From d07a223df83267d59ae6f3a97671656b8e44ca72 Mon Sep 17 00:00:00 2001 From: wangjiawei04 Date: Mon, 20 Jul 2020 21:15:45 +0800 Subject: [PATCH] add pdserving ocr --- deploy/pdserving/det_local_server.py | 71 ++++++++++++++++++ deploy/pdserving/det_web_server.py | 72 +++++++++++++++++++ deploy/pdserving/ocr_local_server.py | 103 +++++++++++++++++++++++++++ deploy/pdserving/ocr_web_client.py | 37 ++++++++++ deploy/pdserving/ocr_web_server.py | 99 +++++++++++++++++++++++++ deploy/pdserving/readme.md | 55 +++++++++++++- deploy/pdserving/rec_local_server.py | 72 +++++++++++++++++++ deploy/pdserving/rec_web_server.py | 71 ++++++++++++++++++ 8 files changed, 578 insertions(+), 2 deletions(-) create mode 100644 deploy/pdserving/det_local_server.py create mode 100644 deploy/pdserving/det_web_server.py create mode 100644 deploy/pdserving/ocr_local_server.py create mode 100644 deploy/pdserving/ocr_web_client.py create mode 100644 deploy/pdserving/ocr_web_server.py create mode 100644 deploy/pdserving/rec_local_server.py create mode 100644 deploy/pdserving/rec_web_server.py diff --git a/deploy/pdserving/det_local_server.py b/deploy/pdserving/det_local_server.py new file mode 100644 index 00000000..acfccdb6 --- /dev/null +++ b/deploy/pdserving/det_local_server.py @@ -0,0 +1,71 @@ +# 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. + +from paddle_serving_client import Client +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +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 +from paddle_serving_server_gpu.web_service import WebService +import time +import re +import base64 + + +class OCRService(WebService): + def init_det(self): + self.det_preprocess = Sequential([ + ResizeByFactor(32, 960), 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, feed=[], fetch=[]): + data = base64.b64decode(feed[0]["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + 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 {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"] + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + det_out = fetch_map["concat_1.tmp_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]) + return {"dt_boxes": dt_boxes.tolist()} + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_det_model") +ocr_service.set_gpus("0") +ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0) +ocr_service.init_det() +ocr_service.run_debugger_service() +ocr_service.run_web_service() diff --git a/deploy/pdserving/det_web_server.py b/deploy/pdserving/det_web_server.py new file mode 100644 index 00000000..dd69be0c --- /dev/null +++ b/deploy/pdserving/det_web_server.py @@ -0,0 +1,72 @@ +# 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. + +from paddle_serving_client import Client +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +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 +from paddle_serving_server_gpu.web_service import WebService +import time +import re +import base64 + + +class OCRService(WebService): + def init_det(self): + self.det_preprocess = Sequential([ + ResizeByFactor(32, 960), 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, feed=[], fetch=[]): + data = base64.b64decode(feed[0]["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + 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 + print(det_img) + return {"image": det_img}, ["concat_1.tmp_0"] + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + det_out = fetch_map["concat_1.tmp_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]) + return {"dt_boxes": dt_boxes.tolist()} + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_det_model") +ocr_service.set_gpus("0") +ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0) +ocr_service.init_det() +ocr_service.run_rpc_service() +ocr_service.run_web_service() diff --git a/deploy/pdserving/ocr_local_server.py b/deploy/pdserving/ocr_local_server.py new file mode 100644 index 00000000..93e2d7a3 --- /dev/null +++ b/deploy/pdserving/ocr_local_server.py @@ -0,0 +1,103 @@ +# 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. + +from paddle_serving_client import Client +from paddle_serving_app.reader import OCRReader +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes +from paddle_serving_server_gpu.web_service import WebService +from paddle_serving_app.local_predict import Debugger +import time +import re +import base64 + + +class OCRService(WebService): + def init_det_debugger(self, det_model_config): + self.det_preprocess = Sequential([ + ResizeByFactor(32, 960), Div(255), + Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( + (2, 0, 1)) + ]) + self.det_client = Debugger() + self.det_client.load_model_config( + det_model_config, gpu=True, profile=False) + self.ocr_reader = OCRReader() + + def preprocess(self, feed=[], fetch=[]): + data = base64.b64decode(feed[0]["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + ori_h, ori_w, _ = im.shape + det_img = self.det_preprocess(im) + _, new_h, new_w = det_img.shape + det_img = det_img[np.newaxis, :] + det_img = det_img.copy() + det_out = self.det_client.predict( + feed={"image": det_img}, fetch=["concat_1.tmp_0"]) + filter_func = FilterBoxes(10, 10) + post_func = DBPostProcess({ + "thresh": 0.3, + "box_thresh": 0.5, + "max_candidates": 1000, + "unclip_ratio": 1.5, + "min_size": 3 + }) + sorted_boxes = SortedBoxes() + ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w] + dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list]) + dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w]) + dt_boxes = sorted_boxes(dt_boxes) + get_rotate_crop_image = GetRotateCropImage() + img_list = [] + max_wh_ratio = 0 + for i, dtbox in enumerate(dt_boxes): + boximg = get_rotate_crop_image(im, dt_boxes[i]) + 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) + if len(img_list) == 0: + return [], [] + _, w, h = self.ocr_reader.resize_norm_img(img_list[0], + max_wh_ratio).shape + imgs = np.zeros((len(img_list), 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 = {"image": imgs.copy()} + fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + return feed, fetch + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True) + res_lst = [] + for res in rec_res: + res_lst.append(res[0]) + res = {"res": res_lst} + return res + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_rec_model") +ocr_service.prepare_server(workdir="workdir", port=9292) +ocr_service.init_det_debugger(det_model_config="ocr_det_model") +ocr_service.run_debugger_service(gpu=True) +ocr_service.run_web_service() diff --git a/deploy/pdserving/ocr_web_client.py b/deploy/pdserving/ocr_web_client.py new file mode 100644 index 00000000..e2a92eb8 --- /dev/null +++ b/deploy/pdserving/ocr_web_client.py @@ -0,0 +1,37 @@ +# 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. +# -*- coding: utf-8 -*- + +import requests +import json +import cv2 +import base64 +import os, sys +import time + +def cv2_to_base64(image): + #data = cv2.imencode('.jpg', image)[1] + return base64.b64encode(image).decode( + 'utf8') #data.tostring()).decode('utf8') + +headers = {"Content-type": "application/json"} +url = "http://127.0.0.1:9292/ocr/prediction" +test_img_dir = "../../doc/imgs/" +for img_file in os.listdir(test_img_dir): + with open(os.path.join(test_img_dir, img_file), 'rb') as file: + image_data1 = file.read() + image = cv2_to_base64(image_data1) + data = {"feed": [{"image": image}], "fetch": ["res"]} + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) diff --git a/deploy/pdserving/ocr_web_server.py b/deploy/pdserving/ocr_web_server.py new file mode 100644 index 00000000..d017f6b9 --- /dev/null +++ b/deploy/pdserving/ocr_web_server.py @@ -0,0 +1,99 @@ +# 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. + +from paddle_serving_client import Client +from paddle_serving_app.reader import OCRReader +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes +from paddle_serving_server_gpu.web_service import WebService +import time +import re +import base64 + + +class OCRService(WebService): + def init_det_client(self, det_port, det_client_config): + self.det_preprocess = Sequential([ + ResizeByFactor(32, 960), Div(255), + Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( + (2, 0, 1)) + ]) + self.det_client = Client() + self.det_client.load_client_config(det_client_config) + self.det_client.connect(["127.0.0.1:{}".format(det_port)]) + self.ocr_reader = OCRReader() + + def preprocess(self, feed=[], fetch=[]): + data = base64.b64decode(feed[0]["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + ori_h, ori_w, _ = im.shape + det_img = self.det_preprocess(im) + det_out = self.det_client.predict( + feed={"image": det_img}, fetch=["concat_1.tmp_0"]) + _, new_h, new_w = det_img.shape + filter_func = FilterBoxes(10, 10) + post_func = DBPostProcess({ + "thresh": 0.3, + "box_thresh": 0.5, + "max_candidates": 1000, + "unclip_ratio": 1.5, + "min_size": 3 + }) + sorted_boxes = SortedBoxes() + ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w] + dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list]) + dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w]) + dt_boxes = sorted_boxes(dt_boxes) + get_rotate_crop_image = GetRotateCropImage() + feed_list = [] + img_list = [] + max_wh_ratio = 0 + for i, dtbox in enumerate(dt_boxes): + boximg = get_rotate_crop_image(im, dt_boxes[i]) + 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) + for img in img_list: + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + feed = {"image": norm_img} + feed_list.append(feed) + fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + return feed_list, fetch + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True) + res_lst = [] + for res in rec_res: + res_lst.append(res[0]) + res = {"res": res_lst} + return res + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_rec_model") +ocr_service.set_gpus("0") +ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0) +ocr_service.init_det_client( + det_port=9293, + det_client_config="ocr_det_client/serving_client_conf.prototxt") +ocr_service.run_rpc_service() +ocr_service.run_web_service() diff --git a/deploy/pdserving/readme.md b/deploy/pdserving/readme.md index 27542774..bab44249 100644 --- a/deploy/pdserving/readme.md +++ b/deploy/pdserving/readme.md @@ -5,24 +5,75 @@ ## 快速启动服务 ### 1. 准备环境 +我们先安装Paddle Serving相关组件 +我们推荐用户使用GPU来做Paddle Serving的OCR服务部署 + +**CUDA版本：9.0以上** +**CUDNN版本：7.0以上** +**操作系统版本：CentOS 6以上** + +``` +python -m pip install paddle_serving_server_gpu paddle_serving_client paddle_serving_app +``` ### 2. 模型转换 +可以使用`paddle_serving_app`提供的模型，执行下列命令 +``` +python -m paddle_serving_app.package --get_model ocr_rec +tar -xzvf ocr_rec.tar.gz +python -m paddle_serving_app.package --get_model ocr_det +tar -xzvf ocr_det.tar.gz +``` +执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。 ### 3. 启动服务 启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表： |版本|特点|适用场景| |-|-|-| -|标准版||| -|快速版||| +|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况| +|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景| #### 方式1. 启动标准版服务 +``` +python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0 +python ocr_web_server.py +``` + #### 方式2. 启动快速版服务 +``` +python ocr_local_server.py +``` ## 发送预测请求 +``` +python ocr_web_client.py +``` + ## 返回结果格式说明 +返回结果是json格式 +``` +{u'result': {u'res': [u'\u571f\u5730\u6574\u6cbb\u4e0e\u571f\u58e4\u4fee\u590d\u7814\u7a76\u4e2d\u5fc3', u'\u534e\u5357\u519c\u4e1a\u5927\u5b661\u7d20\u56fe']}} +``` +我们也可以打印结果json串中`res`字段的每一句话 +``` +土地整治与土壤修复研究中心 +华南农业大学1素图 +``` + ## 自定义修改服务逻辑 + +在`ocr_web_server.py`或是`ocr_debugger_server.py`当中的`preprocess`函数里面做了检测服务和识别服务的前处理，·`postprocess`函数里面做了识别的后处理服务，可以在相应的函数中做修改。调用了`paddle_serving_app`库提供的常见CV模型的前处理/后处理库。 + +如果想要单独启动Paddle Serving的检测服务和识别服务，参见下列表格, 执行对应的脚本即可。 + +| 模型 | 标准版 | 快速版 | +| ---- | ----------------- | ------------------- | +| 检测 | det_web_server.py | det_local_server.py | +| 识别 | rec_web_server.py | rec_local_server.py | + +更多信息参见[Paddle Serving](https://github.com/PaddlePaddle/Serving) diff --git a/deploy/pdserving/rec_local_server.py b/deploy/pdserving/rec_local_server.py new file mode 100644 index 00000000..fbe67aaf --- /dev/null +++ b/deploy/pdserving/rec_local_server.py @@ -0,0 +1,72 @@ +# 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. + +from paddle_serving_client import Client +from paddle_serving_app.reader import OCRReader +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes +from paddle_serving_server_gpu.web_service import WebService +import time +import re +import base64 + + +class OCRService(WebService): + def init_rec(self): + self.ocr_reader = OCRReader() + + def preprocess(self, feed=[], fetch=[]): + img_list = [] + for feed_data in feed: + data = base64.b64decode(feed_data["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + img_list.append(im) + max_wh_ratio = 0 + for i, boximg in enumerate(img_list): + 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((len(img_list), 3, w, h)).astype('float32') + for i, img in enumerate(img_list): + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + imgs[i] = norm_img + feed = {"image": imgs.copy()} + fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + return feed, fetch + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True) + res_lst = [] + for res in rec_res: + res_lst.append(res[0]) + res = {"res": res_lst} + return res + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_rec_model") +ocr_service.set_gpus("0") +ocr_service.init_rec() +ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0) +ocr_service.run_debugger_service() +ocr_service.run_web_service() diff --git a/deploy/pdserving/rec_web_server.py b/deploy/pdserving/rec_web_server.py new file mode 100644 index 00000000..684c313d --- /dev/null +++ b/deploy/pdserving/rec_web_server.py @@ -0,0 +1,71 @@ +# 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. + +from paddle_serving_client import Client +from paddle_serving_app.reader import OCRReader +import cv2 +import sys +import numpy as np +import os +from paddle_serving_client import Client +from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes +from paddle_serving_server_gpu.web_service import WebService +import time +import re +import base64 + + +class OCRService(WebService): + def init_rec(self): + self.ocr_reader = OCRReader() + + def preprocess(self, feed=[], fetch=[]): + # TODO: to handle batch rec images + img_list = [] + for feed_data in feed: + data = base64.b64decode(feed_data["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + img_list.append(im) + feed_list = [] + max_wh_ratio = 0 + for i, boximg in enumerate(img_list): + h, w = boximg.shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + for img in img_list: + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + feed = {"image": norm_img} + feed_list.append(feed) + fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + return feed_list, fetch + + def postprocess(self, feed={}, fetch=[], fetch_map=None): + rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True) + res_lst = [] + for res in rec_res: + res_lst.append(res[0]) + res = {"res": res_lst} + return res + + +ocr_service = OCRService(name="ocr") +ocr_service.load_model_config("ocr_rec_model") +ocr_service.set_gpus("0") +ocr_service.init_rec() +ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0) +ocr_service.run_rpc_service() +ocr_service.run_web_service() -- GitLab