public PWatchlist(PWatchlist other) : this(PapillonPINVOKE.new_PWatchlist__SWIG_1(PWatchlist.getCPtr(other)), true)
{
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
}
public static PResult ReadFromStore(PStore store, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PWatchlist_ReadFromStore(PStore.getCPtr(store), PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public static PResult WriteToFileWatchlist(string filename, PWatchlist arg1, ESerialisationFormat format)
{
PResult ret = new PResult(PapillonPINVOKE.PFileIO_WriteToFileWatchlist__SWIG_0(filename, PWatchlist.getCPtr(arg1), (int)format), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public static PResult ReadFromFileWatchlist(string filename, PWatchlist arg1)
{
PResult ret = new PResult(PapillonPINVOKE.PFileIO_ReadFromFileWatchlist__SWIG_1(filename, PWatchlist.getCPtr(arg1)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult CreateWatchlistFromFolder(string path, PWatchlist watchlist, bool thumbnail, int maxFrames, int maxExamples)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_CreateWatchlistFromFolder__SWIG_0(swigCPtr, path, PWatchlist.getCPtr(watchlist), thumbnail, maxFrames, maxExamples), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult CreateWatchlistFromFolder(string path, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_CreateWatchlistFromFolder__SWIG_3(swigCPtr, path, PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult CreateWatchlist(string configurationFile, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_CreateWatchlist__SWIG_2(swigCPtr, configurationFile, PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(PWatchlist obj)
{
return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr);
}
static void Main(string[] args)
{
PLog.OpenConsoleLogger();
PapillonSDK.Initialise();
string SAMPLE_DIR = PPath.Join(PUtils.GetEnv("PAPILLON_INSTALL_DIR"), "Data", "Samples");
//===========
// Enrollment
//===========
// Enrollment is the process of turning detected faces of a subject into a unique key. In the biometrics world this is often
// referred to as a biometric template. In this SDK we prefer the use of the term 'description'. Think of a description as a
// description of an identity. In this SDK descriptions are fairly generic containers which can be used to store
// a lot of other information as well as the information about the face of the identity.
// The enrollment engine wraps up the whole face-recognition processing chain into an easy to use interface.
// From it you can easily generate 'descriptions' from images and videos. However, all the functions provided
// by the enrollment engine can also be done using the other classes available in this SDK as this enrollment
// engine may not fit all use-cases.
// The following code initialises an enrollment engine and sets it up with a set of sensible options
PEnrollment enrollment = new PEnrollment();
enrollment.AutoConfigureForFaceRecognition(); // this sets-up a sensible set of options for the face-rec enrollment
// Now we can generate two different descriptions of Kieron using two images that were taken at different times.
// For each image, the enrollment engine will locate the face in the input image and generate the description.
// Note, if an image is supplied with more than face, then the enrollment function will generate an error, as it is
// unsure which face to use for the enrollment.
PGuid kieronSubjectId = PGuid.CreateUniqueId();
PDescription descriptionOfKieron1 = new PDescription();
enrollment.EnrollFromImage(PPath.Join(SAMPLE_DIR, "kieron01.jpg"), descriptionOfKieron1, "Kieron", kieronSubjectId);
PDescription descriptionOfKieron2 = new PDescription();
enrollment.EnrollFromImage(PPath.Join(SAMPLE_DIR, "kieron02.jpg"), descriptionOfKieron2, "Kieron", kieronSubjectId);
// Generate two descriptions of Kjetil from images taken at different times
PGuid kjetilSubjectId = PGuid.CreateUniqueId();
PDescription descriptionOfKjetil1 = new PDescription();
enrollment.EnrollFromImage(PPath.Join(SAMPLE_DIR, "kjetil01.jpg"), descriptionOfKjetil1, "Kjetil", kjetilSubjectId);
PDescription descriptionOfKjetil2 = new PDescription();
enrollment.EnrollFromImage(PPath.Join(SAMPLE_DIR, "kjetil02.jpg"), descriptionOfKjetil2, "Kjetil", kjetilSubjectId);
//===========================================================
// Face Verification on Images (also known as Authentication)
//===========================================================
// Verification (commonly referred to as Authentication) is the process of comparing two identities (using their
// 'descriptions' and comparing the result against a threshold. If the probability of match is higher than the
// threshold, then the identity is said to be Verified/Authenticated.
// The value of the threshold can be set using some pre-defined security levels. Alternatively, it can be set
// manually by specifying the value yourself. Care should be taken in setting the right value for your application.
// Papillon has a verification engine which is a helper class to enable you to easily perform verification on descriptions, images
// and videos. All the functionalities of this class can also be done by using the other classes available in
// the SDK.
// The following code, initialises the verification engine, using the enrollment engine and sets the security level.
PVerify verify = new PVerify(enrollment, EVerificationSecurityLevel.E_VERIFICATION_SECURITY_LEVEL_HIGH);
// Next we can try to match a description of Kieron against another description of Kieron, we would expect this to authenticate
// successfully with a high probability.
DoMatch(descriptionOfKieron1, descriptionOfKieron2, verify);
// However, if we match a description of Kieron against a description of Kjetil, we would NOT expect this to authenticate
// successfully. The probability of match will be low.
DoMatch(descriptionOfKieron1, descriptionOfKjetil1, verify);
// We can also match a description of Kjetil against another description of Kjetil. Again, we would expect this to authenticate
// successfully with a high probability.
DoMatch(descriptionOfKjetil1, descriptionOfKjetil2, verify);
//===========================
// Face Verification on Video
//===========================
// Next lets perform enrollment from a video. This is done by processing each frame of the video in turn.
// Because videos can contain multiple faces of the same identity in frames, a collection of faces
// can be used to generate the description. The number of frames to process and the number of examples
// to used to generate the description can be specified as parameters.
// Note, only supply videos which are known to have a single identity present when performing enrollment.
// If you have multiple faces in your video then you need to use the FaceLog2 analytic to generate descriptions
// to use for enrollment.
int maxFramesToProcess = -1; // Process all the frames (up until the maxExamples has been reached)
int maxExamples = 10; // Use the first 5 examples found in the video to generate the description.
PDescription descriptionOfKieronFromVideo = new PDescription();
enrollment.EnrollFromVideo(PPath.Join(SAMPLE_DIR, "kieron01.avi"),
descriptionOfKieronFromVideo, maxFramesToProcess, maxExamples, "Kieron in Video", kieronSubjectId);
PDescription descriptionOfKjetilFromVideo = new PDescription();
enrollment.EnrollFromVideo(PPath.Join(SAMPLE_DIR, "kjetil01.avi"),
descriptionOfKjetilFromVideo, maxFramesToProcess, maxExamples, "Kjetil in Video", kjetilSubjectId);
// Should authenticate
DoMatch(descriptionOfKieron2, descriptionOfKieronFromVideo, verify);
// Should fail to authenticate
DoMatch(descriptionOfKjetil1, descriptionOfKieronFromVideo, verify);
// Should authenticate
DoMatch(descriptionOfKjetil1, descriptionOfKjetilFromVideo, verify);
// Should fail to authenticate
DoMatch(descriptionOfKieron2, descriptionOfKjetilFromVideo, verify);
// =============================
// Merging Descriptions Together
//==============================
// In the above, 5 examples from the video have been used to build each description.
// Each example used has been turned into a descriptor.
// When enrolling from the image, only a single example face was used to generate the description.
// The description will only contain 1 descriptor
// We can see this by requesting the number of descriptors each description holds.
Console.WriteLine("Number of descriptors in image description: " + descriptionOfKieron1.GetDescriptors().Size());
Console.WriteLine("Number of descriptors in video description: " + descriptionOfKieronFromVideo.GetDescriptors().Size());
// When a description is generated a thumbnail for each example (descriptor) is generated and stored in the description
// We can get to these thumbnails and display them
PImage thumbnail = new PImage();
PList thumbnails = descriptionOfKieron1.GetThumbnails();
thumbnails.Get(0, thumbnail);
thumbnail.Display("Thumbnail Of Kieron", 5000); // display for 5 seconds
// It is possible to merge descriptions of the same identity together, generating a more complete 'description' of someone
if (descriptionOfKieron1.AddDescription(descriptionOfKieron2).Failed())
{
Console.WriteLine("Failed to merge descriptions");
System.Environment.Exit(0);
}
// We would now expect there to be two descriptors in the description
// Note, if you try to merge two descriptions with different IdentityId's together this will still work.
// However, the id of base description will be used and the other discarded
Console.WriteLine("Number of descriptors in description: " + descriptionOfKieron1.GetDescriptors().Size());
// Now when we perform the comparison, the match score could change as there is more information
// held in one of the descriptions. You will notice the confidence has increased as we are more
// certain the two identities are the same.
DoMatch(descriptionOfKieron1, descriptionOfKieronFromVideo, verify);
// We now also have two thumbnails associated with this description
thumbnails = descriptionOfKieron1.GetThumbnails();
Console.WriteLine("Number of thumbnails in description: " + thumbnails.Size());
thumbnails.Get(0, thumbnail);
thumbnail.Display("Thumbnail 1 Of Kieron", 5000); // display for 5 seconds
thumbnails.Get(1, thumbnail);
thumbnail.Display("Thumbnail 2 Of Kieron", 5000); // display for 5 seconds
// ============================
// Description Input and Output
//=============================
// It is easy to save any Papillon object to file, including descriptions
if (PFileIO.WriteToFile("descriptionOfKieron1.bin", descriptionOfKieron1).Failed())
{
Console.WriteLine("Failed to save description to file");
System.Environment.Exit(0);
}
if (PFileIO.WriteToFile("descriptionOfKieronFromVideo.bin", descriptionOfKieronFromVideo).Failed())
{
Console.WriteLine("Failed to save description to file");
System.Environment.Exit(0);
}
// We can also easily match descriptions that are stored in file
PMatchScore matchScore = new PMatchScore();
if (verify.VerifyFromDescriptionFile("descriptionOfKieron1.bin", "descriptionOfKieronFromVideo.bin", matchScore).Failed())
{
Console.WriteLine("Failed to match descriptions from file");
System.Environment.Exit(0);
}
Console.WriteLine("Match Score " + matchScore);
// A common requirement is to save descriptions in a database such as MySQL, Postgresql, redis ....
// This can easily be done using memory streams which allows a binary representation of a papillon
// For example to get a binary blob of a description...
PMemoryStream ms = new PMemoryStream();
ms.WriteObjectDescription(descriptionOfKieron1);
PByteArray byteArray = ms.GetByteArray();
Console.WriteLine("Description size in bytes: " + byteArray.Size());
// Also, you can also convert the object to a string which can be stored as a text row of a database table
String str = byteArray.ToString();
// To get from the string back to the byte-array back to your original object you perform the reverse operation
PByteArray byteArray2 = new PByteArray();
PByteArray.FromString(str, byteArray2);
// and then back to the description
PDescription descriptionFromString = new PDescription();
PMemoryStream ms2 = new PMemoryStream(byteArray2);
ms2.ReadObjectDescription(descriptionFromString);
// And we can do our comparison and should get the same match score as before
// and get to the thumbnail
DoMatch(descriptionFromString, descriptionOfKieronFromVideo, verify);
thumbnails = descriptionFromString.GetThumbnails();
thumbnails.Get(0, thumbnail);
thumbnail.Display("Kieron Again", 5000);
//===================================
// Face Identification and Watchlists
//===================================
// Face identification is the process of ascertaining the identity by comparing it against a list of known identities.
// This is done by comparing the 'unknown' description (often referred to as a probe) against a watchlist (sometimes referred
// to as a gallery) of known descriptions and returning the top N matches, that pass the threshold value.
// In Papillon we have a watchlist class that enables you to perform identification.
PWatchlist watchlist = new PWatchlist();
// You can add descriptions to the watchlist
// The descriptions of Kieron
watchlist.Add(descriptionOfKieron1);
watchlist.Add(descriptionOfKieron2);
// The descriptions of Kjetil
watchlist.Add(descriptionOfKjetil1);
watchlist.Add(descriptionOfKjetil2);
// To get the size of the watchlist
// Note, this returns the number of subjects, not the number of descriptions.
Console.WriteLine("Watchlist Size: " + watchlist.Size());
// To search a watch list you need to specify a comparer to use and threshold level to use
PComparer comparer = new PComparer();
PComparer.Create(comparer, 0.55);
// Lets generate an unknown description
PDescription unknownDescription = new PDescription();
enrollment.EnrollFromImage(PPath.Join(SAMPLE_DIR, "kieron_20_years_ago.jpg"), unknownDescription);
// And run a search against the watchlist
PIdentifyResults identifyResults = new PIdentifyResults();
watchlist.Search(unknownDescription, comparer, identifyResults, 1, 0.55f);
// In this example, the identity is returned as Kieron, eventhough there is over a 20 year gap
// between the times the photos were taken
Console.WriteLine(identifyResults.ToString());
thumbnails = unknownDescription.GetThumbnails();
thumbnails.Get(0, thumbnail);
thumbnail.Display("Kieron 20 Years Ago", 5000);
// We can of course load and save watchlists as well
PFileIO.WriteToFileWatchlist("watchlist.bin", watchlist);
//=============================
// Face Identification in Video
//=============================
// There is another example which deals with the complicated task of performing face-identification in video.
// This example is ExampleFaceLog2.cc
}
public PResult EnrollFromCSV(string csvInputFilename, int videoMaxFrames, int videoMaxExamples, string csvOutputFilename, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_EnrollFromCSV__SWIG_1(swigCPtr, csvInputFilename, videoMaxFrames, videoMaxExamples, csvOutputFilename, PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult EnrollFromCSV(string csvInputFilename, int videoMaxFrames, int videoMaxExamples, string csvOutputFilename, PWatchlist watchlist, SWIGTYPE_p_f_r_q_const__papillon__PString__void callback)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_EnrollFromCSV__SWIG_0(swigCPtr, csvInputFilename, videoMaxFrames, videoMaxExamples, csvOutputFilename, PWatchlist.getCPtr(watchlist), SWIGTYPE_p_f_r_q_const__papillon__PString__void.getCPtr(callback)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult EnrollFromVideoStream(PInputVideoStream ivs, int maxFrames, int maxExamples, PGuid subjectId, string subjectName, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_EnrollFromVideoStream__SWIG_0(swigCPtr, PInputVideoStream.getCPtr(ivs), maxFrames, maxExamples, PGuid.getCPtr(subjectId), subjectName, PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public PResult EnrollFromImages(string path, PGuid subjectId, string subjectName, PWatchlist watchlist)
{
PResult ret = new PResult(PapillonPINVOKE.PEnrollment_EnrollFromImages__SWIG_0(swigCPtr, path, PGuid.getCPtr(subjectId), subjectName, PWatchlist.getCPtr(watchlist)), true);
if (PapillonPINVOKE.SWIGPendingException.Pending)
{
throw PapillonPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}