def scripts_serving(file):
target_path = Path(BASE_DIR.joinpath("scripts"), file)
if target_path.exists() and not target_path.is_dir():
return send_file(target_path.open(mode="rb"), attachment_filename=target_path.name)
abort(404)
# train
train_data, train_label = preprocess(config.train_dataset, False, 2)
print('train data: %d x (%d, %d)' % train_data.shape)
print('train label: %d x (%d, %d)' % train_label.shape)
model = FSRCNN(model_dir, scale)
model.train(train_data, train_label, config.batch_size, train_step)
# test
performance = {}
for test_dataset in config.test_dataset:
test_data, test_label = load_dataset(test_dataset, scale)
result_dir = Path(f'result-{scale}-{test_dataset}')
if not result_dir.exists():
result_dir.mkdir()
perf = []
for i, (lr_img, hr_img) in enumerate(zip(test_data, test_label)):
for result in model.predict(lr_img, hr_img):
save_result(result, result_dir, i)
perf.append((i, result['cnn_psnr'], result['cnn_ssim'],
result['bi_psnr'], result['bi_ssim']))
performance[test_dataset] = perf
for test_dataset in config.test_dataset:
print(f'Performance on {test_dataset}')
print('no\tFSRCNN\tBicubic')
for p in performance[test_dataset]:
print(
def windows_path_arg(input: str):
path = WindowsPath(input)
if not path.exists():
raise argparse.ArgumentTypeError(f"'{input}' is not an existing file!")
return path
