public void ComputeMode1()
{
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode1))
using (var img = Image("lenna.png"))
using (var hash = new Mat <byte>())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(121, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[]
{
135, 255, 243, 135, 131, 255, 249, 199, 193, 255, 252, 227, 224, 127, 254, 113, 128, 127, 127,
48, 192, 191, 25, 24, 240, 255, 4, 13, 248, 127, 135, 134, 252, 255, 99, 67, 254, 255, 184, 113,
255, 127, 220, 240, 195, 31, 111, 120, 224, 135, 55, 12, 248, 192, 27, 6, 126, 240, 13, 128, 63,
248, 6, 64, 14, 126, 3, 48, 7, 191, 3, 248, 131, 159, 1, 248, 193, 207, 0, 252, 240, 103, 0,
126, 248, 59, 0, 63, 254, 29, 0, 15, 255, 14, 0, 135, 127, 6, 128, 131, 63, 3, 224, 103, 206, 0,
240, 247, 99, 0, 248, 255, 49, 0, 252, 255, 24, 0, 254, 49, 0
},
hashArray);
}
}
public void CompareSameImage(BlockMeanHashMode mode)
{
using (var model = BlockMeanHash.Create(mode))
using (var img1 = Image("lenna.png", ImreadModes.GrayScale))
{
double hash = model.Compare(img1, img1);
Assert.Equal(0, hash, 6);
}
}
public void CreateAndDispose()
{
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode0))
{
GC.KeepAlive(model);
}
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode1))
{
GC.KeepAlive(model);
}
}
public void CompareDifferentImage(BlockMeanHashMode mode)
{
using (var model = BlockMeanHash.Create(mode))
using (var img1 = Image("lenna.png", ImreadModes.GrayScale))
using (var img2 = Image("building.jpg", ImreadModes.GrayScale))
{
var size = new Size(256, 256);
using (var scaledImg1 = img1.Resize(size))
using (var scaledImg2 = img2.Resize(size))
{
double hash = model.Compare(scaledImg1, scaledImg2);
Assert.Equal(264411, hash, 6);
}
}
}
public void ComputeMode0()
{
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode0))
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(32, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[] { 243, 61, 243, 61, 195, 63, 226, 151, 226, 159, 250, 223, 50, 206, 18, 231, 130, 226, 130, 227, 130, 243, 130, 243, 2, 243, 2, 87, 2, 127, 2, 47 },
hashArray);
}
}
public static ulong[] MakeSoftHash(string path)
{
var image = Cv2.ImRead(path);
var hash = new Mat();
var algorithm = BlockMeanHash.Create(BlockMeanHashMode.Mode1);
if (image.Empty())
{
return(null);
}
algorithm.Compute(image, hash);
image.Dispose();
algorithm.Dispose();
hash.GetArray(out byte[] buffer);
var rr = new ulong[16];
for (int i = 0; i < 15; i++)
{
rr[i] = BitConverter.ToUInt64(buffer, i * 8);
}
rr[15] = buffer[120];
return(rr);
}
static void Main(string[] args)
{
DateTime start = DateTime.Now;
ConsoleSpinner spin = new ConsoleSpinner();
Log.Logger = new LoggerConfiguration()
.MinimumLevel.Debug()
.WriteTo.Console()
.WriteTo.File("log.txt")
.CreateLogger();
//string dir = @"c:\Users\AL\Documents\Visual Studio 2017\Projects\DupFinder\test";
string dir = @"B:\DiskE\BACKUP";
Log.Information("Analyzing {0}...", dir);
DirectoryInfo dirInfo = new DirectoryInfo(dir);
var files = dirInfo.EnumerateDirectories()
.AsParallel()
.SelectMany(di => di.EnumerateFiles("*.*", SearchOption.AllDirectories))
.Where(x => Array.IndexOf(Ext, x.Extension.ToLowerInvariant()) >= 0)
.ToArray();
TimeSpan duration = DateTime.Now - start;
Log.Information("Found {0} file(s), duration {1} sec", files.Count(), duration.TotalSeconds);
Log.Information("Calculating images' hashes...");
CvErrorCallback cvErr = CVError;
IntPtr zero = IntPtr.Zero;
NativeMethods.redirectError(cvErr, zero, ref zero);
Console.SetError(TextWriter.Null);
int c = 0;
Parallel.ForEach(files, f => {
try {
RadialVarianceHash h1 = RadialVarianceHash.Create();
BlockMeanHash h2 = BlockMeanHash.Create();
byte[] data = System.IO.File.ReadAllBytes(f.FullName);
using (Mat mat = Mat.FromImageData(data, ImreadModes.Color)) {
InputOutputArray h1o = new Mat();
InputOutputArray h2o = new Mat();
h1.Compute(mat, h1o);
h2.Compute(mat, h2o);
Files.Add(new File {
FileInfo = f,
RadialVarianceHash = h1o,
BlockMeanHash = h2o,
});
spin.Turn((double)(c++) / (double)files.Count());
}
} catch (Exception ex) {
if (ex.Source != "OpenCvSharp")
{
Log.Error("Error with {0}:\n\t{1}", f.FullName, ex.Message);
}
}
});
Log.Information("Finding duplicates...");
RadialVarianceHash hh1 = RadialVarianceHash.Create();
BlockMeanHash hh2 = BlockMeanHash.Create();
ulong saveSize = 0;
bool dupMsg = true;
int total = Files.Count;
int counter = 0;
for (int x = 0; x < Files.Count; x++)
{
if (Files.ElementAt(x).Duplicate != true)
{
counter++;
dupMsg = true;
for (int y = x + 1; y < Files.Count; y++)
{
if (Files.ElementAt(y).Duplicate == true)
{
continue;
}
double compare1 = hh1.Compare(InputArray.Create(Files.ElementAt(x).RadialVarianceHash.GetMat()), InputArray.Create(Files.ElementAt(y).RadialVarianceHash.GetMat()));
double compare2 = hh2.Compare(InputArray.Create(Files.ElementAt(x).BlockMeanHash.GetMat()), InputArray.Create(Files.ElementAt(y).BlockMeanHash.GetMat()));
if (compare1 > 0.98 && compare2 < 3)
{
saveSize += (ulong)Files.ElementAt(x).FileInfo.Length;
Files.ElementAt(y).Duplicate = true;
total--;
if (dupMsg)
{
Log.Information("Dups for {0}:", Files.ElementAt(x).FileInfo.FullName);
dupMsg = false;
}
Log.Information("\t{0}", Files.ElementAt(y).FileInfo.FullName);
}
spin.Turn((double)counter / (double)total);
}
}
spin.Turn((double)counter / (double)total);
}
Log.Information("Done, possible size save is {0} Mb", saveSize / 1024 / 1024);
Console.ReadKey();
}