def build_quasirandom_transforms(num_transforms, zoom_range, rotation_range, shear_range, translation_range, do_flip=True, allow_stretch=False):
gen = ghalton.Halton(7) # 7 dimensions to sample along
uniform_samples = np.array(gen.get(num_transforms))
tfs = []
for s in uniform_samples:
shift_x = icdf.uniform(s[0], *translation_range)
shift_y = icdf.uniform(s[1], *translation_range)
translation = (shift_x, shift_y)
rotation = icdf.uniform(s[2], *rotation_range)
shear = icdf.uniform(s[3], *shear_range)
if do_flip:
flip = icdf.bernoulli(s[4], p=0.5)
else:
flip = False
log_zoom_range = [np.log(z) for z in zoom_range]
if isinstance(allow_stretch, float):
log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
zoom = np.exp(icdf.uniform(s[5], *log_zoom_range))
stretch = np.exp(icdf.uniform(s[6], *log_stretch_range))
zoom_x = zoom * stretch
zoom_y = zoom / stretch
elif allow_stretch is True: # avoid bugs, f.e. when it is an integer
zoom_x = np.exp(icdf.uniform(s[5], *log_zoom_range))
zoom_y = np.exp(icdf.uniform(s[6], *log_zoom_range))
else:
zoom_x = zoom_y = np.exp(icdf.uniform(s[5], *log_zoom_range))
# the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.
tfs.append(data.build_augmentation_transform((zoom_x, zoom_y), rotation, shear, translation, flip))
return tfs
def build_quasirandom_transforms(num_transforms, zoom_range, rotation_range, shear_range, translation_range, do_flip=True, allow_stretch=False):
gen = ghalton.Halton(7) # 7 dimensions to sample along
uniform_samples = np.array(gen.get(num_transforms))
tfs = []
for s in uniform_samples:
shift_x = icdf.uniform(s[0], *translation_range)
shift_y = icdf.uniform(s[1], *translation_range)
translation = (shift_x, shift_y)
rotation = icdf.uniform(s[2], *rotation_range)
shear = icdf.uniform(s[3], *shear_range)
if do_flip:
flip = icdf.bernoulli(s[4], p=0.5)
else:
flip = False
log_zoom_range = [np.log(z) for z in zoom_range]
if isinstance(allow_stretch, float):
log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
zoom = np.exp(icdf.uniform(s[5], *log_zoom_range))
stretch = np.exp(icdf.uniform(s[6], *log_stretch_range))
zoom_x = zoom * stretch
zoom_y = zoom / stretch
elif allow_stretch is True: # avoid bugs, f.e. when it is an integer
zoom_x = np.exp(icdf.uniform(s[5], *log_zoom_range))
zoom_y = np.exp(icdf.uniform(s[6], *log_zoom_range))
else:
zoom_x = zoom_y = np.exp(icdf.uniform(s[5], *log_zoom_range))
# the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.
tfs.append(data.build_augmentation_transform((zoom_x, zoom_y), rotation, shear, translation, flip))
return tfs
def build_quasirandom_transforms(num_transforms,
color_sigma,
zoom_range,
rotation_range,
shear_range,
translation_range,
do_flip=True,
allow_stretch=False,
skip=0):
gen = ghalton.Halton(10)
uniform_samples = np.array(gen.get(num_transforms + skip))[skip:]
tfs = []
for s in uniform_samples:
rotation = uniform(s[0], *rotation_range)
shift_x = uniform(s[1], *translation_range)
shift_y = uniform(s[2], *translation_range)
translation = (shift_x, shift_y)
# setting shear last because we're not using it at the moment
shear = uniform(s[9], *shear_range)
if do_flip:
flip = bernoulli(s[8], p=0.5)
else:
flip = False
log_zoom_range = [np.log(z) for z in zoom_range]
if isinstance(allow_stretch, float):
log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
zoom = np.exp(uniform(s[6], *log_zoom_range))
stretch = np.exp(uniform(s[7], *log_stretch_range))
zoom_x = zoom * stretch
zoom_y = zoom / stretch
elif allow_stretch is True: # avoid bugs, f.e. when it is an integer
zoom_x = np.exp(uniform(s[6], *log_zoom_range))
zoom_y = np.exp(uniform(s[7], *log_zoom_range))
else:
zoom_x = zoom_y = np.exp(uniform(s[6], *log_zoom_range))
# the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.
tfs.append(
data.build_augmentation_transform((zoom_x, zoom_y), rotation,
shear, translation, flip))
color_vecs = [
normal(s[3:6], avg=0.0, std=color_sigma) for s in uniform_samples
]
return tfs, color_vecs
def build_quasirandom_transforms(
num_transforms,
color_sigma,
zoom_range,
rotation_range,
shear_range,
translation_range,
do_flip=True,
allow_stretch=False,
skip=0,
):
gen = ghalton.Halton(10)
uniform_samples = np.array(gen.get(num_transforms + skip))[skip:]
tfs = []
for s in uniform_samples:
rotation = uniform(s[0], *rotation_range)
shift_x = uniform(s[1], *translation_range)
shift_y = uniform(s[2], *translation_range)
translation = (shift_x, shift_y)
# setting shear last because we're not using it at the moment
shear = uniform(s[9], *shear_range)
if do_flip:
flip = bernoulli(s[8], p=0.5)
else:
flip = False
log_zoom_range = [np.log(z) for z in zoom_range]
if isinstance(allow_stretch, float):
log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
zoom = np.exp(uniform(s[6], *log_zoom_range))
stretch = np.exp(uniform(s[7], *log_stretch_range))
zoom_x = zoom * stretch
zoom_y = zoom / stretch
elif allow_stretch is True: # avoid bugs, f.e. when it is an integer
zoom_x = np.exp(uniform(s[6], *log_zoom_range))
zoom_y = np.exp(uniform(s[7], *log_zoom_range))
else:
zoom_x = zoom_y = np.exp(uniform(s[6], *log_zoom_range))
# the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.
tfs.append(data.build_augmentation_transform((zoom_x, zoom_y), rotation, shear, translation, flip))
color_vecs = [normal(s[3:6], avg=0.0, std=color_sigma) for s in uniform_samples]
return tfs, color_vecs
def build_transforms(**kwargs):
"""
kwargs are lists of possible values.
e.g.: build_transforms(rotation=[0, 90, 180, 270], flip=[True, False])
the names of the arguments are the same as for data.build_augmentation_transform.
"""
transforms = []
k = kwargs.keys()
combinations = list(itertools.product(*kwargs.values()))
combinations = [dict(zip(k, vals)) for vals in combinations]
for comb in combinations:
tf = data.build_augmentation_transform(**comb)
transforms.append(tf)
return transforms
def build_transforms(**kwargs):
"""
kwargs are lists of possible values.
e.g.: build_transforms(rotation=[0, 90, 180, 270], flip=[True, False])
the names of the arguments are the same as for data.build_augmentation_transform.
"""
transforms = []
k = kwargs.keys()
combinations = list(itertools.product(*kwargs.values()))
combinations = [dict(zip(k, vals)) for vals in combinations]
for comb in combinations:
tf = data.build_augmentation_transform(**comb)
transforms.append(tf)
return transforms
def tf2(img):
tf = tf1(img)
tf_center, tf_uncenter = data.build_center_uncenter_transforms(img.shape)
tf_rot = data.build_augmentation_transform(rotation=45)
tf_rot = tf_uncenter + tf_rot + tf_center
return tf + tf_rot
def tf2(img):
tf = tf1(img)
tf_center, tf_uncenter = data.build_center_uncenter_transforms(img.shape)
tf_rot = data.build_augmentation_transform(rotation=45)
tf_rot = tf_uncenter + tf_rot + tf_center
return tf + tf_rot
