DOCS-2086: Add ML, CV, and Data code snippets

services/datamanager/data_manager.go

@@ -31,8 +31,14 @@ func init() {
 }
 
 // Service defines what a Data Manager Service should expose to the users.
+//
+// Sync example:
+//
+//	// Sync data stored on the machine to the cloud.
+//	err := data.Sync(context.Background(), nil)
 type Service interface {
 	resource.Resource
+	// Sync will sync data stored on the machine to the cloud.
 	Sync(ctx context.Context, extra map[string]interface{}) error
 }
 

services/mlmodel/mlmodel.go

@@ -28,9 +28,22 @@ func init() {
 // Service defines the ML Model interface, which takes a map of inputs, runs it through
 // an inference engine, and creates a map of outputs. Metadata is necessary in order to build
 // the struct that will decode that map[string]interface{} correctly.
+//
+// Infer example:
+//
+//	input_tensors := ml.Tensors{"0": tensor.New(tensor.WithShape(1, 2, 3), tensor.WithBacking(6))}
+//
+//	output_tensors, err := myMLModel.Infer(context.Background(), input_tensors)
+//
+// Metadata example:
+//
+//	metadata, err := myMLModel.Metadata(context.Background())
 type Service interface {
 	resource.Resource
+	// Infer returns an output tensor map after running an input tensor map through an interface model.
 	Infer(ctx context.Context, tensors ml.Tensors) (ml.Tensors, error)
+
+	// Metadata returns the metadata: name, data type, expected tensor/array shape, inputs, and outputs associated with the ML model.
 	Metadata(ctx context.Context) (MLMetadata, error)
 }
 

services/vision/vision.go

@@ -29,24 +29,104 @@ func init() {
 }
 
 // A Service that implements various computer vision algorithms like detection and segmentation.
+//
+// DetectionsFromCamera example:
+//
+//	// Get detections from the camera output
+//	detections, err := visService.DetectionsFromCamera(context.Background(), myCam, nil)
+//	if err != nil {
+//			logger.Fatalf("Could not get detections: %v", err)
+//	}
+//	if len(detections) > 0 {
+//			logger.Info(detections[0])
+//	}
+//
+// Detections example:
+//
+//	// 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()
+//
+//	// Get the detections from the image
+//	detections, err := visService.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:
+//
+//	// Get the 2 classifications with the highest confidence scores from the camera output
+//	classifications, err := visService.ClassificationsFromCamera(context.Background(), myCam, 2, nil)
+//	if err != nil {
+//			logger.Fatalf("Could not get classifications: %v", err)
+//	}
+//	if len(classifications) > 0 {
+//			logger.Info(classifications[0])
+//	}
+//
+// Classifications example:
+//
+//	// 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()
+//
+//	// Get the 2 classifications with the highest confidence scores from the image
+//	classifications, err := visService.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:
+//
+//	// Get the objects from the camera output
+//	objects, err := visService.GetObjectPointClouds(context.Background(), "cam1", nil)
+//	if err != nil {
+//			logger.Fatalf("Could not get point clouds: %v", err)
+//	}
+//	if len(objects) > 0 {
+//			logger.Info(objects[0])
+//	}
 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)
-	// classifier methods
+
+	// 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)
-	// segmenter methods
+
+	// 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)
 }