det_img}, ["concat_1.tmp_0"]
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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det_out = fetch_map["concat_1.tmp_0"]
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ratio_list = [
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float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
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]
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dt_boxes_list = self.post_func(det_out, [ratio_list])
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dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
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return {"dt_boxes": dt_boxes.tolist()}
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("ocr_det_model")
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if sys.argv[1] == 'gpu':
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ocr_service.set_gpus("0")
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ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
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elif sys.argv[1] == 'cpu':
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ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
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ocr_service.init_det()
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ocr_service.run_rpc_service()
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ocr_service.run_web_service()
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