kdtree

How to find set of points in x,y grid using KDTree.query_ball_tree

由 北城以北 提交于 2020-06-24 14:41:27
问题 I am working in python and I have a x,y mesh grid which are numpy arrays. I need to find for each point (x1,y1) in the grid, the points which are present at a distance r from (x1,y1). Scipy has a function KDTree.query_ball_tree which takes as input, a KD Tree object (which can be constructed from the numpy arrays) and a distance r, but I am not able to understand how it works. For example, consider the following points below: [(1, 1), (2, 1), (3, 1), (4, 1), (1, 2), (2, 2), (3, 2), (4, 2), (1

基于Kinect 2.0深度摄像头的三维重建

由 情到浓时终转凉″ 提交于 2020-04-26 15:42:40
刚今天验收的实验，记录一下。 是比较基础的三维重建内容。 算是三维重建入门。 系统： windows 环境： visual studio 2013 语言： c++ 相关： OpenCV 2、Kinect SDK 2.0、PCL库 内容： 　　使用Kinect 2.0拍摄获取深度图，将彩色图与深度图配准生成点云； 　　然后每次拍摄得到的点云用ICP算法进行融合，形成完整点云（每次拍摄仅做微小偏移）； 　　之后稍微对点云做了些许处理； 　　还添加了回档的功能； 声明： 　　有挺多借鉴博客与参考资料的，太多懒得写，假装忘了~ 原理：（以下不对变量作用作解释，具体可参照变量名猜测，完整代码最后给出） 　　 流程图如下 　　1.关于彩色图与深度图的配准，官方文档给出了如下3个坐标系： 　　Kinect中总共有着3种坐标空间： 　　　　1.相机空间(Camera space)：拥有三个坐标轴，假设kinect面朝正前方，那么X轴向左递增，Y轴向上递增，Z轴向前递增。 　　　　2.深度空间(Depth space)：拥有三个坐标轴，其中x、y分别是深度图中像素的位置，z轴为像素的深度值。 　　　　3.色彩空间(Color space)：拥有两个坐标轴，其中x、y分别是彩色图像中像素的位置。 　　由此，如果知道参数其实自己也能算，但是kinect事实上已经给出了函数。如下图所示 　

Use KDTree/KNN Return Closest Neighbors

由 允我心安 提交于 2020-01-25 07:56:06
问题 I have two python pandas dataframes. One contains all NFL Quarterbacks' College Football statistics since 2007 and a label on the type of player they are (Elite, Average, Below Average). The other dataframe contains all of the college football qbs' data from this season along with a prediction label. I want to run some sort of analysis to determine the two closest NFL comparisons for every college football qb based on their labels. I'd like to add to two comparable qbs as two new columns to

nearest neighbour search kdTree

由 心不动则不痛 提交于 2020-01-24 17:30:05
问题 To a list of N points [(x_1,y_1), (x_2,y_2), ... ] I am trying to find the nearest neighbours to each point based on distance. My dataset is too large to use a brute force approach so a KDtree seems best. Rather than implement one from scratch I see that sklearn.neighbors.KDTree can find the nearest neighbours. Can this be used to find the nearest neighbours of each particle, i.e return a dim(N) list? 回答1: This question is very broad and missing details. It's unclear what you did try, how

Scipy: how to convert KD-Tree distance from query to kilometers (Python/Pandas)

由 只愿长相守 提交于 2020-01-23 00:39:12
问题 This post builds upon this one. I got a Pandas dataframe containing cities with their geo-coordinates (geodetic) as longitude and latitude. import pandas as pd df = pd.DataFrame([{'city':"Berlin", 'lat':52.5243700, 'lng':13.4105300}, {'city':"Potsdam", 'lat':52.3988600, 'lng':13.0656600}, {'city':"Hamburg", 'lat':53.5753200, 'lng':10.0153400}]); For each city I'm trying to find two other cities that are closest. Therefore I tried the scipy.spatial.KDTree. To do so, I had to convert the

scipy.spatial ValueError: “x must consist of vectors of length %d but has shape %s”

由 淺唱寂寞╮ 提交于 2020-01-16 01:12:07
问题 Scipy has an excelent spatial analysis pack which includes a K-dimensional tree. I am attempting to use the query function and it is returning this error: ValueError: x must consist of vectors of length 6 but has shape (2,) Does anyone know what this error is referring to? From some google searching I have discovered it has this general format: raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) I believe This is the source code. 回答1: Figured it

Python KD Tree Nearest Neigbour where distance is greater than zero

由 寵の児 提交于 2020-01-04 14:06:09
问题 I am trying to implement a Nearest neighbour search for Lat and Lon data. Here is the Data.txt 61.3000183105 -21.2500038147 0 62.299987793 -23.750005722 1 66.3000488281 -28.7500038147 2 40.8000183105 -18.250005722 3 71.8000183105 -35.7500038147 3 39.3000183105 -19.7500019073 4 39.8000183105 -20.7500038147 5 41.3000183105 -20.7500038147 6 The problem is, when I want to do the nearest neighbour for each of the Lat and Lon on the data set, it is searching it self. e.g Nearest Neighbour of (-21

What is better way represent a spatial data

由 痴心易碎 提交于 2019-12-26 16:32:06
问题 I have the following problem in my system : my system is client-server architectural . my application is about recognition building in a city .so i decide to separate the map of the city into grids each grid has an area equal 30 x 30 m . for each grid region i store the center point of the grid (lat,long) . so my question is if a user is located in a specific grid x it's location are send to the server how i can decide in which grid the user are located ? Here's a photo clarify the problem:

Building a k-d tree using MapReduce?

由 非 Y 不嫁゛ 提交于 2019-12-25 04:17:57
问题 I am trying to build the KD tree(independent) for image features. I have extracted the image features,the feature contains suppose 1000 float values. Using map-reduce to distribute the images among the nodes of the cluster according to classification(eg, cat,dog,guns)ie. each node will contain the bunch of the similar images & then build KD tree of the images on each node. I am confused about how the tree can be built. So how can I build the KD tree using map-reduce? Each node will contain

Comparison search time between K-D tree and Brute-force

由 南笙酒味 提交于 2019-12-25 02:22:52
问题 This is a graph of the execution speed according to the dimension of the k - d tree and brute-force that I wrote. The number of pointer sets was fixed at 1 M (1,000,000), and Query measured the speed performed 1000 times. The increase in the k - d tree is huge, But brute-force is not. I wonder why these results have come out and how they can be improved. 回答1: Some ideas: The performance may depend a lot on the characteristics of the data. For example, are the data points evenly distributed,
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