def test_resize_in_memory():
tmpdir = tempfile.mkdtemp()
tmp = tempfile.NamedTemporaryFile(dir=tmpdir, suffix='.JPEG')
im = Image.new('RGB', original_dimensions)
im.save(tmp.name, 'JPEG')
resize = Operations.Resize(probability=1,
width=larger_dimensions[0],
height=larger_dimensions[1],
resample_filter="BICUBIC")
im = [im]
im_resized = resize.perform_operation(im)
assert im_resized[0].size == larger_dimensions
resize_smaller = Operations.Resize(probability=1,
width=smaller_dimensions[0],
height=smaller_dimensions[1],
resample_filter="BICUBIC")
im_resized_smaller = resize_smaller.perform_operation(im)
assert im_resized_smaller[0].size == smaller_dimensions
tmp.close()
def __init__(self):
self.imgaug_transform = iaa.Scale(size=512, interpolation="linear")
self.solt_stream = slc.Stream([slt.Resize(resize_to=(512, 512))])
self.augmentor_op = Operations.Resize(probability=1,
width=512,
height=512,
resample_filter="BILINEAR")
def __init__(self):
self.augmentor_pipeline = Pipeline()
self.augmentor_pipeline.add_operation(Operations.Crop(probability=1, width=64, height=64, centre=False))
self.augmentor_pipeline.add_operation(
Operations.Resize(probability=1, width=512, height=512, resample_filter="BILINEAR")
)
self.imgaug_transform = iaa.Sequential(
[iaa.CropToFixedSize(width=64, height=64), iaa.Scale(size=512, interpolation="linear")]
)
self.solt_stream = slc.Stream(
[slt.CropTransform(crop_size=(64, 64), crop_mode="r"), slt.ResizeTransform(resize_to=(512, 512))]
)
def get_augmentation_group(data_aug_group,
input_size,
center=True,
resize=True):
resize_prob = 1 if resize else 0
center_prob = 1 if center else 0
DATA_AUGMENTATION_GROUPS = [
# GROUP 0 (NO DATA AUGMENTATION)
[
# Center crop
CropCenter(probability=center_prob),
# Resize the image to the desired input size
Operations.Resize(probability=resize_prob,
width=input_size[0],
height=input_size[1],
resample_filter="BICUBIC")
],
# GROUP 1 (Common transformations: rotations, flips, crops, shears)
[
# Rotate the image by 90 degrees randomly
Operations.Rotate(probability=0.5, rotation=-1),
# Flip top/bottom
Operations.Flip(probability=0.5,
top_bottom_left_right="TOP_BOTTOM"),
# Flip left/right
Operations.Flip(probability=0.5,
top_bottom_left_right="LEFT_RIGHT"),
# Random crops
Operations.CropPercentage(probability=0.5,
percentage_area=0.85,
centre=center,
randomise_percentage_area=True),
# Resize the image to the desired input size
Operations.Resize(probability=resize_prob,
width=input_size[0],
height=input_size[1],
resample_filter="BICUBIC")
],
# GROUP 2 (Pixel intensity transformations)
[
# Rotate the image by 90 degrees randomly
Operations.Rotate(probability=0.5, rotation=-1),
# Flip top/bottom
Operations.Flip(probability=0.5,
top_bottom_left_right="TOP_BOTTOM"),
# Flip left/right
Operations.Flip(probability=0.5,
top_bottom_left_right="LEFT_RIGHT"),
# Random change brightness of the image
Operations.RandomBrightness(probability=0.5,
min_factor=0.5,
max_factor=1.5),
# Random change saturation of the image
Operations.RandomColor(probability=0.5,
min_factor=0.5,
max_factor=1.5),
# Random change saturation of the image
Operations.RandomContrast(probability=0.5,
min_factor=0.5,
max_factor=1.5),
# Random crops
Operations.CropPercentage(probability=0.5,
percentage_area=0.85,
centre=center,
randomise_percentage_area=True),
# Resize the image to the desired input size
Operations.Resize(probability=resize_prob,
width=input_size[0],
height=input_size[1],
resample_filter="BICUBIC")
],
# GROUP 3 (Perspective transformations)
[
# Rotate the image by 90 degrees randomly
Operations.Rotate(probability=0.5, rotation=-1),
# Flip top/bottom
Operations.Flip(probability=0.5,
top_bottom_left_right="TOP_BOTTOM"),
# Flip left/right
Operations.Flip(probability=0.5,
top_bottom_left_right="LEFT_RIGHT"),
# Shear Image
Operations.Shear(probability=0.5,
max_shear_left=20,
max_shear_right=20),
# Random Tilt up down
Operations.Skew(probability=0.5,
skew_type="TILT_TOP_BOTTOM",
magnitude=1.0),
# Random Tilt left right
Operations.Skew(probability=0.5,
skew_type="TILT_LEFT_RIGHT",
magnitude=1.0),
# Random Skew CORNER
Operations.Skew(probability=0.5, skew_type="CORNER",
magnitude=1.3),
# Random crops
Operations.CropPercentage(probability=0.5,
percentage_area=0.85,
centre=center,
randomise_percentage_area=True),
# Resize the image to the desired input size
Operations.Resize(probability=resize_prob,
width=input_size[0],
height=input_size[1],
resample_filter="BICUBIC")
],
# GROUP 4 (Noise transformations)
[
# Center crop
CropCenter(probability=1),
# Rotate the image by 90 degrees randomly
Operations.Rotate(probability=0.5, rotation=-1),
# Flip top/bottom
Operations.Flip(probability=0.5,
top_bottom_left_right="TOP_BOTTOM"),
# Flip left/right
Operations.Flip(probability=0.5,
top_bottom_left_right="LEFT_RIGHT"),
# Random distortions
Operations.Distort(probability=0.5,
grid_width=8,
grid_height=8,
magnitude=15),
# Random erasing
Operations.RandomErasing(probability=0.5, rectangle_area=0.25),
# Random crops
Operations.CropPercentage(probability=0.5,
percentage_area=0.85,
centre=center,
randomise_percentage_area=True),
# Resize the image to the desired input size
Operations.Resize(probability=resize_prob,
width=input_size[0],
height=input_size[1],
resample_filter="BICUBIC")
]
]
return DATA_AUGMENTATION_GROUPS[data_aug_group]
