凸包算法是计算几何中的最经典问题之一了。给定一个点集,计算其凸包。凸包是什么就不罗嗦了
本文给出了《计算几何——算法与应用》中一书所列凸包算法的Python实现和Matlab实现,并给出了一个Matlab动画演示程序。
啊,实现谁都会实现啦╮(╯▽╰)╭,但是演示就不一定那么好做了。
算法CONVEXHULL(P) 输入:平面点集P 输出:由CH(P)的所有顶点沿顺时针方向组成的一个列表 1. 根据x-坐标,对所有点进行排序,得到序列p1, …, pn 2. 在Lupper中加入p1和p2(p1在前) 3. for(i←3 ton) 4. do 在Lupper中加入pi 5. while(Lupper中至少还有三个点,而且最末尾的三个点所构成的不是一个右拐) 6. do 将倒数第二个顶点从Lupper中删去 7. 在Llower 中加入pn和pn-1(pn在前) 8. for(i←n-2 downto1) 9. do 在Llower 中加入pi 10. while(Llower 中至少还有三个点,而且最末尾的三个点所构成的不是一个右拐) 11. do 将倒数第二个顶点从Llower 中删去 12. 将第一个和最后一个点从Llower 中删去 (以免在上凸包与下凸包联接之后,出现重复顶点) 13. 将Llower 联接到Lupper后面(将由此得到的列表记为L) 14. return(L)
看看,不是很多的样子是吧。
这里面需要说明的地方只有一点,那就是方向的判定问题。
设有三个点P,Q,R,现需求R位于向量PQ的左侧还是右侧(或者R在PQ上)。
计算PR与PQ的叉积,也就是外积。
如果将向量PR以P为旋转中心旋转只向量PQ的方向走过一个正角(逆时针),意味着R在PQ的右侧,此时外积为正。
另外需要注意的是,如果在凸包上有三点共线的情况,在本例中三点是均位于凸包边界点集中的。如果想避免这一点,可以通过微量抖动数据的方式解决。
废话不多说,Python实现如下:
1 #!/usr/bin/env python 2 # coding: gbk 3 4 ######################################################################## 5 #Author: Feng Ruohang 6 #Create: 2014/10/02 13:39 7 #Digest: Computate the convex hull of a given point list 8 ######################################################################## 9 10 11 direction = lambda m: (m[2][0] - m[0][0]) * (m[1][1] - m[0][1]) - (m[1][0] - m[0][0]) * (m[2][1] - m[0][1]) 12 ''' 13 A Quick Side_check version Using Lambda expression 14 Input: Given a list of three point : m should like [(p_x,p_y), (q_x,q_y), (r_x,r_y)] 15 Output: Return a Number to indicate whether r on the right side of vector(PQ). 16 Positive means r is on the right side of vector(PQ). 17 This is negative of cross product of PQ and PR: Defined by:(Qx-Px)(Ry-Py)-(Rx-Px)(Qy-Py) 18 Which 'negative' indicate PR is clockwise to PQ, equivalent to R is on the right side of PQ 19 ''' 20 21 22 def convex_hull(point_list): 23 ''' 24 Input: Given a point List: A List of Truple (x,y) 25 Output: Return a point list: A List of Truple (x,y) which is CONVEX HULL of input 26 For the sake of effeciency, There is no error check mechanism here. Please catch outside 27 ''' 28 n = len(point_list) #Total Length 29 point_list.sort() 30 31 #Valid Check: 32 if n < 3: 33 return point_list 34 35 #Building Upper Hull: Initialized with first two point 36 upper_hull = point_list[0:1] 37 for i in range(2, n): 38 upper_hull.append(point_list[i]) 39 while len(upper_hull) >= 3 and not direction(upper_hull[-3:]): 40 del upper_hull[-2] 41 42 #Building Lower Hull: Initialized with last two point 43 lower_hull = [point_list[-1], point_list[-2]] 44 for i in range(n - 3, -1, -1): #From the i-3th to the first point 45 lower_hull.append(point_list[i]) 46 while len(lower_hull) >= 3 and not direction(lower_hull[-3:]): 47 del lower_hull[-2] 48 upper_hull.extend(lower_hull[1:-1]) 49 return upper_hull 50 51 52 #========Unit Test: 53 if __name__ == '__main__': 54 test_data = [(i, i ** 2) for i in range(1, 100)] 55 result = convex_hull(test_data) 56 print result 57 58 2015年1月23日
使用很简单,看DocString就行。
下面顺便给出了Matlab 的实现,以及可视化的算法演示:
效果就是个小动画,像这样吧。
Matlab的凸包算法有三个文件:
side_check2:检查三个点构成的弯折的方向
Convex_hull: 凸包算法Matlab实现
Convex_hull_demo:凸包算法的演示。
拷在一个目录里
运行convex_hull_demo( randn(200,2)*100); 就可以看到可视化演示了
这个是辅助函数
%filename: side_check2.m %Input: Matrix of three point: (2x3 or 3x2) % P(p_x,p_y),Q(q_x,q_y),R(r_x,r_y) %Output: 如果P Q R三点构成一个右拐,返回True % 右拐意味着点R在PQ向量的右侧.此时 function result = side_check2(D) if all(size(D) ~= [3,2]) if all(size(D)==[2,3]) D = D'; else error('error dimension') end end result = (det([[1;1;1], D]) < 0 );
这个是纯算法实现。
%filename: convex_hull.m %CONVEX_HULL %INPUT: Point Set:(n x 2) %OUPUT: HULL Point List: (x x 2) function L=t(P) [num,dimension] = size(P); if dimension ~= 2 error('dimension error') end P = sortrows(P,[1,2]); %if there is only one or two point remain,return it if num < 3 L = P; return end %STEP ONE: Upper Hull: L_upper = P([1,2],:); %Take first two points for i = 3:num L_upper = [L_upper;P(i,:)]; %add the point into list while size(L_upper,1) >= 3 l_size = size(L_upper,1); if det([ones(3,1),L_upper(l_size-2:l_size,:)])= 3 l_size = size(L_lower,1); if det([ones(3,1),L_lower(l_size-2:l_size,:)])
这个是演示:
%CONVEX_HULL %INPUT: Point Set:(n x 2) %OUPUT: HULL Point List: (x x 2) %Samples: convex_hull_demo( randn(200,2)*100) function L=convex_hull_demo(P) %Test Data %data_size = data_size %P = randi([-50,50],[data_size,2]); [num,dimension] = size(P); if dimension ~= 2 error('dimension error') end P = sortrows(P,[1,2]); %====Visual Lization board_left = min(P(:,1)); board_right = max(P(:,1)); board_bottom = min(P(:,2)); board_up = max(P(:,2)); x_padding = (board_right- board_left)*0.1; y_padding = (board_up- board_bottom)*0.1; plot_range= [board_left - x_padding,board_right + x_padding,board_bottom-y_padding,board_up+y_padding]; clf; scatter(P(:,1),P(:,2),'b.'); axis(plot_range); hold on %====VisualLization %if there is only one or two point remain,return it if num < 3 L = P; end %STEP ONE: Upper Hull: L_upper = P([1,2],:); %Take first two points hull_handle = plot(L_upper(:,1),L_upper(:,2),'ob-'); for i = 3:num L_upper = [L_upper;P(i,:)]; %add the point into list while size(L_upper,1) >= 3 l_size = size(L_upper,1); if side_check2(L_upper(l_size-2:l_size,:)) %Check if it is valid break; %Quit if Valid else L_upper(l_size-1,:) = []; %Remove the inner point and continue if not end set(hull_handle,'XData',L_upper(:,1),'YData',L_upper(:,2));drawnow; end set(hull_handle,'XData',L_upper(:,1),'YData',L_upper(:,2));drawnow; end %Visualization plot(L_upper(:,1),L_upper(:,2),'bo-'); %Visualization %STEP Two: Build the lower hull L_lower = [P([num,num-1],:)]; % Add P(n) and P(n-1) set(hull_handle,'XData',L_lower(:,1),'YData',L_lower(:,2));drawnow; for i = num-2:-1:1 L_lower = [L_lower;P(i,:)]; while size(L_lower,1) >= 3 l_size = size(L_lower,1); if side_check2(L_lower(l_size-2:l_size,:)) %Check if it is valid break; %Quit if Valid else L_lower(l_size-1,:) = []; %Remove the inner point and continue if not end set(hull_handle,'XData',L_lower(:,1),'YData',L_lower(:,2));drawnow; end set(hull_handle,'XData',L_lower(:,1),'YData',L_lower(:,2));drawnow; end L_lower([1,size(L_lower,1)],:) = []; if isempty(L_lower) L = L_upper; else L = [L_upper;L_lower(2:size(L_lower,1)-1,:)]; end hold off; return