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infer_det.py
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infer_det.py
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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:https://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
from copy import deepcopy
import json
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
def set_paddle_flags(**kwargs):
for key, value in kwargs.items():
if os.environ.get(key, None) is None:
os.environ[key] = str(value)
# NOTE(paddle-dev): All of these flags should be
# set before `import paddle`. Otherwise, it would
# not take any effect.
set_paddle_flags(
FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
)
from paddle import fluid
from ppocr.utils.utility import create_module, get_image_file_list
import program
from ppocr.utils.save_load import init_model
from ppocr.data.reader_main import reader_main
import cv2
from ppocr.utils.utility import initial_logger
logger = initial_logger()
def draw_det_res(dt_boxes, config, img, img_name):
if len(dt_boxes) > 0:
import cv2
src_im = img
for box in dt_boxes:
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
if not os.path.exists(save_det_path):
os.makedirs(save_det_path)
save_path = os.path.join(save_det_path, os.path.basename(img_name))
cv2.imwrite(save_path, src_im)
logger.info("The detected Image saved in {}".format(save_path))
def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
print(config)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(use_gpu)
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
det_model = create_module(config['Architecture']['function'])(params=config)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, eval_outputs = det_model(mode="test")
fetch_name_list = list(eval_outputs.keys())
eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
eval_prog = eval_prog.clone(for_test=True)
exe.run(startup_prog)
# load checkpoints
checkpoints = config['Global'].get('checkpoints')
if checkpoints:
path = checkpoints
fluid.load(eval_prog, path, exe)
logger.info("Finish initing model from {}".format(path))
else:
raise Exception("{} not exists!".format(checkpoints))
save_res_path = config['Global']['save_res_path']
if not os.path.exists(os.path.dirname(save_res_path)):
os.makedirs(os.path.dirname(save_res_path))
with open(save_res_path, "wb") as fout:
test_reader = reader_main(config=config, mode='test')
tackling_num = 0
for data in test_reader():
img_num = len(data)
tackling_num = tackling_num + img_num
logger.info("tackling_num:%d", tackling_num)
img_list = []
ratio_list = []
img_name_list = []
for ino in range(img_num):
img_list.append(data[ino][0])
ratio_list.append(data[ino][1])
img_name_list.append(data[ino][2])
img_list = np.concatenate(img_list, axis=0)
outs = exe.run(eval_prog,\
feed={'image': img_list},\
fetch_list=eval_fetch_list)
global_params = config['Global']
postprocess_params = deepcopy(config["PostProcess"])
postprocess_params.update(global_params)
postprocess = create_module(postprocess_params['function'])\
(params=postprocess_params)
if config['Global']['algorithm'] == 'EAST':
dic = {'f_score': outs[0], 'f_geo': outs[1]}
elif config['Global']['algorithm'] == 'DB':
dic = {'maps': outs[0]}
else:
raise Exception("only support algorithm: ['EAST', 'DB']")
dt_boxes_list = postprocess(dic, ratio_list)
for ino in range(img_num):
dt_boxes = dt_boxes_list[ino]
img_name = img_name_list[ino]
dt_boxes_json = []
for box in dt_boxes:
tmp_json = {"transcription": ""}
tmp_json['points'] = box.tolist()
dt_boxes_json.append(tmp_json)
otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
src_img = cv2.imread(img_name)
draw_det_res(dt_boxes, config, src_img, img_name)
logger.info("success!")
if __name__ == '__main__':
parser = program.ArgsParser()
FLAGS = parser.parse_args()
main()