public FormViewRect(CascadeClassifier cascade)
{
InitializeComponent();
_cascade = cascade;
_imgBlank = new Image <Gray, Byte>(cascade.Size);
_imgBlank.SetValue(new Gray(128.0));
_imgPos = new Image <Bgr, Byte>(@"D:\My Documents\MATLAB\MyResearch\haarlike\对称正样本\pic004.bmp");
_imgPos = _imgPos.Copy(new Rectangle(Point.Empty, cascade.Size));
imageBox1.Image = _imgBlank;
imageBox2.Image = _imgPos;
imageBox1.SetZoomScale(16, Point.Empty);
imageBox2.SetZoomScale(16, Point.Empty);
InitTree();
CountClassifiers(cascade);
}
private void button_viewCascade_Click(object sender, EventArgs e)
{
if (_cascadeClassifier == null || _cascadeClassifier.StageCount <= 0)
{
_cascadeClassifier = XmlClass.Load(@"cascade_0.000001_0.5_0.99_gray_saturation_20150507_step2neg_height14.xml");
}
//查看生成的所有特征矩形
// cascadeClassifier = new CascadeClassifier();
// cascadeClassifier.Size = _size;
// WeakClassifierManager.Instance.CreateHaarFeatures(_size.Width, _size.Height / 2, colorType);
// WeakClassifierManager.Instance.AddSymmetricHaarFeatures(_size.Width, _size.Height, colorType);
// StageClassifier stage = new StageClassifier(0);
// stage.Classifiers = WeakClassifierManager.Instance.WeakClassifiers;
// cascadeClassifier.Classifiers=new StageClassifier[]{stage};
FormViewRect fv = new FormViewRect(_cascadeClassifier);
fv.Show();
}
private void button_train_Click(object sender, EventArgs e)
{
textBox_debug.BringToFront();
if (_cascadeClassifier != null)
{
if (MessageBox.Show("已存在分类器，在现有分类器上继续训练吗？", "", MessageBoxButtons.YesNo) == DialogResult.No)
{
_cascadeClassifier = new CascadeClassifier();
}
}
else
{
_cascadeClassifier = new CascadeClassifier();
}
if (!inited)
{
button_loadSamples_Click(sender, e);
// WeakClassifierManager.Instance.CreateHaarFeatures(_size.Width, _size.Height , _colorType);
WeakClassifierManager.Instance.CreateHaarFeatures(_size.Width, _size.Height / 2, _colorType);
WeakClassifierManager.Instance.AddSymmetricHaarFeatures(_size.Width, _size.Height, _colorType);
}
//StageClassifier.ViewId = int.Parse(textBox1.Text);
GC.Collect();
GC.WaitForFullGCComplete();
BackgroundWorker worker = new BackgroundWorker();
worker.DoWork += new DoWorkEventHandler(
(a, b) =>
{
//cascadeClassifier.Train(posSamples, negSamples, (MyFloat)0.001, (MyFloat)0.5, (MyFloat)0.99);
_cascadeClassifier.Train(_posSamples, _negSamples, _validateSamples, _size, _targetFalsePositiveRate, _maxFalsePositiveRate, _minHitRate);
});
worker.ProgressChanged += new ProgressChangedEventHandler(worker_ProgressChanged);
worker.RunWorkerCompleted += new RunWorkerCompletedEventHandler(worker_RunWorkerCompleted);
worker.WorkerReportsProgress = true;
DebugMsg.Init(worker);
worker.RunWorkerAsync();
inited = true;
}
private void CountClassifiers(CascadeClassifier cascade)
{
int count = 0, gray = 0, saturation = 0;
foreach (StageClassifier stage in cascade.Classifiers)
{
foreach (WeakClassifier weak in stage.Classifiers)
{
switch (weak.Feature.ColorType)
{
case ColorType.Gray: gray++;
break;
case ColorType.Saturation: saturation++;
break;
}
count++;
}
}
this.Text = string.Format("共{0}个弱分类器，其中灰度：{1}，饱和度{2}",
count, gray, saturation);
}
public CascadeDetector(CascadeClassifier cascade, int xStep, int yStep)
{
_cascade = cascade;
_xStep = xStep;
_yStep = yStep;
}