def build_image_batch(images, dtype, channelfirst=False):
"""
Return batch of images.
If images have no channel a channel axis is added. For channelfirst=True
it will be added/moved to front otherwise the channel comes last.
All images in batch will have a channel axis. Batch is of shape
(n, c, h, w) or (n, h, w, c) depending on channelfirst, where n is
the number of images in the batch.
>>> from nutsml.datautil import shapestr
>>> images = [np.zeros((2, 3)), np.ones((2, 3))]
>>> batch = build_image_batch(images, 'uint8', True)
>>> shapestr(batch)
'2x1x2x3'
>>> batch
array([[[[0, 0, 0],
[0, 0, 0]]],
<BLANKLINE>
<BLANKLINE>
[[[1, 1, 1],
[1, 1, 1]]]], dtype=uint8)
:param numpy array images: Images to batch. Must be of shape (w,h,c)
or (w,h). Gray-scale with channel is fine (w,h,1) and also
alpha channel is fine (w,h,4).
:param numpy data type dtype: Data type of batch, e.g. 'uint8'
:param bool channelfirst: If True, channel is added/moved to front.
:return: Image batch with shape (n, c, h, w) or (n, h, w, c).
:rtype: np.array
"""
def _targetshape(image):
shape = image.shape
return (shape[0], shape[1], 1) if image.ndim == 2 else shape
n = len(images)
if not n:
raise ValueError('No images to build batch!')
h, w, c = _targetshape(images[0]) # shape of first(=all) images
if c > w or c > h:
raise ValueError('Channel not at last axis: ' + str((h, w, c)))
batch = np.empty((n, c, h, w) if channelfirst else (n, h, w, c))
for i, image in enumerate(images):
image = ni.add_channel(image, channelfirst)
if image.shape != batch.shape[1:]:
raise ValueError('Images vary in shape: ' + str(image.shape))
batch[i, :, :, :] = image
return batch.astype(dtype)
def test_add_channel():
image = np.ones((10, 20))
assert ni.add_channel(image, True).shape == (1, 10, 20)
assert ni.add_channel(image, False).shape == (10, 20, 1)
image = np.ones((10, 20, 3))
assert ni.add_channel(image, True).shape == (3, 10, 20)
assert ni.add_channel(image, False).shape == (10, 20, 3)
with pytest.raises(ValueError) as ex:
image = np.ones((10,))
ni.add_channel(image, True)
assert str(ex.value).startswith('Image must be 2 or 3 channel!')
with pytest.raises(ValueError) as ex:
image = np.ones((10, 20, 3, 1))
ni.add_channel(image, True)
assert str(ex.value).startswith('Image must be 2 or 3 channel!')