LDOUBLEV
5 years ago
parent
e3388a2440
commit
3f2d384faa
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shapely
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imgaug
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pyclipper
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# 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 os
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import sys
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import time
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import numpy as np
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from copy import deepcopy
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import json
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# from paddle.fluid.contrib.model_stat import summary
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def set_paddle_flags(**kwargs):
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for key, value in kwargs.items():
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if os.environ.get(key, None) is None:
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os.environ[key] = str(value)
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# NOTE(paddle-dev): All of these flags should be
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# set before `import paddle`. Otherwise, it would
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# not take any effect.
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set_paddle_flags(
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FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
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)
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from paddle import fluid
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from ppocr.utils.utility import create_module
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import program
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from ppocr.utils.save_load import init_model
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from ppocr.data.reader_main import reader_main
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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def draw_det_res(dt_boxes, config, img_name, ino):
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if len(dt_boxes) > 0:
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img_set_path = config['TestReader']['img_set_dir']
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img_path = img_set_path + img_name
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import cv2
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src_im = cv2.imread(img_path)
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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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save_det_path = os.path.basename(config['Global'][
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'save_res_path']) + "/det_results/"
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if not os.path.exists(save_det_path):
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os.makedirs(save_det_path)
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save_path = os.path.join(save_det_path, "det_{}.jpg".format(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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def main():
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config = program.load_config(FLAGS.config)
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program.merge_config(FLAGS.opt)
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print(config)
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# check if set use_gpu=True in paddlepaddle cpu version
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use_gpu = config['Global']['use_gpu']
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program.check_gpu(use_gpu)
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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det_model = create_module(config['Architecture']['function'])(params=config)
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startup_prog = fluid.Program()
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, startup_prog):
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with fluid.unique_name.guard():
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_, eval_outputs = det_model(mode="test")
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fetch_name_list = list(eval_outputs.keys())
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eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
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eval_prog = eval_prog.clone(for_test=True)
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exe.run(startup_prog)
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# load checkpoints
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checkpoints = config['Global'].get('checkpoints')
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if checkpoints:
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path = checkpoints
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fluid.load(eval_prog, path, exe)
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logger.info("Finish initing model from {}".format(path))
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else:
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raise Exception("{} not exists!".format(checkpoints))
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save_res_path = config['Global']['save_res_path']
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with open(save_res_path, "wb") as fout:
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test_reader = reader_main(config=config, mode='test')
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tackling_num = 0
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for data in test_reader():
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img_num = len(data)
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tackling_num = tackling_num + img_num
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logger.info("tackling_num:%d", tackling_num)
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img_list = []
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ratio_list = []
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img_name_list = []
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for ino in range(img_num):
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img_list.append(data[ino][0])
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ratio_list.append(data[ino][1])
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img_name_list.append(data[ino][2])
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img_list = np.concatenate(img_list, axis=0)
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outs = exe.run(eval_prog,\
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feed={'image': img_list},\
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fetch_list=eval_fetch_list)
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global_params = config['Global']
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postprocess_params = deepcopy(config["PostProcess"])
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postprocess_params.update(global_params)
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postprocess = create_module(postprocess_params['function'])\
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(params=postprocess_params)
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dt_boxes_list = postprocess(outs, ratio_list)
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for ino in range(img_num):
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dt_boxes = dt_boxes_list[ino]
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img_name = img_name_list[ino]
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dt_boxes_json = []
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for box in dt_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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otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
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fout.write(otstr.encode())
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draw_det_res(dt_boxes, config, img_name, ino)
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logger.info("success!")
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if __name__ == '__main__':
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parser = program.ArgsParser()
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FLAGS = parser.parse_args()
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main()
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