87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
# 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 cv2
|
|
import base64
|
|
# from paddle_serving_app.reader import OCRReader
|
|
from ocr_reader import OCRReader, DetResizeForTest
|
|
from paddle_serving_app.reader import Sequential, ResizeByFactor
|
|
from paddle_serving_app.reader import Div, Normalize, Transpose
|
|
|
|
_LOGGER = logging.getLogger()
|
|
|
|
|
|
class RecOp(Op):
|
|
def init_op(self):
|
|
self.ocr_reader = OCRReader(
|
|
char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
|
|
|
|
def preprocess(self, input_dicts, data_id, log_id):
|
|
(_, input_dict), = input_dicts.items()
|
|
raw_im = base64.b64decode(input_dict["image"].encode('utf8'))
|
|
data = np.fromstring(raw_im, np.uint8)
|
|
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
|
|
feed_list = []
|
|
max_wh_ratio = 0
|
|
## 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 = max_batch_size
|
|
rem = boxes_size % max_batch_size
|
|
|
|
h, w = im.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(im, max_wh_ratio).shape
|
|
norm_img = self.ocr_reader.resize_norm_img(im, max_batch_size)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
feed = {"x": norm_img.copy()}
|
|
feed_list.append(feed)
|
|
return feed_list, False, None, ""
|
|
|
|
def postprocess(self, input_dicts, fetch_data, log_id):
|
|
res_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:
|
|
res_list.append(res[0])
|
|
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:
|
|
res_list.append(res[0])
|
|
|
|
res = {"res": str(res_list)}
|
|
return res, None, ""
|
|
|
|
|
|
class OcrService(WebService):
|
|
def get_pipeline_response(self, read_op):
|
|
rec_op = RecOp(name="rec", input_ops=[read_op])
|
|
return rec_op
|
|
|
|
|
|
uci_service = OcrService(name="ocr")
|
|
uci_service.prepare_pipeline_config("config.yml")
|
|
uci_service.run_service()
|