Python albumentations.RandomGamma() Examples
The following are 4
code examples of albumentations.RandomGamma().
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Example #1
| Source File: dataset.py From kaggle-kuzushiji-2019 with MIT License | 5 votes |
|
def get_transform(train: bool) -> Callable:
train_initial_size = 2048
crop_min_max_height = (400, 533)
crop_width = 512
crop_height = 384
if train:
transforms = [
A.LongestMaxSize(max_size=train_initial_size),
A.RandomSizedCrop(
min_max_height=crop_min_max_height,
width=crop_width,
height=crop_height,
w2h_ratio=crop_width / crop_height,
),
A.HueSaturationValue(
hue_shift_limit=7,
sat_shift_limit=10,
val_shift_limit=10,
),
A.RandomBrightnessContrast(),
A.RandomGamma(),
]
else:
test_size = int(train_initial_size *
crop_height / np.mean(crop_min_max_height))
print(f'Test image max size {test_size} px')
transforms = [
A.LongestMaxSize(max_size=test_size),
]
transforms.extend([
ToTensor(),
])
return A.Compose(
transforms,
bbox_params={
'format': 'coco',
'min_area': 0,
'min_visibility': 0.5,
'label_fields': ['labels'],
},
)
Example #2
| Source File: augmentation.py From EfficientDet.Pytorch with MIT License | 4 votes |
|
def get_augumentation(phase, width=512, height=512, min_area=0., min_visibility=0.):
list_transforms = []
if phase == 'train':
list_transforms.extend([
albu.augmentations.transforms.LongestMaxSize(
max_size=width, always_apply=True),
albu.PadIfNeeded(min_height=height, min_width=width,
always_apply=True, border_mode=0, value=[0, 0, 0]),
albu.augmentations.transforms.RandomResizedCrop(
height=height,
width=width, p=0.3),
albu.augmentations.transforms.Flip(),
albu.augmentations.transforms.Transpose(),
albu.OneOf([
albu.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
albu.RandomGamma(gamma_limit=(50, 150)),
albu.NoOp()
]),
albu.OneOf([
albu.RGBShift(r_shift_limit=20, b_shift_limit=15,
g_shift_limit=15),
albu.HueSaturationValue(hue_shift_limit=5,
sat_shift_limit=5),
albu.NoOp()
]),
albu.CLAHE(p=0.8),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
])
if(phase == 'test' or phase == 'valid'):
list_transforms.extend([
albu.Resize(height=height, width=width)
])
list_transforms.extend([
albu.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225), p=1),
ToTensor()
])
if(phase == 'test'):
return albu.Compose(list_transforms)
return albu.Compose(list_transforms, bbox_params=albu.BboxParams(format='pascal_voc', min_area=min_area,
min_visibility=min_visibility, label_fields=['category_id']))
Example #3
| Source File: dataset.py From kaggle-kuzushiji-2019 with MIT License | 4 votes |
|
def get_transform(
*,
train: bool,
test_height: int,
crop_width: int,
crop_height: int,
scale_aug: float,
color_hue_aug: int,
color_sat_aug: int,
color_val_aug: int,
normalize: bool = True,
) -> Callable:
train_initial_size = 3072 # this value should not matter any more?
crop_ratio = crop_height / test_height
crop_min_max_height = tuple(
int(train_initial_size * crop_ratio * (1 + sign * scale_aug))
for sign in [-1, 1])
if train:
transforms = [
LongestMaxSizeRandomSizedCrop(
max_size=train_initial_size,
min_max_height=crop_min_max_height,
width=crop_width,
height=crop_height,
w2h_ratio=crop_width / crop_height,
),
A.HueSaturationValue(
hue_shift_limit=color_hue_aug,
sat_shift_limit=color_sat_aug,
val_shift_limit=color_val_aug,
),
A.RandomBrightnessContrast(),
A.RandomGamma(),
]
else:
transforms = [
A.LongestMaxSize(max_size=test_height),
]
if normalize:
transforms.append(A.Normalize())
transforms.extend([
ToTensor(),
])
return A.Compose(
transforms,
bbox_params={
'format': 'coco',
'min_area': 0,
'min_visibility': 0.99,
'label_fields': ['labels'],
},
)
Example #4
| Source File: apolloscape.py From pytorch-segmentation with MIT License | 4 votes |
|
def __init__(self,
base_dir='../../data/apolloscape',
road_record_list=[{'road':'road02_seg','record':[22, 23, 24, 25, 26]}, {'road':'road03_seg', 'record':[7, 8, 9, 10, 11, 12]}],
split='train',
ignore_index=255,
debug=False):
self.debug = debug
self.base_dir = Path(base_dir)
self.ignore_index = ignore_index
self.split = split
self.img_paths = []
self.lbl_paths = []
for road_record in road_record_list:
self.road_dir = self.base_dir / Path(road_record['road'])
self.record_list = road_record['record']
for record in self.record_list:
img_paths_tmp = self.road_dir.glob(f'ColorImage/Record{record:03}/Camera 5/*.jpg')
lbl_paths_tmp = self.road_dir.glob(f'Label/Record{record:03}/Camera 5/*.png')
img_paths_basenames = {Path(img_path.name).stem for img_path in img_paths_tmp}
lbl_paths_basenames = {Path(lbl_path.name).stem.replace('_bin', '') for lbl_path in lbl_paths_tmp}
intersection_basenames = img_paths_basenames & lbl_paths_basenames
img_paths_intersection = [self.road_dir / Path(f'ColorImage/Record{record:03}/Camera 5/{intersection_basename}.jpg')
for intersection_basename in intersection_basenames]
lbl_paths_intersection = [self.road_dir / Path(f'Label/Record{record:03}/Camera 5/{intersection_basename}_bin.png')
for intersection_basename in intersection_basenames]
self.img_paths += img_paths_intersection
self.lbl_paths += lbl_paths_intersection
self.img_paths.sort()
self.lbl_paths.sort()
print(len(self.img_paths), len(self.lbl_paths))
assert len(self.img_paths) == len(self.lbl_paths)
self.resizer = albu.Resize(height=512, width=1024)
self.augmenter = albu.Compose([albu.HorizontalFlip(p=0.5),
# albu.RandomRotate90(p=0.5),
albu.Rotate(limit=10, p=0.5),
# albu.CLAHE(p=0.2),
# albu.RandomContrast(p=0.2),
# albu.RandomBrightness(p=0.2),
# albu.RandomGamma(p=0.2),
# albu.GaussNoise(p=0.2),
# albu.Cutout(p=0.2)
])
self.img_transformer = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
self.lbl_transformer = torch.LongTensor
