三维点云处理： k-nn、radius邻近查找作业

k-nn、radius邻近查找作业：

本文这套代码是开源的，来自黎嘉信老师的github.

import random
import math
import  numpy as npfrom result_set import  KNNResultSet,RadiusNNResultSetclass Node:                          #节点，每一个数都是一个分支节点def __init__(self,key,value=-1):self.left = Noneself.right = Noneself.key =keyself.value = value      #value可以用作储存其他数值，譬如点原来的序号def __str__(self):return "key: %s, value: %s" % (str(self.key), str(self.value))def insert(root,key,value=-1):    #构建二叉树if root is None:root = Node(key,value)      #赋初值else:if key < root.key:root.left = insert(root.left,key,value)   #小数放左边elif key > root.key:root.right = insert(root.right,key,value)  #大数放右边else:   # don't insert if key already exist in the treepassreturn  root#二叉树的三种应用
def inorder(root):# Inorder (Left, Root, Right)if root is not None:inorder(root.left)print(root)inorder(root.right)def preorder(root):# Preorder (Root, Left, Right)if root is not None:print(root)preorder(root.left)preorder(root.right)def postorder(root):# Postorder (Left, Right, Root)if root is not None:postorder(root.left)postorder(root.right)print(root)def knn_search(root:Node,result_set:KNNResultSet,key):if root is None:return False# compare the root itselfresult_set.add_point(math.fabs(root.key - key),root.value)       #计算worst_dist ,并把当前root.value(index二叉树)里的值加入到resut_set 中if result_set.worstDist() == 0:return Trueif root.key >= key:# iterate left branch firstif knn_search(root.left, result_set, key):return Trueelif math.fabs(root.key-key) < result_set.worstDist():return knn_search(root.right, result_set, key)return Falseelse:# iterate right branch firstif knn_search(root.right, result_set, key):return Trueelif math.fabs(root.key-key) < result_set.worstDist():return knn_search(root.left, result_set, key)return Falsedef radius_search(root: Node, result_set: RadiusNNResultSet, key):if root is None:return False# compare the root itselfresult_set.add_point(math.fabs(root.key - key), root.value)if root.key >= key:# iterate left branch firstif radius_search(root.left, result_set, key):return Trueelif math.fabs(root.key-key) < result_set.worstDist():return radius_search(root.right, result_set, key)return Falseelse:# iterate right branch firstif radius_search(root.right, result_set, key):return Trueelif math.fabs(root.key-key) < result_set.worstDist():return radius_search(root.left, result_set, key)return Falsedef search_recursively(root,key):               #1NN 搜索 ，递归法if root is None or root.key == key:return rootif key < root.key:return search_recursively(root.left,key)elif key > root.key:return search_recursively(root.right,key)def search_iterative(root, key):                #1NN 搜索 ，循环判断current_node = rootwhile current_node is not None:if current_node.key == key:return current_nodeelif key < current_node.key:current_node = current_node.leftelif key > current_node.key:current_node = current_node.rightreturn current_nodedef main():# Data generationdb_size = 100k = 5    #搜寻5个点radius = 2.0data = np.random.permutation(db_size).tolist()   #random.permutation 随机排列一个数组root =Nonefor i,point in enumerate(data):root = insert(root,point,i)query_key = 6result_set = KNNResultSet(capacity=k)knn_search(root, result_set, query_key)print('kNN Search:')print('index - distance')print(result_set)result_set = RadiusNNResultSet(radius=radius)radius_search(root, result_set, query_key)print('Radius NN Search:')print('index - distance')print(result_set)# print("inorder")# inorder(root)# print("preorder")# preorder(root)# print("postorder")# postorder(root)# node = search_recursive(root, 2)# print(node)## node = search_iterative(root, 2)# print(node)if __name__ == '__main__':main()

结果：

import copyclass DistIndex:def __init__(self, distance, index):self.distance = distanceself.index = indexdef __lt__(self, other):return self.distance < other.distanceclass KNNResultSet:def __init__(self, capacity):self.capacity = capacityself.count = 0self.worst_dist = 1e10self.dist_index_list = []for i in range(capacity):self.dist_index_list.append(DistIndex(self.worst_dist, 0))self.comparison_counter = 0def size(self):return self.countdef full(self):return self.count == self.capacitydef worstDist(self):return self.worst_distdef add_point(self, dist, index):self.comparison_counter += 1if dist > self.worst_dist:returnif self.count < self.capacity:self.count += 1i = self.count - 1while i > 0:if self.dist_index_list[i - 1].distance > dist:self.dist_index_list[i] = copy.deepcopy(self.dist_index_list[i - 1])i -= 1else:breakself.dist_index_list[i].distance = distself.dist_index_list[i].index = indexself.worst_dist = self.dist_index_list[self.capacity - 1].distancedef __str__(self):output = ''for i, dist_index in enumerate(self.dist_index_list):output += '%d - %.2f\n' % (dist_index.index, dist_index.distance)output += 'In total %d comparison operations.' % self.comparison_counterreturn outputclass RadiusNNResultSet:def __init__(self, radius):self.radius = radiusself.count = 0self.worst_dist = radiusself.dist_index_list = []self.comparison_counter = 0def size(self):return self.countdef worstDist(self):return self.radiusdef add_point(self, dist, index):self.comparison_counter += 1if dist > self.radius:returnself.count += 1self.dist_index_list.append(DistIndex(dist, index))def __str__(self):self.dist_index_list.sort()output = ''for i, dist_index in enumerate(self.dist_index_list):output += '%d - %.2f\n' % (dist_index.index, dist_index.distance)output += 'In total %d neighbors within %f.\nThere are %d comparison operations.' \% (self.count, self.radius, self.comparison_counter)return output

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三维点云处理： k-nn、radius邻近查找作业

k-nn、radius邻近查找作业：

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