# 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_client import Client from paddle_serving_app.reader import OCRReader import cv2 import sys import numpy as np import os from paddle_serving_client import Client from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor from paddle_serving_app.reader import Div, Normalize, Transpose from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes if sys.argv[1] == 'gpu': from paddle_serving_server.web_service import WebService elif sys.argv[1] == 'cpu': from paddle_serving_server.web_service import WebService import time import re import base64 class OCRService(WebService): def init_det_client(self, det_port, det_client_config): self.det_preprocess = Sequential([ ResizeByFactor(32, 960), Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( (2, 0, 1)) ]) self.det_client = Client() self.det_client.load_client_config(det_client_config) self.det_client.connect(["127.0.0.1:{}".format(det_port)]) self.ocr_reader = OCRReader() def preprocess(self, feed=[], fetch=[]): data = base64.b64decode(feed[0]["image"].encode('utf8')) data = np.fromstring(data, np.uint8) im = cv2.imdecode(data, cv2.IMREAD_COLOR) ori_h, ori_w, _ = im.shape det_img = self.det_preprocess(im) det_out = self.det_client.predict( feed={"image": det_img}, fetch=["concat_1.tmp_0"], batch=False) _, new_h, new_w = det_img.shape filter_func = FilterBoxes(10, 10) post_func = DBPostProcess({ "thresh": 0.3, "box_thresh": 0.5, "max_candidates": 1000, "unclip_ratio": 1.5, "min_size": 3 }) sorted_boxes = SortedBoxes() ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w] dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list]) dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w]) dt_boxes = sorted_boxes(dt_boxes) get_rotate_crop_image = GetRotateCropImage() feed_list = [] img_list = [] max_wh_ratio = 0 for i, dtbox in enumerate(dt_boxes): boximg = get_rotate_crop_image(im, dt_boxes[i]) img_list.append(boximg) h, w = boximg.shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for img in img_list: norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) feed_list.append(norm_img[np.newaxis, :]) feed_batch = {"image": np.concatenate(feed_list, axis=0)} fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] return feed_batch, fetch, True def postprocess(self, feed={}, fetch=[], fetch_map=None): rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True) res_lst = [] for res in rec_res: res_lst.append(res[0]) res = {"res": res_lst} return res ocr_service = OCRService(name="ocr") ocr_service.load_model_config("ocr_rec_model") if sys.argv[1] == 'gpu': ocr_service.set_gpus("0") ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu") elif sys.argv[1] == 'cpu': ocr_service.prepare_server(workdir="workdir", port=9292) ocr_service.init_det_client( det_port=9293, det_client_config="ocr_det_client/serving_client_conf.prototxt") ocr_service.run_rpc_service() ocr_service.run_web_service()