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transforms.py
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transforms.py
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import cv2
import random
import numpy as np
from albumentations.core.serialization import SERIALIZABLE_REGISTRY
from albumentations import ImageOnlyTransform, DualTransform
from albumentations.core.transforms_interface import to_tuple
from albumentations.augmentations import functional as F
from isegm.utils.misc import get_bbox_from_mask, expand_bbox, clamp_bbox, get_labels_with_sizes
class UniformRandomResize(DualTransform):
def __init__(self, scale_range=(0.9, 1.1), interpolation=cv2.INTER_LINEAR, always_apply=False, p=1):
super().__init__(always_apply, p)
self.scale_range = scale_range
self.interpolation = interpolation
def get_params_dependent_on_targets(self, params):
scale = random.uniform(*self.scale_range)
height = int(round(params['image'].shape[0] * scale))
width = int(round(params['image'].shape[1] * scale))
return {'new_height': height, 'new_width': width}
def apply(self, img, new_height=0, new_width=0, interpolation=cv2.INTER_LINEAR, **params):
return F.resize(img, height=new_height, width=new_width, interpolation=interpolation)
def apply_to_keypoint(self, keypoint, new_height=0, new_width=0, **params):
scale_x = new_width / params["cols"]
scale_y = new_height / params["rows"]
return F.keypoint_scale(keypoint, scale_x, scale_y)
def get_transform_init_args_names(self):
return "scale_range", "interpolation"
@property
def targets_as_params(self):
return ["image"]
class ZoomIn(DualTransform):
def __init__(
self,
height,
width,
bbox_jitter=0.1,
expansion_ratio=1.4,
min_crop_size=200,
min_area=100,
always_resize=False,
always_apply=False,
p=0.5,
):
super(ZoomIn, self).__init__(always_apply, p)
self.height = height
self.width = width
self.bbox_jitter = to_tuple(bbox_jitter)
self.expansion_ratio = expansion_ratio
self.min_crop_size = min_crop_size
self.min_area = min_area
self.always_resize = always_resize
def apply(self, img, selected_object, bbox, **params):
if selected_object is None:
if self.always_resize:
img = F.resize(img, height=self.height, width=self.width)
return img
rmin, rmax, cmin, cmax = bbox
img = img[rmin:rmax + 1, cmin:cmax + 1]
img = F.resize(img, height=self.height, width=self.width)
return img
def apply_to_mask(self, mask, selected_object, bbox, **params):
if selected_object is None:
if self.always_resize:
mask = F.resize(mask, height=self.height, width=self.width,
interpolation=cv2.INTER_NEAREST)
return mask
rmin, rmax, cmin, cmax = bbox
mask = mask[rmin:rmax + 1, cmin:cmax + 1]
if isinstance(selected_object, tuple):
layer_indx, mask_id = selected_object
obj_mask = mask[:, :, layer_indx] == mask_id
new_mask = np.zeros_like(mask)
new_mask[:, :, layer_indx][obj_mask] = mask_id
else:
obj_mask = mask == selected_object
new_mask = mask.copy()
new_mask[np.logical_not(obj_mask)] = 0
new_mask = F.resize(new_mask, height=self.height, width=self.width,
interpolation=cv2.INTER_NEAREST)
return new_mask
def get_params_dependent_on_targets(self, params):
instances = params['mask']
is_mask_layer = len(instances.shape) > 2
candidates = []
if is_mask_layer:
for layer_indx in range(instances.shape[2]):
labels, areas = get_labels_with_sizes(instances[:, :, layer_indx])
candidates.extend([(layer_indx, obj_id)
for obj_id, area in zip(labels, areas)
if area > self.min_area])
else:
labels, areas = get_labels_with_sizes(instances)
candidates = [obj_id for obj_id, area in zip(labels, areas)
if area > self.min_area]
selected_object = None
bbox = None
if candidates:
selected_object = random.choice(candidates)
if is_mask_layer:
layer_indx, mask_id = selected_object
obj_mask = instances[:, :, layer_indx] == mask_id
else:
obj_mask = instances == selected_object
bbox = get_bbox_from_mask(obj_mask)
if isinstance(self.expansion_ratio, tuple):
expansion_ratio = random.uniform(*self.expansion_ratio)
else:
expansion_ratio = self.expansion_ratio
bbox = expand_bbox(bbox, expansion_ratio, self.min_crop_size)
bbox = self._jitter_bbox(bbox)
bbox = clamp_bbox(bbox, 0, obj_mask.shape[0] - 1, 0, obj_mask.shape[1] - 1)
return {
'selected_object': selected_object,
'bbox': bbox
}
def _jitter_bbox(self, bbox):
rmin, rmax, cmin, cmax = bbox
height = rmax - rmin + 1
width = cmax - cmin + 1
rmin = int(rmin + random.uniform(*self.bbox_jitter) * height)
rmax = int(rmax + random.uniform(*self.bbox_jitter) * height)
cmin = int(cmin + random.uniform(*self.bbox_jitter) * width)
cmax = int(cmax + random.uniform(*self.bbox_jitter) * width)
return rmin, rmax, cmin, cmax
def apply_to_bbox(self, bbox, **params):
raise NotImplementedError
def apply_to_keypoint(self, keypoint, **params):
raise NotImplementedError
@property
def targets_as_params(self):
return ["mask"]
def get_transform_init_args_names(self):
return ("height", "width", "bbox_jitter",
"expansion_ratio", "min_crop_size", "min_area", "always_resize")
def remove_image_only_transforms(sdict):
if not 'transforms' in sdict:
return sdict
keep_transforms = []
for tdict in sdict['transforms']:
cls = SERIALIZABLE_REGISTRY[tdict['__class_fullname__']]
if 'transforms' in tdict:
keep_transforms.append(remove_image_only_transforms(tdict))
elif not issubclass(cls, ImageOnlyTransform):
keep_transforms.append(tdict)
sdict['transforms'] = keep_transforms
return sdict