135 lines
4.9 KiB
Python
135 lines
4.9 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 tools.infer.predict_cls import TextClassifier
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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 TextClassifierHelper(TextClassifier):
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def __init__(self, args):
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self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
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self.cls_batch_num = args.rec_batch_num
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self.label_list = args.label_list
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self.cls_thresh = args.cls_thresh
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self.fetch = [
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"save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
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]
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def preprocess(self, img_list):
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args = {}
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img_num = len(img_list)
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args["img_list"] = img_list
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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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# Sorting can speed up the cls process
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indices = np.argsort(np.array(width_list))
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args["indices"] = indices
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cls_res = [['', 0.0]] * img_num
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batch_num = self.cls_batch_num
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predict_time = 0
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beg_img_no, end_img_no = 0, 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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norm_img = self.resize_norm_img(img_list[indices[ino]])
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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if img_num > 1:
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feed = [{
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"image": norm_img_batch[x]
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} for x in range(norm_img_batch.shape[0])]
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else:
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feed = {"image": norm_img_batch[0]}
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return feed, self.fetch, args
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def postprocess(self, outputs, args):
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prob_out = outputs[0]
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label_out = outputs[1]
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indices = args["indices"]
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cls_res = [['', 0.0]] * len(label_out)
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if len(label_out.shape) != 1:
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prob_out, label_out = label_out, prob_out
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for rno in range(len(label_out)):
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label_idx = label_out[rno]
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score = prob_out[rno][label_idx]
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label = self.label_list[label_idx]
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cls_res[indices[rno]] = [label, score]
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if '180' in label and score > self.cls_thresh:
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img_list[indices[rno]] = cv2.rotate(img_list[indices[rno]], 1)
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return args["img_list"], cls_res
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class OCRService(WebService):
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def init_rec(self):
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self.ocr_reader = OCRReader()
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self.text_classifier = TextClassifierHelper(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_classifier.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_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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_, rec_res = self.text_classifier.postprocess(outputs, self.tmp_args)
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res = {
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"direction": [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.cls_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_rpc_service()
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ocr_service.run_web_service()
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