def __call__(self, sample):
instances_info = {
obj_id: obj_info
for obj_id, obj_info in sample['instances_info'].items()
if not obj_info['ignore']
}
obj_id, obj_info = random.choice(list(instances_info.items()))
sample['instances_info'] = {obj_id: obj_info}
obj_mask = sample['instances_mask'] == obj_id
crop_height, crop_width = self.crop_size
obj_bbox = get_bbox_from_mask(obj_mask)
obj_bbox = fit_bbox_ratio(obj_bbox, crop_height / crop_width)
expand_k = np.random.uniform(self.min_expand, self.max_expand)
obj_bbox = expand_bbox(obj_bbox, expand_ratio=expand_k)
obj_bbox = clamp_bbox(obj_bbox, 0, sample['image'].shape[0] - 1, 0,
sample['image'].shape[1] - 1)
sample['image'] = sample['image'][obj_bbox[0]:obj_bbox[1] + 1,
obj_bbox[2]:obj_bbox[3] + 1, :]
sample['instances_mask'] = sample['instances_mask'][
obj_bbox[0]:obj_bbox[1] + 1, obj_bbox[2]:obj_bbox[3] + 1]
sample['image'] = cv2.resize(sample['image'],
(crop_width, crop_height))
sample['instances_mask'] = cv2.resize(sample['instances_mask'],
(crop_width, crop_height),
interpolation=cv2.INTER_NEAREST)
return sample
def get_object_roi(pred_mask, clicks_list, expansion_ratio, min_crop_size):
pred_mask = pred_mask.copy()
for click in clicks_list:
if click.is_positive:
pred_mask[int(click.coords[0]), int(click.coords[1])] = 1
bbox = get_bbox_from_mask(pred_mask)
bbox = expand_bbox(bbox, expansion_ratio, min_crop_size)
h, w = pred_mask.shape[0], pred_mask.shape[1]
bbox = clamp_bbox(bbox, 0, h - 1, 0, w - 1)
return bbox
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}