Images

How to glitch images using RGB channel shifting

Channel shifting is the process of taking the red, green or blue values of pixels in an image and applying those values to pixels in different positions on the image. In this tutorial we are going to accomplish this effect using the Processing language.

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

glitch4ios

To get started download and install the latest version of Processing, version 3.1.1 at the time of writing this. I’ve written a channel shifting script you can download here, you’ll have to unzip it once it’s downloaded. Once you’ve installed and opened Processing you can load the script by accessing the menu.

File > Open

And navigating to the ChannelShiftGlitch.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 glitch. It’s easiest to place the image in the same directory as the script file.

// image path is relative to sketch directory
String imgFileName = "MyImage";
String fileType = "jpg";

I’ve set up some settings variables to make the script easier to use, you can see these towards the top of the script under the comment of script settings.

// repeat the process this many times
int iterations = 5;

// use result image as new source for iterations
boolean recursiveIterations = false;

// shift the image vertically true/false
boolean shiftVertically = false;

// shift the image horizontally true/false
boolean shiftHorizontally = true;

This script is able to apply the same channel shifting effect multiple times, the number of times is specified by the iterations variable, currently set to 5. This variable drives a for loop around the channel shifting code as seen below.

// repeat the process according 
// to the iterations variable
    for(int i = 0;i < iterations;i++)
    {
      // generate random numbers 
      // for which channels to swap
      int sourceChannel = int(random(3));
      int targetChannel = int(random(3));

You can also see in this code where the script generates a random number which will determine which of the three channels, red, green or blue are used as a source, and which of the three channels are used as a target. Next the script sets up the shifting positions, how far vertically and how far horizontally the channel should be shifted. These are either 0 if shifting is set to false for that plane (determined by the shiftHorizontally and shiftVertically settings), or a random number between the 0 and the height or width of the image.

// start with no horizontalShift 
int horizontalShift = 0; 

// if shiftHorizontally is true generate a 
// random number to shift horizontally by
if(shiftHorizontally)
  horizontalShift = int(random(targetImg.width));
      
// start with no verticalShift 
int verticalShift = 0;
      
// if shiftVertically is true generate a 
// random number to shift vertically by
if(shiftVertically)
  verticalShift = int(random(targetImg.height));

Next the script calls the main copyChannel method. This method accepts pixel arrays of the source and target images and will copy one channel to another from one part of the image to another and wrap around both horizontally and vertically if it runs out of space.

// shift the channel
copyChannel(
  sourceImg.pixels, 
  targetImg.pixels, 
  verticalShift, 
  horizontalShift, 
  sourceChannel, 
  targetChannel
  );

The method starts by starting a counter to loop through the rows of pixels in the image, top-to-bottom. This counter is added to the sourceYOffset variable to apply the vertical shift. If the vertical shift plus the counter is higher than the image height we subtract the image height to wrap the shift around to the top of the image.

// starting at the sourceY and pointerY
// loop through the rows
for(int y = 0; y < targetImg.height; y++) { 
  // add y counter to sourceY 
  int sourceYOffset = sourceY + y; 

  // wrap around the top of the 
  // image if we've hit the bottom 
  if(sourceYOffset >= targetImg.height)
    sourceYOffset -= targetImg.height;

Within the row loop the script starts another counter to loop through the columns in that row, left-to-right. It also adds that counter to the sourceXOffset to apply the horizontal shift. If the horizontal shift plus the counter is wider than the image width we subtract the image width to wrap the shift around to the left of the image.

// starting at the sourceX and pointerX 
// loop through the pixels in this row
for(int x = 0; x < targetImg.width; x++) 
{ 
  // add x counter to sourceX 
  int sourceXOffset = sourceX + x; 

  // wrap around the left side of the 
  // image if we've hit the right side 
  if(sourceXOffset >= targetImg.width)
    sourceXOffset -= targetImg.width;

Processing stores image pixels in an array as illustrated in the image below.

pixelarray

In order to access a pixel at specific x/y coordinates in the image we use the formula below.

y * width + x

Next the script isolates the RGB (red, green, blue) values for both the source and target pixels by using the formula above to access the pixel and then some Processing methods to extract the separate RGB channel values.

// get the color of the source pixel
color sourcePixel = 
  sourcePixels[sourceYOffset * targetImg.width + sourceXOffset];
            
// get the RGB values of the source pixel
float sourceRed = red(sourcePixel);
float sourceGreen = green(sourcePixel);
float sourceBlue = blue(sourcePixel);
   
// get the color of the target pixel
color targetPixel = targetPixels[y * targetImg.width + x]; 

// get the RGB of the target pixel
// two of the RGB channel values are required 
// to create the new target color
// the new target color is two of the target
// RGB channel values and one RGB channel value 
// from the source
float targetRed = red(targetPixel);
float targetGreen = green(targetPixel);
float targetBlue = blue(targetPixel);

Now that the script has the RGB of the source pixel and RGB of the target pixel we can proceed to shift one of the channels. We use a switch statement for this, deciding which source channel to use based on the sourceChannel variable which has a holds a random number we generated earlier, either 0, 1 or 2.

// create a variable to hold 
// the new source RGB channel value
float sourceChannelValue = 0;
            
// assigned the source channel value 
// based on sourceChannel random number passed in
switch(sourceChannel)
{
  case 0:
    // use red channel from source
    sourceChannelValue = sourceRed;
    break;
  case 1:
    // use green channel from source
    sourceChannelValue = sourceGreen;
    break;
  case 2:
    // use blue channel from source
    sourceChannelValue = sourceBlue;
    break;
}

After selecting a source channel we apply that channel value to either the red, green or blue channel of the target pixel, again using a switch statement, this time based on the targetChannel variable.

// assigned the source channel value to a 
// target channel based on targetChannel 
// random number passed in
switch(targetChannel)
{
  case 0:
    // assign value to target red channel
    targetPixels[y * targetImg.width + x] = 
      color(sourceChannelValue, 
        targetGreen, 
        targetBlue);
    break;
 case 1:
    // assign value to target green channel
    targetPixels[y * targetImg.width + x] = 
      color(targetRed, 
        sourceChannelValue, 
        targetBlue);
    break;
 case 2:
    // assign value to target blue channel
    targetPixels[y * targetImg.width + x] = 
      color(targetRed, 
        targetGreen, 
        sourceChannelValue);
    break;
}

That’s it for the copyChannel method. The channel has been shifted in the target image at this point. Back in the main draw method of the script there is an if statement that determines whether or not the next iteration (if the iterations variable is to to greater than 1) will use the original image as a source, or use the new shifted image as a source.

// use the target as the new source 
// for the next iteration
if(recursiveIterations)
  sourceImg.pixels = targetImg.pixels;

Using the original image as a source for more than 3 iterations is rather pointless because there are only three channels in the original image to shift to around, always resulting in three shifted ghost images. So if you set iterations higher than 3 you should probably set recursiveIterations to true.

Vertical-Channel-Shift

Setting the recursiveIterations variable to true at the beginning of the script will use each new shifted image as a source for the next iteration and will result in much more dynamic results when iterations is set higher than 3, say 25 or 50.

Recursive-Channel-Shift

Personally I prefer restricting the shifting to either horizontal or vertical alone, but the script allows for the combination  by changing the shiftVertically and shiftHorizontally settings. You can find more Processing tutorials here, and remember, if you’re going to corrupt, corrupt absolutely. #corruptabsolutely

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How to glitch images with WordPad

Sometimes glitching images can be as easy as opening them and saving them again — and that’s exactly the case when image bending with Microsoft WordPad. WordPad is a basic word processor that is included with almost all versions of Microsoft Windows from Windows 95 onwards. If you are running Microsoft Windows then you’ve probably got WordPad on your system, and you can datamosh images with it.

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

glitch4ios

To start glitching with WordPad we need to convert our source image to a BMP (Bitmap) file. This can be done with any image editing software, but since this tutorial is about WordPad we’re going to use one of its fellow Microsoft applications, Microsoft Paint, which is also included with most versions of Microsoft Windows. If you can’t find Paint, press the Win + R keys, type in “pbrush” and press Enter, Paint should open. Open your file and then choose Save as and BMP picture.

Paint-save-as

Then select 24-bit Bitmap (*.bmp;*.dib) from the Save as type dropdown, other types of BMP will work as well to varying degrees.

Paint-save-as-bitmap

Next we need to open the BMP in WordPad. If you can’t find WordPad, press the Win + R keys, type in “write” and press Enter, WordPad should open. Open your BMP image in WordPad by selecting All Documents(*.*) in the dropdown beside the File name field.

WordPad-open

Wait for WordPad to load the image as a document and then press Ctrl+S on your keyboard to Save or use the menu to Save the file.

That’s all there is to it. The process of opening and saving the image using WordPad invokes what has been dubbed the WordPad Effect. You can also try deleting some random text from the file before saving, but it’s not required to glitch the image. The splash image for this post was simply opened and then saved, the WordPad Effect did the rest.

WordPad-opened

So what’s going on here? For the curious, a cursory comparison of the before and after files seems to confirm my suspicion that the transformation, at least in some part, has to do with WordPad converting line breaks when it loads in the image. Line breaks are some bytes that tell text editors and word processors where they should break the text and continue on the next line.

Different operating systems handled them slightly differently, the bytes involved are what’s called a Line Feed, represented by 0x0A in hexadecimal, and a carriage return, 0x0D in hexadecimal. This nomenclature comes from mechanical typewriters where a line of physical paper is fed through the roller (line feed) and the carriage is returned to the beginning of the line (carriage return). In text editors the meaning is the same, a 0x0A tells the editor to move to the next line and a 0x0D tells the editor to move to the beginning of the line.

Image files don’t contain any line feeds or carriage returns, but since WordPad looks at the image data as if it were text it looks for 0x0D and 0x0A in the image file which mean something completely unrelated for images. WordPad attempts to clean up what it thinks is a text file by ensuring that all line breaks have a line feed and a carriage return, so when it finds 0x0D without an accompanying 0x0A, it adds one, and when it finds an 0x0A without an 0x0D it adds one there as well. Because 0x0A and 0x0D mean different things for images, when WordPad thinks it’s correcting a text file in actuality its corrupting the image — et voilà, the WordPad Effect. #corruptabsolutely

How to glitch images using Processing scripts

Datamoshing images, also known as databending or glitching images can be done in a number of ways, some of the most interesting glitches are accomplished by using the Processing programming language. From the beginning the Processing language, was designed as a first programming language. It was inspired by earlier languages like BASIC and Logo.

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

glitch4ios

To get started download and install the latest version of Processing, version 3.1.1 at the time of writing this. I’ve written a simple script you can download here, you’ll have to unzip it once it’s downloaded. Once you’ve installed and opened Processing you can load the script by accessing the menu.

File > Open

And navigating to the SimpleGlitch.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 = "jpg";

In the simple glitch sketch we’re doing a little more. For each pixel in the image the script generates some random numbers to determine whether or not to glitch that pixel. It also keeps track of whether or not the previous pixel was glitched by setting the previousPixelGlitched variable to true or false, if it was, there’s a higher chance that the code will glitch the current pixel. This type of structure will result in lines of glitched pixels, rather that just randomly positioned glitched pixels, which ends up looking like static.

The sketch generates a new random color for the randomColor variable before glitching any pixels and each time a pixel is not glitched. This means that each line of glitched pixels will have a new random color available to it.

// random color 
// 0-255, red, green, blue, alpha
color randomColor = color(random(255), random(255), random(255), 255);

The sketch generates another random number, this time between .5 and 1, and uses this as a mix ratio to mix the random color with the current pixel’s color.

// percentage to mix
float mixPercentage = .5 + random(50)/100;

// mix colors by random percentage of new random color
img.pixels[y + x * img.height] = lerpColor(pixelColor, randomColor, mixPercentage);

For the featured image of this post I adjusted the random color generator to always use 255 (the maximum) blue and thus the resulting image contains colors from the cool range of the spectrum.

In short, this script creates lines of random length and of random colors and mixes them into the original image. I also added some commented out lines that illustrate how to apply filters to the entire image in Processing, uncomment them to see how they affect the result.

// apply some filters
// https://processing.org/reference/filter_.html

// posterize filter
// filter(POSTERIZE, 4);

// dilate filter
// filter(DILATE);

Some ideas for experimenting with this script might be changing the mixPercentage randomness, or, as I did, adjust the random color to be less random by replacing any of the three random(255) with a number between 0 and 255. Instead of glitching pixels randomly you could use a counter, or geometric function (sin, cos, etc) in the loop to glitch pixels in mathematical patterns.

If you’re feeling intimidated by the simple glitch script I created a super simple glitch script you can download here. The super simple glitch script doesn’t actually do anything except loop through each pixel in the image, so it’s ready for experimentation. #corruptabsolutely

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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

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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

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How to glitch JPG images with data corruption

Glitching JPG (or JPEG) images by corrupting their data is a relatively straight-forward affair. Simply open up the file in a hex editor and wreck up the place. Corrupted JPG images can be identified by the telltale offset horizontal bands of changing hues and small square sequences of artifact patterns at the beginning or end of these bands.

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

glitch4ios

If you don’t have a hex editor installed there are some freeware options list at the bottom of this post. Hex editors allow us to view and edit the bytes of a file using hexadecimal. Editing the file using hex rather than text allows greater flexibility since we’re no longer restricted to text characters (which are each represented by two hex digits). Most hex editors display both hex and text (also known as ASCII) in the same view but in separate columns. You can see a JPG open in a hex editor in the image below.

hex-exif

Get started by making a copy of a JPG and open the copy in a hex editor — never edit the original file. The first bytes of a JPG file contain what’s know as the file header. The header contains information that is required for the image to be displayed at all and should be left intact (though feel free to experiment). We need to locate the meat of the file, the raw image data, we can usually tell the raw data apart from the header and other important structural data by it’s garbled nature.

hex-jpg

We can see where some data ripe for glitching is in the above example where the file changes from structured, to seemingly random data. Not to say that there’s no structure, it’s just harder to discern in compressed image data. Once we’ve identified the raw image data we can copy/paste chunks, search/replace sequences or just manually corrupt the data by changing the text (on the right side in the example) or the hex (on the left side in the example) of the raw data.

Now we can begin the process of making changes and checking the result in our favorite image viewer. Making backup copies after every successful change will avoid heartaches when, not if, a misstep renders the image unviewable.

artifacts

Corrupting JPG images often results in interesting patterns due to the corrupt data and the compression algorithms used, as seen enlarged in the example above. Decreasing the quality of the JPG itself, which can be done with image editing software, can sometimes increase the likelihood of generating these artifacts through corruption.

This method can also be used to glitch some other formats as well, most notably BMP files.

As with any glitch-by-corruption technique, too little has no effect, too much can destroy the file, but just enough results in glorious, glorious corruption. #corruptabsolutely

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