internal static WeakClassifier Load(float threshold, bool posLargeThanThreshold, float weight, System.Drawing.Rectangle[] rects, int[] weights, ColorType colorType)
{
HaarFeature feature = new HaarFeature(0, rects, weights, colorType);
WeakClassifier weak = new WeakClassifier(feature);
weak._threshold = threshold;
weak._posLargeThanThreshold = posLargeThanThreshold;
weak._weight = weight;
return(weak);
}
private void AddRect(int x1, int y1, int w1, int h1, int ww1, int x2, int y2, int w2, int h2, int ww2)
{
Rectangle[] rects = new Rectangle[] {
new Rectangle(x1, y1, w1, h1),
new Rectangle(x2, y2, w2, h2),
};
int[] weights = new int[] { ww1, ww2 };
if (_gray)
{
HaarFeature gray = new HaarFeature(_weakId, rects, weights, ColorType.Gray);
_weakClassifiers[_weakId] = new WeakClassifier(gray);
_weakId++;
}
if (_saturation)
{
HaarFeature saturation = new HaarFeature(_weakId, rects, weights, ColorType.Saturation);
_weakClassifiers[_weakId] = new WeakClassifier(saturation);
_weakId++;
}
}
private void treeView1_AfterSelect(object sender, TreeViewEventArgs e)
{
if ((e.Node.Tag is WeakClassifier) == false)
{
return;
}
WeakClassifier weak = e.Node.Tag as WeakClassifier;
textBox1.Text = weak.GetNumString();
Image <Gray, Byte> imgBlank = _imgBlank.Copy();
Image <Bgr, Byte> imgPos = _imgPos.Copy();
HaarFeature feature = (HaarFeature)weak.Feature;
for (int i = 0; i < feature._rects.Length; i++)
{
Image <Gray, Byte> mask = new Image <Gray, Byte>(imgBlank.Size);
int weight = feature._weights[i];
Rectangle rect = feature._rects[i];
rect.Height--;
rect.Width--;
mask.Draw(rect, new Gray(Math.Abs(weight)), 0);
if (weight > 0)
{
imgBlank = imgBlank.Add(mask);
}
else
{
imgBlank = imgBlank.Sub(mask);
}
imgPos.Draw(rect, new Bgr(), 1);
}
Image <Gray, Byte> light = imgBlank.ThresholdBinary(new Gray(128), new Gray(255));
Image <Gray, Byte> dark = imgBlank.ThresholdBinaryInv(new Gray(127), new Gray(255));
imgBlank.SetValue(new Gray(255), light);
imgBlank.SetValue(new Gray(0), dark);
imageBox1.Image = imgBlank;
imageBox2.Image = imgPos;
}
