public override void OnStateEnter(Animator animator, AnimatorStateInfo stateInfo, int layerIndex)
{
base.OnStateEnter(animator, stateInfo, layerIndex);
if (!proxy)
{
proxy = animator.gameObject.GetComponent <AIProxy>();
}
}
public override void OnStateEnter(Animator animator, AnimatorStateInfo stateInfo, int layerIndex)
{
base.OnStateEnter(animator, stateInfo, layerIndex);
if (!proxy)
{
proxy = animator.gameObject.GetComponent <AIProxy>();
}
proxy.owner.Attack();
animator.SetBool("isAttacking", proxy.owner.isAttacking());
}
public override void OnStateEnter(Animator animator, AnimatorStateInfo stateInfo, int layerIndex)
{
base.OnStateEnter(animator, stateInfo, layerIndex);
if (!proxy)
{
proxy = animator.gameObject.GetComponent <AIProxy>();
}
waitTimeProgress = 0;
animator.SetBool("isWaiting", true);
}
void Update()
{
if (Input.GetKeyDown(keyIdeal))
{
// settings
// define tollerance (in this case is used the same tollerance for all variables and articulations
float tolleranceRadius = 0.2f;
ArticolationTollerance tollerance = new ArticolationTollerance();
tollerance.positionSpeedTolleranceRadius = tolleranceRadius;
tollerance.positionTolleranceRadius = tolleranceRadius;
tollerance.rotationSpeedTolleranceRadius = tolleranceRadius;
tollerance.rotationTolleranceRadius = tolleranceRadius;
// Configure limb, one per exercise per limb (if you want an exercise that involves to limbs, use two limbs configurations)
// example made for the arm
// instance sensors
Sensor shoulderSensor = new Sensor(shoulder, tollerance, "spalla");
Sensor elbowSensor = new Sensor(elbow, tollerance, "gomito");
Sensor handSensor = new Sensor(hand, tollerance, "mano");
// with the sensors instances create a limb configuration
LimbConfiguration config = new LimbConfiguration(shoulderSensor, elbowSensor, handSensor);
// configure exercise
ExerciseConfiguration exerciseConfiguration = new ExerciseConfiguration(
config,
(EvaluationResults results) =>
{
AIProxy aiProxy = new AIProxy(); // should be taken from context
//ArticolationError elbowError = aiProxy.UnwrapFromResults("gomito", results);
}
);
// to add more events handler:
exerciseConfiguration.OnExecutionStepEvaluated += (EvaluationResults results) => { }; // exercise configuration should be taken from somewhere
// configure the virtual physioteraphyst
VirtualPhysioterphyst eval = VirtualPhysioterphyst.Instance;
// define a timing for the sampling
float timing = 0.5f, totalDuration = 2f;
eval.timingBetweenSamples = timing;
// setup the exercise with the built configuration
eval.ExerciseSetup(exerciseConfiguration);
// start recording
eval.StartSetup();
// Start playing a new exercise (guided by the real physioteraphyst)
Sequence sequence = DOTween.Sequence();
sequence.Append(hand.transform.DOMove(hand.transform.position * 2, totalDuration));
// once finished the exercise, stop setup
sequence.OnComplete(() => {
eval.EndSetup(); // stop registering
// ... evaluating if the movement has to be saved or discarded
eval.SaveSetup(); // save registration
// eval.DiscardSetup(); // discard registration
});
}
if (Input.GetKeyDown(keyReal))
{
// turn back to initial position
hand.transform.position = initialPositionHand;
elbow.transform.position = initialPositionElbow;
shoulder.transform.position = initialPositionShoulder;
// start evaluation of the exercise
VirtualPhysioterphyst.Instance.StartEvaluation();
// play the exercise
Sequence sequence = DOTween.Sequence();
sequence.Append(hand.transform.DOMove(hand.transform.position * 2, timing / 2));
// on finish stop evaluating
sequence.OnComplete(() => VirtualPhysioterphyst.Instance.StopEvaluation());
}
}
private void CreateAI()
{
Sensor shoulderSensor = new Sensor(shoulder, shoulderTollerance, "spalla");
Sensor elbowSensor = new Sensor(elbow, elbowTollerance, "gomito");
Sensor handSensor = new Sensor(hand, handTollerance, "mano");
LimbConfiguration config = new LimbConfiguration(shoulderSensor, elbowSensor, handSensor);
// check that there is the ghost
if (sampleRecorder.trackersPreview.Count != 3)
{
throw new System.Exception("Ghost non correttamente configurato");
}
LimbConfiguration ghostConfig = new LimbConfiguration(new Sensor(sampleRecorder.trackersPreview[0]), new Sensor(sampleRecorder.trackersPreview[1]), new Sensor(sampleRecorder.trackersPreview[2]));
exerciseConfiguration = new ExerciseConfiguration(
config,
(EvaluationResults results) =>
{
AIProxy aiProxy = new AIProxy(); // should be taken from context
List <string> articolationNames = new List <string>()
{
"spalla", "gomito", "mano"
};
foreach (string articolationName in articolationNames)
{
ArticolationError error = aiProxy.UnwrapFromResults(articolationName, results, articolationNames);
bool isPositionCorrect = error.Position.IsSpeedCorrect && error.Position.IsMagnitudeCorrect,
isRotationCorrect = error.Angle.IsSpeedCorrect && error.Angle.IsMagnitudeCorrect;
Debug.Log(articolationName + ": POSITION IS CORRECT - " + isPositionCorrect.ToString()
+ " # ROTATION IS CORRECT - " + isRotationCorrect.ToString());
GameObject trackerOb;
string nameID = "";
switch (articolationName)
{
case "spalla":
nameID = "Shoulder";
trackerOb = shoulder;
break;
case "gomito":
nameID = "Elbow";
trackerOb = elbow;
break;
case "mano":
nameID = "Wrist";
trackerOb = hand;
break;
default:
trackerOb = hand;
break;
}
//Genera evento con risultati AI -> contesto
SendResult(nameID, isPositionCorrect);
if (isPositionCorrect)
{
trackerOb.GetComponent <MeshRenderer>().material.color = Color.green;
}
else
{
trackerOb.GetComponent <MeshRenderer>().material.color = Color.red;
}
}
},
ghostConfig
);
VirtualPhysioterphyst.Instance.ExerciseSetup(exerciseConfiguration);
}
