I would like to proceduraly generate 2D caves. I already tried out using some 1D simplex noise to determine the terrain of the floor, which is basically everything you can change in a sidescroller, but it turned out rather unimpressive.
I would like to have an interesting terrain for my cave/dungeon and if possible some alternative paths.
I couldn't come up with any ideas for this kind of terrain and I also could not find any promising ways to do this kind of stuff on the internet.
Assuming you've tried something like abs(simplex) > 0.1 ? nocave : cave, maybe the problem you're running into is that it's too connected, and that there are no dead-ends anywhere. You could extend this by doing the following simplex1*simplex1 > threshold(simplex2) where threshold(t)=a*t/(t+b) where a roughly controls the max threshold (cave opening size) and b roughly controls how much of the underground is caves vs not. The function looks like this, and the fact that it quickly rushes to zero is supposed to make dead ends look more rounded and less pointy. Working with the squared noise value is also for this purpose.
Have you tried Perlin Noise? Seems to be the standard way of doing this sort of thing.
Related
The problem is that I didn't learned yet adders or VHDL (which a lot of people are telling me to use them) but all I have is 16-to-1 MUXs.
Should I link each MUX with the other from the select input? (Knowing that I have 4 inputs and 4 outputs obviously)
P.S: I am new to this kind of stuff and I am having a hard time to solve this.
Thank you in advance.
Converting binary to Gray code is pretty easy. In C it's just gray = bin ^ (bin>>1). You need on XOR gate for each bit except for the highest one.
There's a nice schematic over here: https://www.electrical4u.com/binary-to-gray-code-converter-and-grey-to-binary-code-converter/
You can make an XOR easily with a 4-to-1 MUX. You can, of course, make one with a 16-to-1 MUX too, by tying two of the inputs to ground, but it's a big waste of gates.
I'm using Selenium to automate webpage functional testing. It's important for us to do a pixel-by-pixel comparison when we roll out new code, so we're using Selenium to take screenshots and comparing the base64 encoded strings to see if anything has changed.
We're finding that in practice, it's hard to get complete pixel consistency, especially with images. I would like minor blurriness / rendering artifacts to count as a "pass" instead of a "fail", so I'm wondering if there's a way of doing a fuzzy comparison to make our tests a bit less fragile.
I was thinking of maybe looking at the Levenshtein distance between the base64 strings as a starting point, but I don't really know if that's a good approach, or what the tolerances should be that distinguish "something moved on the page" from "rendering artifact". Any ideas / approaches?
So I ended up going with the ImageMagick command-line tool (because why re-invent image comparison). The "Peak Absolute Error" metric of the "compare" tool tells you how much you have to fuzz pixels before two images are identical. This seems to work well... for an image with slight graphical distortions, there might be a lot of pixels that don't match, but slight fuzzing is enough to make them match; but for two images that are actually different, even though most pixels might match, the ones that don't tend to be very different. Right now I'm checking for a PAE of less than 15% to see if the images should be counted as identical. Command line I'm using is:
compare -metric PAE original.png new.png comparison.png
Documentation on ImageMagick's compare tool is here: http://www.imagemagick.org/script/compare.php
I've been using perceptualdiff which uses a model of the human visual system to try to avoid reporting unnoticeable changes (the authors used for renderer regression testing). Usage is quite simple:
perceptualdiff -output diff.ppm baseline.png test.png
(where diff.ppm is a PPM format image highlighting the areas of difference)
The needle regression testing framework has support for using pdiff to compare screenshots:
http://needle.readthedocs.org/en/latest/#engines
Use an image format that does not create artifacts (like BMP or PNG) then you can do a per-pixel comparison.
I think you can check each pixel with a common Euclidean Distance.
To improve performance a little, do not calculate the square root but check the squares of the distances
// Maximum color distance allowed to define pixel consistency.
const float maxDistanceAllowed = 5.0;
// Square of the distance, used in calculations.
float maxD = maxDistanceAllowed * maxDistanceAllowed;
public bool isPixelConsistent(Color pixel1, Color pixel2)
{
// Euclidean distance in 3-dimensions.
float distanceSquared = (pixel1.R - pixel2.R)*(pixel1.R - pixel2.R) + (pixel1.G - pixel2.G)*(pixel1.G - pixel2.G) + (pixel1.B - pixel2.B)*(pixel1.B - pixel2.B);
// If the actual distance is less than the max allowed, the pixel is
// consistent and the method returns TRUE
return distanceSquared <= maxD;
}
Didn't test the C# code, but it should give you the idea. Give some tries and adjust the maxDistanceAllowed to your needs.
If anyone else is looking for something similar there is Depicted-dpxdt. It is designed to be used as part of a CI/CD process.
It combines perceptual diff with server, commandline tool, wrapper for phantom js.
It has functionality demonstrated like crawling entire site and comparing pages for differences.
I'm attempting to gauge the percentage difference between two images.
Having done a lot of reading I seem to have a number of options but I'm not sure what the best method to follow for:
Ease of coding
Performance.
The methods I've seen are:
Non language specific - academic Image comparison - fast algorithm and Mac specific direct pixel access http://www.markj.net/iphone-uiimage-pixel-color/
Does anyone have any advice about what solutions make most sense for the above two cases and have code samples to show how to apply them?
I've had success calculating the difference between two images using the histogram technique mentioned here. redmoskito's answer in the SO question you linked to was actually my inspiration!
The following is an overview of the algorithm I used:
Convert the images to grayscale—compare one channel instead of three.
Divide each image into an n * n grid of "subimages". Then, for subimage pair:
Calculate their colour composition histograms.
Calculate the absolute difference between the two histograms.
The maximum difference found between two subimages is a measure of the two images' difference. Other metrics could also be used (e.g. the average difference betwen subimages).
As tskuzzy noted in his answer, if your ultimate goal is a binary "yes, these two images are (roughly) the same" or "no, they're not", you need some meaningful threshold value. You could produce such a value by passing images into the algorithm and tweaking the threshold based on its output and how similar you think the images are. A form of machine learning, I suppose.
I recently wrote a blog post on this very topic, albeit as part of a larger goal. I also created a simple iPhone app to demonstrate the algorithm. You can find the source on GitHub; perhaps it will help?
It is really difficult to suggest something when you don't tell us more about the images or the variations. Are they shapes? Are they the different objects and you want to know what class of objects? Are they the same object and you want to distinguish the object instance? Are they faces? Are they fingerprints? Are the objects in the same pose? Under the same illumination?
When you say performance, what exactly do you mean? How large are the images? All in all it really depends. With what you've said if it is only ease of coding and performance I would suggest to just find the absolute value of the difference of pixels. That is super easy to code and about as fast as it gets, but really unlikely to work for anything other than the most synthetic examples.
That being said I would like to point you to: DHOG, GLOH, SURF and SIFT.
You can use fairly basic subtraction technique that the lads above suggested. #carlosdc has hit the nail on the head with regard to the type of image this basic technique can be used for. I have attached an example so you can see the results for yourself.
The first shows a image from a simulation at some time t. A second image was subtracted away from the first which was taken some (simulation) time later t + dt. The subtracted image (in black and white for clarity) then shows how the simulation has changed in that time. This was done as described above and is very powerful and easy to code.
Hope this aids you in some way
This is some old nasty FORTRAN, but should give you the basic approach. It is not that difficult at all. Due to the fact that I am doing it on a two colour pallette you would do this operation for R, G and B. That is compute the intensities or values in each cell/pixal, store them in some array. Do the same for the other image, and subtract one array from the other, this will leave you with some coulorfull subtraction image. My advice would be to do as the lads suggest above, compute the magnitude of the sum of the R, G and B componants so you just get one value. Write that to array, do the same for the other image, then subtract. Then create a new range for either R, G or B and map the resulting subtracted array to this, the will enable a much clearer picture as a result.
* =============================================================
SUBROUTINE SUBTRACT(FNAME1,FNAME2,IOS)
* This routine writes a model to files
* =============================================================
* Common :
INCLUDE 'CONST.CMN'
INCLUDE 'IO.CMN'
INCLUDE 'SYNCH.CMN'
INCLUDE 'PGP.CMN'
* Input :
CHARACTER fname1*(sznam),fname2*(sznam)
* Output :
integer IOS
* Variables:
logical glue
character fullname*(szlin)
character dir*(szlin),ftype*(3)
integer i,j,nxy1,nxy2
real si1(2*maxc,2*maxc),si2(2*maxc,2*maxc)
* =================================================================
IOS = 1
nomap=.true.
ftype='map'
dir='./pictures'
! reading first image
if(.not.glue(dir,fname2,ftype,fullname))then
write(*,31) fullname
return
endif
OPEN(unit2,status='old',name=fullname,form='unformatted',err=10,iostat=ios)
read(unit2,err=11)nxy2
read(unit2,err=11)rad,dxy
do i=1,nxy2
do j=1,nxy2
read(unit2,err=11)si2(i,j)
enddo
enddo
CLOSE(unit2)
! reading second image
if(.not.glue(dir,fname1,ftype,fullname))then
write(*,31) fullname
return
endif
OPEN(unit2,status='old',name=fullname,form='unformatted',err=10,iostat=ios)
read(unit2,err=11)nxy1
read(unit2,err=11)rad,dxy
do i=1,nxy1
do j=1,nxy1
read(unit2,err=11)si1(i,j)
enddo
enddo
CLOSE(unit2)
! substracting images
if(nxy1.eq.nxy2)then
nxy=nxy1
do i=1,nxy1
do j=1,nxy1
si(i,j)=si2(i,j)-si1(i,j)
enddo
enddo
else
print *,'SUBSTRACT: Different sizes of image arrays'
IOS=0
return
endif
* normal finishing
IOS=0
nomap=.false.
return
* exceptional finishing
10 write (*,30) fullname
return
11 write (*,32) fullname
return
30 format('Cannot open file ',72A)
31 format('Improper filename ',72A)
32 format('Error reading from file ',72A)
end
! =============================================================
Hope this is of some use. All the best.
Out of the methods described in your first link, the histogram comparison method is by far the simplest to code and the fastest. However key point matching will provide far more accurate results since you want to know a precise number describing the difference between two images.
To implement the histogram method, I would do the following:
Compute the red, green, and blue histograms of each image
Add up the differences between each bucket
If the difference is above a certain threshold, then the percentage is 0%
Otherwise the colors found in the images are similar. So then do a pixel by pixel comparison and convert the difference into a percentage.
I don't know any precise algorithms for finding the key points of an image. However once you find them for each image you can do a pixel by pixel comparison for each of the key points.
The only effect AudioUnit on iOS is the "iTunes EQ", which only lets you use EQ pre-sets. I would like to use a customized eq in my audio graph
I came across this question on the subject and saw an answer suggesting using this DSP code in the render callback. This looks promising and people seem to be using this effectively on various platforms. However, my implementation has a ton of noise even with a flat eq.
Here's my 20 line integration into the "MixerHostAudio" class of Apple's "MixerHost" example application (all in one commit):
https://github.com/tassock/mixerhost/commit/4b8b87028bfffe352ed67609f747858059a3e89b
Any ideas on how I could get this working? Any other strategies for integrating an EQ?
Edit: Here's an example of the distortion I'm experiencing (with the eq flat):
http://www.youtube.com/watch?v=W_6JaNUvUjA
In the code in EQ3Band.c, the filter coefficients are used without being initialized. The init_3band_state method initialize just the gains and frequencies, but the coefficients themselves - es->f1p0 etc. are not initialized, and therefore contain some garbage values. That might be the reason for the bad output.
This code seems wrong in more then one way.
A digital filter is normally represented by the filter coefficients, which are constant, the filter inner state history (since in most cases the output depends on history) and the filter topology, which is the arithmetic used to calculate the output given the input and the filter (coeffs + state history). In most cases, and of course when filtering audio data, you expect to get 0's at the output if you feed 0's to the input.
The problems in the code you linked to:
The filter coefficients are changed in each call to the processing method:
es->f1p0 += (es->lf * (sample - es->f1p0)) + vsa;
The input sample is usually multiplied by the filter coefficients, not added to them. It doesn't make any physical sense - the sample and the filter coeffs don't even have the same physical units.
If you feed in 0's, you do not get 0's at the output, just some values which do not make any sense.
I suggest you look for another code - the other option is debugging it, and it would be harder.
In addition, you'd benefit from reading about digital filters:
http://en.wikipedia.org/wiki/Digital_filter
https://ccrma.stanford.edu/~jos/filters/
How'd I go about this one? I want to tween a value from one to another in x time. While also taking into account that it'd be nice to have an 'ease' at the start and end.
I know, I shouldn't ask really, but I've tried myself, and I'm stuck.
Please assume that to cause a delay, you need to call function wait(time).
One simple approach that might work for you is to interpolate along the unit circle:
To do this, you simply evaluate points along the circle, which ensures a fairly smooth movement, and ease-in as well as ease-out. You can control the speed of the interpolation by changing how quickly you alter the angle.
Assuming you're doing 1-dimensional interpolation (i.e. a simple scalar interpolation, like from 3.5 to 6.9 or whatever), it might be handy to use Y-values from -π/2 to π/2. These are given by the sine function, all you need to do is apply suitable scaling:
angle = -math.pi / 2
start = 3.5
end = 6.9
radius = (end - start) / 2
value = start + radius + radius * math.sin(angle)
I'm not 100% sure if this is legal Lua, didn't test it. If not, it's probably trivial to convert.
You may look at Tweener ActionScript library for inspiration.
For instance, you may borrow necessary equations from here.
If you need further help, please ask.