def test_resizer(axes):
rng = np.random.RandomState(42)
resizer = PadAndCropResizer()
checker = NoResizer()
for _ in range(50):
imdims = list(rng.randint(20,40,size=len(axes)))
div_by = list(rng.randint(1,20,size=len(axes)))
u = np.empty(imdims,np.float32)
if any(s%div_n!=0 for s, div_n in zip(imdims, div_by)):
with pytest.raises(ValueError):
checker.before(u, axes, div_by)
v = resizer.before(u, axes, div_by)
assert all (
s_v >= s_u and s_v%div_n==0
for s_u, s_v, div_n in zip(u.shape, v.shape, div_by)
)
w = resizer.after(v, axes)
assert u.shape == w.shape
d = rng.choice(len(axes))
_axes = axes.replace(axes[d],'')
_u = np.take(u,0,axis=d)
_v = np.take(v,0,axis=d)
_w = resizer.after(_v, _axes)
assert _u.shape == _w.shape
def predict(self, img, resizer=PadAndCropResizer(), **predict_kwargs):
"""Predict.
Parameters
----------
img : :class:`numpy.ndarray`
Input image
resizer : :class:`csbdeep.data.Resizer` or None
If necessary, input image is resized to enable neural network prediction and result is (possibly)
resized to yield original image size.
Returns
-------
(:class:`numpy.ndarray`,:class:`numpy.ndarray`)
Returns the tuple (`prob`, `dist`) of per-pixel object probabilities and star-convex polygon distances.
"""
if resizer is None:
resizer = NoResizer()
isinstance(resizer, Resizer) or _raise(ValueError())
img.ndim in (2, 3) or _raise(ValueError())
x = img
if x.ndim == 2:
x = np.expand_dims(x, (-1 if backend_channels_last() else 0))
channel = x.ndim - 1 if backend_channels_last() else 0
axes = 'YXC' if backend_channels_last() else 'CYX'
self.config.n_channel_in == x.shape[channel] or _raise(ValueError())
# resize: make divisible by power of 2 to allow downsampling steps in unet
axes_div_by = tuple(2**self.config.unet_n_depth if a != 'C' else 1
for a in axes)
x = resizer.before(x, axes, axes_div_by)
if backend_channels_last():
sh = x.shape[:-1] + (1, )
else:
sh = (1, ) + x.shape[1:]
dummy = np.empty((1, ) + sh, np.float32)
prob, dist = self.keras_model.predict([np.expand_dims(x, 0), dummy],
**predict_kwargs)
prob, dist = prob[0], dist[0]
prob = resizer.after(prob, axes)
dist = resizer.after(dist, axes)
prob = np.take(prob, 0, axis=channel)
dist = np.moveaxis(dist, channel, -1)
return prob, dist
