def augment(self, n_augm, out_fname, update=False):
# Define augmenter
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
seq = 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
sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
sometimes(iaa.Affine(
scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, # 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 -20 to +20 percent (per axis)
rotate=(-20, 20), # 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)
)),
sometimes(
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5) # add gaussian noise to images
),
],
random_order=True
)
with h5py.File(self.path+out_fname, 'r+') as hf:
for subject_k,subject_v in hf['train/pose6/'].items():
if subject_k.split('_')[1] in ['F017', 'F018', 'F019','F020']:
for segment_k,segment_v in subject_v.items():
labels = np.asarray(segment_v['aus'])
idxs = self.balance(labels)
labels = labels[idxs]
for i in range(n_augm):
node = 'train/pose6/'+subject_k+'/'+segment_k+'_'+str(i)
_ = hf.create_group(node)
for tp in ['faces', 'leye', 'reye', 'beye', 'nose', 'mouth', 'lmouth', 'rmouth']:
print '{}/{}/{}'.format(subject_k, segment_k, tp)
images = np.asarray(segment_v[tp])
if np.max(idxs)<images.shape[0]:
images = images[idxs]
else:
print ('-------An index error-------')
labels = np.asarray(segment_v['aus'])
for i in range(n_augm):
augm_images = seq.augment_images(images)
node = 'train/pose6/'+subject_k+'/'+segment_k+'_'+str(i)
print ' Writing node {} with data of shape {}'.format(node, augm_images.shape)
segment = hf[node]
segment.create_dataset(tp, data=augm_images)
if tp=='faces':
segment.create_dataset('aus', data=labels)
def get_augment_parameters():
# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
parameters = [
# apply the following augmenters to most images
iaa.Fliplr(0.5), # horizontally flip a percentage of all images
iaa.Flipud(0.2), # vertically flip a percentage of all images
sometimes(iaa.Crop(percent=(0, 0.05))), # crop images by percentage of their height/width
sometimes(iaa.Affine(
scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, # scale images to percentage of their size, individually per axis
translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}, # translate by percent (per axis)
rotate=(-15, 15), # rotate by degrees
shear=(-10, 10), # shear by 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 in imgaug repo)
)),
# 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),
[
sometimes(iaa.Superpixels(p_replace=(0, 0.5), n_segments=(100, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur((0, 1.0)), # blur images with a sigma value
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes
iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.25)), # sharpen images
iaa.Emboss(alpha=(0, 1.0), strength=(0, 1.0)), # emboss images
# search either for all edges or for directed edges
sometimes(iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.5)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7), 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 percentage of the pixels
iaa.CoarseDropout((0.03, 0.1), size_percent=(0.02, 0.1), 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
iaa.Multiply((0.5, 1.5), per_channel=0.5), # change brightness of images
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(iaa.ElasticTransformation(alpha=(0.1, 2.0), sigma=0.2)), # move pixels locally around
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.03))) # sometimes move parts of the image around
],
random_order=True
)
]
return parameters
def get_basic_imgaug_seq(seed):
determinist = {
"deterministic": True,
"random_state": seed
}
train_seq = iaa.Sequential([
iaa.Affine(translate_px=(-25, 25),
scale=(0.85, 1.15),
rotate=(-65, 65),
mode='reflect',
**determinist),
],
random_order=True,
**determinist
)
return train_seq
def get_high_zoom_imgaug_seq(seed):
determinist = {
"deterministic": True,
"random_state": seed
}
train_seq = iaa.Sequential([
# iaa.Sometimes(0.45, iaa.Sharpen(alpha=0.9, lightness=(0.5, 1.15), **determinist), **determinist),
iaa.Sometimes(0.45, iaa.ContrastNormalization(alpha=(0.75, 1.15), **determinist), **determinist),
# iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(scale=(0, 0.01 * 255),
# per_channel=True, **determinist), **determinist),
iaa.Affine(translate_px=(-5, 5),
scale=(1.0, 1.75),
rotate=(-65, 65),
mode='reflect',
**determinist),
# iaa.Add(value=(-35, 35), per_channel=True), # Probably, can change nature of label
],
random_order=True,
**determinist
)
return train_seq