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_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_compare_random_color_op(degrees=None, plot=False):
"""
Compare Random Color op in Python and Cpp
"""
logger.info("test_random_color_op")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Decode with rgb format set to True
data1 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
if degrees is None:
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = F.ComposeOp(
[lambda img: img.astype(np.uint8),
F.ToPIL(), p_op, np.array])
data1 = data1.map(input_columns=["image"],
operations=[vision.Decode(), c_op])
data2 = data2.map(input_columns=["image"], operations=[vision.Decode()])
data2 = data2.map(input_columns=["image"],
operations=transforms_random_color_py())
image_random_color_op = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
actual = item1["image"]
expected = item2["image"]
image_random_color_op.append(actual)
image.append(expected)
assert actual.shape == expected.shape
mse = diff_mse(actual, expected)
logger.info("MSE= {}".format(str(np.mean(mse))))
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op)
def test_invert_py_c(plot=False):
"""
Test Invert Cpp op and python op
"""
logger.info("Test Invert cpp and python op")
# Invert 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_invert = ds.map(input_columns="image", operations=C.Invert())
ds_c_invert = ds_c_invert.batch(512)
for idx, (image, _) in enumerate(ds_c_invert):
if idx == 0:
images_c_invert = image
else:
images_c_invert = np.append(images_c_invert, image, axis=0)
# invert 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_invert = F.ComposeOp(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Invert(), np.array])
ds_p_invert = ds.map(input_columns="image",
operations=transforms_p_invert())
ds_p_invert = ds_p_invert.batch(512)
for idx, (image, _) in enumerate(ds_p_invert):
if idx == 0:
images_p_invert = image
else:
images_p_invert = np.append(images_p_invert, image, axis=0)
num_samples = images_c_invert.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_invert[i], images_c_invert[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
def test_to_pil_01():
"""
Test ToPIL Op with md5 comparison: input is already PIL image
Expected to pass
"""
logger.info("test_to_pil_01")
# Generate dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
# If input is already PIL image.
py_vision.ToPIL(),
py_vision.CenterCrop(375),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"], operations=transform())
# Compare with expected md5 from images
filename = "to_pil_01_result.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
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)
