public void BKTreeShouldQueryBestMatchesBelowGivenThreshold()
{
BKTree <ExampleMetric> tree = new BKTree <ExampleMetric>();
ExampleMetric search = new ExampleMetric(new int[] { 399, 400, 400 });
ExampleMetric best1 = new ExampleMetric(41, new int[] { 400, 400, 400 });
ExampleMetric best2 = new ExampleMetric(42, new int[] { 403, 403, 403 });
ExampleMetric best3 = new ExampleMetric(43, new int[] { 406, 406, 406 });
tree.Add(new ExampleMetric(1, new int[] { 100, 100, 100 }));
tree.Add(new ExampleMetric(2, new int[] { 200, 200, 200 }));
tree.Add(new ExampleMetric(3, new int[] { 300, 300, 300 }));
tree.Add(best1);
tree.Add(best2);
tree.Add(new ExampleMetric(5, new int[] { 500, 500, 500 }));
// Query for match within distance of 1 (best1 is only expected result)
IDictionary <ExampleMetric, int> results = tree.Query(search, 1);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(1, results.Values.ElementAt(0));
Assert.AreEqual(41, results.Keys.ElementAt(0).Id);
Assert.AreEqual(best1.Data, results.Keys.ElementAt(0).Data);
// Query for match within distance of 10 (best1 & best2 are expected results)
tree.Add(best3); // exercise adding another node after already queried
results = tree.Query(search, 10);
Assert.AreEqual(2, results.Count);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(10, DistanceMetric.CalculateLeeDistance(search.Data, best2.Data));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best1, 1)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best2, 10)));
// Query for matches within distance of 20 (best1, best2 & best3 are expected results)
results = tree.Query(search, 20);
Assert.AreEqual(3, results.Count);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(10, DistanceMetric.CalculateLeeDistance(search.Data, best2.Data));
Assert.AreEqual(19, DistanceMetric.CalculateLeeDistance(search.Data, best3.Data));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best1, 1)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best2, 10)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best3, 19)));
}
private static void ExecuteSearch(string[] args)
{
string photoFilePath = GetPhotoPath(args);
string databaseMetaTablePath = GetDatabaseMetaTable(args);
if (string.IsNullOrWhiteSpace(photoFilePath) || string.IsNullOrWhiteSpace(databaseMetaTablePath))
{
PrintHelp("Photo path or database metatable path not provided");
return;
}
if (File.Exists(photoFilePath) == false)
{
PrintHelp("Photo file does not exist");
return;
}
if (File.Exists(databaseMetaTablePath) == false)
{
PrintHelp("Database MetaTable does not exist");
return;
}
using (Image frame = Image.FromFile(photoFilePath))
{
ulong providedPhotoHash = FrameIndexer.CalculateFramePerceptionHashOnly(frame);
VideoFingerPrintDatabaseMetaTableWrapper metaTable = VideoFingerPrintDatabaseMetaTableLoader.Load(databaseMetaTablePath);
BKTree <FrameMetricWrapper> bktree = ModelMetricUtils.CreateBKTree(metaTable);
IDictionary <FrameMetricWrapper, int> treeResults = bktree.Query(
new PhotoMetricWrapper
{
Photo = new PhotoFingerPrintWrapper
{
FilePath = photoFilePath,
PHash = providedPhotoHash,
},
},
2
);
foreach (KeyValuePair <FrameMetricWrapper, int> kvp in treeResults.OrderBy(e => e.Value))
{
FrameMetricWrapper frameWrapper = kvp.Key;
int distance = kvp.Value;
Console.WriteLine(string.Format("Distance {0} for {1} at Frame {2}", distance, frameWrapper.Video.FilePath, frameWrapper.Frame.FrameNumber));
}
}
}
private static IDictionary <string, ISet <PhotoFingerPrintWrapper> > MapPhotosToVideos(
PhotoFingerPrintDatabaseWrapper photoDatabase,
VideoFingerPrintDatabaseMetaTableWrapper metatable
)
{
IDictionary <string, ISet <PhotoFingerPrintWrapper> > resultMap = new Dictionary <string, ISet <PhotoFingerPrintWrapper> >();
IDictionary <string, VideoFingerPrintWrapper> fileNameToVideoFingerPrintMap = MetaTableUtils.EnumerateVideoFingerPrints(metatable).ToDictionary(e => e.FilePath);
BKTree <FrameMetricWrapper> bktree = ModelMetricUtils.CreateBKTree(metatable);
foreach (PhotoFingerPrintWrapper photo in photoDatabase.PhotoFingerPrints)
{
// 1. Find bucket of possible candidates
IDictionary <FrameMetricWrapper, int> treeResults = bktree.Query(
new PhotoMetricWrapper
{
Photo = photo,
},
DefaultMetricThreshold
);
IDictionary <string, ISet <FrameMetricWrapper> > collapsedTreeResults = ModelMetricUtils.CollapseTreeResults(treeResults);
// 2. Find most likely result and add it to the bucket
if (treeResults.Count > 0)
{
VideoFingerPrintWrapper mostLikelyVideo = FindMostLikelyVideo(photo, collapsedTreeResults, fileNameToVideoFingerPrintMap);
// In the case where we didn't get any results, we just skip this photo and move alone
if (mostLikelyVideo == null)
{
continue;
}
ISet <PhotoFingerPrintWrapper> bucket;
string videoFileName = mostLikelyVideo.FilePath;
if (resultMap.TryGetValue(videoFileName, out bucket) == false)
{
bucket = new HashSet <PhotoFingerPrintWrapper>();
resultMap.Add(videoFileName, bucket);
}
}
}
return(resultMap);
}