It only takes a minute to sign up. I'm working on a tile based game and already got a generator working. But it doesn't seem to be smooth enough to look realistic. It currently looks like this The terrain on the screenshot isn't the full terrain it's just the terrain on the screen :. Primarily the issue is that you are using a non realistic persistence of 2. Persistence should be between 0 and 1, where closer to 1 is more rough and closer to 0 is more smooth, see here for more details.
Larger than the maximum output is supposed to be. Correcting this will cause you to have a continuos 0 output, this is because you have cast your output to intstop doing this and you will start getting an output between -1 and 1 that is smooth. The following graph is for values between 0 and 20 with persistance of 0. You are also effectively using the same seed for all your octaves, each octave uses the same 3 prime numbers in the Noise function so actually your octaves are the same noise at different scales.
This can cause difficulties. Each octave should have its own primes. You might consider using the midpoint displacement algorithm vice perlin noise depending on the terrain you'd like to have.
Why would someone chose midpoint displacement over perlin noise for 3D terrain generation? What is the simplest method to generate smooth terrain for a 2d game? Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Sidescrolling terrain 1D perlin noise Ask Question. Asked 6 years, 7 months ago.Thank you for helping us improve the quality of Unity Documentation.
Although we cannot accept all submissions, we do read each suggested change from our users and will make updates where applicable. For some reason your suggested change could not be submitted. And thank you for taking the time to help us improve the quality of Unity Documentation. Return value might be slightly below 0. Perlin noise is a pseudo-random pattern of float values generated across a 2D plane although the technique does generalise to three or more dimensions, this is not implemented in Unity.
The noise does not contain a completely random value at each point but rather consists of "waves" whose values gradually increase and decrease across the pattern. The noise can be used as the basis for texture effects but also for animation, generating terrain heightmaps and many other things. Perlin noise sampled in the range The same coordinates will always return the same sample value but the plane is essentially infinite so it is easy to avoid repetition by choosing a random area to sample from.
Although the noise plane is two-dimensional, it is easy to use just a single one-dimensional line through the pattern, say for animation effects. Note: It is possible for the return value to be slightly less than 0. You may need to clamp the return value if the 0. Is something described here not working as you expect it to? It might be a Known Issue. Please check with the Issue Tracker at issuetracker. Version: Language English. Scripting API. Suggest a change.
Submission failed For some reason your suggested change could not be submitted. Parameters x X-coordinate of sample point. Returns float Value between 0. SetPixels pix ; noiseTex.Perlin noise in two dimensions, generated using the code below. Ahead of tomorrow's blog, I thought I'd briefly discuss Perlin Noise. Perlin Noise If you've worked with 3D graphics programs, you're already well familiar with Ken Perlin's famous noise function which gives rise to so-called Perlin noise.
The code for it looks a little scary, but intuitively it's an easy function to understand. Let's take the 2D case although you can generate Perlin noise for any number of dimensions. Imagine that you have a pixel-square image blank, all white. Now, imagine that I come along and tell you to mark the canvas off into 32 rows and 32 columns of 8x8-pixel squares. Further imagine that I ask you to assign a random grey value to each square. You've now got a kind of checkerboard pattern of random greys.
What differentiates Perlin noise from random checkboard noise is that in Perlin's case, the color values are interpolated smoothly from the center of each tile outward, in such a way that you don't see an obvious gridlike pattern. In other words, when you cross a tile boundary, you want the slope of the pixel intensity to be constant no discontinuities. You can visualize the end result if you took the 32x32 random checkboard pattern and passed it through a Gaussian blur a few times.
Pretty soon, you wouldn't even be able to tell that gridlines ever existed in the first place. That's the idea with Perlin noise. You want to interpolate colors from one block to the next in such a way that there are no discontinuities at the cell boundaries. It turns out this requirement can be met in quite a variety of ways by using cubic splines, quartics, or even sine- or cosine-based interpolation between squares, for example; or by using Perlin's gain function.
Using Perlin Noise to Generate 2D Terrain and Water
There's no one "correct" way to do it. I'd love to be able to link to a good Perlin noise tutorial on the Web, but so far I haven't found one that doesn't try to conflate fractal noise, turbulence, and other topics with Perlin noise.
The best treatment I've come across, frankly, is not surprisingly in Perlin's own Texturing and Modeling book which is truly a first-rate book, "must reading" for graphics programmers. Fortunately, Ken Perlin has done all the hard work for us in writing the necessary interpolation and other code for noiseand he has kindly provided a 3D reference implementation of the noise function in highly optimized Java.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I did find that aforge. It shows an example ofusing the 2D noise to generate clouds. Only difference you need to make going from 1D to 2D is to calculate the slope.
Once you have the slope and the 1D noise, just add that noise in the direction perpendicular to the direction of slope. I saw your question hoping for pseudocode as an answer. I ended up not using the gradient I already have a simplex noise function that would be difficult to make compatible but doing something else.
We now have a mapping from an angle to a noise value. Given the radius we wish our circle to have, we can set the minimum and maximum values of the noise then, for any theta we wish to evaluate, just add the corresponding value to the radius of the circle. In polar coordinates, this looks like:. You will want a tight spread between noisemax and noisemin, else your circle will be mostly wobble.
For a square the process it the same but you do not need any controls or interpolation, just the scaling factors and an interval square side length. You can get fancy and do a nonlinear interpolation but I don't really see the need, and it might mess up the simplex noise. Learn more. Asked 7 years, 11 months ago. Active 7 months ago. Viewed 1k times. Glorfindel Active Oldest Votes.
ElKamina ElKamina 7, 22 22 silver badges 39 39 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home? Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Triage needs to be fixed urgently, and users need to be notified upon….
Dark Mode Beta - help us root out low-contrast and un-converted bits. Technical site integration observational experiment live on Stack Overflow. Related 0.This post is going to be the Perlin noise tutorial that I've always wanted to see. Perlin noise was invented in the eighties and has since been used countless times to generate natural-looking visual effects in films and games.
If you google "perlin noise", you will get a trove of articles and code. However, in my opinion, a beginner will have a hard time figuring out how it really works. I think that a lot of articles on the subject don't provide enough context, jump into too much detail too quickly and use scary terminology like "surflets".
I've even stumbled upon some articles presenting blurred white noise as perlin noise, which is not the case! And that's why I decided to try and write a more beginner-friendly article on the subject, that starts from a simple and easy to understand case, and builds up towards a more advanced one.
Hopefully, people who read this tutorial won't have to struggle any more! Before we go any further, I want to make sure a simple, but very important point is understood: noise is a mathematical function, just like the sine or the exponent. The gray, amorphous, lumpy fog that you see in most pictures is just one of the possible graphical representations of said function. The reason I want to make this explicit is that some people may think about calculating noise in terms of pixels being affected by neighbor pixels, which really isn't the correct way to think about Perlin noise.
Most of us are familiar with mathematical functions of a single variable again, sine for examplebut functions can depend on multiple variables. It's fairly easy to visually represent a function of two variables. For a function of three variables, you can imagine the time, tas the third variable, and the graphical representation would be animated: the values of the function along the z at each point x, y would change with the passage of time.
Now, if you image-search for perlin noise, what you'll usually see is the graphical representation of a two-dimensional function, where lighter pixels correspond to higher values at particular x and y coordinates.
But the thing is, perlin noise doesn't have to be two-dimensional. The concept can be extended to any number of dimensions. But more importantly for us, it can be reduced to just a single dimension! And, for us mere mortals, it's much easier to understand how the one-dimensional case works. Armed with that knowledge, we'll be ready to tackle the more complex cases.
That is the approach that this post is going to take. Regardless of dimensionality, the noise function must be continuous, smooth and random-looking. Of course, there are strict mathematical definitions for what things like "continuous" and "smooth" mean, but you needn't worry about that right now - just go with your intuitive understanding.
In the one-dimensional case, the graph of a function possessing the above-mentioned properties will look like a neat squiggly line. Our first goal will be to construct such a function. It should be noted that there are multiple methods to construct a function fitting these criteria, Perlin's technique is just one of them.
As promised, we're going to consider the single-variable case first. Thus, our noise function n is defined in the domain of real numbers, R. Now, let's imagine that for each integer, a value of either 1 or -1 is chosen randomly.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Strangely the generated noise looks a lot different from what I've expected. It looks more like cosine interpolation. It seems Perlin uses the lerp function in a different way.
Shouldn't be u and v represent the point before and the point after sx and therefor don't change? Here you can find a Java applet from the original Perlin's talk that explains the whole 2D noise computation process very clearly. The perlin's noise function is continuous because, at each point in 1Dit is the linear interpolation of two "smoothed" linear gradients.
Subscribe to RSS
It's pretty basic and only takes a x value.
Subscribe to RSS
I've been messing around with it for a while but can't work out what to pass into the function as the x value. I first tried passing just the x value of the column I'm finding the noise for. This just returned 0 every time. This sort of worked, but the wider I made the terrain, the more spaced out it became. What Pikalek said is absolutely true about the return value, but the workaround he suggest is not very good.
Instead of adding a random offset, you should divide the current x position with a cobstant value. Let's call this value "smoothness". Higher smoothness means smokther terrain:. Since the column number is an integer coord, passing it as input will always give 0 as output. An easy way around that is to add a constant decimal offset to the column number:. Sign up to join this community.Generating 1D Perlin Noise With Python
The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. How should I use this 1D perlin noise function? Ask Question. Asked 3 years, 6 months ago. Active 3 years, 6 months ago. Viewed 1k times. Hope you understand, what I do give the function for the x value? Thanks in advance. IHazza IHazza 13 5 5 bronze badges. Active Oldest Votes. The result is the same everytime, and I'm wondering if I add a random offset will it be random everytime?
If OP wants smoother noise, the library already supports that without applying transforms to the input. Pikalek Pikalek 7, 2 2 gold badges 27 27 silver badges 39 39 bronze badges. But correct me if I'm wrong. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog.
Podcast Cryptocurrency-Based Life Forms. Q2 Community Roadmap. Featured on Meta.