def _sample3D(in_shape, out_shape, seed):
X, Y, X_Batches, Y_Batches, indices = create_data(in_shape, out_shape)
np.random.seed(seed)
Noise2VoidDataWrapper.patch_sampler(X,
Y,
X_Batches,
Y_Batches,
indices,
range=in_shape[1:4] -
out_shape[1:4],
shape=out_shape[1:4])
assert ([*X_Batches.shape] == out_shape).all()
np.random.seed(seed)
range_z = in_shape[1] - out_shape[1]
range_y = in_shape[2] - out_shape[2]
range_x = in_shape[3] - out_shape[3]
for j in indices:
assert np.sum(X_Batches[j]) != 0
assert np.sum(Y_Batches[j]) != 0
z_start = np.random.randint(0, range_z + 1)
y_start = np.random.randint(0, range_y + 1)
x_start = np.random.randint(0, range_x + 1)
assert np.sum(X_Batches[j] -
X[j, z_start:z_start + out_shape[1],
y_start:y_start + out_shape[2],
x_start:x_start + out_shape[3]]) == 0
for j in range(in_shape[0]):
if j not in indices:
assert np.sum(X_Batches[j]) == 0
assert np.sum(Y_Batches[j]) == 0
def test_random_float_coords():
boxsize = 13
np.random.seed(1)
coords = (np.random.rand() * boxsize, np.random.rand() * boxsize)
np.random.seed(1)
assert next(Noise2VoidDataWrapper.__rand_float_coords2D__(boxsize)) == coords
boxsize = 3
np.random.seed(1)
coords = (np.random.rand() * boxsize, np.random.rand() * boxsize, np.random.rand() * boxsize)
np.random.seed(1)
assert next(Noise2VoidDataWrapper.__rand_float_coords3D__(boxsize)) == coords
def test_coord_gen():
coord_gen = Noise2VoidDataWrapper.__rand_float_coordsND__(13, 2)
shape = [128, 128]
for i in range(100):
coords = next(coord_gen)
assert coords[0] < shape[0]
assert coords[1] < shape[1]
coord_gen = Noise2VoidDataWrapper.__rand_float_coordsND__(4, 3)
shape = [55, 55, 55]
for i in range(100):
coords = next(coord_gen)
assert coords[0] < shape[0]
assert coords[1] < shape[1]
assert coords[2] < shape[2]
def _getitem3D(y_shape):
Y = create_data(y_shape)
X = Y[:,:,:,:,0][:,:,:,:,np.newaxis]
val_manipulator = random_neighbor_withCP_uniform
dw = Noise2VoidDataWrapper(X, Y, 4, num_pix=64, shape=(32, 32, 32), value_manipulation=val_manipulator)
x_batch, y_batch = dw.__getitem__(0)
assert x_batch.shape == (4, 32, 32, 32, 1)
assert y_batch.shape == (4, 32, 32, 32, 2)
assert np.sum(y_batch[:,:,:,:,1]) == 64*4
assert np.sum(X[y_batch[:,:,:,:,1] == 1] - Y[y_batch[:,:,:,:,1] == 1][:,0]) <= 10e-12