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.
#Scale image on vstack free
Update: today I encountered Robust Segmentation Free Algorithm for Homogeneity Quantification in Images.ĢD wavelet transform is well suited.
#Scale image on vstack full
The full code is available on my StackExchange Signal Processing Q75536 GitHub Repository (Look at the SignalProcessing\Q75536 folder). A Super Pixel with more features will be less homogenous. Then count how many of those are found within each Super Pixel. Those are very popular in segmentation.Īnother approach would be using more advanced features such as: In a more general form if you look for features for homogeneity and use them to find homogenous regions and then select the inverse. Apply Super Pixel based Segmentation (SLIC Based).Ĭalculate the mean value of each Super Pixel by the indices of each super pixel.Ĭalculate the variance of each super pixel using only its pixels.In my opinion the Super Pixel result is the best of all 3. But the super pixel approach seems to be more robust. It seems that for high SNR images you can work with the local Variance. Using the Weak Texture from Noise Level Estimation from a Single Image (By Masayuki Tanaka).Probably the easiest one would be by local variance. There are many approaches to shape such a feature.
#Scale image on vstack mp4
Scales the resulting video to force the height to be divisible by 2 cause mp4 videos return errors if the height is not divisible by 2.In general, the approach to take, is to have a local feature which has high value for such areas in the image. Positions the video as overlay under the top part of the image: Sets the video height according to the black area: ffmpeg -i input.mp4 -i img.jpg -filter_complex "scale2ref=w=iw:h=ow/mdar scale2ref=h=ih*((533-(53+118))/533) overlay=x=0:y=H*((53)/533) scale=iw:-2" -map 0:a:0 -map -shortest -vsync 0 -c:v libx264 -c:a copy output.mp4Įxplaining: This part sets the image same width and proportional height as video width: In theory at least form my calculations this should have worked perfectly but I still got some black strip between the video and the bottom part but no big mess. In this example I used the image provided by the topic creator. In this example I cut the top and bottom part in 2 separate images: ffmpeg -i Input.mp4 -i Top.jpg -i Bottom.jpg -filter_complex "scale2ref=w=iw:h=ow/mdar scale2ref=w=iw:h=ow/mdar vstack=inputs=3 scale=iw:-2" -map -map 0:a:0 -shortest -vsync 0 -c:v libx264 "Output.mp4"
This is the code I used in my tests, please see if it gets you the expected output
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