176 lines
7.0 KiB
Python
176 lines
7.0 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_rec import TextRecognizer
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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 TextRecognizerHelper(TextRecognizer):
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def __init__(self, args):
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super(TextRecognizerHelper, self).__init__(args)
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if self.loss_type == "ctc":
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self.fetch = ["save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"]
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def preprocess(self, img_list):
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img_num = len(img_list)
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args = {}
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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indices = np.argsort(np.array(width_list))
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args["indices"] = indices
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predict_time = 0
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beg_img_no = 0
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end_img_no = img_num
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].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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for ino in range(beg_img_no, end_img_no):
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if self.loss_type != "srn":
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norm_img = self.resize_norm_img(img_list[indices[ino]],
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max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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else:
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norm_img = self.process_image_srn(img_list[indices[ino]],
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self.rec_image_shape, 8, 25,
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self.char_ops)
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encoder_word_pos_list = []
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gsrm_word_pos_list = []
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gsrm_slf_attn_bias1_list = []
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gsrm_slf_attn_bias2_list = []
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encoder_word_pos_list.append(norm_img[1])
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gsrm_word_pos_list.append(norm_img[2])
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gsrm_slf_attn_bias1_list.append(norm_img[3])
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gsrm_slf_attn_bias2_list.append(norm_img[4])
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norm_img_batch.append(norm_img[0])
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norm_img_batch = np.concatenate(norm_img_batch, axis=0).copy()
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feed = {"image": norm_img_batch.copy()}
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return feed, self.fetch, args
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def postprocess(self, outputs, args):
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if self.loss_type == "ctc":
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rec_idx_batch = outputs[0]
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predict_batch = outputs[1]
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rec_idx_lod = args["save_infer_model/scale_0.tmp_0.lod"]
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predict_lod = args["save_infer_model/scale_1.tmp_0.lod"]
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indices = args["indices"]
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rec_res = [['', 0.0]] * (len(rec_idx_lod) - 1)
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for rno in range(len(rec_idx_lod) - 1):
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beg = rec_idx_lod[rno]
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end = rec_idx_lod[rno + 1]
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rec_idx_tmp = rec_idx_batch[beg:end, 0]
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preds_text = self.char_ops.decode(rec_idx_tmp)
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beg = predict_lod[rno]
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end = predict_lod[rno + 1]
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probs = predict_batch[beg:end, :]
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ind = np.argmax(probs, axis=1)
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blank = probs.shape[1]
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valid_ind = np.where(ind != (blank - 1))[0]
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if len(valid_ind) == 0:
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continue
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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rec_res[indices[rno]] = [preds_text, score]
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elif self.loss_type == 'srn':
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char_num = self.char_ops.get_char_num()
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preds = rec_idx_batch.reshape(-1)
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elapse = time.time() - starttime
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predict_time += elapse
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total_preds = preds.copy()
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for ino in range(int(len(rec_idx_batch) / self.text_len)):
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preds = total_preds[ino * self.text_len:(ino + 1) *
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self.text_len]
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ind = np.argmax(probs, axis=1)
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valid_ind = np.where(preds != int(char_num - 1))[0]
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if len(valid_ind) == 0:
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continue
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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preds = preds[:valid_ind[-1] + 1]
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preds_text = self.char_ops.decode(preds)
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rec_res[indices[ino]] = [preds_text, score]
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else:
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for rno in range(len(rec_idx_batch)):
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end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
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if len(end_pos) <= 1:
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preds = rec_idx_batch[rno, 1:]
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score = np.mean(predict_batch[rno, 1:])
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else:
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preds = rec_idx_batch[rno, 1:end_pos[1]]
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score = np.mean(predict_batch[rno, 1:end_pos[1]])
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preds_text = self.char_ops.decode(preds)
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rec_res[indices[rno]] = [preds_text, score]
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return rec_res
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class OCRService(WebService):
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def init_rec(self):
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self.text_recognizer = TextRecognizerHelper(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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img_list = []
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for feed_data in feed:
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data = base64.b64decode(feed_data["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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img_list.append(im)
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feed, fetch, self.tmp_args = self.text_recognizer.preprocess(img_list)
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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_recognizer.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_recognizer.postprocess(outputs, self.tmp_args)
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res = []
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for i in range(len(rec_res)):
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res.append({
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"text": rec_res[i][0],
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"confidence": float(rec_res[i][1])
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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_server_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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