/// <summary>
/// Gets the sum of the areas of the rectangular features in an integral image.
/// </summary>
///
public double GetSum(SIntegralImage image, int x, int y)
{
double sum = 0.0;
if (!Tilted)
{
// Compute the sum for a standard feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSum(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
else
{
// Compute the sum for a rotated feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSumT(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
return(sum);
}
/// <summary>
/// Detects the presence of an object in a given window.
/// </summary>
///
public bool Compute(SIntegralImage 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 factor = image.GetSum2(x, y, w, h) * invArea - (mean * mean);
factor = (factor >= 0) ? Math.Sqrt(factor) : 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, factor) == 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.
}
/// <summary>
/// Classifies an image as having the searched object or not.
/// </summary>
public bool Classify(SIntegralImage image, int x, int y, double factor)
{
double value = 0;
// For each feature in the feature tree of the current stage,
foreach (HaarFeatureNode[] tree in Trees)
{
int current = 0;
do
{
// Get the feature node from the tree
HaarFeatureNode node = tree[current];
// Evaluate the node's feature
double sum = node.Feature.GetSum(image, x, y);
// And increase the value accumulator
if (sum < node.Threshold * factor)
{
value += node.LeftValue;
current = node.LeftNodeIndex;
}
else
{
value += node.RightValue;
current = node.RightNodeIndex;
}
} while (current > 0);
// Stop early if we have already surpassed the stage threshold value.
//if (value > this.Threshold) return true;
}
// After we have evaluated the output for the
// current stage, we will check if the value
// is still lesser than the stage threshold.
if (value < this.Threshold)
{
// If it is, the stage has rejected the current
// image and it doesn't contains our object.
return(false);
}
else
{
// The stage has accepted the current image
return(true);
}
}
/// <summary>
/// Performs object detection on the given frame.
/// </summary>
///
public Rectangle[] ProcessFrame(UnmanagedImage image)
{
// Creates an integral image representation of the frame
SIntegralImage integralImage = SIntegralImage.FromBitmap(
image, channel, classifier.Cascade.HasTiltedFeatures);
// Creates a new list of detected objects.
this.detectedObjects.Clear();
int width = integralImage.Width;
int height = integralImage.Height;
// Update parameters only if different size
if (steps == null || width != lastWidth || height != lastHeight)
{
update(width, height);
}
Rectangle window = Rectangle.Empty;
// For each scaling step
foreach (float scaling in steps)
{
// Set the classifier window scale
classifier.Scale = scaling;
// Get the scaled window size
window.Width = (int)(baseWidth * scaling);
window.Height = (int)(baseHeight * scaling);
// Check if the window is lesser than the minimum size
if (window.Width < minSize.Width && window.Height < minSize.Height &&
window.Width > maxSize.Width && window.Height > maxSize.Height)
{
// If we are searching in greater to smaller mode,
if (scalingMode == ObjectDetectorScalingMode.GreaterToSmaller)
{
goto EXIT; // it won't get bigger, so we should stop.
}
else
{
continue; // continue until it gets greater.
}
}
// Grab some scan loop parameters
int xStep = window.Width >> 3;
int yStep = window.Height >> 3;
int xEnd = width - window.Width;
int yEnd = height - window.Height;
// Check if we should run in parallel or sequential
if (!UseParallelProcessing)
{
// Sequential mode. Scan the integral image searching
// for objects in the window without parallelization.
// For every pixel in the window column
for (int y = 0; y < yEnd; y += yStep)
{
window.Y = y;
// For every pixel in the window row
for (int x = 0; x < xEnd; x += xStep)
{
window.X = x;
if (searchMode == ObjectDetectorSearchMode.NoOverlap && overlaps(window))
{
continue; // We have already detected something here, moving along.
}
// Try to detect and object inside the window
if (classifier.Compute(integralImage, window))
{
// an object has been detected
detectedObjects.Add(window);
if (searchMode == ObjectDetectorSearchMode.Single)
{
goto EXIT; // Stop on first object found
}
}
}
}
}
else
{
// Parallel mode. Scan the integral image searching
// for objects in the window with parallelization.
ConcurrentBag <Rectangle> bag = new ConcurrentBag <Rectangle>();
int numSteps = (int)Math.Ceiling((double)yEnd / yStep);
// For each pixel in the window column
Parallel.For(0, numSteps, (j, options) => {
int y = j * yStep;
// Create a local window reference
Rectangle localWindow = window;
localWindow.Y = y;
// For each pixel in the window row
for (int x = 0; x < xEnd; x += xStep)
{
if (options.ShouldExitCurrentIteration)
{
return;
}
localWindow.X = x;
// Try to detect and object inside the window
if (classifier.Compute(integralImage, localWindow))
{
// an object has been detected
bag.Add(localWindow);
if (searchMode == ObjectDetectorSearchMode.Single)
{
options.Stop();
}
}
}
});
// If required, avoid adding overlapping objects at
// the expense of extra computation. Otherwise, only
// add objects to the detected objects collection.
switch (searchMode)
{
case ObjectDetectorSearchMode.NoOverlap:
foreach (Rectangle obj in bag.Where(obj => !overlaps(obj)))
{
detectedObjects.Add(obj);
}
break;
case ObjectDetectorSearchMode.Single:
if (bag.TryPeek(out window))
{
detectedObjects.Add(window);
goto EXIT;
}
break;
default:
foreach (Rectangle obj in bag)
{
detectedObjects.Add(obj);
}
break;
}
}
}
EXIT:
Rectangle[] objects = detectedObjects.ToArray();
checkSteadiness(objects);
lastObjects = objects;
return(objects); // Returns the array of detected objects.
}