152 lines
5.3 KiB
Python
152 lines
5.3 KiB
Python
# 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.visual import draw_ser_results
|
|
from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps
|
|
import tools.program as program
|
|
|
|
|
|
def to_tensor(data):
|
|
import numbers
|
|
from collections import defaultdict
|
|
data_dict = defaultdict(list)
|
|
to_tensor_idxs = []
|
|
for idx, v in enumerate(data):
|
|
if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
|
|
if idx not in to_tensor_idxs:
|
|
to_tensor_idxs.append(idx)
|
|
data_dict[idx].append(v)
|
|
for idx in to_tensor_idxs:
|
|
data_dict[idx] = paddle.to_tensor(data_dict[idx])
|
|
return list(data_dict.values())
|
|
|
|
|
|
class SerPredictor(object):
|
|
def __init__(self, config):
|
|
global_config = config['Global']
|
|
|
|
# build post process
|
|
self.post_process_class = build_post_process(config['PostProcess'],
|
|
global_config)
|
|
|
|
# build model
|
|
self.model = build_model(config['Architecture'])
|
|
|
|
load_model(
|
|
config, self.model, model_type=config['Architecture']["model_type"])
|
|
|
|
from paddleocr import PaddleOCR
|
|
|
|
self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False)
|
|
|
|
# create data ops
|
|
transforms = []
|
|
for op in config['Eval']['dataset']['transforms']:
|
|
op_name = list(op)[0]
|
|
if 'Label' in op_name:
|
|
op[op_name]['ocr_engine'] = self.ocr_engine
|
|
elif op_name == 'KeepKeys':
|
|
op[op_name]['keep_keys'] = [
|
|
'input_ids', 'labels', 'bbox', 'image', 'attention_mask',
|
|
'token_type_ids', 'segment_offset_id', 'ocr_info',
|
|
'entities'
|
|
]
|
|
|
|
transforms.append(op)
|
|
if config["Global"].get("infer_mode", None) is None:
|
|
global_config['infer_mode'] = True
|
|
self.ops = create_operators(config['Eval']['dataset']['transforms'],
|
|
global_config)
|
|
self.model.eval()
|
|
|
|
def __call__(self, data):
|
|
with open(data["img_path"], 'rb') as f:
|
|
img = f.read()
|
|
data["image"] = img
|
|
batch = transform(data, self.ops)
|
|
batch = to_tensor(batch)
|
|
preds = self.model(batch)
|
|
post_result = self.post_process_class(
|
|
preds,
|
|
attention_masks=batch[4],
|
|
segment_offset_ids=batch[6],
|
|
ocr_infos=batch[7])
|
|
return post_result, batch
|
|
|
|
|
|
if __name__ == '__main__':
|
|
config, device, logger, vdl_writer = program.preprocess()
|
|
os.makedirs(config['Global']['save_res_path'], exist_ok=True)
|
|
|
|
ser_engine = SerPredictor(config)
|
|
|
|
if config["Global"].get("infer_mode", None) is False:
|
|
data_dir = config['Eval']['dataset']['data_dir']
|
|
with open(config['Global']['infer_img'], "rb") as f:
|
|
infer_imgs = f.readlines()
|
|
else:
|
|
infer_imgs = get_image_file_list(config['Global']['infer_img'])
|
|
|
|
with open(
|
|
os.path.join(config['Global']['save_res_path'],
|
|
"infer_results.txt"),
|
|
"w",
|
|
encoding='utf-8') as fout:
|
|
for idx, info in enumerate(infer_imgs):
|
|
if config["Global"].get("infer_mode", None) is False:
|
|
data_line = info.decode('utf-8')
|
|
substr = data_line.strip("\n").split("\t")
|
|
img_path = os.path.join(data_dir, substr[0])
|
|
data = {'img_path': img_path, 'label': substr[1]}
|
|
else:
|
|
img_path = info
|
|
data = {'img_path': img_path}
|
|
|
|
save_img_path = os.path.join(
|
|
config['Global']['save_res_path'],
|
|
os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
|
|
logger.info("process: [{}/{}], save result to {}".format(
|
|
idx, len(infer_imgs), save_img_path))
|
|
|
|
result, _ = ser_engine(data)
|
|
result = result[0]
|
|
fout.write(img_path + "\t" + json.dumps(
|
|
{
|
|
"ocr_info": result,
|
|
}, ensure_ascii=False) + "\n")
|
|
img_res = draw_ser_results(img_path, result)
|
|
cv2.imwrite(save_img_path, img_res)
|