我有一个点列表（数量级为数万），我需要使用python识别两件事：
1-这些点中的连续点组（abs（x2-x1）＆lt; = 1和abs（y2-y1）＆lt; = 1）
2-每组的弧度/半径
以下是一组示例：
[[331,400]，[331,1200]，[332,400]，[332,486]，[332,522]，[332， 655]，[332,1200]，[332,3800]，[332,3877]，[332,3944]，[332,3963]， [332,3992]，[332,4050]，[333,400]，[333,486]，[333,522]，[333， 560]，[333,588]，[333,655]，[333,700]，[333,1200]，[333,3800]， [333,3877]，[333,3944]，[333,3963]，[333,3992]，[333,4050]，[334， 400]，[334,486]，[334,522]，[334,558]，[334,586]，[334,654]， [334,697]，[334,1200]，[334,3800]，[334,3877]，[334,3944]，[334， 3963]，[334,3992]，[334,4050]，[335,400]，[335,486]，[335,521]， [335,556]，[335,585]，[335,653]，[335,695]，[335,1200]，[335， 3800]，[335,3877]，[335,3944]，[335,3963]，[335,3992]，[335， 4050]，[336,400]，[336,486]，[336,520]，[336,555]，[336,584]， [336,651]，[336,693]，[336,1200]，[336,3800]，[336,3877]，[336， 3944]，[336,3963]，[336,3992]，[336,4050]，[337,400]，[337,486]， [337,554]，[337,583]，[337,649]，[337,692]，[337,1200]，[337， 3800]，[337,3877]，[337,3944]，[337,3963]，[337,3992]，[337， 4050]，[338,377]，[338,400]，[338,486]，[338,553]，[338,582]， [338,647]，[338,691]，[338,1200]，[338,3800]，[338,3877]，[338， 3944]，[338,3963]，[338,3992]，[338,4050]，[339,377]，[339,400]， [339,486]，[339,553]，[339,581]，[339,585]，[339,644]，[339， 654]，[339,690]，[339,706]，[339,1200]，[339,3800]，[339,3877]， [339,3944]，[339,3963]，[339,3992]，[339,4050]，[340,376]，[340， 400]，[340,486]，[340,552]，[340,580]，[340,585]，[340,641]， [340,655]，[340,689]，[340,713]，[340,1200]，[340,3800]，[340， 3877]，[340,3944]，[340,3963]，[340,3992]，[340,4050]，[341,376]， [341,400]，[341,486]，[341,552]，[341,579]，[341,585]，[341， 639]，[341,655]，[341,688]，[341,715]，[341,1200]，[341,3800]， [341,3877]，[341,3944]，[341,3963]，[341,3992]，[341,4050]，[342， 375]，[342,400]，[342,486]，[342,552]，[342,578]，[342,585]， [342,637]，[342,655]，[342,688]，[342,717]，[342,1200]，[342， 3800]，[342,3858]，[342,3925]，[342,3954]，[342,4011]，[342， 4050]，[342,4107]，[343,374]，[343,400]，[343,486]，[343,521]， [343,552]，[343,577]，[343,585]，[343,635]，[343,642]，[343， [687]，[343,718]，[343,1200]，[343,3800]，[343,3858]，[343,3925]， [343,3954]，[343,4011]，[343,4050]，[343,4107]，[344,373]，[344， 400]，[344,486]，[344,521]，[344,552]，[344,576]，[344,585]， [344,633]，[344,642]，[344,687]，[344,719]，[344,1200]，[344， 3800]，[344,3858]，[344,3925]，[344,3954]，[344,4011]，[344， 4050]，[344,4107]，[345,372]，[345,400]，[345,486]，[345,521]， [345,552]，[345,575]，[345,585]，[345,630]，[345,642]，[345， [687]，[345,720]，[345,1200]，[345,3800]，[345,3858]，[345,3925]， [345,3954]，[345,4011]，[345,4050]，[345,4107]，[346,370]，[346， 400]，[346,486]，[346,521]，[346,552]，[346,574]，[346,585]， [346,628]，[346,642]，[346,686]，[346,721]，[346,1200]，[346， 3800]，[346,3858]，[346,3925]，[346,3954]，[346,4011]，[346， 4050]，[346,4107]，[347,368]，[347,400]，[347,486]，[347,521]， [347,552]，[347,572]，[347,585]，[347,626]，[347,642]，[347， 686]，[347,721]，[347,1200]，[347,3800]，[347,3858]，[347,3925]， [347,3954]，[347,4011]，[347,4050]，[347,4107]，[348,366]，[348， 400]，[348,487]，[348,521]，[348,552]，[348,570]，[348,585]， [348,624]，[348,642]，[348,686]，[348,721]，[348,1200]，[348， 3800]，[348,3858]，[348,3925]，[348,3954]，[348,4011]，[348， 4050]，[348,4107]，[349,364]，[349,400]，[349,487]，[349,521]， [349,553]，[349,568]，[349,585]，[349,622]，[349,642]，[349， 686]，[349,722]，[349,1200]，[349,3800]，[349,3858]，[349,3925]， [349,3954]，[349,4011]，[349,4050]，[349,4107]，[350,362]，[350， 400]，[350,487]，[350,521]，[350,553]，[350,585]，[350,619]， [350,642]，[350,686]，[350,722]，[350,1200]，[350,3800]，[350， 3858]，[350,3925]，[350,3954]，[350,4011]，[350,4050]，[350， 4107]，[351,357]，[351,400]，[351,487]，[351,521]，[351,554]， [351,585]，[351,619]，[351,642]，[351,686]，[351,722]，[351， 1200]，[351,3800]，[351,3819]，[351,3858]，[351,3877]，[351， 3915]，[351,3934]，[351,3963]，[351,3992]，[351,4050]，[351， 4069]，[351,4107]，[352,355]，[352,373]，[352,400]，[352,487]， [352,520]，[352,555]，[352,585]，[352,621]，[352,642]，[352， 686]，[352,722]，[352,1200]，[352,3800]，[352,3819]，[352,3858]， [352,3877]，[352,3915]，[352,3934]，[352,3963]，[352,3992]，[352， 4050]，[352,4069]，[352,4107]，[353,353]，[353,375]，[353,400]， [353,487]，[353,520]，[353,556]，[353,585]，[353,623]，[353， 642]，[353,686]，[353,722]，[353,1200]，[353,3800]，[353,3819]， [353,3858]，[353,3877]，[353,3915]，[353,3934]，[353,3963]，[353， 3992]，[353,4050]，[353,4069]，[353,4107]，[354,351]，[354,376]， [354,400]，[354,487]，[354,520]，[354,558]，[354,584]，[354， 625]，[354,642]，[354,686]，[354,721]，[354,1200]，[354,3800]， [354,3819]，[354,3858]，[354,3877]]
答案 0 :(得分：2)
这将为您提供群集和list of angles：
from sklearn.cluster import DBSCAN
from scipy.spatial import distance
from scipy.optimize import curve_fit
import numpy as np, math
data = [[331, 400], [331, 1200], [332, 400], [332, 486], [332, 522]] #....
def angle(pt1, pt2):
x1, y1 = pt1
x2, y2 = pt2
inner_product = x1*x2 + y1*y2
len1 = math.hypot(x1, y1)
len2 = math.hypot(x2, y2)
return math.acos(inner_product/(len1*len2))
db=DBSCAN(eps=1,min_samples=2,metric='precomputed').fit(
distance.squareform(distance.pdist(data)))
core_samples = db.core_sample_indices_
labels = db.labels_
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
unique_labels = set(labels)
for k in unique_labels:
class_members = [index[0] for index in np.argwhere(labels == k)]
cluster_core_samples = [index for index in core_samples
if labels[index] == k]
curve = np.array([data[index] for index in class_members])
print k, curve, [angle(p1,p2) for p1,p2 in zip(curve,curve[1:])]
答案 1 :(得分：1)
获得群集后，你必须得到角度。
现在获得这个角度有点棘手，因为正如我在评论中所说，它不太可能保持不变。
所以我打算做的是将每个簇转换为多项式并得到它的导数
通过这种方式，您将能够首先获得给定点的角度polyfit。
polyfit将获取一个点列表并将其转换为给定顺序的多项式。基本上，订单越高，您获得的准确性就越高，但它有问题。您可能需要进行实验。
所以
z = np.polyfit(ClusterXCoordinates, ClusterYCoordinates, 5)// last part are the degree, this is where you experiment
没有一些ploy1d魔法
p = np.poly1d
我们采取衍生工具 现在获取曲线中两点之间的角度
point = p(x_coordinate)//will give you the y of this cord after polyfit so it's continues and derivable
所以请derivative
// remember p computes the value of the function
def df(x, h=0.1e-5):
//All above code to create p
return ( p(x+h/2) - p(x-h/2) )/h
//Get slope
slope = df(pointToCheckFromCluster)
//calculate angle in rad by defintion
math.atan(slope);
http://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html#numpy.poly1d