def __call__(self, results):
if 'lazy' not in results:
raise ValueError('No lazy operation detected')
lazyop = results['lazy']
imgs = results['imgs']
# crop
left, top, right, bottom = lazyop['crop_bbox'].round().astype(int)
imgs = [img[top:bottom, left:right] for img in imgs]
# resize
img_h, img_w = results['img_shape']
if lazyop['interpolation'] is None:
interpolation = 'bilinear'
else:
interpolation = lazyop['interpolation']
imgs = [
mmcv.imresize(img, (img_w, img_h), interpolation=interpolation)
for img in imgs
]
# flip
if lazyop['flip']:
for img in imgs:
mmcv.imflip_(img, lazyop['flip_direction'])
results['imgs'] = imgs
del results['lazy']
return results
def _augment(img):
if hflip:
mmcv.imflip_(img, 'horizontal')
if vflip:
mmcv.imflip_(img, 'vertical')
if rot90:
img = img.transpose(1, 0, 2)
return img
def _augment_flow(flow):
if hflip:
mmcv.imflip_(flow, 'horizontal')
flow[:, :, 0] *= -1
if vflip:
mmcv.imflip_(flow, 'vertical')
flow[:, :, 1] *= -1
if rot90:
flow = flow.transpose(1, 0, 2)
flow = flow[:, :, [1, 0]]
return flow
def test_imflip_(self):
# test horizontal flip (color image)
img = np.random.rand(80, 60, 3)
h, w, c = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[i, w - 1 - j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test vertical flip (color image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test horizontal flip (grayscale image)
img = np.random.rand(80, 60)
h, w = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[i, w - 1 - j]
assert flipped_img[i, j] == img_for_flip[i, j]
# test vertical flip (grayscale image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, j]
assert flipped_img[i, j] == img_for_flip[i, j]
def __call__(self, results):
"""Performs the Flip augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
modality = results['modality']
assert modality == 'RGB'
if np.random.rand() < self.flip_ratio:
flip = True
else:
flip = False
results['flip'] = flip
results['flip_direction'] = self.direction
if not self.lazy:
if flip:
for i, img in enumerate(results['imgs']):
mmcv.imflip_(img, self.direction)
img_h, img_w = results['img_shape']
for idx in range(len(results['detections'])):
cur_detections = results['detections'][idx].copy()
if self.direction == 'horizontal':
cur_detections[:, 0] = img_w - results['detections'][
idx][:, 2]
cur_detections[:, 2] = img_w - results['detections'][
idx][:, 0]
else:
cur_detections[:, 1] = img_h - results['detections'][
idx][:, 3]
cur_detections[:, 3] = img_h - results['detections'][
idx][:, 1]
results['detections'][idx] = cur_detections
else:
results['imgs'] = list(results['imgs'])
else:
raise NotImplementedError
return results
def __call__(self, results):
"""Performs the Flip augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
modality = results['modality']
if modality == 'Flow':
assert self.direction == 'horizontal'
if np.random.rand() < self.flip_ratio:
flip = True
else:
flip = False
results['flip'] = flip
results['flip_direction'] = self.direction
if not self.lazy:
if flip:
for i, img in enumerate(results['imgs']):
mmcv.imflip_(img, self.direction)
lt = len(results['imgs'])
for i in range(0, lt, 2):
# flow with even indexes are x_flow, which need to be
# inverted when doing horizontal flip
if modality == 'Flow':
results['imgs'][i] = mmcv.iminvert(results['imgs'][i])
else:
results['imgs'] = list(results['imgs'])
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Use one Flip please')
lazyop['flip'] = flip
lazyop['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]:
mmcv.imflip_(v, self.direction)
else:
mmcv.imflip_(results[key], self.direction)
results['flip'] = flip
results['flip_direction'] = self.direction
return results
def test_imflip_(self):
# direction must be "horizontal" or "vertical" or "diagonal"
with pytest.raises(AssertionError):
mmcv.imflip_(np.random.rand(80, 60, 3), direction='random')
# test horizontal flip (color image)
img = np.random.rand(80, 60, 3)
h, w, c = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[i, w - 1 - j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test vertical flip (color image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test diagonal flip (color image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='diagonal')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, w - 1 - j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test horizontal flip (grayscale image)
img = np.random.rand(80, 60)
h, w = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[i, w - 1 - j]
assert flipped_img[i, j] == img_for_flip[i, j]
# test vertical flip (grayscale image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, j]
assert flipped_img[i, j] == img_for_flip[i, j]
# test diagonal flip (grayscale image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='diagonal')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, w - 1 - j]
assert flipped_img[i, j] == img_for_flip[i, j]
