art a practical guide using processing foreword by marius watz matt pearson Generative art is neither programming nor art, in their conventional sense. The Kindle version is text only, but the PDF includes all the code and imagery. " Matt Pearson has provided an excellent introduction to generative art using. Request PDF on ResearchGate | On Dec 1, , Gary Greenfield and others published Generative art: a practical guide using Processing, by Matt Pearson.
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generative art a practical guide using processingmatt pearson MANNINGforeword by marius watz generative art ii. What distinguishes Pearson's book from the others is generative art was first sparked decades ago when I . Since my review copy was a pdf version of. Manning Publications. Paperback. Book Condition: new. BRAND. NEW, Generative Art, Matt Pearson, DESCRIPTION Artists have always explored new media.
Generative Art presents both the technique and the beauty of algorithmic art. The book includes high-quality examples of generative art, along with the specific programmatic steps author and artist Matt Pearson followed to create each unique piece using the Processing programming language. Artists have always explored new media, and computer-based artists are no exception. Generative art, a technique where the artist creates print or onscreen images by using computer algorithms, finds the artistic intersection of programming, computer graphics, and individual expression. The book includes a tutorial on Processing, an open source programming language and environment for people who want to create images, animations, and interactions. Generative Art presents both the techniques and the beauty of algorithmic art. In it, you'll find dozens of high-quality examples of generative art, along with the specific steps the author followed to create each unique piece using the Processing programming language.
In this case, you draw a tiny square, using the noise factor to determine its size. How about visualizing the noise as a rotation? The functions pushMatrix and popMatrix are a way of storing the previous drawing position before you perform the translation and rotation, and then restoring it afterward.
Figure 5. Okay, one more. Change background to background 0 in the setup function, and then rewrite drawPoint so it varies the rotation, size, color, and alpha, all using the same noise factor: Point 20, 30 becomes the new origin from which to start drawing.
For some actions, such as rotating about a point, you will find it is much easier to do this by moving the paper than it is the pen.
This, folks, is what we call animation: Refer back to section 2. The following listing uses the draw function to turn the two-dimensional noise grid into something that moves. There are no moving parts—the illusion is created by scrolling across the noise plane.
This movement seems a little predictable for my liking, though. If only there were some way to produce a smooth, natural variance. Take a look at the next listing. Adding time to this particular visualization produces a nice illusion of ruffling fur, or the wind blowing through grass.
Run it to see it in action. Note that there is no limit to the number of noise sequences you can have in a single script. This is how you tell Processing which rendering engine to use: The highest-quality 2D renderer.
Prioritizes speed and file size over quality. A better high-speed 3D renderer that utilizes OpenGL graphics hardware, if available. You need to import the OpenGL library to use this renderer see listing 5. A renderer for writing directly to PDF files, rather than the screen.
Requires the PDF library. For more information, see http: Adding Dimensions Table 5. You can begin using these to get a sense of drawing in a three-dimensional space. To draw in 3D, you need to add an extra position—a z value—to measure how far you are above or below the 2D plane.
The size command then sets the canvas size and selects this renderer. Next, the sphereDetail command tells the renderer how much detail to use when drawing a sphere. The final two lines create the sphere by moving the drawing point to the middle of the canvas and drawing a sphere around that point with a radius of The output is shown in figure 5. Change that line to translate , , 50 ; the sphere now appears bigger. Begin by entering the code from the following listing into a new window.
The main difference is to the drawPoint function.
Instead of translating to a 2D point, you translate to a 3D point. The code reuses the y value for the z depth as a shortcut; doing so creates a tiered square, sloping Figure 5. At each 3D point, you create a sphere with a radius, fill color and alpha relative to the noise value.
The result is another gently moving cloudscape, but with a better perspective see figure 5. But the noise function can take up to three parameters, so you have the capability to generate noise in three dimensions.
The visual metaphor for three-dimensional noise will no longer be a mountain range: You can begin by building a controlled block of this noise.
The changes are made in the next listing. But it comes with practice. See the following listing for one application of this technique. The t angle circles around the central axis many times in degree jumps, while the s angle does one slow degree rotation turn. Generative Art Pearson Figure 5. A video work created by applying Perlin noise to a deconstructed sphere: Be it a dimension in space or time. I leave you with this magic to make your own, to take and pervert into your own creations.
Hopefully you now have a good appreciation of the range of effects you can produce with it. Anywhere we need a nice naturalistic variance it can be employed, which makes it such a useful convenience it is never going to fall too far down the toy box. I imagine it would be possible to spend a lifetime coming up with new ways to visualize this single algorithm, and when you tired of it you can change algorithm and waste a second lifetime. Figure 6. Low-level rules creating higher-level organization.
I moved here after a lifetime of living in urban environments; so I was, and remain, enraptured by the view to the south, where there is nothing but sea and sky. Chapter Six 6 I find these formations mesmerizing, because they appeal to both sides of my brain. One hemisphere appreciates the natural aesthetic beauty of these creatures in motion and the shapes they form, but there is also the mathematical beauty of the flocking algorithm they are following, that challenges my logical side.
Each bird in the formation is obeying simple instincts, oblivious to the higher-level pattern the flock is creating. Their behavior is defined by a small set of rules—orienting themselves in relation to their immediate neighbors, seeking the center of the flock, avoiding the paths of other birds—but these simple low-level rules, when applied en masse, create patterns of abstract beauty on a higher level.
The term was first coined in the midth Century by George Henry Lewes, the English psychologist, philosopher, and sometime lover of George Eliot, in a paper on the mind; but notions of the whole being greater than the sum of its parts can be dated back as far as Aristotle. In recent decades, the concept of emergence has found a new relevance and wider popularity through its application to Complexity theory, first by John Holland in his book Emergence , and later for a more popular audience by Steven Johnson in a book of the same name.
In a nutshell, emergence is the observation of how complex and coherent patterns can arise from a large number of small, very simple interactions.
The classic example is the ant colony, an organism that has clearly defined, logical and coherent behaviors observable on two different scales. When we study the patterns of each ant, we see that each one has needs, abilities, and pheromone responses that define its behavior as an individual insect.
But when we study the collective behavior of the colony, we again see sophisticated behavioral patterns; the colony operating like a city, with factories, defenses, and waste-disposal facilities.
These behaviors, seemingly insignificant on the micro level, form a more complex macroorganism when viewed collectively. When you create a large number of small agents with simple behaviors, collectively they can become capable of producing a more complex, emergent, behavior on the macro level. With our human-centric perspective, this is the type of organizational patterns we find ourselves seeking; but the myth has been debunked in studies as far back as the s.
No central organization defines the structure and operation of an ant colony. All these globallevel patterns and structures arise as the result of a large number of local-level micro-decisions. Design, hierarchy, pacemakers, and any other form of higher-level influence have no part in it. The role of the queen is as perfunctory as that of every other ant with a job to perform within the greater whole. It is grass-roots. The flock is made up of individuals, with individual behavioral impulses.
Each starling looks only to its immediate surroundings and the movement of its closest neighbors to define the speed and orientation of its flight. This effect has been modeled in computing, most famously by Craig Reynolds in his Boids algorithm, first published in Reynolds discovered that to produce a realistic flocking simulation in code, he needed only three rules: More complex rules can be added object avoidance, for example , but these three are all that Pearson are required to achieve a complexity comparable with the West Pier starlings.
Dan Shiffman and others have created Boids implementations in Processing, which are fun to adapt. One example of my experiments with this code is shown in figure 6. But before we do, I want to give you a little more context on what emergence means from a sociological perspective.
The idea that a self-organized collective—be it the single cells of a slime mold, the ants of a colony, the human inhabitants of a city, or the globally distributed users of the web—can organize themselves into coherence is understandable and acceptable in a world where we conduct much of our intellectual discourse via the distributed nodes of the internet. Most of us can grasp how a network like Twitter, for example, can have a significant sociological meaning, even though each of its single character tweets is near meaningless.
But the meta-patterns of this chatter, when multiplied by hundreds of thousands, shape themselves into cultural shifts and global opinions. All we need is the right perspective, tools, or visualizations to be able to appreciate them. Emergent networks like this have incredible but subtle strength and resilience. Their power is in their distribution. The internet avoids the vulnerabilities of a centralized organization in the same way a slime mold does: Distributed file-sharing networks share this principle, resilient to legislation because there is no central point to target.
The Twitter protocol itself may be seen as an emergent phenomenon.
Its syntax hashtags, addresses, and so on were never designed by its creators but have been adopted from the bottom up, through their use by the collective.
Emergence may suggest that, if it works so well for the natural world, central organization in the human world — governments, institutions — may be largely redundant. Ants follow local pheromone cues, oblivious to the effect of their decisions on the colony as a whole, just as humans give their local concerns wealth, status, security, and so on priority over the needs of the macro-organisms they form groups, cities, countries.
Many newer, more radical groups have acknowledged and embraced emergence as an organizing principle. Anti-globalization and environmental protest groups, for example, have explicitly modeled themselves after distributed, self-organized systems, as have the champions of file sharing. There is a theory from the realms of neuroscience that argues that perhaps consciousness itself — the sense of self that is interpreting these words — is an example of emergence, being a macro-level coherence formed from the micro-interactions between firing synapses within our brains.
Our whole sense of being may be an emergent effect. If you wish to resonate with this aesthetic, you should pay attention to how it works. Second, within a methodology whereby you work with the simple logic of the programming language, where you encourage artworks to grow from logical seeds, and you wish to evolve a complexity sufficient to be visually interesting, the principles of emergence seem to be an ideal tool for getting complex results from simple code.
Chapter 8, on the subject of fractals, then builds on this way of programming, adding the further concept of self-similarity.
You can program algorithms with a simplicity your animal brain can understand, but which through minimal abstraction can develop a complexity beyond what you could predict. But in order to do this through the medium of code, we first need several new tools.
The following listing creates a script that draws a handful of randomly placed circles every time the mouse is clicked. The script creates 10 circles at random positions on the screen every time you click the mouse. Try it a few times to get something similar to figure 6.
Imagine a rock: It has properties—color, weight, roughness—just as the circle object has an x,y and a radius. There are also things you can do with the rock: Defining an object is very similar to defining a function.
Add the contents of the next listing to the bottom of your sketch. Listing 6. Think of it as a template that you can reuse. A class is a definition for a collection of objects. For example, if we define a class 80s English indie band, it describes a number of objects with shared characteristics guitars, backcombed hair, maudlin lyrics.
We can make an instance of that class called The Smiths and a second instance called Echo and The Bunnymen. Both Emergence The difference between classes and instances of those classes is important. If you can 6 are independent of each other but can share common functionality; recordAlbum , featureInJohnHughesFilm , splitAcrimoniously , etc.
The class is a template: A class becomes an object only when you make an instance of it. In one of our lazy human languages, you might say you once saw an 80s English indie band, but what you mean is that you once saw one or more instances of 80s English indie band. Note that so far, this is purely conceptual.
To create an actual object from this template, an instance of the class, you call the constructor by using the new keyword, as demonstrated in this new version of the drawCircles function: If you want to do something with the class, you need to add some methods functions to it, as per the following listing. Pearson Listing 6. In listing 6. Give each circle a movement factor in the x and y directions. Give each circle an updateMe method, called every frame by the draw loop.
The drawMe method then renders the instance to the screen. You can specify what those five items are when you define the array using the braces syntax: The first position is index 0, so the third item is index 2. An empty array, with no items, is defined as follows: Note that you need to cast the new array returned by this command to the correct type, such as int: Depending on the speed of your machine, you can probably comfortably manage a few hundred circles independently moving around the screen before you see any sign of the animation slowing down, but you should start to get an appreciation of the limits of your processor.
Generative Art Notice how, when you click to add more circles, the previous circles stay onscreen unchanged. Next, lets look at how you might tweak this system and tease out some emergent patterns. If this was one of the murmating starlings, it would be flying blind, unable to see its flock-mates. You can change this though, and give the objects created from the class some rudimentary senses. You have all the circle objects in an array; so by looping through this array, which is available globally to all objects , each object can test itself against every other object regardless of whether that object is onscreen or not.
This is called, in gaming parlance, collision detection. Collision detection and collision correction — making an object react realistically to a collision is one of the dark arts of games programming. To have a realistic physics systems, objects need to be able to have an awareness of the environment around them and its inhabitants, and be able to react to their surroundings.
As environments get more complex, and the number of inhabiting objects increases, this becomes quite a processor intensive action to perform every frame loop, especially if objects are of peculiar shapes. This calculation requires a few extra lines in the updateMe function, as you can see in the following listing.
You need to ensure that all bases are covered. If the x position of the current circle is greater than the x position of the intersecting circle, the midpoint x is This is one way of doing it, but there is a neater way that will cover both possibilities with a single line of code. The average of the two x values will give us the midpoint between them, you can calculate this by adding them together and dividing by 2. When you have the x and y position, you can mark it by drawing a circle at that point, with the radius of the circle dictated by the size of the overlap.
The updated method is shown next. But in the interaction between the circles, a new, emergent complexity is apparent. I can only guess how many actual circles are on the screen. If you comment out, or remove, the call to drawMe in the updateMe function, you get rid of the original circles. Next, add a line to the mouse-press function to trace the current number of circles to the console window, so you can see how many circles you need to produce what effect: The pattern is a byproduct of the interactions between the behaviors you programmed, not the behaviors themselves.
A single starling follows a flight pattern that obeys a set of rules; but place that bird in a flock, and the complexity of the pattern increases several-fold, while the rule set remains unchanged. And the drawing style was pretty basic, too.
If you want to take this further, you can follow a lot of paths. To start you off, here are a few ideas. How about: Reducing the alpha and stroke weight—Make the lines more subtle to turn hard edges into an organic blur see figure 6. How about plotting movement along a curve, for example? You can even import images to be the visual representations of objects. Draw a transparent rectangle over everything instead as described in 2.
I hope this has demonstrated how easy it is: Any system of many parts above a certain relatively low level of complexity, will be prone to emergent complexity. The only trick is exploiting it. Simple rules creating complex results. This will open us up to more advanced coding possibilities in the remaining chapters and beyond. If you chose to define them so, you could give your objects feelings, aspirations, failings, and destinies.
They can be active citizens in a virtual world, albeit a highly abstract one. Your objects can be more than data holders: Autonomy Object-oriented programming OOP is a programming conceit. Objects are for organizing conceptual things into conceptual structures. Chapter Seven 7 The difference between an object and an agent, in programming parlance, is that agents, specifically, observe and interact with their environment. They may have more sophisticated behaviors, such as goals, beliefs, and prejudices.
They can also harbor imprecisions, self-interests, and irrationalities too, which is what makes them so interesting to work with. This is what we mean when we speak of autonomy: The difference between an agent and an object is the difference between an animal and a rock. The most familiar single example of an autonomous agent, to you at least, is the one staring at this page. You, dear reader, with your behavior on any particular day defined by a complex mix of biology, psychology, and the comfortableness of your footwear, are an autonomous object, creating interesting patterns in the data you create.
Most everything you do these days leaves a data trail: The cards in your wallet are writing your economic autobiography, your tweets and SMSs are writing an ASCII diary as part of a social map, and the phone in your pocket is drawing a GPS picture of your daily psycho-geography. In the s, the field of computer science was obsessed with CA. Today, that kind of computing power is available in our mobile phones, so we can easily simulate cellular automata in a lightweight Processing sketch.
A 2D1 CA is a grid of cells see figure 7. Each cell has limited local knowledge, only able to see its eight immediate neighbors. In a series of cycles, each cell decides its next state based on the current states of its surrounding cells. Listing 7. At the bottom of the In setup, you calculate the number of rows and columns based on the width and height and the size you want the cells; then, you call the restart function to fill a 2D array with this grid. After the grid is created, there is a second pass through the array to tell each of the cells who its neighbors are above, below, left, and right of them.
Autonomy script you define an object, a Cell, which has x and y positions and a state, either on or off. It also has a holder for its next state, nextState, the state it will enter the next time its drawMe method is called. The first pass triggers each cell to calculate its next state, and the second pass triggers each to make the transition and draw itself.
Note that this needs to be done in two stages so each cell has a static set of neighbors on which to base its next state calculation. Now that you have the grid of cells as shown in figure 7. Multidimensional arrays A one-dimensional array, as you saw in chapter 6, is a data structure for storing a list of elements.
You define one as follows: A 2D array is initialized like so: In listing 7. Generative Art If you want more dimensions, you can have them. Biologists, economists, sociologists, and neuroscientists, amongst others, have discussed at length the parallels between the behavior of CA obeying GOL rules and the results of studies in their respective fields.
The rules for GOL are as follows: If a live black cell has two or three neighbors, it continues to live. Otherwise it dies, of either loneliness or overcrowding. If a dead cell has exactly three neighbors, a miracle occurs: You write this in code as follows. Complete the calcNextState function in accordance with the following listing.
Triangle width is the neighbor count. The color of the shape is mixed using a combination of the two. Generative Art Pearson Figure 7. Lines and their rotations are determined by cell age. In the two examples in figures 7. You may like to try similar experiments. GOL is only one of many potential rule sets. Each cell is particularly susceptible to peer-group pressure and looks to its neighbors to observe the current trend. To ensure that there is an odd number of cells on which to make the decision, the cell includes its own current state in addition to its eight neighbors in making the calculation.
To see the vote in action, rewrite the calcNextState function using the code from the following listing. You can see how the clustering effect creates patterns similar to the hide of a cow, or the contours of a landscape seen from above. With the Vichniac Vote, the rules you apply are sociological —agents succumbing to the peergroup pressure from their neighbors—but the results have an aesthetic more familiar from biology or geology. Might this imply that common computational principles underpin these disciplines?
The rules are as follows: If the state is resting, the next state is off. If the state is off, and exactly two neighbors are firing, the state becomes firing. Note that you have to change the type of state to int, so it can have more than two values. The integers 0, 1, and 2 indicate off, firing, and resting, respectively.
Spaceships the CA term, not mine move across the plane in horizontal, vertical and sometimes diagonal directions.
Does this behavior reflect the way your thoughts form from the electronic bursts of firing synapses? Not one of the famous rule sets, but a custom behavior instead. The patterns it produces should be quite familiar though. There is no reason a cell has to be limited to two or three distinct values: If the average of the neighboring states is 0, the state becomes If the new state goes over , make it If the new state goes under 0, make it 0.
The code is in the next listing. The output is shown in figure 7. Like rain on a shallow pool of water, it churns and varies. The reason for this example is to emphasize how you can tweak not only the rules but also the framework itself.
Pearson 7. The real purpose of your experiments so far has been to drum in the agent-oriented approach, and the ways you can study the patterns that objects unwittingly create. CAs, for all their nerdy amusement value, may be only the simplest of possible systems you can visualize this way. The coding part of this chapter is finished now, but there is plenty of scope to develop these approaches in more meaningful directions.
The interactions of these simplified souls can say something about their less simple analogues: For example, you can query the thoughts or feelings of an agent, perhaps something like this: He constructed a system whereby a number of colored cubes are given the ability to trade their own color value in the pursuit of profit see figure 7.
The set of rules each cube followed was the same, but each cube had different leanings and idiosyncrasies influencing its self-interested behavior; these collectively defined the unstable economics of the system. Generative Art Given an infinite number of coders at an infinite number of MacBooks, you might follow this to its logical extreme and code an entire human being, and pack a bunch of them into a CA grid to see how they got on. Or, more interestingly, you could code up an artificial city environment to house them, and see the patterns they traced.
His piece was more than a nice aesthetic: The shot of Earth shown in figure 7. Before this photograph, we had highly detailed maps of the world and knew pretty much what it would look like when we got up there, but this was the first time we gained the perspective to see it for ourselves from so far away.
It was a great leap in our anthropic subjectivity. Figure 7.
Your everyday movements leave a trail on the surface of the planet, and this trail draws a pattern. Autonomy It took only 40 years before the power of satellite imagery was on every laptop. Couple this with a related technology, Global Positioning Systems GPS , and you have the potential for a new form of unwitting artistic expression.
If the human agent is acting unaware, or uninterested, of the effect their actions are having on the system, they become as valid a data source as any other autonomous object. As an exercise in coding minimalism, he built an AS3 version of the old Lunar Lander arcade game see figure 7. Later, he used this as the basis for experiments with multiuser gaming, building a system to enable many users to play the game over the web. Seb threw his toy out there, inviting any of the great unwashed visiting his site to give it a play.
But, unbeknownst to his users, he had the system record the paths they took as they maneuvered their landers safely to the lunar surface. When he traced this data out onto a composite image, the result, through no intent or design, was surprisingly beautiful see figure 7.
So, it would appear that there are only two challenges in using human agents in the creation of generative art: Data, particularly that relating to the hairless apes and their consumption habits, is being stockpiled as if it were the most valuable commodity we have—which some may argue it is.
There are patterns in Twitter Autonomy streams, web browsing, and GPS location data that can be accessed via a variety of APIs available to programmers, usually for free. The agents in this system are journalists, doing their jobs and writing their articles, obliviously creating meta-patterns in the commonality of their language usage. With this approach, you can begin realizing more sophisticated systems. Your objects can be autonomous agents, interacting with each other in an artificial environment, producing emergent complexity for you to visualize, an idea you explored through cellular automaton experiments.
The potential in data visualization opens up a new field of art. Pearson Figure 8. Making a New Science , which popularized the mathematics of chaos theor y, fractal imager y, in par ticular the Mandelbrot set, was overly familiar from the kind of posters you might expect to see on the walls of hippie math grads.
Anyway, I want this preconception of fractal art flushed from your mind as we explore self-similarity in this chapter. Fractal art can also be subtle and beautiful see figures 8. But this is about as difficult as generative art ever needs to get.
But this is a different breed of iteration, a reflexive iteration—a type that, unchecked, tends toward the infinite. Generative Art Figure 8. As with emergent patterns, fractal structures are everywhere in nature: The leaf of a fern is one particularly good example see figure 8.
This self-similarity repeats, continuing downward in scale, beyond the limits of the human eye. You see a reflection of a reflection of a reflection of a reflection, onward toward the limit of perception. Strange things happen when you have an infinite recursion like this.
Small changes, micro-movements, become massively amplified within this closed loop. In the same way as the small interactions between many agents gave rise to an emergent complexity in the previous chapters, small changes in infinite, self-similar structures create a similar complexity, but on a more ineffable scale.
For example, you can tell at a glance that the following code will never reach a state where it stops: Infinite loops are easy to code but are infinitely tedious to perform. This is one thing to bear in mind as you begin coding reflexive iteration in the following section.
Although fractals in the natural world have the potential to be infinitely recursive, you can only simulate infinite recursion in electronics. And of course, if one of the defining characteristics of an object is that it multiplies, you know that inevitably the objects will breed like rabbits. Fractals 8. If you keep the numbers low to start with, you can maintain an appreciation for what is going on; then, you can kick the numbers up gently when you have something interesting.
Adding 3 levels, each with 3 children, is the equivalent of 3 3, which means 27 objects. Add 1 more child, and it becomes 3 4, which is 81 objects. Five levels with 5 children gives you 3, elements to control. This is why you need to tread carefully. You can think of the first Branch as the trunk of the tree.
The following listing is your starting point. Generative Art Listing 8. Call drawMe on that object. As long as you include the conditional level Fractals trunk, is 1, and its index is 0 because there is only one of it. The object, when created, calls updateMe, which calculates an end point based on the start point you passed it. The drawMe function draws a line between those two points and a little circle to show its origin. Simple so far. The command trickles down to the children. Change updateMe so it calculates the end point randomly, but weighted according to the level, as follows: Figure 8.
Next, you need to add a few new properties to the Branch class to enable you to control drawing style, line length, and rotation put them under the line float endx, endy;: Add this code before the line updateMe ex, why ; in the Branch constructor function: With this new version, the end point will be calculated according to the angle and length—plotting it as a point on the circumference of a circle of radius len.
The angle to that point will change every frame, as will the length of the line. The new updateMe function is as follows. Pearson Listing 8. But there are some things computers can do better than nature—one of which is space clockwork.
Generative Art Normally, the message at this point in the chapter would be to throw what you can at your structure and see what flies. Instead, try pushing up the numbers more gently. Already this was nearing the point when my machine was starting to grumble. My amorphous blob was still spinning, but with a little less pizzazz.
Pushing that one further, to seven levels, yields something like figure 8. I found that was about the limit of what would still animate on my machine.
The complete code for this final version is in the following listing. Listing 8. Sutcliffe Pentagons In I went to a meeting of the Computer Arts Society CAS in London, an organization that was remarkably, for a body devoted to computing celebrating its fortieth anniversary with that event.
Fractals name. Sutcliffe noticed that if you draw a pentagon, then plot the midpoints of each of its five sides and extend a line perpendicular to each of these points, you can connect the end of these lines to make another pentagon. In doing this, the remaining area of the shape also ends up being subdivided into further pentagons, meaning that within each pentagon are six subpentagons.
And within each of those, another six sub-pentagons, repeated downward toward the infinitesimal. For example, if you plot a point every 72 degrees, you get a pentagon. Generative Art Pearson 3. Cannon, W. Floyd, and W. Kenneth Stephenson, Introduction to Circle Packing: It fills that array by rotating an angle around a center point in degree jumps. It then uses that array of points to construct the first Branch object. The Branch object is your self-replicating fractal object. A Programming Language 2.
What is Processing? Structure, logic, and animation. Saving, publishing, and distributing your work. Randomness and not-so-randomness. Rotational drawing.
Chapter 5 Adding Dimensions 5. Two-dimensional noise. Chapter 6 Emergence 6. Emergence defined. Chapter 7 Autonomy 7. Cellular automata. Chapter 8 Fractals 8. Infinite recursion. Case study: Sutcliffe Pentagons. About the Technology Artists have always explored new media, and computer-based artists are no exception. About the book Generative Art presents both the techniques and the beauty of algorithmic art.
What's inside The principles of algorithmic art A Processing language tutorial Using organic, pseudo-random, emergent, and fractal processes. Generative Art combo added to cart. Your book will ship via to:. Commercial Address. As Matt Pearson writes, generative art is easyat least, sometimes. Using Processing as his tool of choice, Matt shows how to progress from primitive drawing to more complex topics like interactive animation and simulated phenomena such as cellular automata.
But rather than just demonstrating syntax, he describes the creative process involved in designing generative systems, showing how manipulating parameters and tweaking algorithms can result in radically different outcomes. Having grown up with the ZX Spectrum and worked many years as a programmer while also being involved in the arts, Matt is perfectly placed at the intersection of code and creative thinking.
In this book, he sets out to provide the reader with a toolbox of recommended practices while simultaneously introducing a deeper cultural context to the work. It should have readers quickly thinking beyond simple code tricks to the more complex ideas that underlie a computational model of form.
My personal favorites are the sections on wrong ways to do things, showing how a simple form like a line or a circle can be transformed into complex systems by thinking creatively about the way theyre constructed. Happy coding! Marius Watz Marius Watz is an artist working with code as his material, who has shown his work around the world. He is the founder of the Generator. He is currently based in Oslo and New York.
But I failed. I started a computer science degree, but dropped out after about a year and a half. Im sorry, but it bored me senseless.
My studies of early s ideas of computing had so repulsed me that I made efforts to stay as far away from computers as I could for the next 10 years. For much of the 90s, I didnt even own a computer; instead I had a guitar, an attitude, and an ill-advised haircut. I was only drawn back toward the end of the decade when the web started to take off, and a lot of creative people suddenly discovered that what theyd been doing recently with video cameras, photography, and hypertext was now being called New Media, and everyone was doing it.
This rehabilitation of computing has continued unabated, to the point that today, to say you work with computers is about as meaningful as saying you breathe air.