mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
# 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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import os
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import sys
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import json
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import paddle
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from paddle.jit import to_static
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from ppocr.data import create_operators, transform
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.utils.save_load import load_model
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from ppocr.utils.utility import get_image_file_list
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import tools.program as program
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import cv2
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def main(config, device, logger, vdl_writer):
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global_config = config['Global']
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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global_config)
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# build model
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if hasattr(post_process_class, 'character'):
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config['Architecture']["Head"]['out_channels'] = len(
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getattr(post_process_class, 'character'))
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model = build_model(config['Architecture'])
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load_model(config, model)
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# create data ops
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transforms = []
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use_padding = False
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for op in config['Eval']['dataset']['transforms']:
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op_name = list(op)[0]
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if 'Label' in op_name:
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continue
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if op_name == 'KeepKeys':
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op[op_name]['keep_keys'] = ['image']
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if op_name == "ResizeTableImage":
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use_padding = True
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padding_max_len = op['ResizeTableImage']['max_len']
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transforms.append(op)
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global_config['infer_mode'] = True
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ops = create_operators(transforms, global_config)
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model.eval()
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for file in get_image_file_list(config['Global']['infer_img']):
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logger.info("infer_img: {}".format(file))
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with open(file, 'rb') as f:
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img = f.read()
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data = {'image': img}
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batch = transform(data, ops)
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images = np.expand_dims(batch[0], axis=0)
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images = paddle.to_tensor(images)
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preds = model(images)
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post_result = post_process_class(preds)
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res_html_code = post_result['res_html_code']
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res_loc = post_result['res_loc']
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img = cv2.imread(file)
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imgh, imgw = img.shape[0:2]
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res_loc_final = []
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for rno in range(len(res_loc[0])):
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x0, y0, x1, y1 = res_loc[0][rno]
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left = max(int(imgw * x0), 0)
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top = max(int(imgh * y0), 0)
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right = min(int(imgw * x1), imgw - 1)
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bottom = min(int(imgh * y1), imgh - 1)
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cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
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res_loc_final.append([left, top, right, bottom])
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res_loc_str = json.dumps(res_loc_final)
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logger.info("result: {}, {}".format(res_html_code, res_loc_final))
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logger.info("success!")
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if __name__ == '__main__':
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config, device, logger, vdl_writer = program.preprocess()
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main(config, device, logger, vdl_writer)
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