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PointCloudData.cpp
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PointCloudData.cpp
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#include "stdafx.h"
#include "PointCloudData.h"
PointCloudData::PointCloudData()
{
m_PointSumNumber = 0;
b_OneStepMode = false;
b_AllPoint = false;
}
PointCloudData::~PointCloudData()
{
}
//获取点云数量
int PointCloudData::GetPointSumNumber()
{
return m_PointSumNumber;
}
//设置点云数量
void PointCloudData::SetPointSumNumber(int number)
{
m_PointSumNumber = number;
}
//获取点云数据
PCPoint* PointCloudData::GetPointCloud()
{
return m_PointCloud;
}
//初始化点云数据
void PointCloudData::InitPointCloud(PCPoint* pointCloud)
{
m_PointCloud = (PCPoint*)new PCPoint[m_PointSumNumber];//点云数据
m_OriginPointCloud = (PCPoint*)new PCPoint[m_PointSumNumber];//单位化后的点云数据
for (int num = 0; num < m_PointSumNumber; num++)
{
//初始化点云坐标和颜色
for (int i = 0; i < 3; i++)
{
m_PointCloud[num].m_Coordinate[i] = pointCloud[num].m_Coordinate[i];
m_PointCloud[num].m_Color[i] = pointCloud[num].m_Color[i];
}
m_PointCloud[num].m_ID = num;
}
PointMoveToOrigin(pointCloud);//将模型置于显示中心
for (int num = 0; num < m_PointSumNumber; num++)
{
for (int j = 0; j < 3; j++)
{
m_OriginPointCloud[num].m_Coordinate[j] = pointCloud[num].m_Coordinate[j];
m_OriginPointCloud[num].m_Color[j] = pointCloud[num].m_Color[j];
}
//初始化ID
m_OriginPointCloud[num].m_ID = num;
}
//初始化PCL kd树
cloud = pcl::PointCloud<pcl::PointXYZ>::Ptr(new pcl::PointCloud<pcl::PointXYZ>());
cloud->width = m_PointSumNumber;//初始化 PCL 点云
cloud->height = 1;
cloud->points.resize(cloud->width * cloud->height);
int cloudCount = 0;
for (int num = 0; num < m_PointSumNumber; num++)
{
cloud->points[cloudCount].x = m_OriginPointCloud[num].m_Coordinate[0];
cloud->points[cloudCount].y = m_OriginPointCloud[num].m_Coordinate[1];
cloud->points[cloudCount].z = m_OriginPointCloud[num].m_Coordinate[2];
cloudCount++;
}
kdtree.setInputCloud(cloud);
}
//获取移动到原点后的点云数据
PCPoint* PointCloudData::GetOriginPointCloud()
{
return m_OriginPointCloud;
}
//从文件中读取点云数据
bool PointCloudData::ReadPointCloudFile(char* fileName)
{
m_MaxX = -10000000.0;//给定它一个非常小的初始值
m_MaxY = -10000000.0;//给定它一个非常小的初始值
m_MaxZ = -10000000.0;//给定它一个非常小的初始值
m_MinX = DBL_MAX;//给定它最大值
m_MinY = DBL_MAX;//给定它最大值
m_MinZ = DBL_MAX;//给定它最大值
char str[256];
int i = 0;
FILE *fp;
PCPoint sumPoint;
PCPoint* tempPointData;//点云数据临时存储器
//获取点云模型点数量
if (fopen_s(&fp, fileName, "r") == 0)
{
while (fscanf_s(fp, "%s", str, 256) != EOF)
{
fscanf_s(fp, "%s", str, 256);
fscanf_s(fp, "%s", str, 256);
m_PointSumNumber++;
}
fclose(fp);
}
//初始化临时点云数据
tempPointData = (PCPoint*) new PCPoint[m_PointSumNumber];
sumPoint.m_Coordinate[0] = 0.0;
sumPoint.m_Coordinate[1] = 0.0;
sumPoint.m_Coordinate[2] = 0.0;
if (fopen_s(&fp, fileName, "r") == 0)
{
while (fscanf_s(fp, "%s", str, 256) != EOF)
{
//将文件中读取数据点的信息存入数组中
double v[3];
int c[3];
int index;
v[2] = (double)atof(str);
fscanf_s(fp, "%s", str, 256);
v[0] = (double)atof(str);
fscanf_s(fp, "%s", str, 256);
v[1] = (double)atof(str);
c[0] = 0.0; c[1] = 0.0; c[2] = 0.0;//设置初始颜色为黑色
index = i++;
for (int j = 0; j<3; j++)//把文件中的点存入数组RangeData中
{
tempPointData[index].m_Coordinate[j] = v[j];
tempPointData[index].m_Color[j] = c[j];
sumPoint.m_Coordinate[j] += v[j];
}
if (v[0] > m_MaxX)
{
m_MaxX = v[0];//得到最大的x坐标值
}
if (v[1] > m_MaxY)
{
m_MaxY = v[1];//得到最大的y坐标值
}
if (v[2] > m_MaxZ)
{
m_MaxZ = v[2];//得到最大的z坐标值
}
if (v[0] < m_MinX)
{
m_MinX = v[0];//得到最小的x坐标值
}
if (v[1] < m_MinY)
{
m_MinY = v[1];//得到最小的y坐标值
}
if (v[2] < m_MinZ)
{
m_MinZ = v[2];//得到最小的z坐标值
}
}
fclose(fp);
//获取模型的中心点
m_MidPoint.m_Coordinate[0] = sumPoint.m_Coordinate[0] / m_PointSumNumber;
m_MidPoint.m_Coordinate[1] = sumPoint.m_Coordinate[1] / m_PointSumNumber;
m_MidPoint.m_Coordinate[2] = sumPoint.m_Coordinate[2] / m_PointSumNumber;
InitPointCloud(tempPointData);//初始化点云数据
delete[] tempPointData;//释放内存
return 1;//文件打得开则返回1
}
else
{
return 0;//文件打不开则返回0
}
}
void PointCloudData::PointMoveToOrigin(PCPoint* & pointCloud)//将模型置于显示中心
{
m_MaxX -= m_MidPoint.m_Coordinate[0];
for (int num = 0; num < m_PointSumNumber; num++)
{
pointCloud[num].m_Coordinate[0] -= m_MidPoint.m_Coordinate[0];
pointCloud[num].m_Coordinate[1] -= m_MidPoint.m_Coordinate[1];
pointCloud[num].m_Coordinate[2] -= m_MidPoint.m_Coordinate[2];
pointCloud[num].m_Coordinate[0] = (pointCloud[num].m_Coordinate[0])
/ (m_MaxX);
pointCloud[num].m_Coordinate[1] = (pointCloud[num].m_Coordinate[1])
/ (m_MaxX);
pointCloud[num].m_Coordinate[2] = (pointCloud[num].m_Coordinate[2])
/ (m_MaxX);
}
}
//带云参数的PCL kdtree k近邻搜索
void PointCloudData::PCLCoefficientsKDtreeNKSearch(PCPoint lpoint, int Knum, pcl::PointCloud<pcl::PointXYZ>::Ptr coeCloud
, pcl::KdTreeFLANN<pcl::PointXYZ> coeKdtree)
{
pcl::PointXYZ searchPoint;//初始化 搜索点
searchPoint.x = lpoint.m_Coordinate[0];
searchPoint.y = lpoint.m_Coordinate[1];
searchPoint.z = lpoint.m_Coordinate[2];
std::vector<int> pointIdxNKNSearch(Knum);
std::vector<float> pointNKNSquaredDistance(Knum);
PCPoint Ptemp;
if (coeKdtree.nearestKSearch(searchPoint, Knum, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
{
for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i)
{
Ptemp.m_Coordinate[0] = coeCloud->points[pointIdxNKNSearch[i]].x;
Ptemp.m_Coordinate[1] = coeCloud->points[pointIdxNKNSearch[i]].y;
Ptemp.m_Coordinate[2] = coeCloud->points[pointIdxNKNSearch[i]].z;
RkNearestPoints.push_back(Ptemp);
}
}
}
//PCL KDtree k近邻搜索
void PointCloudData::PCLKDtreeNKSearch(PCPoint lpoint, int Knum)
{
pcl::PointXYZ searchPoint;//初始化 搜索点
searchPoint.x = lpoint.m_Coordinate[0];
searchPoint.y = lpoint.m_Coordinate[1];
searchPoint.z = lpoint.m_Coordinate[2];
std::vector<int> pointIdxNKNSearch(Knum);
std::vector<float> pointNKNSquaredDistance(Knum);
PCPoint Ptemp;
if (kdtree.nearestKSearch(searchPoint, Knum, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
{
for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i)
{
Ptemp.m_Coordinate[0] = cloud->points[pointIdxNKNSearch[i]].x;
Ptemp.m_Coordinate[1] = cloud->points[pointIdxNKNSearch[i]].y;
Ptemp.m_Coordinate[2] = cloud->points[pointIdxNKNSearch[i]].z;
RkNearestPoints.push_back(Ptemp);
}
}
}
//边界检测
bool PointCloudData::BoundaryCheck(PCPoint lpoint)
{
double array[BCNum][3], Y[3];
double array_ML[BCNum][3];
double A, B, C;
A = B = C = 0.0;
ZeroMemory(array, sizeof(array));
ZeroMemory(Y, sizeof(Y));
PCLKDtreeNKSearch(lpoint, BCNum);
for (int i = 0; i < BCNum; i++)
{
array_ML[i][0] = array[i][0] = RkNearestPoints[i].m_Coordinate[0];
array_ML[i][1] = array[i][1] = RkNearestPoints[i].m_Coordinate[1];
array_ML[i][2] = array[i][2] = RkNearestPoints[i].m_Coordinate[2];
}
RkNearestPoints.clear();
double *Matrix[3], *IMatrix[3];
for (int i = 0; i < 3; i++)
{
Matrix[i] = new double[3];
IMatrix[i] = new double[3];
}
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
*(Matrix[i] + j) = 0.0;//全部赋值为0.0
}
}
for (int j = 0; j < 3; j++)
{
for (int i = 0; i < BCNum; i++)
{
*(Matrix[0] + j) += array[i][0] * array[i][j];
*(Matrix[1] + j) += array[i][1] * array[i][j];
*(Matrix[2] + j) += array[i][2] * array[i][j];
Y[j] -= array[i][j];
}
}
double d = Determinant(Matrix, 3);
Inverse(Matrix, IMatrix, 3, d);
for (int i = 0; i < 3; i++)
{
A += *(IMatrix[0] + i)*Y[i];
B += *(IMatrix[1] + i)*Y[i];
C += *(IMatrix[2] + i)*Y[i];
}
for (int i = 0; i < 3; i++)
{
delete[] Matrix[i];
delete[] IMatrix[i];
}
for (int i = 0; i<BCNum; i++){
double t = (A*array[i][0] + B*array[i][1] + C*array[i][2] + 1) / (A*A + B*B + C*C);
array[i][0] = array[i][0] - A*t;
array[i][1] = array[i][1] - B*t;
array[i][2] = array[i][2] - C*t;
}
double t = (A*lpoint.m_Coordinate[0] + B*lpoint.m_Coordinate[1] + C*lpoint.m_Coordinate[2] + 1) / (A*A + B*B + C*C);
lpoint.m_Coordinate[0] = lpoint.m_Coordinate[0] - A*t;
lpoint.m_Coordinate[1] = lpoint.m_Coordinate[1] - B*t;
lpoint.m_Coordinate[2] = lpoint.m_Coordinate[2] - C*t;
PCPoint* pt3d = new PCPoint[BCNum];
// Point* pt2d = new Point[10];
PCPoint averagePoint; // average point.
averagePoint.m_Coordinate[0] = 0;
averagePoint.m_Coordinate[1] = 0;
averagePoint.m_Coordinate[2] = 0;
for (int i = 0; i < BCNum; i++)
{
pt3d[i].m_Coordinate[0] = array[i][0];
pt3d[i].m_Coordinate[1] = array[i][1];
pt3d[i].m_Coordinate[2] = array[i][2];
averagePoint.m_Coordinate[0] += array[i][0];//get averagePoint
averagePoint.m_Coordinate[1] += array[i][1];
averagePoint.m_Coordinate[2] += array[i][2];
}
averagePoint.m_Coordinate[0] = averagePoint.m_Coordinate[0] / BCNum;
averagePoint.m_Coordinate[1] = averagePoint.m_Coordinate[1] / BCNum;
averagePoint.m_Coordinate[2] = averagePoint.m_Coordinate[2] / BCNum;
int side1 = 0;
int side2 = 0;
PCPoint s;
s.m_Coordinate[0] = averagePoint.m_Coordinate[0] - lpoint.m_Coordinate[0];
s.m_Coordinate[1] = averagePoint.m_Coordinate[1] - lpoint.m_Coordinate[1];
s.m_Coordinate[2] = averagePoint.m_Coordinate[2] - lpoint.m_Coordinate[2];
for (int i = 0; i < BCNum; i++)
{
double t = s.m_Coordinate[0] * (pt3d[i].m_Coordinate[0] - lpoint.m_Coordinate[0])
+ s.m_Coordinate[1] * (pt3d[i].m_Coordinate[1] - lpoint.m_Coordinate[1])
+ s.m_Coordinate[2] * (pt3d[i].m_Coordinate[2] - lpoint.m_Coordinate[2]);
if (t > 0)
{
side1++;
}
else
{
side2++;
}
}
if (abs(((double)side1 - (double)side2) / BCNum) > Threadshold)
{
return true;
}
return false;
}
//进行孔洞修补
bool PointCloudData::HoleRepair()
{
//先清空数组
vector<PCPoint> m_SelectedPoint;//被选中的约束点
vector<PCPoint> newSelectedPoint;//不含附加约束点
vector<PCPoint> m_SelectedBoundaryPoint;//被选中的边界点
//新的修补点集合
vector<PCPoint> repairedPoint;
//获取选中的边界点集合
for (int num = 0; num < m_PointSumNumber; num++)
{
if (m_OriginPointCloud[num].b_Selected &&
m_OriginPointCloud[num].b_BoundaryPoint)
{
m_SelectedBoundaryPoint.push_back(m_OriginPointCloud[num]);
}
}
//将选中的边界点向周围进行扩充，扩充的点即为约束点，每个点向其周围扩充
//新的knum个点
int knum = 15;
for (int num = 0; num < m_SelectedBoundaryPoint.size(); num++)
{
PCLKDtreeNKSearch(m_SelectedBoundaryPoint[num], knum);
for (int i = 1; i < knum; i++)
{
if (NotIn(RkNearestPoints[i], m_SelectedBoundaryPoint,m_SelectedPoint))
{
m_SelectedPoint.push_back(RkNearestPoints[i]);
newSelectedPoint.push_back(RkNearestPoints[i]);
}
}
RkNearestPoints.clear();
}
gProgress = 10;//用于显示进度条
//获取隐式曲面方程的系数
double* coefficients = GetRBFCoefficients(m_SelectedPoint);
//test begin
for (int i = 0; i < m_SelectedPoint.size(); i++)
{
m_TestSeletedPointCloud.push_back(m_SelectedPoint[i]);
}
//test end
gProgress = 30;//用于显示进度条
//插入新的孔洞修补点，病对修补点进行位置的调整
bool repairingResult = InsertRepairPoint(coefficients, repairedPoint, m_SelectedBoundaryPoint, m_SelectedPoint);
gProgress = 90;//用于显示进度条
//将所有的数据重置到初始的未选中状态
for (int num = 0; num < m_PointSumNumber; num++)
{
m_OriginPointCloud[num].b_Selected = false;
}
gProgress = 95;//用于显示进度条
//进行张量投票特征点检测
//TensorVoting(repairedPoint);
//向点云中插入新的修补点
for (int num = 0; num < repairedPoint.size(); num++)
{
/*if (num % 2 == 0 && repairedPoint[num].b_TVPoint == false && num != 0)
{
continue;
}*/
m_AddedPoingCLoud.push_back(repairedPoint[num]);
}
gProgress = -1;//销毁进度条
if (repairingResult)
{
return true;
}
else
{
return false;
}
}
//是否在选中的边界集合中
bool PointCloudData::NotIn(PCPoint lpoint, vector<PCPoint> m_SelectedBoundaryPoint, vector<PCPoint> m_SelectedPoint)
{
for (int num = 0; num < m_SelectedBoundaryPoint.size(); num++)
{
if (fabs(lpoint.m_Coordinate[0] - m_SelectedBoundaryPoint[num].m_Coordinate[0]) < 0.0001
&& fabs(lpoint.m_Coordinate[1] - m_SelectedBoundaryPoint[num].m_Coordinate[1]) < 0.0001
&& fabs(lpoint.m_Coordinate[2] - m_SelectedBoundaryPoint[num].m_Coordinate[2]) < 0.0001)
{
return false;
}
}
for (int num = 0; num < m_SelectedPoint.size(); num++)
{
if (fabs(lpoint.m_Coordinate[0] - m_SelectedPoint[num].m_Coordinate[0]) < 0.0001
&& fabs(lpoint.m_Coordinate[1] - m_SelectedPoint[num].m_Coordinate[1]) < 0.0001
&& fabs(lpoint.m_Coordinate[2] - m_SelectedPoint[num].m_Coordinate[2]) < 0.0001)
{
return false;
}
}
return true;
}
//获取径向基函数的系数
double* PointCloudData::GetRBFCoefficients(vector<PCPoint>& m_SelectedPoint)
{
//定义附加约束点
int RBFNum = m_SelectedPoint.size();
PCPoint* additionalPoint = new PCPoint[RBFNum];
//利用选择的约束点获取附加约束点
for (int num = 0; num < m_SelectedPoint.size(); num++)
{
//获取每个数据点的法矢量值及坐标值
additionalPoint[num].m_Normal = GetNormalVector(m_SelectedPoint[num]);
additionalPoint[num].m_Coordinate[0] = m_SelectedPoint[num].m_Coordinate[0];
additionalPoint[num].m_Coordinate[1] = m_SelectedPoint[num].m_Coordinate[1];
additionalPoint[num].m_Coordinate[2] = m_SelectedPoint[num].m_Coordinate[2];
}
//进行法矢量的方向矫正
RectifyNormals(additionalPoint, RBFNum);
for (int num = 0; num < RBFNum; num++)
{
//获取附加约束点的值
GetAdditionalPoint(&additionalPoint[num]);
//为了方便直接将得到的附加约束点加入约束点集合中
m_SelectedPoint.push_back(additionalPoint[num]);
}
//RBF矩阵的大小
int newRBFNum = 2 * RBFNum;
//初始化RBF元素值
double** RBFValue = new double*[newRBFNum];
for (int num = 0; num < newRBFNum; num++)
{
RBFValue[num] = new double[newRBFNum];
}
for (int i = 0; i < newRBFNum; i++)
{
for (int j = 0; j < newRBFNum; j++)
{
RBFValue[i][j] = GetRBFValue(GetTwoPointsDistance(m_SelectedPoint[i],m_SelectedPoint[j]));
}
}
//初始化RBF矩阵
double** RBFMatrix = new double*[newRBFNum + 4];
for (int num = 0; num < newRBFNum + 4; num++)
{
RBFMatrix[num] = new double[newRBFNum + 5];
}
for (int i = 0; i < newRBFNum; i++)
{
for (int j = 0; j < newRBFNum; j++)
{
RBFMatrix[i][j] = RBFValue[i][j];
}
}
for (int num = 0; num < newRBFNum; num++)
{
RBFMatrix[num][newRBFNum] = 1;
RBFMatrix[num][newRBFNum + 1] = m_SelectedPoint[num].m_Coordinate[0];
RBFMatrix[num][newRBFNum + 2] = m_SelectedPoint[num].m_Coordinate[1];
RBFMatrix[num][newRBFNum + 3] = m_SelectedPoint[num].m_Coordinate[2];
}
for (int num = 0; num < newRBFNum; num++)
{
RBFMatrix[newRBFNum][num] = 1;
RBFMatrix[newRBFNum + 1][num] = m_SelectedPoint[num].m_Coordinate[0];
RBFMatrix[newRBFNum + 2][num] = m_SelectedPoint[num].m_Coordinate[1];
RBFMatrix[newRBFNum + 3][num] = m_SelectedPoint[num].m_Coordinate[2];
}
for (int i = newRBFNum; i < newRBFNum + 4; i++)
{
for (int j = newRBFNum; j < newRBFNum + 4; j++)
{
RBFMatrix[i][j] = 0;
}
}
for (int num = 0; num < RBFNum; num++)
{
RBFMatrix[num][newRBFNum + 4] = 0;
}
for (int num = RBFNum; num < newRBFNum; num++)
{
RBFMatrix[num][newRBFNum + 4] = 1;
}
for (int num = newRBFNum; num < newRBFNum + 4; num++)
{
RBFMatrix[num][newRBFNum + 4] = 0;
}
double* coefficients = new double[newRBFNum + 4];
//利用列主元高斯消去法对矩阵进行计算，得到隐式曲面方程的系数
colunmPrincipleGauss(newRBFNum + 4, RBFMatrix);
//获取隐式曲面方程的系数
for (int num = 0; num < newRBFNum + 4; num++)
{
coefficients[num] = RBFMatrix[num][newRBFNum + 4];
}
return coefficients;
}
//法向矫正
void PointCloudData::RectifyNormals(PCPoint* additionalPoint, int RBFNum)
{
for (int num = 0; num < RBFNum; num++)
{
if (additionalPoint[0].m_Normal[0] * additionalPoint[num].m_Normal[0]
+ additionalPoint[0].m_Normal[1] * additionalPoint[num].m_Normal[1]
+ additionalPoint[0].m_Normal[2] * additionalPoint[num].m_Normal[2]
< 0)
{
additionalPoint[num].m_Normal[0] = -additionalPoint[num].m_Normal[0];
additionalPoint[num].m_Normal[1] = -additionalPoint[num].m_Normal[1];
additionalPoint[num].m_Normal[2] = -additionalPoint[num].m_Normal[2];
}
}
for (int num = 0; num < RBFNum; num++)
{
int knum = 10;
PCLKDtreeNKSearch(additionalPoint[num],knum);
for (int i = 1; i < knum; i++)
{
for (int j = 0; j < RBFNum; j++)
{
if (fabs(RkNearestPoints[i].m_Coordinate[0] - additionalPoint[j].m_Coordinate[0]) < 0.0001
&& fabs(RkNearestPoints[i].m_Coordinate[1] - additionalPoint[j].m_Coordinate[1]) < 0.0001
&& fabs(RkNearestPoints[i].m_Coordinate[2] - additionalPoint[j].m_Coordinate[2]) < 0.0001)
{//找到该近邻点
if (additionalPoint[num].m_Normal[0] * additionalPoint[j].m_Normal[0]
+ additionalPoint[num].m_Normal[1] * additionalPoint[j].m_Normal[1]
+ additionalPoint[num].m_Normal[2] * additionalPoint[j].m_Normal[2]
< 0)
{//如果两法矢量之间的夹角大于90度，则改变法矢量的方向
additionalPoint[j].m_Normal[0] = -additionalPoint[j].m_Normal[0];
additionalPoint[j].m_Normal[1] = -additionalPoint[j].m_Normal[1];
additionalPoint[j].m_Normal[2] = -additionalPoint[j].m_Normal[2];
}
break;
}
}
}
RkNearestPoints.clear();
}
}
//获取数据点的法矢量
double* PointCloudData::GetNormalVector(PCPoint lpoint)
{
/*double* normalVector = new double[3];//定义面法向量
//以下法向量求法由行列式推理得到
//lpoint（x1,y1,z1）
//nearest(x2,y2,z2)
//nearest2(x3,y3,z3)
//o(0,0,0)，a(a1,a2,a3)，b(b1,b2,b3)
//x=a2b3-a3b2,y=a3b1-a1b3,z=a1b2-a2b1
PCLKDtreeNKSearch(lpoint,10);
double a1, a2, a3, b1, b2, b3;
a1 = RkNearestPoints[3].m_Coordinate[0] - lpoint.m_Coordinate[0];
a2 = RkNearestPoints[3].m_Coordinate[1] - lpoint.m_Coordinate[1];
a3 = RkNearestPoints[3].m_Coordinate[2] - lpoint.m_Coordinate[2];
b1 = RkNearestPoints[5].m_Coordinate[0] - lpoint.m_Coordinate[0];
b2 = RkNearestPoints[5].m_Coordinate[1] - lpoint.m_Coordinate[1];
b3 = RkNearestPoints[5].m_Coordinate[2] - lpoint.m_Coordinate[2];
normalVector[0] = a2 * b3 - a3 * b2;
normalVector[1] = a3 * b1 - a1 * b3;
normalVector[2] = a1 * b2 - a2 * b1;
RkNearestPoints.clear();
return normalVector;*/
//利用数据点拟合其最小二乘平面，用最小二乘平面的法矢量代替点的法矢量
double* normalVector = new double[3];
double array[NORMAL_VECTOR_NUM][3], Y[3];
double array_ML[NORMAL_VECTOR_NUM][3];
double A, B, C;
A = B = C = 0.0;
ZeroMemory(array, sizeof(array));
ZeroMemory(Y, sizeof(Y));
PCLKDtreeNKSearch(lpoint, NORMAL_VECTOR_NUM);
for (int i = 0; i < NORMAL_VECTOR_NUM; i++)
{
array_ML[i][0] = array[i][0] = RkNearestPoints[i].m_Coordinate[0];
array_ML[i][1] = array[i][1] = RkNearestPoints[i].m_Coordinate[1];
array_ML[i][2] = array[i][2] = RkNearestPoints[i].m_Coordinate[2];
}
RkNearestPoints.clear();
double *Matrix[3], *IMatrix[3];
for (int i = 0; i < 3; i++)
{
Matrix[i] = new double[3];
IMatrix[i] = new double[3];
}
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
*(Matrix[i] + j) = 0.0;//全部赋值为0.0
}
}
for (int j = 0; j < 3; j++)
{
for (int i = 0; i < NORMAL_VECTOR_NUM; i++)
{
*(Matrix[0] + j) += array[i][0] * array[i][j];
*(Matrix[1] + j) += array[i][1] * array[i][j];
*(Matrix[2] + j) += array[i][2] * array[i][j];
Y[j] -= array[i][j];
}
}
double d = Determinant(Matrix, 3);
Inverse(Matrix, IMatrix, 3, d);
for (int i = 0; i < 3; i++)
{
A += *(IMatrix[0] + i)*Y[i];
B += *(IMatrix[1] + i)*Y[i];
C += *(IMatrix[2] + i)*Y[i];
}
normalVector[0] = A;
normalVector[1] = B;
normalVector[2] = C;
return normalVector;
}
//求得张量投票算法的特征点法矢量
double* PointCloudData::GetTVNormalVector(PCPoint lpoint, pcl::PointCloud<pcl::PointXYZ>::Ptr coeCloud
, pcl::KdTreeFLANN<pcl::PointXYZ> coeKdtree)
{
double* normalVector = new double[3];
double array[NORMAL_VECTOR_NUM][3], Y[3];
double array_ML[NORMAL_VECTOR_NUM][3];
double A, B, C;
A = B = C = 0.0;
ZeroMemory(array, sizeof(array));
ZeroMemory(Y, sizeof(Y));
PCLCoefficientsKDtreeNKSearch(lpoint, NORMAL_VECTOR_NUM, coeCloud, coeKdtree);
for (int i = 0; i < NORMAL_VECTOR_NUM; i++)
{
array_ML[i][0] = array[i][0] = RkNearestPoints[i].m_Coordinate[0];
array_ML[i][1] = array[i][1] = RkNearestPoints[i].m_Coordinate[1];
array_ML[i][2] = array[i][2] = RkNearestPoints[i].m_Coordinate[2];
}
RkNearestPoints.clear();
double *Matrix[3], *IMatrix[3];
for (int i = 0; i < 3; i++)
{
Matrix[i] = new double[3];
IMatrix[i] = new double[3];
}
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
*(Matrix[i] + j) = 0.0;//全部赋值为0.0
}
}
for (int j = 0; j < 3; j++)
{
for (int i = 0; i < NORMAL_VECTOR_NUM; i++)
{
*(Matrix[0] + j) += array[i][0] * array[i][j];
*(Matrix[1] + j) += array[i][1] * array[i][j];
*(Matrix[2] + j) += array[i][2] * array[i][j];
Y[j] -= array[i][j];
}
}
double d = Determinant(Matrix, 3);
Inverse(Matrix, IMatrix, 3, d);
for (int i = 0; i < 3; i++)
{
A += *(IMatrix[0] + i)*Y[i];
B += *(IMatrix[1] + i)*Y[i];
C += *(IMatrix[2] + i)*Y[i];
}
normalVector[0] = A;
normalVector[1] = B;
normalVector[2] = C;
return normalVector;
}
//获取附加约束点的值
void PointCloudData::GetAdditionalPoint(PCPoint* lpoint)
{
double l = 0.1;
double pNormal[3];
double temp[3];
for (int num = 0; num < 3; num++)
{
pNormal[num] = lpoint->m_Normal[num];
}
temp[0] = l * pNormal[0] / sqrt(pNormal[0] * pNormal[0]
+ pNormal[1] * pNormal[1] + pNormal[2] * pNormal[2]) + lpoint->m_Coordinate[0];
temp[1] = l * pNormal[1] / sqrt(pNormal[0] * pNormal[0]
+ pNormal[1] * pNormal[1] + pNormal[2] * pNormal[2]) + lpoint->m_Coordinate[1];
temp[2] = l * pNormal[2] / sqrt(pNormal[0] * pNormal[0]
+ pNormal[1] * pNormal[1] + pNormal[2] * pNormal[2]) + lpoint->m_Coordinate[2];
lpoint->m_Coordinate[0] = temp[0];
lpoint->m_Coordinate[1] = temp[1];
lpoint->m_Coordinate[2] = temp[2];
}
//获取两个数据点之间的距离
double PointCloudData::GetTwoPointsDistance(PCPoint p, PCPoint q)
{
double result = sqrt((p.m_Coordinate[0] - q.m_Coordinate[0]) * (p.m_Coordinate[0] - q.m_Coordinate[0])
+ (p.m_Coordinate[1] - q.m_Coordinate[1]) * (p.m_Coordinate[1] - q.m_Coordinate[1])
+ (p.m_Coordinate[2] - q.m_Coordinate[2]) * (p.m_Coordinate[2] - q.m_Coordinate[2]));
return result;
}
//插入新的修补点
bool PointCloudData::InsertRepairPoint(double* coefficients, vector<PCPoint> & repairedPoint, vector<PCPoint> m_SelectedBoundaryPoint, vector<PCPoint> m_SelectedPoint)
{
PCPoint midPoint;//备选约束点的中心点，不包括附加约束点
for (int num = 0; num < m_SelectedBoundaryPoint.size(); num++)
{
midPoint.m_Coordinate[0] += m_SelectedBoundaryPoint[num].m_Coordinate[0];
midPoint.m_Coordinate[1] += m_SelectedBoundaryPoint[num].m_Coordinate[1];
midPoint.m_Coordinate[2] += m_SelectedBoundaryPoint[num].m_Coordinate[2];
}
//获得中心点
midPoint.m_Coordinate[0] /= (m_SelectedBoundaryPoint.size());
midPoint.m_Coordinate[1] /= (m_SelectedBoundaryPoint.size());
midPoint.m_Coordinate[2] /= (m_SelectedBoundaryPoint.size());
//获取随机两点之间的距离
PCLKDtreeNKSearch(m_SelectedBoundaryPoint[0], 2);
double dp = GetTwoPointsDistance(m_SelectedBoundaryPoint[0], RkNearestPoints[1]);
RkNearestPoints.clear();
//中点
repairedPoint.push_back(midPoint);
//依据隐式曲面方程调整孔洞修补点的位置
RepairedPointDisjust(coefficients, repairedPoint[repairedPoint.size() - 1], m_SelectedPoint);
//进行插点操作
for (int num = 0; num < m_SelectedBoundaryPoint.size(); num++)
{
if (gProgress > 0)
{
gProgress = 30 + 60 * ((double)num / (double)m_SelectedBoundaryPoint.size());//用于显示进度条
}
if (true)
{
//获取两点之间的距离
double distance = GetTwoPointsDistance(midPoint, m_SelectedBoundaryPoint[num]);
//获取插入点的个数
int pointNum = distance / dp;
for (int i = 1; i <= pointNum; i++)
{
double l = ((double)i / (double)pointNum) * distance;
PCPoint tempPoint = GetInsertPoint(midPoint, m_SelectedBoundaryPoint[num], l);
repairedPoint.push_back(tempPoint);
if (i != 1 && GetTwoPointsDistance(repairedPoint[repairedPoint.size() - 1], repairedPoint[repairedPoint.size() - 2]) > 4.5 * dp)
{
//表示该孔洞不能用径向基函数拟合，因此不能自动修补
repairedPoint.clear();
return false;
}
else
{
//依据隐式曲面方程调整孔洞修补点的位置
RepairedPointDisjust(coefficients, repairedPoint[repairedPoint.size() - 1], m_SelectedPoint);
}
}
}
}
return true;
}
//获取新插入的修补点
PCPoint PointCloudData::GetInsertPoint(PCPoint p, PCPoint q, double l)
{
//获取两点的方向向量
double normal[3];
normal[0] = q.m_Coordinate[0] - p.m_Coordinate[0];
normal[1] = q.m_Coordinate[1] - p.m_Coordinate[1];
normal[2] = q.m_Coordinate[2] - p.m_Coordinate[2];
//新插入的修补点
PCPoint repairedPoint;
repairedPoint.m_Coordinate[0] = l * normal[0] / sqrt(normal[0] * normal[0]
+ normal[1] * normal[1] + normal[2] * normal[2]) + p.m_Coordinate[0];
repairedPoint.m_Coordinate[1] = l * normal[1] / sqrt(normal[0] * normal[0]
+ normal[1] * normal[1] + normal[2] * normal[2]) + p.m_Coordinate[1];
repairedPoint.m_Coordinate[2] = l * normal[2] / sqrt(normal[0] * normal[0]
+ normal[1] * normal[1] + normal[2] * normal[2]) + p.m_Coordinate[2];
//设为
repairedPoint.m_Color[0] = 1.0;
repairedPoint.m_Color[1] = 0.0;
repairedPoint.m_Color[2] = 1.0;
return repairedPoint;
}
//获取修补点集合
vector<PCPoint>* PointCloudData::GetNewAddedPoint()
{
return &m_AddedPoingCLoud;
}
/*
//获取约束点集合
vector<PCPoint>* PointCloudData::GetSelectedPoint()
{
return &m_SelectedPoint;
}*/
//调整孔洞修补点
void PointCloudData::RepairedPointDisjust(double* coefficients, PCPoint& repairedPoint, vector<PCPoint> m_SelectedPoint)
{
double lastFr = DBL_MAX;//设为最大的double值
while (true)
{
double frPoint[3];
double frX = 0;
double frY = 0;
double frZ = 0;
for (int i = 0; i < m_SelectedPoint.size(); i++)
{
frX += coefficients[i] * (repairedPoint.m_Coordinate[0] - m_SelectedPoint[i].m_Coordinate[0])
* GetTwoPointsDistance(repairedPoint, m_SelectedPoint[i]);
frY += coefficients[i] * (repairedPoint.m_Coordinate[1] - m_SelectedPoint[i].m_Coordinate[1])
* GetTwoPointsDistance(repairedPoint, m_SelectedPoint[i]);
frZ += coefficients[i] * (repairedPoint.m_Coordinate[2] - m_SelectedPoint[i].m_Coordinate[2])
* GetTwoPointsDistance(repairedPoint, m_SelectedPoint[i]);
}
frX = 3 * frX + coefficients[m_SelectedPoint.size() + 1];
frY = 3 * frY + coefficients[m_SelectedPoint.size() + 2];
frZ = 3 * frZ + coefficients[m_SelectedPoint.size() + 3];
frPoint[0] = frX;
frPoint[1] = frY;
frPoint[2] = frZ;
double Fr = 0;
for (int i = 0; i < m_SelectedPoint.size(); i++)
{
Fr += coefficients[i] * GetRBFValue(GetTwoPointsDistance(repairedPoint, m_SelectedPoint[i]));
}
Fr = Fr + coefficients[m_SelectedPoint.size()] + repairedPoint.m_Coordinate[0] * coefficients[m_SelectedPoint.size() + 1]
+ repairedPoint.m_Coordinate[1] * coefficients[m_SelectedPoint.size() + 2]
+ repairedPoint.m_Coordinate[2] * coefficients[m_SelectedPoint.size() + 3];
double tempFr = Fr / (frPoint[0] * frPoint[0]
+ frPoint[1] * frPoint[1] + frPoint[2] * frPoint[2]);
if (fabs(Fr) < fabs(lastFr))
{
lastFr = Fr;
repairedPoint.m_Coordinate[0] = repairedPoint.m_Coordinate[0] - tempFr * frPoint[0];
repairedPoint.m_Coordinate[1] = repairedPoint.m_Coordinate[1] - tempFr * frPoint[1];
repairedPoint.m_Coordinate[2] = repairedPoint.m_Coordinate[2] - tempFr * frPoint[2];
}
else
{
break;
}
}
}
//进行边界聚类
void PointCloudData::BoundaryClustering()
{
//获取选中的边界点集合
vector<PCPoint> m_SelectedBoundaryPoint;
for (int num = 0; num < m_PointSumNumber; num++)
{
if (m_OriginPointCloud[num].b_Selected &&
m_OriginPointCloud[num].b_BoundaryPoint)
{
m_SelectedBoundaryPoint.push_back(m_OriginPointCloud[num]);
}
}
//利用PCL中的聚类函数对得到的边界点进行聚类操作
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr boundarycloud(new pcl::PointCloud<pcl::PointXYZ>);
for (int num = 0; num < m_PointSumNumber; num++)
{//获取边界点集合
if (m_OriginPointCloud[num].b_BoundaryPoint)
{
m_SelectedBoundaryPoint.push_back(m_OriginPointCloud[num]);
}
m_OriginPointCloud[num].m_Color[0] = 0.0;//全部设为蓝色
m_OriginPointCloud[num].m_Color[1] = 0.0;//全部设为蓝色
m_OriginPointCloud[num].m_Color[2] = 1.0;//全部设为蓝色
}
boundarycloud->width = m_SelectedBoundaryPoint.size();//初始化 PCL 点云
boundarycloud->height = 1;
boundarycloud->points.resize(boundarycloud->width * boundarycloud->height);
//初始化点云
int cloudCount = 0;
for (int num = 0; num < m_SelectedBoundaryPoint.size(); num++)
{
boundarycloud->points[cloudCount].x = m_SelectedBoundaryPoint[num].m_Coordinate[0];
boundarycloud->points[cloudCount].y = m_SelectedBoundaryPoint[num].m_Coordinate[1];
boundarycloud->points[cloudCount].z = m_SelectedBoundaryPoint[num].m_Coordinate[2];
cloudCount++;
}
tree->setInputCloud(boundarycloud);
//初始化聚类参数并进行聚类
vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance(m_clusterDistace);
ec.setMinClusterSize(m_clusterNum);
ec.setMaxClusterSize(5000);
ec.setSearchMethod(tree);
ec.setInputCloud(boundarycloud);
ec.extract(cluster_indices);
//提取聚类完成的各个点云集合
m_CloudClusterNum = cluster_indices.size();
pcl::PointCloud<pcl::PointXYZ>::Ptr* cloud_cluster; //(new pcl::PointCloud<pcl::PointXYZ>);
cloud_cluster = new pcl::PointCloud<pcl::PointXYZ>::Ptr[m_CloudClusterNum];
for (int num = 0; num < m_CloudClusterNum; num++)
{
cloud_cluster[num] = pcl::PointCloud<pcl::PointXYZ>::Ptr(new pcl::PointCloud<pcl::PointXYZ>);
}
int i = 0;
for (vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin();
it != cluster_indices.end(); i++, it++)
{
for (vector<int>::const_iterator pit = it->indices.begin();
pit != it->indices.end(); pit++)
{
cloud_cluster[i]->points.push_back(boundarycloud->points[*pit]);
}
cloud_cluster[i]->width = cloud_cluster[i]->points.size();
cloud_cluster[i]->height = 1;
cloud_cluster[i]->is_dense = true;
}
//将得到的聚类划分好的点云集合存储起来
m_BoundaryPointClusters = new vector<PCPoint>[m_CloudClusterNum];
for (int i = 0; i < m_CloudClusterNum; i++)
{
for (int num = 0; num < cloud_cluster[i]->points.size(); num++)
{
PCPoint tempPoint;
tempPoint.m_Color[0] = ((double)i / double(m_CloudClusterNum)) * 1.0;
tempPoint.m_Color[1] = 1.0 - ((double)i / double(m_CloudClusterNum)) * 1.0;
tempPoint.m_Color[2] = 0.5;
tempPoint.m_Coordinate[0] = cloud_cluster[i]->points[num].x;
tempPoint.m_Coordinate[1] = cloud_cluster[i]->points[num].y;
tempPoint.m_Coordinate[2] = cloud_cluster[i]->points[num].z;
m_BoundaryPointClusters[i].push_back(tempPoint);
}
}
}
//获取边界的聚类数量
int PointCloudData::GetCloudClusterNum()
{
return m_CloudClusterNum;
}
//获取边界聚类集合
vector<PCPoint>* PointCloudData::GetBoundaryPointClusters()
{
return m_BoundaryPointClusters;
}
//自动进行孔洞修补
void PointCloudData::AutomaticHoleRepair()
{
m_AddedPoingCLoud.clear();
//对聚类出的孔洞边界集合分别进行孔洞修补的工作
//先判断出不进行修补的孔洞
//存储判断距离