161 lines
6.0 KiB
Python
161 lines
6.0 KiB
Python
# Copyright (c) 2021 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_server.web_service import WebService, Op
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import logging
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import numpy as np
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import cv2
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import base64
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# from paddle_serving_app.reader import OCRReader
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from ocr_reader import OCRReader, DetResizeForTest
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from paddle_serving_app.reader import Sequential, 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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_LOGGER = logging.getLogger()
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class DetOp(Op):
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def init_op(self):
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self.det_preprocess = Sequential([
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DetResizeForTest(), 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.filter_func = FilterBoxes(10, 10)
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self.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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def preprocess(self, input_dicts, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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data = base64.b64decode(input_dict["image"].encode('utf8'))
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self.raw_im = data
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data = np.fromstring(data, np.uint8)
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# Note: class variables(self.var) can only be used in process op mode
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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self.ori_h, self.ori_w, _ = im.shape
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det_img = self.det_preprocess(im)
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_, self.new_h, self.new_w = det_img.shape
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return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
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def postprocess(self, input_dicts, fetch_dict, log_id):
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det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
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ratio_list = [
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float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
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]
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dt_boxes_list = self.post_func(det_out, [ratio_list])
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dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
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out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im}
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return out_dict, None, ""
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class RecOp(Op):
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def init_op(self):
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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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self.get_rotate_crop_image = GetRotateCropImage()
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self.sorted_boxes = SortedBoxes()
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def preprocess(self, input_dicts, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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raw_im = input_dict["image"]
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data = np.frombuffer(raw_im, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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dt_boxes = input_dict["dt_boxes"]
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dt_boxes = self.sorted_boxes(dt_boxes)
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feed_list = []
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img_list = []
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max_wh_ratio = 0
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## Many mini-batchs, the type of feed_data is list.
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max_batch_size = 6 # len(dt_boxes)
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# If max_batch_size is 0, skipping predict stage
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if max_batch_size == 0:
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return {}, True, None, ""
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boxes_size = len(dt_boxes)
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batch_size = boxes_size // max_batch_size
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rem = boxes_size % max_batch_size
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for bt_idx in range(0, batch_size + 1):
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imgs = None
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boxes_num_in_one_batch = 0
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if bt_idx == batch_size:
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if rem == 0:
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continue
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else:
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boxes_num_in_one_batch = rem
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elif bt_idx < batch_size:
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boxes_num_in_one_batch = max_batch_size
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else:
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_LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
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format(bt_idx, batch_size))
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break
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start = bt_idx * max_batch_size
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end = start + boxes_num_in_one_batch
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img_list = []
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for box_idx in range(start, end):
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boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
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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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_, 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((boxes_num_in_one_batch, 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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feed_list.append(feed)
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return feed_list, False, None, ""
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def postprocess(self, input_dicts, fetch_data, log_id):
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res_list = []
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if isinstance(fetch_data, dict):
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if len(fetch_data) > 0:
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rec_batch_res = self.ocr_reader.postprocess(
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fetch_data, with_score=True)
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for res in rec_batch_res:
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res_list.append(res[0])
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elif isinstance(fetch_data, list):
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for one_batch in fetch_data:
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one_batch_res = self.ocr_reader.postprocess(
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one_batch, with_score=True)
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for res in one_batch_res:
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res_list.append(res[0])
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res = {"res": str(res_list)}
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return res, None, ""
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class OcrService(WebService):
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def get_pipeline_response(self, read_op):
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det_op = DetOp(name="det", input_ops=[read_op])
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rec_op = RecOp(name="rec", input_ops=[det_op])
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return rec_op
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uci_service = OcrService(name="ocr")
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uci_service.prepare_pipeline_config("config.yml")
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uci_service.run_service()
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