rm ocr_detection dir

python/examples/ocr_detection/text_det_client.py

-# Copyright (c) 2020 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.
-
-import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, File2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes
-
-client = Client()
-client.load_client_config("ocr_det_client/serving_client_conf.prototxt")
-client.connect(["127.0.0.1:9494"])
-
-read_image_file = File2Image()
-preprocess = Sequential([
-    ResizeByFactor(32, 960), Div(255),
-    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
-        (2, 0, 1))
-])
-post_func = DBPostProcess({
-    "thresh": 0.3,
-    "box_thresh": 0.5,
-    "max_candidates": 1000,
-    "unclip_ratio": 1.5,
-    "min_size": 3
-})
-filter_func = FilterBoxes(10, 10)
-
-img = read_image_file(name)
-ori_h, ori_w, _ = img.shape
-img = preprocess(img)
-new_h, new_w, _ = img.shape
-ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
-outputs = client.predict(feed={"image": img}, fetch=["concat_1.tmp_0"])
-dt_boxes_list = post_func(outputs["concat_1.tmp_0"], [ratio_list])
-dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])