diff --git a/bob/ip/binseg/data/transforms.py b/bob/ip/binseg/data/transforms.py
index 1336ff135c89f4e33db6add57910f3894cbe5dcb..b97fe7959ba95e571462d6f6d9d2ddade9fad226 100644
--- a/bob/ip/binseg/data/transforms.py
+++ b/bob/ip/binseg/data/transforms.py
@@ -11,6 +11,7 @@ import math
from math import floor
import warnings
import collections
+import bob.core
_pil_interpolation_to_str = {
Image.NEAREST: 'PIL.Image.NEAREST',
@@ -22,7 +23,7 @@ _pil_interpolation_to_str = {
}
Iterable = collections.abc.Iterable
-# Compose
+# Compose
class Compose:
"""Composes several transforms.
@@ -62,7 +63,7 @@ class CenterCrop:
"""
def __init__(self, size):
self.size = size
-
+
def __call__(self, *args):
return [VF.center_crop(img, self.size) for img in args]
@@ -70,24 +71,24 @@ class CenterCrop:
class Crop:
"""
Crop at the given coordinates.
-
+
Attributes
----------
- i : int
+ i : int
upper pixel coordinate.
- j : int
+ j : int
left pixel coordinate.
- h : int
+ h : int
height of the cropped image.
- w : int
+ w : int
width of the cropped image.
"""
def __init__(self, i, j, h, w):
self.i = i
self.j = j
- self.h = h
- self.w = w
-
+ self.h = h
+ self.w = w
+
def __call__(self, *args):
return [img.crop((self.j, self.i, self.j + self.w, self.i + self.h)) for img in args]
@@ -97,34 +98,61 @@ class Pad:
Attributes
----------
- padding : int or tuple
- padding on each border. If a single int is provided this is used to pad all borders.
+ padding : int or tuple
+ padding on each border. If a single int is provided this is used to pad all borders.
If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively.
If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
-
+
fill : int
- pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
- This value is only used when the padding_mode is constant
+ pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ This value is only used when the padding_mode is constant
"""
def __init__(self, padding, fill=0):
self.padding = padding
self.fill = fill
-
+
def __call__(self, *args):
return [VF.pad(img, self.padding, self.fill, padding_mode='constant') for img in args]
-
+
+class AutoLevel16to8:
+ """Converts a 16-bit image to 8-bit representation using "auto-level"
+
+ This transform assumes that the input images are gray-scaled.
+
+ To auto-level, we calculate the maximum and the minimum of the image, and
+ consider such a range should be mapped to the [0,255] range of the
+ destination image.
+ """
+ def _process_one(self, img):
+ return Image.fromarray(bob.core.convert(img, 'uint8', (0,255),
+ img.getextrema()))
+
+ def __call__(self, *args):
+ return [self._process_one(img) for img in args]
+
+class ToRGB:
+ """Converts from any input format to RGB, using an ADAPTIVE conversion.
+
+ This transform takes the input image and converts it to RGB using
+ py:method:`Image.Image.convert`, with `mode='RGB'` and using all other
+ defaults. This may be aggressive if applied to 16-bit images without
+ further considerations.
+ """
+ def __call__(self, *args):
+ return [img.convert(mode="RGB") for img in args]
+
class ToTensor:
"""Converts :py:class:`PIL.Image.Image` to :py:class:`torch.Tensor` """
def __call__(self, *args):
return [VF.to_tensor(img) for img in args]
-
+
# Augmentations
class RandomHFlip:
"""
Flips horizontally
-
+
Attributes
----------
prob : float
@@ -132,50 +160,50 @@ class RandomHFlip:
"""
def __init__(self, prob = 0.5):
self.prob = prob
-
+
def __call__(self, *args):
if random.random() < self.prob:
return [VF.hflip(img) for img in args]
-
+
else:
return args
-
-
+
+
class RandomVFlip:
"""
Flips vertically
-
+
Attributes
----------
- prob : float
+ prob : float
probability at which imgage is flipped. Defaults to ``0.5``
"""
def __init__(self, prob = 0.5):
self.prob = prob
-
+
def __call__(self, *args):
if random.random() < self.prob:
return [VF.vflip(img) for img in args]
-
+
else:
return args
-
+
class RandomRotation:
"""
Rotates by degree
-
+
Attributes
----------
degree_range : tuple
range of degrees in which image and ground truth are rotated. Defaults to ``(-15, +15)``
- prob : float
+ prob : float
probability at which imgage is rotated. Defaults to ``0.5``
"""
def __init__(self, degree_range = (-15, +15), prob = 0.5):
self.prob = prob
self.degree_range = degree_range
-
+
def __call__(self, *args):
if random.random() < self.prob:
degree = random.randint(*self.degree_range)
@@ -184,21 +212,21 @@ class RandomRotation:
return args
class ColorJitter(object):
- """
+ """
Randomly change the brightness, contrast, saturation and hue
-
+
Attributes
----------
- brightness : float
+ brightness : float
how much to jitter brightness. brightness_factor
is chosen uniformly from ``[max(0, 1 - brightness), 1 + brightness]``.
contrast : float
how much to jitter contrast. contrast_factor
is chosen uniformly from ``[max(0, 1 - contrast), 1 + contrast]``.
- saturation : float
+ saturation : float
how much to jitter saturation. saturation_factor
is chosen uniformly from ``[max(0, 1 - saturation), 1 + saturation]``.
- hue : float
+ hue : float
how much to jitter hue. hue_factor is chosen uniformly from
``[-hue, hue]``. Should be >=0 and <= 0.5
prob : float
@@ -247,21 +275,21 @@ class ColorJitter(object):
class RandomResizedCrop:
"""Crop to random size and aspect ratio.
- A crop of random size of the original size and a random aspect ratio of
- the original aspect ratio is made. This crop is finally resized to
+ A crop of random size of the original size and a random aspect ratio of
+ the original aspect ratio is made. This crop is finally resized to
given size. This is popularly used to train the Inception networks.
-
+
Attributes
----------
- size : int
+ size : int
expected output size of each edge
- scale : tuple
+ scale : tuple
range of size of the origin size cropped. Defaults to ``(0.08, 1.0)``
ratio : tuple
range of aspect ratio of the origin aspect ratio cropped. Defaults to ``(3. / 4., 4. / 3.)``
interpolation :
Defaults to ``PIL.Image.BILINEAR``
- prob : float
+ prob : float
probability at which the operation is applied. Defaults to ``0.5``
"""
@@ -332,7 +360,7 @@ class RandomResizedCrop:
class Resize:
"""Resize to given size.
-
+
Attributes
----------
size : tuple or int