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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.
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']
self.algorithm = config['Architecture']["algorithm"]
# 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,
rec_model_dir=global_config.get("kie_rec_model_dir", None),
det_model_dir=global_config.get("kie_det_model_dir", None),
use_gpu=global_config['use_gpu'])
# 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', 'bbox', 'attention_mask', 'token_type_ids',
'image', 'labels', '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, 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")
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)
logger.info("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
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