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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.
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 numpy as np
import time
import tools.infer.utility as utility
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppstructure.utility import parse_args
from picodet_postprocess import PicoDetPostProcess
logger = get_logger()
class LayoutPredictor(object):
def __init__(self, args):
pre_process_list = [{
'Resize': {
'size': [800, 608]
}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image']
}
}]
postprocess_params = {
'name': 'PicoDetPostProcess',
"layout_dict_path": args.layout_dict_path,
"score_threshold": args.layout_score_threshold,
"nms_threshold": args.layout_nms_threshold,
}
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'layout', logger)
def __call__(self, img):
ori_im = img.copy()
data = {'image': img}
data = transform(data, self.preprocess_op)
img = data[0]
if img is None:
return None, 0
img = np.expand_dims(img, axis=0)
img = img.copy()
preds, elapse = 0, 1
starttime = time.time()
self.input_tensor.copy_from_cpu(img)
self.predictor.run()
np_score_list, np_boxes_list = [], []
output_names = self.predictor.get_output_names()
num_outs = int(len(output_names) / 2)
for out_idx in range(num_outs):
np_score_list.append(
self.predictor.get_output_handle(output_names[out_idx])
.copy_to_cpu())
np_boxes_list.append(
self.predictor.get_output_handle(output_names[
out_idx + num_outs]).copy_to_cpu())
preds = dict(boxes=np_score_list, boxes_num=np_boxes_list)
post_preds = self.postprocess_op(ori_im, img, preds)
elapse = time.time() - starttime
return post_preds, elapse
def main(args):
image_file_list = get_image_file_list(args.image_dir)
layout_predictor = LayoutPredictor(args)
count = 0
total_time = 0
repeats = 50
for image_file in image_file_list:
img, flag, _ = check_and_read(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
layout_res, elapse = layout_predictor(img)
logger.info("result: {}".format(layout_res))
if count > 0:
total_time += elapse
count += 1
logger.info("Predict time of {}: {}".format(image_file, elapse))
if __name__ == "__main__":
main(parse_args())
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