Tag Archives: how

How to datamosh videos with automation

Datamoshing videos can be a time-consuming process, automation can help. For Windows users AutoHotkey is free, open-source macro-creation and automation software that can handle some of the repetitive tasks involved in datamoshing.

The following script for AutoHotkey automates I-frame removal in Avidemux, normally a manual process described in this tutorial. The video above was datamoshed using this automation script.

Load AutoHotkey with the script below and then when it comes time to remove I-frames in Avidemux simply focus the slider below the video and press Control+F to trigger the AutoHotkey script. The script will send the appropriate key strokes to remove the next 10 I-frames while you pop out for a break.

; Remove next 10 I-frames
^f::
Loop 10 {
	Send,{Up}
	Sleep, 500
	Send,[
	Sleep, 500
	Send,{Right}
	Sleep, 500
	Send,]
	Sleep, 500
	Send,{Delete}
	Sleep, 1000
}

These types of scripts could also be used to automate key strokes while hex editing images, consider a script which would move a certain number of characters across and then insert a character — that could glitch out an image quite nicely. Similarly one could experiment with automating photo editing processes by scripting with a program like AutoHotkey.

Some of these types of automation could be accomplished through the usage of a programming framework, or scripting language, but automating at the user interface level can remove a lot of overhead and restrictions.

Windows

How to glitch images using pixel sorting

Pixel sorting is the process of isolating a horizontal or vertical line of pixels in an image and sorting their positions based on any number of criteria. For instance pixels positions may be sorted by each pixel’s luminosity, hue or saturation. Manual pixel sorting, while possible, would be overly time consuming, instead Pixel sorting is accomplished using scripting or programming languages.

If you don’t have the time or inclination to pixel sort images using scripts you can use dedicated apps such as Glitch for iOS.

glitch4ios

One popular programming language for pixel sorting is Processing. To get started download and install the latest version of Processing, version 3.1.1 at the time of writing this. Next select a pixel sorting script to start from, my own pixel sorting scripts are not written for Processing so for the purposes of this tutorial we’ll use a popular script made available by glitch artist Kim Asendorf, the ASDF Pixel Sort.

Download the ASDFPixelSort.pde from Kim’s GitHub repository by clicking the green button labelled Clone or download and select Download ZIP. If you’re familiar with GitHub you can do this a number of other ways. Once you’ve downloaded the ZIP you can extract the sorting script and open it in Processing by selecting:

File > Open

And navigating to the ASDFPixelSort.pde script file. In the script, which are referred to as sketches in Processing, you’ll need to change the following lines to point the script at the image you want to pixel sort:

// image path is relative to sketch directory
PImage img;
String imgFileName = "MyImage";
String fileType = "png";

For this tutorial we’ll use a PNG, though Processing supports GIF, JPG and TGA as well. Place your PNG in the same directory as the ASDFPixelSort.pde (which Processing may have placed in a new sub directory) and update the script with the filename.

Once you’ve updated the script with the name of your file simply press the Run button at the top left of the Processing window (it looks like a play button) and in a few seconds you should see a window with the results, a new image should also be saved to the sketch directory.

pixel-sort-run

This particular script loops through both the columns and the rows of the image, but it doesn’t pixel sort the entire column or row, if it did, the result would look more like a blank gradient than anything interesting. Instead for each column and row it looks for a pixel to start sorting on and then it looks for a pixel to stop sorting on — this makes the algorithm somewhat intelligent resulting in identifiable elements of the image being left untouched.

In order to decide which pixel to start sorting on and which to stop sorting on this script can operate in three different modes. The mode can be changed by adjusting the mode variable, by default it is set to 1, but can be changed to either 0 or 2 as well. Different modes will work better depending on the image itself.

 sorting modes
 
 0 = black
 1 = brightness
 2 = white
 
 */

In mode 0, or black mode, the script will begin sorting when it finds a pixel which is not black in the column or row, and will stop sorting when it finds a black pixel. The script identifies black pixels by comparing the pixel’s color value to a threshold, if it’s lower than the black threshold the pixel is deemed to not be black, if it’s higher it’s deemed to be black. You can adjust this threshold by changing the blackValue variable which is by default set to -16000000.

suit-0

In mode 1, or brightness mode, the script will begin sorting when it finds a pixel which is bright in the column or row, and will stop sorting when it finds a dark pixel. The script identifies black pixels by comparing the pixel’s brightness value to a threshold, if it’s lower than the brightness threshold the pixel is deemed to be dark, if it’s higher it’s deemed to be bright. You can adjust this threshold by changing the brightnessValue variable which is by default set to 60.

suit-0

In mode 2, or white mode, the script will begin sorting when it finds a pixel which is not white in the column or row, and will stop sorting when it finds a white pixel. The script identifies white pixels by comparing the pixel’s color value to a threshold, if it’s lower than the white threshold the pixel is deemed to not be white, if it’s higher it’s deemed to be white. You can adjust this threshold by changing the whiteValue variable which is by default set to -13000000.

suit-0

The script can also be run many times to apply the pixel sorting effect multiple times. This can be set by adjusting the loops variable which is by default set to 1.

int loops = 1;

// threshold values to determine sorting start and end pixels
int blackValue = -16000000;
int brightnessValue = 60;
int whiteValue = -13000000;

Pixel sorting is a powerful, and fun, concept. Start by trying out different modes and adjusting the various threshold values. From there you can try moving the row sorting above the column sorting, this will result in more visible vertical sorting (similar to the featured image of this post) as whichever sort is performed last will have the greatest impact on the final image. Alternately you can rotate your image in image editing software before pixel sorting it and then rotate it back to accomplish a similar result. If you break the script, just download the original and get back to experimenting.

  // loop through columns
  while(column < width-1) {
    println("Sorting Column " + column);
    img.loadPixels(); 
    sortColumn();
    column++;
    img.updatePixels();
  }
  
  // loop through rows
  while(row < height-1) {
    println("Sorting Row " + column);
    img.loadPixels(); 
    sortRow();
    row++;
    img.updatePixels();
  }

Some scripts haven’t been updated in a while. If the script you are trying to use is having errors (that don’t seem to do with not finding your image) right from the get-go you might want to try an early version of Processing, versions 2.2.1 and 1.5.1 are listed towards the bottom of the download page.

For more advanced pixel sorting scripts you can have a look at Jeff Thompson’s GitHub which has a number of examples. At the time of writing this I was experiencing errors running them though — they may need updating to function correctly, or at all. If you’ve got the hang of Processing than there’s no reason why you can’t craft an entirely original script. There are two main concepts to explore here, the first is how to determine which pixels to sort, and the second is how to sort them. I’ve had success experimenting with hue based sorting, as well as combining pixel sorting with Sobel edge detection algorithms. #corruptabsolutely

iOS (iPhone, iPad)

Windows

OSX

Processing

How to glitch images using audio editing software

Images can be pleasantly destroyed in a great number of ways, some of the best results come from applying transformation algorithms to the raw image data. Applying filter algorithms to images is something one would normally use software like Adobe Photoshop for, however, using audio processing software instead can yield much more interesting, and unexpected, results.

If you don’t have the time or inclination to manually glitch images with audio processing software you can use dedicated apps such as Glitch for iOS.

glitch4ios

Firstly we’ll need some audio processing software, many will work, Audacity is free, supports many platforms and works quite well for glitching images. Secondly we’ll need a databending-friendly image, the BMP format works well for this type of bending. Once we have an image and Audacity installed, open Audacity and import the image by selecting:

File > Import > Raw Data

audacity-importraw

Audacity will then ask for some information about the file we are importing, we’re going to lie, for Encoding select either U-Law or A-Law. We will have to export with the same encoding setting so remember which was selected. Defaults will work fine for the rest of the import options.

audacity-import

The image will now be open as an audio file, I don’t suggest pressing play. Now we can select any portion of the file or its entirety by clicking and dragging on the waveform (the chart-like display). In some cases it’s better avoid selecting the beginning (first 5-10 seconds of the waveform) of the file as this contains the file header, a section of the file which contains information needed to display the image, if the image won’t display after exporting consider leaving the header intact.

Once we have a selection, we can apply any of the filters under the Effects menu. I have found the Invert, Reverb, Reverse, Wahwah, Compressor and Echo work quite well, but here is where you can experiment. You’ll see the waveform change as each filter effect is applied. The hero image of this post was created using the Compressor and Echo filters applied to the entire file.

Once we’ve applied one or more filter effects we can export the data back to an image by selecting:

File > Export Audio

Change the filename back to the proper image extension, in this case BMP. The Save as type should be set to Other uncompressed files, the Header should be set to RAW (header-less) and the Encoding should be set to either U-Law or A-Law depending on which was chosen during the import process.

audacity-export

Audacity may complain that not all is well with the filename, and perhaps prompt for metadata but these prompts can be accepted and ignored.

All that’s left is to check the result — in an image viewer, not a music player. #corruptabsolutely

Audacity
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