add pdserving ocr
This commit is contained in:
wangjiawei04
parent
7e1420f243
commit
d07a223df8
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# Copyright (c) 2020 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_client import Client
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import cv2
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import sys
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import numpy as np
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import os
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from paddle_serving_client import Client
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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
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from paddle_serving_server_gpu.web_service import WebService
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det(self):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), 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, feed=[], fetch=[]):
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data = base64.b64decode(feed[0]["image"].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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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 {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"]
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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det_out = fetch_map["concat_1.tmp_0"]
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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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return {"dt_boxes": dt_boxes.tolist()}
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("ocr_det_model")
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ocr_service.set_gpus("0")
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ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
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ocr_service.init_det()
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ocr_service.run_debugger_service()
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ocr_service.run_web_service()
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# Copyright (c) 2020 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_client import Client
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import cv2
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import sys
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import numpy as np
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import os
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from paddle_serving_client import Client
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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
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from paddle_serving_server_gpu.web_service import WebService
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det(self):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), 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, feed=[], fetch=[]):
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data = base64.b64decode(feed[0]["image"].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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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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print(det_img)
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return {"image": det_img}, ["concat_1.tmp_0"]
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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det_out = fetch_map["concat_1.tmp_0"]
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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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return {"dt_boxes": dt_boxes.tolist()}
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("ocr_det_model")
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ocr_service.set_gpus("0")
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ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
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ocr_service.init_det()
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ocr_service.run_rpc_service()
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ocr_service.run_web_service()
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@ -0,0 +1,103 @@
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# Copyright (c) 2020 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_client import Client
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from paddle_serving_app.reader import OCRReader
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import cv2
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import sys
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import numpy as np
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import os
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from paddle_serving_client import Client
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from paddle_serving_app.reader import Sequential, URL2Image, 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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from paddle_serving_server_gpu.web_service import WebService
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from paddle_serving_app.local_predict import Debugger
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det_debugger(self, det_model_config):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), 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.det_client = Debugger()
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self.det_client.load_model_config(
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det_model_config, gpu=True, profile=False)
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self.ocr_reader = OCRReader()
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def preprocess(self, feed=[], fetch=[]):
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data = base64.b64decode(feed[0]["image"].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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ori_h, ori_w, _ = im.shape
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det_img = self.det_preprocess(im)
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_, new_h, new_w = det_img.shape
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det_img = det_img[np.newaxis, :]
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det_img = det_img.copy()
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det_out = self.det_client.predict(
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feed={"image": det_img}, fetch=["concat_1.tmp_0"])
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filter_func = FilterBoxes(10, 10)
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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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sorted_boxes = SortedBoxes()
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ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
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dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
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dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
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dt_boxes = sorted_boxes(dt_boxes)
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get_rotate_crop_image = GetRotateCropImage()
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img_list = []
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max_wh_ratio = 0
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for i, dtbox in enumerate(dt_boxes):
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boximg = get_rotate_crop_image(im, dt_boxes[i])
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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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if len(img_list) == 0:
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return [], []
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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((len(img_list), 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 = {"image": imgs.copy()}
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fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
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return feed, fetch
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
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res_lst = []
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for res in rec_res:
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res_lst.append(res[0])
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res = {"res": res_lst}
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return res
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("ocr_rec_model")
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ocr_service.prepare_server(workdir="workdir", port=9292)
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ocr_service.init_det_debugger(det_model_config="ocr_det_model")
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ocr_service.run_debugger_service(gpu=True)
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ocr_service.run_web_service()
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# Copyright (c) 2020 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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# -*- coding: utf-8 -*-
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import requests
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import json
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import cv2
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import base64
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import os, sys
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import time
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def cv2_to_base64(image):
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#data = cv2.imencode('.jpg', image)[1]
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return base64.b64encode(image).decode(
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'utf8') #data.tostring()).decode('utf8')
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headers = {"Content-type": "application/json"}
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url = "http://127.0.0.1:9292/ocr/prediction"
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test_img_dir = "../../doc/imgs/"
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for img_file in os.listdir(test_img_dir):
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with open(os.path.join(test_img_dir, img_file), 'rb') as file:
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image_data1 = file.read()
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image = cv2_to_base64(image_data1)
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data = {"feed": [{"image": image}], "fetch": ["res"]}
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r = requests.post(url=url, headers=headers, data=json.dumps(data))
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print(r.json())
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# Copyright (c) 2020 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_client import Client
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from paddle_serving_app.reader import OCRReader
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import cv2
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import sys
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import numpy as np
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import os
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from paddle_serving_client import Client
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from paddle_serving_app.reader import Sequential, URL2Image, 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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from paddle_serving_server_gpu.web_service import WebService
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import time
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import re
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import base64
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class OCRService(WebService):
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def init_det_client(self, det_port, det_client_config):
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self.det_preprocess = Sequential([
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ResizeByFactor(32, 960), 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.det_client = Client()
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self.det_client.load_client_config(det_client_config)
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self.det_client.connect(["127.0.0.1:{}".format(det_port)])
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self.ocr_reader = OCRReader()
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def preprocess(self, feed=[], fetch=[]):
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data = base64.b64decode(feed[0]["image"].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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ori_h, ori_w, _ = im.shape
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det_img = self.det_preprocess(im)
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det_out = self.det_client.predict(
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feed={"image": det_img}, fetch=["concat_1.tmp_0"])
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_, new_h, new_w = det_img.shape
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filter_func = FilterBoxes(10, 10)
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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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sorted_boxes = SortedBoxes()
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ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
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dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
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dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
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dt_boxes = sorted_boxes(dt_boxes)
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get_rotate_crop_image = GetRotateCropImage()
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feed_list = []
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img_list = []
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max_wh_ratio = 0
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for i, dtbox in enumerate(dt_boxes):
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boximg = get_rotate_crop_image(im, dt_boxes[i])
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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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for img in img_list:
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norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
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feed = {"image": norm_img}
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feed_list.append(feed)
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fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
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return feed_list, fetch
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def postprocess(self, feed={}, fetch=[], fetch_map=None):
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rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
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res_lst = []
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for res in rec_res:
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res_lst.append(res[0])
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res = {"res": res_lst}
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return res
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ocr_service = OCRService(name="ocr")
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ocr_service.load_model_config("ocr_rec_model")
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ocr_service.set_gpus("0")
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ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
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ocr_service.init_det_client(
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det_port=9293,
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det_client_config="ocr_det_client/serving_client_conf.prototxt")
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ocr_service.run_rpc_service()
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ocr_service.run_web_service()
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@ -5,24 +5,75 @@
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## 快速启动服务
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### 1. 准备环境
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我们先安装Paddle Serving相关组件
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我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
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**CUDA版本：9.0以上**
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**CUDNN版本：7.0以上**
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**操作系统版本：CentOS 6以上**
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```
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python -m pip install paddle_serving_server_gpu paddle_serving_client paddle_serving_app
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```
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### 2. 模型转换
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可以使用`paddle_serving_app`提供的模型，执行下列命令
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```
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python -m paddle_serving_app.package --get_model ocr_rec
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tar -xzvf ocr_rec.tar.gz
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python -m paddle_serving_app.package --get_model ocr_det
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tar -xzvf ocr_det.tar.gz
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```
|
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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)
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
@ -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()
|
||||
Loading…
Reference in New Issue