public static SampleCollection LoadPosSamples(string dir, bool isPositive, ColorType colorType)
{
if (false == Directory.Exists(dir))
{
throw new DirectoryNotFoundException(string.Format("目录\"{0}\"不存在", dir));
}
string[] files = ListImageFiles(dir);
SampleCollection coll = new SampleCollection(files.Length * 4);
foreach (string filename in files)
{
Image <Bgr, Byte> img = new Image <Bgr, Byte>(filename);
ISample sample;
sample = new HaarSample(img, isPositive, colorType);
coll.Add(sample);
img._Flip(FLIP.HORIZONTAL);
sample = new HaarSample(img, isPositive, colorType);
coll.Add(sample);
img._Flip(FLIP.VERTICAL);
sample = new HaarSample(img, isPositive, colorType);
coll.Add(sample);
img._Flip(FLIP.HORIZONTAL);
sample = new HaarSample(img, isPositive, colorType);
coll.Add(sample);
}
return(coll);
}
public SampleCollection GetNegSamples(int maxSampleNum)
{
int[] indices = RandomList(_samples.Count, maxSampleNum);
SampleCollection coll = new SampleCollection(indices.Length);
foreach (int i in indices)
{
coll.Add(_samples[i]);
}
return(coll);
}
/// <summary>
/// 使用当前的级联分类器对负目标negSamples进行分类，返回错分的目标，作为新的负样本集negSamples
/// </summary>
/// <param name="negSamples"></param>
/// <returns></returns>
private SampleCollection GetPositivePredictedSamples(SampleCollection samplecoll)
{
SampleCollection newCollection = new SampleCollection(samplecoll.Count);
foreach (ISample sample in samplecoll)
{
if (true == this.Predict(sample))
{
newCollection.Add(sample);
}
}
newCollection.TrimExcess();
return(newCollection);
}