def test_random_sharpness_py(degrees=(0.7, 0.7), plot=False):
"""
Test RandomSharpness python op
"""
logger.info("Test RandomSharpness python op")
# Original Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Random Sharpness Adjusted Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
py_op = F.RandomSharpness()
if degrees is not None:
py_op = F.RandomSharpness(degrees)
transforms_random_sharpness = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)), py_op,
F.ToTensor()])
ds_random_sharpness = data.map(input_columns="image",
operations=transforms_random_sharpness())
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness):
if idx == 0:
images_random_sharpness = np.transpose(image, (0, 2, 3, 1))
else:
images_random_sharpness = np.append(images_random_sharpness,
np.transpose(
image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_sharpness)
def test_random_sharpness_md5():
"""
Test RandomSharpness with md5 comparison
"""
logger.info("Test RandomSharpness with md5 comparison")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms = [
F.Decode(),
F.RandomSharpness((0.1, 1.9)),
F.ToTensor()
]
transform = F.ComposeOp(transforms)
# Generate dataset
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
# check results with md5 comparison
filename = "random_sharpness_01_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_sharpness_c_py(degrees=(1.0, 1.0), plot=False):
"""
Test Random Sharpness C and python Op
"""
logger.info("Test RandomSharpness C and python Op")
# RandomSharpness Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((200, 300))])
python_op = F.RandomSharpness(degrees)
c_op = C.RandomSharpness(degrees)
transforms_op = F.ComposeOp(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])()
ds_random_sharpness_py = data.map(input_columns="image",
operations=transforms_op)
ds_random_sharpness_py = ds_random_sharpness_py.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness_py):
if idx == 0:
images_random_sharpness_py = image
else:
images_random_sharpness_py = np.append(images_random_sharpness_py,
image,
axis=0)
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((200, 300))])
ds_images_random_sharpness_c = data.map(input_columns="image",
operations=c_op)
ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512)
for idx, (image, _) in enumerate(ds_images_random_sharpness_c):
if idx == 0:
images_random_sharpness_c = image
else:
images_random_sharpness_c = np.append(images_random_sharpness_c,
image,
axis=0)
num_samples = images_random_sharpness_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness_c[i],
images_random_sharpness_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_random_sharpness_c,
images_random_sharpness_py,
visualize_mode=2)
def test_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test UniformAugment")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, label) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transform_list = [
F.RandomRotation(45),
F.RandomColor(),
F.RandomSharpness(),
F.Invert(),
F.AutoContrast(),
F.Equalize()
]
transforms_ua = F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.UniformAugment(transforms=transform_list, num_ops=num_ops),
F.ToTensor()
])
ds_ua = ds.map(input_columns="image", operations=transforms_ua())
ds_ua = ds_ua.batch(512)
for idx, (image, label) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image, (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_ua[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_ua)