def test_equalize(plot=False):
"""
Test Equalize
"""
logger.info("Test Equalize")
# 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)
# Color Equalized Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_equalize = F.ComposeOp(
[F.Decode(),
F.Resize((224, 224)),
F.Equalize(),
F.ToTensor()])
ds_equalize = ds.map(input_columns="image",
operations=transforms_equalize())
ds_equalize = ds_equalize.batch(512)
for idx, (image, label) in enumerate(ds_equalize):
if idx == 0:
images_equalize = np.transpose(image, (0, 2, 3, 1))
else:
images_equalize = np.append(images_equalize,
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_equalize[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_equalize)
def test_equalize_py_c(plot=False):
"""
Test Equalize Cpp op and python op
"""
logger.info("Test Equalize cpp and python op")
# equalize Images in cpp
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
ds_c_equalize = ds.map(input_columns="image", operations=C.Equalize())
ds_c_equalize = ds_c_equalize.batch(512)
for idx, (image, _) in enumerate(ds_c_equalize):
if idx == 0:
images_c_equalize = image
else:
images_c_equalize = np.append(images_c_equalize, image, axis=0)
# Equalize images in python
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
transforms_p_equalize = F.ComposeOp(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Equalize(), np.array])
ds_p_equalize = ds.map(input_columns="image",
operations=transforms_p_equalize())
ds_p_equalize = ds_p_equalize.batch(512)
for idx, (image, _) in enumerate(ds_p_equalize):
if idx == 0:
images_p_equalize = image
else:
images_p_equalize = np.append(images_p_equalize, image, axis=0)
num_samples = images_c_equalize.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
def test_equalize_md5_py():
"""
Test Equalize py op with md5 check
"""
logger.info("Test Equalize")
# First dataset
data1 = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms = F.ComposeOp([F.Decode(), F.Equalize(), F.ToTensor()])
data1 = data1.map(input_columns="image", operations=transforms())
# Compare with expected md5 from images
filename = "equalize_01_result.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
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)