Tag Archives: Python

TouchDesigner | Reusable Code Segmentation with Python

reusable-code-segmentation.PNG

Thinking about how to approach re-usability isn’t a new topic here, in fact there’s been plenty of discussion about how to re-use components saved as tox files, how to build out modular pieces, and how to think about using Extensions for building components you want to re-use with special functions.

That’s wonderful and exciting, but for any of us that have built deployed projects have quickly started to think about how to build a standard project that any given installation is just a variation of… a flavor, if you will, of a standard project build. Much of that customization can be handled by a proper configuration process, but there are some outliers in every project… that special method that breaks our beautiful project paradigm with some feature that’s only useful for a single client or application.

What if we could separate our functions into multiple classes – those that are universal to every project we build, and another that’s specific to just the single job we’re working on? Could that help us find a way to preserve a canonical framework with beautiful abstractions while also making space for developing the one-off solutions? What if we needed a solution to handle the above in conjunction with sending messages over a network?  Finally, what if we paired this with some thoughts about how we handle switch-case statements in Python? Could we find a way to help solve this problem more elegantly so we could work smarter, not harder?

Well, that’s exactly what we’re going to take a look at here.

First a little disclaimer, this approach might not be right for everyone, and there’s a strong assumption here that you’ve got some solid Python under your belt before you tackle this process / working style. If you need to tool up a little bit before you dig in, that’s okay. Take a look at the Python posts to help get situated then come back to really dig in.


Getting Set-up

In order to see this approach really sing we need to do a few things to get set-up. We’ll need a few operators in place to see how this works, so before we dig into the python let’s get our network in order.

First let’s create a new base:

base.PNG

Next, inside of our new base let’s set up a typical AB Deck of TOPs with a constant CHOP to swap between them:

typical-ab-deck.PNG

Above we have two moviefilein TOPS connected to a switch TOP that’s terminated in a null TOP. We also have a constant CHOP terminated in a null whose chan1 value is exported to the index parameter of our switch TOP.

Let’s also use the new Layout TOP in another TOP chain:

layout-top.PNG

Here we have a single layout TOP that’s set-up with an export table. If you’ve never used DAT Exports before you might quickly check out the article on the wiki to see how that works. The dime tour of that ideal is that we use a table DAT to export vals to another operator. This is a powerful approach for setting parameters, and certainly worth knowing more about / exploring.

Whew. Okay, now it’s time to set up our extensions. Let’s start by creating a textDAT called messageParserEXT, generalEXT, and one called jobEXT.

parser-general-job.PNG


The Message Parser

A quick note about our parser. The idea here is that a control machine is going to pass along a message to a set of other machines also running on the network. We’re omitting the process of sending and receiving a JSON blob over UPD, but that would be the idea. The control machine passes a JSON message over the network to render nodes who in turn need to decode the message and perform some action. We want a generalized approach to sending those blobs, and we want both the values and the control messages to be embedded in that JSON blob. In our very simple example our JSON blob has only two keys, messagekind and vals:

message = {
        'messagekind' : 'some_method_name',
        'vals' : 'some_value'
}

In this example, I want the messagekind key to be the same as a method name in our General or Specific classes.

Pero, like why?!

Before we get too far ahead of ourselves, let’s first copy and past the code below into our messageParserEXT text DAT, add our General and Specific Classes, and finish setting up our Extensions.

The General Code Bits

In our generalEXT we’re going to create a General class. This works hand in hand with our parser. The parser is going to be our helper class to handle how we pass around commands. The General class is going to handle anything that we need to have persist between projects. The examples here are not representative of the kind of code you’d have your project, instead they’re just here to help us see what’s happening in this approach.

The Specific Code Bits

Here in our Specific class we have the operations that are specific to this single job – or maybe they’re experimental features that we’re not ready to roll into our General class just yet, regardless, these are methods that don’t yet have a place in our canonical code base. For now let’s copy this code block into our jobEXT text DAT.

At this point we’re just about ready to pull apart what on earth is happening. First let’s make sure our extension is correctly set-up. Let’s go back up a level and configure our base component to have the correct path to our newly created extension:

 

reusable-ext-settings.PNG

Wait wait wait… that’s only one extension? What gives? Part of what we’re seeing here is inheritance. Our Specific class inherits from our General class, which inherits form our MessageParser. If you’re scratching your head, consider that a null TOP is also a TOP is also an OP. In the same way we’re taking advantage of Python’s Object oriented nature so we can treat a Specific class as a special kind of General operation that’s related to sending messages between our objects. All of his leads me to believe that we should really talk about object oriented programming… but that’s for another post.

Alright… ALMOST THERE! Finally, let’s head back inside of our base and create three buttons. Lets also create a panel execute for each button:

buttons.PNG

Our first panel execute DAT needs to be set up to watch the state panel value, and to run on Value Change:

change-switch.PNG

Inside of our panel execute DAT our code looks like:

# me - this DAT
# panelValue - the PanelValue object that changed# # Make sure the corresponding toggle is enabled in the Panel Execute DAT.
def onOffToOn(panelValue):
    return
def whileOn(panelValue):
    return
def onOnToOff(panelValue):
    return
def whileOff(panelValue):
    return
def onValueChange(panelValue):
    message = {
        'messagekind' : 'Change_switch',
        'vals' : panelValue } 
    parent().Process_message(message)
    return

If we make our button viewer active, and click out button we should see our constant1 CHOP update, and our switch TOP change:

switch-gif.gif

AHHHHHHHHH!

WHAT JUST HAPPENED?!


The Black Magic

The secret here is that our messagekind key in our panel execute DAT matches an existing method name in our General class. Our ProcessMessage() method accepts a dictionary then extracts the key for messagekind. Next it checks to see if that string matches an existing method in either our General or Specific classes. If it matches, it then calls that method, and passes along the same JSON message blob (which happens to contain our vals) to the correct method for execution.

In this example the messagekind key was Change_switch(). The parser recognized that Change_switch was a valid method for our parent() object, and then called that method and passed along the message JSON blob. If we take a look at the Change_switch() method we can see that it extracts the vals key from the JSON blob, then changes the constant CHOP’s value0 parameter to match the incoming val.

This kind of approach let’s you separate out your experimental or job specific methods from your tried and true methods making it easier in the long run to move from job to job without having to crawl through your extensions to see what can be tossed or what needs to be kept. What’s better still is that this imposes minimal restrictions on how we work – I don’t need to call a separate extension, or create complex branching if-else trees to get the results I want – you’ll also see that in the MessageParser we have a system for managing elegant failure with our simple if hasattr() check – this step ensure that we log that something went wrong, but don’t just throw an error. You’d probably want to also print the key for the method that wasn’t successfully called, but that’s up to you in terms of how you want to approach this challenge.

Next see if you can successfully format a message to call the Image_order() method with another panel execute.

What happens if you call a method that doesn’t exist? Don’t forget to check your text port for this one.

If you’re really getting stuck you might check the link to the repo below so you can see exactly how I’ve set this process up.

If you got this far, here are some other questions to ponder:

  • How would you  use this in production?
  • What problems does this solve for you… does it solve any problems?
  • Are there other places you could apply this same idea in your projects?

At the end of the day this kind of methodology is really looking to help us stop writing the same code bits and bobs, and instead to figure out how to build soft modules for our code so we can work smarter not harder.

With any luck this helps you do just that.

Happy Programming.


Take a look at the sample Repo for this example on Github:
touchdesigner-reusable-code-segmentation-python

TouchDesigner | Switch Statements in Python

python-switch-statements.PNG

Hang onto your socks programmers, we’re about to dive deep. What are we up to here today? Well, we’re going to look into switch statement alternatives in Python (if you don’t know what a switch statement is don’t worry we’ll cover that bit), how you might use that in a practical real-world situation, and why that’s even an idea worth considering. With that in mind let’s dig-in and start to pull apart what Switch Statements are, and why you should care.

From 20,000 feet, switch-case statements are an approach to handling different situations by way of a look-up table rather than with a series of if-else statements. If you’re furrowing your brow consider situations when you may have encountered complex if-else statements where once change breaks everything… for so so much longer than you might want. Also consider what happens if you want to extend that if-else ladder into something more complicated… maybe you want to call different functions or methods based on input conditions, maybe you need to control a remote machine and suddenly you’re scratching your head as you ponder how on earth you’re going to handle complex logic statements across a network. Maybe you’re just after a better code-segmentation solution. Or maybe you’ve run into a function so long you’re starting to loose cycles to long execution times. These are just a few of the situations you might find yourself in and a switch statement might just be the right tool to help – except that there are no switch-case statements in Python.

What gives?!

While there aren’t any switch-case statements, we can use dictionary mappings to get to a similar result… a result so powerful we’re really in for a treat. Before we get there though, we need to look at the situation we’re trying to avoid.

So what exactly is that situation? Let’s consider a problem where we want to only call one function and then let that code block handle all of the various permutations of our actions. That might look like our worst case solution below.

Worst

To get started, what do we have above? We have a single function called switcher() that takes three arguments – the name of the function we want to call, and two values. In this example we have four different math operations, and we want to be able to access any of the four as well as pass in two values and get a result just by calling a single function. That doesn’t seem so hideous on the face of it, so why is this the worst approach?

This example probably isn’t so terrible, but what it does do is bury all the functional mathematical portions of our code inside of a single function. It means we can’t add and test a new element without possibly breaking our whole functional code block, we can only access these operations from within switcher(), and if we decide to add additional operations in the future our code block will just continue to accrue lines of code. It’s a naive approach (naive in the programming sense – as in the first brute force solution you might think of), but it doesn’t give us much room for modularity or growth that doesn’t also come with some unfortunate side effects.

Okay… fine… so what’s a good solution then?


Good

A good solution segments our functions into their own blocks. This allows us to develop functions outside of our switcher() function, call them independently, and have a little more flexible modularity. You might well be thinking that this seems like a LOT more lines… can we really say this is better?! Sure. The additional lines are worth it if we also get some more handles on what we’re doing. It also means we probably save some serious debugging time by being able to isolate where a problem is happening. In our worst case approach we’re stuck with a single function that if it breaks, none of our functions work… and if our logic got sufficiently complex we might be sifting through a whole heap of code before we can really track down what’s happening. Here at least there’s a better chance that a problem is going to be isolated to a single function block – that alone is a HUGE help.

All that said, we’re still not really getting to switch-case statements… we’re still stuck in if-else hell where we’ll have to evaluate our incoming string against potentially all of the possible options before we actually execute our actual code block. At four functions this isn’t so bad, but if we had hundreds we might really be kicking ourselves.

So how can we do better?


Better

Better is to remember that the contents of a python dictionary can be any data type – in fact they can even be function names, or Python objects. How does that help use? Well, it means we can look up what function we want to call on the fly, call it, and even pass in variables. In the example above our switcher() function holds a dictionary of all the possible functions at our disposal – when we call our switcher we pass in the name of the function with the variables that will in turn get passed to the function. Above our active_function variable becomes the variable that’s fetched from our dictionary, which we in turn pass our incoming variables along to.

That’s great in a lot of ways, but especially in that it gets us away from long complicated if-else trees. We can also use this as a mechanism for handling short-hand names for our methods, or multiple assignments – we might want two different keys to access the same function (maybe “mult” and “Multiple” both call the same function for example).

So far this is far away a better approach, so how might we make this better still?


Best

We might take this one step further and start to consider how we might address accepting an arbitrary number of vals. Above we have a simple way to tackle this – probably not what you’d end up with in production, but something that should hopefully get you thinking. Here the variable vals becomes a list that can be any number of values. In the case of both our Add() and Subtract() functions we loop through all of the values – adding each val, or subtracting each val respectively. In the case of our Multiply() and Divide() functions we limit these operations to only two values for the sake of our example. What’s interesting here is that we can return can think about error handling based on the array of values that’s coming into our function.


The above is great, of course, but it’s really just the beginning of the puzzle. Where this really starts to become interesting is how you might think of integrating this approach in your python extensions.

Or if vals is a a dictionary in it’s own right rather than a simple list.

Or if you can send a command like this over the network.

Or if you can start to think about how to build out blocks of code that are specific to a single job, and universal blocks that apply to all of your projects.

Next we’ll start to pull apart some of those very ideas and see where this concept really gets exciting and creates spaces for building tools that persists right alongside the tools that you have to build for a single job.

In the meantime, experiment with some Python style switch statements to see if you can get a handle on what’s happening here, and how you might take better advantage of this method.

Happy programming!


References

Looking for another perspective on this approach form a more pure Python perspective? Check out this post on Jaxenter.com.

TouchDesigner | Delay Scripts

It’s hard to appreciate some of the stranger complexities of working in a programming environment until you stumble on something good and strange. Strange how Matt? What a lovely question, and I’m so glad that you asked!

Time is a strange animal – our relationship to it is often changed by how we perceive the future or the past, and our experience of the now is often clouded by what we’re expecting to need to do soon or reflections of what we did some time ago. Those same ideas find their way into how we program machines, or expect operations to happen – I need some-something to happen at some time in the future. Well, that’s simple enough on the face of it, but how do we think about that when we’re programming?

Typically we start to consider this through operations that involve some form of delay. I might issue the command for an operation now, but I want the environment to wait some fixed period of time before executing those instructions. In Python we have a lovely option for using the time module to perform an operation called sleep – this seems like a lovely choice, but in fact you’ll be oh so sorry if you try this approach:

But whyyyyyyyy?!

Well, Python is blocking inside of TouchDesigner. This means that all of the Python code needs to execute before you can proceed to the next frame. So what does that mean? Well, copy and paste the code above into a text DAT and run this script.

time.sleep

If you keep an eye on the timeline at the bottom of the screen, you should see it pause for 1 second while the time.sleep() operation happens, then we print “oh, hello there” to the text port and we start back up again. In practice this will seem like Touch has frozen, and you’ll soon be cursing yourself for thinking that such a thing would be so easy.

So, if that doesn’t work… what does? Is there any way to delay operations in Python? What do we do?!

Well, as luck would have it there’s a lovely method called run() in the td module. That’s lovely and all, but it’s a little strange to understand how to use this method. There’s lots of interesting nuance to this method, but for now let’s just get a handle on how to use it – both from a simple standpoint, and with more complex configurations.

To get started let’s examine the same idea that we saw above. Instead of using time.sleep() we can instead use run() with an argument called delayFrames. The same operation that we looked at above, but run in a non-blocking way would look like this:

If you try copying and pasting the code above into a text DAT you should have much better results – or at least results where TouchDesigner doesn’t stop running while it waits for the Python bits to finish.

Okay… so that sure is swell and all, so what’s so complicated? Well, let’s suppose you want to pass some arguments into that script – in fact we’ll see in a moment that we sometimes have to pass arguments into that script. First things first – how does that work?

Notice how when we wrote our string we used args[some_index_value] to indicate how to use an argument. That’s great, right? I know… but why do we need that exactly? Well, as it turns out there are some interesting things to consider about running scripts. Let’s think about a situation where we have a constant CHOP whose parameter value0 we want to change each time in a for loop. How do we do that? We need to pass a new value into our script each time it runs. Let’s try something like:

What you should see is that your constant CHOP increments every second:

for-loop-delay

But that’s just the tip of the iceberg. We can run strings, whole DATs, even the contents of a table cell.

This approach isn’t great for everything… in fact, I’m always hesitant to use delay scripts too heavily – but sometimes they’re just what you need, and for that very reason they’re worth understanding.

If you’ve gotten this far and are wondering why on earth this is worth writing about – check out this post on the forum: Replicator set custom parms error. It’s a pretty solid example of how and why it’s worth having a better understanding of how delay scripts work, and how you can make them better work for you.

Happy Programming.

 

TouchDesigner | Finding Dominant Color

Programming is a strange practice. It’s not uncommon that in order to make what’s really interesting, or what you promised the client, or what’s driving a part of your project you have to build another tool.

You want to detect motion, so you need to build out a means of comparing frames, and then determining where the most change has occurred. You want to make visuals that react to audio, but first you need to build out the process for finding meaningful patterns in the audio. And on and on and on.

And so it goes that I’ve been thinking about finding dominant color in an image. There are lots of ways to do this, and one approach is to use a technique called KMeans clustering. This approach isn’t without its faults, but it is interesting and relatively straightforward to implement. The catch is that it’s not fast enough for a realtime application – at least not if you’re using Python. So what can we do? Well, we can still use KMeans clustering, but we need to understand how to use multi-threading in python so we don’t block our main thread in TouchDesigner.

The project / tool / example below does just that – it’s a mechanism for finding dominant color in an image with an approach that uses a background thread for processing that request.


TouchDesigner Dominant Color

An approach for finding dominant color in an image using KMeans clustering with scikit learn and openCV. The approach here is built for realtime applications using TouchDesigner and python multi-threading.

TouchDesigner Version

099
Build 2018.22800

Python Dependencies

  • numpy
  • scipy
  • sklearn
  • cv2

Overview

base_dominant_color

A tool for finding Dominant Color with openCV.

Here we find an attempt at locating dominant colors from a source image with openCV and KMeans clustering. The large idea is to sample colors from a source image build averages from clustered samples and return a best estimation of dominant color. While this works well, it’s not perfect, and in this class you’ll find a number of helper methods to resolve some of the shortcomings of this process.

Procedurally, you’ll find that that the process starts by saving out a small resolution version of the sampled file. This is then hadned over to openCV for some preliminary analysis before being again handed over to sklearn (sci-kit learn) for the KMeans portion of the process. While there is a built-in function for KMeans sorting in openCV the sklearn method is a little less cumbersome and has better reference documentation for building functionality. After the clustering process each resulting sample is processed to find its luminance. Luminance values outside of the set bounds are discarded before assembling a final array of pixel values to be used.

It’s worth noting that this method relies on a number of additional python libraries. These can all be pip installed, and the recommended build approach here would be to use Python35. In the developer’s experience this produces the least number of errors and issues – and boy did the developer stumble along the way here.

Other considerations you’ll find below are that this extension supports a multi-threaded approach to finding results.

Parameters

Dominant Color

  • Image Process Status – (string) The thread process status.
  • Temp Image Cache – (folder) A directory location for a temp image file.
  • Source Image – (TouchDesigner TOP) A TOP (still) used for color analysis.
  • Clusters – (int) The number of requested clusters.
  • Luminance Bounds – (int, tuple) Luminance bounds, mine and max expressed as value between 0 and 1.
  • Clusters within Bounds – (int) The number of clusters within the Luminance Bounds.
  • Smooth Ramp – (toggle) Texture interpolation on output image.
  • Ramp Width – (int) Number of pixels in the output Ramp.
  • Output Image – (menu) A drop-down menu for selecting a ramp or only the returned clusters.
  • Find Colors – (pulse) Issues the command to find dominant colors.

Python

  • Python Externals – (path) A path to the directory with python external libraries.
  • Check Imports – (pulse) A pulse button to check if sklearn was correctly imported.

Using this Module

To use this module there are a few essential elements to keep in mind.

Getting Python in Order

If you haven’t worked with external Python Libraries inside of Touch yet, please take a moment to familiarize yourself with the process. You can read more about it on the Derivative Wiki – Importing Modules

Before you can run this module you’ll need to ensure that your Python environment is correctly set-up. I’d recommend that you install Python 3.5+ as that matches the Python installation in Touch. In building out this tool I ran into some wobbly pieces that largely centered around installing sklearn using Python 3.6 – so take it from someone whose already ran into some issues, you’ll encounter the fewest challenges / configuration issues if you start there. Sklearn (the primary external library used by this module) requires both scipy and numpy – if you have pip installed the process is straightforward. From a command prompt you can run each of these commands consecutively:

pip install numpy
pip install scipy
pip install sklearn

Once you’ve installed the libraries above, you can confirm that they’re available in python by invoking python in your command prompt, and then importing the libraries one by one. Testing to make sure you’ve correctly installed your libraries in a Python only environment first, will help ensure that any debugging you need to do in TouchDesigner is more straightforward.

python-externals-confirmation

Working with TouchDesigner

Python | Importing Modules

If you haven’t imported external libraries in TouchDesigner before there’s an additional step you’ll need to take care of – adding your external site-packages path to TouchDesigner. You can do this with a regular text DAT and by modifying the example below:

import sys
mypath = "C:/Python35/Lib/site-packages/mymodule"
if mypath not in sys.path:
    sys.path.append(mypath)

Copy and paste the above into your text DAT, and modify mypath to be a string that points do your Python externals site-packages directory.

python-page-dominant-color

If that sounds a little out of your depth, you can use a helper feature on the Dominant Color module. On the Python page, navigate to your Python Externals directory. It should likely be a path like: C:\Program Files\Python35\Lib\site-packages

Your path may be different, especially if when you installed Python you didn’t use the checkbox to install for all users. After navigating to your externals directory, pulse the Check imports parameter. If you don’t see a pop-up window then sklearnwas successfully imported. If you do see a pop-up window then something is not quite right, and you’ll need to do a bit of leg-work to get your Python pieces in order before you can use the module.

Using the Dominant Color

With all of your Python elements in order, you’re ready to start using this module.

dominant-colors-parameters

The process for finding dominant color uses a KMeans clustering algorithm for grouping similar values. Luckily we don’t need to know all of the statistics that goes into that mechanism in order to take full advantage of the approach, but it is important to know that we need to be mindful a few elements. For this to work efficiently, we’ll need to save our image out to an external file. For this to work you need to make sure that this module has a cache for saving temporary images. The process will verify that the directory you’ve pointed it to exists before saving out a file, and will create a directory if one doesn’t yet exist. That’s mostly sanity checking to ensure that you don’t have to loose time trying to figure out why your file isn’t saving.

Give that this process happens in another thread, it’s also important to consider that this functions based on a still image, not on a moving one. While it would be slick to have a fast operation for finding KMeans clusters in video, that’s not what this tool does. Instead the assumption here is that you’re using a single frame of reference content, not video. You point this module to a target source, by dropping a TOP onto the Source Image parameter.

Next you’ll need to define the number of clusters you want to look for. Here the term clusters is akin to what’s the target number of dominant colors you’re looking to find – the top 3, the top 10, the top 20? It’s up to you, but keep in mind that more clusters takes longer to produce a result. You’re also likely to want to bound your results with some luminance measure – for example, you probably don’t want colors that are too dark, or too light. The luminance bounds parameters are for luminance measures that are normalized as 0 to 1. Clusters within bounds, then, tells you how many clusters were returned from the process that fell within your specified regions. This is, essentially, a way to know how many swatches work within the brightness ranges you’ve set.

The output ramp from this process can be interpolated and smooth, or Nearest Pixel swatches. You can also choose to output a ramp that’s any length. You might, for example, want a gradient that’s spread over 100 or 1000 pixels rather than just the discrete samples. You can set the number of output pixels with the ramp width parameter.

On the otherside of that equation, you might just want only the samples that came out of the process. In the Output Image parameter, if you choose clusters from the drop down menu you’ll get only the valid samples that fell within your specified luminance bounds.

Finally, to run the operation pulse Find Colors. As an operational note, this process would normally block / lock-up TouchDesigner. To avoid that unsavory circumstance, this module runs the KMeans clustering process in another thread. It’s slightly slower than if it ran in the main thread, but the benefit is that Touch will continue running. You’ll notice that Image Process Status parameter displays Processing while the separate thread is running. Once the result has been returned you’ll Ready displayed in the parameter.

Download from Github

https://github.com/raganmd/touchdesigner-dominant-color

References


Notes from Other Programmers

I don’t use CONDA, but for those of you that do, you can install sklearn with the following command:
conda install scikit-learn

TouchDesigner | Multi-Threading

Multi-threading is no easy task to wrap your head around, and there are plenty of pit-falls when it comes to using it in in Touch. Below we have three simple examples of seeing how that works. It’s not the most thrilling topic in the world, until it’s something that you need – desperately – then it might just save your project.

You can grab the whole repo here: touchdesigner-multi-threading

Three Examples

text_pyThreads
A simple example of creating a function that runs in another thread. As noted in the forum reference post it’s important to consider what operations can potentially create race conditions in Touch. The suggested consideration here is to avoid the use of any operations that will interact with a touch Object. Looking more closely at this example we can see here that we’re using only Pythonic approaches to editing an external file. In our first example we use a simple text file approach to ensure that we have the simplest possible exploration of a concept.


text_pyThreads_openCV
In the opeCV example we look at how one might consider taking the approach of working with image processing through the openCV library. While this example only creates a random red circle, with a little imagination we might see how this would be useful for doing an external image processing pass – finding image features, identifying colors, etc. Running this as a for loop makes it easy to see how this process can block TouchDesigner’s main thread, and why it would be useful to have a means of executing this function in a way that minimizes impact on the running project.


text_pyThreads_queue
While the example pyThreads_opneCV is an excellent start, that doesn’t help us if we need to know when an outside operation has completed. The use of Queue helps resolve this issue. A Queue object can be placed into storage and act as an interchange between threads. It’s marked as a thread safe operation, and in our case is used to help track when we’re working function is “Processing” or “Ready”. You’ll notice that the complications here are that we have a less than ideal need to use an execute DAT to run a frame start script to check for our completed status each frame. This is less than ideal but a reasonable solution for an otherwise blocking operation. Notice that the execute DAT will disable the operation of the frame start script after it’s “Ready”. This kind of approach helps to ensure that the execute DAT only runs the frame start script when necessary and not every frame.

Forum Example

Forum Example 1 python_threading_sample.toe

An example from the forum used to sort out the essential pieces of working with multiple threads, queues, and how to approach this issue without crashing Touch. Many thanks to the original authors for their work helping to shed some light on this murkey part of working in TouchDesigner.

presets and cue building | TouchDesigner 099

I’ve been thinking about state machines and cuing systems lately. Specifically, that there aren’t many good resources that I’ve found so far that talk new artist programmers through how to think about or wrestle with these ideas. Like many Touch programmers I’ve tried lots of different ways of thinking about this problem, and just today I saw someone post on the Facebook help group.

from the facebook help group:

Hi, i’m working arround Matthews AME394 Simple VJ-Setup Tutorial. No Questions, but how can i do nearly the same with different blending times between the moduls. I tried a lot with getting different values out of a table DAT into the length parameter of a timerCHOP. But cannot figur out the right steps to get my goal. Any helps? this i need in a theater situation with different scenes to blend one after another with scenebuttons or only one button and a countCHOP or something else.

This challenge is so very familiar, and while there are lots of ways to solve this problem sometimes the hardest part is having an idea of where to start.  Today what I want to look at is just that – where do we start? This isn’t the best solution, or the only solution – it’s just a starting point solution. It’s a pass at the most basic parts of this equation to help us get started in thinking about what the real problems are, how we want to tackle them, and how we can go about exposes the real issues we need to solve for.

So where do we start? In this simple little state machine we’re going to start with a table full of states. For the sake of simplicity I’m going to keep this as simple as possible… though it might well uncover the more interesting and more challenging pieces that lie ahead.

I’m going to start with the idea that I’ve got a piece of content (an image or a movie) that I want to play. I want to apply some post process effects (in this case just black level and image inversion changes), and I want to have different transition times between these fixed states. Here the only transition I’m worrying about is one that goes from one chain of operations to another. I’m also going to stick with images for now.

So what do we need in our network to get started?!

We’re going to borrow from an idea that often gets used in these kinds of challenges, and we’re going to think of this as operating with two decks – an A deck, and a B deck. Our deck is essentially a chain of operators that allow for all of the possibilities that we might want to explore in our application. In this case I’m only working with a level TOP, but you can imagine that we might use all sorts of operations to make for interesting composition choices.

Alright, so we’re going to lay out a quick easy deck:

moviefilein > level > fit 

adeck.PNG

Next we’re going to repeat this whole chain, then connect both of our fit TOPs to a cross TOP:

ab_deck.PNG

If you’re scratching your head at this fit TOP in line, that’s okay. For us, the fit TOP is going to act as our safety. This makes sure that no matter what the resolution of the incoming file might be, that we always make sure that both decks have matching proportions. We’d probably want a little more thought in how this would work for an event or a show, but for now this is enough to help ensure that don’t experience any unexpected resolution shifts during our transitions.

Next we’re going to add a simple tweening system to our network to control how we blend between states. In this case I’m going to use a constant, a speed, and a null. I need to make sure that my speed is set to clamp, and that my min and max values are 0 and 1 respectively. Right now I only have two different decks, so I don’t want to go any higher that 1 or any lower than 0.

Now we’re cooking with propane! So where do we go next?

some simple cues

movie_file trans_time blk_lvl invert
Banana.tif 1 0 0
Butterfly1.tif 2 0.12 1
Butterfly5.tif 5 0.2 0
Mettler.2.jpg 10 0.05 0
OilDrums.jpg 0.5 0.25 1
Starfish.tif 1 0 1

In this simple examination of this challenge I’m going to use a table to store our cues. In a larger system I’d probably use python storage (which is really a dictionary), but for the sake of keeping it simple let’s start with just a table. Our simple cues are organized above, and we can put all of those values into a table DAT. You’ll notice that for now I’m only worrying about file name and not path – all of these files come from the same directory so we can treat them mostly the same way. We’ll also notice that I’m thinking of my transition times in terms of seconds. All of this can, of course, be much more complicated. The trick is to sort out a very simple example first to identify pressure points and challenges before you dig yourself into a hole.

Okay, let’s add a table DAT to our network and copy all over our cues over.

table_dat.PNG

Now that we have all of our pieces organized it is time to think through the logic of how we make this all work. For now let’s use a button, a count CHOP, and a CHOP Execute DAT. We need to make sure our button is set to be momentary, and we also need to make sure our count CHOP is set to loop – starting at 1 and ending at 6. That matches our row indices from our table DAT.

move-through-cues.PNG

This is great Matt, but why python?

Well, we could do a lot of this with a complex set of CHOPs and selects but these kinds of states tend to be better handled, logically at least, through written code. Python will let us explicitly describe exactly what happens, and in what order those things happen. That’s no small thing, and while it might be a little rocky to wrap your head around using Python in Touch at first, it’s well worth it in the end. So what do we write in our CHOP Execute?

a little bit of logic | python

Uhhhhhhh… wait. What?

Okay. First off we just define a set of variables that we’re going to use. This makes our code a little easier to write, and easier to read. Next all of the action really happens in our onValueChange function.

We’re going to do all of this in a little logical if statement. If this thing, do that thing… in all the other cases, do something else.

First we check to see what our deck position is… which means that we check to see which output we’re currently seeing more of. If our cross TOP’s index is greater that 0.5 we know that we’re closer to 1, which also means we’re seeing more of deck B than deck A. That means that we want to make changes in deck A before we start transitioning. First we change our file, change all of our settings, then finally set a value in our constant CHOP. But why 1 / that value? And why multiplied by -1?

A default network runs at 60 fps. A speed CHOP fed by a constant with a value of 1 will rise a count of 1 over 60 frames. Said another way, an input value of 1 to our speed in a default network will increase by a count of one every second. If we divide that number in half we go twice as slow. A value of 0.5 will increase by a count of 1 every 2 seconds. 1 / our table value will let us think in seconds rather than in fractions while we’re writing our cues. Yeah, but what about being multiplied by -1?! Well, if we want to get back to the 0 index in our cross TOP we need a negative value feeding our speed CHOP. Multiplying by -1 here means that we don’t need to think about the order of cues in our table DAT, and instead our bits of Python will keep us on the rails. Our else statement does all of the same things, but to our B deck. It also uses a positive value to feed our speed CHOP – since we need an increasing value.

There you have it, a simple cuing system.

simple cues.gif

This is great Matt, but what if I want to tween settings on that level TOP? Or any other set of complicated things?! Well, I’d be that at this point you’ve got enough to get you started. You might use a column to indicate if you’re transitioning to a totally new cue or just to new values in the same source image. You could also choose to put your parameter values in CHOPs instead so you could manipulate them with other CHOPs before exporting them to your decks.

What if I don’t want linear transitions?! A speed is just a linear ramp! That’s okay. You might choose to use a lookup CHOP and a more complicated curve. You could even make several types of curves with animation COMPs and switch between them. Or you could use a lag  CHOP to change your attack and release slopes. Or you could use a trigger CHOP, or a fileter CHOP. There are lots of ways to shape curves with math, now it’s up to you to figure out exactly what you’re after.

Happy programming!

pull it apart

Pull apart the example in 088
Pull apart the example in 099

textport for performance | TouchDesiger

consoleText

I love a good challenge, and today on the TouchDesigner slack channel there was an interesting question about how you might go about getting the contents of the textport into a texture to display. That’s a great question, and I can imagine a circumstance where that might be a fun and interesting addition to a set. Sadly, I have no idea about how you might make that happen. I looked through the wiki a bit to see if there were any leads, and it’s difficult to see if there’s actually a good way to grab the contents of the textport.

What do we do then?!

Well, it just so happens that this might be another great place to look at how to take advantage of using extensions in TouchDesigner. Here our extension is going to do some double duty for us. The big picture idea is that we’ll want to be able to use a single call to either display a string, or print and display a string. If you only wanted to print it you could just use print(), so we’ll leave that one out of the mix for now.

Let’s take a look at the extension and then pull apart what’s happening in side.

Okay, so what exactly are we doing here?!

The big picture is that we want a way to be able to log something to a text  object that can be displayed. In this case I choose a table DAT. The reasoning here is that a table DAT before being converted to just a text DAT allows us to do some simple clean up and line adjustments. Each new entry is posted in a row – which makes for an easy way to limit the number of displayed rows. We can do this with a select DAT – which is where we use our StartRow and EndRow members.

Why exactly do we use these? Well, this helps ensure that we can keep our newest row displayed. A text TOP can accept a text DAT of any length, but at some point the text will spill off the bottom – unless you use adaptive sizing. The catch there is that at some point the text will become impossible to read. A top and bottom boundary ensures that we can always have something portion of our text displayed. We use a simple logical test in our Display() method to see if we’ve hit that boundary yet, and if we have we can update our members plus one… moving them both along at the same time.

You may also notice that we have a separate method to display and print… why not just do this in a single method. Well, that’s a great question. We could just use a single method for this with another argument. That’s probably a better way to tackle this challenge, but I wanted to use this opportunity to show how we might call another method from within our class. This can be helpful in a number of different situations, and while this application is a little too simple to really take advantage of that technique, it gives you a peak into how it might work.

Want to download the tox and take it for a test drive? You can find the source code here.

scriptDAT | Tips and Tricks | TouchDesigner

If you spend lots of time setting up parameters in your UI elements and want a faster way to use a set of presets to populate some parameters, then the Script DAT might be just what you’re looking for.

Let’s look at a fast simple example that might have you re-thinking how to quickly set up pars in a project. Keep in mind that this won’t work in every situation, but it might work for an awful lot of them and in ways that you might not have expected.

To get started let us imagine that we have a simple set-up where we have a UI element and a display element. We want a fast way to quickly update their parameters. For the sake of this example let’s imagine that we do not need any fancy scaling or changes on the fly. This is going to be used on a set of displays where we know exactly how they’re going to display. We might think about using storage to set and pull parameters, but you might be hesitant to use too much python for those bits and bobs. Okay, so exports it is… they’re a little more cumbersome to set up, but they are much faster – fine.

Sigh.

I guess we need to start setting up an export table, or a constant CHOP and dragging and dropping all over creation. Before you do that though, take a closer look that is the majesty of the Script DAT:

The Script DAT runs a script each time the DAT cooks and can build/modify the output table based in the optional input tables. The Script DAT is created with a docked (attached) DAT that contains three Python methods: cook, onPulse, and setupParameters. The cook method is run each time the Script DAT cooks. The setupParameters method is run whenever the Setup Parameter button on the Script page is pressed. The onPulse method is run whenever a custom pulse parameter is pushed.

Maybe we can use the Script DAT to make an export table for us with just a little bit of python.

We can start by putting a few things into storage. Let’s create a new dictionary but follow some simple rules:

  • The keys in this dictionary are going to be operator names or paths
  • Each operator is itself a key for another dictionary
  • The keys of that dictionary must be proper parameter names
  • The values associated with these keys need to be legal entries for parameters

Okay, with these rules in mind let’s see what we can do. Open up a new project, in project1 let’s create two new containers:

  • container_ui
  • continer_led_display

Add a new text DAT and create a simple dictionary to put into storage, and let’s follow the rules we described above:

Alright, so far so good. Now let’s add a Script DAT.

We’re going to use our Script DAT to look at our stored vals and create an export table on the fly for whatever is in the storage dictionary “attr” – easy.

Let’s edit our Script DAT to have the following contents:

Finally, let’s turn on the green export flag at the bottom of our Script DAT:

script_dat.PNG

And just like that we’ve set-up an auto-export system. Now every time we update our dictionary run our script to put the contents into storage we’ll automatically push those changes to an export table.

Looking for an example to pull apart – head over to github and download a simple example to look over.

Python in TouchDesigner | Extensions | TouchDesigner

Rounding out some of our work here with Python is to look extensions. If you’ve been following along with other posts you’ve probably already looked over some extensions in this post. If you’re brand new to this idea, check out that example first.

Rather than re-inventing the wheel and setting up a completely new example, let’s instead look at our previous example of making a logger and see how that would be different with extensions as compared to a module on demand.

A warning for those following along at home, we’re now knee deep in Python territory, so what’ we’ll find here is less specific to TouchDesigner and more of a look at using Classes in Python.

In our previous example looking at modules we wrote several functions that we left in a text DAT. We used the mod class in TouchDesigner to treat this text DAT as a python module. That’s a neat trick, and for some applications and situations this might be the right approach to take for a given problem. If we’re working on a stand alone component that we want to use and re-use with a minimal amount of additional effort, then extensions might be a great solution for us to consider.

Wait, what are extensions?! Extensions are way that we can extend a custom component that we make in TouchDesigner. This largely makes several scripting processes much simpler and allows us to treat a component more like an autonomous object rather than a complex set of dependent objects. If you find yourself re-reading that last statement let’s take a moment to see if we can find an analogy to help understand this abstract concept.

When we talked about for loops we used a simple analogy about washing dishes. In that example we didn’t bother to really think deeply about the process of washing dishes – we didn’t think about how much detergent, how much water, water temperature, washing method, et cetera. If we were to approach this problem more programatically we’d probably consider making a whole class to deal with the process of washing dishes:

Okay, so the above seems awfully silly… what’s going on here? In our silly example we can see that rather than one long function for Wash() or Dry(), we instead use several smaller helper functions in the process. Why do this? Well, for one thing it lets you debug your code much more easily. It also means that by separating out some of these elements into different processes we can fix a single part of our pipeline without having to do a complete refactor of the entire Wash() or Dry() methods. Why does that matter? Well, what if we decide that there’s a better way to determine how much soap to use. Rather than having to sort through a single long complex method for Wash() we can instead just look at Set_soap(). It makes unit testing easier, and allows us to replace or develop that method outside the context of the larger method. It also means that if we’re collaborating with other programmers we can divide up the process of writing methods.

What’s this self. business? self. allows us to call a method from inside another method in the same class. This is where extensions begin to really shine as opposed to using modules on demand. We an also use things like attributes andinheritance.

Okay, so how can we think about actually using this?

Let’s take a look at what this approach looks like in python:

So getting started we set up a couple of variables that we were going to reuse several times. Next we declared our class, and then nested our methods inside of that class. You’ll notice that different from methods, we need to include the argument self in our method definitions:

# syntax for just a function
def Clear_log():
    return

# syntax for just a method in a class
def Clear_log( self ):
    return

We can also see how we’ve got several helper functions here to get us up and running – ways to add to the log, save the log to an external file, a way to clear the log. We can imagine in the future we might want to add another method that both clears the log, and saves an empty file, like a reset. Since we’ve already broken those functions into their own methods we could simply add this method like this:

def Reset_log():
    self.Clear_log()
    self.Save_log()
    return

Now it’s time to experiment.

Over the course of this series we’ve looked at lots of fundamental pieces of working with Python in TouchDesigner. Now it’s your turn to start playing and experimenting.

Happy programming!

Python in TouchDesigner | Modules | TouchDesigner

There are a number of ways that we might use modules on demand in TouchDesigner. Before we get too far along, however, we might first ask “what is a module on demand?”

According to the TouchDesigner wiki:

The MOD class provides access to Module On Demand object, 
which allows DATs to be dynamically imported as modules. 
It can be accessed with the mod object, found in the automatically 
imported td module. 

Alternatively, one can use the regular python statement: import. 
Use of the import statement is limited to modules in the search path, 
where as the mod format allows complete statements in one line, 
which is more useful for entering expressions. Also note that DAT modules 
cannot be organized into packages as regular file system based 
python modules can be.

What does this mean? It’s hard to sum up in just a single sentence, but the big thing to take away is that we can essentially use any text DAT to hold whole functions for us that we can then call whenever we want.

Let’s take a closer look at this process. We’ll start with some simple ideas, then work our way up to something a little more complicated.

First we turn things way down, and just think about storing variables. To be clear, we probably wouldn’t use this in a project, but it can be helpful for us when we’re trying to understand what exactly is going on here.

Let’s create a new text DAT and call it “text_variables”, inside let’s put the following text:

width       = 1280
height      = 720

budget      = 'small'

Using the mod class we can access these variables in other operators! To do this we’ll use the following syntax:

mod( 'text_variables' ).width
mod( 'text_variables' ).height
mod( 'text_variables' ).budget

Try adding a constant CHOP, and a text TOP to your network and using the expressions above to retrieve these values.

Next try printing these values:

print( mod( 'text_variables' ).width )
print( mod( 'text_variables' ).height )
print( mod( 'text_variables' ).budget )

So, it looks like we can access the contents of a module as a means of storing variables. That’s hip. Let’s take a moment and circle back to one of the other use cases that we’ve already seen for a module. More than just a single value, we can also put a whole dictionary in a module and then call it on demand. We’ve already done this in some of our previous examples, but we can take a quick look at that process again to make sure we understand.

Let’s create a new text DAT called “text_dictionary_as_module”, inside of this text DAT let’s define the following dictionary:

fruit = {
    "apple" : 10,
    "orange"    : 5,
    "kiwi"  : 16
}

Let’s first print the whole dictionary object:

print( mod( 'text_dictionary_as_module' ).fruit )

Alternatively, we can also access individual keys in the dictionary:

mod( 'text_dictionary_as_module' ).fruit[ 'apple' ]
mod( 'text_dictionary_as_module' ).fruit[ 'orange' ]
mod( 'text_dictionary_as_module' ).fruit[ 'kiwi' ]

What can you do with this?! Well, you might store your config file in a text DAT that you can call from a module on demand. You might use this to store configuration variables for your UI – colors, fonts, etc. ; you might decide to use this to configure some portion of a network, or to hold on to data that you want to recall later; or really any number of things.

Before we get too excited about storing variables in modules on demand, let’s look at an even more powerful feature that will help us better understand where they really start to shine.

Up next we’re going to look at writing a simple function that we can use as a module on demand. In addition to writing some simple little functions, we’re also going to embrace docstrings – a feature of the python language that makes documenting your work easier. Docstings allow us to leave behind some notes for our future selves, or other programmers. One of the single most powerful changes you can make to how you work is to document your code. It’s a difficult practice to establish, and can be frustrating to maintain – but it is hands down one of the most important changes you can make in how you work.

Alright, I’ll get off my documentation soapbox for now. Let’s write a few methods and see how this works in TouchDesigner.

We can start by creating a new text DAT called “text_simple_reutrn”, inside of this DAT we’ll write out our new functions:

Great! But what can we do with these? We can start by using some eval DATs or print statements to see what we’ve got. I’m going to use eval DATs. Let’s add several to our network and try out some calls to our new module on demand. First let’s look at the generic syntax:

mod( name_of_text_dat ).name_of_method

In practice that will look like:

mod( 'text_simple_reutrn' ).multi_by_two( 5 )
mod( 'text_simple_reutrn' ).multi_by_two( 2.5524 )
mod( 'text_simple_reutrn' ).logic_test( 5 )
mod( 'text_simple_reutrn' ).logic_test( 6 )
mod( 'text_simple_reutrn' ).logic_test_two( "TouchDesigner" )

Now we can see that we wrote several small functions that we can then call from anywhere, as long as we know the path to the text DAT we’re using as a module on demand! Here’s where we start to really unlock the potential of modules on demand. As we begin to get a better handle on the kind of function we might write / need for a project we can begin to better understand how to take full advantage of this feature in TouchDesiger.

Doc Strings

Since we took the time to write out all of those doc strings, let’s look at how we might be able to print them out! Part of what’s great about doc strings is that there’s a standard way to retrieve them, and therefore to print them. This means that you can quickly get a some information about your function printed right in the text port. Let’s take a closer look by printing out the doc stings for all of our functions:

That worked pretty well! But looking back at this it seems like we repeated a lot of work. We just learned about for loops, so let’s look at how we could do the same thing with a loop instead:

A Practical Example

This is all fun and games, but what can we do with this? There are any number of functions you might write for a project, but part of what’s exciting here is the ability to write something re-usable in Python. What might that look like? Well, let’s look at an example of a logger. There are a number of events we might want to log in TouchDesigner when we have a complex project.

In our case we’ll write out a method that allows a verbose or compact message, a way to print it to the text port or not, and a way to append a file or not. Alright, here goes:

So now that we’ve written out the method, what would call for this look like?

operator = me

message ='''
Just a friendly message from your TouchDesigner Network.
Anything could go here, an error message an init message.

You dream it up, and it'll print
'''

# print and append log file with a verbose log message
mod( 'text_module1' ).Log_message( operator, message, verbose = True )

# print and append log file with a compact log message
# mod( 'text_module1' ).Log_message( operator, message )


# append log file with verbose log message
# mod( 'text_module1' ).Log_message( operator, message, verbose = True, text_port_print = False )


# print a compact log message
# mod( 'text_module1' ).Log_message( operator, message, append_log = False )

Take a moment to look at the example network and then un-comment a line at a time in the text DAT with the script above. Take note of how things are printed in the text port, or how they’re appended to a file. This is our first generalized function that has some far reaching implications for our work in touch. Here we’ve started with a simple way to log system events, both to a file and to the text port. This is also a very re-usable piece of code. There’s nothing here that’s highly specific to this project, and with a little more thought we could turn this into a module that could be dropped into any project.

Local Modules

We’ve learned a lot so far about modules on demand, but the one glaring shortcoming here is that we need the path to the text DAT in question. That might not be so bad in some cases, but in complex networks writing a relative path might be complicated, and using an absolute path might be limiting. What can we do to solve this problem. We’re in luck, as there’s one feature of modules we haven’t looked at just yet. We can simplify the calling / locating of modules with a little extra organization.

First we need to add a base and rename it to “local”, inside of this base add another base and rename it to “modules”. Perfect. I’m going to reuse one of our existing code examples so we can see a small change in syntax here. I’ve also changed the name of the text DAT inside of local>modules to “simple_return”.

mod.simple_return.multi_by_two( 5 )
mod.simple_return.multi_by_two( 2.5524 )
mod.simple_return.logic_test( 5 )
mod.simple_return.logic_test( 6 )
mod.simple_return.logic_test_two( "TouchDesigner" )
mod.simple_return.logic_test_two( 10 )

Looking at the above, we can see that we were able to remove the parentheses after “mod”. But what else changed? Why is this any better? The benefit to placing this set of functions in local>modules is that as long as you’re inside of this component, you no longer need to use a path to locate the module you’re looking for.

Alright, now it’s time for you to take these ideas out for a test drive and see what you can learn.