108 lines
3.6 KiB
Python
108 lines
3.6 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
|
|
import json
|
|
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.append(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 init_model
|
|
from ppocr.utils.utility import get_image_file_list
|
|
import tools.program as program
|
|
import cv2
|
|
|
|
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'])
|
|
|
|
init_model(config, model, logger)
|
|
|
|
# create data ops
|
|
transforms = []
|
|
use_padding = False
|
|
for op in config['Eval']['dataset']['transforms']:
|
|
op_name = list(op)[0]
|
|
if 'Label' in op_name:
|
|
continue
|
|
if op_name == 'KeepKeys':
|
|
op[op_name]['keep_keys'] = ['image']
|
|
if op_name == "ResizeTableImage":
|
|
use_padding = True
|
|
padding_max_len = op['ResizeTableImage']['max_len']
|
|
transforms.append(op)
|
|
|
|
global_config['infer_mode'] = True
|
|
ops = create_operators(transforms, global_config)
|
|
|
|
model.eval()
|
|
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)
|
|
images = paddle.to_tensor(images)
|
|
preds = model(images)
|
|
post_result = post_process_class(preds)
|
|
res_html_code = post_result['res_html_code']
|
|
res_loc = post_result['res_loc']
|
|
img = cv2.imread(file)
|
|
imgh, imgw = img.shape[0:2]
|
|
res_loc_final = []
|
|
for rno in range(len(res_loc[0])):
|
|
x0, y0, x1, y1 = res_loc[0][rno]
|
|
left = max(int(imgw * x0), 0)
|
|
top = max(int(imgh * y0), 0)
|
|
right = min(int(imgw * x1), imgw - 1)
|
|
bottom = min(int(imgh * y1), imgh - 1)
|
|
cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
|
|
res_loc_final.append([left, top, right, bottom])
|
|
res_loc_str = json.dumps(res_loc_final)
|
|
logger.info("result: {}, {}".format(res_html_code, res_loc_final))
|
|
logger.info("success!")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
config, device, logger, vdl_writer = program.preprocess()
|
|
main(config, device, logger, vdl_writer)
|
|
|