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| # 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 | |
| __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 cv2 | |
| import json | |
| import paddle | |
| 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 | |
| import tools.program as program | |
| def draw_det_res(dt_boxes, config, img, img_name, save_path): | |
| import cv2 | |
| src_im = img | |
| for box in dt_boxes: | |
| box = np.array(box).astype(np.int32).reshape((-1, 1, 2)) | |
| cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) | |
| if not os.path.exists(save_path): | |
| os.makedirs(save_path) | |
| save_path = os.path.join(save_path, os.path.basename(img_name)) | |
| cv2.imwrite(save_path, src_im) | |
| logger.info("The detected Image saved in {}".format(save_path)) | |
| def main(): | |
| global_config = config['Global'] | |
| # build model | |
| model = build_model(config['Architecture']) | |
| load_model(config, model) | |
| # build post process | |
| post_process_class = build_post_process(config['PostProcess']) | |
| # create data ops | |
| transforms = [] | |
| for op in config['Eval']['dataset']['transforms']: | |
| op_name = list(op)[0] | |
| if 'Label' in op_name: | |
| continue | |
| elif op_name == 'KeepKeys': | |
| op[op_name]['keep_keys'] = ['image', 'shape'] | |
| transforms.append(op) | |
| ops = create_operators(transforms, global_config) | |
| save_res_path = config['Global']['save_res_path'] | |
| if not os.path.exists(os.path.dirname(save_res_path)): | |
| os.makedirs(os.path.dirname(save_res_path)) | |
| model.eval() | |
| with open(save_res_path, "wb") as fout: | |
| 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) | |
| src_img = cv2.imread(file) | |
| dt_boxes_json = [] | |
| # parser boxes if post_result is dict | |
| if isinstance(post_result, dict): | |
| det_box_json = {} | |
| for k in post_result.keys(): | |
| boxes = post_result[k][0]['points'] | |
| dt_boxes_list = [] | |
| for box in boxes: | |
| tmp_json = {"transcription": ""} | |
| tmp_json['points'] = np.array(box).tolist() | |
| dt_boxes_list.append(tmp_json) | |
| det_box_json[k] = dt_boxes_list | |
| save_det_path = os.path.dirname(config['Global'][ | |
| 'save_res_path']) + "/det_results_{}/".format(k) | |
| draw_det_res(boxes, config, src_img, file, save_det_path) | |
| else: | |
| boxes = post_result[0]['points'] | |
| dt_boxes_json = [] | |
| # write result | |
| for box in boxes: | |
| tmp_json = {"transcription": ""} | |
| tmp_json['points'] = np.array(box).tolist() | |
| dt_boxes_json.append(tmp_json) | |
| save_det_path = os.path.dirname(config['Global'][ | |
| 'save_res_path']) + "/det_results/" | |
| draw_det_res(boxes, config, src_img, file, save_det_path) | |
| otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n" | |
| fout.write(otstr.encode()) | |
| logger.info("success!") | |
| if __name__ == '__main__': | |
| config, device, logger, vdl_writer = program.preprocess() | |
| main() | |