def test__pass_through_user_img(self):
F = np.eye(2, dtype=np.float)
image_deformer = vlab.imageDeformer_from_defGrad(F)
downsampler = vlab.Downsampler(image_shape=self.img_shape,
factor=1,
fill=1.,
pixel_offset_stddev=0.0)
noise_injector = lambda img: img
virtualTest = vlab.SyntheticImageGenerator(
speckle_image=self.image,
image_deformer=image_deformer,
downsampler=downsampler,
noise_injector=noise_injector,
n=10)
deviation = np.abs(virtualTest(1) - self.image)
if np.max(deviation) > self.tol:
self.fail(
"Image changed value or orientation. Largest error is%f" %
np.max(deviation))
speckle_image = vlab.rosta_speckle(super_image_shape,
dot_size=4,
density=0.5,
smoothness=2.0)
displacement_function = vlab.deformation_fields.harmonic_bilat
# Make an image deformed
image_deformer = vlab.imageDeformer_from_uFunc(displacement_function,
omega=2 * np.pi /
(500. * downsample_factor),
amp=2.0 * downsample_factor)
# Make an image down-sampler including downscaling, fill-factor and sensor grid irregularities
downsampler = vlab.Downsampler(image_shape=super_image_shape,
factor=downsample_factor,
fill=.95,
pixel_offset_stddev=0.05)
# Make a noise injector producing 2% gaussian additive noise
noise_injector = vlab.noise_injector("gaussian", sigma=.02)
# Make an synthetic image generation pipeline
image_generator = vlab.SyntheticImageGenerator(speckle_image=speckle_image,
image_deformer=image_deformer,
downsampler=downsampler,
noise_injector=noise_injector,
n=n_imgs)
# Put it into an image stack
image_stack = dic.ImageStack(image_generator)
# Now, make a mesh. Make sure to use enough elements