/// <summary>
/// Detects the presence of an object in a given window.
/// </summary>
///
public bool Compute(IntegralImage2 image, Rectangle rectangle)
{
int x = rectangle.X;
int y = rectangle.Y;
int w = rectangle.Width;
int h = rectangle.Height;
double mean = image.GetSum(x, y, w, h) * invArea;
double var = image.GetSum2(x, y, w, h) * invArea - (mean * mean);
double sdev = (var >= 0) ? Math.Sqrt(var) : 1;
// For each classification stage in the cascade
foreach (HaarCascadeStage stage in cascade.Stages)
{
// Check if the stage has rejected the image
if (stage.Classify(image, x, y, sdev) == false)
{
return(false); // The image has been rejected.
}
}
// If the object has gone all stages and has not
// been rejected, the object has been detected.
return(true); // The image has been detected.
}
public void lena_test()
{
string localPath = NUnit.Framework.TestContext.CurrentContext.TestDirectory;
#region doc_lena
// In this example, we will compute an integral image
// representation of Lena Söderberg's famous picture:
TestImages testImages = new TestImages(path: localPath);
Bitmap lena = testImages["lena.bmp"]; // get the image
// Create a new Integral Image (squared and tilted) from Lena's picture:
IntegralImage2 ii = IntegralImage2.FromBitmap(lena, computeTilted: true);
// Let's say we would like to get the summed area in the rectangular region
// delimited by pixel (34, 50) until pixels (60, 105). This is equivalent to
// the region under the rectangle (34, 50, 34+60, 50+105) = (34, 50, 94, 155):
long sum = ii.GetSum(34, 50, 94, 155); // this is the sum of values (1760032)
// Now let's say we would like to get the squared sum and tilted sum as well:
long ssum = ii.GetSum2(34, 50, 94, 155); // this is the sum of squared values (229508896)
long tsum = ii.GetSumT(34, 50, 94, 155); // this is the sum of tilted values (-593600)
#endregion
Assert.AreEqual(1760032, sum);
Assert.AreEqual(229508896, ssum);
Assert.AreEqual(-593600, tsum);
}