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140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
import numpy as np
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import torch as t
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from scipy import ndimage
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from cdtools.tools import image_processing, interactions
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def test_centroid():
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# Test single im
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im = t.rand((30, 40))
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sp_centroid = ndimage.center_of_mass(im.numpy())
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centroid = image_processing.centroid(im)
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assert t.allclose(centroid, t.Tensor(sp_centroid))
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# Test stack o' ims
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ims = t.rand((5, 30, 40))
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sp_centroids = [ndimage.center_of_mass(im.numpy())
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for im in ims]
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centroids = image_processing.centroid(ims)
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assert t.allclose(centroids, t.Tensor(sp_centroids))
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def test_centroid_sq():
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# Test single im
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im = t.rand((30, 40))
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sp_centroid = ndimage.center_of_mass(im.numpy()**2)
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centroid = image_processing.centroid_sq(im)
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assert t.allclose(centroid, t.Tensor(sp_centroid))
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# Test complex with multiple ims
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ims = t.rand((5, 30, 40)) + 1j * t.rand((5, 30, 40))
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np_ims = ims.numpy()
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sp_centroids = [ndimage.center_of_mass(np.abs(im)**2)
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for im in np_ims]
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centroids = image_processing.centroid_sq(ims, comp=True)
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assert t.allclose(centroids, t.Tensor(np.array(sp_centroids)))
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def test_sinc_subpixel_shift():
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im = np.zeros((512, 512), dtype=np.complex128)
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im[256, 256] = 1
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# test it by creating a single pixel object and seeing that it is
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# shifted correctly
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xs = np.arange(512) - 256
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Ys, Xs = np.meshgrid(xs, xs)
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sinc_im = np.sinc(Xs - 0.3) * np.sinc(Ys - 0.6)
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torch_im = t.as_tensor(im)
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test_im = image_processing.sinc_subpixel_shift(torch_im, (0.3, 0.6))
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# The fidelity isn't great due to the FFT-based approach, so we need
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# a pretty relaxed condition
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assert np.max(np.abs(sinc_im - test_im.numpy())) < 0.005
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def test_find_pixel_shift():
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# Test two real ims
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big_im = t.rand((30, 70))
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im1 = big_im[3:, :-20]
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im2 = big_im[:-3, 20:]
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assert t.all(image_processing.find_pixel_shift(im1, im2) == t.LongTensor([-3, 20]))
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# Test a real and complex im
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big_im = t.rand((30, 70))
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im1 = big_im[:-5, 10:].to(dtype=t.complex64)
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im2 = big_im[5:, :-10]
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assert t.all(image_processing.find_pixel_shift(im1, im2) == t.LongTensor([5, -10]))
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assert t.all(image_processing.find_pixel_shift(im2, im1) == t.LongTensor([-5, 10]))
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# Test two complex ims
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big_im = t.rand((45, 45)) + 1j * t.rand((45, 45))
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im1 = big_im[:-5, :-4]
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im2 = big_im[5:, 4:]
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assert t.all(image_processing.find_pixel_shift(im1, im2) == t.LongTensor([5, 4]))
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def test_find_subpixel_shift():
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# We can do this by creating a test probe and a test object
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test_probe = t.rand((70, 70)) + 1j * t.rand((70, 70))
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test_obj = t.ones((300, 300)) + 1j * t.rand((300, 300))
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shift = t.tensor((0.8, 0.75))
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im = interactions.ptycho_2D_sinc(test_probe, test_obj, shift, multiple_modes=False)
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retrieved_shift = image_processing.find_subpixel_shift(im, test_probe, search_around=(0, 0), resolution=50)
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# tolerance of 0.03 on this measurement
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assert t.all(t.abs(shift - retrieved_shift) < 0.03)
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def test_find_shift():
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# We can do this by creating a test probe and a test object
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test_probe = t.rand((200, 200)) + 1j * t.rand((200, 200))
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test_obj = t.ones((300, 300)) + 1j * t.rand((300, 300))
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shift = t.tensor((0.8, 0.75))
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im = interactions.ptycho_2D_sinc(test_probe, test_obj, shift,
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multiple_modes=False)[:-40, :-6]
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retrieved_shift = image_processing.find_shift(im, test_probe[40:, 6:], resolution=50)
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# tolerance of 0.03 on this measurement
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assert t.all(t.abs(shift + t.Tensor((40, 6)) - retrieved_shift) < 0.03)
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def test_convolve_1d():
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test_image = np.random.rand(400, 300)
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# test_image = np.hstack((np.ones((400,150)),np.zeros((400,150))))
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xs = np.linspace(-100, 100, 300)
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kernel = 1 / (1 + xs**2)
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# First, we test with everything real, dim=1
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convolved = image_processing.convolve_1d(t.as_tensor(test_image),
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t.as_tensor(kernel), dim=1)
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np_result = np.abs(np.fft.ifft(np.fft.fft(test_image, axis=1) * np.fft.fft(np.fft.ifftshift(kernel)), axis=1))
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assert np.allclose(convolved.numpy(), np_result)
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xs = np.linspace(-100, 100, 400)
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kernel = 1 / (1 + xs**2)
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# Then with dim=0, and a non-fftshifted kernel
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convolved = image_processing.convolve_1d(t.as_tensor(test_image),
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t.as_tensor(np.fft.ifftshift(kernel)),
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fftshift_kernel=False)
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np_result = np.abs(np.fft.ifft(np.fft.fft(test_image, axis=0) * np.fft.fft(np.fft.ifftshift(kernel))[:, None], axis=0))
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assert np.allclose(convolved.numpy(), np_result)
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# And finally with complex input
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convolved = image_processing.convolve_1d(t.as_tensor(test_image, dtype=t.complex64),
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t.as_tensor(kernel, dtype=t.complex64)).numpy()
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np_result = np.fft.ifft(np.fft.fft(test_image, axis=0) * np.fft.fft(np.fft.ifftshift(kernel))[:, None], axis=0)
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assert np.allclose(convolved, np_result)
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