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    <pre><code>import numpy as np from sklearn.neighbors import NearestNeighbors import networkx as nx #Get coordinates f_name = 'horse' coord = np.genfromtxt(str(f_name)+'.txt',autostrip=True) coord = np.hstack((coord, np.zeros((coord.shape[0], 3), dtype=coord.dtype))) def Normals(XYZ): #The below code uses the PCA Eigen method to fit plane. #Get the covariance matrix average=sum(XYZ)/XYZ.shape[0] b = np.transpose(XYZ - average) cov=np.cov(b) #Get eigen val and vec e_val,e_vect = np.linalg.eigh(cov) #Diagonlize eigen vals e_val_d = np.diag(e_val) #Find min eigen val h = np.rank(min(e_val)) #Find normal norm = e_vect[:,h] #Calc curvature curvature = e_val[0]/(e_val[0]+e_val[1]+e_val[2]) return norm[0],norm[1],norm[2] print 'CALCULATE NORMALS' k = 8 neigh = NearestNeighbors(k) neigh.fit(coord) for i in range(0,len(coord)): #Determine the neighbours of point d = neigh.kneighbors(coord[i]) #Add coordinates of neighbours , dont include center point to array. Determine coordinate by the index of the neighbours. print i*100/len(coord) ## k = 8 #number neighbours y = np.zeros((k-1,3)) for c in range(1,k): #Get coordinate y[c-1,0] = coord[d[1][0][c],0] y[c-1,1] = coord[d[1][0][c],1] y[c-1,2] = coord[d[1][0][c],2] #Get information content b = Normals(y) #Assign information content to each point i.e xyzb coord[i,3] = b[0] coord[i,4] = b[1] coord[i,5] = b[2] print 'CALCULATE NORMAL ORIENTATION' #coord = np.random.randn(40, 6) k=4 ###Fit nearest neighbours to coordinates neigh = NearestNeighbors(k) neigh.fit(coord[:,:3]) #Get the point with highest z value , this will be used as the starting point for my depth search z_max_point = np.where(coord[:,2]==np.max(coord[:,2])) z_max_point=int(z_max_point[0]) print z_max_point if coord[z_max_point,5] &lt; 0 : #ie normal doesnt point out coord[z_max_point,3:6]=-coord[z_max_point,3:6] print 'flip' #Create a graph G = nx.Graph() #Add all points and there neighbours to graph, make the weight equal to the distance between points for i in range(0,len(coord)): d = neigh.kneighbors(coord[i,:3]) for c in range(1,k): p1 = d[1][0][0] p2 = d[1][0][c] n1 = coord[d[1][0][0],3:6] n2 = coord[d[1][0][c],3:6] dot = np.dot(n1,n2) G.add_edge(p1,p2,weight =1-np.abs(dot)) T=nx.minimum_spanning_tree(G) x=[] for i in nx.dfs_edges(T,z_max_point): x+=i inds = np.where(np.diff(x))[0] out = np.split(x,inds[np.diff(inds)==1][1::2]+1) print len(out) for j in range(0,len(out)): print j*100/len(out) for i in range(0,len(out[j])-1): n1 = coord[out[j][i],3:6] n2 = coord[out[j][i+1],3:6] if np.dot(n2,n1)&lt;0: coord[out[j][i+1],3:6]=-coord[out[j][i+1],3:6] np.savetxt('horsenormalsorientated.txt',coord) </code></pre>
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