/// <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.
}
/// <summary>
/// Performs object detection on the given frame.
/// </summary>
///
//public Rectangle[] ProcessFrame(Bitmap frame)
//{
// using (FastBitmap fastBitmap = new FastBitmap(frame))
// {
// return ProcessFrame(fastBitmap);
// }
//}
/// <summary>
/// Performs object detection on the given frame.
/// </summary>
///
public Rectangle[] ProcessFrame(Bitmap image)
{
// int colorChannel =
// image.PixelFormat == PixelFormat.Format8bppIndexed ? 0 : channel;
Rectangle[] objects;
// Creates an integral image representation of the frame
using (var fastBitmap = new FastBitmap(image, _classifier.Cascade.HasTiltedFeatures))
{
// Creates a new list of detected objects.
_detectedObjects.Clear();
var width = fastBitmap.Width;
var height = fastBitmap.Height;
// Update parameters only if different size
if (_steps == null || width != _lastWidth || height != _lastHeight)
{
Update(width, height);
}
var window = Rectangle.Empty;
// For each scaling step
for (var i = 0; i < _steps.Length; i++)
{
var scaling = _steps[i];
// 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)
{
// If we are searching in greater to smaller mode,
if (_scalingMode == ObjectDetectorScalingMode.GreaterToSmaller)
{
break; // it won't get bigger, so we should stop.
}
else
{
continue; // continue until it gets greater.
}
}
// Check if the window is greater than the maximum size
else if (window.Width > _maxSize.Width || window.Height > _maxSize.Height)
{
// If we are searching in greater to smaller mode,
if (_scalingMode == ObjectDetectorScalingMode.GreaterToSmaller)
{
continue; // continue until it gets smaller.
}
break; // it won't get smaller, so we should stop.
}
// Grab some scan loop parameters
var xStep = window.Width >> 3;
var yStep = window.Height >> 3;
var xEnd = width - window.Width;
var yEnd = height - window.Height;
// Parallel mode. Scan the integral image searching
// for objects in the window with parallelization.
var bag = new System.Collections.Concurrent.ConcurrentBag <Rectangle>();
var numSteps = (int)Math.Ceiling((double)yEnd / yStep);
// For each pixel in the window column
var window1 = window;
Parallel.For(
0,
numSteps,
(j, options) =>
{
var y = j * yStep;
// Create a local window reference
var localWindow = window1;
localWindow.Y = y;
// For each pixel in the window row
for (var x = 0; x < xEnd; x += xStep)
{
if (options.ShouldExitCurrentIteration)
{
return;
}
localWindow.X = x;
// Try to detect and object inside the window
if (_classifier.Compute(fastBitmap, 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.
if (_searchMode == ObjectDetectorSearchMode.NoOverlap)
{
foreach (var obj in bag)
{
if (!Overlaps(obj))
{
_detectedObjects.Add(obj);
}
}
}
else if (_searchMode == ObjectDetectorSearchMode.Single)
{
if (bag.TryPeek(out window))
{
_detectedObjects.Add(window);
break;
}
}
else
{
foreach (var obj in bag)
{
_detectedObjects.Add(obj);
}
}
}
}
objects = _detectedObjects.ToArray();
if (_searchMode == ObjectDetectorSearchMode.Average)
{
objects = _match.Group(objects);
}
CheckSteadiness(objects);
_lastObjects = objects;
return(objects); // Returns the array of detected objects.
}
