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OpenCV中内置了很多滤波器#xff0c;这里我们讨论cv::ximgproc其中包含的滤波器。 https://docs.opencv.org/3.4/da/d17/group__ximgproc__filters.html 需要注意的是#xff0c;默认安装的OpenCV中不包含cv::ximgproc#xff0c;请从源码重修编译。 在这里贴上我…1 源码
OpenCV中内置了很多滤波器这里我们讨论cv::ximgproc其中包含的滤波器。 https://docs.opencv.org/3.4/da/d17/group__ximgproc__filters.html 需要注意的是默认安装的OpenCV中不包含cv::ximgproc请从源码重修编译。 在这里贴上我的测试源码。
/*
cv::ximgproc::AdaptiveManifoldFilter
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[151] Wolfram Mathematica. Ridge filter mathematica.*/#include ros/ros.h
#include sensor_msgs/Image.h
#include cv_bridge/cv_bridge.h
#include image_transport/image_transport.h
#include opencv2/imgproc/imgproc.hpp
#include opencv2/highgui/highgui.hpp
#include opencv2/ximgproc.hpp
#include message_filters/subscriber.h
#include message_filters/time_synchronizer.h
#include message_filters/sync_policies/approximate_time.h#include pcl/point_types.h
#include pcl_ros/point_cloud.h
#include pcl/common/common.h
#include pcl/common/pca.h
#include Eigen/Denseclass ImageProcessor
{
public:ImageProcessor(): it_(nh_), image_sub_(nh_, /camera/color/image_raw, 1), depth_sub_(nh_, /camera/aligned_depth_to_color/image_raw, 1){typedef message_filters::sync_policies::ApproximateTimesensor_msgs::Image, sensor_msgs::Image MySyncPolicy;// ApproximateTime或者ExactTimesync_.reset(new message_filters::SynchronizerMySyncPolicy(MySyncPolicy(10), image_sub_, depth_sub_));sync_-registerCallback(boost::bind(ImageProcessor::callback, this, _1, _2));pub_ it_.advertise(/fbs/depth_processed, 1);cloud_pub_ nh_.advertisepcl::PointCloudpcl::PointXYZRGB(/fbs/output_cloud, 1);cloud2_pub_ nh_.advertisepcl::PointCloudpcl::PointXYZRGB(/fbs/output_cloud2, 1);}void callback(const sensor_msgs::ImageConstPtr color_msg, const sensor_msgs::ImageConstPtr depth_msg){cv::Mat color_image, depth_image;try {color_image cv_bridge::toCvShare(color_msg, rgb8)-image;depth_image cv_bridge::toCvShare(depth_msg, sensor_msgs::image_encodings::TYPE_16UC1)-image;cv::Mat confidence;generateConfidence(depth_image, confidence);// 将confidence图像标准化到0-255的范围cv::Mat normalized_confidence;confidence.convertTo(normalized_confidence, CV_8UC1, 255.0); // 将浮点值乘以255以进行转换// // 显示信心图// cv::namedWindow(Confidence, cv::WINDOW_AUTOSIZE); // 创建一个窗口// cv::imshow(Confidence, normalized_confidence); // 显示信心图// cv::waitKey(1); // 等待1毫秒让显示函数有机会更新窗口cv::Mat filtered_image;applyFastBilateralSolverFilter(depth_image, color_image, confidence, filtered_image);// applyFastGlobalSmootherFilter(depth_image, color_image, filtered_image);// applyGuidedFilter(depth_image, color_image, filtered_image);// applyRidgeDetectionFilter(depth_image, filtered_image);// 转换回ROS图像并发布sensor_msgs::ImagePtr msg_out cv_bridge::CvImage(std_msgs::Header(), sensor_msgs::image_encodings::TYPE_16UC1, filtered_image).toImageMsg();pub_.publish(msg_out);// 发布点云pcl::PointCloudpcl::PointXYZRGB::Ptr cloud(new pcl::PointCloudpcl::PointXYZRGB());depthToPointCloud(depth_image, color_image, *cloud);cloud-header.stamp pcl_conversions::toPCL(color_msg-header.stamp); // Use the correct message for the timestampcloud_pub_.publish(cloud);// 发布点云// pcl::PointCloudpcl::PointXYZRGB::Ptr cloud(new pcl::PointCloudpcl::PointXYZRGB());depthToPointCloud(filtered_image, color_image, *cloud);// cloud-header.stamp pcl_conversions::toPCL(color_msg-header.stamp); // Use the correct message for the timestampcloud2_pub_.publish(cloud);} catch (const cv_bridge::Exception e) {ROS_ERROR(cv_bridge exception: %s, e.what());return;}}void generateConfidence(const cv::Mat depth, cv::Mat confidence){confidence cv::Mat::zeros(depth.size(), CV_32F);confidence.setTo(1, depth ! 0);}void applyFastBilateralSolverFilter(const cv::Mat depth, const cv::Mat color, const cv::Mat confidence, cv::Mat output){auto filter cv::ximgproc::createFastBilateralSolverFilter(color, 10, 1, 10);filter-filter(depth, confidence, output);}void applyFastGlobalSmootherFilter(const cv::Mat depth, const cv::Mat color, cv::Mat output){// 将depth转换为16S格式cv::Mat depth16S;depth.convertTo(depth16S, CV_16S);// 创建Fast Global Smoother滤波器auto filter cv::ximgproc::createFastGlobalSmootherFilter(color, 128, 10);// 使用16S格式的depth进行滤波filter-filter(depth16S, output);}void applyGuidedFilter(const cv::Mat depth, const cv::Mat color, cv::Mat output){// 将depth图像转换为CV_32F类型cv::Mat depth32F;cv::Mat output32F;depth.convertTo(depth32F, CV_32F, 1.0 / 255.0);auto filter cv::ximgproc::createGuidedFilter(color, 5, 0.01);filter-filter(depth32F, output32F);output32F.convertTo(output, CV_16U, 255.0);}void applyRidgeDetectionFilter(const cv::Mat depth, cv::Mat output){// 将图像转换为浮点类型这是脊线检测滤波器的要求cv::Mat depth32F;depth.convertTo(depth32F, CV_32FC1, 1.0 / 255.0);// 创建脊线检测滤波器int ddepth -1; // 使用相同的图像深度作为输入和输出double scale 1; // Sobel算子的比例因子double delta 0; // 添加到结果中的可选增量值int borderType cv::BORDER_DEFAULT; // 边缘填充方式cv::Ptrcv::ximgproc::RidgeDetectionFilter ridgeFilter cv::ximgproc::RidgeDetectionFilter::create();// 应用脊线检测滤波器cv::Mat output32F;ridgeFilter-getRidgeFilteredImage(depth32F, output32F);// 将结果转换回8位图像以便显示output32F.convertTo(output, CV_16UC1, 255.0);}void depthToPointCloud(const cv::Mat depth_img, const cv::Mat rgb_img, pcl::PointCloudpcl::PointXYZRGB cloud) {// 假设 fx, fy, cx, cy 是相机内参float fx 644.7825927734375; // 焦距xfloat fy 644.0340576171875; // 焦距yfloat cx 636.1322631835938; // 主点xfloat cy 367.4698486328125; // 主点ycloud.width depth_img.cols;cloud.height depth_img.rows;cloud.is_dense false;cloud.points.resize(cloud.width * cloud.height);cloud.header.frame_id map;for (int v 0; v depth_img.rows; v) {for (int u 0; u depth_img.cols; u) {pcl::PointXYZRGB point cloud.points[v * depth_img.cols u];float depth depth_img.atushort(v, u) / 1000.0f; // 深度单位转换为米if (depth 0) continue; // 无效点跳过// 从像素坐标转换到相机坐标系point.x (u - cx) * depth / fx;point.y (v - cy) * depth / fy;point.z depth;// 设置颜色cv::Vec3b rgb rgb_img.atcv::Vec3b(v, u);uint32_t rgb_val ((uint32_t)rgb[0] 16 | (uint32_t)rgb[1] 8 | (uint32_t)rgb[2]);point.rgb *reinterpret_castfloat*(rgb_val);}}}private:ros::NodeHandle nh_;image_transport::ImageTransport it_;image_transport::Publisher pub_;message_filters::Subscribersensor_msgs::Image image_sub_, depth_sub_;boost::shared_ptrmessage_filters::Synchronizermessage_filters::sync_policies::ApproximateTimesensor_msgs::Image, sensor_msgs::Image sync_;ros::Publisher cloud_pub_;ros::Publisher cloud2_pub_;
};int main(int argc, char** argv)
{ros::init(argc, argv, image_processor);ImageProcessor ip;ros::spin();return 0;
}
这里FBS和其他的滤波器的本质区别是FBS引入了信心图confidence这使得其在进行滤波时不会被无效像素所影响因此我建议当使用无信心图的滤波器时尽量使用其他后处理方法填充空洞减少无效像素当使用FBS时建议深度图宁缺毋滥尽量保持真值。
2 效果展示
这是realsense D455的原始深度图与经过FBS的深度图是对比。
nullrawFBS深度图深度图点云
看起来似乎不错但我们将其转换为点云就会发现原始深度图干干净净不同深度处的物体分离性很好但是经过优化的深度图产生了大量的离群点这些是FBS“猜”错的点。 这个视角下我们看到FBS确实补全了部分缺失的深度值但也带来了很大问题最主要的问题就是这些错误的点。
