# 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
import json
__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 paddle
from paddle.jit import to_static
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.utility import get_image_file_list
from ppocr.utils.visual import draw_rectangle
from tools.infer.utility import draw_boxes
import tools.program as program
import cv2
@paddle.no_grad()
def main(config, device, logger, vdl_writer):
global_config = config['Global']
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
if hasattr(post_process_class, 'character'):
config['Architecture']["Head"]['out_channels'] = len(
getattr(post_process_class, 'character'))
model = build_model(config['Architecture'])
algorithm = config['Architecture']['algorithm']
load_model(config, model)
# create data ops
transforms = []
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Encode' in op_name:
continue
if op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['image', 'shape']
transforms.append(op)
global_config['infer_mode'] = True
ops = create_operators(transforms, global_config)
save_res_path = config['Global']['save_res_path']
os.makedirs(save_res_path, exist_ok=True)
model.eval()
with open(
os.path.join(save_res_path, 'infer.txt'), mode='w',
encoding='utf-8') as f_w:
for file in get_image_file_list(config['Global']['infer_img']):
logger.info("infer_img: {}".format(file))
with open(file, 'rb') as f:
img = f.read()
data = {'image': img}
batch = transform(data, ops)
images = np.expand_dims(batch[0], axis=0)
shape_list = np.expand_dims(batch[1], axis=0)
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds, [shape_list])
structure_str_list = post_result['structure_batch_list'][0]
bbox_list = post_result['bbox_batch_list'][0]
structure_str_list = structure_str_list[0]
structure_str_list = [
'<html>', '<body>', '<table>'
] + structure_str_list + ['</table>', '</body>', '</html>']
bbox_list_str = json.dumps(bbox_list.tolist())
logger.info("result: {}, {}".format(structure_str_list,
bbox_list_str))
f_w.write("result: {}, {}\n".format(structure_str_list,
bbox_list_str))
if len(bbox_list) > 0 and len(bbox_list[0]) == 4:
img = draw_rectangle(file, bbox_list)
else:
img = draw_boxes(cv2.imread(file), bbox_list)
cv2.imwrite(
os.path.join(save_res_path, os.path.basename(file)), img)
logger.info('save result to {}'.format(save_res_path))
logger.info("success!")
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main(config, device, logger, vdl_writer)