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# Copyright (c) 2022 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 json
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.visual import draw_ser_results
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppstructure.utility import parse_args
from paddleocr import PaddleOCR
logger = get_logger()
class SerPredictor(object):
def __init__(self, args):
self.ocr_engine = PaddleOCR(
use_angle_cls=args.use_angle_cls,
det_model_dir=args.det_model_dir,
rec_model_dir=args.rec_model_dir,
show_log=False,
use_gpu=args.use_gpu)
pre_process_list = [{
'VQATokenLabelEncode': {
'algorithm': args.kie_algorithm,
'class_path': args.ser_dict_path,
'contains_re': False,
'ocr_engine': self.ocr_engine,
'order_method': args.ocr_order_method,
}
}, {
'VQATokenPad': {
'max_seq_len': 512,
'return_attention_mask': True
}
}, {
'VQASerTokenChunk': {
'max_seq_len': 512,
'return_attention_mask': True
}
}, {
'Resize': {
'size': [224, 224]
}
}, {
'NormalizeImage': {
'std': [58.395, 57.12, 57.375],
'mean': [123.675, 116.28, 103.53],
'scale': '1',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': [
'input_ids', 'bbox', 'attention_mask', 'token_type_ids',
'image', 'labels', 'segment_offset_id', 'ocr_info',
'entities'
]
}
}]
postprocess_params = {
'name': 'VQASerTokenLayoutLMPostProcess',
"class_path": args.ser_dict_path,
}
self.preprocess_op = create_operators(pre_process_list,
{'infer_mode': True})
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'ser', logger)
def __call__(self, img):
ori_im = img.copy()
data = {'image': img}
data = transform(data, self.preprocess_op)
if data[0] is None:
return None, 0
starttime = time.time()
for idx in range(len(data)):
if isinstance(data[idx], np.ndarray):
data[idx] = np.expand_dims(data[idx], axis=0)
else:
data[idx] = [data[idx]]
for idx in range(len(self.input_tensor)):
self.input_tensor[idx].copy_from_cpu(data[idx])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
post_result = self.postprocess_op(
preds, segment_offset_ids=data[6], ocr_infos=data[7])
elapse = time.time() - starttime
return post_result, data, elapse
def main(args):
image_file_list = get_image_file_list(args.image_dir)
ser_predictor = SerPredictor(args)
count = 0
total_time = 0
os.makedirs(args.output, exist_ok=True)
with open(
os.path.join(args.output, 'infer.txt'), mode='w',
encoding='utf-8') as f_w:
for image_file in image_file_list:
img, flag, _ = check_and_read(image_file)
if not flag:
img = cv2.imread(image_file)
img = img[:, :, ::-1]
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
ser_res, _, elapse = ser_predictor(img)
ser_res = ser_res[0]
res_str = '{}\t{}\n'.format(
image_file,
json.dumps(
{
"ocr_info": ser_res,
}, ensure_ascii=False))
f_w.write(res_str)
img_res = draw_ser_results(
image_file,
ser_res,
font_path=args.vis_font_path, )
img_save_path = os.path.join(args.output,
os.path.basename(image_file))
cv2.imwrite(img_save_path, img_res)
logger.info("save vis result to {}".format(img_save_path))
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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