diff --git a/deploy/pdserving/web_service_det.py b/deploy/pdserving/web_service_det.py new file mode 100644 index 0000000000000000000000000000000000000000..25ac2f37dbd3cdf05b3503abaab0c5651867fae9 --- /dev/null +++ b/deploy/pdserving/web_service_det.py @@ -0,0 +1,77 @@ +# 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 +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": 0.5, + "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, log_id): + det_out = fetch_dict["save_infer_model/scale_0.tmp_1"] + 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": str(dt_boxes)} + + return out_dict, None, "" + + +class OcrService(WebService): + def get_pipeline_response(self, read_op): + det_op = DetOp(name="det", input_ops=[read_op]) + return det_op + + +uci_service = OcrService(name="ocr") +uci_service.prepare_pipeline_config("config.yml") +uci_service.run_service() diff --git a/deploy/pdserving/web_service_rec.py b/deploy/pdserving/web_service_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..6b3cf707f0f19034a0734fd27824feb4fb6cce20 --- /dev/null +++ b/deploy/pdserving/web_service_rec.py @@ -0,0 +1,86 @@ +# 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()