1、添加鱼眼相机支持 2、添加在原始图像上进行检测逻辑，通过CameraBevParam.yaml文件original_image_detect中字段为true控制。

launch.sh

@@ -15,7 +15,10 @@
 # gnome-terminal --tab --title="driver" --working-directory="/home/nvidia" \
 # -- bash -c  "awe run --killall; awe run -a; exec bash "
 
-gnome-terminal --tab --title="ros_camera_bev" --working-directory="/home/nvidia/Projects/ros_camera_bev" \
+#gnome-terminal --tab --title="ros_camera_bev" --working-directory="/home/nvidia/Projects/ros_camera_bev" \
+#-- bash -c  "source  ./devel/setup.bash; rosrun perception_camera_bev camera_bev_infer; exec bash "
+
+gnome-terminal --tab --title="ros_camera_bev" --working-directory="/home/cqs/projects/camera_detect/ros_camera_bev/" \
 -- bash -c  "source  ./devel/setup.bash; rosrun perception_camera_bev camera_bev_infer; exec bash "
 
 # gnome-terminal --tab --title="camera_back_warn" --working-directory="/home/nvidia/Projects/perception/ros_camera_bev" \

src/camera_bev_infer/configs/CameraBevParam.yaml

-rgb_sub_topic_name: /sensor/camera/inner/image # /sensor/camera/front_middle/image/bgr
-pointcloud_sub_topic_name: /livox/lidar_hap # /total_calib_pointcloud
+rgb_sub_topic_name: /front/image/bgr # /sensor/camera/front_middle/image/bgr
+pointcloud_sub_topic_name: /merged_lidar_points # /total_calib_pointcloud
 detection_pub_topic_name: /camera/objects
 # static_gridmap_pub_topic_name: /perception/camera/bev_static_gridmap
 creator_id: 0000
 creator_name: camera_bev_node
 sensor_frame_id: camera_01
 # config_root_path: /home/nvidia/Projects/perception/eon_camera_bev
-config_root_path: /home/ubuntu/projects/ros_camera_bev/src/camera_bev_infer
+config_root_path: /home/cqs/projects/camera_detect/ros_camera_bev/src/camera_bev_infer
 rgb_config_path: configs/weights/multi_yolov5m.yaml
 device: 0
 period_time: 5
 vis_res: true
-vis_frame_id: livox_frame_hap # vehicle_link
-vis_project: false
+vis_frame_id:  vehicle_link #livox_frame_hap #
+vis_project: true
 # ["pedestrian", "vehicle", "stone"]
 pub_classes: ["pedestrian", "vehicle"] # ["stone", "cone"]
 obstacle_height_threshold: 25 # cm
-camera_intrinsic: [1018.532796,    0,                 1001.098828,
-                   0,                   1017.127611,  599.751335,
-                   0,                   0,                 1 ]
-distortion_coefficients: [-0.385858, 0.125180, -0.004831, -0.001959, 0.000000]
+camera_intrinsic: [513.2025464568752,     0.     ,  967.0902739804869,
+                   0.     ,  513.0840642375775,   541.0220773650327,
+                   0.     ,     0.     ,     1. ]
+distortion_coefficients: [0.1307926007445786, -0.02713495183259862, -0.007965716375255127, 0.002596178751112769]
 
-camera2lidar_extrinsic:  [-0.05073, -0.993745, 0.0994831, -0.255167,
-                          -0.130468, -0.0921635, -0.98716, -0.0198193,
-                          0.990154, -0.063058, -0.124977, 0.76145,
+camera2lidar_extrinsic:  [-0.0052204, 0.999982, -0.00280128, 0.0881569,
+                          -0.999893, -0.0051816, 0.0136814, 0.0371389,
+                          0.0136667, 0.0028724, 0.999902, -0.0601866,
                           0,          0,          0,          1 ]
+camera2lidar_extrinsic1:  [-0.0052204, -0.999893, 0.0136667, 0.0384177,
+                          0.999982, -0.00518161, 0.0028724, -0.08779,
+                          -0.00280128, 0.0136814, 0.999902, 0.0599195,
+                          0,          0,          0,          1 ]
+
+original_image_detect: false
+
 src_img_width: 1920
 src_img_height: 1080
 dst_img_width: 960

src/camera_bev_infer/src/sensor_manager.cpp

@@ -177,8 +177,8 @@ void sensorManager::publishDetection()
             }
             
             // 2d->bev，结果保存到vGrid中 (frame->objs_有track_id, vGrid->vInsObjs没有)
-            // vision_converter_->Process_objects(frame_ptr->img_src_, pcl_cloud, frame_ptr->objs_, *vGrid);
-            vision_converter_->Process_objects(frame_ptr->img_src_, frame_ptr->depth_, pcl_cloud, frame_ptr->objs_, *vGrid);
+			// vision_converter_->Process_objects(frame_ptr->img_src_, pcl_cloud, frame_ptr->objs_, *vGrid);
+			vision_converter_->Process_objects(frame_ptr->img_src_, frame_ptr->depth_, pcl_cloud, frame_ptr->objs_, *vGrid);
         }else{
             std::cout<<"--------------------------------------------- no pcl, using all depth -------------------------------------------- "<<std::endl;
             vision_converter_->Process_objects(frame_ptr->img_src_, frame_ptr->depth_, frame_ptr->objs_, *vGrid);

src/camera_bev_infer/src/undistort/camera_param.h

@@ -17,12 +17,10 @@
 #include <common/common.h>
 #include <interfaces/base_io.h>
 
-
 namespace waytous {
 namespace deepinfer {
 namespace ios {
 
-
 class CameraParam: public interfaces::BaseIO{
 
 public:
@@ -31,6 +29,12 @@ public:
     Eigen::Matrix<float, 3, 3, Eigen::RowMajor> new_camera_intrinsic;
     Eigen::Matrix<float, 1, 14, Eigen::RowMajor> distortion_coefficients;
 
+	//add 20250514 by chengqingshui
+	cv::Mat mat_distortion_coeffs_;
+	cv::Mat mat_camera_matrix_;
+	cv::Mat mat_camera_matrix_estimate_;
+	bool original_image_detect = true;  //是否在原始图像上检测
+
     // 相机外参
     Eigen::Matrix4d camera2lidar_extrinsic;
 

src/camera_bev_infer/src/undistort/undistort_v2.cpp

@@ -45,7 +45,8 @@ bool UndistortV2::InitParam(){
     }
     d_mapx_.Reshape({param_->dst_height_, param_->dst_width_});
     d_mapy_.Reshape({param_->dst_height_, param_->dst_width_});
-    InitUndistortRectifyMap();
+	//InitUndistortRectifyMap();
+	InitUndistortRectifyMap_opencv();
     
     dst_img = std::make_shared<base::Image8U>(param_->dst_height_, param_->dst_width_, base::Color::BGR);
     auto output = std::make_shared<ios::CameraSrcOut>(dst_img);
@@ -111,6 +112,45 @@ void UndistortV2::InitUndistortRectifyMap() {
     }
 }
 
+void UndistortV2::InitUndistortRectifyMap_opencv() {
+
+	if(param_->original_image_detect){
+		float fx =  float(param_->src_width_) / float(param_->dst_width_) ;
+		float fy =  float(param_->src_height_) /float(param_->dst_height_) ;
+		for (int v = 0; v < param_->dst_height_; ++v) {
+			float *x_ptr = d_mapx_.mutable_cpu_data() + d_mapx_.offset(v);
+			float *y_ptr = d_mapy_.mutable_cpu_data() + d_mapy_.offset(v);
+			for (int u = 0; u < param_->dst_width_; ++u) {
+				x_ptr[u] = (u+0.5f)*fx;
+				y_ptr[u] = (v+0.5f)*fy;
+			}
+		}
+	}
+	else
+	{
+		cv::Size image_size(param_->dst_width_, param_->dst_height_);
+		cv::Mat R = cv::Mat::eye(3, 3, CV_32FC1);
+		cv::Mat map_x, map_y;
+		if( 4 == param_->mat_distortion_coeffs_.rows*param_->mat_distortion_coeffs_.cols) {
+			cv::fisheye::initUndistortRectifyMap(param_->mat_camera_matrix_, param_->mat_distortion_coeffs_, R,
+				param_->mat_camera_matrix_estimate_, image_size, CV_32FC1, map_x, map_y);
+		}
+		else {
+			cv::initUndistortRectifyMap(param_->mat_camera_matrix_, param_->mat_distortion_coeffs_, R,
+				param_->mat_camera_matrix_estimate_, image_size, CV_32FC1, map_x, map_y);
+		}
+
+		for (int v = 0; v < param_->dst_height_; ++v) {
+			float *x_ptr = d_mapx_.mutable_cpu_data() + d_mapx_.offset(v);
+			float *y_ptr = d_mapy_.mutable_cpu_data() + d_mapy_.offset(v);
+			for (int u = 0; u < param_->dst_width_; ++u) {
+				x_ptr[u] = map_x.at<float>(v, u)*2;
+				y_ptr[u] = map_y.at<float>(v, u)*2;
+			}
+		}
+	}
+}
+
 
 /**
  * @name: Exec

src/camera_bev_infer/src/undistort/undistort_v2.h

@@ -69,6 +69,9 @@ public:
      */    
     void InitUndistortRectifyMap();
 
+	//add 20250514 by chengqingshui  用opencv的方法改写InitUndistortRectifyMap（）
+	void InitUndistortRectifyMap_opencv();
+
     UndistortV2(){};
     ~UndistortV2(){};
 
@@ -80,7 +83,7 @@ public:
     base::Blob<float> d_mapy_;
     // 畸变矫正后图像
     base::Image8UPtr dst_img;
-    bool inited_ = 0;
+    bool inited_ = false;
 
 };
 

src/camera_bev_infer/src/utils.cpp

@@ -49,6 +49,7 @@ bool readInferParam(ImageInferNodeParam& param,  const std::string& param_path){
         param.vis_project = node["vis_project"].as<bool>();
     }
 
+
     param.obstacle_height_threshold = node["obstacle_height_threshold"].as<float>();
 
     param.cameraParam_ = std::make_shared<waytous::deepinfer::ios::CameraParam>();
@@ -56,11 +57,13 @@ bool readInferParam(ImageInferNodeParam& param,  const std::string& param_path){
     param.cameraParam_->src_width_ = node["src_img_width"].as<int>();
     param.cameraParam_->dst_height_ = node["dst_img_height"].as<int>();
     param.cameraParam_->dst_width_ = node["dst_img_width"].as<int>();
+	if(node["original_image_detect"].IsDefined() && (!node["original_image_detect"].IsNull())){
+		param.cameraParam_->original_image_detect = node["original_image_detect"].as<bool>();
+	}
 
     // 内参
     std::vector<float> camera_intrinsic = node["camera_intrinsic"].as<std::vector<float>>();
     if(camera_intrinsic.size() != 9){
-        // eon::log::info() << "Error: camera intrinsic number != 9, cannot init node.";
         std::cout << "Error: camera intrinsic number != 9, cannot init node." << std::endl;
         return false;
     }
@@ -95,6 +98,37 @@ bool readInferParam(ImageInferNodeParam& param,  const std::string& param_path){
         }
     }
 
+	//add 20250514 by chengqingshui  添加矩阵形式的相机畸变系数和内参  和估计的内参
+	// 生成内参矩阵
+	param.cameraParam_->mat_camera_matrix_ = cv::Mat::zeros(3, 3, CV_64F);
+	param.cameraParam_->mat_camera_matrix_.at<double>(0, 0) = param.cameraParam_->new_camera_intrinsic(0);  // fx
+	param.cameraParam_->mat_camera_matrix_.at<double>(0, 2) = param.cameraParam_->new_camera_intrinsic(2);  // cx
+	param.cameraParam_->mat_camera_matrix_.at<double>(1, 1) = param.cameraParam_->new_camera_intrinsic(4);  // fy
+	param.cameraParam_->mat_camera_matrix_.at<double>(1, 2) = param.cameraParam_->new_camera_intrinsic(5);  // cy
+	param.cameraParam_->mat_camera_matrix_.at<double>(2, 2) = 1.0;
+	// 生成畸变系数
+	param.cameraParam_->mat_distortion_coeffs_ = cv::Mat::zeros(1, effs.size(), CV_64F);
+	for (int i = 0; i < effs.size(); ++i) {
+		param.cameraParam_->mat_distortion_coeffs_.at<double>(0, i) = effs[i];
+	}
+	// 针对先矫正逻辑生成估计的内参矩阵  不矫正使用缩放内参矩阵
+	if(param.cameraParam_->original_image_detect){
+		param.cameraParam_->mat_camera_matrix_estimate_ = param.cameraParam_->mat_camera_matrix_.clone();
+	}
+	else
+	{
+		cv::Size image_size(param.cameraParam_->dst_width_, param.cameraParam_->dst_height_);
+		cv::Mat R = cv::Mat::eye(3, 3, CV_32FC1);
+		if( 4 == param.cameraParam_->mat_distortion_coeffs_.rows*param.cameraParam_->mat_distortion_coeffs_.cols) {
+			cv::fisheye::estimateNewCameraMatrixForUndistortRectify(param.cameraParam_->mat_camera_matrix_, param.cameraParam_->mat_distortion_coeffs_,
+				image_size,	R, param.cameraParam_->mat_camera_matrix_estimate_, 0, image_size);
+		}
+		else {
+			param.cameraParam_->mat_camera_matrix_estimate_ = cv::getOptimalNewCameraMatrix(param.cameraParam_->mat_camera_matrix_,
+				param.cameraParam_->mat_distortion_coeffs_, image_size, 0, image_size);
+		}
+	}
+
     auto ex_param = node["camera2lidar_extrinsic"].as<std::vector<float>>();
     int ex_size = ex_param.size();
     if(ex_size != 16){

src/camera_bev_infer/src/vision_transform/vision_3d.h

@@ -172,7 +172,7 @@ public:
      * @param {BEV_GridMap&} vGrid  gridmap，保存bev结果
      * @return {*}
      */
-    int Process_objects(const cv::Mat& image, Cloud::Ptr& pcl_cloud, std::vector<object_Seg_info>&objs, BEV_GridMap& vGrid);
+    //int Process_objects(const cv::Mat& image, Cloud::Ptr& pcl_cloud, std::vector<object_Seg_info>&objs, BEV_GridMap& vGrid);
 
     /**
      * @name: Process_objects
@@ -232,6 +232,7 @@ private:
     // std::map<int, std::pair<int, int>> unvalidTracks;
     cv::Mat m_matrixQ;
     Eigen::Matrix4d m_lidar2camera;
+	waytous::deepinfer::ios::CameraParamPtr camera_param_ptr_;
 	// Eigen::Matrix4d m_lidar2odom;
 
     float obstacle_height_threshold = 30; // 障碍物高差阈值
@@ -262,6 +263,8 @@ private:
     //最大车辆长度
     int m_nMaxBEVVehicleLength;
 
+	//颜色映射表
+	cv::Mat mat_colormap_;
 };
 
 #endif