/
cutPlane.py
136 lines (128 loc) · 3.74 KB
/
cutPlane.py
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from sklearn.neighbors import NearestNeighbors
import numpy as np
from scipy.spatial import distance
from numpy import linalg as LA
def localFitPlane(ptList, k, centroid):
mean = centroid
res = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
temp = []
for i in range(len(ptList)):
pt = ptList[i]
temp = [pt[0] - mean[0], pt[1] - mean[1], pt[2] - mean[2]]
res[0][0] += temp[0] * temp[0]
res[0][1] += temp[0] * temp[1]
res[0][2] += temp[0] * temp[2]
res[1][0] += temp[1] * temp[0]
res[1][1] += temp[1] * temp[1]
res[1][2] += temp[1] * temp[2]
res[2][0] += temp[2] * temp[0]
res[2][1] += temp[2] * temp[1]
res[2][2] += temp[2] * temp[2]
for i in range(0, 3):
for j in range(0, 3):
res[i][j] = res[i][j] / k
matrix = np.array(res)
val, vec = LA.eig(matrix)
smallest_evec = vec[:, np.argmin(val)]
# minEigInd = np.where(val == np.amin(val))
# eigTemp = vec[minEigInd]
# norm = LA.norm(eigTemp)
norm = LA.norm(smallest_evec)
# print(eigTemp)
eVec = []
for v in smallest_evec:
v = v / norm
eVec.append(v)
# print(eVec[0])
return eVec
fin = open('cerealBowl.txt')
filtered = []
for line in fin:
string = line.split(' ')
x = float(string[0])
y = float(string[1])
z = float(string[2])
filtered.append([x, y, z])
for i in range(len(filtered)):
if filtered[i][1] < -0.33:
filtered[i] = None
pointLst = []
fin.close()
for item in filtered:
if item:
pointLst.append(item)
X = np.array(pointLst)
neighbors = NearestNeighbors(n_neighbors=16, algorithm='ball_tree').fit(X)
for ind in range(len(pointLst)):
knnInd = neighbors.kneighbors([pointLst[ind]], 16, return_distance=False)
knn = []
for i in knnInd[0][1:]:
knn.append(pointLst[i])
dist = 0
for temp in knn:
if temp != None:
dist += distance.euclidean(temp, pointLst[ind])
break
# print(dist)
# dist > 0.002 for campbells
if dist > 0.004:
pointLst[ind] = None
pointLstNew = []
for pt in pointLst:
if pt != None:
pointLstNew.append(pt)
temp = np.array(pointLstNew)
xmean = np.mean(temp[:, 0])
ymean = np.mean(temp[:, 1])
zmean = np.mean(temp[:, 2])
centroid = [xmean, ymean, zmean]
# for i in range(len(pointLstNew)):
# d = distance.euclidean(pointLstNew[i], centroid)
# # campbells: 0.08
# if d > 0.1:
# pointLstNew[i] = None
ptLst = []
for pt in pointLstNew:
if pt != None:
ptLst.append(pt)
X = np.array(ptLst)
neighbors = NearestNeighbors(n_neighbors=6, algorithm='ball_tree').fit(X)
for point in ptLst:
knnInd = neighbors.kneighbors([point], 6, return_distance=False)
knn = []
for i in knnInd[0][1:]:
knn.append(ptLst[i])
normal = localFitPlane(knn, 5, centroid)
# print(normal)
for n in knn:
sub = [0, 0, 0]
sub[0] = n[0] - point[0]
sub[1] = n[1] - point[1]
sub[2] = n[2] - point[2]
offset = normal[0] * sub[0] + normal[1] * sub[1] + normal[2] * sub[2]
n[0] -= offset * normal[0]
n[1] -= offset * normal[1]
n[2] -= offset * normal[2]
temp = np.array(ptLst)
xmean = np.mean(temp[:, 0])
ymean = np.mean(temp[:, 1])
zmean = np.mean(temp[:, 2])
centroid = [xmean, ymean, zmean]
# for i in range(len(ptLst)):
# d = distance.euclidean(ptLst[i], centroid)
# if d > 0.1:
# ptLst[i] = None
# print('klkl')
out = []
for pt in ptLst:
if pt != None:
out.append(pt)
fout = open('cerealCut.txt', 'wt')
# Postprocess
for pt in out:
tempStr = str(pt)
tempStr = tempStr.replace('[', '')
tempStr = tempStr.replace(']', '')
tempStr = tempStr.replace(',', '')
fout.write(tempStr + "\n")
fout.close()