public void processResponse(String jsonResponse)
{
// The response will be in Json format
// therefore it needs to be deserialized into the classes AnalysedObject and TagData
AnalysedObject analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedObject>(jsonResponse);
if (analysedObject.tags == null)
{
Debug.Log("analysedObject.tagData is null");
}
else
{
Dictionary <string, float> tagsDictionary = new Dictionary <string, float>();
foreach (TagData td in analysedObject.tags)
{
TagData tag = td as TagData;
tagsDictionary.Add(tag.name, tag.confidence);
Debug.Log(tag.name + " " + tag.confidence);
}
//ResultsLabel.instance.SetTagsToLastLabel(tagsDictionary);
}
}
/// <summary>
/// Begin process of Image Capturing and send To Azure
/// Computer Vision service.
/// </summary>
private async Task ExecuteImageCaptureAndAnalysis()
{
var isNewCapture = false;
if (AppSession.Capabilities.isWindowsMR)
{
isNewCapture = TakeScreenshotMixedReality();
}
else if (AppSession.Capabilities.isHololens)
{
isNewCapture = TakeScreenshotHololens();
}
else //we assume PC
{
isNewCapture = TakeScreenshotPC();
}
if (isNewCapture)
{
AnalysedObject result = null;
var taskResult = await _azureController.PostVisionAnalysisAsync <AnalysedObject>(GetScreenshotFilePath());
imgHandler.SetConfidenceLevel(1);
//TODO: update color indicator
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured()
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(visionAnalysisEndpoint, webForm))
{
// gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader(ocpApimSubscriptionKeyHeader, authorizationKey);
// the download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// the upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
yield return(unityWebRequest.SendWebRequest());
long responseCode = unityWebRequest.responseCode;
try
{
string jsonResponse = null;
jsonResponse = unityWebRequest.downloadHandler.text;
// The response will be in Json format
// therefore it needs to be deserialized into the classes AnalysedObject and TagData
AnalysedObject analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedObject>(jsonResponse);
if (analysedObject.tags == null)
{
Debug.Log("analysedObject.tagData is null");
}
else
{
Dictionary <string, float> tagsDictionary = new Dictionary <string, float>();
foreach (TagData td in analysedObject.tags)
{
TagData tag = td as TagData;
tagsDictionary.Add(tag.name, tag.confidence);
}
ResultsLabel.instance.SetTagsToLastLabel(tagsDictionary);
}
}
catch (Exception exception)
{
Debug.Log("Json exception.Message: " + exception.Message);
}
yield return(null);
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured()
{
WWWForm webForm = new WWWForm();
// gets a byte array out of the saved image
byte[] imageBytes = GetImageAsByteArray(imagePath);
// put in base64 format for endpoint and use with field "image"
String s = Convert.ToBase64String(imageBytes);
webForm.AddField("image", s);
// display the image that was taken and being uploaded to the endpoint
Texture2D tex = new Texture2D(2, 2);
tex.LoadImage(imageBytes);
GameObject taken_image_object = ResultsLabel.instance.lastLabelPlaced.transform.Find("TakenImage").gameObject;
UnityEngine.UI.RawImage taken_raw_image = taken_image_object.GetComponent <UnityEngine.UI.RawImage>();
taken_raw_image.texture = tex;
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(endpoint, webForm))
{
yield return(unityWebRequest.SendWebRequest());
if (unityWebRequest.isNetworkError || unityWebRequest.isHttpError)
{
print(unityWebRequest.error);
}
else
{
// print("finished uploading image");
String json_response_text = unityWebRequest.downloadHandler.text;
analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedObject>(json_response_text);
masterdictionary = GetDictFromJsonText(analysedObject);
// get the first objectid
string current_objectid = analysedObject.items_info[0].objectid;
// set the title
WriteTitle(masterdictionary, current_objectid);
// set the information
WriteInformation(masterdictionary, current_objectid);
// set the image from the base64 string
WriteImageToScreenFromBase64(analysedObject.img_str);
}
}
}
public IEnumerator TakeImageAnalysis(Texture2D tex)
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest webReq = UnityWebRequest.Post(visionEndpoint, webForm))
{
byte[] imageBytes = tex.EncodeToPNG();
webReq.SetRequestHeader("Content-Type", "application/octet-stream");
webReq.SetRequestHeader("Accept", "application/json");
webReq.SetRequestHeader(subKeyHeader, authKey);
webReq.downloadHandler = new DownloadHandlerBuffer();
webReq.uploadHandler = new UploadHandlerRaw(imageBytes);
webReq.uploadHandler.contentType = "application/octet-stream";
yield return(webReq.SendWebRequest());
long responseCode = webReq.responseCode;
try
{
string jsonResponse = null;
jsonResponse = webReq.downloadHandler.text;
// The response will be in Json format
// therefore it needs to be deserialized into the classes AnalysedObject and TagData
AnalysedObject analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedFaces>("{\"faces\": " + jsonResponse + "}").faces[0];
if (analysedObject.faceAttributes == null)
{
Debug.Log("analysedObject.tagData is null");
}
else
{
EmotionManager.Instance.faceAttributes = analysedObject.faceAttributes;
EmotionManager.Instance.EmotionsUpdated();
}
}
catch (Exception exception)
{
Debug.Log("Json exception.Message: " + exception.Message);
}
yield return(null);
}
}
// returns the deserlized object from the json text from the endpoint response
public Dictionary <string, Dictionary <string, string> > GetDictFromJsonText(AnalysedObject analysedObject)
{
Dictionary <string, Dictionary <string, string> > temp_masterdictionary = new Dictionary <string, Dictionary <string, string> >();
foreach (Item i in analysedObject.items_info)
{
// Debug.Log(i.objectid);
Dictionary <string, string> EmployeeList = new Dictionary <string, string>();
foreach (Info info in i.information)
{
// Debug.Log(info.title);
// Debug.Log(info.description);
EmployeeList.Add(info.title, info.description);
}
temp_masterdictionary.Add(i.objectid, EmployeeList);
}
return(temp_masterdictionary);
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured()
{
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(visionAnalysisEndpoint, webForm))
{
// gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/octet-stream");
unityWebRequest.SetRequestHeader(ocpApimSubscriptionKeyHeader, authorizationKey);
// the download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
// the upload handler will help uploading the byte array with the request
unityWebRequest.uploadHandler = new UploadHandlerRaw(imageBytes);
unityWebRequest.uploadHandler.contentType = "application/octet-stream";
yield return(unityWebRequest.SendWebRequest());
long responseCode = unityWebRequest.responseCode;
try
{
string jsonResponse = null;
jsonResponse = unityWebRequest.downloadHandler.text;
// The response will be in Json format
// therefore it needs to be deserialized into the classes AnalysedObject and TagData
AnalysedObject analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedObject>(jsonResponse);
if (analysedObject.tags == null)
{
Debug.Log("analysedObject.tagData is null");
}
else
{
Dictionary <string, string> tagsDictionary = new Dictionary <string, string>();
foreach (TagData td in analysedObject.tags)
{
TagData tag = td as TagData;
if ("fruit".Equals(tag.name, StringComparison.OrdinalIgnoreCase))
{
tagsDictionary = new Dictionary <string, string>();
tagsDictionary.Add(tag.name, "calories:52kcal\nfat:0.2g\npolyunsatured fat:0.1g\ncholesterol:0mg\nsodium:1mg\npotassium:107mg\ncarbs:14g\nfood fiber:1.4g\nsugar: 10g\nproteins:0.3g");
break;
}
else
{
tagsDictionary.Add(tag.name, tag.confidence.ToString("0.00 \n"));
}
}
ResultsLabel.instance.SetTagsToLastLabel(tagsDictionary);
}
}
catch (Exception exception)
{
Debug.Log("Json exception.Message: " + exception.Message);
}
yield return(null);
}
}
/// <summary>
/// Call the Computer Vision Service to submit the image.
/// </summary>
public IEnumerator AnalyseLastImageCaptured()
{
lc.LogMessage("Sende Data");
WWWForm webForm = new WWWForm();
using (UnityWebRequest unityWebRequest = UnityWebRequest.Post(visionAnalysisEndpoint, webForm))
{
// gets a byte array out of the saved image
imageBytes = GetImageAsByteArray(imagePath);
unityWebRequest.SetRequestHeader("Content-Type", "application/json");
unityWebRequest.SetRequestHeader(ocpApimSubscriptionKeyHeader, authorizationKey);
byte[] bytes = Encoding.ASCII.GetBytes("{((char)34)url((char)34):((char)34)https://img.purch.com/w/660/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA5Ny85NTkvb3JpZ2luYWwvc2h1dHRlcnN0b2NrXzYzOTcxNjY1LmpwZw==((char)34)}");
unityWebRequest.uploadHandler = new UploadHandlerRaw(bytes);
unityWebRequest.uploadHandler.contentType = "application/json";
// the download handler will help receiving the analysis from Azure
unityWebRequest.downloadHandler = new DownloadHandlerBuffer();
try
{
unityWebRequest.SendWebRequest();
}catch (Exception e) { lc.LogMessage(e.Message); }
yield return(unityWebRequest.SendWebRequest());
long responseCode = unityWebRequest.responseCode;
lc.LogMessage("ResponseCode = " + responseCode);
try
{
string jsonResponse = null;
jsonResponse = unityWebRequest.downloadHandler.text;
lc.LogMessage("Antwort: " + responseCode + " " + jsonResponse);
// The response will be in Json format
// therefore it needs to be deserialized into the classes AnalysedObject and TagData
AnalysedObject analysedObject = new AnalysedObject();
analysedObject = JsonUtility.FromJson <AnalysedObject>(jsonResponse);
if (analysedObject.tags == null)
{
Debug.Log("analysedObject.tagData is null");
}
else
{
Dictionary <string, float> tagsDictionary = new Dictionary <string, float>();
foreach (TagData td in analysedObject.tags)
{
TagData tag = td as TagData;
tagsDictionary.Add(tag.name, tag.confidence);
}
ResultsLabel.instance.SetTagsToLastLabel(tagsDictionary);
}
}
catch (Exception exception)
{
Debug.Log("Json exception.Message: " + exception.Message);
}
}
}
