Data-driven testing with fbp-spec

Automated testing is a key part of software development toolkit and practice. fbp-spec is a testing framework especially designed for Flow-based Programming(FBP)/dataflow programming, which can be used with any FBP runtime.

For imperative or object-oriented code good frameworks are commonly available. For JavaScript, using for example Mocha with Chai assertions is pretty nice. These existing tools can be used with FBP also, as the runtime is implemented in a standard programming language. In fact we have used JavaScript (or CoffeeScript) with Mocha+Chai extensively to test NoFlo components and graphs. However the experience is less nice than it could be:

  • A high degree of amount of setup code is needed to test FBP components
  • Mental gymnastics when developing in FBP/dataflow, but testing with imperative code
  • The most critical aspects like inputs and expectations can drown in setup code
  • No integration with FBP tooling like the Flowhub visual programming IDE

A simple FBP specification

In FBP, code exists as a set of black-box components. Each component defines a set of inports which it receives data on, and a set of outports where data is emitted. Internal state, if any, should be observable through the data sent on outports.

A trivial FBP component

So the behavior of a component is defined by how data sent to input ports causes data to be emitted on output ports.
An fbp-spec is a set of testcases: Examples of input data along with the corresponding output data that is expected. It is stored as a machine-readable datastructure. To also make it nice to read/write also for humans, YAML is used as the preferred format.

topic: myproject/ToBoolean
cases:
-
  name: 'sending a boolean'
  assertion: 'should repeat the same'
  inputs:
    in: true
  expect:
    out:
      equals: true
- 
  name: 'sending a string'
  assertion: 'should convert to boolean'
  inputs: { in: 'true' }
  expect: { out: { equals: true } }

This kind of data-driven declaration of test-cases has the advantage that it is easy to see which things are covered – and which things are not. What about numbers? What about falsy cases? What about less obvious situations like passing { x: 3.0, y: 5.0 }?
And it would be similarly easy to add these cases in. Since unit-testing is example-based, it is critical to cover a diverse set of examples if one is to hope to catch the majority of bugs.

equals here is an assertion function. A limited set of functions are supported, including above/below, contains, and so on. And if the data output is a compound object, and possibly not all parts of the data are relevant to check, one can use a JSONPath to extract the relevant bits to run the assertion function against. There can also be multiple assertions against a single output.

topic: myproject/Parse
cases:
-
  name: 'sending a boolean'
  assertion: 'should repeat the same'
  inputs:
    in: '{ "json": { "number": 4.0, "boolean": true } }'
  expect:
    out:
    - { path: $.json.number,  equals: 4.0 }
    - { path: $.json.boolean, type: boolean }

Stateful components

A FBP component should, when possible, be state-free and not care about message ordering. However it is completely legal, and often useful to have stateful components. To test such a component one can specify a sequence of multiple input packets, and a corresponding expected output sequence.

topic: myproject/Toggle
cases:
-
  name: 'sending two packets'
  assertion: 'should first go high, then low'
  inputs:
  - { in: 0 }
  - { in: 0 }
  expect:
  -
    out: { equals: true }
    inverted: { equals: false }
  -
    out: { equals: false }
    inverted: { equals: true }

This still assumes that the component sends one set of packet out per input packet in. And that we can express our verification with the limited set of assertion operators. What if we need to test more complex message sending patterns, like a component which drops every second packet (like a decimation filter)? Or what if we’d like to verify the side-effects of a component?

Fixtures using FBP graphs

The format of fbp-spec is deliberately simple, designed to support the most common axes-of-freedom in tests as declarative data. For complex assertions, complex input data generation, or component setup, one should use a FBP graph as a fixture.

For instance if we wanted to test an image processing operation, we may have reference out and input files stored on disk. We can read these files with a set of components. And another component can calculate the similarity between the processed out, as a number that we can assert against in our testcases. The fixture graph could look like this:

Example fixture for testing image processing operation, as a FBP graph.

This can be stored using the .FBP DSL into the fbp-spec YAML file:

topic: my/Component
fixture:
 type: 'fbp'
 data: |
  INPORT=readimage.IN:INPUT
  INPORT=testee.PARAM:PARAM
  INPORT=reference.IN:REFERENCE
  OUTPORT=compare.OUT:SIMILARITY

  readimage(test/ReatImage) OUT -> IN testee(my/Component)
  testee OUT -> ACTUAL compare(test/CompareImage)
  reference(test/ReadImage) OUT -> REFERENCE compare
cases:
-
  name: 'testing complex data with custom components fixture'
  assertion: 'should pass'
  inputs:
    input: someimage
    param: 100
    reference: someimage-100-result
  expect:
    similarity:
      above: 0.99

Since FBP is a general purpose programming system, you can do arbitrarily complex things in such a fixture graph.

Flowhub integration

The Flowhub IDE is a client-side browser application. For it to actually cause changes in a live program, it communicate using the FBP runtime protocol to the FBP runtime, typically over WebSocket. This standardized protocol is what makes it possible to program such diverse targets, from webservers in Node.js, to image processing in C, sound processing with SuperCollider, bare-metal microcontrollers and distributed systems. And since fbp-spec uses the same protocol to drive tests, we can edit & run tests directly from Flowhub.

This gives Flowhub a basic level of integrated testing support. This is useful right now, and unlocks a number of future features.

On-device testing with MicroFlo

When programming microcontrollers, automated testing is still not as widely used as in web programming, at least outside very advanced or safety-critical industries. I believe this is largely because the tooling is far from as good. Which is why I’m pretty excited about fbp-spec for MicroFlo, since it makes it exactly as easy to write tests that run on microcontrollers as for any other FBP runtime.

Testing microcontroller code using fbp-spec

To summarize, with fbp-spec 0.2 there is an easy way to test FBP components, for any runtime which supports the FBP runtime protocol (and thus anything Flowhub supports). Check the documentation for how to get started.

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Live programming IoT systems with MsgFlo+Flowhub

Last weekend at FOSDEM I presented in the Internet of Things (IoT) devroom,
showing how one can use MsgFlo with Flowhub to visually live-program devices that talk MQTT.

If the video does not work, try the alternatives here. See also full presentation notes, incl example code.

Background

Since announcing MsgFlo in 2015, it has mostly been used to build scalable backend systems (“cloud”), using AMQP and RabbitMQ. At The Grid we’ve been processing hundred thousands of jobs each week, so that usecase is pretty well tested by now.

However, MsgFlo was designed from the beginning to support multiple messaging systems (including MQTT), as well as other kinds of distributed systems – like a networks of embedded devices working together (one aspect of “IoT”). And in MsgFlo 0.10 this is starting to work pretty nicely.

Visual system architecture

Typical MQTT devices have the topic names hidden in code. Any documentation is typically kept in sync (or not…) by hand.
MsgFlo lets you represent your devices and services as FBP/dataflow “components”, and a system as a connected graph of component instances. Each device periodically sends a discovery message to the broker. This message describing the role name, as well as what ports exists (including the MQTT topic name). This leads to a system architecture which can be visualized easily:

Imaginary solution to a typically Norwegian problem: Avoiding your waterpipes freezing in the winter.

Rewire live system

In most MQTT devices, output is sent directly to the input of another device, by using the same MQTT topic name. This hardcodes the system functionality, reducing encapsulation and reusability.
MsgFlo each device *should* send output and receive inports on topic namespaced to the device.
Connections between devices are handled on the layer above, by the broker/router binding different topics together. With Flowhub, one can change these connections while the system is running.

Change program values on the fly

Changing a parameter or configuration of an embedded device usually requires changing the code and flashing it. This means recompiling and usually being connected to the device over USB. This makes the iteration cycle pretty slow and tedious.
In MsgFlo, devices can (and should!) expose their parameters on MQTT and declare them as inports.
Then they can be changed in Flowhub, the device instantly reflecting the new values.

Great for exploratory coding; quickly trying out different values to find the right one.
Examples are when tweaking animations or game mechanics, as it is near impossible to know up front what feels right.

Add component as adapters

MsgFlo encourages devices to be fairly stupid, focused on a single generally-useful task like providing sensor data, or a way to cause actions in the real world. This lets us define “applications” without touching the individual devices, and adapt the behavior of the system over time.

Imagine we have a device which periodically sends current temperature, as a floating-point number in Celcius unit. And a display device which can display text (for instance a small OLED). To show current temperature, we could wire them directly:

Our display would show something like “22.3333333”. Not very friendly, how does one know what this number means?

Better add a component to do some formatting.

Adding a Python component

Component formatting incoming temperature number to a friendly text string

And then insert it before the display. This will create a new process, and route the data through it.

Our display would now show “Temperature: 22.3 C”

Over time maybe the system grows further

Added another sensor, output now like “Inside 22.2 C Outside: -5.5 C”.

Getting started with MsgFlo

If you have existing “things” that support MQTT, you can start using MsgFlo by either:
1) Modifying the code to also send the discovery message.
2) Use the msgflo-foreign-participant tool to provide discovery without code changes

If you have new things, using one of the MsgFlo libraries is a quick way to support MQTT and MsgFlo. Right now there are libraries for Python, C++11, Node.js, NoFlo and Arduino.

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guv: Automatic scaling of Heroku workers

At The Grid we do a lot of computationally heavy work server-side, in order to produce websites from user-provided content. This includes image analytics (for understanding the content), constraint solving (for page layout) and image processing (optimization and filtering to achieve a particular look). Currently we serve some thousand sites, with some hundred thousands sites expected by the time we’ve completed beta – so scalability is a core concern.

All computationally intensive work is put as jobs in a AMQP/RabbitMQ message queue, which are consumed by Heroku workers. To make it easy to manage many queues and worker roles we also use MsgFlo.
This provides us with the required flexibility to scale: the queues buffer the in-progress work, broker distributes evenly between available workers, and with Heroku we can change number of workers with one command. But, it still leaves us with the decision on how much compute capacity to provision. And when load is dynamic, it is tedious & inefficient to do it manually – especially as Heroku bills workers used by the second.

RabbitMQ and Heroku dashboards

Monitoring RabbitMQ queues and scaling Heroku workers manually when demand changes; not fun.

If we would instead regulate this every 1-5 minute based on demand, we would reduce costs. Or alternatively, with a fixed budget, provide a better quality-of-service. And most importantly, let developers worry about other things.

Of course, there already exists a number of solutions for this. However, some used particular metrics providers which we were not using, some used metrics with unclear relationship to required workers (like number of users), or had unacceptable limitations (only one worker per service, only run as a service with pay-by-number-of-workers).

guv

guv 0.1 implements a simple proportional scaling model. Based the current number of jobs in the queue, and an estimate of job processing time – it calculates the number of workers required for all work to be completed within a configured deadline.

guv system model

The deadline is the maximum time you allow for your users to wait for a completed job. The job processing time [average, deviation] can be calculated from metrics of previous jobs. And the number of jobs in queue is read directly from RabbitMQ.

# A simple guv config for one worker role.
# One guv instance typically manages many worker roles
'*':
  app: my-heroku-app
analyze:
  queue: 'analyze.IN' # RabbitMQ queue name
  worker: analyzeworker # Heroku dyno role name
  process: 20
  deadline: 120.0
  min: 1 # keep something always running
  max: 15 # budget limits

Now there are a couple limitations of this model. Primarily, it is completely reactive; we do not attempt to predict how traffic will develop in the future. Prediction is after all terribly tricky business – better not go there if it can be avoided.
And since it takes a non-zero amount of time to spin up a new worker (about 45-60 seconds), on a sudden spike in demand may cause some jobs to miss a tight deadline, as the workers can’t spin up fast enough. To compensate for this, there is some simple hysteresis: scale up more aggressively, and scale down a bit reluctanctly – we might need the workers next couple of minutes.

As a bonus, guv includes some integration with common metrics services: The statuspage.io metrics about ‘jobs-in-flight’ on status.thegrid.io, come directly from guv. And using New Relic Insights, we can analyze how the scaling is performing.

Last 2 days of guv scaling history on some of the workers roles at The Grid.

If we had a manual scaling with a constant number over 48 hours period, workers=35 (Max), then we would have paid at least 3-4 times more than we did with autoscaling (difference in size of area under Max versus area under the 10 minute line). Alternatively we could have provisioned a lower number of workers, but then with spikes above that number – our users would have suffered because things would be taking longer than normal.

We’ve been running this in production since early June. Back then we had 25 users, where as now we have several thousand. Apart from updating the configuration to reflect service changes we do not deal with scaling – the minute to minute decisions are all done by guv. Not much is planned in terms of new features for guv, apart from some more tools to analyze configuration. For more info on using guv, see the README.

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Announcing MsgFlo, a distributed FBP runtime

At The Grid we do a lot of CPU intensive work on the backend as part of producing web pages. This includes content extraction, normalization, image analytics, webpage auto-layout using constraint solvers, webpage optimization (GSS to CSS compilation) and image processing.

The system runs on Heroku, and spreads over some 10 different dyno roles, communicating between each other using AMQP message queues. Some of the dyno separation also deals with external APIs, allowing us to handle service failures and API rate limiting in a robust manner.

Majority of the workers are implemented using NoFlo, a flow-based-programming for Node.js (and browser), using Flowhub as our IDE. This gives us a strictly encapsulated, visual, introspectable view of the worker; making for a testable and easy-to-understand architecture.

Inside a process: In NoFlo each node is a JavaScript class

However NoFlo is only concerned about an individual worker process: it does not comprehend that it is a part of a bigger system.

Enter MsgFlo

MsgFlo is a new FBP runtime designed for distributed systems. Each node represents a separate process, and the connections (edges) between nodes are message queues in a broker process.
To make this distinction clearer, we’ve adopted the term participant for a node which participates in a MsgFlo network.
Because MsgFlo implements the same FBP runtime protocol and JSON graph format as NoFlo, imgflo, MicroFlo – we can use the same tools, including the .FBP DSL and Flowhub IDE.

Distributed MsgFlo system: HTTP frontends + workers in separate processes.

The graph above represents how different roles are wired together. There may be 1-N participants in the same role, for instance 10 dynos of the same dyno type on Heroku.
There can also be multiple participants in a single process. This can be useful to make different independent facets show up as independent nodes in a graph, even if they happen to be executing in the same process. One could use the same mechanism to implement a shared-nothing message-passing multithreading model, with the limitation that every message will pass through a broker.

Connections have pub-sub semantics, so generally each of the individual dynos will receive messages sent on the connection.
The special component msgflo/RoundRobin specifies that messages should be delivered in a round-robin fashion: new message goes only to the next process in that role with available capacity. The RoundRobin component also supports dead-lettering, so failed jobs can be routed to another queue. For instance to be re-processed at a later point automatically, or manually after developers have located and fixed the issue. This way one never loose pending work.
On AMQP roundrobin delivery and deadlettering can be fulfilled by the broker (e.g. RabbitMQ), so there is no dedicated process for that node.

Messaging systems

People use different messaging systems. We’ve tried to make sure that MsgFlo architecture and tools can be used with many different. The format and delivery of discovery messages is specified, and the tools have a transport abstraction layer. Currently there is production-level support for AMQP 0-9-1 (tested with RabbitMQ). Basic support exists for MQTT, a simple protocol popular in distributed “Internet-of-Things” type systems. Support for more transports can be added by implementing two classes.

Polyglot participation

MsgFlo itself only handles the discovery of participants and setup of the connections between them, as well as providing debug capabilities like Flowhub endpoint support. Having participants in a particular language requires implementing . We do provide a set of libraries that makes this easy for popular languages:

Using noflo-runtime-msgflo makes it super simple to use NoFlo as MsgFlo participants. The exported ports of the NoFlo graph or component (for instance ‘in’, ‘out’, and ‘error’) will be automatically made available as queues in MsgFlo, and one can connect this into a bigger system.

noflo-runtime-msgflo --name compute_foo --graph project/MyGraph

 

If you have some plain Node.js you can use msgflo-nodejs, like this real-life example from imgflo-server.

msgflo = require 'msgflo'

ProcessImageParticipant = (client, role) ->

  definition =
    component: 'imgflo-server/ProcessImage'
    icon: 'file-image-o'
    label: 'Executes image processing jobs'
    inports: [
      id: 'job'
      type: 'object'
    ]
    outports: [
      id: 'jobresult'
      type: 'object'
    ]

  func = (inport, job, send) ->
    throw new Error 'Unsupported port: ' + inport if inport != 'job'

    # XXX: use an error queue?
    @executor.doJob job, (result) ->
      send 'jobresult', null, result

  return new msgflo.participant.Participant client, definition, func, role

In addition to node.js and NoFlo, there is basic participant support provided for Python and for C++ (with AMQP). It took about about half a day and 2-300 lines of code, so adding support for more languages should be pretty simple. There are even tests you can reuse.

Example in Python using msgflo-python:

import msgflo

class Repeat(msgflo.Participant):
  def __init__(self, role):
    d = {
      'component': 'PythonRepeat',
      'label': 'Repeat input data without change',
    }
    msgflo.Participant.__init__(self, d, role)

  def process(self, inport, msg):
    self.send('out', msg.data)
    self.ack(msg)

Example becomes a bit more verbose in C++11, using msgflo-cpp.


class Repeat : public msgflo::Participant
{
    struct Def : public msgflo::Definition {
        Def(void) : msgflo::Definition()
        {
            component = "C++Repeat";
            label = "Repeats input on outport unchanged";
            outports = {
                { "out", "any", "" }
            };
        }
    };

public:
    Repeat(std::string role)
        : msgflo::Participant(role, Def())
    {
    }

private:
    virtual void process(std::string port, msgflo::Message msg)
    {
        std::cout << "Repeat.process()" << std::endl;
        msgflo::Message out;
        out.json = msg.json;
        send("out", out);
        ack(msg);
    }
};

Next

Since MsgFlo 0.3, we are using MsgFlo in production for all workers across The Grid backends. After migrating we’ve also moved more things into dedicated participants, because we now have the tooling that makes managing that complexity easy. Our short term focus now is more tools around MsgFlo, like deadline-based autoscaling and integration of data-driven testing using fbp-spec. Features planned for MsgFlo itself includes live introspection of messages in Flowhub.

Looking further ahead, we would like to make more use of the polyglot capabilities, for instance by move some of our image analytics out from NoFlo/node.js participants (with C/C++ libs) to pure C++ 11 participants.
I also hope to do some fun projects with MQTT and MicroFlo – and validate MsgFlo for Embedded/Internet-of-Things-type.

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sndflo 0.1: Visual sound programming in SuperCollider

SuperCollider is an open source project for real-time audio synthesis and algorithmic composition.
It is split into two parts; an interpreter (sclang) implementing the SuperCollider language and the audio synthesis server (scsynth).
The server has an directed acyclic graph of nodes which it executes to produce the audio output (paper|book on internals). It is essentially a dataflow runtime, specialized for the problem domain of real-time audio processing. The client controls the server through OSC messages which manipulates this graph. Typically the client is some SuperCollider code in the sclang interpreter, but one can also use Clojure, Python or other clients. It is in many ways quite similar to the Flowhub visual IDE (a FBP protocol client) and runtimes like NoFlo, imgflo and MicroFlo.
So we decided to make SuperCollider a runtime too: sndflo.

flowhub-runtimes-withsndflo

Growing list of runtimes that Flowhub can target

We used SuperCollider for Piksels & Lines Orchestra, a audio performance system which hooked into graphics applications like GIMP, Inkscape, MyPaint, Scribus – and sonified the users actions in the application. A lot of time was spent wrestling with SuperCollider, due to the number of new concepts and myriad of ways to do things, and
lack of (well documented) best practices.
There is also a tendency to favor very short, expressive constructs (often opaque). An extreme example, here is an album of SuperCollider pieces composed with <140 characters (+ an analysis of some of them).

On the contrary sndflo is very focused and opinionated. It exposes Synths as components, which are be wired together using Busses (edges in the graph), allowing to build audio effect pipelines. There are several known issues and limitations, but it has now reached a minimally useful state. Creating Synths components (the individual effects) as a visual graph of UGen (primitives like Sin,Cos,Min,Max,LowPass) components is also within scope and planned for next release.

Simple substrative audio synthesis using sawwave and low-pass filter

Simple substrative audio synthesis using sawwave and low-pass filter

The sndflo runtime is itself written in SuperCollider, as an extension. This is to make it easier for those familiar with SuperCollider to understand the code, and to facilitate integration with existing SuperCollider code and tools. For instance setting up a audio pipeline visually using Flowhub+sndflo, then using the Event/Pattern/Stream system in SuperCollider to create an algorithmic composition that drives this pipeline.
Because a web browser cannot talk OSC (UDP/TCP) and SuperCollider does not talk WebSocket a node.js wrapper converts messages on the FBP protocol between JSON over WebSocket to JSON over OSC.

sndflo also implements the remote runtime part of the FBP protocol, which allows seamless interconnection between runtimes. One can export ports in one runtime, and then use it as a component in another runtime, communicating over one of the supported transports (typically JSON over WebSocket).

YouTube demo video

In above example sndflo runs on a Raspberry Pi, and is then used as a component in a NoFlo browser runtime to providing a web interface, both programmed with Flowhub. We could in the same way wire up another FBP runtime, for instance use MicroFlo on Arduino to integrate some physical sensors into the system.
Pretty handy for embedded systems, interactive art installations, internet-of-things or other heterogenous systems.

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MicroFlo 0.3 & Flowhub Beta

Its been nearly 6 months since the previous release of MicroFlo, which was the first that allowed you to visually program your Arduino using NoFlo UI. While we are still just getting started, lots of things have changed since then.

Flowhub

Flowhub is the name of the officially supported, packaged and hosted version of the open source NoFlo UI. This is the IDE used for programming with MicroFlo, and a lot of work has been put into it the last couple of months. Today we released the beta version.

You can test it out for browser, node.js and microcontrollers now.

Other runtimes are in development, and you can add your own by implementing the FBP runtime protocol. For instance there are runtimes for desktop development for GNOME, image processing using GEGL, audio synthesis using SuperCollider and for Python.

Heterogenous FBP

Lightbulb idea: two microcontrollers programmed with MicroFlo used as components in a NoFlo program

Often microcontrollers act as sensors and actuators in a larger system, where embedded computers, mobile devices and servers are used to provide the data storage and processing as well as user interfaces and connectivity with other systems.

With MicroFlo 0.3 one can visually create a microcontroller program, and then export ports on this program to make the entire microcontroller available as a component in NoFlo on node.js. This allows to seamlessly create programs which combine microcontrollers and embedded computers.
We made use of this functionality when we created an interactive table that shows the status of the Ingress virtual reality game.

Platform support

Lunchbox electronics: Some boards that can run MicroFlo

MicroFlo has worked on AVR-based Arduinos from day 1, but it was always the goal to not be specific to Arduino. Therefore I’m happy to say that there are now basic platform implementations for:

  • AVR-based Arduino and derivatives
  • Atmel AVR8 (without using Arduino)
  • mbed LPC1768 (ARM Cortex M3)
  • Texas Instruments Tiva-C (ARM Cortex M4)
  • Embedded Linux (RPi, BeagleBone Black)

Basic bring-up up of new platform can be done in a couple of days, and components which do not use platform-dependent libraries can be used immediately. The goal is to be portable enough that you can pick up whatever capable device you find in your parts-bin, prototype your initial code there – then move the program over to a more ideal device if/when appropriate.
If you have particular platforms you’d like to see supported, leave a note.

Simulation & Automated testing

Automated testing in the embedded world using C/C++ is very painful compared to that of recent web technologies. CoffeScript (or JavaScript) in combination with a modern BDD framework like Mocha makes for simple and beautiful tests.

These tests are ran in a simulator which implements the MicroFlo I/O backend (C++) in JavaScript using a Node.js addon. Unfortunately there are not many good open source instruction-level simulators for the platforms MicroFlo support, so the platform backends can only be tested once we support on-device testing.

Automated testing is a critical piece to making sure that MicroFlo is not only a fun and rewarding way to create microcontroller programs, but also an excellent way to make industrial quality devices.

Easier to get started

A Chrome app is slightly less intimidating for users than a terminal

MicroFlo now ships a Chrome app, used to let Flowhub communicate with the MicroFlo runtime running on device over serial/USB/Bluetooth. This means  it is no longer neccesary to run node.js in a terminal, removing a usability issue in getting started.

In the future, this functionality will be baked into the Flowhub Chrome app itself. With time component code editing, building the firmware and flashing the device will also be available there, making Flowhub a true integrated development environment for MicroFlo.

 

 

 

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imgflo 0.1: An image processing server and Flowhub runtime

At TheGrid we are in need for a flexible service for doing server-side image processing. So after some discussion at LGM in Leipzig, I started writing one based on GEGL, the image processing library that will power the upcoming GIMP 2.10 release. The library provides a demand-driven graph-based API , with a ton of operations included and support for GPU processing using OpenCL.

Runtime

For creating image processing pipelines for the server, imgflo acts as a runtime for Flowhub, our open source visual programming IDE. It adds to the existing NoFlo browser, Node.js and MicroFlo microcontroller runtime targets, all possible thanks to the runtime-agnostic protocol.

The preview is live and changes whenever changes are made to the graph. This allows to quickly experiment and develop new graphs.

Server

As a server, imgflo provides a simple HTTP API where you specify the input image as an URL, the graph to process it through and any parameters exposed on that graph.

Requests being made to the imgflo server HTTP API on demo page

Processed images are cached, so that subsequent request on the same url just returns the image out of the cache.
The git repository includes configuration and build setup for Heroku, so deploying an instance is a 5 minute job.

Next

imgflo 0.1 is now minimally useful as image processing server, but there are many more enhancements on the todo list. Scalability and integration with NoFlo are two big topics, as as expanding the pool of available operations and graphs. Porting filters from GIMP to GEGL is a way of helping with the latter.

Additionally it would be interesting to provide a way of using imgflo with GIMP. First of all one could use graphs made with Flowhub via GEGL meta-operations. A more crazy idea is to integrate directly,  using Flowhub as a companion node-based editor.

 

 

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MicroFlo 0.2.0, visual Arduino programming

Two months after MicroFlo 0.1.0, another important milestone has been reached. This release brings a basic visual programming environment and initial support for all major desktop platforms (Win/OSX/Linux). The project is still very much experimental, but it is now starting to demonstrate potential advantages over traditional Arduino programming.

Official release notes and announcement here.

The start of something visual

The “Hello World” adopted from Arduino, a program that blinks the built-in LED a couple of times per second. Pressing Play (>) uploads the program to the Arduino using MicroFlo.

The IDE shown is NoFlo UI, a visual programming environment which can also be used to program JavaScript for the browser and Node.js using the NoFlo runtime. This project is developed by Henri Bergius and rest of the NoFlo team. For more details about the NoFlo IDE project, check their latest update and follow their Kickstarter project.

Talk

At Piksel 2013 in Bergen, I also presented MicroFlo for the first time, to an audience of mostly new media and experimental sound artists. The talk goes into detail about the motivations behind the project, from the quite practical to the more philosophical considerations. Not my most coherent talk, but it gives some insight.

Link

Next

For the next milestone, MicroFlo 0.3, several things are already planned. Focus is mostly on practical improvements to the system, but I also hope to complete prototype support for “heterogeneous FBP”: Allowing to program systems consisting of both host computer and microcontroller programs in a unified manner using NoFlo+MicroFlo.

I am also planning a MicroFlo workshop at Bitraf some time in December and to demo the project at Maker Faire Oslo.

In the meantime, you can get started with MicroFlo for Arduino by following this tutorial. Feedback and contributions welcomed!

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