mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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135 lines
4.7 KiB
Python
Executable File
135 lines
4.7 KiB
Python
Executable File
# 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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__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 cv2
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import json
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import paddle
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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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def draw_det_res(dt_boxes, config, img, img_name, save_path):
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if len(dt_boxes) > 0:
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import cv2
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src_im = img
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for box in dt_boxes:
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box = box.astype(np.int32).reshape((-1, 1, 2))
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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save_path = os.path.join(save_path, os.path.basename(img_name))
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cv2.imwrite(save_path, src_im)
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logger.info("The detected Image saved in {}".format(save_path))
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@paddle.no_grad()
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def main():
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global_config = config['Global']
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# build model
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model = build_model(config['Architecture'])
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load_model(config, model)
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# build post process
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post_process_class = build_post_process(config['PostProcess'])
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# create data ops
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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 'Label' in op_name:
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continue
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elif 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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ops = create_operators(transforms, global_config)
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save_res_path = config['Global']['save_res_path']
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if not os.path.exists(os.path.dirname(save_res_path)):
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os.makedirs(os.path.dirname(save_res_path))
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model.eval()
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with open(save_res_path, "wb") as fout:
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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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src_img = cv2.imread(file)
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dt_boxes_json = []
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# parser boxes if post_result is dict
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if isinstance(post_result, dict):
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det_box_json = {}
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for k in post_result.keys():
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boxes = post_result[k][0]['points']
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dt_boxes_list = []
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for box in boxes:
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tmp_json = {"transcription": ""}
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tmp_json['points'] = box.tolist()
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dt_boxes_list.append(tmp_json)
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det_box_json[k] = dt_boxes_list
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save_det_path = os.path.dirname(config['Global'][
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'save_res_path']) + "/det_results_{}/".format(k)
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draw_det_res(boxes, config, src_img, file, save_det_path)
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else:
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boxes = post_result[0]['points']
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dt_boxes_json = []
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# write result
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for box in boxes:
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tmp_json = {"transcription": ""}
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tmp_json['points'] = box.tolist()
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dt_boxes_json.append(tmp_json)
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save_det_path = os.path.dirname(config['Global'][
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'save_res_path']) + "/det_results/"
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draw_det_res(boxes, config, src_img, file, save_det_path)
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otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
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fout.write(otstr.encode())
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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()
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