x,y 的匹配集指向另一个已缩放、旋转、平移且缺少元素的集

代码有效(它实际上生成了上面提到的解决方案)，但由于解决方案的数量(即每个自由度的可接受值的组合)随范围扩大，它可能会很快变得不可接受地缓慢(它也会吃掉把我系统中的所有内存都用完了)

如何提高代码的性能?我愿意接受任何解决方案，包括 numpy 和/或 scipy.也许像

注意集合A(上面使用)中的点是标准字段中标记的星星，集合B是观察字段中检测到的那些星星(上面用红色标记)

即使在今天，在观察场中识别与标准场中标记的恒星相对应的恒星的过程也是通过肉眼完成的.这是由于任务的复杂性.

在上面观察到的图像中，有相当多的缩放，但没有旋转和平移.这是一个相当有利的情况；情况可能会更糟.我正在尝试开发一种简单的算法，以避免必须手动将观测场中的恒星一一识别为标准场中的恒星.

litepresence提出的解决方案

这是我根据 litepresence 的回答制作的脚本.

导入 itertools将 numpy 导入为 np将 matplotlib.pyplot 导入为 pltdef getTriangles(set_X, X_combs):"""获取每个三角形边长的低效方法.标准化，使得最小长度为 1."""三角= []对于 X_combs 中的 p0、p1、p2:d1 = np.sqrt((set_X[p0][0] - set_X[p1][0]) ** 2 +(set_X[p0][1] - set_X[p1][1]) ** 2)d2 = np.sqrt((set_X[p0][0] - set_X[p2][0]) ** 2 +(set_X[p0][1] - set_X[p2][1]) ** 2)d3 = np.sqrt((set_X[p1][0] - set_X[p2][0]) ** 2 +(set_X[p1][1] - set_X[p2][1]) ** 2)d_min = min(d1, d2, d3)d_unsort = [d1/d_min, d2/d_min, d3/d_min]triang.append(排序(d_unsort))返回三角def sumTriangles(A_triang，B_triang):"""对于 A 中的每个归一化三角形，与每个归一化三角形进行比较在 B. 找到它们两侧之间的差异，将它们的绝对值相加，并选择绝对差之和最小的两个三角形."""tr_sum, tr_idx = [], []对于我，枚举中的 A_tr(A_triang):对于 j，枚举中的 B_tr(B_triang):# 长度差异的绝对值.tr_diff = abs(np.array(A_tr) - np.array(B_tr))# 求和tr_sum.append(sum(tr_diff))tr_idx.append([i, j])# A 和 B 中绝对和最小的三角形的索引# 长度差异.tr_idx_min = tr_idx[tr_sum.index(min(tr_sum))]A_idx, B_idx = tr_idx_min[0], tr_idx_min[1]print("最小差异:{}".format(min(tr_sum)))返回 A_idx, B_idx# 数据.set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],[1964.91, 1994.565], [1894.41, 1957.065]]设置_B = [[2689.28、3507.04、2895.67、1051.3、1929.49、1035.97、752.44、130.62、620.06、2769.06、1580.77、281.76、224.54、3848.3]、[2061.19、3700.27、2131.2、1837.3、2017.52、80.96、3524.61、3821.22、3711.53、1812.12、1868.33、3865.77、3273.77、2100.71]]set_B = zip(*set_B)# 所有可能的三角形.A_combs = list(itertools.combinations(range(len(set_A)), 3))B_combs = list(itertools.combinations(range(len(set_B)), 3))# 获取归一化三角形.A_triang, B_triang = getTriangles(set_A, A_combs), getTriangles(set_B, B_combs)# 差异最小的 A 和 B 三角形的索引.A_idx, B_idx = sumTriangles(A_triang, B_triang)# 最佳匹配三角形的 A 和 B 中的点的索引.A_idx_pts, B_idx_pts = A_combs[A_idx], B_combs[B_idx]print '三角形 A %s 匹配三角形 B %s' % (A_idx_pts, B_idx_pts)# A 和 B 中的匹配点.print "A:", [set_A[_] for _ in A_idx_pts]print "B:", [set_B[_] for _ in B_idx_pts]# 阴谋A_pts = zip(*[set_A[_] for _ in A_idx_pts])B_pts = zip(*[set_B[_] for _ in B_idx_pts])plt.scatter(*A_pts, s=10, c='k')plt.scatter(*B_pts, s=10, c='r')plt.show()

该方法几乎是瞬时的并产生正确的匹配:

最小差异:0.0314154749597三角形 A (0, 1, 4) 匹配三角形 B (3, 4, 10)答:[[2015.81, 1981.665], [1967.31, 1960.865], [1894.41, 1957.065]]B: [(1051.3, 1837.3), (1929.49, 2017.52), (1580.77, 1868.33)]

1) 我会通过标记所有点并从每组中找到 3 个点的所有可能组合来解决这个问题.

# 将 B 数据标准化为与 A 相同的格式set_Bx, set_By = (set_B)设置_B = []对于我在范围内(len(set_Bx)):set_B.append([set_Bx[i],set_By[i]])'''set_B = [[2689.28, 2061.19], [3507.04, 3700.27], [2895.67, 2131.2],[1051.3, 1837.3], [1929.49, 2017.52], [1035.97, 80.96], [752.44,3524.61]、[130.62、3821.22]、[620.06、3711.53]、[2769.06、1812.12]、[1580.77, 1868.33], [281.76, 3865.77], [224.54, 3273.77], [3848.3,2100.71]]'''列表(itertools.combinations(范围(len(set_A))，3))列表(itertools.combinations(范围(len(set_B))，3))

ETA1:草稿脚本

ETA2:评论中讨论的修补错误:使用 sum(abs()) 而不是 abs(sum()).现在它可以工作了，速度也很快！

'''已知正确解A = [[1894.41, 1957.065],[1967.31, 1960.865],[2015.81, 1981.665]]B = [[1051.3, 1837.3],[1580.77, 1868.33],[1929.49, 2017.52]]'''将 numpy 导入为 np导入迭代工具导入数学进口经营者set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],[1964.91, 1994.565], [1894.41, 1957.065]]set_B = [[2689.28, 3507.04, 2895.67, 1051.3, 1929.49, 1035.97, 752.44, 130.62,620.06、2769.06、1580.77、281.76、224.54、3848.3]、[2061.19、3700.27、2131.2、1837.3、2017.52、80.96、3524.61、3821.22、3711.53、1812.12、1868.33、3865.77、3273.77、2100.71]]# 将集合 B 数据归一化为集合 A 格式set_Bx, set_By = (set_B)设置_B = []对于我在范围内(len(set_Bx)):set_B.append([set_Bx[i],set_By[i]])'''set_B = [[2689.28, 2061.19], [3507.04, 3700.27], [2895.67, 2131.2],[1051.3, 1837.3], [1929.49, 2017.52], [1035.97, 80.96], [752.44, 3524.61],[130.62, 3821.22], [620.06, 3711.53], [2769.06, 1812.12], [1580.77, 1868.33],[281.76, 3865.77], [224.54, 3273.77], [3848.3, 2100.71]]'''打印集_A打印集_B打印镜头(set_A)打印镜头(set_B)set_A_tri = list(itertools.combinations(range(len(set_A)), 3))set_B_tri = list(itertools.combinations(range(len(set_B)), 3))打印 set_A_tri打印 set_B_tri打印 len(set_A_tri)打印长度(set_B_tri)'''set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],[1964.91, 1994.565], [1894.41, 1957.065]]set_A_tri = [(0, 1, 2), (0, 1, 3), (0, 1, 4), (0, 2, 3), (0, 2, 4), (0, 3, 4),(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]'''定义距离(x1，y1，x2，y2):返回 math.sqrt((x2 - x1)**2 + (y2 - y1)**2 )def tri_sides(set_x, set_x_tri):三角形 = []对于我在范围内(len(set_x_tri)):point1 = set_x_tri[i][0]point2 = set_x_tri[i][1]point3 = set_x_tri[i][2]点1x，点1y = set_x[point1][0]，set_x[point1][1]point2x, point2y = set_x[point2][0], set_x[point2][1]point3x, point3y = set_x[point3][0], set_x[point3][1]len1 = 距离(point1x，point1y，point2x，point2y)len2 = 距离(point1x，point1y，point3x，point3y)len3 = 距离(point2x，point2y，point3x，point3y)min_side = min(len1,len2,len3)len1/=min_sidelen2/=min_sidelen3/=min_sidet=[len1,len2,len3]t.sort()三角形.append(t)返回三角形A_triangles = tri_sides(set_A, set_A_tri)B_triangles = tri_sides(set_B, set_B_tri)打印 A_triangles'''[[1.0, 5.0405616860744304, 5.822935502560814],[1.0, 1.5542012854321234, 1.5619803879976761],[1.0, 1.3832883678507584, 2.347214708755337],[1.0, 1.2141910838179129, 1.4096730529373076],[1.0, 1.1275138587537166, 2.0318412465223665],[1.0, 1.5207417600732074, 2.3589630093994876],[1.0, 3.2270326342163584, 4.13069930678442],[1.0, 6.565440477766354, 6.972550347780966],[1.0, 2.1606693015281997, 2.3635387983160885],[1.0、1.589425903498476、1.846471085870448]]'''打印 B_trianglesdef list_subtract(list1,list2):返回 np.absolute(np.array(list1)-np.array(list2))总和 = []阈值 = 1对于我在范围内(len(A_triangles)):对于范围内的 j(len(B_triangles)):k = sum(list_subtract(A_triangles[i], B_triangles[j]))如果 k <临界点:sums.append([i,j,k])# 按最小和排序sums = sorted(sums, key=operator.itemgetter(2))打印总和打印获胜者 %s"% 总和 [0]打印总和[0][0]打印总和[0][1]match_A = set_A_tri[sums[0][0]]match_B = set_B_tri[sums[0][1]]print '三角形 A %s 匹配三角形 B %s' % (match_A, match_B)match_A_pts = []match_B_pts = []对于范围内的 i (3):match_A_pts.append(set_A[match_A[i]])match_B_pts.append(set_B[match_B[i]])print '三角形 A 有点 %s' % match_A_ptsprint '三角形 B 有点 %s' % match_B_pts'''获胜者 [2, 204, 0.031415474959738399]2204三角形 A (0, 1, 4) 匹配三角形 B (3, 4, 10)三角形 A 有点 [[2015.81, 1981.665], [1967.31, 1960.865], [1894.41, 1957.065]]三角形 B 有点 [[1051.3, 1837.3], [1929.49, 2017.52], [1580.77, 1868.33]]'''

(Why am I doing this? See explanation below)

Consider two sets of points, A and B as shown below

It might not look like it, but set A is "hidden" within set B. It can not be easily seen because points in B are scaled, rotated, and translated in (x, y) with respect to A. Even worse, some points that are present in A are missing in B, and B contains many points that are not in A.

I need to find the appropriate scaling, rotation, and translation that must be applied to the B set in order to match it with set A. In the case shown above, the correct values are:

scale = 0.14, rot_angle = 0.0, x_transl = 35.0, y_transl = 2.0

which produces the (good enough) match

(in red, only the matched B points are shown; these are located in the sector 1000<x<2000, y~2000 in the first figure to the right). Given the many degrees of freedom (DoF: scaling + rotation + 2D translation) I am aware of the possibility of a miss-match, but the coordinates of the points are not random (although they may look like it) so the probability of this happening is very small.

The code I wrote (see below) uses brute force to loop through all possible DoF values taken from pre-defined ranges for each one. The core of the code is based on minimizing the distance of each point in A to any point in B

The code works (it actually generated the solution mentioned above), but since the number of solutions (i.e., the combinations of accepted values for each DoF) scales with larger ranges, it can become unacceptably slow pretty quick (also it eats up all the RAM in my system)

How can I improve the performance of the code? I'm open to any solution including numpy and/or scipy. Perhaps something like Basing-Hopping to search for the best match (or a relatively close one) instead of the brute force method I currently employ?

import numpy as np
from scipy.spatial import distance
import math


def scalePoints(B_center, delta_x, delta_y, scale):
    """
    Scales xy points.

    http://codereview.stackexchange.com/q/159183/35351
    """
    x_scale = B_center[0] - scale * delta_x
    y_scale = B_center[1] - scale * delta_y

    return x_scale, y_scale


def rotatePoints(center, x, y, angle):
    """
    Rotates points in 'xy' around 'center'. Angle is in degrees.
    Rotation is counter-clockwise

    http://stackoverflow.com/a/20024348/1391441
    """
    angle = math.radians(angle)
    xy_rot = x - center[0], y - center[1]
    xy_rot = (xy_rot[0] * math.cos(angle) - xy_rot[1] * math.sin(angle),
              xy_rot[0] * math.sin(angle) + xy_rot[1] * math.cos(angle))
    xy_rot = xy_rot[0] + center[0], xy_rot[1] + center[1]

    return xy_rot


def distancePoints(set_A, x_transl, y_transl):
    """
    Find the sum of the minimum distance of points in set_A to points in set_B.
    """
    d = distance.cdist(set_A, zip(*[x_transl, y_transl]), 'euclidean')
    # Sum of all minimal distances.
    d_sum = np.sum(np.min(d, axis=1))

    return d_sum


def match_frames(
        set_A, B_center, delta_x, delta_y, tol, sc_min, sc_max, sc_step,
        ang_min, ang_max, ang_step, xmin, xmax, xstep, ymin, ymax, ystep):
    """
    Process all possible solutions in the defined ranges.
    """
    N = len(set_A)
    # Ranges
    sc_range = np.arange(sc_min, sc_max, sc_step)
    ang_range = np.arange(ang_min, ang_max, ang_step)
    x_range = np.arange(xmin, xmax, xstep)
    y_range = np.arange(ymin, ymax, ystep)
    print("Total solutions: {:.2e}".format(
          np.prod([len(_) for _ in [sc_range, ang_range, x_range, y_range]])))

    d_sum, params_all = [], []
    for scale in sc_range:
        # Scaled points.
        x_scale, y_scale = scalePoints(B_center, delta_x, delta_y, scale)
        for ang in ang_range:
            # Rotated points.
            xy_rot = rotatePoints(B_center, x_scale, y_scale, ang)
            # x translation
            for x_tr in x_range:
                x_transl = xy_rot[0] + x_tr
                # y translation
                for y_tr in y_range:
                    y_transl = xy_rot[1] + y_tr

                    # Find minimum distance sum.
                    d_sum.append(distancePoints(set_A, x_transl, y_transl))

                    # Store solutions.
                    params_all.append([scale, ang, x_tr, y_tr])

                    # Condition to break out if given tolerance for match
                    # is achieved.
                    if d_sum[-1] <= tol * N:
                        print("Match found:", scale, ang, x_tr, y_tr)
                        i_min = d_sum.index(min(d_sum))
                        return i_min, params_all

        # Print best solution found so far.
        i_min = d_sum.index(min(d_sum))
        print("d_sum_min = {:.2f}".format(d_sum[i_min]))

    return i_min, params_all


# Data.
set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],
         [1964.91, 1994.565], [1894.41, 1957.065]]
set_B = [
    [2689.28, 3507.04, 2895.67, 1051.3, 1929.49, 1035.97, 752.44, 130.62,
     620.06, 2769.06, 1580.77, 281.76, 224.54, 3848.3],
    [2061.19, 3700.27, 2131.2, 1837.3, 2017.52, 80.96, 3524.61, 3821.22,
     3711.53, 1812.12, 1868.33, 3865.77, 3273.77, 2100.71]]

# This is necessary to apply the scaling.
x, y = np.asarray(set_B)
# Center of B points, defined as the center of the minimal rectangle that
# contains all points.
B_center = [(min(x) + max(x)) * .5, (min(y) + max(y)) * .5]
# Difference between the center coordinates and the xy points.
delta_x, delta_y = B_center[0] - x, B_center[1] - y

# Tolerance in pixels for match.
tol = 1.
# Ranges for each DoF.
sc_min, sc_max, sc_step = .01, .2, .01
ang_min, ang_max, ang_step = -30., 30., 1.
xmin, xmax, xstep = -150., 150., 1.
ymin, ymax, ystep = -150., 150., 1.

# Find proper scaling + rotation + translation for set_B.
i_min, params_all = match_frames(
    set_A, B_center, delta_x, delta_y, tol, sc_min, sc_max, sc_step,
    ang_min, ang_max, ang_step, xmin, xmax, xstep, ymin, ymax, ystep)

# Best match found
print(params_all[i_min])

Why am I doing this?

When an astronomer observes a star field, it also needs to observe what is called a "standard field of stars". This is needed to be able to transform the "instrumental magnitudes" (a logarithmic measure of the brightness) of the observed stars to a common universal scale, since these magnitudes depend on the optical system used (telescope + CCD array). In the example shown here, the standard field can be seen below to the left, and the observed one to the right.

Notice that the points in the set A (used above) are the marked stars in the standard field, and the set B are those stars detected in the observed field (marked in red above)

The process of identifying those stars in the observed field that correspond to the stars marked in the standard field is done by-eye, even today. This is due to the complexity of the task.

In the observed image above, there's quite a bit of scaling, but no rotation and little translation. This constitutes a rather favorable scenario; it could be much worse. I'm trying to develop a simple algorithm to avoid having to manually identify stars in the observed field as stars in the standard field, one by one.

Solution proposed by litepresence

This is a script I made following the answer by litepresence.

import itertools
import numpy as np
import matplotlib.pyplot as plt


def getTriangles(set_X, X_combs):
    """
    Inefficient way of obtaining the lengths of each triangle's side.
    Normalized so that the minimum length is 1.
    """
    triang = []
    for p0, p1, p2 in X_combs:
        d1 = np.sqrt((set_X[p0][0] - set_X[p1][0]) ** 2 +
                     (set_X[p0][1] - set_X[p1][1]) ** 2)
        d2 = np.sqrt((set_X[p0][0] - set_X[p2][0]) ** 2 +
                     (set_X[p0][1] - set_X[p2][1]) ** 2)
        d3 = np.sqrt((set_X[p1][0] - set_X[p2][0]) ** 2 +
                     (set_X[p1][1] - set_X[p2][1]) ** 2)
        d_min = min(d1, d2, d3)
        d_unsort = [d1 / d_min, d2 / d_min, d3 / d_min]
        triang.append(sorted(d_unsort))

    return triang


def sumTriangles(A_triang, B_triang):
    """
    For each normalized triangle in A, compare with each normalized triangle
    in B. find the differences between their sides, sum their absolute values,
    and select the two triangles with the smallest sum of absolute differences.
    """
    tr_sum, tr_idx = [], []
    for i, A_tr in enumerate(A_triang):
        for j, B_tr in enumerate(B_triang):
            # Absolute value of lengths differences.
            tr_diff = abs(np.array(A_tr) - np.array(B_tr))
            # Sum the differences
            tr_sum.append(sum(tr_diff))
            tr_idx.append([i, j])

    # Index of the triangles in A and B with the smallest sum of absolute
    # length differences.
    tr_idx_min = tr_idx[tr_sum.index(min(tr_sum))]
    A_idx, B_idx = tr_idx_min[0], tr_idx_min[1]
    print("Smallest difference: {}".format(min(tr_sum)))

    return A_idx, B_idx


# Data.
set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],
         [1964.91, 1994.565], [1894.41, 1957.065]]
set_B = [
    [2689.28, 3507.04, 2895.67, 1051.3, 1929.49, 1035.97, 752.44, 130.62,
     620.06, 2769.06, 1580.77, 281.76, 224.54, 3848.3],
    [2061.19, 3700.27, 2131.2, 1837.3, 2017.52, 80.96, 3524.61, 3821.22,
     3711.53, 1812.12, 1868.33, 3865.77, 3273.77, 2100.71]]
set_B = zip(*set_B)

# All possible triangles.
A_combs = list(itertools.combinations(range(len(set_A)), 3))
B_combs = list(itertools.combinations(range(len(set_B)), 3))

# Obtain normalized triangles.
A_triang, B_triang = getTriangles(set_A, A_combs), getTriangles(set_B, B_combs)

# Index of the A and B triangles with the smallest difference.
A_idx, B_idx = sumTriangles(A_triang, B_triang)

# Indexes of points in A and B of the best match triangles.
A_idx_pts, B_idx_pts = A_combs[A_idx], B_combs[B_idx]
print 'triangle A %s matches triangle B %s' % (A_idx_pts, B_idx_pts)

# Matched points in A and B.
print "A:", [set_A[_] for _ in A_idx_pts]
print "B:", [set_B[_] for _ in B_idx_pts]

# Plot
A_pts = zip(*[set_A[_] for _ in A_idx_pts])
B_pts = zip(*[set_B[_] for _ in B_idx_pts])
plt.scatter(*A_pts, s=10, c='k')
plt.scatter(*B_pts, s=10, c='r')
plt.show()

The method is almost instantaneous and produces the correct match:

Smallest difference: 0.0314154749597
triangle A (0, 1, 4) matches triangle B (3, 4, 10)
A: [[2015.81, 1981.665], [1967.31, 1960.865], [1894.41, 1957.065]]
B: [(1051.3, 1837.3), (1929.49, 2017.52), (1580.77, 1868.33)]

1) I would approach this problem by labeling all points and finding all possible combinations of 3 points from each set.

# normalize B data to same format as A
set_Bx, set_By = (set_B)
set_B = []
for i in range(len(set_Bx)):
    set_B.append([set_Bx[i],set_By[i]])
'''
set_B = [[2689.28, 2061.19], [3507.04, 3700.27], [2895.67, 2131.2], 
[1051.3, 1837.3], [1929.49, 2017.52], [1035.97, 80.96], [752.44, 
3524.61], [130.62, 3821.22], [620.06, 3711.53], [2769.06, 1812.12], 
[1580.77, 1868.33], [281.76, 3865.77], [224.54, 3273.77], [3848.3, 
2100.71]]
'''

list(itertools.combinations(range(len(set_A)), 3))
list(itertools.combinations(range(len(set_B)), 3))

How to generate all permutations of a list in Python

2) For each 3 point group, calculate the sides of the respective triangle; repeating the process for group A and group B.

dist = sqrt( (x2 - x1)**2 + (y2 - y1)**2 )

How do I find the distance between two points?

3) Then reduce the ratio of sides for each such that each triangle's smallest side was then equal to 1; the other sides reduced in respective ratio.

In two similar triangles:

The perimeters of the two triangles are in the same ratio as the sides. The corresponding sides, medians and altitudes will all be in this same ratio.

http://www.mathopenref.com/similartrianglesparts.html

4) Finally for each a triangle from group A, compare to each triangle from group B, with element-wise subtraction. Then sum the resulting elements and find the triangles from A and B with the smallest sum.

list(numpy.array(list1)-numpy.array(list2))

Subtracting 2 lists in Python

5) Given matched triangles; finding the appropriate scaling, translation, and rotation should be relatively trivial in terms of CPU/RAM.

ETA1: rough draft script

ETA2: patched error discussed in comments: with sum(abs()) instead of abs(sum()). Now it works, fast too!

'''
known correct solution

A = [[1894.41, 1957.065],[1967.31, 1960.865],[2015.81, 1981.665]]
B = [[1051.3, 1837.3],[1580.77, 1868.33],[1929.49, 2017.52]]

'''
import numpy as np
import itertools
import math
import operator

set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365],
        [1964.91, 1994.565], [1894.41, 1957.065]]
set_B = [[2689.28, 3507.04, 2895.67, 1051.3, 1929.49, 1035.97, 752.44, 130.62,
        620.06, 2769.06, 1580.77, 281.76, 224.54, 3848.3],
        [2061.19, 3700.27, 2131.2, 1837.3, 2017.52, 80.96, 3524.61, 3821.22,
        3711.53, 1812.12, 1868.33, 3865.77, 3273.77, 2100.71]]

# normalize set B data to set A format
set_Bx, set_By = (set_B)
set_B = []
for i in range(len(set_Bx)):
    set_B.append([set_Bx[i],set_By[i]])

'''
set_B = [[2689.28, 2061.19], [3507.04, 3700.27], [2895.67, 2131.2], 
[1051.3, 1837.3], [1929.49, 2017.52], [1035.97, 80.96], [752.44, 3524.61], 
[130.62, 3821.22], [620.06, 3711.53], [2769.06, 1812.12], [1580.77, 1868.33], 
[281.76, 3865.77], [224.54, 3273.77], [3848.3, 2100.71]]
'''

print set_A
print set_B
print len(set_A)
print len(set_B)

set_A_tri = list(itertools.combinations(range(len(set_A)), 3))
set_B_tri = list(itertools.combinations(range(len(set_B)), 3))

print set_A_tri
print set_B_tri
print len(set_A_tri)
print len(set_B_tri)

'''
set_A = [[2015.81, 1981.665], [1967.31, 1960.865], [1962.91, 1951.365], 
[1964.91, 1994.565], [1894.41, 1957.065]]

set_A_tri = [(0, 1, 2), (0, 1, 3), (0, 1, 4), (0, 2, 3), (0, 2, 4), (0, 3, 4), 
(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]
'''

def distance(x1,y1,x2,y2):
    return math.sqrt((x2 - x1)**2 + (y2 - y1)**2 )

def tri_sides(set_x, set_x_tri):

    triangles = []
    for i in range(len(set_x_tri)):

        point1 = set_x_tri[i][0]
        point2 = set_x_tri[i][1]
        point3 = set_x_tri[i][2]

        point1x, point1y = set_x[point1][0], set_x[point1][1]
        point2x, point2y = set_x[point2][0], set_x[point2][1]
        point3x, point3y = set_x[point3][0], set_x[point3][1] 

        len1 = distance(point1x,point1y,point2x,point2y)
        len2 = distance(point1x,point1y,point3x,point3y)
        len3 = distance(point2x,point2y,point3x,point3y)

        min_side = min(len1,len2,len3)
        len1/=min_side
        len2/=min_side
        len3/=min_side
        t=[len1,len2,len3]
        t.sort()
        triangles.append(t)

    return triangles

A_triangles = tri_sides(set_A, set_A_tri)
B_triangles = tri_sides(set_B, set_B_tri)

print A_triangles
'''
[[1.0, 5.0405616860744304, 5.822935502560814], 
[1.0, 1.5542012854321234, 1.5619803879976761], 
[1.0, 1.3832883678507584, 2.347214708755337], 
[1.0, 1.2141910838179129, 1.4096730529373076], 
[1.0, 1.1275138587537166, 2.0318412465223665], 
[1.0, 1.5207417600732074, 2.3589630093994876], 
[1.0, 3.2270326342163584, 4.13069930678442], 
[1.0, 6.565440477766354, 6.972550347780966], 
[1.0, 2.1606693015281997, 2.3635387983160885], 
[1.0, 1.589425903498476, 1.846471085870448]]
'''
print B_triangles

def list_subtract(list1,list2):

    return np.absolute(np.array(list1)-np.array(list2))

sums = []
threshold = 1
for i in range(len(A_triangles)):
    for j in range(len(B_triangles)):
        k = sum(list_subtract(A_triangles[i], B_triangles[j]))
        if k < threshold:
            sums.append([i,j,k])
# sort by smallest sum
sums = sorted(sums, key=operator.itemgetter(2))

print sums
print 'winner %s' % sums[0]
print sums[0][0]
print sums[0][1]
match_A = set_A_tri[sums[0][0]]
match_B = set_B_tri[sums[0][1]]
print 'triangle A %s matches triangle B %s' % (match_A, match_B)

match_A_pts = []
match_B_pts = []
for i in range(3):
    match_A_pts.append(set_A[match_A[i]])
    match_B_pts.append(set_B[match_B[i]])

print 'triangle A has points %s' % match_A_pts
print 'triangle B has points %s' % match_B_pts

'''
winner [2, 204, 0.031415474959738399]
2
204
triangle A (0, 1, 4) matches triangle B (3, 4, 10)
triangle A has points [[2015.81, 1981.665], [1967.31, 1960.865], [1894.41, 1957.065]]
triangle B has points [[1051.3, 1837.3], [1929.49, 2017.52], [1580.77, 1868.33]]
'''

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