def validation_pipeline(mask_db, path):
image, mask = read_image_and_mask(mask_db, path)
args = Compose([
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
ChannelsFirst()
])(image=image, mask=mask)
return args['image'], args.get('mask')
def train_pipeline(cache, mask_db, path):
image, mask = read_image_and_mask_cached(cache, mask_db, (101, 101), path)
args = Compose([
LabelMaskBorder(),
HorizontalFlip(p=0.5),
OneOf([
ShiftScaleRotate(rotate_limit=15,
border_mode=cv2.BORDER_REPLICATE),
RandomSizedCrop(min_max_height=(70, 100), height=101, width=101)
],
p=0.2),
GaussNoise(p=0.2),
OneOf([
RandomBrightness(limit=0.4),
RandomGamma(),
], p=0.5),
OneOf([Blur(), MedianBlur(), MotionBlur()], p=0.2),
OneOf([
ElasticTransform(alpha=10, sigma=10, alpha_affine=10),
GridDistortion()
],
p=0.2),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
PadIfNeeded(128, 128, cv2.BORDER_REPLICATE),
ChannelsFirst()
])(image=image, mask=mask)
return args['image'], args.get('mask')
def validation_pipeline(cache, mask_db, path):
image, mask = read_image_and_mask_cached(cache, mask_db, (101, 101), path)
args = Compose([
LabelMaskBorder(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
PadIfNeeded(128, 128, cv2.BORDER_REPLICATE),
ChannelsFirst()
])(image=image, mask=mask)
return args['image'], args.get('mask')
def validation_pipeline(cache, sample_db, image_path):
image = read_image(image_path)
bboxes = load_bboxes(sample_db, image_path)
args = Compose([
ConvertBboxesToAlbumentations(),
Resize(256, 256),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
ChannelsFirst(),
ConvertBboxesToOriginal()
])(image=image, bboxes=bboxes)
return args['image'], args.get('bboxes')
