MissPenguin
4 years ago
committed by
GitHub
commit
b1623d69a5
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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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if sys.argv[1] == 'gpu':
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from paddle_serving_server_gpu.web_service import WebService
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elif sys.argv[1] == 'cpu':
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from paddle_serving_server.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.init_det()
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if sys.argv[1] == 'gpu':
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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.run_debugger_service(gpu=True)
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elif sys.argv[1] == 'cpu':
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ocr_service.prepare_server(workdir="workdir", port=9292)
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ocr_service.run_debugger_service()
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ocr_service.init_det()
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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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if sys.argv[1] == 'gpu':
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from paddle_serving_server_gpu.web_service import WebService
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elif sys.argv[1] == 'cpu':
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from paddle_serving_server.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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if sys.argv[1] == 'gpu':
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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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elif sys.argv[1] == 'cpu':
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ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
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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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# 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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if sys.argv[1] == 'gpu':
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from paddle_serving_server_gpu.web_service import WebService
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elif sys.argv[1] == 'cpu':
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from paddle_serving_server.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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if sys.argv[1] == 'gpu':
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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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elif sys.argv[1] == 'cpu':
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self.det_client.load_model_config(
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det_model_config, gpu=False, 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.init_det_debugger(det_model_config="ocr_det_model")
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if sys.argv[1] == 'gpu':
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ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
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ocr_service.run_debugger_service(gpu=True)
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elif sys.argv[1] == 'cpu':
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ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
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ocr_service.run_debugger_service()
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ocr_service.run_web_service()
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@ -1,37 +0,0 @@
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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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@ -1,105 +0,0 @@
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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.
|
|
||||||
# See the License for the specific language governing permissions and
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|
||||||
# limitations under the License.
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||||||
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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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if sys.argv[1] == 'gpu':
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from paddle_serving_server_gpu.web_service import WebService
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elif sys.argv[1] == 'cpu':
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from paddle_serving_server.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):
|
|
||||||
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")
|
|
||||||
if sys.argv[1] == 'gpu':
|
|
||||||
ocr_service.set_gpus("0")
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
|
|
||||||
elif sys.argv[1] == 'cpu':
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292)
|
|
||||||
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()
|
|
||||||
@ -1,79 +0,0 @@
|
|||||||
# 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
|
|
||||||
if sys.argv[1] == 'gpu':
|
|
||||||
from paddle_serving_server_gpu.web_service import WebService
|
|
||||||
elif sys.argv[1] == 'cpu':
|
|
||||||
from paddle_serving_server.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.init_rec()
|
|
||||||
if sys.argv[1] == 'gpu':
|
|
||||||
ocr_service.set_gpus("0")
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
|
|
||||||
ocr_service.run_debugger_service(gpu=True)
|
|
||||||
elif sys.argv[1] == 'cpu':
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
|
|
||||||
ocr_service.run_debugger_service()
|
|
||||||
ocr_service.run_web_service()
|
|
||||||
@ -1,77 +0,0 @@
|
|||||||
# 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
|
|
||||||
if sys.argv[1] == 'gpu':
|
|
||||||
from paddle_serving_server_gpu.web_service import WebService
|
|
||||||
elif sys.argv[1] == 'cpu':
|
|
||||||
from paddle_serving_server.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.init_rec()
|
|
||||||
if sys.argv[1] == 'gpu':
|
|
||||||
ocr_service.set_gpus("0")
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
|
|
||||||
elif sys.argv[1] == 'cpu':
|
|
||||||
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
|
|
||||||
ocr_service.run_rpc_service()
|
|
||||||
ocr_service.run_web_service()
|
|
||||||
Loading…
Reference in new issue