123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168 |
- # Copyright (c) 2021 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_server.web_service import WebService, Op
- import logging
- import numpy as np
- import copy
- import cv2
- import base64
- # from paddle_serving_app.reader import OCRReader
- from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
- from paddle_serving_app.reader import Sequential, ResizeByFactor
- from paddle_serving_app.reader import Div, Normalize, Transpose
- from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
- _LOGGER = logging.getLogger()
- class DetOp(Op):
- def init_op(self):
- self.det_preprocess = Sequential([
- DetResizeForTest(), Div(255),
- Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
- (2, 0, 1))
- ])
- self.filter_func = FilterBoxes(10, 10)
- self.post_func = DBPostProcess({
- "thresh": 0.3,
- "box_thresh": 0.6,
- "max_candidates": 1000,
- "unclip_ratio": 1.5,
- "min_size": 3
- })
- def preprocess(self, input_dicts, data_id, log_id):
- (_, input_dict), = input_dicts.items()
- data = base64.b64decode(input_dict["image"].encode('utf8'))
- self.raw_im = data
- data = np.fromstring(data, np.uint8)
- # Note: class variables(self.var) can only be used in process op mode
- im = cv2.imdecode(data, cv2.IMREAD_COLOR)
- self.ori_h, self.ori_w, _ = im.shape
- det_img = self.det_preprocess(im)
- _, self.new_h, self.new_w = det_img.shape
- return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
- def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
- det_out = list(fetch_dict.values())[0]
- ratio_list = [
- float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
- ]
- dt_boxes_list = self.post_func(det_out, [ratio_list])
- dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
- out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im}
- return out_dict, None, ""
- class RecOp(Op):
- def init_op(self):
- self.ocr_reader = OCRReader(
- char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
- self.get_rotate_crop_image = GetRotateCropImage()
- self.sorted_boxes = SortedBoxes()
- def preprocess(self, input_dicts, data_id, log_id):
- (_, input_dict), = input_dicts.items()
- raw_im = input_dict["image"]
- data = np.frombuffer(raw_im, np.uint8)
- im = cv2.imdecode(data, cv2.IMREAD_COLOR)
- self.dt_list = input_dict["dt_boxes"]
- self.dt_list = self.sorted_boxes(self.dt_list)
- # deepcopy to save origin dt_boxes
- dt_boxes = copy.deepcopy(self.dt_list)
- feed_list = []
- img_list = []
- max_wh_ratio = 320 / 48.
- ## Many mini-batchs, the type of feed_data is list.
- max_batch_size = 6 # len(dt_boxes)
- # If max_batch_size is 0, skipping predict stage
- if max_batch_size == 0:
- return {}, True, None, ""
- boxes_size = len(dt_boxes)
- batch_size = boxes_size // max_batch_size
- rem = boxes_size % max_batch_size
- for bt_idx in range(0, batch_size + 1):
- imgs = None
- boxes_num_in_one_batch = 0
- if bt_idx == batch_size:
- if rem == 0:
- continue
- else:
- boxes_num_in_one_batch = rem
- elif bt_idx < batch_size:
- boxes_num_in_one_batch = max_batch_size
- else:
- _LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
- format(bt_idx, batch_size))
- break
- start = bt_idx * max_batch_size
- end = start + boxes_num_in_one_batch
- img_list = []
- for box_idx in range(start, end):
- boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
- 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)
- _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
- max_wh_ratio).shape
- imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
- for id, img in enumerate(img_list):
- norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
- imgs[id] = norm_img
- feed = {"x": imgs.copy()}
- feed_list.append(feed)
- return feed_list, False, None, ""
- def postprocess(self, input_dicts, fetch_data, data_id, log_id):
- rec_list = []
- dt_num = len(self.dt_list)
- if isinstance(fetch_data, dict):
- if len(fetch_data) > 0:
- rec_batch_res = self.ocr_reader.postprocess(
- fetch_data, with_score=True)
- for res in rec_batch_res:
- rec_list.append(res)
- elif isinstance(fetch_data, list):
- for one_batch in fetch_data:
- one_batch_res = self.ocr_reader.postprocess(
- one_batch, with_score=True)
- for res in one_batch_res:
- rec_list.append(res)
- result_list = []
- for i in range(dt_num):
- text = rec_list[i]
- dt_box = self.dt_list[i]
- if text[1] >= 0.5:
- result_list.append([text, dt_box.tolist()])
- res = {"result": str(result_list)}
- return res, None, ""
- class OcrService(WebService):
- def get_pipeline_response(self, read_op):
- det_op = DetOp(name="det", input_ops=[read_op])
- rec_op = RecOp(name="rec", input_ops=[det_op])
- return rec_op
- uci_service = OcrService(name="ocr")
- FLAGS = ArgsParser().parse_args()
- uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
- uci_service.run_service()
|