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xyzi
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//��C++���е�������������ͷ�ļ���
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd ��д����ص�ͷ�ļ���
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL��֧�ֵĵ�����ͷ�ļ���
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
//#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/radius_outlier_removal.h>
#include "LasEdit.h"
using namespace std;
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZ>& cloud);
float computeRange(pcl::PointCloud<pcl::PointXYZI>& trail, float r, int index, int k);
void loadLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZI>::Ptr input_cloud);
void saveLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZI>::Ptr save_cloud);
int main() {
//������ƣ��켣�ߣ���������
pcl::PointCloud<pcl::PointXYZI>::Ptr input_cloud(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr trail(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr part(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr midle_filtered(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr result(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointXYZI tempPnt;
std::vector<int>idx;
std::vector<int>part_idx;
//����
float r = 10000; //o1,ʹ��+ 2.5��5��10ƫС��5�պã�10����
int k = 6;//20
//const std::string path=
//��ȡ�������
cout << "read cloud" << endl;
CLasEdit le;
le.readLas2PointCloudXYZI("/home/qrh/桌面/experimentData/3data/data1.las",input_cloud);
//loadLASFileRGB("/home/qrh/桌面/experimentData/3data/data1.las",input_cloud);
//��ȡ�켣��
cout << "read trajectory" << endl;
le.readLas2PointCloudXYZI("/home/qrh/桌面/experimentData/3data/trajectory1.las", trail);
//loadLASFileRGB("/home/qrh/桌面/experimentData/3data/trajectory1.las", trail);
cout << "cloud_size:::::" << input_cloud->width << " \t" << "trajectory_size:::::: " << trail->width << endl;
//cloud�����˲���
cout << "�����˲���" << endl;
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZI> octree(resolution);
octree.setInputCloud(input_cloud);
octree.addPointsFromInputCloud();
cout << "�������" << endl;
/**
�и����·�沿��
*/
cout << "�и����·�沿��" << endl;
for (size_t i = 8000;i<9600; i += k) {
cout << "-------------------------------------------------------------" << endl;
float range = computeRange(*trail, r, i, k);
float center = trail->points[i].x;
float Ymin = min(trail->points[i].y, trail->points[i + k].y);
float Ymax = max(trail->points[i].y, trail->points[i + k].y);
float Zmin = -10000;
float Zmax = 10000;
cout << "range: " << range << endl;
//�ֶ��и����ӵ�
Eigen::Vector3f Emin(center - range, Ymin, Zmin);//0xc0c0c0c0 0x3f3f3f3f
Eigen::Vector3f Emax(center + range, Ymax, Zmax);
octree.boxSearch(Emin, Emax, idx);
cout << i << "\t�����������\t" << idx.size() << endl;
part_idx.insert(part_idx.end(), idx.begin(), idx.end());
}
//��ȡmidle
boost::shared_ptr<std::vector<int>> midle_ptr = boost::make_shared<std::vector<int>>(part_idx);
pcl::ExtractIndices<pcl::PointXYZI> extract;
extract.setInputCloud(input_cloud);
extract.setIndices(midle_ptr);
extract.setNegative(false);//�����Ϊtrue,������ȡָ��index֮��ĵ���
extract.filter(*part);
cout << "С�α���Ϊ: data1_1.las" << endl;
le.savePointCloudXYZI2Las( part,"/home/qrh/桌面/experimentData/data1_1/record.las");
//saveLASFileRGB("/home/qrh/桌面/experimentData/data1_1/record.las", part);
//pcl::io::savePCDFileASCII("o_midle_filtered.pcd", *midle_filtered);
cout << "����ɹ�" << endl;
//��ȡ�켣��
cout << "�켣����Ϊ: data1_1.las" << endl;
tempCloud->width = 1600;
tempCloud->height = 1;
tempCloud->points.resize(tempCloud->width*tempCloud->height);
int count = 0;
// for (int i = 8000;i < 9600;++i) {
for (int i = 9599;i >=8000;--i) {
cout << 1 << endl;
/*tempPnt.x = trail->points[i].x;
tempPnt.y = trail->points[i].y;
tempPnt.z = trail->points[i].z;
tempPnt.rgb = trail->points[i].rgb;*/
//tempCloud->points[i] = tempPnt;
tempCloud->points[count++] = trail->points[i];
cout << "ok" << endl;
}
le.savePointCloudXYZI2Las(tempCloud,"/home/qrh/桌面/experimentData/data1_1/trajectory.las");
//saveLASFileRGB("/home/qrh/桌面/experimentData/data1_1/trajectory.las",tempCloud);
return 0;
}//main
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZ>& cloud) {
/*
*��ȡlas�ļ�
*/
std::ifstream ifs(s, std::ios::in | std::ios::binary); // ��las�ļ�
liblas::ReaderFactory f;
liblas::Reader reader = f.CreateWithStream(ifs); // ��ȡlas�ļ�
unsigned long int nbPoints = reader.GetHeader().GetPointRecordsCount();//��ȡlas���ݵ�ĸ���
cloud.width = nbPoints; //��֤��las���ݵ�ĸ���һ��
cloud.height = 1;
cloud.is_dense = false;
cloud.points.resize(cloud.width * cloud.height);
int i = 0;
uint16_t r1, g1, b1;
int r2, g2, b2;
uint32_t rgb;
while (reader.ReadNextPoint()) {
// ��ȡlas���ݵ�x��y��z��Ϣ
cloud.points[i].x = (reader.GetPoint().GetX());
cloud.points[i].y = (reader.GetPoint().GetY());
cloud.points[i].z = (reader.GetPoint().GetZ());
i++;
}
}
/**
* ����켣�㴦x�����䷶Χ
*/
float computeRange(pcl::PointCloud<pcl::PointXYZI>& trail, float r, int index, int k) {
//����n1��n2,a,bΪ�켣����2��
float x1, y1, x2, y2, a1, a2, a3, b1, b2, b3;
x2 = 1; y2 = 0;//n2Ϊx��������λ����
a1 = trail.points[index].x;
a2 = trail.points[index].y;
a3 = trail.points[index].z;
b1 = trail.points[index + k].x;
b2 = trail.points[index + k].y;
b3 = trail.points[index + k].z;
x1 = a1 - b1;//�켣�߷�������
y1 = a2 - b2;
float cosa = (x1 * x2 + y1 * y2) / (sqrt(x1 * x1 + y1 * y1) * sqrt(x2 * x2 + y2 * y2));
float sina = sqrt(1 - cosa * cosa);
float range = r / sina;
return range;
}
/**
* ��ȡRGB las�ļ�
*/
void loadLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr input_cloud) {
// ��las�ļ�
std::ifstream ifs(s, std::ios::in | std::ios::binary);
// ��ȡlas�ļ�
liblas::ReaderFactory f;
liblas::Reader reader = f.CreateWithStream(ifs);
//��ȡ����ͷ
liblas::Header const& header = reader.GetHeader();
//���õ�������
input_cloud->width = header.GetPointRecordsCount(); //��֤��las���ݵ�ĸ���һ��
input_cloud->height = 1;//��ʾ�������
input_cloud->is_dense = false;//��ʾ���ܼ�����
input_cloud->points.resize(input_cloud->width * input_cloud->height);//���µ�ĸ���
int index = 0;
uint16_t red_1, green_1, black_1;
int red_2, green_2, black_2;
uint32_t rgb;
while (reader.ReadNextPoint()) {
liblas::Point const& temp_point = reader.GetPoint();
// ��ȡlas�ļ�3D����
input_cloud->points[index].x = (temp_point.GetX());//x����
input_cloud->points[index].y = (temp_point.GetY());//y����
input_cloud->points[index].z = (temp_point.GetZ());//z����
//��ȡlas�ļ���ɫ��Ϣ
red_1 = (temp_point.GetColor().GetRed());//red
green_1 = (temp_point.GetColor().GetGreen());//green
black_1 = (temp_point.GetColor().GetBlue());//black
//������ɫת��
red_2 = ceil(((float)red_1 / 65536) * (float)256);
green_2 = ceil(((float)green_1 / 65536) * (float)256);
black_2 = ceil(((float)black_1 / 65536) * (float)256);
rgb = ((int)red_2) << 16 | ((int)green_2) << 8 | ((int)black_2);
input_cloud->points[index].rgb = *reinterpret_cast<float*>(&rgb);
index++;
}
ifs.close();
}
/**
* ����RGB las�ļ�
*/
void saveLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr save_cloud) {
cout << save_cloud->points.size() << endl;
//���ļ�
std::ofstream ofs(s, ios::out | ios::binary);
liblas::Header header;
//���õ�����
header.SetPointRecordsCount(save_cloud->points.size());
//����x��y��z��������
header.SetScale(0.0001, 0.0001, 0.0001);
//ƫ����
header.SetOffset(0.0, 0.0, 0.0);
// fill other header members
// here the header has been serialized to disk into the *file.las*
liblas::Writer writer(ofs, header);
liblas::Point point(&header);
// fill other properties of point record
for (int i = 0;i < save_cloud->points.size();++i) {
//���õ�x��y��z����
point.SetCoordinates(save_cloud->points[i].x, save_cloud->points[i].y, save_cloud->points[i].z);
point.SetColor(liblas::Color(save_cloud->points[i].r, save_cloud->points[i].g, save_cloud->points[i].b));
writer.WritePoint(point);
}
//writer.SetHeader(header);
//ofs.flush();
ofs.close();
}
xyzrgb
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//标准C++库中的输入输出类相关头文件。
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd 读写类相关的头文件。
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL中支持的点类型头文件。
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
//#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/radius_outlier_removal.h>
using namespace std;
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZ>& cloud);
float computeRange(pcl::PointCloud<pcl::PointXYZRGB>& trail, float r, int index, int k);
void loadLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr input_cloud);
void saveLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr save_cloud);
int main() {
//输入点云，轨迹线，保存种子
pcl::PointCloud<pcl::PointXYZRGB>::Ptr input_cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr trail(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr part(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr midle_filtered(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr result(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointXYZRGB tempPnt;
std::vector<int>idx;
std::vector<int>part_idx;
//变量
float r = 10000; //o1,使用+ 2.5，5，10偏小，5刚好，10覆盖
int k = 6;//20
//读取输入点云
cout << "读取点云" << endl;
loadLASFileRGB("data1.las",input_cloud);
//读取轨迹线
cout << "读取轨迹线" << endl;
loadLASFileRGB("trajectory1.las", trail);
cout << "原始点数: " << input_cloud->width << " \t" << "轨迹线点数: " << trail->width << endl;
//cloud构建八叉树
cout << "构建八叉树" << endl;
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);
octree.setInputCloud(input_cloud);
octree.addPointsFromInputCloud();
cout << "构建完成" << endl;
/**
切割宽于路面部分
*/
cout << "切割宽于路面部分" << endl;
for (size_t i = 8000;i<9600; i += k) {
cout << "-------------------------------------------------------------" << endl;
float range = computeRange(*trail, r, i, k);
float center = trail->points[i].x;
float Ymin = min(trail->points[i].y, trail->points[i + k].y);
float Ymax = max(trail->points[i].y, trail->points[i + k].y);
float Zmin = -10000;
float Zmax = 10000;
cout << "range: " << range << endl;
//分段切割种子点
Eigen::Vector3f Emin(center - range, Ymin, Zmin);//0xc0c0c0c0 0x3f3f3f3f
Eigen::Vector3f Emax(center + range, Ymax, Zmax);
octree.boxSearch(Emin, Emax, idx);
cout << i << "\t这段种子数：\t" << idx.size() << endl;
part_idx.insert(part_idx.end(), idx.begin(), idx.end());
}
//提取midle
boost::shared_ptr<std::vector<int>> midle_ptr = boost::make_shared<std::vector<int>>(part_idx);
pcl::ExtractIndices<pcl::PointXYZRGB> extract;
extract.setInputCloud(input_cloud);
extract.setIndices(midle_ptr);
extract.setNegative(false);//如果设为true,可以提取指定index之外的点云
extract.filter(*part);
cout << "小段保存为: data1_1.las" << endl;
saveLASFileRGB("data1_1.las", part);
//pcl::io::savePCDFileASCII("o_midle_filtered.pcd", *midle_filtered);
cout << "保存成功" << endl;
//提取轨迹线
cout << "轨迹保存为: data1_1.las" << endl;
tempCloud->width = 1600;
tempCloud->height = 1;
tempCloud->points.resize(tempCloud->width*tempCloud->height);
int count = 0;
for (int i = 8000;i < 9600;++i) {
cout << 1 << endl;
/*tempPnt.x = trail->points[i].x;
tempPnt.y = trail->points[i].y;
tempPnt.z = trail->points[i].z;
tempPnt.rgb = trail->points[i].rgb;*/
//tempCloud->points[i] = tempPnt;
tempCloud->points[count++] = trail->points[i];
cout << "ok" << endl;
}
saveLASFileRGB("data1_1_trajectory.las",tempCloud);
return 0;
}//main
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZ>& cloud) {
/*
*读取las文件
*/
std::ifstream ifs(s, std::ios::in | std::ios::binary); // 打开las文件
liblas::ReaderFactory f;
liblas::Reader reader = f.CreateWithStream(ifs); // 读取las文件
unsigned long int nbPoints = reader.GetHeader().GetPointRecordsCount();//获取las数据点的个数
cloud.width = nbPoints; //保证与las数据点的个数一致
cloud.height = 1;
cloud.is_dense = false;
cloud.points.resize(cloud.width * cloud.height);
int i = 0;
uint16_t r1, g1, b1;
int r2, g2, b2;
uint32_t rgb;
while (reader.ReadNextPoint()) {
// 获取las数据的x，y，z信息
cloud.points[i].x = (reader.GetPoint().GetX());
cloud.points[i].y = (reader.GetPoint().GetY());
cloud.points[i].z = (reader.GetPoint().GetZ());
i++;
}
}
/**
* 计算轨迹点处x轴两变范围
*/
float computeRange(pcl::PointCloud<pcl::PointXYZRGB>& trail, float r, int index, int k) {
//向量n1，n2,a,b为轨迹线上2点
float x1, y1, x2, y2, a1, a2, a3, b1, b2, b3;
x2 = 1; y2 = 0;//n2为x轴正方向单位向量
a1 = trail.points[index].x;
a2 = trail.points[index].y;
a3 = trail.points[index].z;
b1 = trail.points[index + k].x;
b2 = trail.points[index + k].y;
b3 = trail.points[index + k].z;
x1 = a1 - b1;//轨迹线方向向量
y1 = a2 - b2;
float cosa = (x1 * x2 + y1 * y2) / (sqrt(x1 * x1 + y1 * y1) * sqrt(x2 * x2 + y2 * y2));
float sina = sqrt(1 - cosa * cosa);
float range = r / sina;
return range;
}
/**
* 读取RGB las文件
*/
void loadLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr input_cloud) {
// 打开las文件
std::ifstream ifs(s, std::ios::in | std::ios::binary);
// 读取las文件
liblas::ReaderFactory f;
liblas::Reader reader = f.CreateWithStream(ifs);
//获取公共头
liblas::Header const& header = reader.GetHeader();
//设置点云属性
input_cloud->width = header.GetPointRecordsCount(); //保证与las数据点的个数一致
input_cloud->height = 1;//表示无序点云
input_cloud->is_dense = false;//表示非密集点云
input_cloud->points.resize(input_cloud->width * input_cloud->height);//更新点的个数
int index = 0;
uint16_t red_1, green_1, black_1;
int red_2, green_2, black_2;
uint32_t rgb;
while (reader.ReadNextPoint()) {
liblas::Point const& temp_point = reader.GetPoint();
// 获取las文件3D坐标
input_cloud->points[index].x = (temp_point.GetX());//x坐标
input_cloud->points[index].y = (temp_point.GetY());//y坐标
input_cloud->points[index].z = (temp_point.GetZ());//z坐标
//获取las文件颜色信息
red_1 = (temp_point.GetColor().GetRed());//red
green_1 = (temp_point.GetColor().GetGreen());//green
black_1 = (temp_point.GetColor().GetBlue());//black
//进行颜色转换
red_2 = ceil(((float)red_1 / 65536) * (float)256);
green_2 = ceil(((float)green_1 / 65536) * (float)256);
black_2 = ceil(((float)black_1 / 65536) * (float)256);
rgb = ((int)red_2) << 16 | ((int)green_2) << 8 | ((int)black_2);
input_cloud->points[index].rgb = *reinterpret_cast<float*>(&rgb);
index++;
}
ifs.close();
}
/**
* 保存RGB las文件
*/
void saveLASFileRGB(string s, pcl::PointCloud<pcl::PointXYZRGB>::Ptr save_cloud) {
cout << save_cloud->points.size() << endl;
//打开文件
std::ofstream ofs(s, ios::out | ios::binary);
liblas::Header header;
//设置点数量
header.SetPointRecordsCount(save_cloud->points.size());
//设置x，y，z比例因子
header.SetScale(0.0001, 0.0001, 0.0001);
//偏移量
header.SetOffset(0.0, 0.0, 0.0);
// fill other header members
// here the header has been serialized to disk into the *file.las*
liblas::Writer writer(ofs, header);
liblas::Point point(&header);
// fill other properties of point record
for (int i = 0;i < save_cloud->points.size();++i) {
//设置点x，y，z坐标
point.SetCoordinates(save_cloud->points[i].x, save_cloud->points[i].y, save_cloud->points[i].z);
point.SetColor(liblas::Color(save_cloud->points[i].r, save_cloud->points[i].g, save_cloud->points[i].b));
writer.WritePoint(point);
}
//writer.SetHeader(header);
//ofs.flush();
ofs.close();
}
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文章浏览阅读4.5k次，点赞2次，收藏3次。首先要有一个color-picker组件 <el-color-picker v-model="headcolor"></el-color-picker>在data里面data() { return {headcolor: ’ #278add ’ //这里可以选择一个默认的颜色} }然后在你想要改变颜色的地方用v-bind绑定就好了，例如：这里的:sty..._vue el-color-picker
文章浏览阅读640次。基于芯片日益增长的问题，所以内核开发者们引入了新的方法，就是在内核中只保留函数，而数据则不包含，由用户（应用程序员）自己把数据按照规定的格式编写，并放在约定的地方，为了不占用过多的内存，还要求数据以根精简的方式编写。boot启动时，传参给内核，告诉内核设备树文件和kernel的位置，内核启动时根据地址去找到设备树文件，再利用专用的编译器去反编译dtb文件，将dtb还原成数据结构，以供驱动的函数去调用。firmware是三星的一个固件的设备信息，因为找不到固件，所以内核启动不成功。_exynos 4412 刷机
文章浏览阅读2w次，点赞24次，收藏42次。Linux系统配置jdkLinux学习教程，Linux入门教程（超详细）_linux配置jdk
文章浏览阅读3.3k次，点赞5次，收藏19次。xlabel('\delta');ylabel('AUC');具体符号的对照表参照下图：_matlab微米怎么输入
文章浏览阅读119次。顺序读写指的是按照文件中数据的顺序进行读取或写入。对于文本文件，可以使用fgets、fputs、fscanf、fprintf等函数进行顺序读写。在C语言中，对文件的操作通常涉及文件的打开、读写以及关闭。文件的打开使用fopen函数，而关闭则使用fclose函数。在C语言中，可以使用fread和fwrite函数进行二进制读写。 Biaoge 于2024-03-09 23:51发布 阅读量：7 ️文章类型：【 C语言程序设计 】在C语言中，用于打开文件的函数是____，用于关闭文件的函数是____。
文章浏览阅读3.4k次，点赞2次，收藏13次。跟随鼠标移动的粒子以grid（SOP）为partical（SOP）的资源模板，调整后连接【Geo组合+point spirit（MAT)】，在连接【feedback组合】适当调整。影响粒子动态的节点【metaball(SOP)+force(SOP)】添加mouse in（CHOP)鼠标位置到metaball的坐标，实现鼠标影响。..._touchdesigner怎么让一个模型跟着鼠标移动
文章浏览阅读178次。项目运行环境配置：Jdk1.8 + Tomcat7.0 + Mysql + HBuilderX（Webstorm也行）+ Eclispe（IntelliJ IDEA,Eclispe,MyEclispe,Sts都支持）。项目技术：Springboot + mybatis + Maven +mysql5.7或8.0+html+css+js等等组成，B/S模式 + Maven管理等等。环境需要1.运行环境：最好是java jdk 1.8，我们在这个平台上运行的。其他版本理论上也可以。_基于java技术的停车场管理系统实现与设计
文章浏览阅读3.5k次。前言对于MediaPlayer播放器的源码分析内容相对来说比较多，会从Java-&amp;gt;Jni-&amp;gt;C/C++慢慢分析，后面会慢慢更新。另外，博客只作为自己学习记录的一种方式，对于其他的不过多的评论。MediaPlayerDemopublic class MainActivity extends AppCompatActivity implements SurfaceHolder.Cal..._android多媒体播放源码分析 时序图
文章浏览阅读2.4k次，点赞41次，收藏13次。java 数据结构与算法 ——快速排序法_快速排序法