mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
# Copyright (c) 2021 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_server.web_service import WebService, Op
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import logging
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import numpy as np
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import cv2
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import base64
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# from paddle_serving_app.reader import OCRReader
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from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
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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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_LOGGER = logging.getLogger()
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class RecOp(Op):
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def init_op(self):
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self.ocr_reader = OCRReader(
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char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
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def preprocess(self, input_dicts, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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raw_im = base64.b64decode(input_dict["image"].encode('utf8'))
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data = np.fromstring(raw_im, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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feed_list = []
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max_wh_ratio = 0
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## Many mini-batchs, the type of feed_data is list.
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max_batch_size = 6 # len(dt_boxes)
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# If max_batch_size is 0, skipping predict stage
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if max_batch_size == 0:
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return {}, True, None, ""
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boxes_size = max_batch_size
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rem = boxes_size % max_batch_size
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h, w = im.shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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_, w, h = self.ocr_reader.resize_norm_img(im, max_wh_ratio).shape
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norm_img = self.ocr_reader.resize_norm_img(im, max_batch_size)
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norm_img = norm_img[np.newaxis, :]
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feed = {"x": norm_img.copy()}
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feed_list.append(feed)
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return feed_list, False, None, ""
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def postprocess(self, input_dicts, fetch_data, data_id, log_id):
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res_list = []
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if isinstance(fetch_data, dict):
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if len(fetch_data) > 0:
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rec_batch_res = self.ocr_reader.postprocess(
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fetch_data, with_score=True)
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for res in rec_batch_res:
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res_list.append(res[0])
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elif isinstance(fetch_data, list):
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for one_batch in fetch_data:
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one_batch_res = self.ocr_reader.postprocess(
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one_batch, with_score=True)
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for res in one_batch_res:
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res_list.append(res[0])
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res = {"res": str(res_list)}
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return res, None, ""
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class OcrService(WebService):
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def get_pipeline_response(self, read_op):
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rec_op = RecOp(name="rec", input_ops=[read_op])
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return rec_op
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uci_service = OcrService(name="ocr")
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FLAGS = ArgsParser().parse_args()
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uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
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uci_service.run_service()
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