I have a problem when trying to draw a pie chart.
Of course, there is no problem with drawing the chart, the problem is the icon placement.
Ideally, the icons should be placed on a circle (let's forget the percent labels for now).
However, the design obviously breaks when there are neighbor items with small values.
Could you recommend an algorithm solving this issue? To simplify, as input we have:
PIE_RADIUS - The outer radius of the pie.
ICON_RADIUS - The radius of the icon circle.
ICON_PLACEMENT_RADIUS - The radius of the circle when icon center should be ideally placed.
NUM_ICONS - Number of icons to place.
iconAngles Angle for every icon, in the center of its section
Required output:
Either iconAngles for items placed around the pie or iconPositions when moving the icons out of their ideal circle.
I know how to check whether two icons overlap.
We can consider the center of the pie to be at (0, 0).
(The implementation is part of an iOS application but I am interested in a general algorihm).
A first naive algorithm , we "push" the icons that overlap with an other icon:
FOR iconToPlace in icons do:
isPlaced = false
WHILE(not isPlaced) DO:
isPlaced = true
FOR icon in icons DO:
IF overlap(iconToPlace, icon) AND iconToPlace != icon THEN:
isPlaced = false
push(iconToPlace) // same angle but the icon is now further
BREAK
ENDIF
ENDFOR
ENDWHILE
ENDFOR
With this first algorithm some icons will be futher from the center than other. But it does not exploit the possible place by changing the angle. By applying this to your second design (with small values) it is clear that the solution will be far away from the ideal one.
A second less naive algorithm, first we allocate a new angle (difference less than DeltaAngleMax) for each icon then we apply the first algo:
icons = SORT(icons)
iconsRef = icons
isFinished = false
WHILE(not isFinished) DO:
isFinished = true
FOR i = 0 TO i = NUM_ICONS-1 DO:
IF overlap(icons(i), icons(i+1 % NUM_ICONS))
AND not overlap(icons(i), icons(i-1 % NUM_ICONS)) //seems useless
AND not overlap(icons(i)-DeltaAngle % 360, icons(i-1 % NUM_ICONS))
AND ABS(icons(i)-iconsRef(i)) <= DeltaAngleMax THEN:
//overlap with next icon but not with previous,
//if we decrease angle we still not overlap with previous icon and
//the futur delta angle is less than DeltaAngleMax
//then we can move the icon :
icons(i) = icons(i)-DeltaAngle
isFinished = false
ELSE IF overlap(icons(i), icons(i-1 % NUM_ICONS))
AND not overlap(icons(i), icons(i+1 % NUM_ICONS)) //seems useless
AND not overlap(icons(i)+DeltaAngle % 360, icons(i+1 % NUM_ICONS))
AND ABS(icons(i)-iconsRef(i)) <= DeltaAngleMax THEN:
//vice et versa:
icons(i) = icons(i)+DeltaAngle
isFinished = false
ENDFOR
ENDWHILE
APPLY_FIRST_ALGO
Choose wisely deltaAngle and DeltaAngleMax. A too little deltaAngle will lead to a big running time.
To go further you should have a look at the force-directed graph drawing algorithm which is much more robust method to achieve your goal, one of the difficulty is to find the correct forces of the nodes (your icons, you have no edges).
Just brainstorming:
A genetic algorithm with a fitness function that has a high penalty for overlaps plus a penalty equal to the sum of the squares of the angular distances between each candidate location and its ideal location (centered relative to its slice).
The solution I implemented was the following:
Calculate the position for all the icons relative to their slice (icon centered on ICON_PLACEMENT_RADIUS)
Find sequences of overlapping icons (iterate the icons and check if the next is overlapping with the previous).
Calculate the minimum angular distance between two icons (approximately (2.0f * ICON_RADIUS + 1.0f) / ICON_PLACEMENT_RADIUS)
Calculate the center of the sequence (sum all the slices for the sequence and find the center), place the icons together (distance between them is the minimum angular distance).
When all icons placed, check if icons overlap, if yes, merge their sequences and iterate.
Note this algorithm works only if all the number of icons is small comparing to the size of the circle but it's simple and very fast.
The result is:
Related
I'm currently developing an application that will alert users of incoming rain. To do this I want to check certain area around user location for rainfall (different pixel colours for intensity on rainfall radar image). I would like the checked area to be a circle but I don't know how to do this efficiently.
Let's say I want to check radius of 50km. My current idea is to take subset of image with size 100kmx100km (user+50km west, user+50km east, user+50km north, user+50km south) and then check for each pixel in this subset if it's closer to user than 50km.
My question here is, is there a better solution that is used for this type of problems?
If the occurrence of the event you are searching for (rain or anything) is relatively rare, then there's nothing wrong with scanning a square or pixels and then, only after detecting rain in that square, checking whether that rain is within the desired 50km circle. Note that the key point here is that you don't need to check each pixel of the square for being inside the circle (that would be very inefficient), you have to search for your event (rain) first and only when you found it, check whether it falls into the 50km circle. To implement this efficiently you also have to develop some smart strategy for handling multi-pixel "stains" of rain on your image.
However, since you are scanning a raster image, you can easily implement the well-known Bresenham circle algorithm to find the starting and the ending point of the circle for each scan line. That way you can easily limit your scan to the desired 50km radius.
On the second thought, you don't even need the Bresenham algorithm for that. For each row of pixels in your square, calculate the points of intersection of that row with the 50km circle (using the usual schoolbook formula with square root), and then check all pixels that fall between these intersection points. Process all rows in the same fashion and you are done.
P.S. Unfortunately, the Wikipedia page I linked does not present Bresenham algorithm at all. It has code for Michener circle algorithm instead. Michener algorithm will also work for circle rasterization purposes, but it is less precise than Bresenham algorithm. If you care for precision, find a true Bresenham on somewhere. It is actually surprisingly diffcult to find on the net: most search hits erroneously present Michener as Bresenham.
There is, you can modify the midpoint circle algorithm to give you an array of for each y, the x coordinate where the circle starts (and ends, that's the same thing because of symmetry). This array is easy to compute, pseudocode below.
Then you can just iterate over exactly the right part, without checking anything.
Pseudo code:
data = new int[radius];
int f = 1 - radius, ddF_x = 1;
int ddF_y = -2 * radius;
int x = 0, y = radius;
while (x < y)
{
if (f >= 0)
{
y--;
ddF_y += 2; f += ddF_y;
}
x++;
ddF_x += 2; f += ddF_x;
data[radius - y] = x; data[radius - x] = y;
}
Maybe you can try something that will speed up your algorithm.
In brute force algorithm you will probably use equation:
(x-p)^2 + (y-q)^2 < r^2
(p,q) - center of the circle, user position
r - radius (50km)
If you want to find all pixels (x,y) that satisfy above condition and check them, your algorithm goes to O(n^2)
Instead of scanning all pixels in this circle I will check only only pixels that are on border of the circle.
In that case, you can use some more clever way to define circle.
x = p+r*cos(a)
y = q*r*sin(a)
a - angle measured in radians [0-2pi]
Now you can sample some angles, for example twenty of them, iterate and find all pairs (x,y) that are border for radius 50km. Now check are they on the rain zone and alert user.
For more safety I recommend you to use multiple radians (smaller than 50km), because your whole rain cloud can be inside circle, and your app will not recognize him. For example use 3 incircles (r = 5km, 15km, 30km) and do same thing. Efficiency of this algorithm only depends on number of angles and number of incircles.
Pseudocode will be:
checkRainDanger()
p,q <- position
radius[] <- array of radii
for c = 1 to length(radius)
a=0
while(a<2*pi)
x = p + radius[c]*cos(a)
y = q + radius[c]*sin(a)
if rainZone(x,y)
return true
else
a+=pi/10
end_while
end_for
return false //no danger
r2=r*r
for x in range(-r, +r):
max_y=sqrt(r2-x*x)
for y in range(-max_y, +max_y):
# x,y is in range - check for rain
I'm trying to work out how to write an algorithm to randomly place circles of R radius, in a 2d rectangle of arbitrary dimensions, such that each placed circle is at least D distance away from other circles in the rectangle,
The rectangle doesn't need to be filled, to be more specific older circles may be destroyed, so I need to be able to place a new circle that respects the positions of the last N circles I've already placed (say 5 for eg), if it can't satisfy these conditions then I could handle it seperately.
Can anyone help me how to deduce such an algorithm, or perhaps point to some research that may cover this?
1 Place circle at random location
2 Loop over previous circles
3 if too close
4 delete new circle
5 goto 1
6 if need more circles
7 goto 1
To determine if there is room
Choose resolution required, say delta = D/100
for( x = 0; x < rectangle_size x += delta )
for( y = 0; y < rectangle_size y += delta )
unset failed
loop over circles
if x,y less than 2D from circle
set failed
break from circle loop
if not failed
return 'yes there is room'
return 'no, there is no room'
If you expect to have so many circles that there only a few holes left with room for new circles, then you could do this
clear candidates
Choose resolution required, say delta = D/100
for( x = 0; x < rectangle_size x += delta )
for( y = 0; y < rectangle_size y += delta )
unset failed
loop over circles
if x,y less than 2D from circle
set failed
break from circle loop
if not failed
add x,y to candidates
if no candidates
return 'there is no room'
randomly choose location for new circle from candidates
1. Pick random startingspot.
2. Place circle
3. Move in random direction at least D
4. Goto 2 until distance to edge is < D or the distance to another circles center is < 2D
The first algorithm to come to mind is simulated annealing. Basically, you start out with the easiest solution, probably just a grid, then you "shake the box" in random ways to see if you get better answers. First you do large shakes, then gradually make them smaller. It sounds a little chaotic, and doesn't always produce the absolute best solution, but when something is computationally intensive it usually comes pretty close in a lot shorter time.
It really depends on what you mean by "random". Assuming that you want as close to a uniform distribution as possible, you will probably have to use an iterative solution like the one ravenspoint suggested. It may be slightly faster to place all of the circles randomly and then start replacing circles that don't meet your distance condition.
If the randomness isn't that important - i.e. if it just has to "look" random (which is probably fine if you're not doing something scientific), then grid your space up and place your N circles by choosing N indices in the grid. You could make it slightly more "random" by adding some noise to the location that you place the circle inside the grid. This would work really well for sparse placement.
i'm developing a game.
How can i "fade in" the audio volume for a sound, based on the "distance" of a sprite from the current render scene ?
Suppose i've a world:
WIDTH_WORLD = 10000
HEIGHT_WORLD = 10000
Current Scene
xCurrent = 800 ( + Width Res. = 800 + 1024 = 1824)
yCurrent = 400 ( + Height Res. = 400 + 768 =... )
Far Sprite
xSprite = 7000
ySprite = 3000
What is a good algorithm to "calculate" audio volume (and maybe left/right pan channel ratio) ?
Thanks in advance!
Total Volumn
There are several approaches for the attenuation of the volume. E.g. you could use a linear damping function, like volume = max( 0, max_volume - max_volume/max_distance * distance ), or a function with inverse distance fall off, like volume = min( max_volume, max_volume / distance ).
Balance Ratio
Again, there are several approaches to realize an adequate functionality. In your case, you could say, if the object is at or beyond the left screen border, pan to the left, if the object is at or beyond the right screen border, pan to the right, otherwise use an interpolation function, e.g. balance = (object_x-camera_x) / screen_half_width with values between -1: left, +1: right, and 0: center.
When combining these approaches you have to think about what is suitable for your case: For example only damp the volume if the object is out of screen bounds, what ever...
Use the distance (Dsquared = xdist^2 + ydist ^2) as a scaling factor to downscale your volume. Invert this distance squared value to apply directly to your volume (volume falls off as a square of distance, and distance is easily calculated as a squared value anyway).
Left / right channel ratio is done simply by defining two different points for the left and right channels (to the left and right, respectively, of your render location), perform the above calculation, and find the ratios of your channels that way. Because the above calculation is just a sum of two squares, it's very computationally simple.
Well, the volume of a sound degrades as the square of the distance from the sound source, so that would give you a good place to start.
Knowing what library you are using for your game would be helpful though - many game libraries will have this sort of functionality built in already.
Here's the problem: I have a number of binary images composed by traces of different thickness. Below there are two images to illustrate the problem:
First Image - size: 711 x 643 px
Second Image - size: 930 x 951 px
What I need is to measure the average thickness (in pixels) of the traces in the images. In fact, the average thickness of traces in an image is a somewhat subjective measure. So, what I need is a measure that have some correlation with the radius of the trace, as indicated in the figure below:
Notes
Since the measure doesn't need to be very precise, I am willing to trade precision for speed. In other words, speed is an important factor to the solution of this problem.
There might be intersections in the traces.
The trace thickness might not be constant, but an average measure is OK (even the maximum trace thickness is acceptable).
The trace will always be much longer than it is wide.
I'd suggest this algorithm:
Apply a distance transformation to the image, so that all background pixels are set to 0, all foreground pixels are set to the distance from the background
Find the local maxima in the distance transformed image. These are points in the middle of the lines. Put their pixel values (i.e. distances from the background) image into a list
Calculate the median or average of that list
I was impressed by #nikie's answer, and gave it a try ...
I simplified the algorithm for just getting the maximum value, not the mean, so evading the local maxima detection algorithm. I think this is enough if the stroke is well-behaved (although for self intersecting lines it may not be accurate).
The program in Mathematica is:
m = Import["http://imgur.com/3Zs7m.png"] (* Get image from web*)
s = Abs[ImageData[m] - 1]; (* Invert colors to detect background *)
k = DistanceTransform[Image[s]] (* White Pxs converted to distance to black*)
k // ImageAdjust (* Show the image *)
Max[ImageData[k]] (* Get the max stroke width *)
The generated result is
The numerical value (28.46 px X 2) fits pretty well my measurement of 56 px (Although your value is 100px :* )
Edit - Implemented the full algorithm
Well ... sort of ... instead of searching the local maxima, finding the fixed point of the distance transformation. Almost, but not quite completely unlike the same thing :)
m = Import["http://imgur.com/3Zs7m.png"]; (*Get image from web*)
s = Abs[ImageData[m] - 1]; (*Invert colors to detect background*)
k = DistanceTransform[Image[s]]; (*White Pxs converted to distance to black*)
Print["Distance to Background*"]
k // ImageAdjust (*Show the image*)
Print["Local Maxima"]
weights =
Binarize[FixedPoint[ImageAdjust#DistanceTransform[Image[#], .4] &,s]]
Print["Stroke Width =",
2 Mean[Select[Flatten[ImageData[k]] Flatten[ImageData[weights]], # != 0 &]]]
As you may see, the result is very similar to the previous one, obtained with the simplified algorithm.
From Here. A simple method!
3.1 Estimating Pen Width
The pen thickness may be readily estimated from the area A and perimeter length L of the foreground
T = A/(L/2)
In essence, we have reshaped the foreground into a rectangle and measured the length of the longest side. Stronger modelling of the pen, for instance, as a disc yielding circular ends, might allow greater precision, but rasterisation error would compromise the signicance.
While precision is not a major issue, we do need to consider bias and singularities.
We should therefore calculate area A and perimeter length L using functions which take into account "roundedness".
In MATLAB
A = bwarea(.)
L = bwarea(bwperim(.; 8))
Since I don't have MATLAB at hand, I made a small program in Mathematica:
m = Binarize[Import["http://imgur.com/3Zs7m.png"]] (* Get Image *)
k = Binarize[MorphologicalPerimeter[m]] (* Get Perimeter *)
p = N[2 Count[ImageData[m], Except[1], 2]/
Count[ImageData[k], Except[0], 2]] (* Calculate *)
The output is 36 Px ...
Perimeter image follows
HTH!
Its been a 3 years since the question was asked :)
following the procedure of #nikie, here is a matlab implementation of the stroke width.
clc;
clear;
close all;
I = imread('3Zs7m.png');
X = im2bw(I,0.8);
subplottight(2,2,1);
imshow(X);
Dist=bwdist(X);
subplottight(2,2,2);
imshow(Dist,[]);
RegionMax=imregionalmax(Dist);
[x, y] = find(RegionMax ~= 0);
subplottight(2,2,3);
imshow(RegionMax);
List(1:size(x))=0;
for i = 1:size(x)
List(i)=Dist(x(i),y(i));
end
fprintf('Stroke Width = %u \n',mean(List));
Assuming that the trace has constant thickness, is much longer than it is wide, is not too strongly curved and has no intersections / crossings, I suggest an edge detection algorithm which also determines the direction of the edge, then a rise/fall detector with some trigonometry and a minimization algorithm. This gives you the minimal thickness across a relatively straight part of the curve.
I guess the error to be up to 25%.
First use an edge detector that gives us the information where an edge is and which direction (in 45° or PI/4 steps) it has. This is done by filtering with 4 different 3x3 matrices (Example).
Usually I'd say it's enough to scan the image horizontally, though you could also scan vertically or diagonally.
Assuming line-by-line (horizontal) scanning, once we find an edge, we check if it's a rise (going from background to trace color) or a fall (to background). If the edge's direction is at a right angle to the direction of scanning, skip it.
If you found one rise and one fall with the correct directions and without any disturbance in between, measure the distance from the rise to the fall. If the direction is diagonal, multiply by squareroot of 2. Store this measure together with the coordinate data.
The algorithm must then search along an edge (can't find a web resource on that right now) for neighboring (by their coordinates) measurements. If there is a local minimum with a padding of maybe 4 to 5 size units to each side (a value to play with - larger: less information, smaller: more noise), this measure qualifies as a candidate. This is to ensure that the ends of the trail or a section bent too much are not taken into account.
The minimum of that would be the measurement. Plausibility check: If the trace is not too tangled, there should be a lot of values in that area.
Please comment if there are more questions. :-)
Here is an answer that works in any computer language without the need of special functions...
Basic idea: Try to fit a circle into the black areas of the image. If you can, try with a bigger circle.
Algorithm:
set image background = 0 and trace = 1
initialize array result[]
set minimalExpectedWidth
set w = minimalExpectedWidth
loop
set counter = 0
create a matrix of zeros size w x w
within a circle of diameter w in that matrix, put ones
calculate area of the circle (= PI * w)
loop through all pixels of the image
optimization: if current pixel is of background color -> continue loop
multiply the matrix with the image at each pixel (e.g. filtering the image with that matrix)
(you can do this using the current x and y position and a double for loop from 0 to w)
take the sum of the result of each multiplication
if the sum equals the calculated circle's area, increment counter by one
store in result[w - minimalExpectedWidth]
increment w by one
optimization: include algorithm from further down here
while counter is greater zero
Now the result array contains the number of matches for each tested width.
Graph it to have a look at it.
For a width of one this will be equal to the number of pixels of trace color. For a greater width value less circle areas will fit into the trace. The result array will thus steadily decrease until there is a sudden drop. This is because the filter matrix with the circular area of that width now only fits into intersections.
Right before the drop is the width of your trace. If the width is not constant, the drop will not be that sudden.
I don't have MATLAB here for testing and don't know for sure about a function to detect this sudden drop, but we do know that the decrease is continuous, so I'd take the maximum of the second derivative of the (zero-based) result array like this
Algorithm:
set maximum = 0
set widthFound = 0
set minimalExpectedWidth as above
set prevvalue = result[0]
set index = 1
set prevFirstDerivative = result[1] - prevvalue
loop until index is greater result length
firstDerivative = result[index] - prevvalue
set secondDerivative = firstDerivative - prevFirstDerivative
if secondDerivative > maximum or secondDerivative < maximum * -1
maximum = secondDerivative
widthFound = index + minimalExpectedWidth
prevFirstDerivative = firstDerivative
prevvalue = result[index]
increment index by one
return widthFound
Now widthFound is the trace width for which (in relation to width + 1) many more matches were found.
I know that this is in part covered in some of the other answers, but my description is pretty much straightforward and you don't have to have learned image processing to do it.
I have interesting solution:
Do edge detection, for edge pixels extraction.
Do physical simulation - consider edge pixels as positively charged particles.
Now put some number of free positively charged particles in the stroke area.
Calculate electrical force equations for determining movement of these free particles.
Simulate particles movement for some time until particles reach position equilibrium.
(As they will repel from both stoke edges after some time they will stay in the middle line of stoke)
Now stroke thickness/2 would be average distance from edge particle to nearest free particle.
I know there are lots of posts about collision detection generally for sprites moving about a 2D plane, but my question is slightly different.
I'm inserting circles into a 2D plane. The circles have variable radii. I'm trying to optimize my method of finding a random position within the plane where I can insert a new circle without it colliding with any other circles already on the plane. Right now I'm using a very "un-optimized" approach that simply generates a random point within the plane and then checks it against all the other circles on the plane.
Are there ways to optimize this? For this particular app, the bounds of the plane can only hold 20-25 circles at a time and typically there are between 5-10 present. As you would expect, when the number of circles approaches the max that can fit, you have to test many points before finding one that works. It gets very slow.
Note: safeDistance is the radius of the circle I want to add to the plane.
Here's the code:
- (CGPoint)getSafePosition:(float)safeDistance {
// Point must be far enough from edges
// Point must be far enough from other sprites
CGPoint thePoint;
BOOL pointIsSafe = NO;
int sd = ceil(safeDistance);
while(!pointIsSafe) {
self.pointsTested++; // DEBUG
// generate a random point inside the plane boundaries to test
thePoint = CGPointMake((arc4random() % ((int)self.manager.gameView.frame.size.width - sd*2)) + sd,
(arc4random() % ((int)self.manager.gameView.frame.size.height - sd*2)) + sd);
if(self.manager.gameView.sprites.count > 0) {
for(BasicSprite *theSprite in self.manager.gameView.sprites) {
// get distance between test point and the sprite position
float distance = [BasicSprite distanceBetweenPoints:thePoint b:theSprite.position];
// check if distance is less than the sum of the min safe distances of the two entities
if(distance < (safeDistance + [theSprite minSafeDistance])) {
// point not safe
pointIsSafe = NO;
break;
}
// if we get here, the point did not collide with the last tested point
pointIsSafe = YES;
}
}
else {
pointIsSafe = YES;
}
}
return thePoint;
}
Subdivide your window into w by h blocks. You'll be maintaining a w by h array, dist. dist[x][y] contains the size of the largest circle that can be centred at (x, y). (You can use pixels as blocks, although we'll be updating the entire array with each circle placed, so you may want to choose larger blocks for improved speed, at the cost of slightly reduced packing densities.)
Initialisation
Initially, set all dist[x][y] to min(x, y, w - x, h - y). This encodes the limits given by the bounding box that is the window.
Update procedure
Every time you add a circle to the window, say one positioned at (a, b) with radius r, you need to update all elements of dist.
The update required for each position (x, y) is:
dist[x][y] = min(dist[x][y], sqrt((x - a)^2 + (y - b)^2) - r);
(Obviously, ^2 here means squaring, not XOR.) Basically, we are saying: "Set dist[x][y] to the minimum distance to the circle just placed, unless the situation is already worse than that." dist values for points inside the circle just placed will be negative, but that doesn't matter.
Finding the next location
Then, when you want to insert the next circle of radius q, just scan through dist looking for a location with dist value >= q. (If you want to randomly choose such a location, find the complete list of valid locations and then randomly choose one.)
Honestly, with only 20-25 circles, you're not going to get much of a speed boost by using a fancier algorithm or data structure (e.g. a quadtree or a kd-tree). Everything is fast for small n.
Are you absolutely sure this is the bottleneck in your application? Have you profiled? If yes, then the way you're going to speed this up is through microoptimization, not through advanced algorithms. Are you making lots of iterations through the while loop because most of the plane is unsafe?
You could split your plane in lots of little rectangles (slightly quadtree-related) and save which rectangles are hit by at least one of the circles.
When you look for a insertion-point, you'll just have to look for some "empty" ones (which doesn't need any random jumps and is possible in constant time).
The number and constellation of rectangles can be computed by the radius.
Just an outline, since this solution is fairly involved.
If you want to guarantee you always find a place to put a circle if it's possible, you can do the following. Consider each existing circle C. We will try to find a location where we can place the new circle so that it is touching C. For each circle D (other than C) that is sufficiently close to C, there will be a range of angles where placing a new circle at one of those angles around C will make it intersect with D. Some geometry will give you that range. Similarly, for each of the four boundaries that are close enough to C, there will be a range of angles where placing a new circle at one of those angles will make it intersect with the boundary. If all these intervals cover all 360 degrees around C, then you cannot place a circle adjacent to C, and you will have to try the next circle, until there are no more candidates for C. If you find a place to put the new circle, you can move it some random distance away from C so that all your new circles do not have to be adjacent to an existing circle if that is not necessary.