def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
flip = np.random.random() < self.flip_ratio
if flip:
for key in self.keys:
if isinstance(results[key], list):
for v in results[key]:
if key in ('bbox', 'bboxes'):
bboxflip_(v, self.direction, self.Len)
elif key in ('flow'):
flowflip_(v, self.direction)
else:
imflip_(v, self.direction)
else:
if key in ('bbox', 'bboxes'):
bboxflip_(results[key], self.direction, self.Len)
elif key in ('flow'):
raise NotImplementedError(
"not implemented flow for just one frame")
else:
imflip_(results[key], self.direction)
results['flip'] = flip
results['flip_direction'] = self.direction
return results
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
flip = np.random.random() < self.flip_ratio
if flip:
for key in self.keys:
if isinstance(results[key], list):
for v in results[key]:
imflip_(v, self.direction)
else:
imflip_(results[key], self.direction)
results['flip'] = flip
results['flip_direction'] = self.direction
return results