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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.insert(0, 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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from ppocr.utils.visual import draw_rectangle |
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from tools.infer.utility import draw_boxes |
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import tools.program as program |
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import cv2 |
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@paddle.no_grad() |
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def main(config, device, logger, vdl_writer): |
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global_config = config['Global'] |
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post_process_class = build_post_process(config['PostProcess'], |
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global_config) |
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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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algorithm = config['Architecture']['algorithm'] |
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load_model(config, model) |
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transforms = [] |
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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 'Encode' 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', 'shape'] |
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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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save_res_path = config['Global']['save_res_path'] |
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os.makedirs(save_res_path, exist_ok=True) |
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model.eval() |
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with open( |
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os.path.join(save_res_path, 'infer.txt'), mode='w', |
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encoding='utf-8') as f_w: |
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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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shape_list = np.expand_dims(batch[1], 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, [shape_list]) |
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structure_str_list = post_result['structure_batch_list'][0] |
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bbox_list = post_result['bbox_batch_list'][0] |
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structure_str_list = structure_str_list[0] |
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structure_str_list = [ |
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'<html>', '<body>', '<table>' |
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] + structure_str_list + ['</table>', '</body>', '</html>'] |
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bbox_list_str = json.dumps(bbox_list.tolist()) |
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logger.info("result: {}, {}".format(structure_str_list, |
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bbox_list_str)) |
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f_w.write("result: {}, {}\n".format(structure_str_list, |
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bbox_list_str)) |
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if len(bbox_list) > 0 and len(bbox_list[0]) == 4: |
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img = draw_rectangle(file, bbox_list) |
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else: |
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img = draw_boxes(cv2.imread(file), bbox_list) |
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cv2.imwrite( |
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os.path.join(save_res_path, os.path.basename(file)), img) |
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logger.info('save result to {}'.format(save_res_path)) |
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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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