目录
前言
一、背景知识
Opencv轮廓检测
ROS相关知识
二、环境依赖
三、具体实现
Step1：初始化ROS，订阅话题
Step2：接收话题，进入回调
1. 帧处理
2. 膨胀腐蚀处理
Step3：红绿特征处理
1. 提取绘制轮廓
2. 转换矩形、排序
3. 显示检测结果
四、完整代码
五、使用方法
CMakeLists.txt
package.xml
detect.launch
六、后续改进思路
前言
根据需求需要使用Opencv实现红绿灯检测的功能，于是在猿力猪大佬的【OpenCV】红绿灯识别 轮廓识别 C++ OpenCV 案例实现 文章的基础上，将Opencv 3中的写法改成了Opencv 4，在具体图片处理的部分也按照我自己的逻辑进行了一些改动，并写成ROS工作空间包含了完整的话题读取，图片处理及检测结果显示。
一、背景知识
Opencv轮廓检测
这个部分主要用到Opencv中的findContours函数，具体介绍可以参考：findContours函数参数详解，关于轮廓检测的基本概念参考上面提到的猿力猪大佬的博文即可。
ROS相关知识
ROS编译方式：[详细教程]使用ros编译运行自己写的程序
ROS多节点运行：ROS中的roslaunch命令和launch文件（ROS入门学习笔记四）
ROS话题订阅：ROS消息发布（publish）与订阅(subscribe)（C++代码详解）
二、环境依赖
- OpenCV 4
- cv_bridge
三、具体实现
Step1：初始化ROS，订阅话题
int main(int argc, char **argv)
{
ros::init(argc, argv, "tld_cv_node");
ros::NodeHandle nh;
std::string image_topic;
nh.param<std::string>("sub_topic", image_topic, "/src_rgb/compressed");
std::cout << "image_topic: " << image_topic << std::endl;
ros::Subscriber camera_sub =
nh.subscribe(image_topic, 2, camera_callback);
ros::spin();
ros::waitForShutdown();
return 0;
}
Step2：接收话题，进入回调
1. 帧处理
- 从图像话题中读取图像并转换为BGR格式，调整亮度，而后转为YCrCb格式，提取ROI，根据红绿阈值拆分红色和绿色分量
cv_bridge::CvImagePtr cv_ptr =
cv_bridge::toCvCopy(msg_pic, sensor_msgs::image_encodings::BGR8);
if (rotated)
{
cv::flip(cv_ptr->image, src_image, -1);
}
else
{
src_image = cv_ptr->image;
}
// std::cout << "src_image" << src_image.size() << std::endl;
// 亮度参数
double a = 0.3;
double b = (1 - a) * 125;
// 统计检测用时
clock_t start, end;
start = clock();
src_image.copyTo(frame);
// 调整亮度
src_image.convertTo(img, img.type(), a, b);
// cv::imshow("img",img);
// 使用ROI（感兴趣区域）方式截取图像
cv::Rect roi(0, 0, 2048, 768); // 定义roi，图片尺寸2048*1536
// std::cout << "img size:" << img.size() << std::endl;
cv::Mat roi_image = img(roi);
// 转换为YCrCb颜色空间
cvtColor(roi_image, imgYCrCb, cv::COLOR_BGR2YCrCb);
// cvtColor(img, imgYCrCb, cv::COLOR_BGR2YCrCb);
imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
// 分解YCrCb的三个成分
std::vector<cv::Mat> planes;
split(imgYCrCb, planes);
// 遍历以根据Cr分量拆分红色和绿色
cv::MatIterator_<uchar> it_Cr = planes[1].begin<uchar>(),
it_Cr_end = planes[1].end<uchar>();
cv::MatIterator_<uchar> it_Red = imgRed.begin<uchar>();
cv::MatIterator_<uchar> it_Green = imgGreen.begin<uchar>();
for (; it_Cr != it_Cr_end; ++it_Cr, ++it_Red, ++it_Green)
{
// RED, 145<Cr<470 红色
// if (*it_Cr > 145 && *it_Cr < 470)
if (*it_Cr > 140 && *it_Cr < 470)
*it_Red = 255;
else
*it_Red = 0;
// GREEN 95<Cr<110 绿色
if (*it_Cr > 95 && *it_Cr < 110)
*it_Green = 255;
else
*it_Green = 0;
// YELLOW 黄色
}PS：ROI选取这里只是随意截取了图片的上半部分。
2. 膨胀腐蚀处理
- 膨胀的第三个参数：膨胀操作的内核，我根据实际场景的检测效果进行了修改
// 膨胀和腐蚀
dilate(imgRed, imgRed, cv::Mat(8, 8, CV_8UC1), cv::Point(-1, -1));
erode(imgRed, imgRed, cv::Mat(1, 1, CV_8UC1), cv::Point(-1, -1));
dilate(imgGreen, imgGreen, cv::Mat(12, 12, CV_8UC1), cv::Point(-1, -1));
erode(imgGreen, imgGreen, cv::Mat(1, 1, CV_8UC1), cv::Point(-1, -1));Step3：红绿特征处理
- 这是我改动最大的一个函数，只保留了原作者提取轮廓转换为矩形的思路。先提取、绘制轮廓、显示检测结果，然后对得到的矩形进行位置排序，再对轮廓依次进行显示。
1. 提取绘制轮廓
// 提取轮廓
findContours(tmp_Red, contours_Red, hierarchy_Red, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
findContours(tmp_Green, contours_Green, hierarchy_Green, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
// 绘制轮廓
drawContours(frame, contours_Red, -1, cv::Scalar(0, 0, 255), cv::FILLED); // Red
std::cout << "提取到的红色轮廓数量：" << contours_Red.size() << std::endl;
drawContours(frame, contours_Green, -1, cv::Scalar(0, 255, 0), cv::FILLED); // Green
std::cout << "提取到的绿色轮廓数量：" << contours_Green.size() << std::endl;
// 显示轮廓
// imshow("contours", frame);
trackBox_Red = new cv::Rect[contours_Red.size()];
trackBox_Green = new cv::Rect[contours_Green.size()];2. 转换矩形、排序
- 此处需特别注意trackBox指针的清空
- 对结构体的排序方式参考了用sort对结构体某个元素排序的方法
// 确定要跟踪的区域
for (int i = 0; i < contours_Red.size(); i++)
{
// Yi opencv4 不支持 CvSeq
trackBox_Red[i] = cv::boundingRect(contours_Red[i]);
}
for (int i = 0; i < contours_Green.size(); i++)
{
// Yi opencv4 不支持 CvSeq
trackBox_Green[i] = cv::boundingRect(contours_Green[i]);
}
// imshow("contours", tmp);
// Rect.tl() 返回矩形左上顶点的坐标
for (int i = 0; i < contours_Red.size(); i++)
{
Store_x_color a;
a.x = trackBox_Red[i].tl().x;
a.y = trackBox_Red[i].tl().y;
a.color = 0;
x_color.push_back(a);
}
for (int i = 0; i < contours_Green.size(); i++)
{
Store_x_color a;
a.x = trackBox_Green[i].tl().x;
a.y = trackBox_Green[i].tl().y;
a.color = 1;
x_color.push_back(a);
}
// 清空指针
delete[] trackBox_Red;
delete[] trackBox_Green;
// 对左上顶点横坐标进行排序
sort(x_color.begin(), x_color.end(), CompareByX);3. 显示检测结果
// 显示结果
for (int i = 0; i < x_color.size(); i++)
{
if (0 == x_color[i].color)
cv::putText(frame, "Red", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 0, 255), 2, 8, 0);
else if (1 == x_color[i].color)
cv::putText(frame, "Green", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2, 8, 0);
else if (2 == x_color[i].color)
cv::putText(frame, "Yellow", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 2, 8, 0);
else
cv::putText(frame, "Lights off", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(255, 255, 255), 2, 8, 0);
}
cv::namedWindow("tld_result", 0);
cv::resizeWindow("tld_result", 1920, 1080);
cv::imshow("tld_result", frame);
cv::waitKey(1);实际检测结果如下图所示：
四、完整代码
/*
* @CopyRight: All Rights Reserved by Plusgo
* @Author: Yi
* @E-mail: waterwinsor@gmail.com
* @Date: 2023年 05月 06日 星期六 15:44:35
* @LastEditTime: 2023年 05月 08日 星期一 10:07:30
*/
// requirements: opencv 4
#include <iostream>
#include <fstream>
#include <time.h>
#include <algorithm>
#include <cv_bridge/cv_bridge.h>
#include <image_transport/image_transport.h>
#include <ros/ros.h>
#include <sensor_msgs/Image.h>
#include <sensor_msgs/PointCloud2.h>
#include <opencv2/opencv.hpp>
#include "opencv2/imgproc.hpp"
#include <opencv2/imgproc/types_c.h>
struct Store_x_color
{
int x; // 存储左上顶点横坐标
int y; // 存储左上顶点纵坐标
int color; // 存储当前点颜色
};
// Function headers
void processImg(cv::Mat, cv::Mat); // 前红后绿
bool CompareByX(const Store_x_color &, const Store_x_color &);
// Global variables
cv::Mat src_image;
bool rotated = true; // rotate 180
cv::Mat frame;
cv::Mat img;
cv::Mat imgYCrCb;
cv::Mat imgGreen;
cv::Mat imgRed;
cv::Mat imgYellow;
std::vector<Store_x_color> x_color;
void camera_callback(const sensor_msgs::CompressedImageConstPtr &msg_pic)
{
try
{
cv_bridge::CvImagePtr cv_ptr =
cv_bridge::toCvCopy(msg_pic, sensor_msgs::image_encodings::BGR8);
if (rotated)
{
cv::flip(cv_ptr->image, src_image, -1);
}
else
{
src_image = cv_ptr->image;
}
// std::cout << "src_image" << src_image.size() << std::endl;
// 亮度参数
double a = 0.3;
double b = (1 - a) * 125;
// 统计检测用时
clock_t start, end;
start = clock();
src_image.copyTo(frame);
// 调整亮度
src_image.convertTo(img, img.type(), a, b);
// cv::imshow("img",img);
// 使用ROI（感兴趣区域）方式截取图像
cv::Rect roi(0, 0, 2048, 768); // 定义roi，图片尺寸2048*1536
// std::cout << "img size:" << img.size() << std::endl;
cv::Mat roi_image = img(roi);
// 转换为YCrCb颜色空间
cvtColor(roi_image, imgYCrCb, cv::COLOR_BGR2YCrCb);
// cvtColor(img, imgYCrCb, cv::COLOR_BGR2YCrCb);
imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
// 分解YCrCb的三个成分
std::vector<cv::Mat> planes;
split(imgYCrCb, planes);
// 遍历以根据Cr分量拆分红色和绿色
cv::MatIterator_<uchar> it_Cr = planes[1].begin<uchar>(),
it_Cr_end = planes[1].end<uchar>();
cv::MatIterator_<uchar> it_Red = imgRed.begin<uchar>();
cv::MatIterator_<uchar> it_Green = imgGreen.begin<uchar>();
for (; it_Cr != it_Cr_end; ++it_Cr, ++it_Red, ++it_Green)
{
// RED, 145<Cr<470 红色
// if (*it_Cr > 145 && *it_Cr < 470)
if (*it_Cr > 140 && *it_Cr < 470)
*it_Red = 255;
else
*it_Red = 0;
// GREEN 95<Cr<110 绿色
if (*it_Cr > 95 && *it_Cr < 110)
*it_Green = 255;
else
*it_Green = 0;
// YELLOW 黄色
}
// 膨胀和腐蚀
dilate(imgRed, imgRed, cv::Mat(8, 8, CV_8UC1), cv::Point(-1, -1));
erode(imgRed, imgRed, cv::Mat(1, 1, CV_8UC1), cv::Point(-1, -1));
dilate(imgGreen, imgGreen, cv::Mat(12, 12, CV_8UC1), cv::Point(-1, -1));
erode(imgGreen, imgGreen, cv::Mat(1, 1, CV_8UC1), cv::Point(-1, -1));
// 检测和显示
processImg(imgRed, imgGreen);
// 清空x_color
x_color.clear();
end = clock();
std::cout << "检测时间：" << (double)(end - start) / CLOCKS_PER_SEC << std::endl; // 打印检测时间
}
catch (cv_bridge::Exception e)
{
ROS_ERROR_STREAM("cant't get image");
}
}
int main(int argc, char **argv)
{
ros::init(argc, argv, "tld_cv_node");
ros::NodeHandle nh;
std::string image_topic;
nh.param<std::string>("sub_topic", image_topic, "/src_rgb/compressed");
std::cout << "image_topic: " << image_topic << std::endl;
ros::Subscriber camera_sub =
nh.subscribe(image_topic, 2, camera_callback);
ros::spin();
ros::waitForShutdown();
return 0;
}
void processImg(cv::Mat src_Red, cv::Mat src_Green)
{
cv::Mat tmp_Red;
cv::Mat tmp_Green;
std::vector<std::vector<cv::Point>> contours_Red;
std::vector<std::vector<cv::Point>> contours_Green;
std::vector<cv::Vec4i> hierarchy_Red;
std::vector<cv::Vec4i> hierarchy_Green;
cv::Rect *trackBox_Red;
cv::Rect *trackBox_Green;
src_Red.copyTo(tmp_Red);
src_Green.copyTo(tmp_Green);
// 提取轮廓
findContours(tmp_Red, contours_Red, hierarchy_Red, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
findContours(tmp_Green, contours_Green, hierarchy_Green, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
// 绘制轮廓
drawContours(frame, contours_Red, -1, cv::Scalar(0, 0, 255), cv::FILLED); // Red
std::cout << "提取到的红色轮廓数量：" << contours_Red.size() << std::endl;
drawContours(frame, contours_Green, -1, cv::Scalar(0, 255, 0), cv::FILLED); // Green
std::cout << "提取到的绿色轮廓数量：" << contours_Green.size() << std::endl;
// 显示轮廓
// imshow("contours", frame);
trackBox_Red = new cv::Rect[contours_Red.size()];
trackBox_Green = new cv::Rect[contours_Green.size()];
// 确定要跟踪的区域
for (int i = 0; i < contours_Red.size(); i++)
{
// Yi opencv4 不支持 CvSeq
trackBox_Red[i] = cv::boundingRect(contours_Red[i]);
}
for (int i = 0; i < contours_Green.size(); i++)
{
// Yi opencv4 不支持 CvSeq
trackBox_Green[i] = cv::boundingRect(contours_Green[i]);
}
// imshow("contours", tmp);
// Rect.tl() 返回矩形左上顶点的坐标
for (int i = 0; i < contours_Red.size(); i++)
{
Store_x_color a;
a.x = trackBox_Red[i].tl().x;
a.y = trackBox_Red[i].tl().y;
a.color = 0;
x_color.push_back(a);
}
for (int i = 0; i < contours_Green.size(); i++)
{
Store_x_color a;
a.x = trackBox_Green[i].tl().x;
a.y = trackBox_Green[i].tl().y;
a.color = 1;
x_color.push_back(a);
}
// 清空指针
delete[] trackBox_Red;
delete[] trackBox_Green;
// 对左上顶点横坐标进行排序
sort(x_color.begin(), x_color.end(), CompareByX);
// 显示结果
for (int i = 0; i < x_color.size(); i++)
{
if (0 == x_color[i].color)
cv::putText(frame, "Red", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 0, 255), 2, 8, 0);
else if (1 == x_color[i].color)
cv::putText(frame, "Green", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2, 8, 0);
else if (2 == x_color[i].color)
cv::putText(frame, "Yellow", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 2, 8, 0);
else
cv::putText(frame, "Lights off", cv::Point(x_color[i].x, x_color[i].y - 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(255, 255, 255), 2, 8, 0);
}
cv::namedWindow("tld_result", 0);
cv::resizeWindow("tld_result", 1920, 1080);
cv::imshow("tld_result", frame);
cv::waitKey(1);
return;
}
bool CompareByX(const Store_x_color &a, const Store_x_color &b)
{
return a.x < b.x;
}五、使用方法
编译所需的CMakeLists.txt、package.xml和运行所需roslaunch文件如下
-
CMakeLists.txt
cmake_minimum_required(VERSION 2.8.3)
project(tld_cv)
set(CMAKE_INCLUDE_CURRENT_DIR ON)
set(CMAKE_BUILD_TYPE "Release") # Debug Release RelWithDebInfo
add_definitions(-O2 -pthread)
add_compile_options(-std=c++11)
find_package(OpenCV REQUIRED)
find_package(catkin REQUIRED COMPONENTS
roscpp
std_msgs
sensor_msgs
cv_bridge
image_transport
)
catkin_package(
CATKIN_DEPENDS
roscpp
std_msgs
sensor_msgs
cv_bridge
image_transport
)
include_directories(
# include
${catkin_INCLUDE_DIRS}
${OpenCV_INCLUDE_DIRS}
)
add_executable(tld_cv src/main.cpp)
target_link_libraries(tld_cv
${catkin_LIBRARIES}
${OpenCV_LIBRARIES}
)
-
package.xml
<?xml version="1.0"?>
<package format="2">
<name>tld_cv</name>
<version>0.0.0</version>
<description>The tld_cv package</description>
<maintainer email="royry@foxmail.com">Ru1yi</maintainer>
<license>TODO</license>
<buildtool_depend>catkin</buildtool_depend>
<build_depend>cv_bridge</build_depend>
<build_depend>image_transport</build_depend>
<build_depend>roscpp</build_depend>
<build_depend>sensor_msgs</build_depend>
<build_depend>std_msgs</build_depend>
<build_export_depend>cv_bridge</build_export_depend>
<build_export_depend>image_transport</build_export_depend>
<build_export_depend>roscpp</build_export_depend>
<build_export_depend>sensor_msgs</build_export_depend>
<build_export_depend>std_msgs</build_export_depend>
<exec_depend>cv_bridge</exec_depend>
<exec_depend>image_transport</exec_depend>
<exec_depend>roscpp</exec_depend>
<exec_depend>sensor_msgs</exec_depend>
<exec_depend>std_msgs</exec_depend>
<!-- The export tag contains other, unspecified, tags -->
<export>
<!-- Other tools can request additional information be placed here -->
</export>
</package>
-
detect.launch
<launch>
<arg name="sub_image_topic" value="/camera/image_color/compressed"/>
<param name="sub_topic" value="$(arg sub_image_topic)"/>
<node pkg="tld_cv" type="tld_cv" name="tld_cv" output="screen" />
</launch>
六、后续改进思路
改进可有如下几个方向：
- ROI
根据具体自动驾驶场景，可以通过红绿灯位置、高度、相机安装方式、车辆定位和IMU信息实时计算出一个更为精确的ROI，检测再进行排序即可确定红绿灯的个数和顺序，方便编写后处理中的逻辑。文章来源：https://www.toymoban.com/news/detail-604487.html
- 筛选面积
根据检测后的结果筛选较大的几个轮廓，可以排除掉某些较小物体的误检干扰 文章来源地址https://www.toymoban.com/news/detail-604487.html
本人接触OpenCV时间尚短、经验尚浅，如果有任何疏漏、错误还请大家指出～
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