public void IndexFiles(FileInfo[] imageFiles, BackgroundWorker IndexBgWorker, object argument = null)
{
BKTree<CEDDTreeNode> ceddtree = new BKTree<CEDDTreeNode>();
double[] ceddDiscriptor = null;
int totalFileCount = imageFiles.Length;
CEDD cedd = new CEDD();
for (int i = 0; i < totalFileCount; i++)
{
var fi = imageFiles[i];
using (Bitmap bmp = new Bitmap(Image.FromFile(fi.FullName)))
{
ceddDiscriptor = cedd.Apply(bmp);
}
CEDDTreeNode ceddTreeNode = new CEDDTreeNode
{
Id = i,
ImageName = fi.Name,
ImagePath = fi.FullName,
CEDDDiscriptor = ceddDiscriptor
};
ceddtree.add(ceddTreeNode);
IndexBgWorker.ReportProgress(i);
}
CEDDRepository<BKTree<CEDDTreeNode>> repo = new CEDDRepository<BKTree<CEDDTreeNode>>();
repo.Save(ceddtree);
CacheHelper.Remove("CeddIndexTree");
}
public void BKTree_should_FindBestDistance()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
TestNode search = new TestNode(new int[] { 118, 223, 316 });
TestNode best = new TestNode(3, new int[] { 120, 220, 320 });
tree.add(new TestNode(1, new int[] { 100, 200, 300 }));
tree.add(new TestNode(2, new int[] { 110, 210, 310 }));
tree.add(best);
tree.add(new TestNode(4, new int[] { 130, 230, 330 }));
tree.add(new TestNode(5, new int[] { 140, 240, 340 }));
Assert.Equal(9, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(9, tree.findBestDistance(search));
}
public void BKTree_should_FindBestDistance()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
TestNode search = new TestNode(new int[] { 118, 223, 316 });
TestNode best = new TestNode(3, new int[] { 120, 220, 320 });
tree.add(new TestNode(1, new int[] { 100, 200, 300 }));
tree.add(new TestNode(2, new int[] { 110, 210, 310 }));
tree.add(best);
tree.add(new TestNode(4, new int[] { 130, 230, 330 }));
tree.add(new TestNode(5, new int[] { 140, 240, 340 }));
Assert.Equal(9, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(9, tree.findBestDistance(search));
}
public void IndexFiles(FileInfo[] imageFiles, BackgroundWorker IndexBgWorker, object argument = null)
{
BKTree <CEDDTreeNode> ceddtree = new BKTree <CEDDTreeNode>();
double[] ceddDiscriptor = null;
int totalFileCount = imageFiles.Length;
CEDD cedd = new CEDD();
for (int i = 0; i < totalFileCount; i++)
{
var fi = imageFiles[i];
using (Bitmap bmp = new Bitmap(Image.FromFile(fi.FullName)))
{
ceddDiscriptor = cedd.Apply(bmp);
}
CEDDTreeNode ceddTreeNode = new CEDDTreeNode
{
Id = i,
ImageName = fi.Name,
ImagePath = fi.FullName,
CEDDDiscriptor = ceddDiscriptor
};
ceddtree.add(ceddTreeNode);
IndexBgWorker.ReportProgress(i);
}
CEDDRepository <BKTree <CEDDTreeNode> > repo = new CEDDRepository <BKTree <CEDDTreeNode> >();
repo.Save(ceddtree);
CacheHelper.Remove("CeddIndexTree");
}
public void BKTree_should_ThrowUponAddingNullNode()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
tree.add(new TestNode(1, new int[] { 100, 200, 300 }));
tree.add(new TestNode(2, new int[] { 110, 210, 310 }));
tree.add(new TestNode(3, new int[] { 130, 230, 330 }));
tree.add(new TestNode(4, new int[] { 140, 240, 340 }));
Assert.ThrowsDelegate boom =
delegate
{
tree.add(null);
};
Assert.Throws <NullReferenceException>(boom);
}
/*
* To use BKTree:
* 1. Create a class dervied from BKTreeNode
* 2. Add a member variable of your data to be sorted / retrieved
* 3. Override the calculateDistance method to calculate the distance metric
* between two nodes for the data to be sorted / retrieved.
* 4. Instantiate a BKTree with the type name of the class created in (1).
*/
static void Main(string[] args)
{
/*
* NOTE: More comprehensive examples of BK-Tree methods in unit tests
*/
// Exercise static distance metric methods -- just because
Console.WriteLine(
DistanceMetric.calculateHammingDistance(
new byte[] { 0xEF, 0x35, 0x20 },
new byte[] { 0xAD, 0x13, 0x87 }));
Console.WriteLine(
DistanceMetric.calculateLeeDistance(
new int[] { 196, 105, 48 },
new int[] { 201, 12, 51 }));
Console.WriteLine(
DistanceMetric.calculateLevenshteinDistance(
"kitten",
"sitting"));
// Create BKTree with derived node class from top of file
BKTree<ExampleNodeRecord> tree = new BKTree<ExampleNodeRecord>();
// Add some nodes
tree.add( new ExampleNodeRecord( 1, new int[] {100,200,300}) );
tree.add( new ExampleNodeRecord( 2, new int[] {110,210,310}) );
tree.add( new ExampleNodeRecord( 3, new int[] {120,220,320}) );
tree.add( new ExampleNodeRecord( 4, new int[] {130,230,330}) );
tree.add( new ExampleNodeRecord( 5, new int[] {140,240,340}) );
// Get best node from our tree with best distance
Dictionary<ExampleNodeRecord, Int32> results =
tree.findBestNodeWithDistance(
new ExampleNodeRecord( new int[] { 103, 215, 303 }) );
// Get best nodes below threshold
results = tree.query(
new ExampleNodeRecord(new int[] { 103, 215, 303 }),
10 ); // arbitrary threshold
// Dictionaries don't print well; so invent your own handy print routine
}
/*
* To use BKTree:
* 1. Create a class dervied from BKTreeNode
* 2. Add a member variable of your data to be sorted / retrieved
* 3. Override the calculateDistance method to calculate the distance metric
* between two nodes for the data to be sorted / retrieved.
* 4. Instantiate a BKTree with the type name of the class created in (1).
*/
static void Main(string[] args)
{
/*
* NOTE: More comprehensive examples of BK-Tree methods in unit tests
*/
// Exercise static distance metric methods -- just because
Console.WriteLine(
DistanceMetric.calculateHammingDistance(
new byte[] { 0xEF, 0x35, 0x20 },
new byte[] { 0xAD, 0x13, 0x87 }));
Console.WriteLine(
DistanceMetric.calculateLeeDistance(
new int[] { 196, 105, 48 },
new int[] { 201, 12, 51 }));
Console.WriteLine(
DistanceMetric.calculateLevenshteinDistance(
"kitten",
"sitting"));
// Create BKTree with derived node class from top of file
BKTree <ExampleNodeRecord> tree = new BKTree <ExampleNodeRecord>();
// Add some nodes
tree.add(new ExampleNodeRecord(1, new int[] { 100, 200, 300 }));
tree.add(new ExampleNodeRecord(2, new int[] { 110, 210, 310 }));
tree.add(new ExampleNodeRecord(3, new int[] { 120, 220, 320 }));
tree.add(new ExampleNodeRecord(4, new int[] { 130, 230, 330 }));
tree.add(new ExampleNodeRecord(5, new int[] { 140, 240, 340 }));
// Get best node from our tree with best distance
Dictionary <ExampleNodeRecord, Int32> results =
tree.findBestNodeWithDistance(
new ExampleNodeRecord(new int[] { 103, 215, 303 }));
// Get best nodes below threshold
results = tree.query(
new ExampleNodeRecord(new int[] { 103, 215, 303 }),
10); // arbitrary threshold
// Dictionaries don't print well; so invent your own handy print routine
}
public void BKTree_should_FindBestNode()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
TestNode search = new TestNode(new int[] { 210, 175, 233 });
TestNode best = new TestNode(2, new int[] { 200, 200, 200 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(best);
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(new TestNode(4, new int[] { 400, 400, 400 }));
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
TestNode found = tree.findBestNode(search);
Assert.Equal(2, found.Id);
Assert.Equal(best.Data, found.Data);
}
public void BKTree_should_FindBestNodeWithDistance()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
TestNode search = new TestNode(new int[] { 365, 422, 399 });
TestNode best = new TestNode(4, new int[] { 400, 400, 400 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary <TestNode, Int32> result = tree.findBestNodeWithDistance(search);
Assert.Equal(1, result.Count);
Assert.Equal(58, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(58, result.Values.ElementAt(0));
Assert.Equal(4, result.Keys.ElementAt(0).Id);
Assert.Equal(best.Data, result.Keys.ElementAt(0).Data);
}
public void BKTree_should_QueryBestMatchesBelowGivenThreshold()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
TestNode search = new TestNode(new int[] { 399, 400, 400 });
TestNode best1 = new TestNode(41, new int[] { 400, 400, 400 });
TestNode best2 = new TestNode(42, new int[] { 403, 403, 403 });
TestNode best3 = new TestNode(43, new int[] { 406, 406, 406 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best1);
tree.add(best2);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary <TestNode, Int32> results;
// Query for match within distance of 1 (best1 is only expected result)
results = tree.query(search, 1);
Assert.Equal(1, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(1, results.Values.ElementAt(0));
Assert.Equal(41, results.Keys.ElementAt(0).Id);
Assert.Equal(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.Equal(2, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(10, DistanceMetric.calculateLeeDistance(search.Data, best2.Data));
Assert.True(results.Contains(new KeyValuePair <TestNode, int>(best1, 1)));
Assert.True(results.Contains(new KeyValuePair <TestNode, int>(best2, 10)));
// Query for matches within distance of 20 (best1, best2 & best3 are expected results)
results = tree.query(search, 20);
Assert.Equal(3, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(10, DistanceMetric.calculateLeeDistance(search.Data, best2.Data));
Assert.Equal(19, DistanceMetric.calculateLeeDistance(search.Data, best3.Data));
Assert.True(results.Contains(new KeyValuePair <TestNode, int>(best1, 1)));
Assert.True(results.Contains(new KeyValuePair <TestNode, int>(best2, 10)));
Assert.True(results.Contains(new KeyValuePair <TestNode, int>(best3, 19)));
}
public void BKTree_should_FindBestNode()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
TestNode search = new TestNode(new int[] { 210, 175, 233 });
TestNode best = new TestNode(2, new int[] { 200, 200, 200 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(best);
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(new TestNode(4, new int[] { 400, 400, 400 }));
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
TestNode found = tree.findBestNode(search);
Assert.Equal(2, found.Id);
Assert.Equal(best.Data, found.Data);
}
public void BKTree_should_ThrowUponAddingNullNode()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
tree.add(new TestNode(1, new int[] { 100, 200, 300 }));
tree.add(new TestNode(2, new int[] { 110, 210, 310 }));
tree.add(new TestNode(3, new int[] { 130, 230, 330 }));
tree.add(new TestNode(4, new int[] { 140, 240, 340 }));
Assert.ThrowsDelegate boom =
delegate
{
tree.add(null);
};
Assert.Throws<NullReferenceException>(boom);
}
public void BKTree_should_QueryBestMatchesBelowGivenThreshold()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
TestNode search = new TestNode(new int[] { 399, 400, 400 });
TestNode best1 = new TestNode(41, new int[] { 400, 400, 400 });
TestNode best2 = new TestNode(42, new int[] { 403, 403, 403 });
TestNode best3 = new TestNode(43, new int[] { 406, 406, 406 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best1);
tree.add(best2);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary<TestNode, Int32> results;
// Query for match within distance of 1 (best1 is only expected result)
results = tree.query(search, 1);
Assert.Equal(1, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(1, results.Values.ElementAt(0));
Assert.Equal(41, results.Keys.ElementAt(0).Id);
Assert.Equal(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.Equal(2, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(10, DistanceMetric.calculateLeeDistance(search.Data, best2.Data));
Assert.True(results.Contains(new KeyValuePair<TestNode, int>(best1, 1)));
Assert.True(results.Contains(new KeyValuePair<TestNode, int>(best2, 10)));
// Query for matches within distance of 20 (best1, best2 & best3 are expected results)
results = tree.query(search, 20);
Assert.Equal(3, results.Count);
Assert.Equal(1, DistanceMetric.calculateLeeDistance(search.Data, best1.Data));
Assert.Equal(10, DistanceMetric.calculateLeeDistance(search.Data, best2.Data));
Assert.Equal(19, DistanceMetric.calculateLeeDistance(search.Data, best3.Data));
Assert.True(results.Contains(new KeyValuePair<TestNode, int>(best1, 1)));
Assert.True(results.Contains(new KeyValuePair<TestNode, int>(best2, 10)));
Assert.True(results.Contains(new KeyValuePair<TestNode, int>(best3, 19)));
}
public void BKTree_should_FindBestNodeWithDistance()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
TestNode search = new TestNode(new int[] { 365, 422, 399 });
TestNode best = new TestNode(4, new int[] { 400, 400, 400 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary<TestNode,Int32> result = tree.findBestNodeWithDistance(search);
Assert.Equal(1, result.Count);
Assert.Equal(58, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(58, result.Values.ElementAt(0));
Assert.Equal(4, result.Keys.ElementAt(0).Id);
Assert.Equal(best.Data, result.Keys.ElementAt(0).Data);
}