/// <summary>
/// Add All detected faces to a specific collection
/// </summary>
/// <param name="collectionId"></param>
/// <param name="imageId"></param>
/// <param name="image"></param>
public FaceRecord AddImageToCollection(string collectionId, Amazon.Rekognition.Model.Image image)
{
AmazonRekognitionClient rekognitionClient = AmazonClient.GetInstance();
//Validate that image contains only one face.
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(new Amazon.Rekognition.Model.DetectFacesRequest
{
Attributes = new List <string> {
"ALL"
},
Image = image
});
if (null != detectFacesResponse.FaceDetails && detectFacesResponse.FaceDetails.Count > 1)
{
throw new ArgumentNullException("Many faces in the image");
}
IndexFacesRequest indexFacesRequest = new IndexFacesRequest()
{
Image = image,
CollectionId = collectionId,
DetectionAttributes = new List <String>()
{
"ALL"
}
};
IndexFacesResponse indexFacesResponse = rekognitionClient.IndexFaces(indexFacesRequest);
return(indexFacesResponse.FaceRecords.FirstOrDefault());
}
public void ScoreFaces(DetectFacesResponse detectFactResponse)
{
LogInfo(JsonConvert.SerializeObject(detectFactResponse));
// LEVEL 4: choose one or more categories to build criteria from
// ageRange, beard, boundingBox, eyeglasses, eyesOpen, gender, mouthOpen, mustache, pose, quality, smile, sunglasses
}
/// <summary>
/// Unmarshaller the response from the service to the response class.
/// </summary>
/// <param name="context"></param>
/// <returns></returns>
public override AmazonWebServiceResponse Unmarshall(JsonUnmarshallerContext context)
{
DetectFacesResponse response = new DetectFacesResponse();
context.Read();
int targetDepth = context.CurrentDepth;
while (context.ReadAtDepth(targetDepth))
{
if (context.TestExpression("FaceDetails", targetDepth))
{
var unmarshaller = new ListUnmarshaller <FaceDetail, FaceDetailUnmarshaller>(FaceDetailUnmarshaller.Instance);
response.FaceDetails = unmarshaller.Unmarshall(context);
continue;
}
if (context.TestExpression("OrientationCorrection", targetDepth))
{
var unmarshaller = StringUnmarshaller.Instance;
response.OrientationCorrection = unmarshaller.Unmarshall(context);
continue;
}
}
return(response);
}
public async static void GetFaceDetailFromS3(IAmazonRekognition rekognitionClient, string bucketName, string keyName)
{
FaceDetail result = null;
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image {
S3Object = new S3Object {
Bucket = bucketName,
Name = keyName
}
},
Attributes = new List <String>()
{
"ALL"
}
};
try
{
Task <DetectFacesResponse> detectTask = rekognitionClient.DetectFacesAsync(detectFacesRequest);
DetectFacesResponse detectFacesResponse = await detectTask;
PrintFaceDetails(detectFacesResponse.FaceDetails);
if (detectFacesResponse.FaceDetails.Count > 0)
{
result = detectFacesResponse.FaceDetails[0]; // take the 1st face only
}
}
catch (AmazonRekognitionException rekognitionException)
{
Console.WriteLine(rekognitionException.Message, rekognitionException.InnerException);
}
}
public static async Task <string> FunctionHandler(String photo)
{
String bucket = "moodanalysis";
//ArrayList result = new ArrayList();
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
Attributes = new List <String>()
{
"ALL"
}
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
int count = detectFacesResponse.FaceDetails.Count;
//return false;
return((count == 1) + "");
}
public async static Task <FaceDetail> GetFaceDetailFromStream(IAmazonRekognition rekognitionClient, MemoryStream stream)
{
FaceDetail result = null;
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image {
Bytes = stream
},
Attributes = new List <String>()
{
"ALL"
}
};
try
{
Task <DetectFacesResponse> detectTask = rekognitionClient.DetectFacesAsync(detectFacesRequest);
DetectFacesResponse detectFacesResponse = await detectTask;
PrintFaceDetails(detectFacesResponse.FaceDetails);
if (detectFacesResponse.FaceDetails.Count > 0)
{
result = detectFacesResponse.FaceDetails[0]; // take the 1st face only
}
}
catch (AmazonRekognitionException rekognitionException)
{
Console.WriteLine(rekognitionException.Message, rekognitionException.InnerException);
}
return(result);
}
public DetectFacesResponse DetectFaces(DetectFaceParams dfp)
{
DetectFacesResponse resp = null;
var conf = new AmazonRekognitionConfig()
{
RegionEndpoint = dfp.RegEndpoint
};
using (recClient = new AmazonRekognitionClient(awsAccessKeyId, awsSecretAccessKey, conf))
{
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = dfp.PhotoName,
Bucket = dfp.BucketName
},
},
// Attributes can be "ALL" or "DEFAULT".
// "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality.
// "ALL": See https://docs.aws.amazon.com/sdkfornet/v3/apidocs/items/Rekognition/TFaceDetail.html
Attributes = new List <String>()
{
"ALL"
}
};
try
{
resp = recClient.DetectFaces(detectFacesRequest);
if (resp == null)
{
throw new Exception("AmazonRekognitionClient DetectFaces method call return null.");
}
//bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
//foreach (FaceDetail face in resp.Result.FaceDetails)
//{
// Console.WriteLine("BoundingBox: top={0} left={1} width={2} height={3}", face.BoundingBox.Left,
// face.BoundingBox.Top, face.BoundingBox.Width, face.BoundingBox.Height);
// Console.WriteLine("Confidence: {0}\nLandmarks: {1}\nPose: pitch={2} roll={3} yaw={4}\nQuality: {5}",
// face.Confidence, face.Landmarks.Count, face.Pose.Pitch,
// face.Pose.Roll, face.Pose.Yaw, face.Quality);
// if (hasAll)
// Console.WriteLine("The detected face is estimated to be between " +
// face.AgeRange.Low + " and " + face.AgeRange.High + " years old.");
//}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
return(resp);
}
async void Analyze_Clicked(System.Object sender, System.EventArgs e)
{
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(AWSAccessKeyID, AWSSecretAccessKey, Amazon.RegionEndpoint.USEast1);
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Amazon.Rekognition.Model.Image()
{
S3Object = new S3Object
{
Bucket = "babytech-images",
Name = "baby-eyes-mouth-open.jpg"
}
},
Attributes = new List <String>()
{
"ALL"
}
};
try
{
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
const float confidence_threshold = 0.8F;
// check if mouth is open
if ((face.MouthOpen != null) && (face.MouthOpen.Confidence > confidence_threshold))
{
FacialAnalysisData += (face.MouthOpen.Value) ? "\n✔ Baby's mouth is open." : "\n❌ Baby's mouth is not open.";
}
else
{
FacialAnalysisData += "\n❌ Unable to determine if baby's mouth is open.";
}
// check if eyes are open
if ((face.EyesOpen != null) && (face.EyesOpen.Confidence > confidence_threshold))
{
FacialAnalysisData += (face.EyesOpen.Value) ? "\n✔ Baby's eyes are open." : "\n❌ Baby's eyes are not open.";
}
else
{
FacialAnalysisData += "\n❌ Unable to determine if baby's eyes are open.";
}
}
DisplayAlert("Analysis Results", FacialAnalysisData, "OK");
}
catch (Exception exception)
{
Console.WriteLine(exception.Message);
}
}
public async Task <State> Run(State state, ILambdaContext context)
{
var photo = new Image
{
S3Object = new Amazon.Rekognition.Model.S3Object
{
Bucket = state.Bucket,
Name = state.ImageKey
}
};
Console.WriteLine($"Analyzing image {state.Bucket}:{state.ImageKey}");
var detectLabels = await this.RekognitionClient.DetectLabelsAsync(new DetectLabelsRequest
{
MinConfidence = MinConfidence,
Image = photo
});
var tags = new Dictionary <string, string>();
foreach (var label in detectLabels.Labels.Take(10))
{
Console.WriteLine($"\tFound Label {label.Name} with confidence {label.Confidence}");
tags.Add(label.Name, label.Confidence.ToString());
}
// Now detect faces
DetectFacesResponse detectFaces = await this.RekognitionClient.DetectFacesAsync(new DetectFacesRequest
{
Attributes = new List <string> {
"ALL"
},
Image = photo
});
foreach (var face in detectFaces.FaceDetails)
{
Console.WriteLine($"\tFound face {face.BoundingBox.ToString()} with age {face.AgeRange.ToString()}");
}
var celebrityResult = await this.RekognitionClient.RecognizeCelebritiesAsync(new RecognizeCelebritiesRequest
{
Image = photo
});
if (celebrityResult.CelebrityFaces.Count > 0)
{
state.Celebrity = celebrityResult.CelebrityFaces.First();
}
state.Labels = tags;
state.Faces = detectFaces.FaceDetails;
return(state);
}
static void IdentifyFaces(string filename)
{
// Using USWest2, not the default region
AmazonRekognitionClient rekoClient = new AmazonRekognitionClient(Amazon.RegionEndpoint.USWest2);
// Request needs image bytes, so read and add to request
byte[] data = File.ReadAllBytes(filename);
DetectFacesRequest dfr = new DetectFacesRequest
{
Image = new Amazon.Rekognition.Model.Image
{
Bytes = new MemoryStream(data)
}
};
DetectFacesResponse outcome = rekoClient.DetectFaces(dfr);
if (outcome.FaceDetails.Count > 0)
{
// Load a bitmap to modify with face bounding box rectangles
Bitmap facesHighlighted = new Bitmap(filename);
Pen pen = new Pen(Color.Black, 3);
// Create a graphics context
using (var graphics = Graphics.FromImage(facesHighlighted))
{
foreach (var fd in outcome.FaceDetails)
{
// Get the bounding box
BoundingBox bb = fd.BoundingBox;
Console.WriteLine($"Bounding box = ({bb.Left}, {bb.Top}, {bb.Height}, {bb.Width})");
// Draw the rectangle using the bounding box values
// They are percentages so scale them to picture
graphics.DrawRectangle(pen,
x: facesHighlighted.Width * bb.Left,
y: facesHighlighted.Height * bb.Top,
width: facesHighlighted.Width * bb.Width,
height: facesHighlighted.Height * bb.Height);
}
}
// Save the image with highlights as PNG
string fileout = filename.Replace(Path.GetExtension(filename), "_faces.png");
facesHighlighted.Save(fileout, System.Drawing.Imaging.ImageFormat.Png);
Console.WriteLine(">>> " + outcome.FaceDetails.Count + " face(s) highlighted in file " + fileout);
}
else
{
Console.WriteLine(">>> No faces found");
}
}
/// <summary>
/// Identifies faces in the image file. If faces are found, the
/// method adds bounding boxes.
/// </summary>
/// <param name="client">The Rekognition client used to call
/// RecognizeCelebritiesAsync.</param>
/// <param name="filename">The name of the file that potentially
/// contins images of celebrities.</param>
public static async Task IdentifyFaces(AmazonRekognitionClient client, string filename)
{
// Request needs image bytes, so read and add to request.
byte[] data = File.ReadAllBytes(filename);
DetectFacesRequest request = new DetectFacesRequest
{
Image = new Amazon.Rekognition.Model.Image
{
Bytes = new MemoryStream(data),
},
};
DetectFacesResponse response = await client.DetectFacesAsync(request);
if (response.FaceDetails.Count > 0)
{
// Load a bitmap to modify with face bounding box rectangles.
Bitmap facesHighlighted = new Bitmap(filename);
Pen pen = new Pen(Color.Black, 3);
// Create a graphics context.
using (var graphics = System.Drawing.Graphics.FromImage(facesHighlighted))
{
foreach (var fd in response.FaceDetails)
{
// Get the bounding box.
BoundingBox bb = fd.BoundingBox;
Console.WriteLine($"Bounding box = ({bb.Left}, {bb.Top}, {bb.Height}, {bb.Width})");
// Draw the rectangle using the bounding box values.
// They are percentages so scale them to the picture.
graphics.DrawRectangle(
pen,
x: facesHighlighted.Width * bb.Left,
y: facesHighlighted.Height * bb.Top,
width: facesHighlighted.Width * bb.Width,
height: facesHighlighted.Height * bb.Height);
}
}
// Save the image with highlights as PNG.
string fileout = filename.Replace(Path.GetExtension(filename), "_faces.png");
facesHighlighted.Save(fileout, System.Drawing.Imaging.ImageFormat.Png);
Console.WriteLine(">>> " + response.FaceDetails.Count + " face(s) highlighted in file " + fileout);
}
else
{
Console.WriteLine(">>> No faces found");
}
}
public async Task <IEnumerable <Rectangle> > ExtractFacesAsync()
{
if (_facesResponse == null)
{
var facesRequest = new DetectFacesRequest()
{
Image = _rekognitionImage
};
_facesResponse = await _client.DetectFacesAsync(facesRequest);
}
return(ExtractFaces());
}
// snippet-start:[Rekognition.dotnetv3.DetectFacesExample]
public static async Task Main()
{
string photo = "input.jpg";
string bucket = "bucket";
var rekognitionClient = new AmazonRekognitionClient();
var detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket,
},
},
// Attributes can be "ALL" or "DEFAULT".
// "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality.
// "ALL": See https://docs.aws.amazon.com/sdkfornet/v3/apidocs/items/Rekognition/TFaceDetail.html
Attributes = new List <string>()
{
"ALL"
},
};
try
{
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
Console.WriteLine($"BoundingBox: top={face.BoundingBox.Left} left={face.BoundingBox.Top} width={face.BoundingBox.Width} height={face.BoundingBox.Height}");
Console.WriteLine($"Confidence: {face.Confidence}");
Console.WriteLine($"Landmarks: {face.Landmarks.Count}");
Console.WriteLine($"Pose: pitch={face.Pose.Pitch} roll={face.Pose.Roll} yaw={face.Pose.Yaw}");
Console.WriteLine($"Brightness: {face.Quality.Brightness}\tSharpness: {face.Quality.Sharpness}");
if (hasAll)
{
Console.WriteLine($"Estimated age is between {face.AgeRange.Low} and {face.AgeRange.High} years old.");
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
private List <Mat> detectFace(Bitmap bitmap)
{
Mat src = null;
try
{
src = OpenCvSharp.Extensions.BitmapConverter.ToMat(bitmap);
}catch (Exception e)
{
}
Amazon.Rekognition.Model.Image image = Utils.bitmapToAWSImage(bitmap);
DetectFacesRequest request = new DetectFacesRequest()
{
Image = image
};
try
{
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(request);
float bitmapWidth = (float)bitmap.Width;
float bitmapHeight = (float)bitmap.Height;
List <Mat> matList = new List <Mat>();
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
int faceLeft = (int)(face.BoundingBox.Left * bitmapWidth);
int faceTop = (int)(face.BoundingBox.Top * bitmapHeight);
int faceWidth = (int)(face.BoundingBox.Width * bitmapWidth);
int faceHeight = (int)(face.BoundingBox.Height * bitmapHeight);
Rect rectCrop = new Rect(faceLeft, faceTop, faceWidth, faceHeight);
//Console.WriteLine("Confidence : {0}\nAge :" + face.Confidence + ", " + face.BoundingBox.Top + ", " + face.BoundingBox.Left + ", " +
// face.BoundingBox.Height + ", " + face.BoundingBox.Width);
Mat img = new Mat(src, rectCrop);
matList.Add(img);
}
return(matList);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
return(null);
}
public IEnumerable <Rectangle> ExtractFaces()
{
if (_facesResponse == null)
{
var facesRequest = new DetectFacesRequest()
{
Image = _rekognitionImage
};
_facesResponse = _client.DetectFacesAsync(facesRequest).Result;
}
return(_facesResponse.FaceDetails.Select(f =>
AmazonRekognitionCoordinateTranslator.RelativeBoxToAbsolute(f.BoundingBox, _width, _height)));
}
public static void Example()
{
String photo = "input.jpg";
String bucket = "bucket";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
// Attributes can be "ALL" or "DEFAULT".
// "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality.
// "ALL": See https://docs.aws.amazon.com/sdkfornet/v3/apidocs/items/Rekognition/TFaceDetail.html
Attributes = new List <String>()
{
"ALL"
}
};
try
{
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(detectFacesRequest);
bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
Console.WriteLine("BoundingBox: top={0} left={1} width={2} height={3}", face.BoundingBox.Left,
face.BoundingBox.Top, face.BoundingBox.Width, face.BoundingBox.Height);
Console.WriteLine("Confidence: {0}\nLandmarks: {1}\nPose: pitch={2} roll={3} yaw={4}\nQuality: {5}",
face.Confidence, face.Landmarks.Count, face.Pose.Pitch,
face.Pose.Roll, face.Pose.Yaw, face.Quality);
if (hasAll)
{
Console.WriteLine("The detected face is estimated to be between " +
face.AgeRange.Low + " and " + face.AgeRange.High + " years old.");
}
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
public static IAsyncOperation <string> GetFaceDetails(string base64, string AccessKey, string SecretKey)
{
return(Task.Run <string>(async() =>
{
byte[] imageBytes;
try
{
base64 = base64.Substring(base64.IndexOf(',') + 1).Trim('\0');
imageBytes = System.Convert.FromBase64String(base64);
}
catch (Exception e) {
return e.Message;
}
string sJSONResponse = "";
AWSCredentials credentials;
try
{
credentials = new BasicAWSCredentials(AccessKey, SecretKey);
}
catch (Exception e)
{
throw new AmazonClientException("Cannot load the credentials from the credential profiles file. "
+ "Please make sure that your credentials file is at the correct "
+ "location (/Users/<userid>/.aws/credentials), and is in a valid format.", e);
}
DetectFacesRequest request = new DetectFacesRequest {
Attributes = new List <string>(new string[] { "ALL" })
};
DetectFacesResponse result = null;
request.Image = new Image {
Bytes = new MemoryStream(imageBytes, 0, imageBytes.Length)
};
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(credentials, RegionEndpoint.USWest2);
try
{
result = await rekognitionClient.DetectFacesAsync(request);
}
catch (AmazonRekognitionException e)
{
throw e;
}
// Return server status as unhealthy with appropriate status code
sJSONResponse = JsonConvert.SerializeObject(result.FaceDetails);
return sJSONResponse;
}).AsAsyncOperation());
}
public string GetImageInfo(ImageData imageData, byte[] imgData = null)
{
try
{
var path = Path.Combine(
Directory.GetCurrentDirectory(), "wwwroot",
imageData.fileName);
imgData = Convert.FromBase64String(imageData.base64Data);
_imageData = new MemoryStream(imgData);
DetectFacesRequest detectFaces = new DetectFacesRequest()
{
Image = new Image()
{
Bytes = _imageData
}
};
DetectFacesResponse facesResponse = _rekognitionClient.DetectFacesAsync(detectFaces).Result;
List <FaceDetail> lstCelebrities = facesResponse.FaceDetails;
FaceDetail faceDetail = new FaceDetail();
StringBuilder sbCelebrities = new StringBuilder();
//foreach (var item in lstCelebrities)
//{
// switch (switch_on)
// {
// default:
// }
//}
string Celebrities = sbCelebrities.ToString().TrimEnd(',');
return(Celebrities);
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
throw;
}
}
public List <Emotion> EmotionDetect(string _image)
{
{
String photo = _image;
String bucket = "ngankhanh98";
List <Emotion> response;
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Amazon.Rekognition.Model.Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
Attributes = new List <String>()
{
"ALL"
}
};
try
{
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(detectFacesRequest);
bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
FaceDetail face = detectFacesResponse.FaceDetails[0];
return(face.Emotions);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
return(null);
}
}
}
private static void detectFace(Amazon.Rekognition.Model.Image image, AmazonRekognitionClient rekognitionClient)
{
DetectFacesRequest request = new DetectFacesRequest()
{
Image = image
};
try
{
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(request);
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
Console.WriteLine("Confidence : {0}\nAge :" + face.Confidence + ", " + face.BoundingBox.Top + ", " + face.BoundingBox.Left + ", " +
face.BoundingBox.Height + ", " + face.BoundingBox.Width);
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
public async Task IdentifyFacesTests()
{
var mockClient = new Mock <AmazonRekognitionClient>();
mockClient.Setup(client => client.DetectFacesAsync(
It.IsAny <DetectFacesRequest>(),
It.IsAny <CancellationToken>()
)).Returns((DetectFacesRequest r, CancellationToken token) =>
{
return(Task.FromResult(new DetectFacesResponse()
{
HttpStatusCode = System.Net.HttpStatusCode.OK,
}));
});
byte[] data = File.ReadAllBytes(_filename);
DetectFacesRequest request = new DetectFacesRequest
{
Image = new Amazon.Rekognition.Model.Image
{
Bytes = new MemoryStream(data),
},
};
var client = mockClient.Object;
DetectFacesResponse response = await client.DetectFacesAsync(request);
bool gotResult = response is not null;
Assert.True(gotResult, "DetectFacesAsync returned a response.");
bool ok = response.HttpStatusCode == System.Net.HttpStatusCode.OK;
Assert.True(ok, $"Successfully searched image for faces.");
}
public static async Task <string> FunctionHandler(String photo)
{
String bucket = "moodanalysis";
//ArrayList result = new ArrayList();
string result = "";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
// Recognizes User's face
CompareFacesRequest CFR = new CompareFacesRequest()
{
//SimilarityThreshold = 50,
SourceImage = new Image()
{
S3Object = new S3Object()
{
Name = "referencePhoto.jpg",
Bucket = bucket
},
},
TargetImage = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
};
CompareFacesResponse compareFacesResponse = await rekognitionClient.CompareFacesAsync(CFR);
string howManyFaces = "";
if (compareFacesResponse.FaceMatches.Count == 0)
{
return("");
}
//int index = 0, bestIndex = 0;
var bestMatch = compareFacesResponse.FaceMatches[0];
float bestMatchResult = compareFacesResponse.FaceMatches[0].Similarity;
BoundingBox bestBoundingBox = compareFacesResponse.FaceMatches[0].Face.BoundingBox;
foreach (var faceMatch in compareFacesResponse.FaceMatches)
{
howManyFaces += faceMatch.Similarity + ",";
if (bestMatchResult < faceMatch.Similarity)
{
bestMatch = faceMatch;
bestBoundingBox = faceMatch.Face.BoundingBox;
bestMatchResult = faceMatch.Similarity;
}
}
// Detects emotions of faces in photo
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
Attributes = new List <String>()
{
"ALL"
}
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
//int i = 0;
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
if (face.BoundingBox.Height == bestBoundingBox.Height &&
face.BoundingBox.Left == bestBoundingBox.Left &&
face.BoundingBox.Top == bestBoundingBox.Top &&
face.BoundingBox.Width == bestBoundingBox.Width)
{
//var emotQuery = FilterEmotions(face, IsLowConfidence);
FilterEmotions filter = delegate(FaceDetail faceFilter, ConfidenceFilterDelegate confFilter)
{
return(faceFilter.Emotions.FindAll(n => confFilter(n)).ToList());
};
var emotQuery = filter(face, IsLowConfidence);
//IEnumerable<Emotion> emotQuery =
// from faceEmotion in face.Emotions
// where faceEmotion.Confidence > 10
// select faceEmotion;
// GRAB THE EMOTION
foreach (Emotion emot in emotQuery)
{
result += emot.Type + ",";
}
break;
}
}
//delete the last ,
if (result.Length != 0)
{
result = result.Substring(0, result.Length - 1);
}
return(result);
}
public EmotionalData DetectImage(byte[] image)
{
var awsImage = new Image();
try
{
//using (FileStream stream = new FileStream(image, FileMode.Open, FileAccess.Read))
//{
// byte[] data = new byte[stream.Length];
// stream.Read(data, 0, (int)stream.Length);
// awsImage.Bytes = new MemoryStream(data);
//}
awsImage.Bytes = new MemoryStream(image);
}
catch (Exception ex)
{
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(
"", "", RegionEndpoint.APSoutheast1
);
DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
{
Image = awsImage,
MaxLabels = 10,
MinConfidence = 77F
};
var faceAttr = new List <string>();
//faceAttr.Add("Emotions");
faceAttr.Add("ALL");
DetectFacesRequest detectFacesRequest = new DetectFacesRequest
{
Attributes = faceAttr,
Image = awsImage
};
try
{
//DetectLabelsResponse detectLabelsResponse =
//rekognitionClient.DetectLabels(detectlabelsRequest);
//foreach (Label label in detectLabelsResponse.Labels)
// Console.WriteLine("{0}: {1}", label.Name, label.Confidence);
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(detectFacesRequest);
if (detectFacesResponse.FaceDetails != null)
{
var customerFace = detectFacesResponse.FaceDetails.OrderByDescending(x => x.Confidence).FirstOrDefault();
var customerEmotionData = customerFace.Emotions.OrderByDescending(x => x.Confidence).FirstOrDefault();
return(new EmotionalData
{
Emotion = customerEmotionData.Type,
Probability = Convert.ToDecimal(customerEmotionData.Confidence)
});
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
return(new EmotionalData());
}
// Face detection method
private async Task FacialRecognitionScan(ApplicationUser user, UsersInGymDetail currentFacilityDetail)
{
// initialize similarity threshold for accepting face match, source and target img.
// S3 bucket img, dynamically selected based on user currently logged in.
float similarityThreshold = 70F;
string photo = $"{user.FirstName}_{user.Id}.jpg";
String targetImage = $"{user.FirstName}_{user.Id}_Target.jpg";
try
{
// create image objects
Image imageSource = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
};
Image imageTarget = new Image()
{
S3Object = new S3Object()
{
Name = targetImage,
Bucket = bucket
},
};
// create a compare face request object
CompareFacesRequest compareFacesRequest = new CompareFacesRequest()
{
SourceImage = imageSource,
TargetImage = imageTarget,
SimilarityThreshold = similarityThreshold
};
// detect face features of img scanned
CompareFacesResponse compareFacesResponse = await AmazonRekognition.CompareFacesAsync(compareFacesRequest);
// Display results
foreach (CompareFacesMatch match in compareFacesResponse.FaceMatches)
{
ComparedFace face = match.Face;
// if confidence for similarity is over 90 then grant access
if (match.Similarity > 90)
{
// if there is a match set scan success
user.IsCameraScanSuccessful = true;
}
else
{
ViewBag.MatchResult = "Facial Match Failed!";
}
}
}
catch (Exception e)
{
_logger.LogInformation(e.Message);
}
// now add get facial details to display in the view.
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = targetImage,
Bucket = bucket
},
},
// "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality.
Attributes = new List <String>()
{
"ALL"
}
};
try
{
DetectFacesResponse detectFacesResponse = await AmazonRekognition.DetectFacesAsync(detectFacesRequest);
bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
// if the face found has all attributes within a Detect Face object then save these values to the database.
if (hasAll)
{
currentFacilityDetail.IsSmiling = face.Smile.Value;
currentFacilityDetail.Gender = face.Gender.Value.ToString();
currentFacilityDetail.AgeRangeLow = face.AgeRange.Low;
currentFacilityDetail.AgeRangeHigh = face.AgeRange.High;
}
}
}
catch (Exception e)
{
_logger.LogInformation(e.Message);
}
}
public static void Example()
{
String photo = "photo.jpg";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
Image image = new Image();
try
{
using (FileStream fs = new FileStream(photo, FileMode.Open, FileAccess.Read))
{
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
int height;
int width;
// Used to extract original photo width/height
using (System.Drawing.Bitmap imageBitmap = new System.Drawing.Bitmap(photo))
{
height = imageBitmap.Height;
width = imageBitmap.Width;
}
Console.WriteLine("Image Information:");
Console.WriteLine(photo);
Console.WriteLine("Image Height: " + height);
Console.WriteLine("Image Width: " + width);
try
{
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = image,
Attributes = new List <String>()
{
"ALL"
}
};
DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(detectFacesRequest);
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
Console.WriteLine("Face:");
ShowBoundingBoxPositions(height, width,
face.BoundingBox, detectFacesResponse.OrientationCorrection);
Console.WriteLine("BoundingBox: top={0} left={1} width={2} height={3}", face.BoundingBox.Left,
face.BoundingBox.Top, face.BoundingBox.Width, face.BoundingBox.Height);
Console.WriteLine("The detected face is estimated to be between " +
face.AgeRange.Low + " and " + face.AgeRange.High + " years old.");
Console.WriteLine();
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
async void Analyze_Clicked(System.Object sender, System.EventArgs e)
{
CognitoAWSCredentials credentials = new CognitoAWSCredentials(
AWS.IdentityPoolId,
RegionEndpoint.USEast1
);
S3Uploader uploader = new S3Uploader(credentials);
string bucketName = "babytech-images";
string keyName = patient.PatientID + ".jpg";
await uploader.UploadFileAsync(bucketName, keyName, PhotoPath);
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(credentials, Amazon.RegionEndpoint.USEast1);
DetectFacesRequest detectFacesRequest = new DetectFacesRequest()
{
Image = new Amazon.Rekognition.Model.Image()
{
S3Object = new S3Object
{
Bucket = bucketName,
Name = keyName
}
},
Attributes = new List <String>()
{
"ALL"
}
};
try
{
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
// check if mouth is open
if ((face.MouthOpen != null) && (face.MouthOpen.Value))
{
FacialAnalysisData += "\n❌ Baby's mouth should be closed";
}
if ((face.MouthOpen != null) && (!face.MouthOpen.Value) && (face.EyesOpen.Confidence > 0.88F))
{
FacialAnalysisData += "\n✔ Baby's mouth should be closed";
}
// check if eyes are open
if ((face.EyesOpen != null) && (face.EyesOpen.Value))
{
FacialAnalysisData += "\n✔ Baby's eyes should be open";
}
if ((face.EyesOpen != null) && (!face.EyesOpen.Value) && (face.EyesOpen.Confidence > 0.93F))
{
FacialAnalysisData += "\n❌ Baby's eyes should be open";
}
// check for eyeglasses
if ((face.Eyeglasses != null) && (face.Eyeglasses.Value))
{
FacialAnalysisData += "\n❌ Baby should not be wearing eyeglasses";
}
if ((face.Eyeglasses != null) && (!face.Eyeglasses.Value))
{
FacialAnalysisData += "\n✔ Baby should not be wearing eyeglasses";
}
//check brightness
if ((face.Quality.Brightness != null) && (face.Quality.Brightness > 0.61F) && (face.Quality.Brightness < 0.97F))
{
FacialAnalysisData += "\n✔ Picture is acceptable brightness";
}
else
{
FacialAnalysisData += "\n❌ Picture is acceptable brightness";
}
//check sharpness
if ((face.Quality.Sharpness != null) && (face.Quality.Sharpness > 0.67F))
{
FacialAnalysisData += "\n✔ Picture is acceptable sharpness";
}
else
{
FacialAnalysisData += "\n❌ Picture is acceptable sharpness";
}
// check for smile
if ((face.Smile != null) && (face.Smile.Value) && (face.Smile.Confidence < 0.83F))
{
FacialAnalysisData += "\n❌ Baby should not be smiling";
}
if ((face.Eyeglasses != null) && (!face.Eyeglasses.Value))
{
FacialAnalysisData += "\n✔ Baby should not be smiling";
}
// check for calm expression
Emotion calmEmotion = face.Emotions.Find(emotion => emotion.Type == "CALM");
if (calmEmotion.Confidence > 0.93F)
{
FacialAnalysisData += "\n ✔ Baby should have a neutral facial expression";
}
else
{
FacialAnalysisData += "\n ❌ BBaby should have a neutral facial expression";
}
//check sharpness
if ((face.Quality.Sharpness != null) && (face.Quality.Sharpness > 0.67F))
{
FacialAnalysisData += "\n✔ Picture is acceptable sharpness";
}
else
{
FacialAnalysisData += "\n❌ Picture is acceptable sharpness";
}
//check brightness
if ((face.Quality.Brightness != null) && (face.Quality.Brightness > 0.61F) && (face.Quality.Brightness < 0.97F))
{
FacialAnalysisData += "\n✔ Picture is acceptable brightness";
}
else
{
FacialAnalysisData += "\n❌ Picture is acceptable brightness";
}
}
await DisplayAlert("Analysis Results", FacialAnalysisData, "OK");
}
catch (Exception exception)
{
Console.WriteLine(exception.Message);
}
}
// snippet-start:[Rekognition.dotnetv3.ImageOrientationBoundingBox]
public static async Task Main()
{
string photo = @"D:\Development\AWS-Examples\Rekognition\target.jpg"; // "photo.jpg";
var rekognitionClient = new AmazonRekognitionClient();
var image = new Amazon.Rekognition.Model.Image();
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
int height;
int width;
// Used to extract original photo width/height
using (var imageBitmap = new Bitmap(photo))
{
height = imageBitmap.Height;
width = imageBitmap.Width;
}
Console.WriteLine("Image Information:");
Console.WriteLine(photo);
Console.WriteLine("Image Height: " + height);
Console.WriteLine("Image Width: " + width);
try
{
var detectFacesRequest = new DetectFacesRequest()
{
Image = image,
Attributes = new List <string>()
{
"ALL"
},
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
detectFacesResponse.FaceDetails.ForEach(face =>
{
Console.WriteLine("Face:");
ShowBoundingBoxPositions(
height,
width,
face.BoundingBox,
detectFacesResponse.OrientationCorrection);
Console.WriteLine($"BoundingBox: top={face.BoundingBox.Left} left={face.BoundingBox.Top} width={face.BoundingBox.Width} height={face.BoundingBox.Height}");
Console.WriteLine($"The detected face is estimated to be between {face.AgeRange.Low} and {face.AgeRange.High} years old.\n");
});
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
