• PM_ME_VINTAGE_30S [he/him]@lemmy.sdf.orgEnglish
    2·
    10 months ago

    I created this on my phone in MATLAB. You can probably do this in Octave with similar or the same code.

    Figure 2024-01-14 17_18_24~2

    First, I downloaded the image from Lemmy, then uploaded it into my MATLAB app. I renamed the image to image.jpg, then ran the following code:

    image=imread(“image.jpg”) imagesc(log10(abs( fftshift(fft2(image)) )))

    fft2 applies a 2D Fast Fourier transform to the image, which creates a complex (as in complex numbers) image. abs takes the magnitude of the complex image elementwise. log10 scales the result for display.

    Then I downloaded the image from the MATLAB app, went into the Photos app and (badly) cropped out the white border.

    Despite how dramatically different it looks, it actually contains the same [1] information as the original image. Said differently, you can actually go back to the original with the inverse functions, specifically by undoing the logarithm and applying the inverse FFT.

    [1] Almost. (1). There will be border problems potentially caused by me sloppily cropping some pixels out of the image. (2). It looks like MATLAB resized the image when rendering the figure. However, if I actually saved the matrix (raw image) rather than the figure, then it would be the correct size. (3) (Thank you to @itslilith@lemmy.blahaj.zone for pointing this out.) You need the phase information to reconstruct the original signal, which I (intentionally) threw out (to get a real image) when I took the absolute value but then completely forgot about it.

    • itslilith@lemmy.blahaj.zone
      1·
      10 months ago

      don’t you lose information by taking the abs(), since to restore the full information you need both complex and imaginary parts of the fft? You could probably get away with encoding Re and Im in different color channels tho

    • PM_ME_VINTAGE_30S [he/him]@lemmy.sdf.orgEnglish
      1·
      10 months ago

      So I took your image and ruined my MATLAB account used the most normal part of your totally normal cow as a 3D [1] cockvolution convolution kernel. So in some sense, I dragged the red and purple part all across your image and added up the results. Here’s the result:

      Figure 2024-01-15 14_46_27

      Here’s the MATLAB code:

      normal_image = imread(“totally_normal_image.png”);

      feature=normal_image(272:350,205:269,:);

      feature_expansion = padarray(feature,[0,ceil((79-65)/2),0],‘replicate’);

      for m = 1:1:3

      new_normal_image(:,:,m) = conv2(normal_image(:,:,m),feature_expansion(:,:,m));

new_normal_image(:,:,m) = new_normal_image(:,:,m)/max(max(new_normal_image(:,:,m)));

      end

      imshow(new_normal_image)

      [1] The original image was practically grayscale, so only a 2D convolution is required, i.e. over 2 spatial dimensions. Since you added color, it adds a extra dimension, one per color channel. Which makes it more annoying to work with in MATLAB. I mean, I could have just dumped everything into grayscale, but I need practice with processing color images anyways.

        • Herbal Gamer@sh.itjust.works
          1·
          10 months ago

          Okay hold on; I did it in a few hours, not weeks but it gets freaky fast:

          1. Played around with the lines; erasing a few bits and rounding off some corners.

          2. Did the outlines in marker

          3. First bit of shading

          4. Left it overnight. Came back today and started building up the shades and lines

          5. Bit more

          6. Bit more and now I think I’m done.