Пример #1
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 public PComparer(PComparer other) : this(PapillonPINVOKE.new_PComparer__SWIG_1(PComparer.getCPtr(other)), true)
 {
     if (PapillonPINVOKE.SWIGPendingException.Pending)
     {
         throw PapillonPINVOKE.SWIGPendingException.Retrieve();
     }
 }
Пример #2
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    public PResult Search(PDescription unknown, PComparer comparer, PWatchlistOptions options, PIdentifyResults results)
    {
        PResult ret = new PResult(PapillonPINVOKE.PWatchlist_Search__SWIG_2(swigCPtr, PDescription.getCPtr(unknown), PComparer.getCPtr(comparer), PWatchlistOptions.getCPtr(options), PIdentifyResults.getCPtr(results)), true);

        if (PapillonPINVOKE.SWIGPendingException.Pending)
        {
            throw PapillonPINVOKE.SWIGPendingException.Retrieve();
        }
        return(ret);
    }
Пример #3
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    public PResult Search(PDescription unknown, PComparer compare, PIdentifyResults results, int topN, float threshold)
    {
        PResult ret = new PResult(PapillonPINVOKE.PWatchlist_Search__SWIG_1(swigCPtr, PDescription.getCPtr(unknown), PComparer.getCPtr(compare), PIdentifyResults.getCPtr(results), topN, threshold), true);

        if (PapillonPINVOKE.SWIGPendingException.Pending)
        {
            throw PapillonPINVOKE.SWIGPendingException.Retrieve();
        }
        return(ret);
    }
Пример #4
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    public static PResult Create(PComparer.EComparerType type, PComparer comparer)
    {
        PResult ret = new PResult(PapillonPINVOKE.PComparer_Create__SWIG_3((int)type, PComparer.getCPtr(comparer)), true);

        if (PapillonPINVOKE.SWIGPendingException.Pending)
        {
            throw PapillonPINVOKE.SWIGPendingException.Retrieve();
        }
        return(ret);
    }
Пример #5
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    public static PResult Create(PComparer comparer, double threshold)
    {
        PResult ret = new PResult(PapillonPINVOKE.PComparer_Create__SWIG_0(PComparer.getCPtr(comparer), threshold), true);

        if (PapillonPINVOKE.SWIGPendingException.Pending)
        {
            throw PapillonPINVOKE.SWIGPendingException.Retrieve();
        }
        return(ret);
    }
Пример #6
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 internal static global::System.Runtime.InteropServices.HandleRef getCPtr(PComparer 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
        }