![]() ![]() Because most client work would like to support as much as users as possible, that means you have to work on an app that supports iOS N-1, N-2, or worse N-3. If you plan to find a job or work on a client project in which you have no control over this OS version, you might want to wait a year or two before considering moving to SwiftUI. If you work on a new app that plans to target only the mentioned OS, I would say yes. It depends since SwiftUI runs on iOS 13, macOS 10.15, tvOS 13, and watchOS 6. Out = np.Frequently asked questions about SwiftUI. # take 3 wavelet transforms on each channel and average S = Scattering2D(shape=img.shape, L=L, J=J) ![]() J, L = 4, 8 # largest scale number of angles Imshow(img, w=.6, h=.6, title="%s x %s image" % img.shape) S2 = np.vstack()ĭef group_by_scale_second_order(out, J, L): S2_slices = np.vstack(S2_slices).mean(axis=0) Imshow(S1_theta, title=f"angle=",ĭef group_by_angle_second_order(out, J, L): S1_theta = np.vstack(list(S1_slices.values())) S1_slices = np.vstack(S1_slices).mean(axis=0) N_S1 = J * L # number of first-order coeffs J=3 no subsampling / lowpassingĬan't do with library code, see comment below answer.Ī conv-net can be trained on top of scattering features with segmentation objective wavelet scattering attains SOTA on many benchmarks in limited data settings. Inspect the wavelets used, and keep a subset (or just one) examples in code. To preserve fastest variations, set J=1, which omits low-freq wavelets and makes lowpass narrow (also reducing subsampling): Too many outputs? ( scale=0 is missing for theoretical reasons) Comparing against first-order, we see bottom-left is more intense, which follows from colors shifting at a changing rate (faster toward fractal singularity). Obtained by taking wavelet transform of wavelet transform, capturing variations of variations: Then we want a large scale wavelet that's steeply oriented. Suppose we wish to detect this transition: Orientation information is obfuscated by averaging over all the scales. Scale=0 captures fast variations over small intervals, while scale=3 captures slow variations over large regions Grouped by angle ExampleĮxample wavelets ( j = scale index (width frequency), theta = angle index): Grouped by scale Outputs are made robust to noise at expense of spatial localization by lowpassing the modulus of output. "greatest variation over a 2cm x 2cm region" by indexing with a proper unit conversion and taking argmax over the 2D slice each slice is an "intensity heatmap" of variations per n.
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