/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured(string imagePath)
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(predictionEndpoint, webForm))
{
// Gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader("Prediction-Key", predictionKey);
// The upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
// The download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// Send the request
yield return(unityWebRequest.SendWebRequest());
string jsonResponse = unityWebRequest.downloadHandler.text;
// The response will be in JSON format, therefore it needs to be deserialized
// The following lines refers to a class that you will build in later Chapters
// Wait until then to uncomment these lines
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
SceneOrganiser.Instance.SetTagsToLastLabel(analysisObject);
}
}
/// <summary>
/// Set the Tags as Text of the last label created.
/// </summary>
public void SetTagsToLastLabel(AnalysisObject analysisObject)
{
lastLabelPlacedText = lastLabelPlaced.GetComponent <TextMesh>();
if (analysisObject.Predictions != null)
{
foreach (Prediction p in analysisObject.Predictions)
{
if (p.Probability > 0.99)
{
lastLabelPlacedText.text += $"Detected: {p.TagName} {p.Probability.ToString("0.00 \n")}";
Debug.Log($"Detected: {p.TagName} {p.Probability.ToString("0.00 \n")}");
nowBloom = p.TagName;
Debug.Log(nowBloom + " " + prevBloom);
if (nowBloom == "1" && prevBloom == "0")
{
#if UNITY_EDITOR
EditorApplication.isPlaying = false;
#elif WINDOWS_UWP
Application.Quit();
Windows.ApplicationModel.Core.CoreApplication.Exit();
#endif
}
prevBloom = nowBloom;
}
}
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseCustomtImageCaptured()
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(customVisionAnalysisEndpoint, webForm))
{
// Gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader("Prediction-Key", customVisionAuthorizationKey);
// The upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
// The download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// Send the request
yield return(unityWebRequest.SendWebRequest());
string jsonResponse = unityWebRequest.downloadHandler.text;
// The response will be in JSON format, therefore it needs to be deserialized
// The following lines refers to a class that you will build in later Chapters
// Wait until then to uncomment these lines
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
if (analysisObject != null)
{
Dictionary <string, double> tagsDictionary = new Dictionary <string, double>();
foreach (Prediction td in analysisObject.Predictions)
{
var accuracyRequest = td.Probability * 100.00;
if (tagsDictionary.ContainsKey(td.TagName))
{
if (tagsDictionary[td.TagName] < td.Probability)
{
tagsDictionary[td.TagName] = td.Probability;
}
}
else
{
tagsDictionary.Add(td.TagName, td.Probability);
}
}
ResultsLabel.instance.SetTagsToCustomLabel(tagsDictionary);
}
//SceneOrganiser.Instance.SetTagsToLastLabel(analysisObject);
}
}
IEnumerator Upload(byte[] data)
{
WWWForm form = new WWWForm();
using (UnityWebRequest www = UnityWebRequest.Post("", form)) // URL aqui
{
www.SetRequestHeader("Prediction-Key", ""); //KEY aqui
www.SetRequestHeader("Content-Type", "application/octet-stream");
www.uploadHandler = new UploadHandlerRaw(data);
www.uploadHandler.contentType = "application/octet-stream";
www.downloadHandler = new DownloadHandlerBuffer();
yield return(www.SendWebRequest());
if (www.isNetworkError || www.isHttpError)
{
Debug.Log(www.error);
}
else
{
string jsonResponse = www.downloadHandler.text;
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
foreach (Prediction predict in analysisObject.Predictions)
{
if (predict.Probability > probabiltyThreshold)
{
boxFactory.GetComponent <scrBoxFactory>().CreateBox(predict.boundingBox, predict.TagName, texture.width, texture.height);
switch (predict.TagName)
{
case "Harry Potter":
infoCharacter.getCharacter(0);
break;
case "Hermione Granger":
infoCharacter.getCharacter(1);
break;
case "Ron Weasley":
infoCharacter.getCharacter(2);
break;
case "Voldemort":
infoCharacter.getCharacter(16);
break;
}
}
}
Debug.Log(analysisObject);
}
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
IEnumerator AnalyseLastImageCaptured(byte[] imageBytes, Action <AzureCVTag, double> onDone)
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(predictionEndpoint, webForm))
{
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader("Prediction-Key", predictionKey);
// The upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
// The download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// Send the request
yield return(unityWebRequest.SendWebRequest());
string jsonResponse = unityWebRequest.downloadHandler.text;
try
{
// The response will be in JSON format, therefore it needs to be deserialized
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
double maxProbability = 0.0;
AzureCVTag resultTag = AzureCVTag.none;
foreach (var prediction in analysisObject.Predictions)
{
if (prediction.Probability > maxProbability)
{
maxProbability = prediction.Probability;
Enum.TryParse(prediction.TagName, out AzureCVTag tag);
resultTag = tag;
}
}
// If the max probability of a tag is lesser than 65%, better keep it to none
if (maxProbability < 0.65)
{
resultTag = AzureCVTag.none;
maxProbability = 0;
}
onDone(resultTag, maxProbability);
}
catch (Exception ex)
{
Debug.LogError(ex.Message);
onDone(AzureCVTag.none, 0);
}
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public static void AnalyseLastImageCaptured(string imagePath)
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(predictionEndpoint, webForm))
{
// Gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader("Prediction-Key", predictionKey);
// The upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
// The download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// Send the request
var res = unityWebRequest.SendWebRequest();
while (!res.isDone)
{
System.Threading.Thread.Sleep(100);
}
string jsonResponse = unityWebRequest.downloadHandler.text;
// The response will be in JSON format, therefore it needs to be deserialized
// The following lines refers to a class that you will build in later Chapters
// Wait until then to uncomment these lines
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
var builder = new StringBuilder();
if (analysisObject.Predictions != null)
{
foreach (var item in analysisObject.Predictions)
{
if (item.Probability > 0.5)
{
builder.AppendLine(item.TagName + " found");
}
}
HintBox.Instance.ShowText(builder.ToString());
}
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured(string imagePath)
{
// Update camera status to analysis.
SceneOrganiser.Instance.SetCameraStatus("Analysis");
//Makes call to the API to analyse the picture and find a tag
//When it is done, SceneOrganiser.Instance.FinaliseLabel is called
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(predictionEndpoint, webForm))
{
// Gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader("Prediction-Key", predictionKey);
// The upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
// The download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// Send the request
yield return(unityWebRequest.SendWebRequest());
if (unityWebRequest.error != "")
{
DialogManager.Instance.LaunchBasicDialog(1, "Internet Error", "Please verify your internet connection");
}
string jsonResponse = unityWebRequest.downloadHandler.text;
Debug.Log("response: " + jsonResponse);
// Create a texture. Texture size does not matter, since
// LoadImage will replace with the incoming image size.
Texture2D tex = new Texture2D(1, 1);
tex.LoadImage(imageBytes);
SceneOrganiser.Instance.quadRenderer.material.SetTexture("_MainTex", tex);
// The response will be in JSON format, therefore it needs to be deserialized
AnalysisObject analysisObject = new AnalysisObject();
analysisObject = JsonConvert.DeserializeObject <AnalysisObject>(jsonResponse);
SceneOrganiser.Instance.FinaliseLabel(analysisObject);
}
}
/// <summary>
/// Set the Tags as Text of the last label created.
/// </summary>
public void SetTagsToLastLabel(AnalysisObject analysisObject)
{
lastLabelPlacedText = lastLabelPlaced.GetComponent <TextMesh>();
if (analysisObject.Predictions != null)
{
foreach (Prediction p in analysisObject.Predictions)
{
if (p.Probability > 0.02)
{
lastLabelPlacedText.text += $"Detected: {p.TagName} {p.Probability.ToString("0.00 \n")}";
Debug.Log($"Detected: {p.TagName} {p.Probability.ToString("0.00 \n")}");
}
}
}
}
/// <summary>
/// Initializes the settings view.
/// </summary>
/// <param name="vis">The visualization that will be configured by this settings view.</param>
/// <param name="showStaticObjects">Whether static study objects should be shown in the settings view.</param>
/// <param name="showSpeedSettings">Whether speed settings should be shown in the settings view.</param>
public override void Init(IConfigurableVisualization vis, bool showStaticObjects = true, bool showSpeedSettings = false)
{
if (vis == null)
{
return;
}
VisProperties settings = vis.Settings;
visId = vis.Settings.VisId;
settingsViewObjects = new List <SettingsViewObject>();
int i = 0;
// create settings prefab for each object
if (SettingsPrefab && Services.DataManager() != null)
{
foreach (var dataSetElem in Services.DataManager().DataSets.Values)
{
AnalysisObject dataSet = (AnalysisObject)dataSetElem;
var settingsViewObject = GameObject.Instantiate <SettingsViewObject>(SettingsPrefab, this.transform);
settingsViewObject.transform.localPosition = new Vector3(StartPositionX, StartPositionY - (i * OffsetY), -0.009f);
settingsViewObject.DataSet = dataSet;
settingsViewObject.Init();
for (int idx = 0; idx < settings.ObjectIds.Count; idx++)
{
if (settings.ObjectIds[idx] == dataSetElem.Id)
{
settingsViewObject.IsObjectSelected = true;
if (settings.TryGet("useSpeed", out List <bool> useSpeedList))
{
if (useSpeedList != null && useSpeedList.Count == settings.ObjectIds.Count)
{
settingsViewObject.IsUseSpeedSelected = useSpeedList[idx];
}
}
}
}
settingsViewObjects.Add(settingsViewObject);
i++;
}
}
}
private void DrawTrajectory(AnalysisObject dataSet, bool useSpeed)
{
// for all sessions that we want to visualize...
for (int s = 0; s < Settings.Sessions.Count; s++)
{
// for all conditions that we want to visualize...
for (int c = 0; c < Settings.Conditions.Count; c++)
{
var line = Instantiate(LinePrefab, Anchor);
var lineComponent = line.GetComponent <CustomTubeRenderer>();
lineComponent.startWidth = 0.0f;
lineComponent.endWidth = 0.006f;
float colorSaturationOffset = ((Settings.Conditions.Count * s) + c) / (float)(Settings.Conditions.Count * Settings.Sessions.Count);
UpdateSegments(dataSet, Settings.Sessions[s], Settings.Conditions[c], useSpeed, colorSaturationOffset, out List <List <Vector3> > segments, out List <List <Color> > colorSegments);
lineComponent.SetPositions(segments, colorSegments);
primitives.Add(lineComponent);
}
}
}
private void UpdateSegments(AnalysisObject dataSet, int session, int condition, bool useSpeed, float colorSaturationOffset, out List <List <Vector3> > segments, out List <List <Color> > colorSegments)
{
Color.RGBToHSV(dataSet.ObjectColor, out float colorH, out float colorS, out float colorV);
if (Settings.Conditions.Count * Settings.Sessions.Count > 3)
{
colorS = Math.Max(0.9f, colorS);
}
Color objectColor = Color.HSVToRGB(colorH, colorS - colorSaturationOffset, colorV);
var infoObjects = dataSet.GetInfoObjects(session, condition); // get data for current session/condition
segments = new List <List <Vector3> >();
List <Vector3> points = new List <Vector3>();
colorSegments = new List <List <Color> >();
List <Color> colors = new List <Color>();
Vector3 previousPosition = new Vector3(0, 0, 0);
long previousTimestamp = 0;
Vector3 currentPosition;
long currentTime = Services.StudyManager().CurrentTimestamp;
int currentIndex = dataSet.GetIndexFromTimestamp(currentTime, session, condition);
int firstIndex = dataSet.GetIndexFromTimestamp(currentTime - (long)(trailTime * TimeSpan.TicksPerSecond), session, condition);
int alphaDropOffIndex = dataSet.GetIndexFromTimestamp(currentTime - (long)(0.75 * trailTime * TimeSpan.TicksPerSecond), session, condition);
for (int i = firstIndex; i < currentIndex; i++)
{
Sample o = infoObjects[i];
if (float.IsNaN(o.Position.x) || float.IsNaN(o.Position.y) || float.IsNaN(o.Position.z))
{
throw new ArgumentException("float.NaN is not a valid position.");
}
currentPosition = o.Position;
// check data to decide if we want to add this measurement
if (!CheckReductionFilter(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 15, 0.03f))
{
continue;
}
// checks our time filter
if (o.Timestamp < currentTimeFilterMin || o.Timestamp > currentTimeFilterMax)
{
continue;
}
// check if probably an outlier (too fast)
if (true || !CheckOutlier(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 10.0f))
{
points.Add(currentPosition); // add current point
// compute color based on speed
float diffSeconds = (float)(o.Timestamp - previousTimestamp) / TimeSpan.TicksPerSecond;
float diffDistance = (currentPosition - previousPosition).magnitude;
float currentSpeed = diffDistance / diffSeconds;
Color color;
if (useSpeed)
{
color = MapSpeedToColor(currentSpeed);
}
else
{
color = objectColor;
}
// set alpha
if (alphaDropOffIndex > firstIndex)
{
color.a = (i - firstIndex) / ((alphaDropOffIndex - firstIndex) * 1.0f);
}
colors.Add(color);
}
else
{
if (points.Count > 3)
{
segments.Add(points);
colors[0] = colors[1]; // first speed value cannot be correct, correct color
colorSegments.Add(colors);
}
points = new List <Vector3>();
points.Add(currentPosition);
colors = new List <Color>();
// compute color based on speed
float diffSeconds = (float)(o.Timestamp - previousTimestamp) / TimeSpan.TicksPerSecond;
float diffDistance = (currentPosition - previousPosition).magnitude;
float currentSpeed = diffDistance / diffSeconds;
Color color;
if (useSpeed)
{
color = MapSpeedToColor(currentSpeed);
}
else
{
color = objectColor;
}
// set alpha
if (alphaDropOffIndex > firstIndex)
{
color.a = (i - firstIndex) / ((alphaDropOffIndex - firstIndex) * 1.0f);
}
colors.Add(color);
}
previousPosition = currentPosition;
previousTimestamp = o.Timestamp;
}
if (points.Count > 3)
{
segments.Add(points);
colors[0] = colors[1]; // first speed value cannot be correct, correct color
colorSegments.Add(colors);
}
}
private void DrawTrajectoryWithSpeed(AnalysisObject dataSet)
{
// for all sessions that we want to visualize...
for (int s = 0; s < Settings.Sessions.Count; s++)
{
// for all conditions that we want to visualize...
for (int c = 0; c < Settings.Conditions.Count; c++)
{
var line = Instantiate(LinePrefab, Anchor);
var lineComponent = line.GetComponent <CustomTubeRenderer>();
lineComponent.startWidth = 0.003f;
lineComponent.endWidth = 0.003f;
var infoObjects = dataSet.GetInfoObjects(Settings.Sessions[s], Settings.Conditions[c]); // get data for current session/condition
// prepare lists for points and colors
List <List <Vector3> > segments = new List <List <Vector3> >();
List <Vector3> points = new List <Vector3>();
List <List <Color> > colorSegments = new List <List <Color> >();
List <Color> colors = new List <Color>();
Vector3 previousPosition = new Vector3(0, 0, 0);
long previousTimestamp = long.MinValue;
Vector3 currentPosition;
for (int i = 0; i < infoObjects.Count; i++)
{
Sample o = infoObjects[i];
if (float.IsNaN(o.Position.x) || float.IsNaN(o.Position.y) || float.IsNaN(o.Position.z))
{
throw new ArgumentException("float.NaN is not a valid position.");
}
currentPosition = o.Position;
// check data to decide if we want to add this measurement
if (!CheckReductionFilter(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 15, 0.03f))
{
continue;
}
// checks our time filter
if (o.Timestamp < currentTimeFilterMin || o.Timestamp > currentTimeFilterMax)
{
continue;
}
// check if probably an outlier (too fast)
if (true || !CheckOutlier(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 10.0f))
{
points.Add(currentPosition); // add current point
// compute color based on speed
////float diffSeconds = (float)(o.Timestamp - previousTimestamp) / TimeSpan.TicksPerSecond;
////float diffDistance = (currentPosition - previousPosition).magnitude;
////float currentSpeed = diffDistance / diffSeconds;
////Color color = MapSpeedToColor(currentSpeed);
Color color = MapSpeedToColor(o.Speed);
colors.Add(color);
}
else
{
if (points.Count > 3)
{
segments.Add(points);
colors[0] = colors[1]; // first speed value cannot be correct, correct color
colorSegments.Add(colors);
}
points = new List <Vector3>();
points.Add(currentPosition);
colors = new List <Color>();
// compute color based on speed
float diffSeconds = (float)(o.Timestamp - previousTimestamp) / TimeSpan.TicksPerSecond;
float diffDistance = (currentPosition - previousPosition).magnitude;
float currentSpeed = diffDistance / diffSeconds;
Color color = MapSpeedToColor(currentSpeed);
colors.Add(color);
}
previousPosition = currentPosition;
previousTimestamp = o.Timestamp;
}
if (points.Count > 3)
{
segments.Add(points);
colors[0] = colors[1]; // first speed value cannot be correct, correct color
colorSegments.Add(colors);
}
lineComponent.SetPositions(segments, colorSegments);
primitives.Add(lineComponent);
}
}
}
private void DrawTrajectoryPlain(AnalysisObject dataSet)
{
// for all sessions that we want to visualize...
for (int s = 0; s < Settings.Sessions.Count; s++)
{
// for all conditions that we want to visualize
for (int c = 0; c < Settings.Conditions.Count; c++)
{
var line = Instantiate(LinePrefab, Anchor);
var lineComponent = line.GetComponent <CustomTubeRenderer>();
lineComponent.startWidth = 0.003f;
lineComponent.endWidth = 0.003f;
float colorSaturationOffset = ((Settings.Conditions.Count * s) + c) / (float)(Settings.Conditions.Count * Settings.Sessions.Count);
Color.RGBToHSV(dataSet.ObjectColor, out float colorH, out float colorS, out float colorV);
if (Settings.Conditions.Count * Settings.Sessions.Count > 3)
{
colorS = Math.Max(0.9f, colorS);
}
Color objectColor = Color.HSVToRGB(colorH, colorS - colorSaturationOffset, colorV);
lineComponent.Color = objectColor;
var infoObjects = dataSet.GetInfoObjects(Settings.Sessions[s], Settings.Conditions[c]); // get data for current session/condition
List <List <Vector3> > segments = new List <List <Vector3> >();
List <Vector3> points = new List <Vector3>();
Vector3 previousPosition = new Vector3(0, 0, 0);
long previousTimestamp = long.MinValue;
Vector3 currentPosition;
for (int i = 0; i < infoObjects.Count; i++)
{
Sample o = infoObjects[i];
if (float.IsNaN(o.Position.x) || float.IsNaN(o.Position.y) || float.IsNaN(o.Position.z))
{
throw new ArgumentException("float.NaN is not a valid position.");
}
currentPosition = o.Position;
// check data to decide if we want to add this measurement
if (!CheckReductionFilter(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 15, 0.03f))
{
continue;
}
// checks our time filter
if (o.Timestamp < currentTimeFilterMin || o.Timestamp > currentTimeFilterMax)
{
continue;
}
// check if probably an outlier (too fast)
if (true || !CheckOutlier(o.Timestamp, currentPosition, ref previousTimestamp, ref previousPosition, 10.0f))
{
points.Add(currentPosition);
}
else
{
if (points.Count > 3)
{
segments.Add(points);
}
points = new List <Vector3>();
points.Add(currentPosition);
}
previousPosition = currentPosition;
previousTimestamp = o.Timestamp;
}
if (points.Count > 3)
{
segments.Add(points);
}
lineComponent.SetPositions(segments);
primitives.Add(lineComponent);
}
}
}
/// <summary>
/// Set the Tags as Text of the last label created.
/// </summary>
public void FinaliseLabel(AnalysisObject analysisObject)
{
if (analysisObject.Predictions != null)
{
// Sort the Predictions to locate the highest one
List <Prediction> sortedPredictions = new List <Prediction>();
sortedPredictions = analysisObject.Predictions.OrderBy(p => p.Probability).ToList();
Prediction bestPrediction = new Prediction();
bestPrediction = sortedPredictions[sortedPredictions.Count - 1];
if (bestPrediction.Probability > probabilityThreshold)
{
// The prediction is considered good enough
quadRenderer = quad.GetComponent <Renderer>() as Renderer;
Bounds quadBounds = quadRenderer.bounds;
//Draw a cube acting like a visible boundingBox for the recognized object
GameObject objBoundingBox = DrawInSpace.Instance.DrawCube((float)bestPrediction.BoundingBox.Width, (float)bestPrediction.BoundingBox.Height);
objBoundingBox.transform.parent = quad.transform;
objBoundingBox.transform.localPosition = CalculateBoundingBoxPosition(quadBounds, bestPrediction.BoundingBox);
DrawInSpace.Instance.ChooseMaterial(objBoundingBox, "BoundingBoxTransparentFlashy"); //optional
//Set the position and scale of the quad depending on user position
objBoundingBox.transform.SetParent(transform); //break the link with quad (picture)
BoundingBoxManager.Instance.MakeBoundingBoxInteractible(objBoundingBox);
objBoundingBox.AddComponent <EventTriggerDelegate>(); //to block picture functions while moving bounding box
// Move the label upward in world space just above the boundingBox.
LastLabelPlaced.transform.position = objBoundingBox.transform.position;
LastLabelPlaced.transform.Translate(Vector3.up * (float)(bestPrediction.BoundingBox.Height / 2 + 0.1f), Space.World); //Vector3 = World Space and Transform = Local Space
LastLabelPlaced.transform.parent = objBoundingBox.transform; //Link the Text label to the object bounding box
// Set the label text and make it face the user
SetLastLabel(bestPrediction.TagName);
LastLabelPlaced.transform.rotation = Quaternion.LookRotation(Camera.main.transform.forward);
RecognizedObject = bestPrediction.TagName;
}
else
{
RecognizedObject = null;
}
}
else
{
RecognizedObject = null;
}
if (ImageCapture.Instance.AppMode == ImageCapture.AppModes.Analysis)
{
if (RecognizedObject == null)
{
SetLastLabel("Unknown");
}
}
else if (ImageCapture.Instance.AppMode == ImageCapture.AppModes.Smart)
{
ImageCapture.Instance.UploadPhoto(RecognizedObject);
}
//Make a sound to notify the user of the new label
AudioPlay.Instance.PlayWithVolume("Bell", 80);
ImageCapture.Instance.ResetImageCapture();
}
