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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.
import os
import sys
from PIL import Image
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, __dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import numpy as np
import math
import time
import traceback
import paddle
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read
logger = get_logger()
class TextSR(object):
def __init__(self, args):
self.sr_image_shape = [int(v) for v in args.sr_image_shape.split(",")]
self.sr_batch_num = args.sr_batch_num
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'sr', logger)
self.benchmark = args.benchmark
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="sr",
model_precision=args.precision,
batch_size=args.sr_batch_num,
data_shape="dynamic",
save_path=None, #args.save_log_path,
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=0,
logger=logger)
def resize_norm_img(self, img):
imgC, imgH, imgW = self.sr_image_shape
img = img.resize((imgW // 2, imgH // 2), Image.BICUBIC)
img_numpy = np.array(img).astype("float32")
img_numpy = img_numpy.transpose((2, 0, 1)) / 255
return img_numpy
def __call__(self, img_list):
img_num = len(img_list)
batch_num = self.sr_batch_num
st = time.time()
st = time.time()
all_result = [] * img_num
if self.benchmark:
self.autolog.times.start()
for beg_img_no in range(0, img_num, batch_num):
end_img_no = min(img_num, beg_img_no + batch_num)
norm_img_batch = []
imgC, imgH, imgW = self.sr_image_shape
for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[ino])
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
if self.benchmark:
self.autolog.times.stamp()
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if len(outputs) != 1:
preds = outputs
else:
preds = outputs[0]
all_result.append(outputs)
if self.benchmark:
self.autolog.times.end(stamp=True)
return all_result, time.time() - st
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_recognizer = TextSR(args)
valid_image_file_list = []
img_list = []
# warmup 2 times
if args.warmup:
img = np.random.uniform(0, 255, [16, 64, 3]).astype(np.uint8)
for i in range(2):
res = text_recognizer([img] * int(args.sr_batch_num))
for image_file in image_file_list:
img, flag, _ = check_and_read(image_file)
if not flag:
img = Image.open(image_file).convert("RGB")
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
valid_image_file_list.append(image_file)
img_list.append(img)
try:
preds, _ = text_recognizer(img_list)
for beg_no in range(len(preds)):
sr_img = preds[beg_no][1]
lr_img = preds[beg_no][0]
for i in (range(sr_img.shape[0])):
fm_sr = (sr_img[i] * 255).transpose(1, 2, 0).astype(np.uint8)
fm_lr = (lr_img[i] * 255).transpose(1, 2, 0).astype(np.uint8)
img_name_pure = os.path.split(valid_image_file_list[
beg_no * args.sr_batch_num + i])[-1]
cv2.imwrite("infer_result/sr_{}".format(img_name_pure),
fm_sr[:, :, ::-1])
logger.info("The visualized image saved in infer_result/sr_{}".
format(img_name_pure))
except Exception as E:
logger.info(traceback.format_exc())
logger.info(E)
exit()
if args.benchmark:
text_recognizer.autolog.report()
if __name__ == "__main__":
main(utility.parse_args())
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