接下来开始Haar特征提取算法的解析：

       在本算法中，Haar特征选取了6种特征子,代码及解析如下：

6种Haar特征描述子计算如下，接下来分析Haar特征的应用：

//生成Haar特征向量  192维    32*6=192维
void HaarFeatures::GenerateSystematic()
{
	float x[] = {0.2f, 0.4f, 0.6f, 0.8f};
	float y[] = {0.2f, 0.4f, 0.6f, 0.8f};
	float s[] = {0.2f, 0.4f};
	for (int iy = 0; iy < 4; ++iy) //取x[]中数，窗口系数
	{
		for (int ix = 0; ix < 4; ++ix) //取y[]中数，窗口系数
		{
			for (int is = 0; is < 2; ++is) //取s[]中数，窗口系数
			{
				FloatRect r(x[ix]-s[is]/2, y[iy]-s[is]/2, s[is], s[is]);//获取窗口大小，共32个
				for (int it = 0; it < 6; ++it)  //it为选取Haar特征描述子类型（共6种）
				{
					m_features.push_back(HaarFeature(r, it));
				}
			}
		}
	}
}

void HaarFeatures::UpdateFeatureVector(const Sample& s)
{
	for (int i = 0; i < m_featureCount; ++i)
	{
		//归一化
		m_featVec[i] = m_features[i].Eval(s);
	}
}