I\'m working on some 2D games with Pygame. I need to place several objects at the same time randomly without them intersecting. I have tried a few obvious
Three ideas:
The first method fails because hitting a random array of 20 non-overlapping objects is highly improbable (actually (1-p)^20
, where 0<p<1
is the probability of two objects colliding). If you could dramatically (orders-of-magnitude drama) decrease their size, it might help.
The most obvious improvement would be:
while len(rectangles)<N:
new_rectangle=get_random_rectangle()
for rectangle in rectangles:
if not any(intersects (rectangle, new_rectangle) for rectangle in rectangles)
rectangles.add(new_rectangle)
This would greatly improve your performance, as having a single intersection will not force you to generate a whole new set, just to pick a different single rectangle.
How often will you be using these sets in your game? Using a different set every second is a different scenario than using a set once in an hour. If you don't use these sets too often, pre-calculate s large-enough set so that the gamer would probably never see the same set twice. When pre-calculating, you don't care too much for the time spent (so you can even use your inefficient first algorithm).
Even if you actually need these rectangles at run time, it might be a good idea to calculate them a bit before you need them, when the CPU is idle for some reason, so that you'll always have a set ready in hand.
At run time, just pick a set at random. This is probably the best approach for real-time gaming.
Note:
This solution assumes that you rectangles are kept in a space-saving manner, e.g. pairs of (x, y)
coordinates. These pairs consume very little space, and you can actually save thousands, and even millions, in a file with reasonable size.
Useful links:
There is a very simple approximation to your problem which worked fine for me:
I used this to randomly generate a 2D map (Zelda like). My objects' images are smaller than <100*100>, so I used grid of size <500*500> and allowed for 1-6 objects in each grid.
list_of_objects = []
for i in range(20):
while True:
new_object = create_object()
if not any(collides(new_object, x) for x in list_of_objects):
break
list_of_objects.append(new_object)
I assume you already have the create_object()
and collides()
functions
You may also need to decrease the size of the rects if this loops too many times
In my case I had a similar problem except that I had some pre-exiting rectangles inside the overall rectangle. So new rectangles had to be placed around those existing ones.
I used a greedy approach:
This requires a conversion from your original coordinate space to/from the grid space but straightforward to do.
(Note that running Kadene directly on the original, global rectangle takes to long. Going via a grid approximation is plenty fast for my application)
I've changed my answer a bit to address your follow-up question about whether it could be modified to instead generate random non-colliding squares rather than arbitrarily rectangles. I did this in the simplest way I could that would work, which was to post-process the rectangular output of my original answer and turn its contents into square sub-regions. I also updated the optional visualization code to show both kinds of output. Obviously this sort of filtering could be extended to do other things like insetting each rectangle or square slightly to prevent them from touching one another.
My answer avoids doing what many of the answers already posted do -- which is randomly generating rectangles while rejecting any that collide with any already created -- because it sounds inherently slow and computationally wasteful. My approach concentrates instead on only generating ones that don't overlap in the first place.
That makes what needs to be done relatively simple by turning it into a simple area subdivision problem which can be performed very quickly. Below is one implementation of how that can be done. It starts with a rectangle defining the outer boundary which it divides into four smaller non-overlapping rectangles. That is accomplished by choosing a semi-random interior point and using it along with the four existing corner points of the outer rectangle to form the four subsections.
Most of the action take place in thequadsect()
function. The choice of the interior point is crucial in determining what the output looks like. You can constrain it any way you wish, such as only selecting one that would result in sub-rectangles of at least a certain minimum width or height or no bigger than some amount. In the sample code in my answer, it's defined as the center point ±1/3 of the width and height of the outer rectangle, but basically any interior point would work to some degree.
Since this algorithm generates sub-rectangles very rapidly, it's OK to spend some computational time determining the interior division point.
To help visualize the results of this approach, there's some non-essential code at the very end that uses thePIL
(Python Imaging Library) module to create an image file displaying the rectangles generated during some test runs I made.
Anyway, here's the latest version of the code and output samples:
import random
from random import randint
random.seed()
NUM_RECTS = 20
REGION = Rect(0, 0, 640, 480)
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
@staticmethod
def from_point(other):
return Point(other.x, other.y)
class Rect(object):
def __init__(self, x1, y1, x2, y2):
minx, maxx = (x1,x2) if x1 < x2 else (x2,x1)
miny, maxy = (y1,y2) if y1 < y2 else (y2,y1)
self.min, self.max = Point(minx, miny), Point(maxx, maxy)
@staticmethod
def from_points(p1, p2):
return Rect(p1.x, p1.y, p2.x, p2.y)
width = property(lambda self: self.max.x - self.min.x)
height = property(lambda self: self.max.y - self.min.y)
plus_or_minus = lambda v: v * [-1, 1][(randint(0, 100) % 2)] # equal chance +/-1
def quadsect(rect, factor):
""" Subdivide given rectangle into four non-overlapping rectangles.
'factor' is an integer representing the proportion of the width or
height the deviatation from the center of the rectangle allowed.
"""
# pick a point in the interior of given rectangle
w, h = rect.width, rect.height # cache properties
center = Point(rect.min.x + (w // 2), rect.min.y + (h // 2))
delta_x = plus_or_minus(randint(0, w // factor))
delta_y = plus_or_minus(randint(0, h // factor))
interior = Point(center.x + delta_x, center.y + delta_y)
# create rectangles from the interior point and the corners of the outer one
return [Rect(interior.x, interior.y, rect.min.x, rect.min.y),
Rect(interior.x, interior.y, rect.max.x, rect.min.y),
Rect(interior.x, interior.y, rect.max.x, rect.max.y),
Rect(interior.x, interior.y, rect.min.x, rect.max.y)]
def square_subregion(rect):
""" Return a square rectangle centered within the given rectangle """
w, h = rect.width, rect.height # cache properties
if w < h:
offset = (h - w) // 2
return Rect(rect.min.x, rect.min.y+offset,
rect.max.x, rect.min.y+offset+w)
else:
offset = (w - h) // 2
return Rect(rect.min.x+offset, rect.min.y,
rect.min.x+offset+h, rect.max.y)
# call quadsect() until at least the number of rects wanted has been generated
rects = [REGION] # seed output list
while len(rects) <= NUM_RECTS:
rects = [subrect for rect in rects
for subrect in quadsect(rect, 3)]
random.shuffle(rects) # mix them up
sample = random.sample(rects, NUM_RECTS) # select the desired number
print '%d out of the %d rectangles selected' % (NUM_RECTS, len(rects))
#################################################
# extra credit - create an image file showing results
from PIL import Image, ImageDraw
def gray(v): return tuple(int(v*255) for _ in range(3))
BLACK, DARK_GRAY, GRAY = gray(0), gray(.25), gray(.5)
LIGHT_GRAY, WHITE = gray(.75), gray(1)
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
CYAN, MAGENTA, YELLOW = (0, 255, 255), (255, 0, 255), (255, 255, 0)
BACKGR, SQUARE_COLOR, RECT_COLOR = (245, 245, 87), (255, 73, 73), (37, 182, 249)
imgx, imgy = REGION.max.x + 1, REGION.max.y + 1
image = Image.new("RGB", (imgx, imgy), BACKGR) # create color image
draw = ImageDraw.Draw(image)
def draw_rect(rect, fill=None, outline=WHITE):
draw.rectangle([(rect.min.x, rect.min.y), (rect.max.x, rect.max.y)],
fill=fill, outline=outline)
# first draw outlines of all the non-overlapping rectanges generated
for rect in rects:
draw_rect(rect, outline=LIGHT_GRAY)
# then draw the random sample of them selected
for rect in sample:
draw_rect(rect, fill=RECT_COLOR, outline=WHITE)
# and lastly convert those into squares and re-draw them in another color
for rect in sample:
draw_rect(square_subregion(rect), fill=SQUARE_COLOR, outline=WHITE)
filename = 'square_quadsections.png'
image.save(filename, "PNG")
print repr(filename), 'output image saved'
Output Sample 1
Output Sample 2
an alternative pseudocode, to those already mentioned:
while not enough objects:
place object randomly
if overlaps with anything else:
reduce size until it fits or has zero size
if zero size:
remove
Or something like that.
But this has the advantage of possibly creating some smaller objects than you intended, and creating objects which almost intersect (i.e. touch).
If it's a map for the player to traverse, they may still not be able to traverse it because their path could be blocked.