# 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 @paddle.no_grad() 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 = [ '', '', '' ] + structure_str_list + ['

', '', ''] 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)