reusex::vision Namespace Reference

Namespaces
namespace	common
namespace	libtorch
namespace	onnx
namespace	osd
namespace	tensor_rt

Classes
struct	AnnotationConfig
	Configuration for ML annotation inference and data loading. More...
class	BackendFactory
class	Dataloader
struct	IData
class	IDataset
class	IMLBackend
class	IModel

Enumerations
enum class	Backend {   opencv , tensor_rt , libtorch , dnn ,   onnx_runtime , openvino , unknown }
enum class	Model { yolo , sam3 }

Functions
auto	annotate (const std::filesystem::path &dbPath, const std::filesystem::path &modelPath, const AnnotationConfig &config=AnnotationConfig{}) -> int
	Run semantic annotation on sensor frames using ML models.
torch::Tensor	xyxy_to_xywh (const torch::Tensor &x)
	Convert bounding boxes from (x1,y1,x2,y2) to (cx,cy,w,h) format.
torch::Tensor	xywh_to_xyxy (const torch::Tensor &x)
	Convert bounding boxes from (cx,cy,w,h) to (x1,y1,x2,y2) format.
torch::Tensor	nms (const torch::Tensor &bboxes, const torch::Tensor &scores, float iou_threshold=0.45)
	Non-maximum suppression on bounding boxes.
torch::Tensor	non_max_suppression (torch::Tensor predictions, float confThreshold=0.25, float iouThreshold=0.45, int maxDetections=300)
	YOLO-style non-maximum suppression with class-aware filtering.
auto	project (ProjectDB &db, CloudConstPtr cloud) -> CloudLPtr
	Project the labels stored in the database on tho the assebled point cloud.
float	generate_scale (cv::Mat &image, const cv::Size &target_size, bool scale_up=false)
float	letterbox (cv::Mat &input_image, cv::Mat &output_image, const cv::Size &target_size)
float	cropbox (cv::Mat &input_image, cv::Mat &output_image, const cv::Size &target_size)

Variables
std::vector< std::string >	Yolov8_className
	YOLO v8 class names for object detection.

Enumeration Type Documentation

Backend

enum class reusex::vision::Backend

strong

Enumerator
opencv
tensor_rt
libtorch
dnn
onnx_runtime
openvino
unknown

Definition at line 28 of file BackendFactory.hpp.

Model

enum class reusex::vision::Model

strong

Enumerator
yolo
sam3

Definition at line 9 of file IMLBackend.hpp.

Function Documentation

annotate()

auto reusex::vision::annotate	(	const std::filesystem::path &	dbPath,
		const std::filesystem::path &	modelPath,
		const AnnotationConfig &	config = AnnotationConfig{} ) -> int

Run semantic annotation on sensor frames using ML models.

Parameters

dbPath	Path to project database containing sensor frames
modelPath	Path to ML model (.pt, .engine, .onnx)
config	Configuration for inference and data loading

Returns

0 on success, error code on failure

cropbox()

float reusex::vision::cropbox	(	cv::Mat &	input_image,
		cv::Mat &	output_image,
		const cv::Size &	target_size )

generate_scale()

float reusex::vision::generate_scale	(	cv::Mat &	image,
		const cv::Size &	target_size,
		bool	scale_up = false )

letterbox()

float reusex::vision::letterbox	(	cv::Mat &	input_image,
		cv::Mat &	output_image,
		const cv::Size &	target_size )

nms()

torch::Tensor reusex::vision::nms	(	const torch::Tensor &	bboxes,
		const torch::Tensor &	scores,
		float	iou_threshold = 0.45 )

Non-maximum suppression on bounding boxes.

Parameters

bboxes	Bounding boxes tensor [N, 4] in xyxy format.
scores	Confidence scores tensor [N].
iou_threshold	IoU threshold for suppression.

Returns

Indices of kept boxes.

non_max_suppression()

torch::Tensor reusex::vision::non_max_suppression	(	torch::Tensor	predictions,
		float	confThreshold = 0.25,
		float	iouThreshold = 0.45,
		int	maxDetections = 300 )

YOLO-style non-maximum suppression with class-aware filtering.

Parameters

predictions	Raw model output [batch_size, 116, 8400].
confThreshold	Confidence threshold for filtering.
iouThreshold	IoU threshold for NMS.
maxDetections	Maximum detections to keep per image.

Returns

Filtered detections [batch_size, maxDetections, 6+32].

project()

auto reusex::vision::project	(	ProjectDB &	db,
		CloudConstPtr	cloud ) -> CloudLPtr

Project the labels stored in the database on tho the assebled point cloud.

For each view in the database this constructs a croped point cloud, computes a z-buffer and the assigns the precomputed labels to the closses point in the point cloud.

xywh_to_xyxy()

torch::Tensor reusex::vision::xywh_to_xyxy

(

const torch::Tensor &

x

)

Convert bounding boxes from (cx,cy,w,h) to (x1,y1,x2,y2) format.

xyxy_to_xywh()

torch::Tensor reusex::vision::xyxy_to_xywh

(

const torch::Tensor &

x

)

Convert bounding boxes from (x1,y1,x2,y2) to (cx,cy,w,h) format.

Variable Documentation

Yolov8_className

std::vector<std::string> reusex::vision::Yolov8_className

extern

YOLO v8 class names for object detection.

Contains the list of class names that can be detected by YOLO v8 model.