private async Task startDetection(MediaFile file)
{
faceDetectResult = await CognitiveService.FaceDetect(file.GetStreamWithImageRotatedForExternalStorage());
//await Task.Delay(5000);
processing = false;
}
public static async Task <FaceDetectResult> DetectFaceAsync(string photoPath)
{
// Face API 呼び出し準備
var apiKey = "YOUR_API_KEY";
var apiEndpoint = "https://YOUR_API_LOCATION.api.cognitive.microsoft.com/face/v1.0";
var client = new FaceServiceClient(apiKey, apiEndpoint);
// Face API で判定
var file = new FileFromPath(photoPath);
var imageStream = file.Open(FileAccess.Read);
var attributes = new FaceAttributeType[] {
FaceAttributeType.Age, FaceAttributeType.Gender, FaceAttributeType.Smile,
};
var result = await client.DetectAsync(imageStream, false, false, attributes);
// 判定結果を代入
var detectResult = new FaceDetectResult();
detectResult.Age = result[0].FaceAttributes.Age;
detectResult.Gender = result[0].FaceAttributes.Gender;
detectResult.Smile = result[0].FaceAttributes.Smile;
return(detectResult);
}
public static async Task <FaceDetectResult> DetectFaceAsync(string photoPath)
{
// Face API 呼び出し準備
var apiKey = "YOUR_API_KEY";
var apiEndpoint = "https://YOUR_API_LOCATION.cognitiveservices.azure.com/";
var client = new FaceClient(new ApiKeyServiceClientCredentials(apiKey))
{
Endpoint = apiEndpoint
};
// Face API で判定
var file = new FileFromPath(photoPath);
var imageStream = file.Open(FileAccess.Read);
var attributes = new FaceAttributeType[] {
FaceAttributeType.Age, FaceAttributeType.Gender, FaceAttributeType.Smile,
};
//var result = await client.Face.DetectWithStreamAsync(imageStream, false, false, attributes);
var result = await client.Face.DetectWithStreamAsync(imageStream, false, false, attributes);
// 判定結果を代入
var detectResult = new FaceDetectResult();
detectResult.Age = (double)result[0].FaceAttributes.Age;
detectResult.Gender = result[0].FaceAttributes.Gender.ToString();
detectResult.Smile = (double)result[0].FaceAttributes.Smile;
return(detectResult);
}
private async Task startDetection(MediaFile file)
{
// Appel de la fonction FaceDetect du dossier Services pour l'analyse du visage
faceDetectResult = await CognitiveService.FaceDetect(file.GetStreamWithImageRotatedForExternalStorage());
processing = false;
}
private async void RunButton_OnClicked(object sender, EventArgs e)
{
// 画像の選択
var photo = "";
var imageChoiceResult = await DisplayAlert("どちらの画像を使いますか", "", "カメラ", "ローカルフォルダー");
try
{
if (imageChoiceResult)
{
photo = await TakePhotoAsync();
}
else
{
photo = await PickPhotoAsync();
}
}
catch (Exception exception)
{
await DisplayAlert("エラーが発生しました", exception.Message, "OK");
}
// 画像の判定
ImagePreview.Source = photo;
var faceResult = new FaceDetectResult();
try
{
faceResult = await DetectFaceAsync(photo);
}
catch (Exception exception)
{
await DisplayAlert("Face API の呼び出しに失敗しました", exception.Message, "OK");
return;
}
// 判定結果を配置
Age.Text = "年齢 : " + faceResult.Age;
Gender.Text = "性別 : " + faceResult.Gender;
Smile.Text = "笑顔 : " + (faceResult.Smile * 100).ToString("##0") + "点";
}
public static async Task <FaceDetectResult> DetectFaceAsync(string photo)
{
// Face API 呼び出し準備
var subKey = "Your_FaceAPISubKey";
var client = new FaceServiceClient(subKey);
// Face API で判定
var file = await FileSystem.Current.GetFileFromPathAsync(photo);
var imageStream = await file.OpenAsync(FileAccess.Read);
var result = await client.DetectAsync(imageStream, false, false, Enum.GetValues(typeof(FaceAttributeType)).OfType <FaceAttributeType>().ToArray());
// 判定結果を代入
var detectResult = new FaceDetectResult();
detectResult.age = result[0].FaceAttributes.Age;
detectResult.gender = result[0].FaceAttributes.Gender;
detectResult.emotionKey = result[0].FaceAttributes.Emotion.ToRankedList().First <KeyValuePair <string, float> >().Key;
detectResult.emotionValue = result[0].FaceAttributes.Emotion.ToRankedList().First <KeyValuePair <string, float> >().Value;
return(detectResult);
}
public static async Task <bool> RunDetect(Guid requestID, string apis, string name, string source,
Stream incomingPicture, string sourceContainerName, string resultContainerName, IAsyncCollector <object> outputItem, TraceWriter log, string videoName = null)
{
string apikey = string.Empty;
try
{
string[] apiArr = apis.Split(',');
int randomApi = FaceHelper.Instance.Next(0, apiArr.Length);
apikey = apiArr[randomApi];
log.Info($"RunDetect request id: {requestID} apiKey: {apikey} ticks: {DateTime.Now.Ticks}");
Tuple <HttpClient, PolicyWrap <HttpResponseMessage> > tuple = FaceHelper.HttpClientList.GetOrAdd(apikey, new Tuple <HttpClient, PolicyWrap <HttpResponseMessage> >(
new HttpClient(),
FaceHelper.DefineAndRetrieveResiliencyStrategy(log)));
HttpClient client = tuple.Item1;
PolicyWrap <HttpResponseMessage> policy = tuple.Item2;
IDatabase cache = FaceHelper.Connection.GetDatabase(int.Parse(FaceHelper.Connection.GetDatabase(1).StringGet(apikey)));
//the large group id it's based on the mac address we get - each MAC address can work with different face api group
string largegroupid = ConfigurationManager.AppSettings[source];
if (videoName == null)
{
log.Info($"Detecting {name} requestId: {requestID} apiKey: {apikey} ticks: {DateTime.Now.Ticks}");
}
else
{
log.Info($"Detecting thumbnail {name} from {videoName} requestId: {requestID} apiKey: {apikey} ticks: {DateTime.Now.Ticks}");
}
byte[] pictureImage;
// Convert the incoming image stream to a byte array.
using (var br = new BinaryReader(incomingPicture))
{
pictureImage = br.ReadBytes((int)incomingPicture.Length);
}
var detectionResult = await new FaceDetect(log, client).DetectFaces(pictureImage, apikey, requestID, policy);
if ((detectionResult != null) && (detectionResult.Length > 0))
{
//prepare identify request
int maxCandidate = int.Parse(ConfigurationManager.AppSettings["maxNumOfCandidatesReturned"]);
double threshold = double.Parse(ConfigurationManager.AppSettings["confidenceThreshold"]);
var identifyResquest = new FaceIdentifyRequest()
{
ConfidenceThreshold = threshold,
MaxNumOfCandidatesReturned = maxCandidate,
LargePersonGroupId = largegroupid,
FaceIds = detectionResult.Select(s => s.FaceId).ToArray()
};
var identifyResult = await new FaceIdentify(log, client).IdentifyFaces(identifyResquest, apikey, requestID, policy);
if ((identifyResult == null) || (identifyResult.Length == 0))
{
log.Info($"No identification result requestId: {requestID}, apiKey:{apikey} ticks: {DateTime.Now.Ticks}");
}
var personResult = new PersonDetails(log, client);
//merging results and find person name
for (int i = 0; i < detectionResult.Length; i++)
{
if (videoName == null)
{
detectionResult[i].FaceBlobName = string.Concat(detectionResult[i].FaceId, "_", name);
}
else
{
detectionResult[i].FaceBlobName = videoName + "/" + name;
}
if ((identifyResult != null) && (identifyResult.Length > 0))
{
detectionResult[i].Candidates = identifyResult[i].Candidates;
for (int j = 0; j < detectionResult[i].Candidates.Length; j++)
{
string personid = detectionResult[i].Candidates[j].PersonId.ToString();
string personName = cache.StringGet(largegroupid + "-" + personid);
if (string.IsNullOrEmpty(personName) == true)
{
log.Info($"Missing cache requestId: {requestID} apiKey: {apikey} personId: {personid} ticks: {DateTime.Now.Ticks}");
var tPerson = await personResult.GetPersonName(personid, apikey, largegroupid, requestID, policy);
personName = tPerson.Name;
cache.StringSet(largegroupid + "-" + personid, personName, null, When.Always);
}
detectionResult[i].Candidates[j].PersonName = new InternalPersonDetails()
{
PersonId = detectionResult[i].Candidates[j].PersonId,
Name = personName
};
}
}
}
}
else
{
log.Info($"No dectection result requestId: {requestID}, apiKey:{apikey} ticks: {DateTime.Now.Ticks}");
//in case of video, we want to create a link to the face detected by AMS (Azure Media Services) although face api didn't recognize it
if (videoName != null)
{
detectionResult = new FaceDetectResult[1] {
new FaceDetectResult()
{
FaceBlobName = videoName + "/" + name
}
}
}
;
}
string blobname = videoName ?? name;
var actionResult = new FaceIdentifyResult()
{
BlobName = blobname,
ContainerName = sourceContainerName,
ResultContainerName = resultContainerName,
BlobLength = incomingPicture.Length,
CreatedDateTime = DateTime.UtcNow,
RequestId = requestID,
ApiKey = apikey,
LargeGroupId = largegroupid,
Source = source,
DetectResultList = detectionResult
};
string strResult = JsonConvert.SerializeObject(actionResult);
await outputItem.AddAsync(strResult);
}
catch (Exception ex)
{
log.Error($"Exception Message: {ex.Message}, requestId: {requestID}, apiKey:{apikey} ticks: {DateTime.Now.Ticks}", ex);
return(false);
}
return(true);
}
private async Task starDetection(Stream data)
{
faceDetectResult = await CognitiveService.FaceDetect(data);
processing = false;
}
private async void RunButton_OnClicked(object sender, EventArgs e)
{
// 画像の選択
var photo = "";
var imageChoiceResult = await DisplayAlert("どちらの画像を使いますか", "", "カメラ", "ローカルフォルダー");
try
{
if (imageChoiceResult)
{
photo = await TakePhotoAsync();
}
else
{
photo = await PickPhotoAsync();
}
}
catch (Exception exception)
{
await DisplayAlert("エラーが発生しました", exception.Message, "OK");
}
// 画像の判定
ImagePreview.Source = photo;
var faceResult = new FaceDetectResult();
try
{
faceResult = await DetectFaceAsync(photo);
}
catch (Exception exception)
{
await DisplayAlert("Face API の呼び出しに失敗しました", exception.Message, "OK");
return;
}
// 判定結果を配置
Age.Text = "年齢 : " + faceResult.age;
Gender.Text = "性別 : " + faceResult.gender;
Emotion.Text = "表情 : ";
switch (faceResult.emotionKey)
{
case "Anger":
Emotion.Text = Emotion.Text + "怒り";
break;
case "Contempt":
Emotion.Text = Emotion.Text + "軽蔑";
break;
case "Disgust":
Emotion.Text = Emotion.Text + "むかつき";
break;
case "Fear":
Emotion.Text = Emotion.Text + "恐れ";
break;
case "Happiness":
Emotion.Text = Emotion.Text + "喜び";
break;
case "Neutral":
Emotion.Text = Emotion.Text + "無表情";
break;
case "Sadness":
Emotion.Text = Emotion.Text + "悲しみ";
break;
case "Surprise":
Emotion.Text = Emotion.Text + "驚き";
break;
default:
break;
}
Emotion.Text = Emotion.Text + "(" + faceResult.emotionValue.ToString("0.00%") + ")";
}
private async Task FaceDetectAsync(FaceDetector detector, MediaCapture capture, CancellationToken token)
{
if (detector == null || capture == null || token == null)
{
throw new ArgumentNullException();
}
var previewProperties = capture.VideoDeviceController.GetMediaStreamProperties(MediaStreamType.VideoPreview) as VideoEncodingProperties;
var videoFrame = new VideoFrame(BitmapPixelFormat.Bgra8, (int)previewProperties.Width, (int)previewProperties.Height);
int width = (int)previewProperties.Width;
int height = (int)previewProperties.Height;
FaceDetectResult result = null;
var stopWatch = Stopwatch.StartNew();
{
using (var currentFrame = await capture.GetPreviewFrameAsync(videoFrame))
using (var softwareBitmap = currentFrame.SoftwareBitmap)
{
if (softwareBitmap == null)
{
return;
}
// SoftwareBitmap -> byte array
var buffer = new byte[4 * width * height];
softwareBitmap.CopyToBuffer(buffer.AsBuffer());
token.ThrowIfCancellationRequested();
// Detect face
result = detector.Detect(buffer, width, height);
token.ThrowIfCancellationRequested();
}
}
stopWatch.Stop();
videoFrame.Dispose();
// Draw result to Canvas
await Dispatcher.RunAsync(CoreDispatcherPriority.High, () =>
{
FaceDrawCanvas.Width = width;
FaceDrawCanvas.Height = height;
// Draw fps
FpsTextBlock.Text = (1000 / stopWatch.ElapsedMilliseconds) + "fps";
// Draw face point
if (_faceDrawer != null && result != null)
{
List <FaceDetectData> faces = new List <FaceDetectData>();
foreach (var f in result.Faces)
{
FaceDetectData data = new FaceDetectData();
data.FaceRect = f.FaceRect;
foreach (var p in f.FacePoints)
{
data.FaceLandmarks.Add(p);
}
faces.Add(data);
}
_faceDrawer.DrawToCanvas(FaceDrawCanvas, faces);
}
});
}