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')