def translate(self,
out_shape,
offset,
direction='horizontal',
fill_val=0,
interpolation='bilinear'):
"""Translate the BitmapMasks.
Args:
out_shape (tuple[int]): Shape for output mask, format (h, w).
offset (int | float): The offset for translate.
direction (str): The translate direction, either "horizontal"
or "vertical".
fill_val (int | float): Border value. Default 0 for masks.
interpolation (str): Same as :func:`mmcv.imtranslate`.
Returns:
BitmapMasks: Translated BitmapMasks.
"""
if len(self.masks) == 0:
translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
else:
translated_masks = mmcv.imtranslate(self.masks.transpose(
(1, 2, 0)),
offset,
direction,
border_value=fill_val,
interpolation=interpolation)
if translated_masks.ndim == 2:
translated_masks = translated_masks[:, :, None]
translated_masks = translated_masks.transpose(
(2, 0, 1)).astype(self.masks.dtype)
return BitmapMasks(translated_masks, *out_shape)
def _translate_seg(self,
results,
offset,
direction='horizontal',
fill_val=255):
"""Translate segmentation maps horizontally or vertically."""
for key in results.get('seg_fields', []):
seg = results[key].copy()
results[key] = mmcv.imtranslate(seg, offset, direction,
fill_val).astype(seg.dtype)
def _translate_img(self, results, offset, direction='horizontal'):
"""Translate the image.
Args:
results (dict): Result dict from loading pipeline.
offset (int | float): The offset for translate.
direction (str): The translate direction, either "horizontal"
or "vertical".
"""
for key in results.get('img_fields', ['img']):
img = results[key].copy()
results[key] = mmcv.imtranslate(
img, offset, direction, self.img_fill_val).astype(img.dtype)
def translate(self,
out_shape,
offset,
direction='horizontal',
fill_val=0,
interpolation='bilinear'):
"""Translate the BitmapMasks.
Args:
out_shape (tuple[int]): Shape for output mask, format (h, w).
offset (int | float): The offset for translate.
direction (str): The translate direction, either "horizontal"
or "vertical".
fill_val (int | float): Border value. Default 0 for masks.
interpolation (str): Same as :func:`mmcv.imtranslate`.
Returns:
BitmapMasks: Translated BitmapMasks.
Example:
>>> from mmdet.core.mask.structures import BitmapMasks
>>> self = BitmapMasks.random(dtype=np.uint8)
>>> out_shape = (32, 32)
>>> offset = 4
>>> direction = 'horizontal'
>>> fill_val = 0
>>> interpolation = 'bilinear'
>>> # Note, There seem to be issues when:
>>> # * out_shape is different than self's shape
>>> # * the mask dtype is not supported by cv2.AffineWarp
>>> new = self.translate(out_shape, offset, direction, fill_val,
>>> interpolation)
>>> assert len(new) == len(self)
>>> assert new.height, new.width == out_shape
"""
if len(self.masks) == 0:
translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
else:
translated_masks = mmcv.imtranslate(
self.masks.transpose((1, 2, 0)),
offset,
direction,
border_value=fill_val,
interpolation=interpolation)
if translated_masks.ndim == 2:
translated_masks = translated_masks[:, :, None]
translated_masks = translated_masks.transpose(
(2, 0, 1)).astype(self.masks.dtype)
return BitmapMasks(translated_masks, *out_shape)
def __call__(self, results):
if np.random.rand() > self.prob:
return results
magnitude = random_negative(self.magnitude, self.random_negative_prob)
for key in results.get('img_fields', ['img']):
img = results[key]
height, width = img.shape[:2]
if self.direction == 'horizontal':
offset = magnitude * width
else:
offset = magnitude * height
img_translated = mmcv.imtranslate(img,
offset,
direction=self.direction,
border_value=self.pad_val,
interpolation=self.interpolation)
results[key] = img_translated.astype(img.dtype)
return results
def test_imtranslate(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.uint8)
assert_array_equal(mmcv.imtranslate(img, 0), img)
# offset=1, horizontal
img_translated = np.array([[128, 1, 2], [128, 4, 5], [128, 7, 8]],
dtype=np.uint8)
assert_array_equal(mmcv.imtranslate(img, 1, border_value=128),
img_translated)
# offset=-1, vertical
img_translated = np.array([[4, 5, 6], [7, 8, 9], [0, 0, 0]],
dtype=np.uint8)
assert_array_equal(mmcv.imtranslate(img, -1, 'vertical'),
img_translated)
# offset=-2, horizontal
img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
img_translated = [[3, 4, 128, 128], [7, 8, 128, 128]]
img_translated = np.stack(
[img_translated, img_translated, img_translated], axis=-1)
assert_array_equal(mmcv.imtranslate(img, -2, border_value=128),
img_translated)
# offset=2, vertical
border_value = (110, 120, 130)
img_translated = np.stack([
np.ones((2, 4)) * border_value[0],
np.ones((2, 4)) * border_value[1],
np.ones((2, 4)) * border_value[2]
],
axis=-1).astype(np.uint8)
assert_array_equal(mmcv.imtranslate(img, 2, 'vertical', border_value),
img_translated)
# test invalid number elements in border_value
with pytest.raises(AssertionError):
mmcv.imtranslate(img, 1, border_value=(1, ))
# test invalid type of border_value
with pytest.raises(ValueError):
mmcv.imtranslate(img, 1, border_value=[1, 2, 3])
# test invalid value of direction
with pytest.raises(AssertionError):
mmcv.imtranslate(img, 1, 'diagonal')
