I had previously talked in detail about Perlin Noise and its properties, here. Now in this blog post I will write about the implementation. My implementation is based on an excellent writeup which I found in this link (http://webstaff.itn.liu.se/~stegu/TNM022-2005/perlinnoiselinks/perlin-noise-math-faq.html).

####

Perlin noise is a function for generating coherent(smooth) noise over a space. I've have come across multiple implementations of Perlin Noise function and most of them are based on smoothing of multiple noise functions to generate a coherent noise. The one I am discussing in this blog post is alittle different and more or less conforms with the original implementation that Ken Perlin came up with.

In the figure,

g(xgrid, ygrid) = (gx, gy)

These gradients can be visualized as:

vec00 = (x-x0,y-y0);

vec01 = (x-x0,y-y1);

vec10 = (x-x1,y-y0);

vec11 = (x-x1,y-y1);

The influence can be calculated by performing dot operation on the gradient and the vector as:

s = g(x0, y0) · vec00;

t = g(x1, y0) · vec10;

u = g(x0, y1) · vec01;

v = g(x1, y1) · vec11;

S

and also at (y-y0) as:

S

where smoothParam(p) = 3p

ax = s + S

We also calculate

bx = u + S

Then the final result

z = ax + S

####
**Short Description**

Fig: Perlin Noise(left), Noise(right)

#### Requirement

Perlin Noise (2D) function is a function that takes two points (*x*and*y*) and returns a noise value (say*z*).*x*,*y*and*z*are all floating point numbers.#### The Grid

The concept of accepting floating point numbers can be explained from the concept of Grid. The space, which the noise function operates on, is assumed to be composed of grids. Grid, in terms of*x*and*y*, are the whole numbers. So, any decimal (fractions) are points lying inside a grid cell.In the figure,

*(x0,y0), (x1,y1), (x0,y1), (x1,y0)*represents grid points and*(x,y)*lies inside a grid.#### Pseudorandom Gradient

Pseudorandom Gradient function takes the grid coordinates and generates pseudorandom gradient of length 1 as:g(xgrid, ygrid) = (gx, gy)

These gradients can be visualized as:

#### Calculating grid influences

Next, We need to calculate a vector directing from grip points to the point*(x,y)*.vec00 = (x-x0,y-y0);

vec01 = (x-x0,y-y1);

vec10 = (x-x1,y-y0);

vec11 = (x-x1,y-y1);

The influence can be calculated by performing dot operation on the gradient and the vector as:

s = g(x0, y0) · vec00;

t = g(x1, y0) · vec10;

u = g(x0, y1) · vec01;

v = g(x1, y1) · vec11;

#### Interpolation

A smoothing function, characterized by the curver 3p^{2}- 2p^{3}, is used to get a weight*Sx*at*(x-x0)*in the curve as:S

_{x}= smoothParam(x-x0);and also at (y-y0) as:

S

_{y}= smoothParam(y-y0);where smoothParam(p) = 3p

^{2}- 2p^{3}, then parameter representing weighted average of*s*and*t*is obtained by constructing a linear function mapping 0 to*s*and 1 to*t*, and evaluating it at our*x*dimension weight S_{x}. We call this ax.ax = s + S

_{x}*(t - s);We also calculate

*bx*using*u*and*v*as:bx = u + S

_{x}*(v - u);Then the final result

*z*can be obtained as:z = ax + S

_{y}*(bx - ax);
Very Useful article

ReplyDeleteVery Useful ARTICLE VISIT www.apponix.com for Java Tutorials

ReplyDeleteinformative blog

ReplyDeletedata science certification

This post is very simple to read and appreciate without leaving any details out. Great work!

ReplyDeleteartificial intelligence course in noida

Stunning! Such an astonishing and supportive post this is. I incredibly love it. It's so acceptable thus wonderful. I am simply astounded.

ReplyDeletehrdf contribution

wonderful bLog! its intriguing. thankful to you for sharing.

ReplyDeletehrdf claimable training

If you don't mind proceed with this extraordinary work and I anticipate a greater amount of your magnificent blog entries

ReplyDeletebusiness analytics course

Your content is very unique and understandable useful for the readers keep update more article like this.

ReplyDeletedigital marketing classes in aurangabad

This blog is very informative and excellent post gained a lot of information. good job..

ReplyDeleteData Science Training in Hyderabad

Data Science Course in Hyderabad

I am sure that this is going to help a lot of individuals. Keep up the good work. It is highly convincing and I enjoyed going through the entire blog.

ReplyDeletedata science course