def test_auto_contrast_invalid_ignore_param_c():
"""
Test AutoContrast C Op with invalid ignore parameter
"""
logger.info("Test AutoContrast C Op with invalid ignore parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])
])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(ignore=255.5))
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value 255.5 is not of type" in str(error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])
])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(ignore=(10, 100)))
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value (10,100) is not of type" in str(
error)
def test_auto_contrast_invalid_cutoff_param_c():
"""
Test AutoContrast C Op with invalid cutoff parameter
"""
logger.info("Test AutoContrast C Op with invalid cutoff parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])
])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=-10.0))
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(
error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])
])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=120.0))
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(
error)
def test_auto_contrast_mnist_c(plot=False):
"""
Test AutoContrast C op with MNIST dataset (Grayscale images)
"""
logger.info("Test AutoContrast C Op With MNIST Images")
ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
ds_auto_contrast_c = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=1,
ignore=(0, 255)))
ds_orig = de.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
images = []
images_trans = []
labels = []
for _, (data_orig,
data_trans) in enumerate(zip(ds_orig, ds_auto_contrast_c)):
image_orig, label_orig = data_orig
image_trans, _ = data_trans
images.append(image_orig)
labels.append(label_orig)
images_trans.append(image_trans)
# Compare with expected md5 from images
filename = "autocontrast_mnist_result_c.npz"
save_and_check_md5(ds_auto_contrast_c,
filename,
generate_golden=GENERATE_GOLDEN)
if plot:
visualize_one_channel_dataset(images, images_trans, labels)
def test_auto_contrast_c(plot=False):
"""
Test AutoContrast C Op
"""
logger.info("Test AutoContrast C Op")
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
python_op = F.AutoContrast()
c_op = C.AutoContrast()
transforms_op = F.ComposeOp(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])()
ds_auto_contrast_py = ds.map(input_columns="image",
operations=transforms_op)
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image,
axis=0)
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op)
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image,
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i],
images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
if plot:
visualize_list(images_auto_contrast_c,
images_auto_contrast_py,
visualize_mode=2)
def test_auto_contrast_c(plot=False):
"""
Test AutoContrast C Op
"""
logger.info("Test AutoContrast C Op")
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20])
c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20])
transforms_op = F.ComposeOp(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])()
ds_auto_contrast_py = ds.map(input_columns="image",
operations=transforms_op)
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image,
axis=0)
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op)
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image,
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i],
images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
# Compare with expected md5 from images
filename = "autocontrast_01_result_c.npz"
save_and_check_md5(ds_auto_contrast_c,
filename,
generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_auto_contrast_c,
images_auto_contrast_py,
visualize_mode=2)