/
test_datagen.py
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/
test_datagen.py
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from __future__ import print_function, unicode_literals, absolute_import, division
from six.moves import range, zip, map, reduce, filter
# import warnings
import numpy as np
import pytest
from tifffile import imread, imsave
from csbdeep.data import RawData, create_patches, create_patches_reduced_target
from csbdeep.io import load_training_data
from csbdeep.utils import Path, axes_dict, move_image_axes, backend_channels_last
def test_create_patches():
rng = np.random.RandomState(42)
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, '')
n_images, n_patches_per_image = 2, 4
def _create(img_size,img_axes,patch_size,patch_axes):
X,Y,XYaxes = create_patches (
raw_data = get_data(n_images, img_axes, img_size),
patch_size = patch_size,
patch_axes = patch_axes,
n_patches_per_image = n_patches_per_image,
)
assert len(X) == n_images*n_patches_per_image
assert np.allclose(X,Y,atol=1e-6)
if patch_axes is not None:
assert XYaxes == 'SC'+patch_axes.replace('C','')
_create((128,128),'YX',(32,32),'YX')
_create((128,128),'YX',(32,32),None)
_create((128,128),'YX',(32,32),'XY')
_create((128,128),'YX',(32,32,1),'XYC')
_create((32,48,32),'ZYX',(16,32,8),None)
_create((32,48,32),'ZYX',(16,32,8),'ZYX')
_create((32,48,32),'ZYX',(16,32,8),'YXZ')
_create((32,48,32),'ZYX',(16,32,1,8),'YXCZ')
def test_create_patches_reduced_target():
rng = np.random.RandomState(42)
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
n_images, n_patches_per_image = 2, 4
def _create(red_none,img_size,img_axes,patch_size,patch_axes):
raw_data, red_axes, keepdims = get_data(n_images, img_axes, img_size)
# change patch_size to (img_size or None) for red_axes
patch_size = list(patch_size)
for a in red_axes:
patch_size[axes_dict(img_axes if patch_axes is None else patch_axes)[a]] = (
None if red_none else img_size[axes_dict(img_axes)[a]]
)
X,Y,XYaxes = create_patches_reduced_target (
raw_data = raw_data,
patch_size = patch_size,
patch_axes = patch_axes,
n_patches_per_image = n_patches_per_image,
reduction_axes = red_axes,
target_axes = rng.choice((None,img_axes)) if keepdims else ''.join(a for a in img_axes if a not in red_axes),
#
normalization = lambda patches_x, patches_y, *args: (patches_x, patches_y),
verbose = False,
)
assert len(X) == n_images*n_patches_per_image
_X = np.mean(X,axis=tuple(axes_dict(XYaxes)[a] for a in red_axes),keepdims=True)
err = np.max(np.abs(_X-Y))
assert err < 1e-5
for b in (True,False):
_create(b,(128,128),'YX',(32,32),'YX')
_create(b,(128,128),'YX',(32,32),None)
_create(b,(128,128),'YX',(32,32),'XY')
_create(b,(128,128),'YX',(32,32,1),'XYC')
_create(b,(32,48,32),'ZYX',(16,32,8),None)
_create(b,(32,48,32),'ZYX',(16,32,8),'ZYX')
_create(b,(32,48,32),'ZYX',(16,32,8),'YXZ')
_create(b,(32,48,32),'ZYX',(16,32,1,8),'YXCZ')
_create(b,(128,2,128),'YCX',(32,2,32),'YCX')
_create(b,(3,128,128),'CYX',(3,32,32),None)
_create(b,(128,128,4),'YXC',(4,32,32),'CXY')
_create(b,(128,128,5),'YXC',(32,32,5),'XYC')
_create(b,(32,48,2,32),'ZYCX',(16,32,2,8),None)
_create(b,(32,3,48,32),'ZCYX',(3,16,32,8),'CZYX')
_create(b,(4,32,48,32),'CZYX',(16,32,8,4),'YXZC')
_create(b,(32,48,32,2),'ZYXC',(16,32,2,8),'YXCZ')
def test_create_save_and_load(tmpdir):
rng = np.random.RandomState(42)
tmpdir = Path(str(tmpdir))
save_file = str(tmpdir / 'data.npz')
n_images, n_patches_per_image = 2, 4
def _create(img_size,img_axes,patch_size,patch_axes):
U,V = (rng.uniform(size=(n_images,)+img_size) for _ in range(2))
X,Y,XYaxes = create_patches (
raw_data = RawData.from_arrays(U,V,img_axes),
patch_size = patch_size,
patch_axes = patch_axes,
n_patches_per_image = n_patches_per_image,
save_file = save_file
)
(_X,_Y), val_data, _XYaxes = load_training_data(save_file,verbose=True)
assert val_data is None
assert _XYaxes[-1 if backend_channels_last else 1] == 'C'
_X,_Y = (move_image_axes(u,fr=_XYaxes,to=XYaxes) for u in (_X,_Y))
assert np.allclose(X,_X,atol=1e-6)
assert np.allclose(Y,_Y,atol=1e-6)
assert set(XYaxes) == set(_XYaxes)
assert load_training_data(save_file,validation_split=0.5)[2] is not None
assert all(len(x)==3 for x in load_training_data(save_file,n_images=3)[0])
_create(( 64,64), 'YX',(16,16 ),None)
_create(( 64,64), 'YX',(16,16 ),'YX')
_create(( 64,64), 'YX',(16,16,1),'YXC')
_create((1,64,64),'CYX',( 16,16),'YX')
_create((1,64,64),'CYX',(1,16,16),None)
_create((64,3,64),'YCX',(3,16,16),'CYX')
_create((64,3,64),'YCX',(16,16,3),'YXC')
def test_rawdata_from_folder(tmpdir):
rng = np.random.RandomState(42)
tmpdir = Path(str(tmpdir))
n_images, img_size, img_axes = 3, (64,64), 'YX'
data = {'X' : rng.uniform(size=(n_images,)+img_size).astype(np.float32),
'Y' : rng.uniform(size=(n_images,)+img_size).astype(np.float32)}
for name,images in data.items():
(tmpdir/name).mkdir(exist_ok=True)
for i,img in enumerate(images):
imsave(str(tmpdir/name/('img_%02d.tif'%i)),img)
raw_data = RawData.from_folder(str(tmpdir),['X'],'Y',img_axes)
assert raw_data.size == n_images
for i,(x,y,axes,mask) in enumerate(raw_data.generator()):
assert mask is None
assert axes == img_axes
assert any(np.allclose(x,u) for u in data['X'])
assert any(np.allclose(y,u) for u in data['Y'])