def get_data(n_images, axes, shape):
def _gen():
for i in range(n_images):
x = rng.uniform(size=shape)
y = 5 + 3*x
yield x, y, axes, None
return RawData(_gen, n_images, '')
def get_data(n_images, axes, shape):
red_n = rng.choice(len(axes) - 1) + 1
red_axes = ''.join(rng.choice(tuple(axes), red_n, replace=False))
keepdims = rng.choice((True, False))
def _gen():
for i in range(n_images):
x = rng.uniform(size=shape)
y = np.mean(x,
axis=tuple(axes_dict(axes)[a] for a in red_axes),
keepdims=keepdims)
yield x, y, axes, None
return RawData(_gen, n_images, ''), red_axes, keepdims
def original():
x = np.load('data/img006_noconv.npy')
x = np.moveaxis(x, 1, 2)
## initial axes are TZCYX, but we
axes = 'CZYX'
mypath = Path('isonet_psf_1')
mypath.mkdir(exist_ok=True)
subsample = 5.0 #10.2
print('image size =', x.shape)
print('image axes =', axes)
print('subsample factor =', subsample)
plt.switch_backend('agg')
if False:
plt.figure(figsize=(15, 15))
plot_some(np.moveaxis(x[0], 1, -1)[[5, -5]],
title_list=[['xy slice', 'xy slice']],
pmin=2,
pmax=99.8)
plt.savefig(mypath / 'datagen_1.png')
plt.figure(figsize=(15, 15))
plot_some(np.moveaxis(np.moveaxis(x[0], 1, -1)[:, [50, -50]], 1, 0),
title_list=[['xz slice', 'xz slice']],
pmin=2,
pmax=99.8,
aspect=subsample)
plt.savefig(mypath / 'datagen_2.png')
def gimmeit_gen():
## iterate over time dimension
for i in range(x.shape[0]):
yield x[i], x[i], axes, None
raw_data = RawData(gimmeit_gen, x.shape[0], "this is great!")
## initial idea
if False:
def buildkernel():
kern = np.exp(-(np.arange(10)**2 / 2))
kern /= kern.sum()
kern = kern.reshape([1, 1, -1, 1])
kern = np.stack([kern, kern], axis=1)
return kern
psf_kern = buildkernel()
## use Martins theoretical psf
if False:
psf_aniso = imread('data/psf_aniso_NA_0.8.tif')
psf_channels = np.stack([
psf_aniso,
] * 2, axis=1)
def buildkernel():
kernel = np.zeros(20)
kernel[7:13] = 1 / 6
## reshape into CZYX. long axis is X.
kernel = kernel.reshape([1, 1, -1])
## repeate same kernel for both channels
kernel = np.stack([kernel, kernel], axis=0)
return kernel
psf = buildkernel()
print(psf.shape)
## use theoretical psf
if False:
psf_channels = np.load('data/measured_psfs.npy')
psf = rotate(psf_channels, 90, axes=(1, 3))
iso_transform = data.anisotropic_distortions(
subsample=subsample,
psf=psf,
)
X, Y, XY_axes = data.create_patches(
raw_data=raw_data,
patch_size=(2, 1, 128, 128),
n_patches_per_image=256,
transforms=[iso_transform],
)
assert X.shape == Y.shape
print("shape of X,Y =", X.shape)
print("axes of X,Y =", XY_axes)
# remove dummy z dim to obtain multi-channel 2D patches
X = X[:, :, 0, ...]
Y = Y[:, :, 0, ...]
XY_axes = XY_axes.replace('Z', '')
assert X.shape == Y.shape
print("shape of X,Y =", X.shape)
print("axes of X,Y =", XY_axes)
np.savez(mypath / 'my_training_data.npz', X=X, Y=Y, axes=XY_axes)
for i in range(2):
plt.figure(figsize=(16, 4))
sl = slice(8 * i, 8 * (i + 1))
plot_some(np.moveaxis(X[sl], 1, -1),
np.moveaxis(Y[sl], 1, -1),
title_list=[np.arange(sl.start, sl.stop)])
plt.savefig(mypath / 'datagen_panel_{}.png'.format(i))