static void Main(string[] args)
{
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
MemoryStream ms = null;
try
{
//Bitmap은 base64형식이 아닌듯 하다. AWS에서는 base64형식의 이미지 파일이 필요한데 이를 바꿔주는 코드는 아래와 같다.
//Base64는 8비트짜리 바이트 3개를 6비트씩 4개로 쪼개어 표현한다.
Bitmap bitmap = new Bitmap(@"D:\picture\insup.bmp");
ms = new MemoryStream();
bitmap.Save(ms, ImageFormat.Jpeg);
byte[] data = new byte[ms.Length];
ms.Read(data, 0, (int)ms.Length);
image.Bytes = ms;
}catch (Exception e)
{
}
searchFace(image, rekognitionClient);
//using (FileStream fs = new FileStream(@"D:\picture\paul.bmp", FileMode.Open, FileAccess.Read))
//{
// byte[] data = null;
// data = new byte[fs.Length];
// fs.Read(data, 0, (int)fs.Length);
// image.Bytes = new MemoryStream(data);
//}
}
/// <summary>
/// Constructor for a feature extractor using Amazon Web Services SDK, using a given annotator client,
/// optimized to be used with a batch of images.
/// </summary>
/// <param name="imagePath"></param>
/// <param name="client"></param>
internal AwsFeatureExtractor(string imagePath, AmazonRekognitionClient client)
{
var size = System.Drawing.Image.FromFile(imagePath).Size;
var orientation = SKCodec.Create(imagePath).EncodedOrigin;
if ((int)orientation % 2 == 1)
{
// use standard bounding if image is not rotated or rotated by 180 degrees
_height = size.Height;
_width = size.Width;
}
else
{
// flip height and width if image is rotated by 90 or 270 degrees
_height = size.Width;
_width = size.Height;
}
_rekognitionImage = new Image();
using FileStream fs = new FileStream(imagePath, FileMode.Open, FileAccess.Read);
var data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
_rekognitionImage.Bytes = new MemoryStream(data);
_client = client;
}
public IHttpActionResult Get(string imageURl)
{
//Get Image
WebClient webClient = new WebClient();
byte[] imageBinary = webClient.DownloadData(imageURl);
//Create Image Model ...
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
image.Bytes = new MemoryStream(imageBinary);
List <FaceRecord> blackListResult = _rekognitionService.Recognize(Constants.BlackListCollectionId, image);
List <FaceRecord> missingResuList = _rekognitionService.Recognize(Constants.WhiteListCollectionId, image);
RecognizResult result = new RecognizResult
{
BlackListFaces = blackListResult,
MissingListFaces = missingResuList
};
//Create Cache Control Header...
CacheControlHeaderValue cacheControlHeader = new CacheControlHeaderValue()
{
Public = true,
MaxAge = new TimeSpan(0, 0, 1)
};
return(new OkResultWithCaching <RecognizResult>(result, this)
{
CacheControlHeader = cacheControlHeader
});
}
// snippet-start:[Rekognition.dotnetv3.CompareFacesExample]
public static async Task Main()
{
float similarityThreshold = 70F;
string sourceImage = "source.jpg";
string targetImage = "target.jpg";
var rekognitionClient = new AmazonRekognitionClient();
Amazon.Rekognition.Model.Image imageSource = new Amazon.Rekognition.Model.Image();
try
{
using FileStream fs = new FileStream(sourceImage, FileMode.Open, FileAccess.Read);
byte[] data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
imageSource.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine($"Failed to load source image: {sourceImage}");
return;
}
Amazon.Rekognition.Model.Image imageTarget = new Amazon.Rekognition.Model.Image();
try
{
using FileStream fs = new FileStream(targetImage, FileMode.Open, FileAccess.Read);
byte[] data = new byte[fs.Length];
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
imageTarget.Bytes = new MemoryStream(data);
}
catch (Exception ex)
{
Console.WriteLine($"Failed to load target image: {targetImage}");
Console.WriteLine(ex.Message);
return;
}
var compareFacesRequest = new CompareFacesRequest
{
SourceImage = imageSource,
TargetImage = imageTarget,
SimilarityThreshold = similarityThreshold,
};
// Call operation
var compareFacesResponse = await rekognitionClient.CompareFacesAsync(compareFacesRequest);
// Display results
compareFacesResponse.FaceMatches.ForEach(match =>
{
ComparedFace face = match.Face;
BoundingBox position = face.BoundingBox;
Console.WriteLine($"Face at {position.Left} {position.Top} matches with {match.Similarity}% confidence.");
});
Console.WriteLine($"Found {compareFacesResponse.UnmatchedFaces.Count} face(s) that did not match.");
}
/// <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());
}
/// <summary>
/// Constructor for a feature extractor using Amazon Web Services SDK, optimized for analysing a single image.
/// </summary>
/// <param name="imageStream"></param>
/// <param name="accessKey"></param>
/// <param name="secretKey"></param>
/// <param name="endpoint"></param>
public AwsFeatureExtractor(Stream imageStream, string accessKey, string secretKey, RegionEndpoint endpoint)
{
var size = System.Drawing.Image.FromStream(imageStream).Size;
var orientation = SKCodec.Create(imageStream).EncodedOrigin;
if ((int)orientation % 2 == 1)
{
// use standard bounding if image is not rotated or rotated by 180 degrees
_height = size.Height;
_width = size.Width;
}
else
{
// flip height and width if image is rotated by 90 or 270 degrees
_height = size.Width;
_width = size.Height;
}
_rekognitionImage = new Image();
var data = new byte[imageStream.Length];
imageStream.Read(data, 0, (int)imageStream.Length);
_rekognitionImage.Bytes = new MemoryStream(data);
_client = new AmazonRekognitionClient(new BasicAWSCredentials(accessKey, secretKey), endpoint);
}
private void btnLoad_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog op = new OpenFileDialog();
op.Title = "Select a picture";
op.Filter = "All supported graphics|*.jpg;*.jpeg;*.png|" +
"JPEG (*.jpg;*.jpeg)|*.jpg;*.jpeg|" +
"Portable Network Graphic (*.png)|*.png";
if (op.ShowDialog() == true)
{
imgPhoto.Source = new BitmapImage(new Uri(op.FileName));
}
String photo = imgPhoto.Source.ToString().Substring(8);
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.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)
{
return;
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectModerationLabelsRequest detectModerationLabelsRequest = new DetectModerationLabelsRequest()
{
Image = image,
MinConfidence = 60F
};
try
{
DetectModerationLabelsResponse detectModerationLabelsResponse = rekognitionClient.DetectModerationLabels(detectModerationLabelsRequest);
List <Moderation> items = new List <Moderation>();
foreach (ModerationLabel label in detectModerationLabelsResponse.ModerationLabels)
{
items.Add(new Moderation()
{
Name = label.Name, Confidence = label.Confidence, ParentName = label.ParentName
});
}
lvModeration.ItemsSource = items;
}
catch (Exception)
{
Console.WriteLine("Error!!!");
return;
}
}
public async Task <IActionResult> Login(IFormFile file)
{
CelebrityModel celeb = new CelebrityModel();
Directory.Delete(_appEnvironment.WebRootPath + "/resources/", true);
Directory.CreateDirectory(_appEnvironment.WebRootPath + "/resources/");
if (null != file && file.Length > 0)
{
string speechFileName = "notjeff.mp3";
string speechText = "Nice try. You're not Jeff, I can't let you in.";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
RecognizeCelebritiesRequest recognizeCelebritiesRequest = new RecognizeCelebritiesRequest();
Amazon.Rekognition.Model.Image img = new Amazon.Rekognition.Model.Image();
MemoryStream memStream = new MemoryStream();
file.CopyTo(memStream);
img.Bytes = memStream;
recognizeCelebritiesRequest.Image = img;
RecognizeCelebritiesResponse recognizeCelebritiesResponse = await rekognitionClient.RecognizeCelebritiesAsync(recognizeCelebritiesRequest);
if (null != recognizeCelebritiesResponse && recognizeCelebritiesResponse.CelebrityFaces.Count > 0)
{
celeb.CelebrityName = recognizeCelebritiesResponse.CelebrityFaces[0].Name;
celeb.Confidence = recognizeCelebritiesResponse.CelebrityFaces[0].MatchConfidence;
if (celeb.CelebrityName == "Jeff Bezos")
{
speechText = "Hello Boss, Welcome to the Deployment Bot. Please continue to start the deployment.";
celeb.IsJeff = true;
speechFileName = "jeff.mp3";
}
}
else
{
celeb.CelebrityName = "Sure, you're popular among your friends. But that doesn't make you a celebrity.";
celeb.Confidence = 0;
}
AmazonPollyClient pollyclient = new AmazonPollyClient();
Amazon.Polly.Model.SynthesizeSpeechResponse speechResponse =
await pollyclient.SynthesizeSpeechAsync(new Amazon.Polly.Model.SynthesizeSpeechRequest()
{
OutputFormat = OutputFormat.Mp3, Text = speechText, VoiceId = VoiceId.Joanna
});
var stream = new FileStream(_appEnvironment.WebRootPath + "/resources/" + speechFileName, FileMode.Create);
await speechResponse.AudioStream.CopyToAsync(stream);
stream.Close();
}
return(View("Login", celeb));
}
private void btnLoad1_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog op = new OpenFileDialog();
op.Title = "Select a picture";
op.Filter = "All supported graphics|*.jpg;*.jpeg;*.png|" +
"JPEG (*.jpg;*.jpeg)|*.jpg;*.jpeg|" +
"Portable Network Graphic (*.png)|*.png";
if (op.ShowDialog() == true)
{
imgPhoto1.Source = new BitmapImage(new Uri(op.FileName));
}
String photo = imgPhoto1.Source.ToString().Substring(8);
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.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)
{
return;
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectTextRequest detectTextRequest = new DetectTextRequest()
{
Image = image,
};
try
{
DetectTextResponse detectTextResponse = rekognitionClient.DetectText(detectTextRequest);
List <Moderation1> items = new List <Moderation1>();
foreach (TextDetection text in detectTextResponse.TextDetections)
{
items.Add(new Moderation1()
{
Confidence = text.Confidence, Detected = text.DetectedText, Type = text.Type
});
}
lvModeration1.ItemsSource = items;
}
catch (Exception)
{
}
}
private List <Guid> SearchOneFace(string faceFileName)
{
List <Guid> facesData = null;
try
{
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image()
{
Bytes = new MemoryStream(System.IO.File.ReadAllBytes(faceFileName))
};
SearchFacesByImageRequest searchFacesByImageRequest = new SearchFacesByImageRequest()
{
CollectionId = awsCollectionId, Image = image, FaceMatchThreshold = awsFaceMatchThreshold, MaxFaces = 1000
};
using (AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(awsAccessKeyId, awsSecretAccessKey, awsRegionEndpoint))
{
SearchFacesByImageResponse searchFacesByImageResponse = rekognitionClient.SearchFacesByImageAsync(searchFacesByImageRequest).Result;
if (searchFacesByImageResponse != null && searchFacesByImageResponse.FaceMatches.Count > 0)
{
facesData = new List <Guid>();
for (int f = 0; f < searchFacesByImageResponse.FaceMatches.Count; f++)
{
string dateMask = searchFacesByImageResponse.FaceMatches[f].Face.ExternalImageId;
if (dateMask.Length > 7)
{
dateMask = dateMask.Substring(0, 8);
if (dates != null && dates.Contains(dateMask))
{
if (searchFacesByImageResponse.FaceMatches[f].Similarity >= awsSimilarityLevel)
{
Guid faceId;
if (Guid.TryParse(searchFacesByImageResponse.FaceMatches[f].Face.FaceId, out faceId))
{
if (!facesData.Contains(faceId))
{
facesData.Add(faceId);
}
}
}
}
}
}
}
}
}
catch (Exception exc)
{
}
if (facesData != null && facesData.Count == 0)
{
facesData = null;
}
return(facesData);
}
public async Task <bool> AuthenticateUserByFace(byte[] targetImage) //FileStream targetImage
{
float similarityThreshold = 90F;
String sourceImage = "https://hackathonimagedump.s3.us-east-2.amazonaws.com/123456.jpeg";
// String targetImage = "https://hackathonimagedump.s3.us-east-2.amazonaws.com/HappyFace.jpeg";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient("AKIAX2ZTBCX4OX6XH77Q", "X/FcCoEFyuIl5+hmwE+IVMk4t1089mgf0jIQI7Xo", RegionEndpoint.USWest2);
Amazon.Rekognition.Model.Image imageSource = new Amazon.Rekognition.Model.Image();
try
{
var webClient = new WebClient();
byte[] imageBytes = webClient.DownloadData(sourceImage);
imageSource.Bytes = new MemoryStream(imageBytes);
}
catch
{
}
Amazon.Rekognition.Model.Image imageTarget = new Amazon.Rekognition.Model.Image();
try
{
imageTarget.Bytes = new MemoryStream(targetImage);
}
catch
{
}
CompareFacesRequest compareFacesRequest = new CompareFacesRequest()
{
SourceImage = imageSource,
TargetImage = imageTarget,
SimilarityThreshold = similarityThreshold
};
// Call operation
CompareFacesResponse compareFacesResponse = await rekognitionClient.CompareFacesAsync(compareFacesRequest);
if (compareFacesResponse.HttpStatusCode == HttpStatusCode.OK)
{
if (compareFacesResponse.SourceImageFace.Confidence > 90F)
{
return(true);
}
else
{
return(false);
}
}
else
{
return(false);
}
}
private void ImageForm_Load(object sender, EventArgs e)
{
// Load images
var image = new Amazon.Rekognition.Model.Image();
try
{
using (var fs = new FileStream(imageUrl, 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)
{
MessageBox.Show("Không thể mở tệp " + imageUrl, "Thông báo", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
var rekognitionClient = new AmazonRekognitionClient();
var detectlabelsRequest = new DetectLabelsRequest()
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F
};
try
{
DetectLabelsResponse detectLabelsResponse = rekognitionClient.DetectLabels(detectlabelsRequest);
//MessageBox.Show("Detected labels for " + imageUrl);
foreach (var label in detectLabelsResponse.Labels)
{
//MessageBox.Show(label.Name + " : " + label.Confidence);
foreach (var item in label.Instances)
{
//MessageBox.Show("Left : " + item.BoundingBox.Left);
boundingEnum.Add(new PositionClass(
item.BoundingBox.Top * bmpHeight,
item.BoundingBox.Left * bmpWidth,
item.BoundingBox.Width * bmpWidth,
item.BoundingBox.Height * bmpHeight)
);
}
}
}
catch (Exception)
{
MessageBox.Show("Không thể phân tích hình ảnh", "Lỗi", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
static void IdentifyCelebrityFaces(string filename)
{
// Using USWest2, not the default region
AmazonRekognitionClient rekoClient = new AmazonRekognitionClient(Amazon.RegionEndpoint.USWest2);
RecognizeCelebritiesRequest dfr = new RecognizeCelebritiesRequest();
// 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.RecognizeCelebrities(dfr);
if (outcome.CelebrityFaces.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);
Font drawFont = new Font("Arial", 12);
// Create a graphics context
using (var graphics = Graphics.FromImage(facesHighlighted))
{
foreach (var fd in outcome.CelebrityFaces)
{
// Get the bounding box
BoundingBox bb = fd.Face.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);
graphics.DrawString(fd.Name, drawFont, Brushes.White, facesHighlighted.Width * bb.Left,
facesHighlighted.Height * bb.Top + facesHighlighted.Height * bb.Height);
}
}
// Save the image with highlights as PNG
string fileout = filename.Replace(Path.GetExtension(filename), "_celebrityfaces.png");
facesHighlighted.Save(fileout, System.Drawing.Imaging.ImageFormat.Png);
Console.WriteLine(">>> " + outcome.CelebrityFaces.Count + " celebrity face(s) highlighted in file " + fileout);
}
else
{
Console.WriteLine(">>> No celebrity faces found");
}
}
static Amazon.Rekognition.Model.Image ToBytesStream(string filename)
{
var image = new Amazon.Rekognition.Model.Image();
using (var fs = new FileStream(filename, FileMode.Open, FileAccess.Read))
{
var data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
return(image);
}
public void FindObject()
{
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
try
{
using (FileStream fs = new FileStream(filePath, 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 " + filePath);
return;
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F
};
try
{
DetectLabelsResponse detectLabelsResponse = rekognitionClient.DetectLabels(detectlabelsRequest);
double width = imgPictureFrame.Width;
double height = imgPictureFrame.Height;
foreach (Label label in detectLabelsResponse.Labels)
{
List <Instance> instances = label.Instances;
foreach (Instance instance in instances)
{
string data = $"{label.Name}: {Math.Round(instance.Confidence,2)}%";
BoundingBox boundingBox = instance.BoundingBox;
BindingBox bindingBox = new BindingBox(width * boundingBox.Width, height * boundingBox.Height, height * boundingBox.Top, width * boundingBox.Left, data);
gContainer.Children.Add(bindingBox);
}
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
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 DetectLabelsResponse GetInfo(string filePath)
{
String photo = filePath;
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.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 ex)
{
Console.WriteLine("Failed to load file " + photo);
return(null);
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F
};
try
{
DetectLabelsResponse detectLabelsResponse = rekognitionClient.DetectLabels(detectlabelsRequest);
Console.WriteLine("Detected labels for " + photo);
foreach (Label label in detectLabelsResponse.Labels)
{
Console.WriteLine("{0}: {1}", label.Name, label.Confidence);
}
return(detectLabelsResponse);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
return(null);
}
public static Amazon.Rekognition.Model.Image matTOAWSImage(Mat mat)
{
try
{
MemoryStream ms = mat.ToMemoryStream(".jpg");
byte[] data = new byte[ms.Length];
ms.Read(data, 0, (int)ms.Length);
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
image.Bytes = ms;
return(image);
}catch (Exception e)
{
}
return(null);
}
public static Amazon.Rekognition.Model.Image bitmapToAWSImage(Bitmap bitmap)
{
try
{
MemoryStream ms = new MemoryStream();
bitmap.Save(ms, ImageFormat.Jpeg);
byte[] data = new byte[ms.Length];
ms.Read(data, 0, (int)ms.Length);
Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
image.Bytes = ms;
return(image);
}catch (Exception e)
{
}
return(null);
}
public static void Example()
{
String photo = "moviestars.jpg";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
RecognizeCelebritiesRequest recognizeCelebritiesRequest = new RecognizeCelebritiesRequest();
Amazon.Rekognition.Model.Image img = new Amazon.Rekognition.Model.Image();
byte[] data = null;
try
{
using (FileStream fs = new FileStream(photo, FileMode.Open, FileAccess.Read))
{
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
}
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
img.Bytes = new MemoryStream(data);
recognizeCelebritiesRequest.Image = img;
Console.WriteLine("Looking for celebrities in image " + photo + "\n");
RecognizeCelebritiesResponse recognizeCelebritiesResponse = rekognitionClient.RecognizeCelebrities(recognizeCelebritiesRequest);
Console.WriteLine(recognizeCelebritiesResponse.CelebrityFaces.Count + " celebrity(s) were recognized.\n");
foreach (Celebrity celebrity in recognizeCelebritiesResponse.CelebrityFaces)
{
Console.WriteLine("Celebrity recognized: " + celebrity.Name);
Console.WriteLine("Celebrity ID: " + celebrity.Id);
BoundingBox boundingBox = celebrity.Face.BoundingBox;
Console.WriteLine("position: " +
boundingBox.Left + " " + boundingBox.Top);
Console.WriteLine("Further information (if available):");
foreach (String url in celebrity.Urls)
{
Console.WriteLine(url);
}
}
Console.WriteLine(recognizeCelebritiesResponse.UnrecognizedFaces.Count + " face(s) were unrecognized.");
}
public List <FaceRecord> Recognize(string collectionId, Amazon.Rekognition.Model.Image image)
{
//1- Detect faces in the input image and adds them to the specified collection.
AmazonRekognitionClient rekognitionClient = AmazonClient.GetInstance();
IndexFacesRequest indexFacesRequest = new IndexFacesRequest()
{
Image = image,
CollectionId = collectionId,
DetectionAttributes = new List <String>()
{
"DEFAULT"
}
};
IndexFacesResponse indexFacesResponse = rekognitionClient.IndexFaces(indexFacesRequest);
//2- Search all detected faces in the collection
SearchFacesResponse searchFacesResponse = null;
List <FaceRecord> matchedFaces = new List <FaceRecord>();
if (null != indexFacesResponse && null != indexFacesResponse.FaceRecords && 0 != indexFacesResponse.FaceRecords.Count)
{
foreach (FaceRecord face in indexFacesResponse.FaceRecords)
{
searchFacesResponse = rekognitionClient.SearchFaces(new SearchFacesRequest
{
CollectionId = collectionId,
FaceId = face.Face.FaceId,
FaceMatchThreshold = 70F,
MaxFaces = 2
});
if (searchFacesResponse.FaceMatches != null && searchFacesResponse.FaceMatches.Count != 0)
{
matchedFaces.Add(face);
}
}
//Remove newly added faces to the collection
_collectionService.RemoveFacesFromCollection(collectionId, indexFacesResponse.FaceRecords.Select(x => x.Face.FaceId).ToList());
}
return(matchedFaces);
}
// snippet-start:[Rekognition.dotnetv3.CelebritiesInImageExample]
public static async Task Main(string[] args)
{
string photo = "moviestars.jpg";
var rekognitionClient = new AmazonRekognitionClient();
var recognizeCelebritiesRequest = new RecognizeCelebritiesRequest();
var img = new Amazon.Rekognition.Model.Image();
byte[] data = null;
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
}
catch (Exception)
{
Console.WriteLine($"Failed to load file {photo}");
return;
}
img.Bytes = new MemoryStream(data);
recognizeCelebritiesRequest.Image = img;
Console.WriteLine($"Looking for celebrities in image {photo}\n");
var recognizeCelebritiesResponse = await rekognitionClient.RecognizeCelebritiesAsync(recognizeCelebritiesRequest);
Console.WriteLine($"{recognizeCelebritiesResponse.CelebrityFaces.Count} celebrity(s) were recognized.\n");
recognizeCelebritiesResponse.CelebrityFaces.ForEach(celeb =>
{
Console.WriteLine($"Celebrity recognized: {celeb.Name}");
Console.WriteLine($"Celebrity ID: {celeb.Id}");
BoundingBox boundingBox = celeb.Face.BoundingBox;
Console.WriteLine($"position: {boundingBox.Left} {boundingBox.Top}");
Console.WriteLine("Further information (if available):");
celeb.Urls.ForEach(url =>
{
Console.WriteLine(url);
});
});
Console.WriteLine($"{recognizeCelebritiesResponse.UnrecognizedFaces.Count} face(s) were unrecognized.");
}
private static void searchFace(Amazon.Rekognition.Model.Image image, AmazonRekognitionClient rekognitionClient)
{
String collectionId = "MyCollection";
SearchFacesByImageRequest request = new SearchFacesByImageRequest()
{
CollectionId = collectionId,
Image = image
};
SearchFacesByImageResponse response = rekognitionClient.SearchFacesByImage(request);
foreach (FaceMatch face in response.FaceMatches)
{
Console.WriteLine("FaceId: " + face.Face.FaceId + ", Similarity: " + face.Similarity);
}
}
private string CompareFaces(string strPersonName, MemoryStream msCapture, MemoryStream msFacePic)
{
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient("", "", Amazon.RegionEndpoint.USEast1);
CompareFacesRequest req = new CompareFacesRequest();
Amazon.Rekognition.Model.Image src = new Amazon.Rekognition.Model.Image();
src.Bytes = msCapture;
req.SourceImage = src;
Amazon.Rekognition.Model.Image trg = new Amazon.Rekognition.Model.Image();
trg.Bytes = msFacePic;
req.TargetImage = trg;
try
{
CompareFacesResponse compareFacesResult = rekognitionClient.CompareFaces(req);
List <CompareFacesMatch> faceDetails = compareFacesResult.FaceMatches;
ComparedFace face = null;
foreach (CompareFacesMatch match in faceDetails)
{
face = match.Face;
BoundingBox position = face.BoundingBox;
System.Diagnostics.Debug.Write("Face at " + position.Left
+ " " + position.Top
+ " matches with " + face.Confidence
+ "% confidence.");
if (face.Confidence > 75)
{
return(strPersonName);
}
}
}
catch (Exception ex)
{
return("Fail");
}
return("Unknown");
}
// snippet-start:[Rekognition.dotnetv3.DetectLabelsLocalFile]
public static async Task Main()
{
string photo = "input.jpg";
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;
}
var rekognitionClient = new AmazonRekognitionClient();
var detectlabelsRequest = new DetectLabelsRequest
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F,
};
try
{
DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest);
Console.WriteLine($"Detected labels for {photo}");
foreach (Label label in detectLabelsResponse.Labels)
{
Console.WriteLine($"{label.Name}: {label.Confidence}");
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
public CompareFacesResponse CompareFaces(byte[] source, byte[] target)
{
AmazonRekognitionClient rekoClient = new AmazonRekognitionClient(_credentials, Amazon.RegionEndpoint.USWest2);
CompareFacesRequest cfr = new CompareFacesRequest();
Amazon.Rekognition.Model.Image sourceImage = new Amazon.Rekognition.Model.Image();
Amazon.Rekognition.Model.Image targetImage = new Amazon.Rekognition.Model.Image();
var sourceStream = new MemoryStream(source);
var targetStream = new MemoryStream(target);
sourceImage.Bytes = sourceStream;
targetImage.Bytes = targetStream;
cfr.SourceImage = sourceImage;
cfr.TargetImage = targetImage;
return(rekoClient.CompareFaces(cfr));
}
public async Task <FaceMatchResponse> CompareFacesAsync(string sourceImage, string targetImage)
{
// Converte a imagem fonte em um objeto MemoryStream
var imageSource = new Amazon.Rekognition.Model.Image();
imageSource.Bytes = _serviceUtils.ConvertImageToMemoryStream(sourceImage);
// Converte a imagem alvo em um objeto MemoryStream
var imageTarget = new Amazon.Rekognition.Model.Image();
imageTarget.Bytes = _serviceUtils.ConvertImageToMemoryStream(targetImage);
// Configura o objeto que fará o request para o AWS Rekognition
// A propriedade SimilarityThreshold ajusta o nível de similaridade na comparação das imagens
var request = new CompareFacesRequest
{
SourceImage = imageSource,
TargetImage = imageTarget,
SimilarityThreshold = 80f
};
// Faz a chamada do serviço de CompareFaces
var response = await _rekognitionClient.CompareFacesAsync(request);
// Verifica se houve algum match nas imagens
var hasMatch = response.FaceMatches.Any();
// Se não houve match ele retorna um objeto não encontrado
if (!hasMatch)
{
return(new FaceMatchResponse(hasMatch, null, string.Empty));
}
// Com a imagem fonte e os parâmetros de retorno do match contornamos o rosto encontrado na imagem
var fileName = _serviceUtils.Drawing(imageSource.Bytes, response.SourceImageFace);
// Pega o percentual de similaridade da imagem encontrada
var similarity = response.FaceMatches.FirstOrDefault().Similarity;
// Retorna o objeto com as informações encontradas e com a URL para verificar a imagem
return(new FaceMatchResponse(hasMatch, similarity, fileName));
}
static Amazon.Rekognition.Model.Image ToBytesStream(string filename)
{
var image = new Amazon.Rekognition.Model.Image();
using (FileStream fs = new FileStream(filename, FileMode.Open, FileAccess.Read))
{
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
var client = new AmazonRekognitionClient();
var request = new DetectLabelsRequest()
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F
};
return(image);
}
public DetectTextResponse ImageToText()
{
Image image = new Image();
using (FileStream fs = new FileStream(filePath, FileMode.Open, FileAccess.Read))
{
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectTextRequest detectTextRequest = new DetectTextRequest()
{
Image = image,
};
DetectTextResponse detectTextResponse = rekognitionClient.DetectText(detectTextRequest);
double width = imgPictureFrame.Width;
double height = imgPictureFrame.Height;
foreach (TextDetection text in detectTextResponse.TextDetections)
{
bool isLine = true;
if (text.Type != TextTypes.LINE)
{
isLine = false;
}
BoundingBox boundingBox = text.Geometry.BoundingBox;
BindingBox bindingBox = new BindingBox(width * boundingBox.Width, height * boundingBox.Height, height * boundingBox.Top, width * boundingBox.Left, text.Id.ToString(), isLine);
gContainer.Children.Add(bindingBox);
bindingBoxes.Add(bindingBox);
}
return(detectTextResponse);
}