在圆内的网格中生成坐标 [英] Generate coordinates in grid that lie within a circle

问题描述

我找到了

解决方案

像这样的东西(对于原点处的圆)怎么办?

  X = int(R)#R是半径对于范围内的x(-X，X + 1):Y = int(((R * R-x * x)** 0.5)#给定y给定x对于范围(-Y，Y + 1)中的y:产量(x，y) 

当圆不以原点为中心时，可以很容易地适应一般情况.

I've found this answer, which seems to be somewhat related to this question, but I'm wondering if it's possible to generate the coordinates one by one without the additional ~22% (1 - pi / 4) loss of comparing each point to the radius of the circle (by computing the distance between the circle's center and that point).

So far I have the following function in Python. I know by Gauss' circle problem the number of coordinates I will end up with, but I'd like to generate those points one by one as well.

from typing import Iterable
from math import sqrt, floor

def circCoord(sigma: float =1.0, centroid: tuple =(0, 0)) -> Iterable[tuple]:
    r""" Generate all coords within $3\vec{\sigma}$ of the centroid """

    # The number of least iterations is given by Gauss' circle problem:
    # http://mathworld.wolfram.com/GausssCircleProblem.html

    maxiterations = 1 + 4 * floor(3 * sigma) + 4 * sum(\
      floor(sqrt(9 * sigma**2 - i**2)) for i in range(1, floor(3 * sigma) + 1)
    )

    for it in range(maxiterations):
       # `yield` points in image about `centroid` over which we loop

What I'm trying to do is iterate over only those pixels lying within 3 * sigma of a pixel (at centroid in the above function).

---

I've since written the following example script that demonstrates that the solution below is accurate.

#! /usr/bin/env python3
# -*- coding: utf-8 -*-


import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import numpy as np
import argparse
from typing import List, Tuple
from math import sqrt


def collect(x: int, y: int, sigma: float =3.0) -> List[Tuple[int, int]]:
    """ create a small collection of points in a neighborhood of some point 
    """
    neighborhood = []

    X = int(sigma)
    for i in range(-X, X + 1):
        Y = int(pow(sigma * sigma - i * i, 1/2))
        for j in range(-Y, Y + 1):
            neighborhood.append((x + i, y + j))

    return neighborhood


def plotter(sigma: float =3.0) -> None:
    """ Plot a binary image """    
    arr = np.zeros([sigma * 2 + 1] * 2)

    points = collect(int(sigma), int(sigma), sigma)

    # flip pixel value if it lies inside (or on) the circle
    for p in points:
        arr[p] = 1

    # plot ellipse on top of boxes to show their centroids lie inside
    circ = Ellipse(\
        xy=(int(sigma), int(sigma)), 
        width=2 * sigma,
        height=2 * sigma,
        angle=0.0
    )

    fig = plt.figure(0)
    ax  = fig.add_subplot(111, aspect='equal')
    ax.add_artist(circ)
    circ.set_clip_box(ax.bbox)
    circ.set_alpha(0.2)
    circ.set_facecolor((1, 1, 1))
    ax.set_xlim(-0.5, 2 * sigma + 0.5)
    ax.set_ylim(-0.5, 2 * sigma + 0.5)

    plt.scatter(*zip(*points), marker='.', color='white')

    # now plot the array that's been created
    plt.imshow(-arr, interpolation='none', cmap='gray')
    #plt.colorbar()

    plt.show()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument('-s', '--sigma', type=int, \
      help='Circle about which to collect points'
    )

    args = parser.parse_args()

    plotter(args.sigma)

And the output for

./circleCheck.py -s 4

is:

解决方案

What about simply something like this (for a circle at origin)?

X = int(R) # R is the radius
for x in range(-X,X+1):
    Y = int((R*R-x*x)**0.5) # bound for y given x
    for y in range(-Y,Y+1):
        yield (x,y)

This can easily be adapted to the general case when the circle is not centred at the origin.

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