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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1.8k次。OpenJudge百练第4088号习题:集合运算题目描述解题思路参考答案测试用例小结题目描述来源OpenJudge网站 —— 百练习题集-第4088号习题要求总时间限制: 3000ms 单个测试点时间限制: 1000ms 内存限制: 65536kB描述设 A = {a1, a2, … , an}, B = {b1, b2, … , bm} 是非负整数集合,其中m = O(logn)。..._设 a = {a1, a2, . . . , an}, b = {b1, b2, . . . , bm} 是整数集合, 其中 m = o
文章浏览阅读410次。一、为什么要使用NoSQL数据库NoSQL数据库在我的理解是一类数据库的统称(如键值存储型,文档型,列存储型等等)。 NoSQL出现的意义是啥呢?适用关系型数据库的时候就使用关系型数据库(目前大部分我们所使用的数据库均为关系型),不适用的时候也没有必要非使用关系型数据库不可,这个时候就可以考虑更加合适的数据库,比如NoSQL数据库。 至于为什么说在这里使用NoSQL数据库会更加_rdbqry
文章浏览阅读644次,点赞23次,收藏14次。综上所述,基于Vue的电商数据分析系统的开发不仅能够提升电商平台的服务质量和运营效率,还能够帮助商家更好地理解和把握市场脉动,实现数据的最大价值。在数据库管理工具的选择上,使用了Navicat 11,这是一个用户友好且功能强大的数据库管理软件,它支持多种数据库系统,包括MySQL,并提供了图形化界面,使得数据库的管理和维护工作更加便捷。开发环境方面,我们选择了PyCharm作为主要的集成开发环境(IDE),它提供了丰富的Python开发工具和插件,支持Django框架,有助于提高开发效率和代码质量。
文章浏览阅读166次。最近需要实现裁剪图片上传,想起之前公司用到的一个插件,却不知道叫什么名字了。在网上找了有些时间,最终找到了这个网站。http://www.croppic.net/因为官网文档全部都是英文,所以看起来有些吃力,可以大概看懂80%,但是缺少详细的案例说明,所以真正配置起来还是非常懵逼。如果完全按照官网文档的步骤,大概就是这样的下载安装官网提供两种下载方式,第一种类似于SD..._croppic 多图上传
文章浏览阅读355次。示例检查路径是目录还是文件该is_dir函数返回参数是否为目录,而is_file返回参数是否为文件。使用file_exists来检查它是否是要么。$dir="/this/is/a/directory";$file="/this/is/a/file.txt";echois_dir($dir)?"$dirisadirectory":"$dirisnotadirecto..._php查询分享文件信息/apaas/1.0/share/list?product=netdisk
文章浏览阅读5.6k次,点赞2次,收藏5次。概念LinkedHashMap继承自HashMap,它的结构如图所示:hashmap是无序的,LinkedHashMap是有序的,且默认为插入顺序。LinkedHashMap通过在HashMap的基础上增加一条双向链表,实现了插入顺序和访问顺序一致。通过对HashMap一些方法的覆盖,例如newNode, replacementNode, replacementTreeNode, newTreeNode,让所有对底层HashMap数据结构修改的同时该链表进行修改,遍历的时候便是遍历这一条有序_linkedhashmap
文章浏览阅读7.7k次,点赞4次,收藏42次。python 基于pytesseract ocr 的视频文字识别_实现视频语义信息提取任务opencv
文章浏览阅读1.7w次,点赞2次,收藏2次。出现错误error: ‘rand’ was not declared in this scope解决方法添加头文件#include_[error] 'rand' was not declared in this scope
文章浏览阅读5.4k次,点赞14次,收藏78次。空洞卷积(扩张卷积,带孔卷积,atrous convolution)是一种区别于普通卷积的卷积方式,从字面理解,就是卷积层中有洞。1.一维理解以一维为例:图中(a)Input feature表示输入特征,Output feature表示输出特征,这是一个正常的kernel = 3; stride = 1; pad = 1的卷积操作。图中(b)下面为Input feature,上面为Output feature,与图(a)不同的是pad = 2,同时引入了一个rate = 2,这个rate_空洞卷积 一维