vision

package
v0.51.2 Latest Latest
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Published: Nov 21, 2024 License: AGPL-3.0 Imports: 28 Imported by: 21

Documentation

Overview

Package vision is the service that allows you to access various computer vision algorithms (like detection, segmentation, tracking, etc) that usually only require a camera or image input. For more information, see the vision service docs.

Index

Constants

View Source
const SubtypeName = "vision"

SubtypeName is the name of the type of service.

Variables

View Source
var API = resource.APINamespaceRDK.WithServiceType(SubtypeName)

API is a variable that identifies the vision service resource API.

Functions

func Named

func Named(name string) resource.Name

Named is a helper for getting the named vision's typed resource name.

func NewRPCServiceServer added in v0.2.36

func NewRPCServiceServer(coll resource.APIResourceCollection[Service]) interface{}

NewRPCServiceServer constructs a vision gRPC service server. It is intentionally untyped to prevent use outside of tests.

Types

type Properties added in v0.28.0

type Properties struct {
	ClassificationSupported bool
	DetectionSupported      bool
	ObjectPCDsSupported     bool
}

Properties returns various information regarding the current vision service, specifically, which vision tasks are supported by the resource.

type Service

type Service interface {
	resource.Resource
	// DetectionsFromCamera returns a list of detections from the next image from a specified camera using a configured detector.
	DetectionsFromCamera(ctx context.Context, cameraName string, extra map[string]interface{}) ([]objectdetection.Detection, error)

	// Detections returns a list of detections from a given image using a configured detector.
	Detections(ctx context.Context, img image.Image, extra map[string]interface{}) ([]objectdetection.Detection, error)

	// ClassificationsFromCamera returns a list of classifications from the next image from a specified camera using a configured classifier.
	ClassificationsFromCamera(
		ctx context.Context,
		cameraName string,
		n int,
		extra map[string]interface{},
	) (classification.Classifications, error)

	// Classifications returns a list of classifications from a given image using a configured classifier.
	Classifications(
		ctx context.Context,
		img image.Image,
		n int,
		extra map[string]interface{},
	) (classification.Classifications, error)

	// GetObjectPointClouds returns a list of 3D point cloud objects and metadata from the latest 3D camera image using a specified segmenter.
	GetObjectPointClouds(ctx context.Context, cameraName string, extra map[string]interface{}) ([]*viz.Object, error)
	// properties
	GetProperties(ctx context.Context, extra map[string]interface{}) (*Properties, error)
	// CaptureAllFromCamera returns the next image, detections, classifications, and objects all together, given a camera name. Used for
	// visualization.
	CaptureAllFromCamera(ctx context.Context,
		cameraName string,
		opts viscapture.CaptureOptions,
		extra map[string]interface{},
	) (viscapture.VisCapture, error)
}

A Service implements various computer vision algorithms like detection and segmentation. For more information, see the vision service docs.

DetectionsFromCamera example:

myDetectorService, err := vision.FromRobot(machine, "my_detector")
if err != nil {
	logger.Error(err)
	return
}

// Get detections from the camera output
detections, err := myDetectorService.DetectionsFromCamera(context.Background(), "my_camera", nil)
if err != nil {
	logger.Fatalf("Could not get detections: %v", err)
}
if len(detections) > 0 {
	logger.Info(detections[0])
}

Detections example:

myCam, err := camera.FromRobot(machine, "my_camera")
if err != nil {
	logger.Error(err)
	return
}
// Get the stream from a camera
camStream, err := myCam.Stream(context.Background())
// Get an image from the camera stream
img, release, err := camStream.Next(context.Background())
defer release()

myDetectorService, err := vision.FromRobot(machine, "my_detector")
if err != nil {
	logger.Error(err)
	return
}
// Get the detections from the image
detections, err := myDetectorService.Detections(context.Background(), img, nil)
if err != nil {
	logger.Fatalf("Could not get detections: %v", err)
}
if len(detections) > 0 {
	logger.Info(detections[0])
}

ClassificationsFromCamera example:

myClassifierService, err := vision.FromRobot(machine, "my_classifier")
if err != nil {
	logger.Error(err)
	return
}
// Get the 2 classifications with the highest confidence scores from the camera output
classifications, err := myClassifierService.ClassificationsFromCamera(context.Background(), "my_camera", 2, nil)
if err != nil {
	logger.Fatalf("Could not get classifications: %v", err)
}
if len(classifications) > 0 {
	logger.Info(classifications[0])
}

Classifications example:

myCam, err := camera.FromRobot(machine, "my_camera")
if err != nil {
	logger.Error(err)
	return
}
// Get the stream from a camera
camStream, err := myCam.Stream(context.Background())
if err!=nil {
		logger.Error(err)
		return
}
// Get an image from the camera stream
img, release, err := camStream.Next(context.Background())
defer release()

myClassifierService, err := vision.FromRobot(machine, "my_classifier")
if err != nil {
	logger.Error(err)
	return
}
// Get the 2 classifications with the highest confidence scores from the image
classifications, err := myClassifierService.Classifications(context.Background(), img, 2, nil)
if err != nil {
	logger.Fatalf("Could not get classifications: %v", err)
}
if len(classifications) > 0 {
	logger.Info(classifications[0])
}

GetObjectPointClouds example:

mySegmenterService, err := vision.FromRobot(machine, "my_segmenter")
if err != nil {
	logger.Error(err)
	return
}
// Get the objects from the camera output
objects, err := mySegmenterService.GetObjectPointClouds(context.Background(), "my_camera", nil)
if err != nil {
	logger.Fatalf("Could not get point clouds: %v", err)
}
if len(objects) > 0 {
	logger.Info(objects[0])
}

CaptureAllFromCamera example:

// The data to capture and return from the camera
captOpts := viscapture.CaptureOptions{
	ReturnImage: true,
	ReturnDetections: true,
}
// Get the captured data for a camera
capture, err := visService.CaptureAllFromCamera(context.Background(), "my_camera", captOpts, nil)
if err != nil {
	logger.Fatalf("Could not get capture data from vision service: %v", err)
}
image := capture.Image
detections := capture.Detections
classifications := capture.Classifications
objects := capture.Objects

func FromDependencies added in v0.2.47

func FromDependencies(deps resource.Dependencies, name string) (Service, error)

FromDependencies is a helper for getting the named vision service from a collection of dependencies.

func FromRobot

func FromRobot(r robot.Robot, name string) (Service, error)

FromRobot is a helper for getting the named vision service from the given Robot.

func NewClientFromConn

func NewClientFromConn(
	ctx context.Context,
	conn rpc.ClientConn,
	remoteName string,
	name resource.Name,
	logger logging.Logger,
) (Service, error)

NewClientFromConn constructs a new Client from connection passed in.

func NewService added in v0.2.36

NewService wraps the vision model in the struct that fulfills the vision service interface.

Directories

Path Synopsis
Package colordetector uses a heuristic based on hue and connected components to create bounding boxes around objects of a specified color.
Package colordetector uses a heuristic based on hue and connected components to create bounding boxes around objects of a specified color.
Package detectionstosegments uses a 2D segmenter and a camera that can project its images to 3D to project the bounding boxes to 3D in order to created a segmented point cloud.
Package detectionstosegments uses a 2D segmenter and a camera that can project its images to 3D to project the bounding boxes to 3D in order to created a segmented point cloud.
Package fake implements a fake vision service which always returns the user specified detections/classifications.
Package fake implements a fake vision service which always returns the user specified detections/classifications.
Package mlvision uses an underlying model from the ML model service as a vision model, and wraps the ML model with the vision service methods.
Package mlvision uses an underlying model from the ML model service as a vision model, and wraps the ML model with the vision service methods.
Package obstaclesdepth uses an underlying depth camera to fulfill GetObjectPointClouds, projecting its depth map to a point cloud, an then applying a point cloud clustering algorithm
Package obstaclesdepth uses an underlying depth camera to fulfill GetObjectPointClouds, projecting its depth map to a point cloud, an then applying a point cloud clustering algorithm
Package obstaclesdistance uses an underlying camera to fulfill vision service methods, specifically GetObjectPointClouds, which performs several queries of NextPointCloud and returns a median point.
Package obstaclesdistance uses an underlying camera to fulfill vision service methods, specifically GetObjectPointClouds, which performs several queries of NextPointCloud and returns a median point.
Package obstaclespointcloud uses the 3D radius clustering algorithm as defined in the RDK vision/segmentation package as vision model.
Package obstaclespointcloud uses the 3D radius clustering algorithm as defined in the RDK vision/segmentation package as vision model.
Package register registers all relevant vision models and also API specific functions
Package register registers all relevant vision models and also API specific functions

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