def get_valid_augmentors(self, input_shape, output_shape, view=False):
print(input_shape, output_shape)
shape_augs = [
imgaug.CenterCrop(input_shape),
]
input_augs = None
label_augs = []
if self.model_type == 'unet' or self.model_type == 'micronet':
label_augs = [GenInstanceUnetMap(crop_shape=output_shape)]
if self.model_type == 'dcan':
label_augs = [GenInstanceContourMap(crop_shape=output_shape)]
if self.model_type == 'dist':
label_augs = [
GenInstanceDistance(crop_shape=output_shape, inst_norm=False)
]
if self.model_type == 'np_hv':
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == 'np_dist':
label_augs = [
GenInstanceDistance(crop_shape=output_shape, inst_norm=True)
]
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs
def get_train_augmentors(self, input_shape, output_shape, view=False):
shape_augs = [
imgaug.Affine(
shear=5, # in degree
scale=(0.8, 1.2),
rotate_max_deg=179,
translate_frac=(0.01, 0.01),
interp=cv2.INTER_NEAREST,
border=cv2.BORDER_CONSTANT),
imgaug.Flip(vert=True),
imgaug.Flip(horiz=True),
imgaug.CenterCrop(input_shape),
]
input_augs = self.input_augs
label_augs = []
if self.model_type == 'unet' or self.model_type == 'micronet':
label_augs =[GenInstanceUnetMap(crop_shape=output_shape)]
if self.model_type == 'dcan':
label_augs =[GenInstanceContourMap(crop_shape=output_shape)]
if self.model_type == 'dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=False)]
if self.model_type == 'np_hv' or self.model_type == 'np_hv_opt':
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == 'np_dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=True)]
if not self.type_classification:
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs
def get_train_augmentors(self, input_shape, output_shape, view=False):
print(input_shape, output_shape)
shape_augs = [
imgaug.Affine(
shear=5, # in degree
scale=(0.8, 1.2),
rotate_max_deg=179,
translate_frac=(0.01, 0.01),
interp=cv2.INTER_NEAREST,
border=cv2.BORDER_CONSTANT),
imgaug.Flip(vert=True),
imgaug.Flip(horiz=True),
imgaug.CenterCrop(input_shape),
]
input_augs = [
imgaug.RandomApplyAug(
imgaug.RandomChooseAug([
GaussianBlur(),
MedianBlur(),
imgaug.GaussianNoise(),
]), 0.5),
# standard color augmentation
imgaug.RandomOrderAug([
imgaug.Hue((-8, 8), rgb=True),
imgaug.Saturation(0.2, rgb=True),
imgaug.Brightness(26, clip=True),
imgaug.Contrast((0.75, 1.25), clip=True),
]),
imgaug.ToUint8(),
]
label_augs = []
if self.model_type == 'unet' or self.model_type == 'micronet':
label_augs = [GenInstanceUnetMap(crop_shape=output_shape)]
if self.model_type == 'dcan':
label_augs = [GenInstanceContourMap(crop_shape=output_shape)]
if self.model_type == 'dist':
label_augs = [
GenInstanceDistance(crop_shape=output_shape, inst_norm=False)
]
if self.model_type == 'np_hv':
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == 'np_dist':
label_augs = [
GenInstanceDistance(crop_shape=output_shape, inst_norm=True)
]
if not self.type_classification:
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs
def get_valid_augmentors(self, view=False):
shape_augs = [
imgaug.CenterCrop(self.infer_input_shape),
]
input_augs = None
# default to 'xy'
if self.model_mode != 'np+dst':
label_augs = [GenInstanceXY(self.infer_mask_shape)]
else:
label_augs = [GenInstanceDistance(self.infer_mask_shape)]
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(self.infer_mask_shape))
return shape_augs, input_augs, label_augs
def get_train_augmentors(self, view=False):
shape_augs = [
imgaug.Affine(
shear=5, # in degree
scale=(0.8, 1.2),
rotate_max_deg=179,
translate_frac=(0.01, 0.01),
interp=cv2.INTER_NEAREST,
border=cv2.BORDER_CONSTANT),
imgaug.Flip(vert=True),
imgaug.Flip(horiz=True),
imgaug.CenterCrop(self.train_input_shape),
]
input_augs = [
imgaug.RandomApplyAug(
imgaug.RandomChooseAug([
GaussianBlur(),
MedianBlur(),
imgaug.GaussianNoise(),
]), 0.5),
# standard color augmentation
imgaug.RandomOrderAug([
imgaug.Hue((-8, 8), rgb=True),
imgaug.Saturation(0.2, rgb=True),
imgaug.Brightness(26, clip=True),
imgaug.Contrast((0.75, 1.25), clip=True),
]),
imgaug.ToUint8(),
]
# default to 'xy'
if self.model_mode != 'np+dst':
label_augs = [GenInstanceXY(self.train_mask_shape)]
else:
label_augs = [GenInstanceDistance(self.train_mask_shape)]
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(self.train_mask_shape))
return shape_augs, input_augs, label_augs
def get_valid_augmentors(self, input_shape, output_shape, view=False):
shape_augs = [
imgaug.CenterCrop(input_shape),
]
input_augs = None
label_augs = []
if self.model_type == "np_hv" or self.model_type == "np_hv_opt":
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == "np_dist":
label_augs = [
GenInstanceDistance(crop_shape=output_shape, inst_norm=True)
]
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs