Spaces:
Runtime error
Runtime error
| # 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 __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import os | |
| import sys | |
| import json | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(__dir__) | |
| sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) | |
| os.environ["FLAGS_allocator_strategy"] = 'auto_growth' | |
| import paddle | |
| from paddle.jit import to_static | |
| from ppocr.data import create_operators, transform | |
| from ppocr.modeling.architectures import build_model | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.save_load import load_model | |
| from ppocr.utils.utility import get_image_file_list | |
| from ppocr.utils.visual import draw_rectangle | |
| from tools.infer.utility import draw_boxes | |
| import tools.program as program | |
| import cv2 | |
| def main(config, device, logger, vdl_writer): | |
| global_config = config['Global'] | |
| # build post process | |
| post_process_class = build_post_process(config['PostProcess'], | |
| global_config) | |
| # build model | |
| if hasattr(post_process_class, 'character'): | |
| config['Architecture']["Head"]['out_channels'] = len( | |
| getattr(post_process_class, 'character')) | |
| model = build_model(config['Architecture']) | |
| algorithm = config['Architecture']['algorithm'] | |
| load_model(config, model) | |
| # create data ops | |
| transforms = [] | |
| for op in config['Eval']['dataset']['transforms']: | |
| op_name = list(op)[0] | |
| if 'Encode' in op_name: | |
| continue | |
| if op_name == 'KeepKeys': | |
| op[op_name]['keep_keys'] = ['image', 'shape'] | |
| transforms.append(op) | |
| global_config['infer_mode'] = True | |
| ops = create_operators(transforms, global_config) | |
| save_res_path = config['Global']['save_res_path'] | |
| os.makedirs(save_res_path, exist_ok=True) | |
| model.eval() | |
| with open( | |
| os.path.join(save_res_path, 'infer.txt'), mode='w', | |
| encoding='utf-8') as f_w: | |
| for file in get_image_file_list(config['Global']['infer_img']): | |
| logger.info("infer_img: {}".format(file)) | |
| with open(file, 'rb') as f: | |
| img = f.read() | |
| data = {'image': img} | |
| batch = transform(data, ops) | |
| images = np.expand_dims(batch[0], axis=0) | |
| shape_list = np.expand_dims(batch[1], axis=0) | |
| images = paddle.to_tensor(images) | |
| preds = model(images) | |
| post_result = post_process_class(preds, [shape_list]) | |
| structure_str_list = post_result['structure_batch_list'][0] | |
| bbox_list = post_result['bbox_batch_list'][0] | |
| structure_str_list = structure_str_list[0] | |
| structure_str_list = [ | |
| '<html>', '<body>', '<table>' | |
| ] + structure_str_list + ['</table>', '</body>', '</html>'] | |
| bbox_list_str = json.dumps(bbox_list.tolist()) | |
| logger.info("result: {}, {}".format(structure_str_list, | |
| bbox_list_str)) | |
| f_w.write("result: {}, {}\n".format(structure_str_list, | |
| bbox_list_str)) | |
| if len(bbox_list) > 0 and len(bbox_list[0]) == 4: | |
| img = draw_rectangle(file, bbox_list) | |
| else: | |
| img = draw_boxes(cv2.imread(file), bbox_list) | |
| cv2.imwrite( | |
| os.path.join(save_res_path, os.path.basename(file)), img) | |
| logger.info('save result to {}'.format(save_res_path)) | |
| logger.info("success!") | |
| if __name__ == '__main__': | |
| config, device, logger, vdl_writer = program.preprocess() | |
| main(config, device, logger, vdl_writer) | |