def get_train_transform(image_size, augmentation=None):
if augmentation is None:
augmentation = 'none'
LEVELS = {
'none': get_none_augmentations,
'light': get_light_augmentations,
'medium': get_medium_augmentations,
'hard': get_hard_augmentations,
'hard2': get_hard_augmentations_v2
}
assert augmentation in LEVELS.keys()
augmentation = LEVELS[augmentation](image_size)
longest_size = max(image_size[0], image_size[1])
return A.Compose([
Resize(int(config.img_height * 1.5), int(config.img_weight * 1.5)),
CenterCrop(config.img_height, config.img_weight),
A.LongestMaxSize(longest_size, interpolation=cv2.INTER_CUBIC),
A.PadIfNeeded(image_size[0],
image_size[1],
border_mode=cv2.BORDER_CONSTANT,
value=0), augmentation,
A.Normalize(),
ToTensor()
])
def get_test_transform(image_size):
longest_size = max(image_size[0], image_size[1])
return A.Compose([
#Resize(int(config.img_height*1.5),int(config.img_weight*1.5)),
CenterCrop(config.img_height, config.img_weight),
A.LongestMaxSize(longest_size, interpolation=cv2.INTER_CUBIC),
A.PadIfNeeded(image_size[0],
image_size[1],
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.Normalize(),
ToTensor()
])
def __init__(self,
df,
df_controls,
stats_experiments,
img_dir,
mode,
verbose=True,
channels=[1, 2, 3, 4, 5, 6]):
self.records = deepcopy(df).to_records(index=False)
df_conts = deepcopy(df_controls)
mask = (df_conts['well_type'] == 'negative_control') & \
(df_conts['well'] == 'B02')
df_neg_conts = df_conts[mask]
self.records_neg_conts = df_neg_conts.to_records(index=False)
mask = (df_conts['well_type'] == 'positive_control')
df_pos_conts = df_conts[mask]
self.records_pos_conts = df_pos_conts.to_records(index=False)
self.stats_exps = stats_experiments
self.mode = mode
self.channels = channels
self.img_dir = img_dir
self.len = df.shape[0]
self.transform_train = Compose([
VerticalFlip(p=0.5),
HorizontalFlip(p=0.5),
ShiftScaleRotate(
shift_limit=0, scale_limit=0, rotate_limit=180, p=1.0),
RandomCrop(height=364, width=364, p=1.0)
],
p=1.0)
self.transform_val = Compose(
[CenterCrop(height=364, width=364, p=1.0)], p=1.0)
if verbose:
print()
self.imgs = self._load_imgs(self.records,
desc='Images',
verbose=verbose)
self.imgs_neg_conts = self._load_imgs(self.records_neg_conts,
desc='Negative controls',
verbose=verbose)
self.imgs_pos_conts = self._load_imgs(self.records_pos_conts,
desc='Positive controls',
verbose=verbose)