Ejemplo n.º 1
0
        public static LDA loadTraining(
            int TRAINING_SIZE, int TEST_SIZE,
            string SKETCH_PATH, string SKETCH_EXTENSION, string LDA_FILE_NAME,
            out List <string> trainingSetSketchesPath, out List <string> testSetSketchesPath
            )
        {
            LDA             lda;
            List <FileInfo> files = new List <FileInfo>();
            DirectoryInfo   dinfo = new DirectoryInfo(SKETCH_PATH);

            files.AddRange(dinfo.GetFiles(SKETCH_EXTENSION));
            Random rnd = new Random();

            if (File.Exists(LDA_FILE_NAME))
            {
                Stream          openFileStream = File.OpenRead(LDA_FILE_NAME);
                BinaryFormatter deserializer   = new BinaryFormatter();
                lda = (LDA)deserializer.Deserialize(openFileStream);
                openFileStream.Close();

                trainingSetSketchesPath = new List <string>();
                testSetSketchesPath     = new List <string>();

                foreach (FileInfo file in files)
                {
                    foreach (string train in lda.trainingSet)
                    {
                        if (!file.Name.StartsWith(train))
                        {
                            testSetSketchesPath.Add(file.FullName);
                            break;
                        }
                    }
                }
                testSetSketchesPath = testSetSketchesPath.OrderBy(x => rnd.Next()).ToList();
                testSetSketchesPath = testSetSketchesPath.Take(TEST_SIZE).ToList();
            }
            else
            {
                lda = new LDA();

                files = files.OrderBy(x => rnd.Next()).ToList();
                List <FileInfo> trainingSet = files.Take(TRAINING_SIZE).ToList();
                List <FileInfo> testSet     = files.Skip(TRAINING_SIZE).Take(TEST_SIZE).ToList();

                trainingSetSketchesPath = trainingSet.Select(file => file.FullName).ToList();
                testSetSketchesPath     = testSet.Select(file => file.FullName).ToList();
                lda.trainingSet         = trainingSetSketchesPath.Select(file => file.Substring(file.LastIndexOf('\\') + 1, 5)).ToList();
            }
            return(lda);
        }
Ejemplo n.º 2
0
        //test on all images
        public void accuracyTest(BackgroundWorker worker, out double rank1, out double rank10, out double rank50, out double rank100, out double rank200)
        {
            rank1   = 0;
            rank10  = 0;
            rank50  = 0;
            rank100 = 0;
            rank200 = 0;
            if (lda == null)
            {
                lda = LDA.loadTraining(
                    TRAINING_SIZE, TEST_SIZE,
                    SKETCH_PATH, SKETCH_EXTENSION, LDA_FILE_NAME,
                    out trainingSetSketchesPath, out testSetSketchesPath
                    );
            }
            for (int i = 0; i < testSetSketchesPath.Count; i++)
            {
                search(testSetSketchesPath[i], worker, true);
                var index = getSubjectIndex();
                if (index == 1)
                {
                    rank1++;
                }
                if (index <= 10)
                {
                    rank10++;
                }
                if (index <= 50)
                {
                    rank50++;
                }
                if (index <= 100)
                {
                    rank100++;
                }
                if (index <= 200)
                {
                    rank200++;
                }
                worker.ReportProgress(i * 100 / testSetSketchesPath.Count);
            }

            rank1   /= testSetSketchesPath.Count;
            rank10  /= testSetSketchesPath.Count;
            rank50  /= testSetSketchesPath.Count;
            rank100 /= testSetSketchesPath.Count;
            rank200 /= testSetSketchesPath.Count;
            worker.ReportProgress(100);
        }
Ejemplo n.º 3
0
        // main query method
        public void search(String sketchPath, BackgroundWorker worker = null, bool progressOnlyDescriptor = false)
        {
            bool progress = (worker != null);

            if (sketchPath.Equals(""))
            {
                return;
            }

            if (lda == null)
            {
                lda = LDA.loadTraining(
                    TRAINING_SIZE, TEST_SIZE,
                    SKETCH_PATH, SKETCH_EXTENSION, LDA_FILE_NAME,
                    out trainingSetSketchesPath, out testSetSketchesPath
                    );
            }

            if (descriptors == null)
            {
                if (File.Exists(DESCRIPTOR_FILE_NAME))
                {
                    Stream          openFileStream = File.OpenRead(DESCRIPTOR_FILE_NAME);
                    BinaryFormatter deserializer   = new BinaryFormatter();
                    descriptors = (List <FaceDescriptor>)deserializer.Deserialize(openFileStream);
                    openFileStream.Close();
                }
                else
                {
                    descriptors = new List <FaceDescriptor>();
                    List <FileInfo> files = new List <FileInfo>();
                    DirectoryInfo   dinfo = new DirectoryInfo(PHOTO_PATH);
                    files.AddRange(dinfo.GetFiles(PHOTO_EXTENSION));
                    dinfo = new DirectoryInfo(OTHER_PHOTO_PATH);
                    files.AddRange(dinfo.GetFiles(PHOTO_EXTENSION));
                    files.AddRange(dinfo.GetFiles(OTHER_PHOTO_EXTENSION));

                    for (int i = 0; i < files.Count; i++)
                    {
                        FaceDescriptor face = processImage(files[i].FullName, files[i].Name, true);
                        if (face != null)
                        {
                            descriptors.Add(face);
                        }

                        if (progress)
                        {
                            worker.ReportProgress(i * 100 / files.Count);
                        }
                    }

                    Stream          SaveFileStream = File.Create(DESCRIPTOR_FILE_NAME);
                    BinaryFormatter serializer     = new BinaryFormatter();
                    serializer.Serialize(SaveFileStream, descriptors);
                    SaveFileStream.Close();
                }

                LDA.training(lda, descriptors, trainingSetSketchesPath,
                             processImage, TRAINING_SIZE, LDA_FILE_NAME, worker);

                for (int i = 0; i < descriptors.Count; i++)
                {
                    for (int k = 0; k < descriptors[i].DescriptorHog.Count(); k++)
                    {
                        descriptors[i].DescriptorHog[k] *= (float)lda.projectingVectorHOG[k];
                    }
                    for (int k = 0; k < descriptors[i].DescriptorSift.Count(); k++)
                    {
                        descriptors[i].DescriptorSift[k] *= (float)lda.projectingVectorSIFT[k];
                    }
                }
            }


            if (progress && !progressOnlyDescriptor)
            {
                worker.ReportProgress(0);
            }

            sketchName = sketchPath.Substring(sketchPath.LastIndexOf('\\') + 1, 5);
            FaceDescriptor sketchFace = processImage(sketchPath, sketchName, false);

            resultHog        = new SortedDictionary <double, string>();
            resultSift       = new SortedDictionary <double, string>();
            resultBordaCount = new SortedDictionary <double, string>();

            for (int i = 0; i < sketchFace.DescriptorHog.Count(); i++)
            {
                sketchFace.DescriptorHog[i] *= (float)lda.projectingVectorHOG[i];
            }
            for (int i = 0; i < sketchFace.DescriptorSift.Count(); i++)
            {
                sketchFace.DescriptorSift[i] *= (float)lda.projectingVectorSIFT[i];
            }

            int hogSize  = 540;
            int siftSize = 512;

            for (int i = 0; i < descriptors.Count; i++)
            {
                addDictionaryUnique(
                    euclideanDistance(
                        sketchFace.DescriptorHog, descriptors[i].DescriptorHog, hogSize
                        ), descriptors[i].Name, resultHog);
                addDictionaryUnique(
                    euclideanDistance(
                        sketchFace.DescriptorSift, descriptors[i].DescriptorSift, siftSize
                        ), descriptors[i].Name, resultSift);
                if (progress && !progressOnlyDescriptor)
                {
                    worker.ReportProgress(i * 100 / descriptors.Count);
                }
            }

            Dictionary <string, int> dictionary = new Dictionary <string, int>();
            int count = 0;

            foreach (var r in resultHog)
            {
                dictionary.Add(r.Value, (resultHog.Count - count) * 2);//weight
                count++;
            }
            count = 0;
            foreach (var r in resultSift)
            {
                dictionary[r.Value] += resultSift.Count - count;
                count++;
            }
            foreach (var r in dictionary)
            {
                double value = r.Value;
                //negative int because sorted dictionary work in ascending mode only
                addDictionaryUnique(-r.Value, r.Key, resultBordaCount);
            }

            if (progress && !progressOnlyDescriptor)
            {
                worker.ReportProgress(100);
            }
        }
Ejemplo n.º 4
0
        public static void training(
            LDA lda, List <FaceDescriptor> descriptors, List <string> trainingSetSketchesPath,
            Func <string, string, bool, FaceDescriptor> processImage,
            int TRAINING_SIZE, string LDA_FILE_NAME, BackgroundWorker worker = null
            )
        {
            bool progress = (worker != null);

            if (lda.projectingVectorHOG == null)
            {
                List <FaceDescriptor> trainingDescriptors = new List <FaceDescriptor>();
                for (int i = 0; i < trainingSetSketchesPath.Count; i++)
                {
                    FaceDescriptor face = processImage(trainingSetSketchesPath[i], lda.trainingSet[i], true);
                    if (face != null)
                    {
                        trainingDescriptors.Add(face);
                    }

                    if (progress)
                    {
                        worker.ReportProgress(i * 100 / trainingSetSketchesPath.Count);
                    }
                }

                if (progress)
                {
                    worker.ReportProgress(0);
                }

                Dictionary <string, FaceDescriptor> descriptorsWithName = new Dictionary <string, FaceDescriptor>();

                foreach (var descriptor in descriptors)
                {
                    string name = descriptor.Name.Substring(0, 5);
                    if (!descriptorsWithName.ContainsKey(name))
                    {
                        descriptorsWithName.Add(name, descriptor);
                    }
                }

                int NPoints   = TRAINING_SIZE * 2;
                int NClasses  = TRAINING_SIZE;
                int NVarsHOG  = trainingDescriptors[0].DescriptorHog.Count();
                int NVarsSIFT = trainingDescriptors[0].DescriptorSift.Count();

                double[,] xyHOG  = new double[NPoints, NVarsHOG + 1];
                double[,] xySIFT = new double[NPoints, NVarsSIFT + 1];
                for (int i = 0; i < TRAINING_SIZE; i++)
                {
                    FaceDescriptor photoDescriptor = descriptorsWithName[trainingDescriptors[i].Name];

                    for (int k = 0; k < NVarsHOG; k++)
                    {
                        xyHOG[i, k] = trainingDescriptors[i].DescriptorHog[k];
                        xyHOG[i + TRAINING_SIZE, k] = photoDescriptor.DescriptorHog[k];
                    }
                    xyHOG[i, NVarsHOG] = i;
                    xyHOG[i + TRAINING_SIZE, NVarsHOG] = i;

                    for (int k = 0; k < NVarsSIFT; k++)
                    {
                        xySIFT[i, k] = trainingDescriptors[i].DescriptorSift[k];
                        xySIFT[i + TRAINING_SIZE, k] = photoDescriptor.DescriptorSift[k];
                    }
                    xySIFT[i, NVarsSIFT] = i;
                    xySIFT[i + TRAINING_SIZE, NVarsSIFT] = i;

                    if (progress)
                    {
                        worker.ReportProgress(i * 100 / TRAINING_SIZE);
                    }
                }

                int      info  = 0;
                double[] wHOG  = new double[0];
                double[] wSIFT = new double[0];

                alglib.lda.fisherlda(xyHOG, NPoints, NVarsHOG, NClasses, ref info, ref wHOG, null);
                alglib.lda.fisherlda(xySIFT, NPoints, NVarsSIFT, NClasses, ref info, ref wSIFT, null);
                lda.projectingVectorHOG  = wHOG;
                lda.projectingVectorSIFT = wSIFT;

                Stream          SaveFileStream = File.Create(LDA_FILE_NAME);
                BinaryFormatter serializer     = new BinaryFormatter();
                serializer.Serialize(SaveFileStream, lda);
                SaveFileStream.Close();
            }
        }