Given the lengths of the sides of 2 triangles. Determine if the second triangle can fit inside the first triangle?
For more detailed info read the full problem statement below:
http://acm.timus.ru/problem.aspx?space=1&num=1566&locale=en
My implementation below tries all the (3!)^2 possible combinations of aligning the bases of the triangles. It then tries to shift the second triangle inside the first triangle while checking that the base of the second triangle doesn't exceed the base of the first triangle.
But I keep getting Wrong Answer(WA) #16.
The case I gave is the second image. It is obvious that if you rotate PQR to align the sides of length 2.77 and 3.0 the third vertex will not be inside triangle ABC. The side of length 4.2 can only be aligned along the side of len 5. Thus this case is satisfied only in the configuration show in the second image.
Can you help me find the bug, suggest some test cases where my algorithm breaks down. Alternative algorithms are also welcome.
#include <cmath>
#include <iostream>
using namespace std;
const double PI = atan(1.0)* 4;
// Traingle ABC (Envelope)
double xa, ya, xb, yb, xc, yc;
// Traingle PQR (Postcard)
double xp, yp, xq, yq, xr, yr;
// Angle between sides AB and AC
double theta;
double signWrtLine(double x1, double y1, double x2, double y2, double x, double y)
{
double A = y2 - y1;
double B = x1 - x2;
double C = -(A * x1 + B * y1);
return (A * x + B * y + C);
}
bool fit()
{
if ((xr > xc) || (yq > yb)) return false;
if (signWrtLine(xa, ya, xb, yb, xq, yq) < 0) {
double d = (yq / tan(theta)) - xq;
return (xr + d <= xc);
}
return (signWrtLine(xa, ya, xb, yb, xq, yq) >= 0 &&
signWrtLine(xb, yb, xc, yc, xq, yq) >= 0 &&
signWrtLine(xc, yc, xa, ya, xq, yq) >= 0);
}
bool fit(double a[], double b[])
{
// generate the 3! permutations of the envelope
// loops i,k
for (int i = 0; i < 3; i++) {
double angle;
double u = a[i], v = a[(i + 1) % 3], w = a[(i + 2) % 3];
for (int k = 0; k < 2; k++) {
switch (k) {
case 0:
xa = 0, ya = 0;
angle = theta = acos((u * u + v * v - w * w) / (2 * u * v));
xb = v * cos(angle), yb = v * sin(angle);
xc = u, yc = 0;
break;
case 1:
// reflect envelope
swap(v, w);
angle = theta = acos((u * u + v * v - w * w) / (2 * u * v));
xb = v * cos(angle), yb = v * sin(angle);
break;
}
// generate the 3! permutations of the postcard
// loops j,k
for (int j = 0; j < 3; j++) {
double angle;
double u = b[j], v = b[(j + 1) % 3], w = b[(j + 2) % 3];
for (int k = 0; k < 2; k++) {
switch (k) {
case 0:
xp = 0, yp = 0;
angle = acos((u * u + v * v - w * w) / (2 * u * v));
xq = v * cos(angle), yq = v * sin(angle);
xr = u, yr = 0;
break;
case 1:
// reflect postcard
swap(v, w);
angle = acos((u * u + v * v - w * w) / (2 * u * v));
xq = v * cos(angle), yq = v * sin(angle);
break;
}
if (fit()) return true;
}
}
}
}
return false;
}
int main()
{
double a[3], b[3];
for (int i = 0; i < 3; i++) cin >> a[i];
for (int i = 0; i < 3; i++) cin >> b[i];
if(fit(a, b)) cout << "YES" << endl;
else cout << "NO" << endl;
return 0;
}
Barycentric coordinates! In detail:
Let the "envelope" triangle have vertices A, B, C; without loss of generality you can place vertex A at the origin and align the side AB with the +x axis. Use the edge lengths of the envelope triangle to find the angle at vertex A, i.e., the angle between the sides AB and AC. Using this angle, you define a new coordinate system (u,v); in this coordinate system the vertex coordinates are A=(0,0), B=(1,0) and C=(0,1).
Now, take the other triangle with vertices A',B',C', and find first the XY coordinates of the 3 vertices for each case of: (A'B', B'C', A'C') aligned with +x coordinate axis. For each such alignment, transform the other two vertices to the UV-coordinate system determined by the envelope triangle. If it happens that both of the other vertices have (u,v) coordinates with 0 <= u,v <= 1 with u+v<=1, the triangle fits within the envelope triangle.
Angle between two sides can be obtained through the sine theorem for planar triangles; though you have to be a bit careful if the angle at a vertex is obtuse (> PI/2) since the sine function is symmetric around PI/2 on the interval [0,PI]. To check whether the angle is obtuse, you also need to use the cosine theorem, though you don't need to calculate the cosine itself: if |AB|^2 + |AC|^2 > |BC|^2, the angle at A is obtuse.
I think that about sums it up.
//23/08/11 13:56
//determine if a triangle will fit inside a second triangle
#include<iostream>
#include<cmath>
#include<algorithm>
using namespace std;
const double pi= 3.1414926;
const double tri=180;//deg in triangle
double find_B_ang(double a,double b,double c);
double find_C_ang(double a,double b,double c);
double movetri_r , tc_ghosthor_B;
int main()
{double a=0.0,b=0.0,c=0.0,x=0.0,y=0.0,z=0.0;
double A=0.0,B=0.0,C=0.0,A1=0.0,B1=0.0,C1=0.0;// L&R base angles
double te_vert_B=0.0,te_hor_B=0.0,te_hor_C=0.0;
double tc_vert_B=0.0,tc_hor_B=0.0,tc_hor_C=0.0;
//points B and B1 are considered co-incedent
cout<<"\n\ndetermine if a triangular card will fit inside\n"
<<"a triangular envelope\n";
//envelope dimensions
cout<<"\nenter lengths of the sides of envelope (space between)\n";
cout<<"ensure longest of them is less than sum of other two\n";
do
{
cin>>a>>b>>c;//the e sides
if(c>a)swap(a,c);//sort sides in decending order
if(b>a)swap(a,b);
if(c>b)swap(b,c);
if(a >(b+c))
cout<<"WRONG...b+c must be greater than a";
}while(a >(b+c));
cout<<"\nthe sides of the envelope are "<<a<<','<<b<<','<<c<<endl;
B=find_B_ang(a,b,c);
C=find_C_ang(a,b,c);
te_vert_B=c*sin(B*pi/tri);//apex to base vertical line
te_hor_B=te_vert_B/tan(B*pi/tri);//B to vertical line
te_hor_C=a-te_hor_B;//C to vertical line
cout<<"-------------------------------------------\n";
//card dimensions
do
{
cout<<"\nenter lengths of sides of card (space between) \n";
cout<<"ensure longest of them is less than sum of other two\n";
do
{
cin>>x>>y>>z;//the c sides
if(z>x)swap(z,x);//sort sides in decending order
if(y>x)swap(y,x);
if(z>y)swap(y,z);
if(x>(y+z))
cout<<"WRONG...y+z must be greater than x\n";
}while(x>(y+z));
cout<<"\nthe sides of card are "<<x<<','<<y<<','<<z<<endl;//x is base
B1=find_B_ang(x,y,z);
C1=find_C_ang(x,y,z);
tc_vert_B=z*sin(B1*pi/tri);//apex to base vertical line
tc_hor_B=tc_vert_B/tan(B1*pi/tri);//B to vertical line
tc_hor_C=x-tc_hor_B;//C to vertical line
tc_ghosthor_B=tc_vert_B/tan(B*pi/tri);
movetri_r= abs(tc_ghosthor_B-tc_hor_B);
cout<<"------------------------------------------------\n";
//determine and advise if card fits within envelope
if(B1<B && tc_vert_B <(tc_hor_C + a-x)*tan(C*pi/tri))cout<<"\ntrue";
else if(B1<B && tc_hor_B< te_hor_B && tc_vert_B<te_vert_B)cout<<"true";
else if(B1>B && movetri_r<a-x && tc_vert_B<te_vert_B)cout<<"true";
else cout<<"\nfalse";
} while(x>0);
cout<<"\npress any key...";
cin.ignore();
cin.get();
return 0;
}
double find_B_ang(double a,double b,double c)
{
double X=0.0;
X=((a*a)+(c*c)-(b*b));
X/=2*a*c;
X=acos(X);
X*=tri/pi;
return X; //degrees
}
double find_C_ang(double a,double b,double c)
{
double X=0.0;
X=((a*a)+(b*b)-(c*c));
X/=2*a*b;
X=acos(X);
X*=tri/pi;
return X;//degrees
}
Use epsilon (1e-10) when comparing doubles!
Might try from here - Link. It seems that the problem is unsolved so far, so best bet to go with some heuristics to get simple cases (like checks for inscribed / circumscribed circles, aligning borders, etc) and hope for the best.
Related
I'm trying to covert an image from Polar Coordinates to Cartesian Coordinates but after applying the formulas I get float coordinates (r and teta) and I don't know how to represent the points in space using floats for x and y. There might be a way of transforming them in int numbers and still preserving the distribution but I don't see how. I know that there are functions in OpenCV like warpPolar that to the work but I would like to implement it by myself. Any ideas would help :)
This is my code:
struct Value
{
double r;
double teta;
int value; // pixel intensity
};
void irisNormalization(Mat img, Circle pupilCircle, Circle irisCircle, int &matrixWidth, int &matrixHeight)
{
int w = img.size().width;
int h = img.size().height;
int X, Y;
double r, teta;
int rayOfIris = irisCircle.getRay();
std::vector<Value> polar;
// consider the rectangle the iris circle is confined in
int xstart = irisCircle.getA() - rayOfIris;
int ystart = irisCircle.getB() - rayOfIris;
int xfinish = irisCircle.getA() + rayOfIris;
int yfinish = irisCircle.getB() + rayOfIris;
for (int x = xstart; x < xfinish; x++)
for (int y = ystart; y < yfinish; y++)
{
X = x - xstart - rayOfIris;
Y = y - ystart - rayOfIris;
r = sqrt(X * X + Y * Y);
if (X != 0)
{
teta = (atan(abs(Y / X)) * double(180 / M_PI));
if (X > 0 && Y > 0) // quadrant 1
teta = teta;
if (X > 0 && Y < 0)
teta = 360 - teta; // quadrant 4
if (X < 0 && Y > 0) // quadrant 2
teta = 180 - teta;
if (X < 0 && Y < 0) // quadrant 3
teta = 180 + teta;
if (r < rayOfIris)
{
polar.push_back({ r, teta, int(((Scalar)(img.at<uchar>(Point(x, y)))).val[0]) });
}
}
}
std::sort(polar.begin(), polar.end(), [](const Value &left, const Value &right) {
return left.r < right.r && left.teta < right.teta;
});
for (std::vector<Value>::const_iterator i = polar.begin(); i != polar.end(); ++i)
std::cout << i->r << ' ' << i->teta << endl;
Your implementation attempts to express every integer-coordinate point inside a given circle in polar-coordinates. In this way, however, you terminate with an array of coordinates toghether with a value.
If instead you want to geometrically transform your image, you should:
create the destination image with proper width (rho resolution) and height (theta resolution);
loop through every pixel of the destination image and map it back into the original image with the inverse transformation;
get the value of the back-transformed point into the original image by eventually interpolating near values.
For interpolating the values different methods are available. A non-exhaustive list includes:
nearest-neighbor interpolation;
bilinear interpolation;
bicubic interpolation.
Problem
I want to get all pixels that are within a circle of a given radius about a given point, where points can only have integer coordinates, i.e. pixels in a canvas.
So I want to obtain all points in the yellow area given (x, y) and r.
Approaches
The most efficient way I can think of is to loop through a square around (x, y) and check the Euclidean distance for each point:
for (int px = x - r; px <= x + r; px++) {
for (int py = y - r; py <= y + r; py++) {
int dx = x - px, dy = y - py;
if (dx * dx + dy * dy <= r * r) {
// Point is part of the circle.
}
}
}
However, this means that this algorithm will check (r * 2)^2 * (4 - pi) / 4 pixels that are not part of the circle. dx * dx + dy * dy <= r * r, which seems rather expensive, is called redundantly almost 1 / 4 of the time.
Integrating something like what was proposed here might enhance performance:
for (int px = x - r; px <= x + r; px++) {
for (int py = y - r; py <= y + r; py++) {
int dx = abs(x - px), dy = abs(y - py);
if (dx + dy <= r || (!(dx > r || dy > r) && (dx * dx + dy * dy <= r * r))) {
// Point is part of the circle.
}
}
}
However as the author themselves pointed out, this is likely not going to be faster when most of the points are going to be inside of the circle (especially because of abs), which pi / 4 are in this case.
I was not able to find any resources on this question. I am looking specifically for a solution in C++ and not something in SQL.
Alright here are the benchmarks I promised.
Setup
I used google benchmark and the task was to insert all points within the perimiter of the circle into a std::vector<point>. I benchmark for a set of radii and a constant center:
radii = {10, 20, 50, 100, 200, 500, 1000}
center = {100, 500}
language: C++17
compiler: msvc 19.24.28316 x64
platform: windows 10
optimization: O2 (full optimization)
threading: single threaded execution
The results of each algorithm is tested for correctness (compared against the output of OPs algorithm).
So far the following algorithms are benchmarked:
OP's algorithm enclosing_square.
My algorithm containing_square.
creativecreatorormaybenot's algorithm edge_walking.
Mandy007's algorithm binary_search.
Results
Run on (12 X 3400 MHz CPU s)
CPU Caches:
L1 Data 32K (x6)
L1 Instruction 32K (x6)
L2 Unified 262K (x6)
L3 Unified 15728K (x1)
-----------------------------------------------------------------------------
Benchmark Time CPU Iterations
-----------------------------------------------------------------------------
binary_search/10/manual_time 804 ns 3692 ns 888722
binary_search/20/manual_time 2794 ns 16665 ns 229705
binary_search/50/manual_time 16562 ns 105676 ns 42583
binary_search/100/manual_time 66130 ns 478029 ns 10525
binary_search/200/manual_time 389964 ns 2261971 ns 1796
binary_search/500/manual_time 2286526 ns 15573432 ns 303
binary_search/1000/manual_time 9141874 ns 68384740 ns 77
edge_walking/10/manual_time 703 ns 5492 ns 998536
edge_walking/20/manual_time 2571 ns 49807 ns 263515
edge_walking/50/manual_time 15533 ns 408855 ns 45019
edge_walking/100/manual_time 64500 ns 1794889 ns 10899
edge_walking/200/manual_time 389960 ns 7970151 ns 1784
edge_walking/500/manual_time 2286964 ns 55194805 ns 308
edge_walking/1000/manual_time 9009054 ns 234575321 ns 78
containing_square/10/manual_time 629 ns 4942 ns 1109820
containing_square/20/manual_time 2485 ns 40827 ns 282058
containing_square/50/manual_time 15089 ns 361010 ns 46311
containing_square/100/manual_time 62825 ns 1565343 ns 10990
containing_square/200/manual_time 381614 ns 6788676 ns 1839
containing_square/500/manual_time 2276318 ns 45973558 ns 312
containing_square/1000/manual_time 8886649 ns 196004747 ns 79
enclosing_square/10/manual_time 1056 ns 4045 ns 660499
enclosing_square/20/manual_time 3389 ns 17307 ns 206739
enclosing_square/50/manual_time 18861 ns 106184 ns 37082
enclosing_square/100/manual_time 76254 ns 483317 ns 9246
enclosing_square/200/manual_time 421856 ns 2295571 ns 1654
enclosing_square/500/manual_time 2474404 ns 15625000 ns 284
enclosing_square/1000/manual_time 9728718 ns 68576389 ns 72
Code
The complete test code is below, you can copy & paste it and test it yourself. fill_circle.cpp contains the implementation of the different algorithms.
main.cpp
#include <string>
#include <unordered_map>
#include <chrono>
#include <benchmark/benchmark.h>
#include "fill_circle.hpp"
using namespace std::string_literals;
std::unordered_map<const char*, circle_fill_func> bench_tests =
{
{"enclosing_square", enclosing_square},
{"containing_square", containing_square},
{"edge_walking", edge_walking},
{"binary_search", binary_search},
};
std::vector<int> bench_radii = {10, 20, 50, 100, 200, 500, 1000};
void postprocess(std::vector<point>& points)
{
std::sort(points.begin(), points.end());
//points.erase(std::unique(points.begin(), points.end()), points.end());
}
std::vector<point> prepare(int radius)
{
std::vector<point> vec;
vec.reserve(10ull * radius * radius);
return vec;
}
void bm_run(benchmark::State& state, circle_fill_func target, int radius)
{
using namespace std::chrono;
constexpr point center = {100, 500};
auto expected_points = prepare(radius);
enclosing_square(center, radius, expected_points);
postprocess(expected_points);
for (auto _ : state)
{
auto points = prepare(radius);
auto start = high_resolution_clock::now();
target(center, radius, points);
auto stop = high_resolution_clock::now();
postprocess(points);
if (expected_points != points)
{
auto text = "Computation result incorrect. Expected size: " + std::to_string(expected_points.size()) + ". Actual size: " + std::to_string(points.size()) + ".";
state.SkipWithError(text.c_str());
break;
}
state.SetIterationTime(duration<double>(stop - start).count());
}
}
int main(int argc, char** argv)
{
for (auto [name, target] : bench_tests)
for (int radius : bench_radii)
benchmark::RegisterBenchmark(name, bm_run, target, radius)->Arg(radius)->UseManualTime();
benchmark::Initialize(&argc, argv);
if (benchmark::ReportUnrecognizedArguments(argc, argv))
return 1;
benchmark::RunSpecifiedBenchmarks();
}
fill_circle.hpp
#pragma once
#include <vector>
struct point
{
int x = 0;
int y = 0;
};
constexpr bool operator<(point const& lhs, point const& rhs) noexcept
{
return lhs.x != rhs.x
? lhs.x < rhs.x
: lhs.y < rhs.y;
}
constexpr bool operator==(point const& lhs, point const& rhs) noexcept
{
return lhs.x == rhs.x && lhs.y == rhs.y;
}
using circle_fill_func = void(*)(point const& center, int radius, std::vector<point>& points);
void enclosing_square(point const& center, int radius, std::vector<point>& points);
void containing_square(point const& center, int radius, std::vector<point>& points);
void edge_walking(point const& center, int radius, std::vector<point>& points);
void binary_search(point const& center, int radius, std::vector<point>& points);
fill_circle.cpp
#include "fill_circle.hpp"
constexpr double sqrt2 = 1.41421356237309504880168;
constexpr double pi = 3.141592653589793238462643;
void enclosing_square(point const& center, int radius, std::vector<point>& points)
{
int sqr_rad = radius * radius;
for (int px = center.x - radius; px <= center.x + radius; px++)
{
for (int py = center.y - radius; py <= center.y + radius; py++)
{
int dx = center.x - px, dy = center.y - py;
if (dx * dx + dy * dy <= sqr_rad)
points.push_back({px, py});
}
}
}
void containing_square(point const& center, int radius, std::vector<point>& points)
{
int sqr_rad = radius * radius;
int half_side_len = radius / sqrt2;
int sq_x_end = center.x + half_side_len;
int sq_y_end = center.y + half_side_len;
// handle inner square
for (int x = center.x - half_side_len; x <= sq_x_end; x++)
for (int y = center.y - half_side_len; y <= sq_y_end; y++)
points.push_back({x, y});
// probe the rest
int x = 0;
for (int y = radius; y > half_side_len; y--)
{
int x_line1 = center.x - y;
int x_line2 = center.x + y;
int y_line1 = center.y - y;
int y_line2 = center.y + y;
while (x * x + y * y <= sqr_rad)
x++;
for (int i = 1 - x; i < x; i++)
{
points.push_back({x_line1, center.y + i});
points.push_back({x_line2, center.y + i});
points.push_back({center.x + i, y_line1});
points.push_back({center.x + i, y_line2});
}
}
}
void edge_walking(point const& center, int radius, std::vector<point>& points)
{
int sqr_rad = radius * radius;
int mdx = radius;
for (int dy = 0; dy <= radius; dy++)
{
for (int dx = mdx; dx >= 0; dx--)
{
if (dx * dx + dy * dy > sqr_rad)
continue;
for (int px = center.x - dx; px <= center.x + dx; px++)
{
for (int py = center.y - dy; py <= center.y + dy; py += 2 * dy)
{
points.push_back({px, py});
if (dy == 0)
break;
}
}
mdx = dx;
break;
}
}
}
void binary_search(point const& center, int radius, std::vector<point>& points)
{
constexpr auto search = []( const int &radius, const int &squad_radius, int dx, const int &y)
{
int l = y, r = y + radius, distance;
while (l < r)
{
int m = l + (r - l) / 2;
distance = dx * dx + (y - m) * (y - m);
if (distance > squad_radius)
r = m - 1;
else if (distance < squad_radius)
l = m + 1;
else
r = m;
}
if (dx * dx + (y - l) * (y - l) > squad_radius)
--l;
return l;
};
int squad_radius = radius * radius;
for (int px = center.x - radius; px <= center.x + radius; ++px)
{
int upper_limit = search(radius, squad_radius, px - center.x, center.y);
for (int py = 2*center.y - upper_limit; py <= upper_limit; ++py)
{
points.push_back({px, py});
}
}
}
for (line = 1; line <= r; line++) {
dx = (int) sqrt(r * r - line * line);
for (ix = 1; ix <= dx; ix++) {
putpixel(x - ix, y + line)
putpixel(x + ix, y + line)
putpixel(x - ix, y - line)
putpixel(x + ix, y - line)
}
}
To avoid repeated generation of pixels at axes, it is worth to start loops from 1 and draw central lines (ix==0 or line==0) in separate loop.
Note that there is also pure integer Bresenham algorithm to generate circumference points.
Alright, first of all we calculate the inner square of the circle. The formula for it is straight forward:
x² + y² = r² // circle formula
2h² = r² // all sides of square are of equal length so x == y, lets define h := x
h = r / sqrt(2) // half side length of the inner square
Now, every point between (-h, -h) and (+h, +h) lies within the circle. Here is an image of what I mean:
The remaining blue part is a bit tricky, but not too complicated either. We start at the very top of the blue circle (x = 0, y = -radius). Next, we walk right (x++) until we leave the circle perimiter (until x²+y² < r² doesn't hold anymore). Everything between (0, y) and (x, y) is within the circle. Because of symmetry we can extend this 8 fold by
(-x, -y), (+x, -y)
(-x, +y), (+x, +y)
(-y, -x), (-y, +x)
(+y, -x), (+y, +x)
now we go down 1 line (y--) and repeat the steps above (while keeping the most recent value of x). Add the center of the circle to each of the points and you're done.
Here is a visualization. There are some artifacts because of the upscaling. The red dot shows what we're testing at each iteration:
Here is the full code (using opencv to draw the stuff):
#include <opencv2/opencv.hpp>
constexpr double sqrt2 = 1.41421356237309504880168;
int main()
{
cv::Point center(200, 200);
constexpr int radius = 180;
// create test image
cv::Mat img(400, 400, CV_8UC3);
cv::circle(img, center, radius, {180, 0, 0}, cv::FILLED);
cv::imshow("img", img);
cv::waitKey();
// calculate inner rectangle
int halfSideLen = radius / sqrt2;
cv::Rect innerRect(center.x - halfSideLen, center.y - halfSideLen, halfSideLen * 2, halfSideLen * 2);
cv::rectangle(img, innerRect, {0, 180, 0}, cv::FILLED);
cv::imshow("img", img);
cv::waitKey();
// probe the rest
int x = 0;
for (int y = radius; y >= halfSideLen; y--)
{
for (; x * x + y * y < radius * radius; x++)
{
// anything between the following points lies within the circle
// each pair of points represents a line
// (-x, -y), (+x, -y)
// (-x, +y), (+x, +y)
// (-y, -x), (-y, +x)
// (+y, -x), (+y, +x)
// center + {(-X..X) x (-Y..Y)} is inside the circle
cv::line(img, cv::Point(center.x - x, center.y - y), cv::Point(center.x + x, center.y - y), {180, 180, 0});
cv::line(img, cv::Point(center.x - x, center.y + y), cv::Point(center.x + x, center.y + y), {180, 180, 0});
cv::line(img, cv::Point(center.x - y, center.y - x), cv::Point(center.x - y, center.y + x), {180, 180, 0});
cv::line(img, cv::Point(center.x + y, center.y - x), cv::Point(center.x + y, center.y + x), {180, 180, 0});
cv::imshow("img", img);
cv::waitKey(20);
}
}
cv::waitKey();
return 0;
}
This is an optimization that reduce 1/4 the dimension of search:
for (int px = x; px <= x + r; ++px) {
bool find = false;
int dx = x - px, dy;
for (int py = y; !find && py <= y + r; ++py) {
dy = y - py;
if (dx * dx + dy * dy <= r * r)) {
/* (px, py), (px, y+y-py+r), (x+x-px+r, py)
& (x+x-px+r, y+y-py+r) are part of the circle.*/
}else{
find = true; //Avoid increasing on the axis y
}
}
}
or better, improving performance the iteration of second circle for avoiding the if conditional
for (int px = x; px <= x + r; ++px) {
int dx = x - px, py = y;
for (; dx * dx + (py-y) * (py-y) <= r * r; ++py) {
/* (px, py), (px, y+y-py+r), (x+x-px+r, py)
& (x+x-px+r, y+y-py+r) are part of the circle.*/
}
}
well i think that other option is a binary search for upper limit:
int binarySearch(int R, int dx, int y){
int l=y, r=y+R;
while (l < r) {
int m = l + (r - l) / 2;
if(dx*dx + (y - m)*(y - m) > R*R) r = m - 1;
else if(dx*dx + (y - m)*(y - m) < R*R) l = m + 1;
else r = m;
}
if(dx*dx + (y - l)*(y - l) > R*R) --l;
return l;
}
for (int px = x; px <= x + r; ++px) {
int upperLimit = binarySearch(r, px-x, y);
for (int py = y; py <= upperLimit; ++py) {
/* (px, py), (px, y+y-py+r), (x+x-px+r, py)
& (x+x-px+r, y+y-py+r) are part of the circle.*/
}
}
The idea of binary search is to find the upper limit optimally, avoiding the if condition and calculations within the for cycle. For this, it is checked which is the largest integer that makes the distance between the current point and the radius within the circle.
PD: Sorry my English.
Code
Based on the idea from #ScottHunter, I came up with the following algorithm:
#include <functional>
// Executes point_callback for every point that is part of the circle
// defined by the center (x, y) and radius r.
void walk_circle(int x, int y, int r,
std::function<void(int x, int y)> point_callback) {
for (int px = x - r; px < x + r; px++)
point_callback(px, y);
int mdx = r;
for (int dy = 1; dy <= r; dy++)
for (int dx = mdx; dx >= 0; dx--) {
if (dx * dx + dy * dy > r * r)
continue;
for (int px = x - dx; px <= x + dx; px++) {
point_callback(px, y + dy);
point_callback(px, y - dy);
}
mdx = dx;
break;
}
}
Algorithm explained
This algorithm performs a minute number of checks. Specifically, it only checks in each row until the first point that is part of the circle is reached. Furthermore, it will skip points to the left of the previously identified point in the next row. Additionally, by using symmetry, only half of the rows (n/2 + 1/2 as we start at 0) are checked.
This is a visualization of the algorithm I created. The red outline indicates the square that would have previously been checked and the black pixels indicate the real circle (with the red pixel in the middle being the center). The algorithm checks points (marked blue) and loops through valid points (marked green).
As you can see, the number of blue pixels at the end is minute, i.e. there are only a few points being looped over that are not part of the circle. Additionally, notice that only the first green pixel each time needs a check, the others are only looped through, which is why they appear instantly.
Notes
The axes could easily be reversed, obviously.
This could be optimized by taking advantage of symmetry even more, i.e. that the rows are going to be the same as the columns (going through all rows is the same as going through all columns, left to right, up to down, vise versa, vise vera) and going down only a quarter of the rows from the center would be enough to determine exactly what points are going to be part of the circle. However, I feel like the minor performance bump this is going to give is not worth the additional code.
If someone wants to code it out, propose an edit to this answer.
Code with comments
#include <functional>
// Executes point_callback for every point that is part of the circle
// defined by the center (x, y) and radius r.
void walk_circle(int x, int y, int r,
std::function<void(int x, int y)> point_callback) {
// Walk through the whole center line as it will always be completely
// part of the circle.
for (int px = x - r; px < x + r; px++)
point_callback(px, y);
// Define a maximum delta x that shrinks whith every row as the arc
// is closing.
int mdx = r;
// Start directly below the center row to make use of symmetry.
for (int dy = 1; dy <= r; dy++)
for (int dx = mdx; dx >= 0; dx--) {
// Check if the point is part of the circle using Euclidean distance.
if (dx * dx + dy * dy > r * r)
continue;
// If a point in a row left to the center is part of the circle,
// all points to the right of it until the center are going to be
// part of the circle as well.
// Then, we can use horizontal symmetry to move the same distance
// to the right from the center.
for (int px = x - dx; px <= x + dx; px++) {
// Use y - dy and y + dy thanks to vertical symmetry
point_callback(px, y + dy);
point_callback(px, y - dy);
}
// The next row will never have a point in the circle further left.
mdx = dx;
break;
}
}
The problem has a fixed complexity of O(n^2) where n is the radius of the circle. The same complexity as a square or any regular 2D shape
There is no getting past the fact that you can not reduce the number of pixels in a circle, even if you take advantage of the symmetry the complexity remains the same.
So ignoring complexity and looking for optimization.
In your question you state that the abs is a little too expensive per pixel (or 4th pixel)
Once per row is better than once per pixel
You can reduce it down to 1 square root per row. For a circle radius 256 that 128 square roots
void circle(int x, int y, int radius) {
int y1 = y, y2 = y + 1, r = 0, rSqr = radius * radius;
while (r < radius) {
int x1 = x, x2 = x + 1, right = x + sqrt(rSqr - r * r) + 1.5;;
while (x2 < right) {
pixel(x1, y1);
pixel(x2, y1);
pixel(x1--, y2);
pixel(x2++, y2);
}
y1--;
y2++;
r++;
}
}
To get more out of it you can crate a lookup table for the sqrt root calculations.
All integer
Alternatively you can use the variation on the bresenham line that replaces the square root with all integer math. However it is a mess and would not be of any benefit unless the device does not have a floating point unit.
void circle(int x, int y, int radius) {
int l, yy = 0, xx = radius - 1, dx = 1, dy = 1;
int err = dx - (radius << 1);
int l2 = x, y0 = y, r2 = x + 1;
int l1 = x - xx, r1 = r2 + xx;
int y2 = y0 - xx, y1 = y0 + 1, y3 = y1 + xx;
while (xx >= yy) {
l = l1;
while (l < r1) {
pixel(l, y1);
pixel(l++, y0);
}
l = l2;
while (l < r2) {
pixel(l, y3);
pixel(l++, y2);
}
err += dy;
dy += 2;
y0--;
yy++;
y1++;
l2--;
r2++;
if (err > 0) {
dx += 2;
err += (-radius << 1) + dx;
xx--;
r1--
l1++
y3--
y2++
}
}
}
You can draw a square that fits inside the circle and it is pretty straightforward to find if the point falls in.
This will solve most points(2 * r^2) in O(1) time, instead of searching all (4 * r^2) points.
Edit: For the rest of the points, you don't need to loop all the other pixels. You need to loop for the 4 rectangles sized with dimensions [(2r/sqrt(2)), r-(r/sqrt(2))] on the 4 sides(north,east,south,west) of the square that is inside. It means that you never have to search for the squares on the corners. Since, it is completely symmetric, we can take the absolute values of input points and search if the point is inside half-squares on the positive side of the coordinate plane. Which means we only loop once instead of 4.
int square_range = r/sqrt(2);
int abs_x = abs(x);
int abs_y = abs(y);
if(abs_x < square_range && abs_y < square_range){
//point is in
}
else if(abs_x < r && abs_y < r){ // if it falls in the outer square
// this is the only loop that has to be done
if(abs_x < abs_y){
int temp = abs_y;
abs_y = abs_x;
abs_x = temp;
}
for(int x = r/sqrt(2) ; x < r ; x++){
for(int y = 0 ; y < r/sqrt(2) ; y++){
if(x*x + y*y < r*r){
//point is in
}
}
}
}
Overall complexity of the code is O((r-r/sqrt(2))* (r/sqrt(2))). Which is only looping for a half of a single rectangle(8 way symmetry) that is in between the inner square and cirle's outer border.
I'm trying to implement the Delaunay triangulation in C++. Currently it's working, but I'm not getting the correct amount of triangles.
I try it with 4 points in a square pattern : (0,0), (1,0), (0,1), (1,1).
Here's the algorithm I use :
std::vector<Triangle> Delaunay::triangulate(std::vector<Vec2f> &vertices) {
// Determinate the super triangle
float minX = vertices[0].getX();
float minY = vertices[0].getY();
float maxX = minX;
float maxY = minY;
for(std::size_t i = 0; i < vertices.size(); ++i) {
if (vertices[i].getX() < minX) minX = vertices[i].getX();
if (vertices[i].getY() < minY) minY = vertices[i].getY();
if (vertices[i].getX() > maxX) maxX = vertices[i].getX();
if (vertices[i].getY() > maxY) maxY = vertices[i].getY();
}
float dx = maxX - minX;
float dy = maxY - minY;
float deltaMax = std::max(dx, dy);
float midx = (minX + maxX) / 2.f;
float midy = (minY + maxY) / 2.f;
Vec2f p1(midx - 20 * deltaMax, midy - deltaMax);
Vec2f p2(midx, midy + 20 * deltaMax);
Vec2f p3(midx + 20 * deltaMax, midy - deltaMax);
// Add the super triangle vertices to the end of the vertex list
vertices.push_back(p1);
vertices.push_back(p2);
vertices.push_back(p3);
// Add the super triangle to the triangle list
std::vector<Triangle> triangleList = {Triangle(p1, p2, p3)};
// For each point in the vertex list
for(auto point = begin(vertices); point != end(vertices); point++)
{
// Initialize the edges buffer
std::vector<Edge> edgesBuff;
// For each triangles currently in the triangle list
for(auto triangle = begin(triangleList); triangle != end(triangleList);)
{
if(triangle->inCircumCircle(*point))
{
Edge tmp[3] = {triangle->getE1(), triangle->getE2(), triangle->getE3()};
edgesBuff.insert(end(edgesBuff), tmp, tmp + 3);
triangle = triangleList.erase(triangle);
}
else
{
triangle++;
}
}
// Delete all doubly specified edges from the edge buffer
// Black magic by https://github.com/MechaRage
auto ite = begin(edgesBuff), last = end(edgesBuff);
while(ite != last) {
// Search for at least one duplicate of the current element
auto twin = std::find(ite + 1, last, *ite);
if(twin != last)
// If one is found, push them all to the end.
last = std::partition(ite, last, [&ite](auto const &o){ return !(o == *ite); });
else
++ite;
}
// Remove all the duplicates, which have been shoved past "last".
edgesBuff.erase(last, end(edgesBuff));
// Add the triangle to the list
for(auto edge = begin(edgesBuff); edge != end(edgesBuff); edge++)
triangleList.push_back(Triangle(edge->getP1(), edge->getP2(), *point));
}
// Remove any triangles from the triangle list that use the supertriangle vertices
triangleList.erase(std::remove_if(begin(triangleList), end(triangleList), [p1, p2, p3](auto t){
return t.containsVertex(p1) || t.containsVertex(p2) || t.containsVertex(p3);
}), end(triangleList));
return triangleList;
}
And here's what I obtain :
Triangle:
Point x: 1 y: 0
Point x: 0 y: 0
Point x: 1 y: 1
Triangle:
Point x: 1 y: 0
Point x: 1 y: 1
Point x: 0 y: 1
Triangle:
Point x: 0 y: 0
Point x: 1 y: 1
Point x: 0 y: 1
While this would be the correct output :
Triangle:
Point x: 1 y: 0
Point x: 0 y: 0
Point x: 0 y: 1
Triangle:
Point x: 1 y: 0
Point x: 1 y: 1
Point x: 0 y: 1
I have no idea why there is a triangle with the (0, 0) and the (1, 1).
I need an outside eye to review the code and find out what's going wrong.
All the sources are on my Github repo. Feel free to fork it and to PR your code.
Thanks!
what about this implementation of Paul Bourke's Delaunay triangulation algorithm. Take a look at Triangulate() I have used this source many times without any complains
#include <iostream>
#include <stdlib.h> // for C qsort
#include <cmath>
#include <time.h> // for random
const int MaxVertices = 500;
const int MaxTriangles = 1000;
//const int n_MaxPoints = 10; // for the test programm
const double EPSILON = 0.000001;
struct ITRIANGLE{
int p1, p2, p3;
};
struct IEDGE{
int p1, p2;
};
struct XYZ{
double x, y, z;
};
int XYZCompare(const void *v1, const void *v2);
int Triangulate(int nv, XYZ pxyz[], ITRIANGLE v[], int &ntri);
int CircumCircle(double, double, double, double, double, double, double, double, double&, double&, double&);
using namespace std;
////////////////////////////////////////////////////////////////////////
// CircumCircle() :
// Return true if a point (xp,yp) is inside the circumcircle made up
// of the points (x1,y1), (x2,y2), (x3,y3)
// The circumcircle centre is returned in (xc,yc) and the radius r
// Note : A point on the edge is inside the circumcircle
////////////////////////////////////////////////////////////////////////
int CircumCircle(double xp, double yp, double x1, double y1, double x2,
double y2, double x3, double y3, double &xc, double &yc, double &r){
double m1, m2, mx1, mx2, my1, my2;
double dx, dy, rsqr, drsqr;
/* Check for coincident points */
if(abs(y1 - y2) < EPSILON && abs(y2 - y3) < EPSILON)
return(false);
if(abs(y2-y1) < EPSILON){
m2 = - (x3 - x2) / (y3 - y2);
mx2 = (x2 + x3) / 2.0;
my2 = (y2 + y3) / 2.0;
xc = (x2 + x1) / 2.0;
yc = m2 * (xc - mx2) + my2;
}else if(abs(y3 - y2) < EPSILON){
m1 = - (x2 - x1) / (y2 - y1);
mx1 = (x1 + x2) / 2.0;
my1 = (y1 + y2) / 2.0;
xc = (x3 + x2) / 2.0;
yc = m1 * (xc - mx1) + my1;
}else{
m1 = - (x2 - x1) / (y2 - y1);
m2 = - (x3 - x2) / (y3 - y2);
mx1 = (x1 + x2) / 2.0;
mx2 = (x2 + x3) / 2.0;
my1 = (y1 + y2) / 2.0;
my2 = (y2 + y3) / 2.0;
xc = (m1 * mx1 - m2 * mx2 + my2 - my1) / (m1 - m2);
yc = m1 * (xc - mx1) + my1;
}
dx = x2 - xc;
dy = y2 - yc;
rsqr = dx * dx + dy * dy;
r = sqrt(rsqr);
dx = xp - xc;
dy = yp - yc;
drsqr = dx * dx + dy * dy;
return((drsqr <= rsqr) ? true : false);
}
///////////////////////////////////////////////////////////////////////////////
// Triangulate() :
// Triangulation subroutine
// Takes as input NV vertices in array pxyz
// Returned is a list of ntri triangular faces in the array v
// These triangles are arranged in a consistent clockwise order.
// The triangle array 'v' should be malloced to 3 * nv
// The vertex array pxyz must be big enough to hold 3 more points
// The vertex array must be sorted in increasing x values say
//
// qsort(p,nv,sizeof(XYZ),XYZCompare);
///////////////////////////////////////////////////////////////////////////////
int Triangulate(int nv, XYZ pxyz[], ITRIANGLE v[], int &ntri){
int *complete = NULL;
IEDGE *edges = NULL;
IEDGE *p_EdgeTemp;
int nedge = 0;
int trimax, emax = 200;
int status = 0;
int inside;
int i, j, k;
double xp, yp, x1, y1, x2, y2, x3, y3, xc, yc, r;
double xmin, xmax, ymin, ymax, xmid, ymid;
double dx, dy, dmax;
/* Allocate memory for the completeness list, flag for each triangle */
trimax = 4 * nv;
complete = new int[trimax];
/* Allocate memory for the edge list */
edges = new IEDGE[emax];
/*
Find the maximum and minimum vertex bounds.
This is to allow calculation of the bounding triangle
*/
xmin = pxyz[0].x;
ymin = pxyz[0].y;
xmax = xmin;
ymax = ymin;
for(i = 1; i < nv; i++){
if (pxyz[i].x < xmin) xmin = pxyz[i].x;
if (pxyz[i].x > xmax) xmax = pxyz[i].x;
if (pxyz[i].y < ymin) ymin = pxyz[i].y;
if (pxyz[i].y > ymax) ymax = pxyz[i].y;
}
dx = xmax - xmin;
dy = ymax - ymin;
dmax = (dx > dy) ? dx : dy;
xmid = (xmax + xmin) / 2.0;
ymid = (ymax + ymin) / 2.0;
/*
Set up the supertriangle
his is a triangle which encompasses all the sample points.
The supertriangle coordinates are added to the end of the
vertex list. The supertriangle is the first triangle in
the triangle list.
*/
pxyz[nv+0].x = xmid - 20 * dmax;
pxyz[nv+0].y = ymid - dmax;
pxyz[nv+1].x = xmid;
pxyz[nv+1].y = ymid + 20 * dmax;
pxyz[nv+2].x = xmid + 20 * dmax;
pxyz[nv+2].y = ymid - dmax;
v[0].p1 = nv;
v[0].p2 = nv+1;
v[0].p3 = nv+2;
complete[0] = false;
ntri = 1;
/*
Include each point one at a time into the existing mesh
*/
for(i = 0; i < nv; i++){
xp = pxyz[i].x;
yp = pxyz[i].y;
nedge = 0;
/*
Set up the edge buffer.
If the point (xp,yp) lies inside the circumcircle then the
three edges of that triangle are added to the edge buffer
and that triangle is removed.
*/
for(j = 0; j < ntri; j++){
if(complete[j])
continue;
x1 = pxyz[v[j].p1].x;
y1 = pxyz[v[j].p1].y;
x2 = pxyz[v[j].p2].x;
y2 = pxyz[v[j].p2].y;
x3 = pxyz[v[j].p3].x;
y3 = pxyz[v[j].p3].y;
inside = CircumCircle(xp, yp, x1, y1, x2, y2, x3, y3, xc, yc, r);
if (xc + r < xp)
// Suggested
// if (xc + r + EPSILON < xp)
complete[j] = true;
if(inside){
/* Check that we haven't exceeded the edge list size */
if(nedge + 3 >= emax){
emax += 100;
p_EdgeTemp = new IEDGE[emax];
for (int i = 0; i < nedge; i++) { // Fix by John Bowman
p_EdgeTemp[i] = edges[i];
}
delete []edges;
edges = p_EdgeTemp;
}
edges[nedge+0].p1 = v[j].p1;
edges[nedge+0].p2 = v[j].p2;
edges[nedge+1].p1 = v[j].p2;
edges[nedge+1].p2 = v[j].p3;
edges[nedge+2].p1 = v[j].p3;
edges[nedge+2].p2 = v[j].p1;
nedge += 3;
v[j] = v[ntri-1];
complete[j] = complete[ntri-1];
ntri--;
j--;
}
}
/*
Tag multiple edges
Note: if all triangles are specified anticlockwise then all
interior edges are opposite pointing in direction.
*/
for(j = 0; j < nedge - 1; j++){
for(k = j + 1; k < nedge; k++){
if((edges[j].p1 == edges[k].p2) && (edges[j].p2 == edges[k].p1)){
edges[j].p1 = -1;
edges[j].p2 = -1;
edges[k].p1 = -1;
edges[k].p2 = -1;
}
/* Shouldn't need the following, see note above */
if((edges[j].p1 == edges[k].p1) && (edges[j].p2 == edges[k].p2)){
edges[j].p1 = -1;
edges[j].p2 = -1;
edges[k].p1 = -1;
edges[k].p2 = -1;
}
}
}
/*
Form new triangles for the current point
Skipping over any tagged edges.
All edges are arranged in clockwise order.
*/
for(j = 0; j < nedge; j++) {
if(edges[j].p1 < 0 || edges[j].p2 < 0)
continue;
v[ntri].p1 = edges[j].p1;
v[ntri].p2 = edges[j].p2;
v[ntri].p3 = i;
complete[ntri] = false;
ntri++;
}
}
/*
Remove triangles with supertriangle vertices
These are triangles which have a vertex number greater than nv
*/
for(i = 0; i < ntri; i++) {
if(v[i].p1 >= nv || v[i].p2 >= nv || v[i].p3 >= nv) {
v[i] = v[ntri-1];
ntri--;
i--;
}
}
delete[] edges;
delete[] complete;
return 0;
}
int XYZCompare(const void *v1, const void *v2){
XYZ *p1, *p2;
p1 = (XYZ*)v1;
p2 = (XYZ*)v2;
if(p1->x < p2->x)
return(-1);
else if(p1->x > p2->x)
return(1);
else
return(0);
}
I didn't go with a debugger, but from the resulting triangles it seems that this is an accuracy/ambiguity problem.
When you are triangulating a square there are two ways to split it into triangles and both are OK from Delaunay criteria (circumscribed circle center is on border of triangle).
So if you evaluate every triangle independently you may sometimes get even 4 triangles (depending on implementation).
Normally in such cases I recommend to build algorithm as a series of questions which cannot produce contradicting answers. In this case the question is "to which point goes triangle based on edge (1,0)-(1,1)". But often this requires significant changes to the algorithm.
A quick fix usually involves adding some tolerances for comparisons and extra checks (like non-intersecting triangles). But usually it just makes problems rarer.
Most likely you didn't delete all the double edges, especially not the edges from same triangles but with vertices only in another order. The correct function is in the answer from #cMinor.
I have two points and I would like to compute n evenly distributed points on top of the line created by the given line. How could I perform this in c++?
Linear interpolation (affectionately called lerp by the Graphics community) is what you want. Given the end points it can generate the points lying in between with a parameter t.
Let the end points be A (Ax, Ay) and B (Bx, By). The vector spanning from A to B would be given by
V = B − A = <Vx, Vy>
L(t) = A + tV
This essentially means that starting from the point A, we scale the vector V with the scalar t; the point A is displaced by this scaled vector and thus the point we get depends on the value of t, the parameter. When t = 0, we get back A, if t = 1 we get B, if it's 0.5 we get the point midway between A and B.
line A----|----|----|----B
t 0 ¼ ½ ¾ 1
It works for any line (slope doesn't matter). Now coming to your problem of N stops. If you need N to be 10, then you'd have t vary by 1/N, so t = i/10, where i would be the loop iterator.
i = 0, t = 0
i = 1, t = 0.1
i = 2, t = 0.2
⋮
i = 9, t = 0.9
i = 10, t = 1.0
Here's one way to implement it:
#include <iostream>
struct Point {
float x, y;
};
Point operator+ (Point const &pt1, Point const &pt2) {
return { pt1.x + pt2.x, pt1.y + pt2.y };
}
Point operator- (Point const &pt1, Point const &pt2) {
return { pt1.x - pt2.x, pt1.y - pt2.y };
}
Point scale(Point const &pt, float t) {
return { pt.x * t, pt.y * t };
}
std::ostream& operator<<(std::ostream &os, Point const &pt) {
return os << '(' << pt.x << ", " << pt.y << ')';
}
void lerp(Point const &pt1, Point const &pt2, float stops) {
Point const v = pt2 - pt1;
float t = 0.0f;
for (float i = 0.0f; i <= stops; ++i) {
t = i / stops;
Point const p = pt1 + scale(v, t);
std::cout << p << '\n';
}
}
int main() {
lerp({0.0, 0.0}, {5.0f, 5.0f}, 5.0f);
}
Output
(0, 0)
(1, 1)
(2, 2)
(3, 3)
(4, 4)
(5, 5)
Aside
Notice that on every iteration t gets incremented by Δt = 1 / N. Thus another way to update t in a loop would be
t₀ = 0
t₁ = t₀ + Δt
t₂ = t₁ + Δt
⋮
t₉ = t₈ + Δt
t₁₀ = t₉ + Δt
However, this isn't very parallelizable since every iteration of the loop would depend on the previous iteration.
You can use the following give_uniform_points_between(M, N, num_points) which gives a number of #num_points points between M and N. I assume here that the line is not vertical (see below if the line can be vertical).
std::vector<Point> give_uniform_points_between(const Point& M, const Point& N, const unsigned num_points) {
std::vector<Point> result;
// get equation y = ax + b
float a = (N.y - M.y) / (N.x - M.x);
float b = N.y - a * N.x;
float step = std::fabs(M.x - N.x) / num_points;
for (float x = std::min(M.x, N.x); x < std::max(M.x, N.x); x += step) {
float y = a*x+b;
result.push_back(Point{x,y});
}
return result;
}
Demo : Live on Coliru
and result is :
(-3,9);(-2.3,7.6);(-1.6,6.2);(-0.9,4.8);(-0.2,3.4);(0.5,2);(1.2,0.6);(1.9,-0.8);(2.6,-2.2);(3.3,-3.6);
Explanation
From two points (x1,y1) and (x2,y2) you can guess the line equation which pass through these points.
This equation takes the form a*x + b*y + c = 0 or simply y = a*x + b if you cannot have vertical line
where a = (y2 - y1) / (x2 - x1) and you deduce b as shown in the code.
Then you can just vary x or y along your line starting for the point with a minimum value coordinate.
All these (x,y) points you find are on your line and should be uniformely distributed (thanks to the fixed step).
View the line as (x1,y1) + λ(x2-x1,y2-y1), i.e. the first point, plus a multiple of the vector between them.
When λ=0 you have the first point and when λ=1 you have the second.
So you just want to take n equally distributed values of λ between 0 and 1.
How you do this depends on what you mean by between: are the end points included or not?
For example you could take λ=0/(n-1), λ=1/(n-1), λ=2/(n-1), ... λ=(n-1)/(n-1).
That would give n equally distributed points including the endpoints.
Or you could take λ=1/(n+1), λ=2/(n+1), ... λ=n/(n+1).
That would give n equally distributed points not including the endpoints.
Not so mcuh math though...
vector<Rect> Utils::createReactsOnLine(Point pt1, Point pt2, int numRects, int height, int width){
float x1 = pt1.x;
float y1 = pt1.y;
float x2 = pt2.x;
float y2 = pt2.y;
float x_range = std::abs(x2 - x1);
float y_range = std::abs(y2 - y1);
// Find center points of rects on the line
float x_step_size = x_range / (float)(numRects-1);
float y_step_size = y_range / (float)(numRects-1);
float x_min = std::min(x1,x2);
float y_min = std::min(x1,x2);
float x_max = std::max(x1,x2);
float y_max = std::max(x1,x2);
cout << numRects << endl;
float next_x = x1;
float next_y = y1;
cout << "Next x, y: "<< next_x << "," << next_y << endl;
for(int i = 0; i < numRects-1; i++){
if (x1 < x2)
next_x = next_x + x_step_size;
else
next_x = next_x - x_step_size;
if (y1 < y2)
next_y = next_y + y_step_size;
else
next_y = next_y - y_step_size;
cout << "Next x, y: "<< next_x << "," << next_y << endl;
}
return vector<Rect>();
}
To get the center, I have tried, for each vertex, to add to the total, divide by the number of vertices.
I've also tried to find the topmost, bottommost -> get midpoint... find leftmost, rightmost, find the midpoint.
Both of these did not return the perfect center because I'm relying on the center to scale a polygon.
I want to scale my polygons, so I may put a border around them.
What is the best way to find the centroid of a polygon given that the polygon may be concave, convex and have many many sides of various lengths?
The formula is given here for vertices sorted by their occurance along the polygon's perimeter.
For those having difficulty understanding the sigma notation in those formulas, here is some C++ code showing how to do the computation:
#include <iostream>
struct Point2D
{
double x;
double y;
};
Point2D compute2DPolygonCentroid(const Point2D* vertices, int vertexCount)
{
Point2D centroid = {0, 0};
double signedArea = 0.0;
double x0 = 0.0; // Current vertex X
double y0 = 0.0; // Current vertex Y
double x1 = 0.0; // Next vertex X
double y1 = 0.0; // Next vertex Y
double a = 0.0; // Partial signed area
// For all vertices except last
int i=0;
for (i=0; i<vertexCount-1; ++i)
{
x0 = vertices[i].x;
y0 = vertices[i].y;
x1 = vertices[i+1].x;
y1 = vertices[i+1].y;
a = x0*y1 - x1*y0;
signedArea += a;
centroid.x += (x0 + x1)*a;
centroid.y += (y0 + y1)*a;
}
// Do last vertex separately to avoid performing an expensive
// modulus operation in each iteration.
x0 = vertices[i].x;
y0 = vertices[i].y;
x1 = vertices[0].x;
y1 = vertices[0].y;
a = x0*y1 - x1*y0;
signedArea += a;
centroid.x += (x0 + x1)*a;
centroid.y += (y0 + y1)*a;
signedArea *= 0.5;
centroid.x /= (6.0*signedArea);
centroid.y /= (6.0*signedArea);
return centroid;
}
int main()
{
Point2D polygon[] = {{0.0,0.0}, {0.0,10.0}, {10.0,10.0}, {10.0,0.0}};
size_t vertexCount = sizeof(polygon) / sizeof(polygon[0]);
Point2D centroid = compute2DPolygonCentroid(polygon, vertexCount);
std::cout << "Centroid is (" << centroid.x << ", " << centroid.y << ")\n";
}
I've only tested this for a square polygon in the upper-right x/y quadrant.
If you don't mind performing two (potentially expensive) extra modulus operations in each iteration, then you can simplify the previous compute2DPolygonCentroid function to the following:
Point2D compute2DPolygonCentroid(const Point2D* vertices, int vertexCount)
{
Point2D centroid = {0, 0};
double signedArea = 0.0;
double x0 = 0.0; // Current vertex X
double y0 = 0.0; // Current vertex Y
double x1 = 0.0; // Next vertex X
double y1 = 0.0; // Next vertex Y
double a = 0.0; // Partial signed area
// For all vertices
int i=0;
for (i=0; i<vertexCount; ++i)
{
x0 = vertices[i].x;
y0 = vertices[i].y;
x1 = vertices[(i+1) % vertexCount].x;
y1 = vertices[(i+1) % vertexCount].y;
a = x0*y1 - x1*y0;
signedArea += a;
centroid.x += (x0 + x1)*a;
centroid.y += (y0 + y1)*a;
}
signedArea *= 0.5;
centroid.x /= (6.0*signedArea);
centroid.y /= (6.0*signedArea);
return centroid;
}
The centroid can be calculated as the weighted sum of the centroids of the triangles it can be partitioned to.
Here is the C source code for such an algorithm:
/*
Written by Joseph O'Rourke
orourke#cs.smith.edu
October 27, 1995
Computes the centroid (center of gravity) of an arbitrary
simple polygon via a weighted sum of signed triangle areas,
weighted by the centroid of each triangle.
Reads x,y coordinates from stdin.
NB: Assumes points are entered in ccw order!
E.g., input for square:
0 0
10 0
10 10
0 10
This solves Exercise 12, p.47, of my text,
Computational Geometry in C. See the book for an explanation
of why this works. Follow links from
http://cs.smith.edu/~orourke/
*/
#include <stdio.h>
#define DIM 2 /* Dimension of points */
typedef int tPointi[DIM]; /* type integer point */
typedef double tPointd[DIM]; /* type double point */
#define PMAX 1000 /* Max # of pts in polygon */
typedef tPointi tPolygoni[PMAX];/* type integer polygon */
int Area2( tPointi a, tPointi b, tPointi c );
void FindCG( int n, tPolygoni P, tPointd CG );
int ReadPoints( tPolygoni P );
void Centroid3( tPointi p1, tPointi p2, tPointi p3, tPointi c );
void PrintPoint( tPointd p );
int main()
{
int n;
tPolygoni P;
tPointd CG;
n = ReadPoints( P );
FindCG( n, P ,CG);
printf("The cg is ");
PrintPoint( CG );
}
/*
Returns twice the signed area of the triangle determined by a,b,c,
positive if a,b,c are oriented ccw, and negative if cw.
*/
int Area2( tPointi a, tPointi b, tPointi c )
{
return
(b[0] - a[0]) * (c[1] - a[1]) -
(c[0] - a[0]) * (b[1] - a[1]);
}
/*
Returns the cg in CG. Computes the weighted sum of
each triangle's area times its centroid. Twice area
and three times centroid is used to avoid division
until the last moment.
*/
void FindCG( int n, tPolygoni P, tPointd CG )
{
int i;
double A2, Areasum2 = 0; /* Partial area sum */
tPointi Cent3;
CG[0] = 0;
CG[1] = 0;
for (i = 1; i < n-1; i++) {
Centroid3( P[0], P[i], P[i+1], Cent3 );
A2 = Area2( P[0], P[i], P[i+1]);
CG[0] += A2 * Cent3[0];
CG[1] += A2 * Cent3[1];
Areasum2 += A2;
}
CG[0] /= 3 * Areasum2;
CG[1] /= 3 * Areasum2;
return;
}
/*
Returns three times the centroid. The factor of 3 is
left in to permit division to be avoided until later.
*/
void Centroid3( tPointi p1, tPointi p2, tPointi p3, tPointi c )
{
c[0] = p1[0] + p2[0] + p3[0];
c[1] = p1[1] + p2[1] + p3[1];
return;
}
void PrintPoint( tPointd p )
{
int i;
putchar('(');
for ( i=0; i<DIM; i++) {
printf("%f",p[i]);
if (i != DIM - 1) putchar(',');
}
putchar(')');
putchar('\n');
}
/*
Reads in the coordinates of the vertices of a polygon from stdin,
puts them into P, and returns n, the number of vertices.
The input is assumed to be pairs of whitespace-separated coordinates,
one pair per line. The number of points is not part of the input.
*/
int ReadPoints( tPolygoni P )
{
int n = 0;
printf("Polygon:\n");
printf(" i x y\n");
while ( (n < PMAX) && (scanf("%d %d",&P[n][0],&P[n][1]) != EOF) ) {
printf("%3d%4d%4d\n", n, P[n][0], P[n][1]);
++n;
}
if (n < PMAX)
printf("n = %3d vertices read\n",n);
else
printf("Error in ReadPoints:\too many points; max is %d\n", PMAX);
putchar('\n');
return n;
}
There's a polygon centroid article on the CGAFaq (comp.graphics.algorithms FAQ) wiki that explains it.
boost::geometry::centroid(your_polygon, p);
Here is Emile Cormier's algorithm without duplicated code or expensive modulus operations, best of both worlds:
#include <iostream>
using namespace std;
struct Point2D
{
double x;
double y;
};
Point2D compute2DPolygonCentroid(const Point2D* vertices, int vertexCount)
{
Point2D centroid = {0, 0};
double signedArea = 0.0;
double x0 = 0.0; // Current vertex X
double y0 = 0.0; // Current vertex Y
double x1 = 0.0; // Next vertex X
double y1 = 0.0; // Next vertex Y
double a = 0.0; // Partial signed area
int lastdex = vertexCount-1;
const Point2D* prev = &(vertices[lastdex]);
const Point2D* next;
// For all vertices in a loop
for (int i=0; i<vertexCount; ++i)
{
next = &(vertices[i]);
x0 = prev->x;
y0 = prev->y;
x1 = next->x;
y1 = next->y;
a = x0*y1 - x1*y0;
signedArea += a;
centroid.x += (x0 + x1)*a;
centroid.y += (y0 + y1)*a;
prev = next;
}
signedArea *= 0.5;
centroid.x /= (6.0*signedArea);
centroid.y /= (6.0*signedArea);
return centroid;
}
int main()
{
Point2D polygon[] = {{0.0,0.0}, {0.0,10.0}, {10.0,10.0}, {10.0,0.0}};
size_t vertexCount = sizeof(polygon) / sizeof(polygon[0]);
Point2D centroid = compute2DPolygonCentroid(polygon, vertexCount);
std::cout << "Centroid is (" << centroid.x << ", " << centroid.y << ")\n";
}
Break it into triangles, find the area and centroid of each, then calculate the average of all the partial centroids using the partial areas as weights. With concavity some of the areas could be negative.