115 lines
4.5 KiB
Python
115 lines
4.5 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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from paddle_serving_client import Client
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from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
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from paddle_serving_app.reader import Div, Normalize, Transpose
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from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
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from ocr_reader import OCRReader
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try:
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from paddle_serving_server_gpu.web_service import WebService
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except ImportError:
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from paddle_serving_server.web_service import WebService
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from paddle_serving_app.local_predict import LocalPredictor
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det_debugger(self, det_model_config):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), Div(255),
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Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
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(2, 0, 1))
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])
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self.det_client = LocalPredictor()
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if sys.argv[1] == 'gpu':
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self.det_client.load_model_config(
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det_model_config, use_gpu=True, gpu_id=0)
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elif sys.argv[1] == 'cpu':
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self.det_client.load_model_config(det_model_config)
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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, feed=[], fetch=[]):
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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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ori_h, ori_w, _ = im.shape
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det_img = self.det_preprocess(im)
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_, new_h, new_w = det_img.shape
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det_img = det_img[np.newaxis, :]
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det_img = det_img.copy()
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det_out = self.det_client.predict(
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feed={"x": det_img}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
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filter_func = FilterBoxes(10, 10)
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post_func = DBPostProcess({
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"thresh": 0.3,
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"box_thresh": 0.5,
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"max_candidates": 1000,
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"unclip_ratio": 1.5,
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"min_size": 3
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})
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sorted_boxes = SortedBoxes()
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ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
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dt_boxes_list = post_func(det_out["save_infer_model/scale_0.tmp_1"], [ratio_list])
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dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
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dt_boxes = sorted_boxes(dt_boxes)
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get_rotate_crop_image = GetRotateCropImage()
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img_list = []
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max_wh_ratio = 0
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for i, dtbox in enumerate(dt_boxes):
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boximg = get_rotate_crop_image(im, dt_boxes[i])
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img_list.append(boximg)
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h, w = boximg.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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if len(img_list) == 0:
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return [], []
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_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
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max_wh_ratio).shape
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imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
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for id, img in enumerate(img_list):
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norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
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imgs[id] = norm_img
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feed = {"x": imgs.copy()}
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fetch = ["save_infer_model/scale_0.tmp_1"]
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return feed, fetch, True
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
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res_lst = []
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for res in rec_res:
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res_lst.append(res[0])
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res = {"res": res_lst}
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return res
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("../ppocr_rec_mobile_2.0_serving")
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ocr_service.prepare_server(workdir="workdir", port=9292)
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ocr_service.init_det_debugger(det_model_config="../ppocr_det_mobile_2.0_serving")
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if sys.argv[1] == 'gpu':
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ocr_service.set_gpus("0")
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ocr_service.run_debugger_service(gpu=True)
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elif sys.argv[1] == 'cpu':
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ocr_service.run_debugger_service()
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ocr_service.run_web_service()
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