1.项目描述
趁课余时间做了一个小作品，项目是校园地图，主要目的是练习Qt和一些基本的数据结构和算法。该项目的主要功能是从下拉列表中选择出发地和目的地，然后地图上可以显示路线。主要的显示方法是通过贴图来显示。时间久远才想起来整理，当时也是经历了一个星期的断断续续的修补，最后形成了一个比较完善的小地图软件。
2.基本思路
01.首先需要构建路网（很重要），我首先写了一个该版本，然后把路网标记后保存。具体用来储存的数据结构是一个具有节点信息的结构体，然后借用C++的 vector (vector真的太好用了)来记录所有的路网节点。
typedef struct road_dot{
int i; // 该节点的id
int vistable; //该节点是否可访问
cv::Point self; //该节点在地图上的位置
std::vector<size_t> others_id; // 保存与该节点相连的节点的id
}Road_node;
下面是我用来标记路网并自动储存的模块：
/************************************************************************************************
*
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* These under codes construct a tool to get the road nodes infomation *
* <2016-10-29><wuhui> *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
*/
void MainWindow::mousePressEvent(QMouseEvent *event)
{
if(0<x&&x<1480&&0<y&&y<800) // if on the pic
{
Road_node new_node;
new_node.self = Point(x,y);
new_node.i = 200; // not a pos
cv::circle(img,Point(x,y),6,cv::Scalar(255,0,255),-1);
qimg = Mat2QImage(img);
ui->pic->setPixmap(QPixmap::fromImage(qimg));
if(!first_node)
{start_node = new_node;start_set_flag = 1; first_node =2;pre_nodes_id = 0;}
for(int i = 0;i<point.size();i++)
{
if((abs(x-point[i].x) <10 )&& (abs(y-point[i].y)<10)) // if at pos
{
new_node.i = i;
qDebug()<<"this is pos "<<i;
break;
}
}
road.push_back(new_node);// record this new node ;
for(int i = 0;i<road.size()-1;i++)
{
if((abs(x-road[i].self.x) <10 )&& (abs(y-road[i].self.y)<10)) // if at node
{ // yes
road.pop_back(); // drop this node
if(event->button() == Qt::LeftButton) // if leftButton
{
start_node = road[i]; // set start node
start_set_flag = 1;
pre_nodes_id = i;
}
else if(event->button() == Qt::RightButton)
{
start_node = road[pre_nodes_id]; // this is start
start_set_flag = 1;
end_node = road[i];
end_set_flag = 1;
road[i].others_id.push_back(pre_nodes_id);
road[pre_nodes_id].others_id.push_back(i);
pre_nodes_id = i;
}
qDebug()<<"this node‘s others.size = "<<road[i].others_id.size();
break;
}
else if(i == road.size() - 2)
{ // no
if(!start_set_flag) // if have no start_node
{
start_node = road[pre_nodes_id];
start_set_flag = 1;
}
if(!end_set_flag)
{
end_node = road[road.size()-1];
end_set_flag = 1;
}
road[pre_nodes_id].others_id.push_back(road.size()-1); // push now node
road[road.size()-1].others_id.push_back(pre_nodes_id);
qDebug()<<"this node‘s others.size = "<<road[road.size()-1].others_id.size();
pre_nodes_id = road.size()-1;
break;
}
}
if(end_set_flag && start_set_flag)
{
end_set_flag = 0;
start_set_flag = 0;
cv::line(img,start_node.self,end_node.self,cv::Scalar(0,0,0),3);
qimg = Mat2QImage(img);
ui->pic->setPixmap(QPixmap::fromImage(qimg)); // flush the pic
}
qDebug()<<"road.size"<<road.size();
}
} // :)
该代码主要通过在地图上点击来标记，右键表示一个连续段的结束，每个节点会记录与之相连的其它节点的id号。结果示意图：（另外，地图图片是Google截图并精心修改而成）
02.标记完了路网后就是设计一个找路算法了，想过深度或广度搜索，但是做过一个demo后觉得稍显复杂并且速度不理想，后来根据地图的形状设计了一个看似很low却很有效的算法：
首先设立两个哨兵，一个在当前出发节点(哨兵A)，另一个在下一个目标出发点(哨兵 B)，下一个目标出发点由所有子节点中距离目的地最近的节点确定。
当B所在的节点具有可走子节点（不是死胡同）并且还未达到目的地时，A走到B（所有走过的路压栈，方便后退），B继续探路，若走到死胡同就后退，走其他的最近子节点。
该寻路算法如下：
int start_id = ui->comboBox->currentIndex(); // get start and end node‘s id /
int end_id = ui->comboBox_2->currentIndex();
for(int i = 0;i<road.size();i++)
{
road[i].vistable = 0; // set all nodes are visitable;
}
// Road_node start_node_temp,end_node_temp; // temp node
int road_start_i,road_end_i;
int road_start_i_temp,road_end_i_temp;
for(int i = 0;i<road.size();i++) // search which node is on the start/end point;
{
if(road[i].i == start_id)
{
qDebug()<<"start"<<i;
start_node = road[i];
road_start_i = i; // get the id of start roadnode;
qDebug()<<"i = "<<start_node.i;
}
else if(road[i].i == end_id)
{
qDebug()<<"end"<<i;
end_node = road[i];
road_end_i = i; // get the id of end roadnode;
qDebug()<<"i = "<<end_node.i;
}
}
/***********/
road_start_i_temp = road_start_i;
road_end_i_temp = road_start_i;
// start_node_temp = start_node;
//end_node_temp = start_node_temp;
std::vector<int> road_temp;
int pre_distance = 9000000;
while(road[road_end_i_temp].i!= end_id)
//for(int xx = 0;xx<10;xx++)
{
int useable_flag = 0;
for(int i = 0;i<road[road_end_i_temp].others_id.size();i++) // if the current endpoint to have next point
{
if(road[road[road_end_i_temp].others_id[i]].vistable == 0)
useable_flag = 1; // have useable point
}
if(useable_flag) // have useable next node
{
// start_node_temp = end_node_temp;
road_start_i_temp = road_end_i_temp;
road[road_start_i_temp].vistable = 1; // have visited it;
//start_node_temp.vistable = 1; // have visited it;
road_temp.push_back(road_start_i_temp); // record the walked node
int temp_min_id;
for(int i = 0;i<road[road_start_i_temp].others_id.size();i++) // find the min dis node // int these next points
{
if(road[road[road_start_i_temp].others_id[i]].vistable == 0 ) // if this node have not visited;
{
int tempx,tempy;
tempx = (road[road[road_start_i_temp].others_id[i]].self.x- end_node.self.x)*(road[road[road_start_i_temp].others_id[i]].self.x - end_node.self.x);
tempy = (road[road[road_start_i_temp].others_id[i]].self.y- end_node.self.y)*(road[road[road_start_i_temp].others_id[i]].self.y - end_node.self.y);
if(pre_distance<(tempx+tempy))
{
qDebug()<<"pre_distance = "<<pre_distance;
qDebug()<<"now distance = "<<tempx+tempy;
// end_node_temp = road[road[road_start_i_temp].others_id[i-1]];
road_end_i_temp = road[road_start_i_temp].others_id[temp_min_id];
}
else {
//end_node_temp = road[start_node_temp.others_id[i]];
road_end_i_temp = road[road_start_i_temp].others_id[i];
temp_min_id = i;
pre_distance = tempx+tempy;
}
}
}
pre_distance = 9000000;
}
else // have no useable next node
{
road[road_end_i_temp].vistable = 1;
loop: for(int i = 0;i<road[road_start_i_temp].others_id.size();i++) // if have other useable nodes
{
if(road[road[road_start_i_temp].others_id[i]].vistable == 0)
useable_flag = 1; // have useable point
}
if(useable_flag)
{
int temp_min_id;
for(int i = 0;i<road[road_start_i_temp].others_id.size();i++) // fine the min dis node
{
if(road[road[road_start_i_temp].others_id[i]].vistable == 0 ) // if this node have not visited;
{
int tempx,tempy;
tempx = (road[road[road_start_i_temp].others_id[i]].self.x- end_node.self.x)*(road[road[road_start_i_temp].others_id[i]].self.x - end_node.self.x);
tempy = (road[road[road_start_i_temp].others_id[i]].self.y- end_node.self.y)*(road[road[road_start_i_temp].others_id[i]].self.y - end_node.self.y);
if(pre_distance<(tempx+tempy))
{
//end_node_temp = road[road[road_start_i_temp].others_id[i-1]];
road_end_i_temp = road[road_start_i_temp].others_id[temp_min_id];
}
else {
//end_node_temp = road[start_node_temp.others_id[i]];
road_end_i_temp = road[road_start_i_temp].others_id[i];
temp_min_id = i;
pre_distance = tempx+tempy;
}
}
}
pre_distance = 9000000;
}
else
{
road_temp.pop_back(); // drop this start_temp node
road_start_i_temp = road_temp[road_temp.size()-1];
goto loop;
}
}
/*
int pre_distance = 90000000;
for(int i = 0;i<start_node_temp.others_id.size();i++) //
{
int tempx,tempy;
tempx = (road[start_node_temp.others_id[i]].self.x- end_node.self.x)*(road[start_node_temp.others_id[i]].self.x - end_node.self.x);
tempy = (road[start_node_temp.others_id[i]].self.y- end_node.self.y)*(road[start_node_temp.others_id[i]].self.y - end_node.self.y);
if(pre_distance<(tempx+tempy))
{
end_node_temp = road[start_node_temp.others_id[i-1]];
}
else {
end_node_temp = road[start_node_temp.others_id[i]];
pre_distance = tempx+tempy;
}
}
cv::line(img,start_node_temp.self,end_node_temp.self,cv::Scalar(0,0,0),3);
qimg = Mat2QImage(img);
ui->pic->setPixmap(QPixmap::fromImage(qimg)); // flush the pic
start_node_temp = end_node_temp;
xx++;*/
}
road_temp.push_back(road_end_i_temp);
for(int i = 0;i<road_temp.size()-1;i++)
{
cv::line(img,road[road_temp[i]].self,road[road_temp[i+1]].self,cv::Scalar(0,0,0),3);
qimg = Mat2QImage(img);
ui->pic->setPixmap(QPixmap::fromImage(qimg)); // flush the pic
}
qDebug()<<road_temp.size();
}
经过实验该算法还是很可靠的 ;)
效果图：
时间： 2024-10-12 13:47:04