def test_gray(self):
pipe_a = utp.Inputer(np.ones([1, 10, 10, 1]))
pipe_b = utp.Inputer(np.zeros([1, 10, 10, 1]))
pipe_merge = utp.Pipe([pipe_a, pipe_b])
pipe_stacker = utp.TensorStacker(pipe_merge)
stacked = next(pipe_stacker.out)
output = stacked.shape
expect = (2, 10, 10, 1)
self.assertTrue(output == expect, msg=utg.errmsg(output, expect))
def test_gray2(self):
data_file = os.path.join(TEST_DATA_PATH, 'img000000001.npy')
imgrgb = np.array(np.load(data_file))
imggray = imgrgb[:, :, 0]
imgin = np.reshape(imggray, [1, 288, 352, 1])
pipe_a = utp.Inputer(imgin)
pipe_b = utp.Inputer(imgin)
pipe_merge = utp.Pipe([pipe_a, pipe_b])
pipe_stacker = utp.TensorStacker(pipe_merge)
stacked = next(pipe_stacker.out)
output = stacked.shape
expect = (2, 288, 352, 1)
self.assertTrue(output == expect, msg=utg.errmsg(output, expect))
def test_auto_after_stacker(self):
data_file = os.path.join(TEST_DATA_PATH, 'img000000001.npy')
imgrgb = np.array(np.load(data_file))
imgin = np.reshape(imgrgb, [1, 288, 352, 3])
pipe_a = utp.Inputer(imgin)
pipe_b = utp.Inputer(imgin)
pipe_merge = utp.Pipe([pipe_a, pipe_b])
pipe_stacker = utp.TensorStacker(pipe_merge)
pipe_tensor = utp.TensorFormater(pipe_stacker)
tensor = next(pipe_tensor.out)
output = tensor.shape
expect = (2, 288, 352, 3)
self.assertTrue(output == expect, msg=utg.errmsg(output, expect))
def _prepare(self):
super(DataSetSuperResolution, self)._prepare()
if not self._is_lock:
data_filename_iter = utp.FileNameLooper(
self._path_data,
prefix=self._prefix_data,
random_shuffle=self._is_shuffle,
ids=self._ids,
max_epoch=self._epoch_max)
self._filename_iter = data_filename_iter
else:
self._filename_iter = utp.FileNameLooper(self._path_data,
prefix=self._prefix_data,
random_shuffle=False,
ids=self._ids,
max_epoch=1)
data_filename_iter = utp.Inputer()
self._filename_inputer = data_filename_iter
self._is_next_file = True
if self._is_single:
data_image = utp.NPYReaderSingle(data_filename_iter)
data_image_copyer = utp.Copyer(data_image, copy_number=2)
data_tensor = utp.TensorFormater(data_image_copyer)
label_tensor = utp.TensorFormater(data_image_copyer)
else:
data_filename_copyer = utp.Copyer(data_filename_iter,
copy_number=2)
label_filename = utp.LabelFinder(data_filename_copyer,
utg.label_name)
data_filename = utp.Pipe(data_filename_copyer)
data_image = utp.NPYReaderSingle(data_filename)
label_image = utp.NPYReaderSingle(label_filename)
data_tensor = utp.TensorFormater(data_image)
label_tensor = utp.TensorFormater(label_image)
if self._is_need_gray:
data_full = utp.ImageGrayer(data_tensor)
label_full = utp.ImageGrayer(label_tensor)
else:
data_full = data_tensor
label_full = label_tensor
merge = utp.Pipe([data_full, label_full])
stacker = utp.TensorStacker(merge)
multi_tensor = utp.TensorFormater(stacker)
stacked_shape = [2] + list(self._shape_o)
patch_generator = utp.PatchGenerator(multi_tensor,
shape=stacked_shape,
n_patches=self._n_patches,
strides=self._strides,
random_gen=self._is_shuffle,
check_all=self._is_check_all)
buffer_stacked = utp.Buffer(patch_generator)
slicer = utp.TensorSlicer(buffer_stacked, self._shape_sample_o)
buffer_hl = utp.Buffer(slicer)
self._label = utp.TensorFormater(buffer_hl)
down_sample = utp.DownSampler(buffer_hl,
self._down_sample_ratio,
method=self._down_sample_method)
self._data = utp.TensorFormater(down_sample)
self._testo = self._filename_iter
self._means = []
self._stds = []