private static List <double[][]> CMCPerComponent(PhotoSketchCBIR cbr, List <PhotoMetadata> sketches)
{
// matrix of accuracies for each component: hair, eyebrows, eye, nose, mouth and shape
var accuraciesNoGender = new double[6][];
var accuraciesWithGender = new double[6][];
foreach (var sketch in sketches)
{
try
{
var ranksNoGender = cbr.ComputeComponentRanks(sketch.AbsolutePath, Gender.UNKNOWN);
var ranksWithGender = cbr.ComputeComponentRanks(sketch.AbsolutePath, sketch.Gender);
for (int i = 0; i < RANKS.Length; i++)
{
for (int j = 0; j < 6; i++)
{
accuraciesNoGender[j][i] += ranksNoGender[j].Take(RANKS[i]).Any(t => t.Item1.Id == sketch.Id) ? 1 : 0;
accuraciesWithGender[j][i] += ranksWithGender[j].Take(RANKS[i]).Any(t => t.Item1.Id == sketch.Id) ? 1 : 0;
}
}
}
catch (ArgumentException e) { }
}
return(new List <double[][]> {
accuraciesNoGender.Select(values => values.Select(v => v / sketches.Count * 100d).ToArray()).ToArray(),
accuraciesWithGender.Select(values => values.Select(v => v / sketches.Count * 100d).ToArray()).ToArray()
});
}
// execute a test for eveluate the performance of cbr using the best features fusion
private static void TestBestCBR(PhotoSketchCBIR cbir, List <PhotoMetadata> sketches)
{
cbir.SearchFusionStrategy = new WeightedSum(1, 1, 1, 0, 1, 1); // hair, eyebrows, eyes, mouth and shape
var accuraciesNoGender = CreateRecognitionCMC(cbir, sketches, false);
var accuraciesWithGender = CreateRecognitionCMC(cbir, sketches, true);
SerializeRecognitionCMC(string.Format("rank_best_cbr.csv"), accuraciesNoGender, accuraciesWithGender);
}
// execute the test that creates CMC for each component
private static void TestSingleComponents(PhotoSketchCBIR cbir, List <PhotoMetadata> sketches)
{
var ranks = CMCPerComponent(cbir, sketches);
var accuraciesNoGender = ranks[0];
var accuraciesWithGender = ranks[1];
// for each component
for (int i = 0; i < 6; i++)
{
SerializeRecognitionCMC(string.Format("rank_performance_{0}.csv", i),
accuraciesNoGender[i],
accuraciesWithGender[i]);
}
}
protected TaskSearchSketch(PhotoSketchCBIR cbr, string sketchPath, Gender gender, int rankSize)
{
Func <Tuple <PhotoMetadata, double>, string> formatLabel = t =>
string.Format("Id: {0}, Gender: {1}, Score: {2}", t.Item1.Id, t.Item1.Gender, t.Item2);
DoWork += (s, e) =>
{
// background work compute a search
var rank = cbr.Search(sketchPath, gender, rankSize);
var images = rank.Select(r => r.Item1.AbsolutePath).ToArray();
var labels = rank.Select(formatLabel).ToArray();
e.Result = Tuple.Create(images, labels);
};
RunWorkerCompleted += (s, e) => OnSearchCompleted.Invoke(this, e.Result as Tuple <string[], string[]>);
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
var featureBlockParams = Params.GetComponentBlockParams();
var boundingBoxParams = Params.GetComponentBoundingBoxParams();
var referenceShape = Params.GetReferenceShape();
var componentExtractor = ComponentAlignerFactory.FromReferenceShape(boundingBoxParams, referenceShape);
var cbr = new PhotoSketchCBIR(PhotoSketchFeatureExtractorFactory.Default(componentExtractor, featureBlockParams));
var view = new FormDashboardCBIR();
var controller = new PresenterCBIR(view, cbr);
Application.Run(view);
}
static void Main()
{
var photos = PhotoMetadataCsv.FromCSV(GALLERY_CSV).ToList();
var sketches = PhotoMetadataCsv.FromCSV(SKETCHS_CSV).ToList();
var extractor = TestUtils.GetPhotoSketchFeatureExtractor(Params.GetReferenceShape());
// extract or load from dumped db
//var db = ExtractFeaturesDB(extractor, true);
var db = FaceFeaturesDB.CreateFromDump(DUMPED_DB);
var cbir = new PhotoSketchCBIR(extractor)
{
Database = db
};
TestSingleComponents(cbir, sketches);
TestDifferentFusion(cbir, sketches);
TestBestCBR(cbir, sketches);
}
private static double[] CreateRecognitionCMC(PhotoSketchCBIR cbr, List <PhotoMetadata> sketches, bool filterGender = true)
{
var maxRank = RANKS.Max();
var accuracies = new double[RANKS.Length];
sketches.ForEach(sketch =>
{
try
{
var ranks = cbr.Search(sketch.AbsolutePath, filterGender ? sketch.Gender : Gender.UNKNOWN, maxRank);
for (int i = 0; i < RANKS.Length; i++)
{
accuracies[i] += ranks.Take(RANKS[i]).Any(t => t.Item1.Id == sketch.Id) ? 1 : 0;
}
}
catch (Exception) { }
});
return(accuracies.Select(v => v / sketches.Count * 100d).ToArray());
}
// execute the test that creates CMC combining the most discriminative components
private static void TestDifferentFusion(PhotoSketchCBIR cbir, List <PhotoMetadata> sketches)
{
var bestFusions = new double[5][]
{
new double[] { 0, 1, 1, 0, 0, 0 }, // eyebrows and eyes
new double[] { 0, 1, 1, 0, 1, 0 }, // eyebrows, eyes and mouth
new double[] { 0, 1, 1, 0, 1, 1 }, // eyebrows, eyes, mouth and shape
new double[] { 1, 1, 1, 0, 1, 1 }, // hair, eyebrows, eyes, mouth and shape
new double[] { 1, 1, 1, 1, 1, 1 } // all components
};
for (int f = 0; f < bestFusions.Length; f++)
{
cbir.SearchFusionStrategy = new WeightedSum(bestFusions[f]);
var cmcData = CreateRecognitionCMC(cbir, sketches, false);
var cmcDataGender = CreateRecognitionCMC(cbir, sketches, true);
SerializeRecognitionCMC(string.Format("rank_performance_fusion_{0}.csv", f),
cmcData,
cmcDataGender);
}
}
public static TaskSearchSketch NewSearch(PhotoSketchCBIR cbr, string sketchPath, Gender gender, int rankSize)
{
return(new TaskSearchSketch(cbr, sketchPath, gender, rankSize));
}
public PresenterCBIR(CBRView view, PhotoSketchCBIR cbr)
{
View = view;
CBR = cbr;
Init();
}
