private Dictionary <string, float> getemotion(MemoryStream image)
{
Dictionary <string, float> emos = new Dictionary <string, float>();
IAmazonRekognition reg = new AmazonRekognitionClient(ConfigurationManager.AppSettings["AWSAccessKey"], ConfigurationManager.AppSettings["AWSSecretKey"], RegionEndpoint.EUWest1);
var request = new DetectFacesRequest()
{
Image = new Amazon.Rekognition.Model.Image {
Bytes = image
},
Attributes = new List <string>
{
"ALL"
}
};
var respFace = reg.DetectFaces(request);
foreach (var detail in respFace.FaceDetails)
{
foreach (var item in detail.Emotions)
{
if (!emos.ContainsKey(item.Type))
{
emos.Add(item.Type, item.Confidence);
}
}
}
return(emos);
}
/// <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());
}
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");
}
}
public DetectFacesResponse IdentifyFaces(byte[] request)
{
AmazonRekognitionClient rekoClient = new AmazonRekognitionClient(_credentials, Amazon.RegionEndpoint.USEast2);
DetectFacesRequest dfr = new DetectFacesRequest();
Amazon.Rekognition.Model.Image img = new Amazon.Rekognition.Model.Image();
img.Bytes = new MemoryStream(request);
dfr.Image = img;
return(rekoClient.DetectFaces(dfr));
}
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 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);
}
}
static void IdentifyFaces(string filename)
{
// Using USWest2, not the default region
AmazonRekognitionClient rekoClient = new AmazonRekognitionClient(Amazon.RegionEndpoint.USWest2);
DetectFacesRequest dfr = new DetectFacesRequest();
// Request needs image butes, so read and add to request
Amazon.Rekognition.Model.Image img = new Amazon.Rekognition.Model.Image();
byte[] data = null;
using (FileStream fs = new FileStream(filename, FileMode.Open, FileAccess.Read))
{
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
}
img.Bytes = new MemoryStream(data);
dfr.Image = img;
var outcome = rekoClient.DetectFaces(dfr);
// Load a bitmap to modify with face bounding box rectangles
System.Drawing.Bitmap facesHighlighted = new System.Drawing.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 = (" + fd.BoundingBox.Left + ", " + fd.BoundingBox.Top + ", " +
fd.BoundingBox.Height + ", " + fd.BoundingBox.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(">>> Faces highlighted in file " + fileout);
}
public void RekognitionDetectFaces()
{
#region to-detect-faces-in-an-image-1481841782793
var client = new AmazonRekognitionClient();
var response = client.DetectFaces(new DetectFacesRequest
{
Image = new Image {
S3Object = new S3Object {
Bucket = "mybucket",
Name = "myphoto"
}
}
});
List <FaceDetail> faceDetails = response.FaceDetails;
string orientationCorrection = response.OrientationCorrection;
#endregion
}
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 DetectFacesResponse Recognize(Bitmap image)
{
MemoryStream memoryStream = new MemoryStream();
image.Save(memoryStream, System.Drawing.Imaging.ImageFormat.Jpeg);
var result = _client.DetectFaces(new DetectFacesRequest()
{
Attributes = new List <string> {
"ALL"
},
Image = new Amazon.Rekognition.Model.Image()
{
Bytes = memoryStream
}
});
var serialized = JsonConvert.SerializeObject(result);
File.WriteAllText("real_result.json", serialized);
return(result);
}
/// <summary>
/// This method will detect the input image is face or not
/// </summary>
/// <param name="filename"></param>
/// <returns></returns>
public bool DetectFaces(string filename)
{
IAmazonRekognition rekoClient = new AmazonRekognitionClient(Amazon.RegionEndpoint.USEast1);
DetectFacesRequest dfr = new DetectFacesRequest();
Amazon.Rekognition.Model.Image img = new Amazon.Rekognition.Model.Image();
// Request needs image butes, so read and add to request
byte[] data = null;
using (FileStream fs = new FileStream(filename, FileMode.Open, FileAccess.Read))
{
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
}
img.Bytes = new MemoryStream(data);
dfr.Image = img;
var outcome = rekoClient.DetectFaces(dfr);
if (outcome.FaceDetails.Count > 0)
{
return(true);
}
else
{
return(false);
}
}
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);
}
}
public void IdentifyFaces(string filename, string path, string bucketName)
{
// Using USWest2, not the default region
IAmazonRekognition rekoClient = new AmazonRekognitionClient("AKIAIYKAM62F6DZ2CEGA", "HmHyI439/ZdyOOxjnrpW3izOzOWcu3kS5qwpV1Kd", Amazon.RegionEndpoint.USEast1);
Amazon.Rekognition.Model.Image img;
var response = rekoClient.DetectFaces(new DetectFacesRequest
{
Image = new Amazon.Rekognition.Model.Image
{
S3Object = new Amazon.Rekognition.Model.S3Object
{
Bucket = bucketName,
Name = path,
}
}
});
int k = response.FaceDetails.Count;
//List<facedetail> faceDetails = response.FaceDetails;
string orientationCorrection = response.OrientationCorrection;
//</facedetail>
DetectFacesRequest dfr = new DetectFacesRequest();
// Request needs image butes, so read and add to request
byte[] data = null;
using (FileStream fs = new FileStream(path, FileMode.Open, FileAccess.Read))
{
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
}
//img.Bytes = new MemoryStream(data);
//if (img.S3Object != null)
//{
// img.S3Object.Name = "blazarName";
// img.S3Object.Bucket = "blazarstorage";
//}
//dfr.Image = img;
//var outcome = rekoClient.DetectFaces(dfr);
if (response.FaceDetails.Count > 0)
{
// Load a bitmap to modify with face bounding box rectangles
System.Drawing.Bitmap facesHighlighted = new System.Drawing.Bitmap(filename);
Pen pen = new Pen(Color.Black, 3);
// Create a graphics context
using (var graphics = 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 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 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());
}
