def chapter_augmenters_coarsesaltandpepper():
fn_start = "arithmetic/coarsesaltandpepper"
aug = iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1))
run_and_save_augseq(fn_start + ".jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95)
aug = iaa.CoarseSaltAndPepper(0.05, size_px=(4, 16))
run_and_save_augseq(fn_start + "_pixels.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95)
aug = iaa.CoarseSaltAndPepper(0.05,
size_percent=(0.01, 0.1),
per_channel=True)
run_and_save_augseq(fn_start + "_per_channel.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95)
def augmentation_pipeline(level):
if level == 'resize_only':
list_augmentations = [iaa.Resize(512)]
elif level == 'light':
list_augmentations = [
iaa.Resize(512),
iaa.Affine(
scale=1.1,
shear=(2.5, 2.5),
rotate=(-5, 5),
),
]
elif level == 'heavy': #no rotation included
list_augmentations = [
iaa.Resize(512),
iaa.Affine(
scale=1.15,
shear=(4.0, 4.0),
),
iaa.Fliplr(0.2), # horizontally flip 20% of the images
iaa.Sometimes(
0.1,
iaa.CoarseSaltAndPepper(p=(0.01, 0.01),
size_percent=(0.1, 0.2))),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0.0, 2.0))),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=(0, 0.04 * 255))),
]
elif level == 'heavy_with_rotations':
list_augmentations = [
iaa.Resize(512),
iaa.Affine(
scale=1.15,
shear=(4.0, 4.0),
rotate=(-6, 6),
),
iaa.Fliplr(0.2), # horizontally flip 20% of the images
iaa.Sometimes(
0.1,
iaa.CoarseSaltAndPepper(p=(0.01, 0.01),
size_percent=(0.1, 0.2))),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0.0, 2.0))),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=(0, 0.04 * 255))),
]
return list_augmentations
def train(self, sess, data, labels, learning_rate):
### do hiding?
if len(self.do_hide) > 0: # do_hide is num of grid
N = np.random.choice(self.do_hide, 1)[0]
### if N == 0: use full image
if N != 0:
n, w, h, _ = data.shape
mask = net.gen_random_patch(shape=(n, w, h), N=N)
mask = np.expand_dims(mask, axis=3)
data = data * mask + (1 - mask) * self.image_mean
### do augmentation?
if self.do_augmentation == 1:
data = iaa.Sequential([
iaa.Fliplr(0.25),
iaa.Flipud(0.25),
iaa.Sometimes(0.25, iaa.Affine(rotate=(-180, 180))),
iaa.Sometimes(
0.2,
iaa.Affine(translate_percent={
'x': (-0.15, 0.15),
'y': (-0.15, 0.15)
}))
]).augment_images(data)
elif self.do_augmentation == 2:
data = iaa.Sequential([
iaa.Fliplr(0.25),
iaa.Flipud(0.25),
iaa.Sometimes(0.25, iaa.Affine(rotate=(-180, 180))),
iaa.Sometimes(
0.2,
iaa.Affine(translate_percent={
'x': (-0.1, 0.1),
'y': (-0.1, 0.1)
})),
iaa.Sometimes(
0.2,
iaa.OneOf([
iaa.CoarseDropout(0.2, size_percent=(0.05, 0.1)),
iaa.CoarseSalt(0.2, size_percent=(0.05, 0.1)),
iaa.CoarsePepper(0.2, size_percent=(0.05, 0.1)),
iaa.CoarseSaltAndPepper(0.2, size_percent=(0.05, 0.1))
]))
]).augment_images(data)
_, loss, scores, hits, summary = sess.run(
[
self.train_op, self.loss_op, self.score_op, self.hit_op,
self.summary_op
],
feed_dict={
self.inputs: data,
self.labels: labels,
self.learning_rate: learning_rate,
self.is_training: True
})
return loss, scores, hits, summary
def get_augmentation_sequence():
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential([
sometimes(iaa.Fliplr(0.5)),
iaa.Sometimes(0.1, iaa.Add((-70, 70))),
sometimes(
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
} # scale images to 80-120% of their size, individually per axis
)),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01))),
iaa.Sometimes(
0.1,
iaa.SimplexNoiseAlpha(iaa.OneOf(
[iaa.Add((150, 255)),
iaa.Add((-100, 100))]),
sigmoid_thresh=5)),
iaa.Sometimes(
0.1,
iaa.OneOf([
iaa.CoarseDropout((0.01, 0.15), size_percent=(0.02, 0.08)),
iaa.CoarseSaltAndPepper(p=0.2, size_percent=0.01),
iaa.CoarseSalt(p=0.2, size_percent=0.02)
])),
iaa.Sometimes(0.25, slice_thickness_augmenter)
])
return seq
def train_transform(image, segmentation_maps=None):
image_aug = iaa.Sequential(
[iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1))],
random_order=False)
geom_aug = iaa.Sequential([
iaa.flip.Fliplr(p=0.5),
iaa.flip.Flipud(p=0.5),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.25, 0.25),
"y": (-0.25, 0.25)
},
rotate=(-180, 180),
shear=(20, 20),
mode='reflect'),
],
random_order=False)
geom_aug_deterministic = geom_aug.to_deterministic()
image = geom_aug_deterministic.augment(image=image)
image = image_aug(image=image)
if segmentation_maps is None:
return image
segmentation_maps = geom_aug_deterministic.augment(image=segmentation_maps)
return image, segmentation_maps
def aug_image(image, is_infer=False, augment = 1):
if is_infer:
flip_code = augment
if flip_code == 1:
seq = iaa.Sequential([iaa.Fliplr(1.0)])
elif flip_code == 2:
seq = iaa.Sequential([iaa.Flipud(1.0)])
elif flip_code == 3:
seq = iaa.Sequential([iaa.Flipud(1.0),
iaa.Fliplr(1.0)])
elif flip_code ==0:
return image
else:
seq = iaa.Sequential([
iaa.Affine(rotate= (-15, 15),
shear = (-15, 15),
mode='edge'),
iaa.SomeOf((0, 2),
[
iaa.GaussianBlur((0, 1.5)),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.01 * 255), per_channel=0.5),
iaa.AddToHueAndSaturation((-5, 5)),
iaa.EdgeDetect(alpha=(0, 0.5)),
iaa.CoarseSaltAndPepper(0.2, size_percent=(0.05, 0.1)),
],
random_order=True
)
])
image = seq.augment_image(image)
return image
def logic(self, image):
for param in self.augmentation_params:
self.augmentation_data.append([
str(param.augmentation_value),
iaa.CoarseSaltAndPepper(
p=0.2, size_percent=param.augmentation_value,
min_size=2).to_deterministic().augment_image(image),
param.detection_tag
])
def heavy_augmentation():
augmenter = iaa.Sequential(
[
iaa.Sometimes(
0.1,
iaa.OneOf([
iaa.CoarseSaltAndPepper(0.02, size_percent=(0.001, 0.02)),
iaa.CoarseSaltAndPepper(
0.02, size_percent=(0.001, 0.02), per_channel=True)
])),
iaa.Sometimes(0.5, iaa.GaussianBlur(
sigma=(0, 3.0))), # blur images with a sigma of 0 to 3.0
iaa.OneOf([
iaa.Sometimes(0.33, iaa.SaltAndPepper(p=(0, 0.05))),
iaa.Sometimes(0.33,
iaa.SaltAndPepper(p=(0, 0.05), per_channel=True))
]),
iaa.Sometimes(
0.6,
iaa.OneOf([
iaa.Multiply((0.6, 1.4), per_channel=0.5),
iaa.Multiply((0.8, 1.2), per_channel=0.5),
])),
iaa.Sometimes(0.6, iaa.LinearContrast((0.6, 1.4))),
# Too slow to do all the time
iaa.Sometimes(
0.2,
iaa.CropAndPad(
percent=(-0.2, 0.2), pad_mode="constant", pad_cval=0)),
iaa.Sometimes(0.1, iaa.imgcorruptlike.ShotNoise(severity=1)),
iaa.Sometimes(
0.2,
iaa.OneOf([
iaa.imgcorruptlike.Pixelate(severity=1),
iaa.imgcorruptlike.GlassBlur(severity=1),
iaa.imgcorruptlike.ZoomBlur(severity=1),
iaa.imgcorruptlike.Fog(severity=1),
iaa.imgcorruptlike.Frost(severity=1),
iaa.imgcorruptlike.Spatter(severity=1)
])),
],
random_order=True)
return augmenter
def __getitem__(self, idx):
patient_id = self.patient_ids[idx]
if self.verbose:
print(patient_id)
img = self.load_image(patient_id)
img_source_h, img_source_w = img.shape[:2]
img_h, img_w = img.shape[:2]
if self.is_training:
cfg = utils.TransformCfg(
crop_size=self.img_size,
src_center_x=img_w / 2 + np.random.uniform(-32, 32),
src_center_y=img_h / 2 + np.random.uniform(-32, 32),
scale_x=self.img_size / img_source_w *
(2**np.random.normal(0, 0.25)),
scale_y=self.img_size / img_source_h *
(2**np.random.normal(0, 0.25)),
angle=np.random.normal(0, 8.0),
shear=np.random.normal(0, 4.0),
hflip=np.random.choice([True, False]),
vflip=False)
else:
cfg = utils.TransformCfg(crop_size=self.img_size,
src_center_x=img_w / 2,
src_center_y=img_h / 2,
scale_x=self.img_size / img_source_w,
scale_y=self.img_size / img_source_h,
angle=0,
shear=0,
hflip=False,
vflip=False)
crop = cfg.transform_image(img)
if self.is_training:
crop = np.power(crop, 2.0**np.random.normal(0, 0.2))
aug = iaa.Sequential([
iaa.Sometimes(
0.1,
iaa.CoarseSaltAndPepper(p=(0.01, 0.01),
size_percent=(0.1, 0.2))),
iaa.Sometimes(0.2, iaa.GaussianBlur(sigma=(0.0, 2.0))),
iaa.Sometimes(0.2,
iaa.AdditiveGaussianNoise(scale=(0, 0.04 * 255)))
])
crop = aug.augment_image(
np.clip(np.stack([crop, crop, crop], axis=2) * 255, 0,
255).astype(np.uint8))[:, :, 0].astype(
np.float32) / 255.0
# soft_label = 1e-4
# labels = self.patient_categories[patient_id] * (1.0 - soft_label * 2) + soft_label
labels = self.patient_categories[patient_id].astype(np.float32)
sample = {'img': crop, 'categories': labels}
return sample
def data_aug2(image):
seq = iaa.Sometimes(
0.5, iaa.Identity(),
iaa.OneOf([
iaa.CoarseDropout((0.1, 0.2), size_percent=(0.01, 0.02)),
iaa.CoarseSaltAndPepper(0.1, size_percent=(0.01, 0.02))
]))
image = seq(image=image)
return image
def setup_augmentation(self):
# Augmentation
# aug = iaa.Sequential([
# #iaa.Sometimes(0.5, iaa.PerspectiveTransform(0.05)),
# #iaa.Sometimes(0.5, iaa.CropAndPad(percent=(-0.05, 0.1))),
# #iaa.Sometimes(0.5, iaa.Affine(scale=(1.0, 1.2))),
# iaa.Sometimes(0.5, iaa.CoarseDropout( p=0.05, size_percent=0.01) ),F
# iaa.Sometimes(0.5, iaa.GaussianBlur(1.2*np.random.rand())),
# iaa.Sometimes(0.5, iaa.Add((-0.1, 0.1), per_channel=0.3)),
# iaa.Sometimes(0.3, iaa.Invert(0.2, per_channel=True)),
# iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4), per_channel=0.5)),
# iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4))),
# iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 2.2), per_channel=0.3))],
# random_order=False)
# aug = iaa.Sequential([
# #iaa.Sometimes(0.5, iaa.CoarseDropout( p=0.25, size_percent=0.02) ),
# iaa.Sometimes(0.5, iaa.GaussianBlur(1.2*np.random.rand())),
# iaa.Sometimes(0.5, iaa.Add((-60, 60), per_channel=0.3)),
# iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4), per_channel=0.5)),
# iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4))),
# iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 2.2), per_channel=0.3))],
# random_order=False)
aug = iaa.Sequential(
[
#iaa.Sometimes(0.5, PerspectiveTransform(0.05)),
#iaa.Sometimes(0.5, CropAndPad(percent=(-0.05, 0.1))),
iaa.Sometimes(0.5, iaa.Affine(scale=(1.0, 1.2))),
#iaa.Sometimes(0.5, iaa.CoarseDropout( p=0.2, size_percent=0.05) ),
iaa.Sometimes(
0.5,
iaa.SomeOf(2, [
iaa.CoarseDropout(p=0.2, size_percent=0.05),
iaa.Cutout(fill_mode="constant",
cval=(0, 255),
fill_per_channel=0.5),
iaa.Cutout(fill_mode="constant", cval=(255)),
iaa.CoarseSaltAndPepper(0.05, size_px=(4, 16)),
iaa.CoarseSalt(0.05, size_percent=(0.01, 0.1))
])),
iaa.Sometimes(0.5, iaa.GaussianBlur(1.2 * np.random.rand())),
iaa.Sometimes(0.5, iaa.Add((-25, 25), per_channel=0.3)),
iaa.Sometimes(0.3, iaa.Invert(0.2, per_channel=True)),
iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4), per_channel=0.5)),
iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4))),
iaa.Sometimes(
0.5, iaa.ContrastNormalization(
(0.5, 2.2), per_channel=0.3))
],
random_order=False)
return aug
def DA_CoarseSaltAndPepper(inputimg):
assert inputimg.ndim in [2, 3], "input invalid! Please check input"
values = np.arange(0.0, 0.21, 0.02)
p_channels = np.arange(0.0, 1.01, 0.1)
ret = []
for i in np.arange(len(values)):
for j in np.arange(len(p_channels)):
Name = "DA_CoarseSalt" + str(values[i]) + "_" + str(p_channels[j])
VALUE = str(values[i]) + "_" + str(p_channels[j])
aug_img = iaa.CoarseSalt(p=values[i],size_percent=(0.5, 1.0), per_channel=p_channels[j]).augment_image(inputimg)
ret.append((Name, VALUE, aug_img))
Name = "DA_CoarseSaltAndPepper" + str(values[i]) + "_" + str(p_channels[j])
VALUE = str(values[i]) + "_" + str(p_channels[j])
aug_img1 = iaa.CoarseSaltAndPepper(p=values[i], size_percent=(0.5, 1.0), per_channel=p_channels[j]).augment_image(inputimg)
ret.append((Name, VALUE, aug_img1))
assert len(ret) == 242, "DA_CoarseDropout output size not match!"
return ret
def __init__(self, size, train):
self.seq = iaa.Sequential([
iaa.OneOf(
[iaa.CropToFixedSize(size, size),
iaa.Resize((size, size))],
random_state=63), # end of OneOf
iaa.Fliplr(0.5),
iaa.PerspectiveTransform(0.01)
])
if train:
self.seq.append(iaa.CoarseSaltAndPepper(0.2, size_percent=0.01))
#self.seq = self.seq.to_deterministic()
self.mean = np.array([123.15163084, 115.90288257, 103.0626238],
dtype=np.float32).reshape(3, 1, 1)
def cpu_augment(self, imgs, boxes):
# for bx in boxes:
# self.assert_bboxes(bx)
ia_bb = []
for n in range(len(imgs)):
c_boxes = []
for i in boxes[n]:
try:
c_boxes.append(
ia.BoundingBox(x1=i[0], y1=i[1], x2=i[2], y2=i[3]))
except AssertionError:
print('Assertion Error: ', i)
ia_bb.append(ia.BoundingBoxesOnImage(c_boxes, shape=imgs[n].shape))
seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.AddElementwise((-20, 20), per_channel=1)),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=(0, 0.10 * 255))),
iaa.Sometimes(0.5, iaa.Multiply((0.75, 1.25), per_channel=1)),
iaa.Sometimes(0.5, iaa.MultiplyElementwise((0.75, 1.25))),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0.0, 1.0))),
iaa.Fliplr(0.5),
iaa.Sometimes(
0.95,
iaa.SomeOf(1, [
iaa.CoarseDropout(p=(0.10, 0.25),
size_percent=(0.25, 0.5)),
iaa.CoarseDropout(p=(0.0, 0.15), size_percent=(0.1, 0.25)),
iaa.Dropout(p=(0, 0.25)),
iaa.CoarseSaltAndPepper(p=(0, 0.25),
size_percent=(0.1, 0.2))
])),
iaa.Affine(scale=iap.Choice(
[iap.Uniform(0.4, 1), iap.Uniform(1, 3)]),
rotate=(-180, 180))
])
seq_det = seq.to_deterministic()
image_b_aug = seq_det.augment_images(imgs)
bbs_b_aug = seq_det.augment_bounding_boxes(ia_bb)
bbs_b_aug = [
b.remove_out_of_image().cut_out_of_image() for b in bbs_b_aug
]
return image_b_aug, [
np.array([self.bbox_r(j) for j in i.bounding_boxes])
for i in bbs_b_aug
]
def __init__(self, args, split, input_size=224, scale=0.05, shift=0.05, flip=0.5, rand_warp=False, skip_warp=0.1, warp_scale=0.05, rotate=20):
self.args = args
self.split = split
self.input_size = input_size
self.scale = scale
self.shift = shift
self.flip = flip
self.rand_warp = rand_warp
self.skip_warp = skip_warp
self.warp_scale = warp_scale
self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
self.data_rng = np.random.RandomState(123)
self.eig_val = np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32)
self.eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352], [-0.5832747, 0.00994535, -0.81221408], [-0.56089297, 0.71832671, 0.41158938]], dtype=np.float32)
self.samples = []
self.seq = iaa.Sequential([
iaa.Sometimes(0.5, [
iaa.Fliplr(flip),
iaa.Affine(scale={"x": (1.0 - shift, 1.0 + shift), "y": (1.0 - shift, 1.0 + shift)}, translate_percent={"x": (-scale, scale), "y": (-scale, scale)}, rotate=(-rotate, rotate)),
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.Sometimes(0.5, iaa.CoarseDropout(0.2, size_percent=0.1)),
iaa.Sometimes(0.5, iaa.PiecewiseAffine(scale=(0.01, warp_scale), nb_cols=3, nb_rows=3)),
iaa.Sometimes(0.5,
iaa.OneOf([
iaa.SaltAndPepper(0.1),
iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1)), ]))]),
])
self.split = ['train', 'val'] if self.split == 'all' else [self.split]
for spl in self.split:
data_path = os.path.join(args.data, spl)
for path, dir, files in os.walk(data_path):
for filename in files:
ext = os.path.splitext(filename)[-1].lower()
if ext in ('.png', '.jpg', '.jpeg'):
label_name = path.split('/')[-1]
self.samples.append((os.path.join(path, filename), int(label_name)))
print('Loaded {} total: {}'.format(self.split[0], len(self.samples)))
def get_augmentation_sequence():
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
slice_thickness_augmenter = iaa.Lambda(
func_images=slice_thickness_func_images,
func_keypoints=slice_thickness_func_keypoints)
seq = iaa.Sequential([
sometimes(iaa.Fliplr(0.5)),
iaa.Sometimes(0.1, iaa.Add((-70, 70))),
sometimes(
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
} # scale images to 80-120% of their size, individually per axis
)),
# sometimes(iaa.Multiply((0.5, 1.5))),
# sometimes(iaa.ContrastNormalization((0.5, 2.0))),
# sometimes(iaa.Affine(
# translate_percent={"x": (-0.02, 0.02), "y": (-0.02, 0.02)}, # translate by -20 to +20 percent (per axis)
# rotate=(-2, 2), # rotate by -45 to +45 degrees
# shear=(-2, 2), # shear by -16 to +16 degrees
# order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
# cval=(0, 255), # if mode is constant, use a cval between 0 and 255
# mode='constant' # use any of scikit-image's warping modes (see 2nd image from the top for examples)
# )),
# sometimes(iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05))),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01))),
iaa.Sometimes(
0.1,
iaa.SimplexNoiseAlpha(iaa.OneOf(
[iaa.Add((150, 255)),
iaa.Add((-100, 100))]),
sigmoid_thresh=5)),
iaa.Sometimes(
0.1,
iaa.OneOf([
iaa.CoarseDropout((0.01, 0.15), size_percent=(0.02, 0.08)),
iaa.CoarseSaltAndPepper(p=0.2, size_percent=0.01),
iaa.CoarseSalt(p=0.2, size_percent=0.02)
])),
iaa.Sometimes(0.25, slice_thickness_augmenter)
])
return seq
def get_augmentation_sequence():
sometimes = lambda aug: iaa.Sometimes(0.1, aug)
seq = iaa.Sequential([
iaa.Sometimes(
0.01,
iaa.OneOf([
iaa.CoarseDropout((0.01, 0.15), size_percent=(0.02, 0.08)),
iaa.CoarseSaltAndPepper(p=0.2, size_percent=0.01),
iaa.CoarseSalt(p=0.2, size_percent=0.02)
])),
iaa.Sometimes(0.2, iaa.LinearContrast((0.25, 0.8))),
iaa.Sometimes(0.2, iaa.Add((-20, 20))),
])
seq2 = iaa.Sequential([
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01), cval=0)),
iaa.Sometimes(
0.2, iaa.ElasticTransformation(alpha=(15, 25), sigma=6, cval=0))
])
return seq, seq2
def noise_tsfm(self, conf):
seq = iaa.Sequential([
iaa.OneOf(children=[
iaa.GaussianBlur((0., 1.2)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.Emboss(alpha=(0, 0.5), strength=(0, 1.0)),
iaa.ElasticTransformation(0.8),
iaa.OneOf([
iaa.CoarseSaltAndPepper(p=(0., 0.15), size_percent=0.3),
iaa.Dropout(p=0.15)
])
])
])
transform = trans.Compose([
trans.Lambda(lambda x: imgaug_on_PIL(seq, x)),
trans.RandomApply([trans.ColorJitter(0.1, 0.15, 0.15)]),
trans.RandomApply([trans.ColorJitter(hue=0.1)]),
trans.ToTensor(),
trans.Normalize(conf.mean, conf.std)
])
return transform
def augment_images(np_img_array, img_dir, img_list):
seq = iaa.Sequential(
[
iaa.Sometimes(
0.8,
iaa.CropAndPad(
percent=(0.1, 0.3),
pad_mode=["edge", "reflect"],
)),
iaa.Sometimes(
0.35,
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
0, iaa.Add((10, 50))))),
iaa.Sometimes(
0.35, iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)),
iaa.Sometimes(0.35,
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.25))),
iaa.Sometimes(
0.35,
iaa.OneOf([
iaa.CoarseDropout((0.15, 0.2),
size_percent=(0.001, 0.02),
per_channel=0.1),
iaa.CoarseSaltAndPepper(
(0.15, 0.2), size_percent=(0.001, 0.02)),
#iaa.Superpixels(p_replace=(0.15, 0.2), n_segments=(128, 256))
]))
],
random_order=True)
images_aug = seq.augment_images(np_img_array)
for image, filepath in zip(images_aug, image_list):
global image_num
image_num += 1
im = Image.fromarray(image)
new_filename = split(filepath)[-1]
new_filename.replace(image_extension, '')
new_filename = new_filename + str(image_num) + image_extension
im.save(join(image_dir, new_filename))
def grayback_gaia():
sequence = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Sometimes(
0.9,
iaa.Affine(scale=[1, 2.5],
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
rotate=(-10, 10),
order=1,
cval=(0, 0),
mode="constant")),
iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 1.5))),
iaa.Sometimes(
0.5, iaa.OneOf([iaa.Add(
(-50, 50)), iaa.Multiply((0.5, 1.5))])),
iaa.Sometimes(
0.2,
iaa.OneOf([
iaa.GaussianBlur(sigma=(0.0, 4.0)),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))
])),
iaa.Sometimes(0.1, iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255))),
iaa.Sometimes(
0.1,
iaa.OneOf([
iaa.Dropout(p=(0, 0.2)),
iaa.CoarseDropout((0.0, 0.025), size_percent=(0.08)),
iaa.SaltAndPepper(p=(0, 0.2)),
iaa.CoarseSaltAndPepper((0.0, 0.025), size_percent=(0.08))
]))
],
random_order=False)
return sequence
def simple_imgaug_example():
image_dir_path = dataset_home_dir_path + '/phenotyping/cvppp2017_lsc_lcc_challenge/package/CVPPP2017_LSC_training/training/A1'
label_dir_path = dataset_home_dir_path + '/phenotyping/cvppp2017_lsc_lcc_challenge/package/CVPPP2017_LSC_training/training/A1'
images, labels = prepare_dataset(image_dir_path, label_dir_path)
image_width, image_height = 200, 200
# FIXME [decide] >> Before or after random transformation?
# Preprocessing (normalization, standardization, etc).
images_pp = images.astype(np.float)
#images_pp /= 255.0
images_pp = standardize_samplewise(images_pp)
#images_pp = standardize_featurewise(images_pp)
if True:
augmenter = iaa.SomeOf(
(1, 2),
[
iaa.OneOf([
iaa.Affine(
scale={
'x': (0.8, 1.2),
'y': (0.8, 1.2)
}, # Scale images to 80-120% of their size, individually per axis.
translate_percent={
'x': (-0.1, 0.1),
'y': (-0.1, 0.1)
}, # Translate by -10 to +10 percent (per axis).
rotate=(-10, 10), # Rotate by -10 to +10 degrees.
shear=(-5, 5), # Shear by -5 to +5 degrees.
#order=[0, 1], # Use nearest neighbour or bilinear interpolation (fast).
order=
0, # Use nearest neighbour or bilinear interpolation (fast).
#cval=(0, 255), # If mode is constant, use a cval between 0 and 255.
#mode=ia.ALL # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
#mode='edge' # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
),
#iaa.PiecewiseAffine(scale=(0.01, 0.05)), # Move parts of the image around. Slow.
iaa.PerspectiveTransform(scale=(0.01, 0.1)),
iaa.ElasticTransformation(
alpha=(20.0, 50.0), sigma=(6.5, 8.5)
), # Move pixels locally around (with random strengths).
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(
0,
3.0)), # Blur images with a sigma between 0 and 3.0.
iaa.AverageBlur(
k=(2, 7)
), # Blur image using local means with kernel sizes between 2 and 7.
iaa.MedianBlur(
k=(3, 11)
), # Blur image using local medians with kernel sizes between 2 and 7.
iaa.MotionBlur(k=(5, 11),
angle=(0, 360),
direction=(-1.0, 1.0),
order=1),
]),
iaa.OneOf([
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.1 * 255, 0.5 * 255),
per_channel=False), # Add Gaussian noise to images.
iaa.AdditiveLaplaceNoise(loc=0,
scale=(0.1 * 255, 0.4 * 255),
per_channel=False),
iaa.AdditivePoissonNoise(lam=(32, 96), per_channel=False),
iaa.CoarseSaltAndPepper(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarseSalt(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarsePepper(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarseDropout(p=(0.1, 0.3),
size_percent=(0.05, 0.3),
per_channel=False),
]),
iaa.OneOf([
iaa.MultiplyHueAndSaturation(mul=(-10, 10),
per_channel=False),
iaa.AddToHueAndSaturation(value=(-255, 255),
per_channel=False),
iaa.LinearContrast(
alpha=(0.5, 1.5),
per_channel=False), # Improve or worsen the contrast.
iaa.Invert(p=1,
per_channel=False), # Invert color channels.
iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75, 1.5)), # Sharpen images.
iaa.Emboss(alpha=(0, 1.0),
strength=(0, 2.0)), # Emboss images.
]),
],
random_order=True)
elif False:
augmenter = iaa.Sequential(
[
# Apply the following augmenters to most images.
iaa.Fliplr(0.5), # Horizontally flip 50% of all images.
iaa.Flipud(0.2), # Vertically flip 20% of all images.
# Crop images by -5% to 10% of their height/width.
iaa.Sometimes(
0.5,
iaa.CropAndPad(percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255))),
iaa.Sometimes(
0.5,
iaa.Affine(
scale={
'x': (0.8, 1.2),
'y': (0.8, 1.2)
}, # Scale images to 80-120% of their size, individually per axis.
translate_percent={
'x': (-0.2, 0.2),
'y': (-0.2, 0.2)
}, # Translate by -20 to +20 percent (per axis).
rotate=(-45, 45), # Rotate by -45 to +45 degrees.
shear=(-16, 16), # Shear by -16 to +16 degrees.
order=[
0,
1
], # Use nearest neighbour or bilinear interpolation (fast).
cval=(
0, 255
), # If mode is constant, use a cval between 0 and 255.
mode=ia.
ALL # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
)),
# Execute 0 to 5 of the following (less important) augmenters per image.
# Don't execute all of them, as that would often be way too strong.
iaa.SomeOf(
(0, 5),
[
iaa.Sometimes(
0.5,
iaa.Superpixels(p_replace=(0, 1.0),
n_segments=(20, 200))
), # Convert images into their superpixel representation.
iaa.OneOf([
iaa.GaussianBlur(
(0, 3.0)
), # Blur images with a sigma between 0 and 3.0.
iaa.AverageBlur(
k=(2, 7)
), # Blur image using local means with kernel sizes between 2 and 7.
iaa.MedianBlur(
k=(3, 11)
), # Blur image using local medians with kernel sizes between 2 and 7.
]),
iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75, 1.5)), # Sharpen images.
iaa.Emboss(alpha=(0, 1.0),
strength=(0, 2.0)), # Emboss images.
# Search either for all edges or for directed edges, blend the result with the original image using a blobby mask.
iaa.SimplexNoiseAlpha(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0),
direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255),
per_channel=0.5), # Add gaussian noise to images.
iaa.OneOf([
iaa.Dropout(
(0.01, 0.1), per_channel=0.5
), # Randomly remove up to 10% of the pixels.
iaa.CoarseDropout((0.03, 0.15),
size_percent=(0.02, 0.05),
per_channel=0.2),
]),
iaa.Invert(0.05,
per_channel=True), # Invert color channels.
iaa.Add(
(-10, 10), per_channel=0.5
), # Change brightness of images (by -10 to 10 of original value).
iaa.AddToHueAndSaturation(
(-20, 20)), # Change hue and saturation.
# Either change the brightness of the whole image (sometimes per channel) or change the brightness of subareas.
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply(
(0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0)))
]),
iaa.ContrastNormalization(
(0.5, 2.0), per_channel=0.5
), # Improve or worsen the contrast.
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.Sometimes(
0.5,
iaa.ElasticTransformation(alpha=(0.5, 3.5),
sigma=0.25)
), # Move pixels locally around (with random strengths).
iaa.Sometimes(
0.5, iaa.PiecewiseAffine(scale=(0.01, 0.05))
), # Sometimes move parts of the image around.
iaa.Sometimes(
0.5, iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
else:
augmenter = iaa.Sequential([
iaa.SomeOf(
1,
[
#iaa.Sometimes(0.5, iaa.Crop(px=(0, 100))), # Crop images from each side by 0 to 16px (randomly chosen).
iaa.Sometimes(0.5, iaa.Crop(percent=(
0,
0.1))), # Crop images by 0-10% of their height/width.
iaa.Fliplr(0.5), # Horizontally flip 50% of the images.
iaa.Flipud(0.5), # Vertically flip 50% of the images.
iaa.Sometimes(
0.5,
iaa.Affine(
scale={
'x': (0.8, 1.2),
'y': (0.8, 1.2)
}, # Scale images to 80-120% of their size, individually per axis.
translate_percent={
'x': (-0.2, 0.2),
'y': (-0.2, 0.2)
}, # Translate by -20 to +20 percent (per axis).
rotate=(-45, 45), # Rotate by -45 to +45 degrees.
shear=(-16, 16), # Shear by -16 to +16 degrees.
#order=[0, 1], # Use nearest neighbour or bilinear interpolation (fast).
order=
0, # Use nearest neighbour or bilinear interpolation (fast).
#cval=(0, 255), # If mode is constant, use a cval between 0 and 255.
#mode=ia.ALL # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
#mode='edge' # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
)),
iaa.Sometimes(0.5, iaa.GaussianBlur(
sigma=(0,
3.0))) # Blur images with a sigma of 0 to 3.0.
]),
iaa.Scale(size={
'height': image_height,
'width': image_width
}) # Resize.
])
for idx in range(images.shape[0]):
images_pp[idx] = (images_pp[idx] - np.min(images_pp[idx])) / (
np.max(images_pp[idx]) - np.min(images_pp[idx])) * 255
images_pp = images_pp.astype(np.uint8)
# Test 1 (good).
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug1 = augmenter_det.augment_images(images)
images_aug1 = augmenter_det.augment_images(images_pp)
labels_aug1 = augmenter_det.augment_images(labels)
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug2 = augmenter_det.augment_images(images)
images_aug2 = augmenter_det.augment_images(images_pp)
labels_aug2 = augmenter_det.augment_images(labels)
#export_images(images, labels, './augmented1/img', '')
export_images(images_pp, labels, './augmented1/img', '')
export_images(images_aug1, labels_aug1, './augmented1/img', '_aug1')
export_images(images_aug2, labels_aug2, './augmented1/img', '_aug2')
# Test 2 (bad).
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug1 = augmenter_det.augment_images(images)
images_aug1 = augmenter_det.augment_images(images_pp)
labels_aug1 = augmenter_det.augment_images(labels)
#images_aug2 = augmenter_det.augment_images(images)
images_aug2 = augmenter_det.augment_images(images_pp)
labels_aug2 = augmenter_det.augment_images(labels)
#export_images(images, labels, './augmented2/img', '')
export_images(images_pp, labels, './augmented2/img', '')
export_images(images_aug1, labels_aug1, './augmented2/img', '_aug1')
export_images(images_aug2, labels_aug2, './augmented2/img', '_aug2')
print('*********************************', images_pp.dtype)
def draw_per_augmenter_images():
print("[draw_per_augmenter_images] Loading image...")
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
keypoints = [ia.Keypoint(x=34, y=15), ia.Keypoint(x=85, y=13), ia.Keypoint(x=63, y=73)] # left ear, right ear, mouth
keypoints = [ia.KeypointsOnImage(keypoints, shape=image.shape)]
print("[draw_per_augmenter_images] Initializing...")
rows_augmenters = [
(0, "Noop", [("", iaa.Noop()) for _ in sm.xrange(5)]),
(0, "Crop\n(top, right,\nbottom, left)", [(str(vals), iaa.Crop(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Pad\n(top, right,\nbottom, left)", [(str(vals), iaa.Pad(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Fliplr", [(str(p), iaa.Fliplr(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Flipud", [(str(p), iaa.Flipud(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Superpixels\np_replace=1", [("n_segments=%d" % (n_segments,), iaa.Superpixels(p_replace=1.0, n_segments=n_segments)) for n_segments in [25, 50, 75, 100, 125]]),
(0, "Superpixels\nn_segments=100", [("p_replace=%.2f" % (p_replace,), iaa.Superpixels(p_replace=p_replace, n_segments=100)) for p_replace in [0, 0.25, 0.5, 0.75, 1.0]]),
(0, "Invert", [("p=%d" % (p,), iaa.Invert(p=p)) for p in [0, 0, 1, 1, 1]]),
(0, "Invert\n(per_channel)", [("p=%.2f" % (p,), iaa.Invert(p=p, per_channel=True)) for p in [0.5, 0.5, 0.5, 0.5, 0.5]]),
(0, "Add", [("value=%d" % (val,), iaa.Add(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Add\n(per channel)", [("value=(%d, %d)" % (vals[0], vals[1],), iaa.Add(vals, per_channel=True)) for vals in [(-55, -35), (-35, -15), (-10, 10), (15, 35), (35, 55)]]),
(0, "AddToHueAndSaturation", [("value=%d" % (val,), iaa.AddToHueAndSaturation(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Multiply", [("value=%.2f" % (val,), iaa.Multiply(val)) for val in [0.25, 0.5, 1.0, 1.25, 1.5]]),
(1, "Multiply\n(per channel)", [("value=(%.2f, %.2f)" % (vals[0], vals[1],), iaa.Multiply(vals, per_channel=True)) for vals in [(0.15, 0.35), (0.4, 0.6), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "GaussianBlur", [("sigma=%.2f" % (sigma,), iaa.GaussianBlur(sigma=sigma)) for sigma in [0.25, 0.50, 1.0, 2.0, 4.0]]),
(0, "AverageBlur", [("k=%d" % (k,), iaa.AverageBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "MedianBlur", [("k=%d" % (k,), iaa.MedianBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "BilateralBlur\nsigma_color=250,\nsigma_space=250", [("d=%d" % (d,), iaa.BilateralBlur(d=d, sigma_color=250, sigma_space=250)) for d in [1, 3, 5, 7, 9]]),
(0, "Sharpen\n(alpha=1)", [("lightness=%.2f" % (lightness,), iaa.Sharpen(alpha=1, lightness=lightness)) for lightness in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "Emboss\n(alpha=1)", [("strength=%.2f" % (strength,), iaa.Emboss(alpha=1, strength=strength)) for strength in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "EdgeDetect", [("alpha=%.2f" % (alpha,), iaa.EdgeDetect(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(0, "DirectedEdgeDetect\n(alpha=1)", [("direction=%.2f" % (direction,), iaa.DirectedEdgeDetect(alpha=1, direction=direction)) for direction in [0.0, 1*(360/5)/360, 2*(360/5)/360, 3*(360/5)/360, 4*(360/5)/360]]),
(0, "AdditiveGaussianNoise", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "AdditiveGaussianNoise\n(per channel)", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255, per_channel=True)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "Dropout", [("p=%.2f" % (p,), iaa.Dropout(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Dropout\n(per channel)", [("p=%.2f" % (p,), iaa.Dropout(p=p, per_channel=True)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(3, "CoarseDropout\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseDropout\n(p=0.2, per channel)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, per_channel=True, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "SaltAndPepper", [("p=%.2f" % (p,), iaa.SaltAndPepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Salt", [("p=%.2f" % (p,), iaa.Salt(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Pepper", [("p=%.2f" % (p,), iaa.Pepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "CoarseSaltAndPepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSaltAndPepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseSalt\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSalt(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarsePepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarsePepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "ContrastNormalization", [("alpha=%.1f" % (alpha,), iaa.ContrastNormalization(alpha=alpha)) for alpha in [0.5, 0.75, 1.0, 1.25, 1.50]]),
(0, "ContrastNormalization\n(per channel)", [("alpha=(%.2f, %.2f)" % (alphas[0], alphas[1],), iaa.ContrastNormalization(alpha=alphas, per_channel=True)) for alphas in [(0.4, 0.6), (0.65, 0.85), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "Grayscale", [("alpha=%.1f" % (alpha,), iaa.Grayscale(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(6, "PerspectiveTransform", [("scale=%.3f" % (scale,), iaa.PerspectiveTransform(scale=scale)) for scale in [0.025, 0.05, 0.075, 0.10, 0.125]]),
(0, "PiecewiseAffine", [("scale=%.3f" % (scale,), iaa.PiecewiseAffine(scale=scale)) for scale in [0.015, 0.03, 0.045, 0.06, 0.075]]),
(0, "Affine: Scale", [("%.1fx" % (scale,), iaa.Affine(scale=scale)) for scale in [0.1, 0.5, 1.0, 1.5, 1.9]]),
(0, "Affine: Translate", [("x=%d y=%d" % (x, y), iaa.Affine(translate_px={"x": x, "y": y})) for x, y in [(-32, -16), (-16, -32), (-16, -8), (16, 8), (16, 32)]]),
(0, "Affine: Rotate", [("%d deg" % (rotate,), iaa.Affine(rotate=rotate)) for rotate in [-90, -45, 0, 45, 90]]),
(0, "Affine: Shear", [("%d deg" % (shear,), iaa.Affine(shear=shear)) for shear in [-45, -25, 0, 25, 45]]),
(0, "Affine: Modes", [(mode, iaa.Affine(translate_px=-32, mode=mode)) for mode in ["constant", "edge", "symmetric", "reflect", "wrap"]]),
(0, "Affine: cval", [("%d" % (int(cval*255),), iaa.Affine(translate_px=-32, cval=int(cval*255), mode="constant")) for cval in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(
2, "Affine: all", [
(
"",
iaa.Affine(
scale={"x": (0.5, 1.5), "y": (0.5, 1.5)},
translate_px={"x": (-32, 32), "y": (-32, 32)},
rotate=(-45, 45),
shear=(-32, 32),
mode=ia.ALL,
cval=(0.0, 1.0)
)
)
for _ in sm.xrange(5)
]
),
(1, "ElasticTransformation\n(sigma=0.2)", [("alpha=%.1f" % (alpha,), iaa.ElasticTransformation(alpha=alpha, sigma=0.2)) for alpha in [0.1, 0.5, 1.0, 3.0, 9.0]]),
(0, "Alpha\nwith EdgeDetect(1.0)", [("factor=%.1f" % (factor,), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0))) for factor in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(4, "Alpha\nwith EdgeDetect(1.0)\n(per channel)", [("factor=(%.2f, %.2f)" % (factor[0], factor[1]), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0), per_channel=0.5)) for factor in [(0.0, 0.2), (0.15, 0.35), (0.4, 0.6), (0.65, 0.85), (0.8, 1.0)]]),
(15, "SimplexNoiseAlpha\nwith EdgeDetect(1.0)", [("", iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0))) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(9, "FrequencyNoiseAlpha\nwith EdgeDetect(1.0)", [("exponent=%.1f" % (exponent,), iaa.FrequencyNoiseAlpha(exponent=exponent, first=iaa.EdgeDetect(1.0), size_px_max=16, upscale_method="linear", sigmoid=False)) for exponent in [-4, -2, 0, 2, 4]])
]
print("[draw_per_augmenter_images] Augmenting...")
rows = []
for (row_seed, row_name, augmenters) in rows_augmenters:
ia.seed(row_seed)
#for img_title, augmenter in augmenters:
# #aug.reseed(1000)
# pass
row_images = []
row_keypoints = []
row_titles = []
for img_title, augmenter in augmenters:
aug_det = augmenter.to_deterministic()
row_images.append(aug_det.augment_image(image))
row_keypoints.append(aug_det.augment_keypoints(keypoints)[0])
row_titles.append(img_title)
rows.append((row_name, row_images, row_keypoints, row_titles))
# matplotlib drawin routine
"""
print("[draw_per_augmenter_images] Plotting...")
width = 8
height = int(1.5 * len(rows_augmenters))
fig = plt.figure(figsize=(width, height))
grid_rows = len(rows)
grid_cols = 1 + 5
gs = gridspec.GridSpec(grid_rows, grid_cols, width_ratios=[2, 1, 1, 1, 1, 1])
axes = []
for i in sm.xrange(grid_rows):
axes.append([plt.subplot(gs[i, col_idx]) for col_idx in sm.xrange(grid_cols)])
fig.tight_layout()
#fig.subplots_adjust(bottom=0.2 / grid_rows, hspace=0.22)
#fig.subplots_adjust(wspace=0.005, hspace=0.425, bottom=0.02)
fig.subplots_adjust(wspace=0.005, hspace=0.005, bottom=0.02)
for row_idx, (row_name, row_images, row_keypoints, row_titles) in enumerate(rows):
axes_row = axes[row_idx]
for col_idx in sm.xrange(grid_cols):
ax = axes_row[col_idx]
ax.cla()
ax.axis("off")
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if col_idx == 0:
ax.text(0, 0.5, row_name, color="black")
else:
cell_image = row_images[col_idx-1]
cell_keypoints = row_keypoints[col_idx-1]
cell_image_kp = cell_keypoints.draw_on_image(cell_image, size=5)
ax.imshow(cell_image_kp)
x = 0
y = 145
#ax.text(x, y, row_titles[col_idx-1], color="black", backgroundcolor="white", fontsize=6)
ax.text(x, y, row_titles[col_idx-1], color="black", fontsize=7)
fig.savefig("examples.jpg", bbox_inches="tight")
#plt.show()
"""
# simpler and faster drawing routine
"""
output_image = ExamplesImage(128, 128, 128+64, 32)
for (row_name, row_images, row_keypoints, row_titles) in rows:
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
misc.imsave("examples.jpg", output_image.draw())
"""
# routine to draw many single files
seen = defaultdict(lambda: 0)
markups = []
for (row_name, row_images, row_keypoints, row_titles) in rows:
output_image = ExamplesImage(128, 128, 128+64, 32)
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
if "\n" in row_name:
row_name_clean = row_name[0:row_name.find("\n")+1]
else:
row_name_clean = row_name
row_name_clean = re.sub(r"[^a-z0-9]+", "_", row_name_clean.lower())
row_name_clean = row_name_clean.strip("_")
if seen[row_name_clean] > 0:
row_name_clean = "%s_%d" % (row_name_clean, seen[row_name_clean] + 1)
fp = os.path.join(IMAGES_DIR, "examples_%s.jpg" % (row_name_clean,))
#misc.imsave(fp, output_image.draw())
save(fp, output_image.draw())
seen[row_name_clean] += 1
markup_descr = row_name.replace('"', '') \
.replace("\n", " ") \
.replace("(", "") \
.replace(")", "")
markup = '' % (markup_descr, fp, markup_descr)
markups.append(markup)
for markup in markups:
print(markup)
def __getitem__(self, idx):
patient_id = self.patient_ids[idx]
img = self.load_image(patient_id)
if self.crop_source != 1024:
img_source_w = self.crop_source
img_source_h = self.crop_source
else:
img_source_h, img_source_w = img.shape[:2]
img_h, img_w = img.shape[:2]
augmentation_sigma = {
10:
dict(scale=0.1, angle=5.0, shear=2.5, gamma=0.2, hflip=False),
15:
dict(scale=0.15,
angle=6.0,
shear=4.0,
gamma=0.2,
hflip=np.random.choice([True, False])),
20:
dict(scale=0.15,
angle=6.0,
shear=4.0,
gamma=0.25,
hflip=np.random.choice([True, False])),
}[self.augmentation_level]
if self.is_training:
cfg = utils.TransformCfg(
crop_size=self.img_size,
src_center_x=img_w / 2 + np.random.uniform(-32, 32),
src_center_y=img_h / 2 + np.random.uniform(-32, 32),
scale_x=self.img_size / img_source_w *
(2**np.random.normal(0, augmentation_sigma['scale'])),
scale_y=self.img_size / img_source_h *
(2**np.random.normal(0, augmentation_sigma['scale'])),
angle=np.random.normal(0, augmentation_sigma['angle']),
shear=np.random.normal(0, augmentation_sigma['shear']),
hflip=augmentation_sigma['hflip'],
vflip=False)
else:
cfg = utils.TransformCfg(crop_size=self.img_size,
src_center_x=img_w / 2,
src_center_y=img_h / 2,
scale_x=self.img_size / img_source_w,
scale_y=self.img_size / img_source_h,
angle=0,
shear=0,
hflip=False,
vflip=False)
crop = cfg.transform_image(img)
if self.is_training:
crop = np.power(
crop, 2.0**np.random.normal(0, augmentation_sigma['gamma']))
if self.augmentation_level == 20:
aug = iaa.Sequential([
iaa.Sometimes(
0.1,
iaa.CoarseSaltAndPepper(p=(0.01, 0.01),
size_percent=(0.1, 0.2))),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0.0, 2.0))),
iaa.Sometimes(
0.5, iaa.AdditiveGaussianNoise(scale=(0, 0.04 * 255)))
])
crop = aug.augment_image(
np.clip(
np.stack([crop, crop, crop], axis=2) * 255, 0,
255).astype(np.uint8))[:, :, 0].astype(
np.float32) / 255.0
if self.augmentation_level == 15:
aug = iaa.Sequential([
iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0.0, 1.0))),
iaa.Sometimes(
0.25, iaa.AdditiveGaussianNoise(scale=(0, 0.02 * 255)))
])
crop = aug.augment_image(
np.clip(
np.stack([crop, crop, crop], axis=2) * 255, 0,
255).astype(np.uint8))[:, :, 0].astype(
np.float32) / 255.0
annotations = []
# print('patient_id', patient_id)
for annotation in self.annotations[patient_id]:
points = cfg.transform().inverse(annotation)
res = np.zeros((1, 5))
p0 = np.min(points, axis=0)
p1 = np.max(points, axis=0)
res[0, 0:2] = p0
res[0, 2:4] = p1
res[0, 4] = 0
annotations.append(res)
if len(annotations):
annotations = np.row_stack(annotations)
else:
annotations = np.zeros((0, 5))
sample = {
'img': crop,
'annot': annotations,
'scale': 1.0,
'category': self.patient_categories[patient_id]
}
return sample
class NeuralNet:
# History of accuracies on train set
accs = []
# History of accuracies on test set
val_accs = []
# Image augmenters
augmenters = [
ia.Noop(),
ia.CoarseSaltAndPepper(p=0.2, size_percent=0.30),
ia.CoarseSaltAndPepper(p=0.4, size_percent=0.30),
ia.Pad(px=(3, 0, 0, 0)),
ia.Pad(px=(0, 3, 0, 0)),
ia.Pad(px=(0, 0, 3, 0)),
ia.Pad(px=(0, 0, 0, 3)),
ia.GaussianBlur(sigma=0.25),
ia.GaussianBlur(sigma=0.5),
ia.GaussianBlur(sigma=1),
ia.GaussianBlur(sigma=2),
ia.Affine(rotate=-2),
ia.Affine(rotate=2),
ia.PiecewiseAffine(scale=0.007)
]
def __init__(
self,
experiment_name: str,
# Input shape
input_shape: Tuple[int, int, int],
# Mini batch size
mb_size: Optional = 32,
# Number of filters in each convolutional layer
filters_count: Optional[List[int]] = None,
# Size of kernel, common for each convolutional layer
kernel_size: Optional[List[int]] = None,
# Neurons count in each dense layer
dense_layers: Optional[List[int]] = None,
# Learning rate
learning_rate: float = 0.005,
# Number of epochs
nb_epochs: int = 50000,
# Steps per epoch. Each |steps_per_epoch| epochs net is evaluated on val set.
steps_per_epoch: int = 1000,
# Dropout after each dense layer (excluding last)
dropout_rate: float = 0.5,
# Whether or not augmentation should be performed when choosing next
# batch (as opposed to augmenting the entire
augment_on_the_fly: bool = True,
augmenters: Optional[List[ia.Augmenter]] = None,
min_label: int = 0,
max_label: int = NUM_CLASSES,
# Whether or not classification should be in binary mode. If yes,
# *please* provide the |positive_class| parameter.
binary_classification: bool = False,
# ID of the subject that is considered "positive" in case of
# binary classification.
positive_class: int = 0,
# If provided, will store checkpoints to ckpt_file
ckpt_file: Optional[str] = None,
):
self.experiment_name = experiment_name
self.input_shape = input_shape
self.mb_size = mb_size
self.learning_rate = learning_rate
self.nb_epochs = nb_epochs
self.steps_per_epoch = steps_per_epoch
self.dropout = dropout_rate
self.augment_on_the_fly = augment_on_the_fly
self.ckpt_file = ckpt_file
self.binary_classification = binary_classification
self.positive_class = positive_class
self.num_classes = NUM_CLASSES if not binary_classification else 1
if dense_layers is None:
dense_layers = [32, self.num_classes]
self.dense_layers = dense_layers
if filters_count is None:
filters_count = [32, 64]
self.filters_count = filters_count
if kernel_size is None:
kernel_size = [5, 5]
self.kernel_size = kernel_size
if binary_classification:
self._confusion_matrix = np.zeros((2, 2))
else:
self._confusion_matrix = np.zeros(
(self.num_classes, self.num_classes))
if augmenters is not None:
self.augmenters = augmenters
self._get_data(range_beg=min_label, range_end=max_label)
# Initialize logging.
self.logger = logging.Logger("main_logger", level=logging.INFO)
log_file = 'log.txt'
formatter = logging.Formatter(fmt='{levelname:<7} {message}',
style='{')
console_handler = logging.StreamHandler()
console_handler.setFormatter(formatter)
file_handler = logging.FileHandler(log_file)
file_handler.setFormatter(formatter)
self.logger.addHandler(console_handler)
self.logger.addHandler(file_handler)
def _augment_single_input(self, inp_x: np.ndarray):
"""
Augments single input with given augmenter.
:param inp_x: single input
:return: augmented input
"""
augmenter = choice(self.augmenters)
inp_x = inp_x.reshape([1] + list(inp_x.shape))
augmented = np.ndarray.astype(
augmenter.augment_images(np.ndarray.astype(inp_x * 256, np.uint8)),
np.float32)
augmented = augmented * (1 / 256)
augmented = augmented.reshape(inp_x.shape[1:])
return augmented
def _augment_train_set(self) -> None:
"""
Augments entire training set with all augmenters.
:return: None, appends augmented images to the train set.
"""
train_augs = []
for augmenter in self.augmenters:
cur_aug = np.ndarray.astype(
augmenter.augment_images(
np.ndarray.astype(self.x_train * 256, np.uint8)),
np.float32)
cur_aug = cur_aug * (1 / 256)
# Display augmented input, if you want
# show_image(cur_aug[0].reshape(NN_INPUT_SIZE))
train_augs.append(cur_aug)
self.x_train = np.concatenate([self.x_train] + train_augs)
self.y_train = np.concatenate([self.y_train] * (1 + len(train_augs)))
def _get_data(self, range_beg: int = 0, range_end: int = 52) -> None:
"""
:param range_beg, range_end: only samples such that label \in [range_beg, range_end) will be
used. Sensible values for (range_beg, range_end) would be:
* 00, 52 -> to use eurecom only
* 52, 78 -> to use ias_lab_rgbd_only
* 78, 98 -> to use superface_dataset only
:return: self.(x|y)_(train|test) are set as a result
"""
# Load stored numpy arrays from files.
logging.info("Loading data..")
self.x_train = np.load(DB_LOCATION + '/gen/' + self.experiment_name +
'_X_train.npy')
self.y_train = np.load(DB_LOCATION + '/gen/' + self.experiment_name +
'_Y_train.npy')
self.x_test = np.load(DB_LOCATION + '/gen/' + self.experiment_name +
'_X_test.npy')
self.y_test = np.load(DB_LOCATION + '/gen/' + self.experiment_name +
'_Y_test.npy')
train_indices = []
test_indices = []
# Filter out samples out of [range_beg, range_end).
for i in range(len(self.y_train)):
if range_end > np.argmax(self.y_train[i]) >= range_beg:
train_indices.append(i)
for i in range(len(self.y_test)):
if range_end > np.argmax(self.y_test[i]) >= range_beg:
test_indices.append(i)
shuffle(train_indices)
self.x_train = self.x_train[train_indices]
self.y_train = self.y_train[train_indices]
self.x_test = self.x_test[test_indices]
self.y_test = self.y_test[test_indices]
if self.binary_classification:
def to_binary(row):
return np.array([
1.
]) if np.argmax(row) == self.positive_class else np.array([0.])
self.y_train = np.apply_along_axis(to_binary, 1, self.y_train)
self.y_test = np.apply_along_axis(to_binary, 1, self.y_test)
# Show first input if you want
show_image(self.x_train[0].reshape(
[self.input_shape[0], self.input_shape[1] * self.input_shape[2]]))
# Image augmentation.
if not self.augment_on_the_fly:
self._augment_train_set()
logging.info("Loaded data..")
def _visualize_kernels(self):
"""
For each convolutional layer, visualizes filters and convolved images.
"""
for layer_no in range(len(self.conv_layers)):
num_filters = self.filters_count[layer_no]
kernels = []
applied_kernels = []
for filter_no in range(num_filters):
inp_x = self.input_shape[0] // (2**layer_no)
inp_y = self.input_shape[1] // (2**layer_no)
if layer_no == 0:
tmp_str = 'conv2d/kernel:0'
else:
tmp_str = 'conv2d_%d/kernel:0' % layer_no
kernel = [
v for v in tf.global_variables() if v.name == tmp_str
][0]
kernel = kernel[:, :, :, filter_no]
cur_conv_layer = self.conv_layers[layer_no]
if layer_no == 0:
kernel = tf.reshape(kernel, [
1, self.kernel_size[0] * self.input_shape[-1],
self.kernel_size[1], 1
])
else:
kernel = tf.reshape(kernel, [1] +\
[self.kernel_size[0] * self.filters_count[layer_no - 1], self.kernel_size[1]] +
[1])
kernels.append(kernel)
applied = tf.reshape(cur_conv_layer[0, :, :, filter_no],
[1, inp_x, inp_y, 1])
tf.summary.image('conv{0}_filter{1}_kernel'.format(
layer_no, filter_no),
kernel,
family='kernels_layer{0}'.format(layer_no),
max_outputs=1)
tf.summary.image('conv{0}_filter{1}_applied'.format(
layer_no, filter_no),
applied,
family='convolved_layer_{0}'.format(layer_no),
max_outputs=1)
applied_kernels.append(applied)
# Write concatenated patches to summary.
concatenated_kernels = tf.concat(kernels, axis=2)
kernels_name = "kernels_layer{0}".format(layer_no)
tf.summary.image(kernels_name,
concatenated_kernels,
family='kernels_all_layers')
concatenated_applieds = tf.concat(applied_kernels, axis=2)
applieds_name = "convolved_layer{0}".format(layer_no)
tf.summary.image(applieds_name,
concatenated_applieds,
family='convolved_all_layers')
if self.conv_layers:
# Merge all visualizations of kernels.
self.merged_summary = tf.summary.merge_all()
def _visualize_exciting_patches(self):
"""
For each convolutional layer, visualizes patches that excite each filter the most.
"""
# Initialize fetch handles for exciting patches and their respective responses.
self.exciting_patches = [[None] * k for k in self.filters_count]
self.patches_responses = [[None] * k for k in self.filters_count]
self.flattened_exciting_patches = [[None] * k
for k in self.filters_count]
self.all_exciting_patches_at_layer = [None for _ in self.filters_count]
for layer_no in range(len(self.conv_layers)):
num_filters = self.filters_count[layer_no]
cur_conv_layer = self.conv_layers[layer_no]
for filter_no in range(num_filters):
# Find top 10 responses to current filter, in the current mini-batch.
inp_x = self.input_shape[0] // (2**layer_no)
inp_y = self.input_shape[1] // (2**layer_no)
single_filtered_flattened = tf.reshape(
cur_conv_layer[:, :, :, filter_no],
[self.eff_mb_size * inp_x * inp_y])
top10_vals, top10_indices = tf.nn.top_k(
single_filtered_flattened, k=10, sorted=True)
top10_reshaped = tf.map_fn(
lambda sxy: [
sxy // (inp_x * inp_y),
(sxy // inp_y) % inp_x, sxy % inp_y
],
top10_indices,
dtype=[tf.int32, tf.int32, tf.int32])
def safe_cut_patch(sxy, size, img, layer_no):
"""
:param (sample_no, x, y)@sxy
:param size: size of patch to cut out
:param img: image to cut it from
:param layer_no: current layer number
:return: Cuts out a patch of size (|size|) located at (x, y) on
input #sample_no in current batch.
"""
sample_no, x, y = sxy
x *= 2**layer_no
y *= 2**layer_no
pad_marg_x = size[0] // 2
pad_marg_y = size[1] // 2
padding = [[0, 0], [pad_marg_x, pad_marg_x],
[pad_marg_y, pad_marg_y], [0, 0]]
padded = tf.pad(img, padding)
return padded[sample_no, x:x + size[0], y:y + size[1], :]
# Find patches corresponding to the top 10 responses.
# Store patches and responses in class-visible array to be retrieved later.
self.exciting_patches[layer_no][filter_no] = \
tf.map_fn(lambda sxy: safe_cut_patch(sxy,
size=(self.kernel_size[0] * (2 ** layer_no),
self.kernel_size[1] * (2 ** layer_no)),
img=tf.expand_dims(self.x[:, :, :, 0], axis=-1),
layer_no=layer_no),
top10_reshaped,
dtype=tf.float32)
self.patches_responses[layer_no][filter_no] = top10_vals
# Flatten and concatenate the 10 patches to 2 dimensions for visualization.
flattened_patches_shape = [1] + \
[10 * self.kernel_size[0] * (2 ** layer_no),
self.kernel_size[1] * (2 ** layer_no)] + \
[1]
# Write patches to summary.
patch_name = "exciting_patches_filter{0}".format(filter_no)
flattened_exciting_patches = tf.reshape(
self.exciting_patches[layer_no][filter_no],
flattened_patches_shape,
name=patch_name)
self.flattened_exciting_patches[layer_no][
filter_no] = flattened_exciting_patches
self.all_exciting_patches_at_layer[layer_no] = tf.concat(
self.flattened_exciting_patches[layer_no], axis=2)
# Write concatenated patches to summary.
all_patches_name = "exciting_patches_layer{0}".format(layer_no)
tf.summary.image(all_patches_name,
self.all_exciting_patches_at_layer[layer_no],
family='exciting_all_layers')
# Merge all summaries.
self.merged_summary = tf.summary.merge_all()
def _visualize_incorrect_answer_images(self):
correct = tf.boolean_mask(self.x, self.correct)
correct = tf.transpose(correct, perm=[0, 1, 3, 2])
correct = tf.reshape(
correct,
shape=[1, -1, self.input_shape[1] * self.input_shape[2], 1])
correct = tf.concat([
correct,
tf.zeros(
shape=[1, 1, self.input_shape[1] * self.input_shape[2], 1])
],
axis=1)
tf.summary.image('correct', correct)
incorrect = tf.boolean_mask(self.x, tf.logical_not(self.correct))
incorrect = tf.transpose(incorrect, perm=[0, 1, 3, 2])
incorrect = tf.reshape(
incorrect,
shape=[1, -1, self.input_shape[1] * self.input_shape[2], 1])
incorrect = tf.concat([
incorrect,
tf.zeros(
shape=[1, 1, self.input_shape[1] * self.input_shape[2], 1])
],
axis=1)
tf.summary.image('incorrect', incorrect)
# Merge all summaries.
self.merged_summary = tf.summary.merge_all()
def _create_convolutional_layers(self) -> None:
signal = self.x
for layer_no in range(len(self.filters_count)):
num_filters = self.filters_count[layer_no]
signal = tf.layers.batch_normalization(signal)
# Init weights with std.dev = sqrt(2 / N)
#
input_size = int(signal.get_shape()[1]) * int(
signal.get_shape()[2]) * int(signal.get_shape()[3])
w_init = tf.initializers.random_normal(stddev=sqrt(2 / input_size))
# Convolutional layer
cur_conv_layer = tf.layers.conv2d(inputs=signal,
filters=num_filters,
kernel_size=self.kernel_size,
kernel_initializer=w_init,
padding='same')
# Reduce image dimensions in half.
cur_pool_layer = tf.layers.max_pooling2d(inputs=cur_conv_layer,
pool_size=[2, 2],
strides=2,
padding='valid')
self.conv_layers.append(cur_conv_layer)
self.pool_layers.append(cur_pool_layer)
# Set pooled image as current signal
signal = cur_pool_layer
return signal
def _create_dense_layers(self) -> None:
signal = self.x if not self.pool_layers else self.pool_layers[-1]
input_size = int(signal.get_shape()[1]) * int(
signal.get_shape()[2]) * int(signal.get_shape()[3])
signal = tf.reshape(signal, [self.eff_mb_size, input_size])
for num_neurons in self.dense_layers[:-1]:
signal = tf.layers.batch_normalization(signal)
# Init weights with std.dev = sqrt(2 / N)
# https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf?spm=5176.100239.blogcont55892.28.pm8zm1&file=He_Delving_Deep_into_ICCV_2015_paper.pdf
input_size = int(signal.get_shape()[1])
w_init = tf.initializers.random_normal(stddev=sqrt(2 / input_size))
cur_dense_layer = tf.layers.dense(inputs=signal,
units=num_neurons,
activation=tf.nn.leaky_relu,
kernel_initializer=w_init)
signal = cur_dense_layer
# Apply dropout
cur_dropout_layer = tf.layers.dropout(inputs=signal,
rate=self.dropout)
signal = cur_dropout_layer
# Init weights with std.dev = sqrt(2 / N)
input_size = int(signal.get_shape()[1])
w_init = tf.initializers.random_normal(
stddev=tf.sqrt(tf.constant(2.) / input_size))
cur_layer = tf.layers.dense(inputs=signal,
activation=tf.nn.sigmoid,
units=self.dense_layers[-1],
kernel_initializer=w_init)
self.output_layer = cur_layer
def _create_training_objectives(self) -> None:
if self.binary_classification:
self.preds = tf.cast(tf.round(self.output_layer), dtype=tf.int64)
self.y_sparse = tf.cast(self.y, dtype=tf.int64)
else:
self.preds = tf.argmax(self.output_layer, axis=1)
self.y_sparse = tf.argmax(self.y, axis=1)
self.loss = tf.losses.log_loss(self.y, self.output_layer)
self.correct = tf.reshape(tf.equal(self.y_sparse, self.preds),
shape=[self.eff_mb_size])
self.accuracy = tf.reduce_mean(tf.cast(self.correct, tf.float32))
self.train_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss)
self.logger.info('list of variables {0}'.format(
list(map(lambda x: x.name, tf.global_variables()))))
def _create_model(self):
self.x = tf.placeholder(dtype=tf.float32,
shape=[None] + list(self.input_shape))
self.y = tf.placeholder(dtype=tf.float32,
shape=[None, self.num_classes])
self.eff_mb_size = tf.shape(self.x)[0] # Effective batch size
self.conv_layers = []
self.pool_layers = []
self._create_convolutional_layers()
self._create_dense_layers()
self._create_training_objectives()
def train_on_batch(self, batch_x, batch_y):
"""
:return: [loss, accuracy]
"""
results = self.sess.run([self.loss, self.accuracy, self.train_op],
feed_dict={
self.x: batch_x,
self.y: batch_y
})
self.accs.append(results[1])
return results[:2]
def test_on_batch(self,
batch_x,
batch_y,
global_step=1) -> Tuple[float, float, List[float]]:
"""
Note that this function does not fetch |self.train_op|, so that the weights
are not updated.
:param batch_x:
:param batch_y:
:param global_step:
:return: (loss, accuracy, probs)
"""
if self.conv_layers:
# Write summary
results = self.sess.run([
self.loss, self.accuracy, self.output_layer, self.preds,
self.merged_summary
],
feed_dict={
self.x: batch_x,
self.y: batch_y
})
msum = results[4]
self.writer.add_summary(msum, global_step=global_step)
self.writer.flush()
else:
results = self.sess.run([self.loss, self.accuracy, self.preds],
feed_dict={
self.x: batch_x,
self.y: batch_y
})
self.val_accs.append(results[1])
# Update confusion matrix
preds = results[3]
for i in range(len(batch_x)):
self._confusion_matrix[np.argmax(batch_y[i]), preds[i]] += 1.
return results[0], results[1], list(results[2])
def validate(self, global_step) -> ClassificationResults:
"""
:return: (loss, accuracy, auc_roc)
Note that if self.binary_classification is False, auc_roc may be anything
"""
losses = []
accs = []
all_pred_probs = []
all_labels = []
for batch_no in range(self.x_test.shape[0] // self.mb_size + 1):
inputs = self.x_test[batch_no * self.mb_size:(batch_no + 1) *
self.mb_size]
labels = self.y_test[batch_no * self.mb_size:(batch_no + 1) *
self.mb_size]
loss, acc, probs = self.test_on_batch(inputs,
labels,
global_step=global_step)
losses.append(loss)
accs.append(acc)
all_pred_probs += probs
all_labels += list(labels)
all_pred_probs = np.array(all_pred_probs)
all_labels = np.array(all_labels)
all_labels = all_labels.astype(dtype=np.bool)
loss = np.mean(losses)
acc = np.mean(accs)
return ClassificationResults(loss=loss,
acc=acc,
pred_probs=all_pred_probs,
labels=all_labels,
binary=self.binary_classification)
def _next_training_batch(self) -> (np.ndarray, np.ndarray):
batch = sample(list(range(self.x_train.shape[0])), self.mb_size)
batch_x, batch_y = self.x_train[batch], self.y_train[batch]
if self.augment_on_the_fly:
for sample_no in range(self.mb_size):
batch_x[sample_no] = self._augment_single_input(
batch_x[sample_no])
return batch_x, batch_y
def train_and_evaluate(self) -> ClassificationResults:
"""
Train and evaluate model.
"""
with tf.Session() as self.sess:
# Initialize computation graph.
self._create_model()
# Add visualizations to computation graph.
self._visualize_kernels()
self._visualize_exciting_patches()
self._visualize_incorrect_answer_images()
# Initialize variables.
if self.ckpt_file:
saver = tf.train.Saver()
try:
saver.restore(self.sess, self.ckpt_file)
except (tf.errors.InvalidArgumentError,
tf.errors.NotFoundError):
tf.global_variables_initializer().run()
else:
tf.global_variables_initializer().run()
# Initialize summary writer.
self.writer = tf.summary.FileWriter(logdir='conv_vis')
# Initialize progress bar.
bar = Bar('',
max=self.steps_per_epoch,
suffix='%(index)d/%(max)d ETA: %(eta)ds')
for epoch_no in range(self.nb_epochs):
self.logger.info("Epoch {epoch_no}/{nb_epochs}".format(
epoch_no=epoch_no, nb_epochs=self.nb_epochs))
for step_no in range(self.steps_per_epoch):
# Train model on next batch
batch_x, batch_y = self._next_training_batch()
results = self.train_on_batch(batch_x, batch_y)
# Update bar
bar.message = 'loss: {0[0]:.8f} acc: {0[1]:.3f} mean_acc: {1:.3f}'. \
format(results, np.mean(self.accs[-1000:]), )
bar.next()
# Re-initialize progress bar
bar.finish()
bar = Bar('',
max=self.steps_per_epoch,
suffix='%(index)d/%(max)d ETA: %(eta)ds')
# Store model
if self.ckpt_file:
saver.save(self.sess, self.ckpt_file)
# Validate
val_results = self.validate(global_step=epoch_no)
loss, acc, auc_roc = val_results.loss, val_results.acc, val_results.get_auc_roc(
)
if self.binary_classification:
self.logger.info(
"Validation results: Loss: {0}, accuracy: {1}, auc_roc: {2}"
.format(loss, acc, auc_roc))
else:
self.logger.info(
"Validation results: Loss: {0}, accuracy: {1}".format(
loss, acc))
# Dipslay confusion matrix
show_image(self._confusion_matrix)
return val_results
scale={
'x': TruncatedNormal(1, .1, low=.8, high=1.2),
'y': TruncatedNormal(1, .1, low=.8, high=1.2),
},
translate_percent={
'x': TruncatedNormal(0, .1, low=-.2, high=.2),
'y': TruncatedNormal(0, .1, low=-.2, high=.2),
},
rotate=(-180, 180),
shear={
'x': TruncatedNormal(0, 10, low=-30, high=30),
'y': TruncatedNormal(0, 10, low=-30, high=30),
},
cval=(0, 255),
),
aug.CoarseSaltAndPepper((.01, .1), size_percent=(5E-3, 5E-2)),
])
class Trainer(DefaultTrainer):
@classmethod
def build_train_loader(cls, cfg):
return build_detection_train_loader(cfg, mapper=augment)
def augment(record):
record = deepcopy(record)
image = plt.imread(record["filepath"])
annotations = record["annotations"]
boxes = [annotation["bbox"] for annotation in annotations]
(gir_face, depth_face) = (face.gir_img, face.depth_img)
if gir_face is None or depth_face is None:
return None
if np.isnan(gir_face).any() or np.isnan(depth_face).any():
return None
try:
face = normalized(face, rotate=False)
face = hog_and_entropy(face)
except ValueError:
return None
return face.get_fd_desc()
augmenters = [
ia.Noop(),
ia.CoarseSaltAndPepper(p=0.2, size_percent=0.30),
ia.CoarseSaltAndPepper(p=0.4, size_percent=0.30),
ia.Pad(px=(3, 0, 0, 0)),
ia.Pad(px=(0, 3, 0, 0)),
ia.Pad(px=(0, 0, 3, 0)),
ia.Pad(px=(0, 0, 0, 3)),
ia.GaussianBlur(sigma=0.25),
ia.GaussianBlur(sigma=0.5),
ia.GaussianBlur(sigma=1),
ia.GaussianBlur(sigma=2),
ia.Affine(rotate=-2),
ia.Affine(rotate=2)
]
def run_preprocess():
# Adds poisson noise (similar to gaussian but different distribution) to an image, sampled once per pixel from
# a poisson distribution Poisson(s), where s is sampled per image and varies between lo and hi for percent of
# all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same poisson distribution:
"Additive_Poisson_Noise": lambda lo, hi, percent:
iaa.AdditivePoissonNoise(lam=(lo, hi), per_channel=percent),
# Adds salt and pepper noise to an image, i.e. some white-ish and black-ish pixels.
# Replaces percent of all pixels with salt and pepper noise
"Salt_And_Pepper": lambda percent: iaa.SaltAndPepper(percent),
# Adds coarse salt and pepper noise to image, i.e. rectangles that contain noisy white-ish and black-ish pixels
# Replaces percent of all pixels with salt/pepper in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt_And_Pepper": lambda percent, lo, hi: iaa.CoarseSaltAndPepper(percent, size_percent=(lo, hi)),
# Adds salt noise to an image, i.e white-ish pixels
# Replaces percent of all pixels with salt noise
"Salt": lambda percent: iaa.Salt(percent),
# Adds coarse salt noise to image, i.e. rectangles that contain noisy white-ish pixels
# Replaces percent of all pixels with salt in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt": lambda percent, lo, hi: iaa.CoarseSalt(percent, size_percent=(lo, hi)),
# Adds Pepper noise to an image, i.e Black-ish pixels
# Replaces percent of all pixels with Pepper noise
"Pepper": lambda percent: iaa.Pepper(percent),
# Adds coarse pepper noise to image, i.e. rectangles that contain noisy black-ish pixels
def transform(aug_type, magnitude, X):
if aug_type == "crop":
X_aug = iaa.Crop(px=(0, int(magnitude * 32))).augment_images(X)
elif aug_type == "gaussian-blur":
X_aug = iaa.GaussianBlur(sigma=(0, magnitude * 25.0)).augment_images(X)
elif aug_type == "rotate":
X_aug = iaa.Affine(rotate=(-180 * magnitude, 180 * magnitude)).augment_images(X)
elif aug_type == "shear":
X_aug = iaa.Affine(shear=(-90 * magnitude, 90 * magnitude)).augment_images(X)
elif aug_type == "translate-x":
X_aug = iaa.Affine(
translate_percent={"x": (-magnitude, magnitude), "y": (0, 0)}
).augment_images(X)
elif aug_type == "translate-y":
X_aug = iaa.Affine(
translate_percent={"x": (0, 0), "y": (-magnitude, magnitude)}
).augment_images(X)
elif aug_type == "horizontal-flip":
X_aug = iaa.Fliplr(magnitude).augment_images(X)
elif aug_type == "vertical-flip":
X_aug = iaa.Flipud(magnitude).augment_images(X)
elif aug_type == "sharpen":
X_aug = iaa.Sharpen(
alpha=(0, 1.0), lightness=(0.50, 5 * magnitude)
).augment_images(X)
elif aug_type == "emboss":
X_aug = iaa.Emboss(
alpha=(0, 1.0), strength=(0.0, 20.0 * magnitude)
).augment_images(X)
elif aug_type == "additive-gaussian-noise":
X_aug = iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, magnitude * 255), per_channel=0.5
).augment_images(X)
elif aug_type == "dropout":
X_aug = iaa.Dropout(
(0.01, max(0.011, magnitude)), per_channel=0.5
).augment_images(
X
) # Dropout first argument should be smaller than second one
elif aug_type == "coarse-dropout":
X_aug = iaa.CoarseDropout(
(0.03, 0.15), size_percent=(0.30, np.log10(magnitude * 3)), per_channel=0.2
).augment_images(X)
elif aug_type == "gamma-contrast":
X_norm = normalize(X)
X_aug_norm = iaa.GammaContrast(magnitude * 1.75).augment_images(
X_norm
) # needs 0-1 values
X_aug = denormalize(X_aug_norm)
elif aug_type == "brighten":
X_aug = iaa.Add(
(int(-40 * magnitude), int(40 * magnitude)), per_channel=0.5
).augment_images(
X
) # brighten
elif aug_type == "invert":
X_aug = iaa.Invert(1.0).augment_images(X) # magnitude not used
elif aug_type == "fog":
X_aug = iaa.Fog().augment_images(X) # magnitude not used
elif aug_type == "clouds":
X_aug = iaa.Clouds().augment_images(X) # magnitude not used
elif aug_type == "histogram-equalize":
X_aug = iaa.AllChannelsHistogramEqualization().augment_images(
X
) # magnitude not used
elif aug_type == "super-pixels": # deprecated
X_norm = normalize(X)
X_norm2 = (X_norm * 2) - 1
X_aug_norm2 = iaa.Superpixels(
p_replace=(0, magnitude), n_segments=(100, 100)
).augment_images(X_norm2)
X_aug_norm = (X_aug_norm2 + 1) / 2
X_aug = denormalize(X_aug_norm)
elif aug_type == "perspective-transform":
X_norm = normalize(X)
X_aug_norm = iaa.PerspectiveTransform(
scale=(0.01, max(0.02, magnitude))
).augment_images(
X_norm
) # first scale param must be larger
np.clip(X_aug_norm, 0.0, 1.0, out=X_aug_norm)
X_aug = denormalize(X_aug_norm)
elif aug_type == "elastic-transform": # deprecated
X_norm = normalize(X)
X_norm2 = (X_norm * 2) - 1
X_aug_norm2 = iaa.ElasticTransformation(
alpha=(0.0, max(0.5, magnitude * 300)), sigma=5.0
).augment_images(X_norm2)
X_aug_norm = (X_aug_norm2 + 1) / 2
X_aug = denormalize(X_aug_norm)
elif aug_type == "add-to-hue-and-saturation":
X_aug = iaa.AddToHueAndSaturation(
(int(-45 * magnitude), int(45 * magnitude))
).augment_images(X)
elif aug_type == "coarse-salt-pepper":
X_aug = iaa.CoarseSaltAndPepper(p=0.2, size_percent=magnitude).augment_images(X)
elif aug_type == "grayscale":
X_aug = iaa.Grayscale(alpha=(0.0, magnitude)).augment_images(X)
else:
raise ValueError
return X_aug
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument(
"--only",
default=None,
help=
"If this is set, then only the results of an augmenter with this name will be shown. "
"Optionally, comma-separated list.",
required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
ia.imresize_single_image(data.astronaut(), (128, 128))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=50, y=40),
ia.Keypoint(x=70, y=38),
ia.Keypoint(x=62, y=52)
],
shape=images[0].shape),
ia.KeypointsOnImage([
ia.Keypoint(x=55, y=32),
ia.Keypoint(x=42, y=95),
ia.Keypoint(x=75, y=89)
],
shape=images[1].shape)
]
bounding_boxes = [
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[0].shape),
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[1].shape)
]
augmenters = [
iaa.Sequential([
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="Sequential"),
iaa.SomeOf(2,
children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="SomeOf"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="OneOf"),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
name="Sometimes"),
iaa.WithColorspace("HSV",
children=[iaa.Add(20)],
name="WithColorspace"),
iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"),
iaa.AddToHueAndSaturation((-20, 20),
per_channel=True,
name="AddToHueAndSaturation"),
iaa.Noop(name="Noop"),
iaa.Resize({
"width": 64,
"height": 64
}, name="Resize"),
iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"),
iaa.Pad(px=(0, 8), name="Pad-px"),
iaa.Crop(px=(0, 8), name="Crop-px"),
iaa.Crop(percent=(0, 0.1), name="Crop-percent"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=10, name="BilateralBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0),
direction=(0, 1.0),
name="DirectedEdgeDetect"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1 * 255),
name="AdditiveGaussianNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05,
size_percent=(0.05, 0.5),
name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.ContrastNormalization(alpha=(0.5, 2.0),
name="ContrastNormalization"),
iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"),
iaa.Salt(p=0.05, name="Salt"),
iaa.Pepper(p=0.05, name="Pepper"),
iaa.CoarseSaltAndPepper(p=0.05,
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"),
iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_px={
"x": (-16, 16),
"y": (-16, 16)
},
rotate=(-45, 45),
shear=(-16, 16),
order=ia.ALL,
cval=(0, 255),
mode=ia.ALL,
name="Affine"),
iaa.PiecewiseAffine(scale=0.03,
nb_rows=(2, 6),
nb_cols=(2, 6),
name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.5, 8.0),
sigma=1.0,
name="ElasticTransformation"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=False,
name="Alpha"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=True,
name="AlphaPerChannel"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaAffine"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=False,
name="AlphaElementwise"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=True,
name="AlphaElementwisePerChannel"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaElementwiseAffine"),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="FrequencyNoiseAlpha")
]
augmenters.append(
iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters],
name="Sequential"))
augmenters.append(
iaa.Sometimes(0.5, [aug.copy() for aug in augmenters],
name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name in [
v.strip() for v in args.only.split(",")
]:
print("Augmenter: %s" % (augmenter.name, ))
grid = []
for image, kps, bbs in zip(images, keypoints, bounding_boxes):
aug_det = augmenter.to_deterministic()
imgs_aug = aug_det.augment_images(
np.tile(image[np.newaxis, ...], (16, 1, 1, 1)))
kps_aug = aug_det.augment_keypoints([kps] * 16)
bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16)
imgs_aug_drawn = [
kps_aug_one.draw_on_image(img_aug)
for img_aug, kps_aug_one in zip(imgs_aug, kps_aug)
]
imgs_aug_drawn = [
bbs_aug_one.draw_on_image(img_aug)
for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug)
]
grid.append(np.hstack(imgs_aug_drawn))
ia.imshow(np.vstack(grid))
def transform(self, image: np.ndarray, target: str,
condition: int) -> Tuple[torch.Tensor, torch.Tensor, int]:
"""Transforms and normalizes the data. If in training mode the data is augmentated.
Args:
image (np.ndarray): Image to transform
target (str): Training target
condition (int): Condition
Returns:
Tuple[torch.Tensor, torch.Tensor, int]: Augmented image, target and condition
"""
# Resize
resize = iaa.Resize({"height": 224, "width": 224})
image = resize.augment_image(image)
# Random horizontal flipping and erase
if self.train:
if random.random() > 0.5:
# flip image
flip = iaa.HorizontalFlip(1.0)
image = flip.augment_image(image)
# flip class
if target == "a":
target = "d"
elif target == "d":
target = "a"
# flip condition
if condition == 2:
condition = 4
elif condition == 4:
condition = 2
#imgaug
seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.Affine(rotate=(-15, 15))),
iaa.Sometimes(0.3, iaa.EdgeDetect(alpha=(0.3, 0.8))),
iaa.Sometimes(0.5, iaa.MotionBlur(k=iap.Choice([3, 5, 7]))),
iaa.OneOf([
iaa.Dropout(p=(0, 0.3), per_channel=0.5),
iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.09))
]),
iaa.Sometimes(0.5, iaa.AllChannelsCLAHE(clip_limit=(1, 10)))
])
image = seq.augment_image(image)
# Transform to tensor
image = TF.to_tensor(image)
# Transform to one hot encoding
target = torch.tensor(self.target_dict[target])
#normalize image to fit pretrained vgg model
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
image = normalize(image)
return image, target, condition