def _aug_occl(self):
from imgaug.augmenters import Sequential,SomeOf,OneOf,Sometimes,WithColorspace,WithChannels, \
Noop,Lambda,AssertLambda,AssertShape,Scale,CropAndPad, \
Pad,Crop,Fliplr,Flipud,Superpixels,ChangeColorspace, PerspectiveTransform, \
Grayscale,GaussianBlur,AverageBlur,MedianBlur,Convolve, \
Sharpen,Emboss,EdgeDetect,DirectedEdgeDetect,Add,AddElementwise, \
AdditiveGaussianNoise,Multiply,MultiplyElementwise,Dropout, \
CoarseDropout,Invert,ContrastNormalization,Affine,PiecewiseAffine, \
ElasticTransformation
return Sequential(
[Sometimes(0.7, CoarseDropout(p=0.4, size_percent=0.01))])
Sometimes(0.5, ContrastNormalization((0.5, 2.2), per_channel=0.3))
], random_order=False)
affine = iaa.Affine(
scale=(0.5, 1.5),
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
rotate=(-180, 180),
#shear=(-8, 8)
)
occlude = iaa.Sequential([
#Sometimes(0.5, PerspectiveTransform(0.05)),
#Sometimes(0.5, CropAndPad(percent=(-0.05, 0.1))),
#Sometimes(0.5, Affine(scale=(1.0, 1.2))),
#Sometimes(0.5, Affine(rotate=(-180, 180))),
Sometimes(0.5, CoarseDropout( p=0.1, size_percent=0.02) ),
#Sometimes(0.5, GaussianBlur(1.2*np.random.rand())),
Sometimes(0.5, Add((-100, 100), per_channel=0.3)),
#Sometimes(0.3, Invert(0.2, per_channel=True)),
#Sometimes(0.5, Multiply((0.6, 1.4), per_channel=0.5)),
#Sometimes(0.5, Multiply((0.6, 1.4))),
Sometimes(0.5, ContrastNormalization((0.5, 2.2), per_channel=0.3))
], random_order=False)
def remove_pixels_outside_bbox(img, bbox_info_short):
# RECREATE BBOX PIXEL DIMENSIONS FROM RELATIVE FLOATS ==================
x_center_p = int(float(bbox_info_short[0]) * img.shape[1])
y_center_p = int(float(bbox_info_short[1]) * img.shape[0])
width = int(float(bbox_info_short[2]) * img.shape[1])
height = int(float(bbox_info_short[3]) * img.shape[0])
class dropout(object):
def __init__(self, rate, size=None):
self.trans = CoarseDropout(rate, size_percent=size)
def __call__(self, image, label=None):
return self.trans.augment_image(image), label
def __init__(self, rate, size=None):
self.trans = CoarseDropout(rate, size_percent=size)
# Validation dataset
# 2017 valset
dataset_val = CocoDataset()
dataset_val.load_coco(args.dataset, "val2007")
dataset_val.prepare()
# Image Augmentation
# Right/Left flip 50% of the time, IOU50=0.029(VALIDATION SET)
# augmentation = imgaug.augmenters.Fliplr(0.5)
# Flip and rotate
augmentation = Sequential([
Fliplr(0.5),
GaussianBlur((0, 1.5)),
CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05)),
Affine(scale=(0.3, 2), rotate=(-20, 20)),
Multiply((0.5, 1.5)),
AddToHueAndSaturation((-20, 20))
])
#augmentation = Sequential([Flipud(0.2), Fliplr(0.5), Affine(scale=(0.2, 2), rotate=(-60,60)), GaussianBlur((0, 2)), AddToHueAndSaturation((-30,30)), GammaContrast((0.5, 1.5)), AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5)])
#augmentation = Sequential([Fliplr(0.5), Affine(scale=(0.3, 3), rotate=(-60,60)), GaussianBlur((0, 3)), AddToHueAndSaturation((-30,30)), GammaContrast((0.5, 1.5)), AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5) ])
'''
iaa.Sequential([
# apply the following augmenters to most images
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.1), # vertically flip 20% of all images
# crop images by -5% to 10% of their height/width
sometimes(iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},