122 lines
4.6 KiB
Python
122 lines
4.6 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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from paddle_serving_app.reader import OCRReader
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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 clas_local_server import TextClassifierHelper
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from det_local_server import TextDetectorHelper
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from rec_local_server import TextRecognizerHelper
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from tools.infer.predict_system import TextSystem, sorted_boxes
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from paddle_serving_app.local_predict import Debugger
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import copy
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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 TextSystemHelper(TextSystem):
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def __init__(self, args):
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self.text_detector = TextDetectorHelper(args)
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self.text_recognizer = TextRecognizerHelper(args)
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self.use_angle_cls = args.use_angle_cls
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if self.use_angle_cls:
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self.clas_client = Debugger()
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self.clas_client.load_model_config(
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global_args.cls_model_dir, gpu=True, profile=False)
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self.text_classifier = TextClassifierHelper(args)
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self.det_client = Debugger()
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self.det_client.load_model_config(
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global_args.det_model_dir, gpu=True, profile=False)
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self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
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def preprocess(self, img):
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feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
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fetch_map = self.det_client.predict(feed, fetch)
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outputs = [fetch_map[x] for x in fetch]
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dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
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if dt_boxes is None:
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return None, None
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img_crop_list = []
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dt_boxes = sorted_boxes(dt_boxes)
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for bno in range(len(dt_boxes)):
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tmp_box = copy.deepcopy(dt_boxes[bno])
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img_crop = self.get_rotate_crop_image(img, tmp_box)
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img_crop_list.append(img_crop)
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if self.use_angle_cls:
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feed, fetch, self.tmp_args = self.text_classifier.preprocess(
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img_crop_list)
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fetch_map = self.clas_client.predict(feed, fetch)
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outputs = [fetch_map[x] for x in self.text_classifier.fetch]
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for x in fetch_map.keys():
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if ".lod" in x:
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self.tmp_args[x] = fetch_map[x]
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img_crop_list, _ = self.text_classifier.postprocess(outputs,
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self.tmp_args)
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feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
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img_crop_list)
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return feed, self.fetch, self.tmp_args
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def postprocess(self, outputs, args):
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return self.text_recognizer.postprocess(outputs, args)
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class OCRService(WebService):
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def init_rec(self):
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self.text_system = TextSystemHelper(global_args)
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def preprocess(self, feed=[], fetch=[]):
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# TODO: to handle batch rec images
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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_system.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 self.text_system.fetch]
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for x in fetch_map.keys():
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if ".lod" in x:
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self.tmp_args[x] = fetch_map[x]
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rec_res = self.text_system.postprocess(outputs, self.tmp_args)
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res = {
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"pred_text": [x[0] for x in rec_res],
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"score": [str(x[1]) for x in rec_res]
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}
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return res
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if __name__ == "__main__":
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config(global_args.rec_model_dir)
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ocr_service.init_rec()
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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_debugger_service()
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ocr_service.run_web_service()
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