public static CascadeClassifier Load(string filename)
{
XmlDocument doc = new XmlDocument();
doc.Load(filename);
XmlNode cascadeNode = doc.SelectSingleNode("CascadeClassifier");
int width = int.Parse(cascadeNode.Attributes["Width"].Value);
int height = int.Parse(cascadeNode.Attributes["Height"].Value);
int stageCount = int.Parse(cascadeNode.Attributes["StageCount"].Value);
//XmlNodeList stageList = doc.SelectNodes("Stage");
StageClassifier[] classifiers = new StageClassifier[stageCount];
for (int i = 0; i < stageCount; i++)
{
XmlNode stageNode = cascadeNode.ChildNodes[i];
string numStr = (i + 1).ToString();
if (false == numStr.Equals(stageNode.Attributes["StageNum"].Value))
{
throw new Exception("Xml文件损坏");
}
classifiers[i] = LoadStageClassifier(i + 1, stageNode);
}
CascadeClassifier cascade = new CascadeClassifier();
cascade.LoadFrom(new Size(width, height), classifiers);
return(cascade);
}
/// <summary>
/// 计算强分类器的阈值
/// </summary>
// private void CalcThreshold()
// {
// MyFloat sum = 0;
// foreach (WeakClassifier weak in _classifiers)
// {
// sum += weak.Weight;
// }
// sum /= 2;
// _threshold = sum;
// }
/// <summary>
/// 调整强分类器的阈值，使得检测率满足要求
/// </summary>
/// <param name="result"></param>
/// <param name="targetHitRate"></param>
// private void AdjustThreshold(PredictResult result,MyFloat targetHitRate)
// {
// _trainPredictValues.Sort();
// int targetHitCount = (int)(result.Count * targetHitRate + 0.5f);
// int currentHitCount = (int)(result.Count * result.HitRate);
// int diff = targetHitCount - currentHitCount;
// int newIndex = BinarySearch(_trainPredictValues, _threshold)-diff;
// if (newIndex < 0)
// newIndex = 0;
// else if (newIndex >= _trainPredictValues.Count)
// newIndex = _trainPredictValues.Count - 1;
// _threshold = _trainPredictValues[newIndex];
//
// }
/// <summary>
/// 二分查找，找到大于等于key且序号最小的元素
/// </summary>
/// <param name="arr"></param>
/// <param name="key">待查找的数值</param>
/// <returns>找到的序号</returns>
// int BinarySearch(List<MyFloat> arr, MyFloat key)
// {
// int low = 0, high = arr.Count - 1;
// int mid = (low + high) / 2;
//
// while (low <= high)
// {
// mid = (high + low) / 2;
// if (arr[mid] == key)
// {
// break;
// }
// else if (arr[mid] < key)
// {
// low = mid + 1;
// }
// else
// {
// high = mid - 1;
// }
// }
// while (mid > 0 && arr[mid - 1] == key)
// mid--;
// if (arr[mid] < key && mid + 1 < arr.Count && arr[mid + 1] > key)
// mid++;
// return mid;
// }
internal static StageClassifier Load(int id, double threshold, WeakClassifier[] weaks)
{
StageClassifier stage = new StageClassifier();
stage._id = id;
stage._threshold = threshold;
stage._classifiers = weaks;
return(stage);
}
private static StageClassifier LoadStageClassifier(int id, XmlNode stageNode)
{
MyFloat threshold = MyFloat.Parse(stageNode.Attributes["Threshold"].Value);
int weakNum = int.Parse(stageNode.Attributes["ClassifiersNum"].Value);
WeakClassifier[] weaks = new WeakClassifier[weakNum];
for (int i = 0; i < weakNum; i++)
{
XmlNode weakNode = stageNode.ChildNodes[i];
weaks[i] = LoadWeakClassifier(weakNode);
}
StageClassifier stage = StageClassifier.Load(id, threshold, weaks);
return(stage);
}
public void Train(SampleCollection posSamples,
SampleCollection allNegSamples,
SampleCollection validateSamples,
Size size,
double targetFalsePositiveRate, double maxFalsePositiveRate, double minHitRate)
{
const int startCapacity = 20;
_size = size;
List <StageClassifier> stageClassifier = new List <StageClassifier>(startCapacity);
double hitRate = 1;
double falsePositiveRate = 1;
int stageCount = 0;
int maxSampleNum;
SampleCollection negSamples;
{ //计算使用的负样本数量
double MaxMemoryUse = MemoryInfo.GetFreePhysicalMemory() - 400e6;
if (MaxMemoryUse < 0)
{
MaxMemoryUse = 1.8e9;
}
//MaxMemoryUse = 2.48e9;
//按照内存大小来选取负样本，在充分利用内存的同时防止使用虚拟内存
int weakNum = WeakClassifierManager.Instance.WeakClassifiers.Length;
maxSampleNum = (int)((
MaxMemoryUse
- 1.5e6f //预留150M给程序其它部分
)
/ weakNum / sizeof(MyFloat)) - posSamples.Count;
if (DebugMsg.Debug)
{
string msg = string.Format("\r\n单次使用负样本：{0}，特征数量：{1}，预计消耗内存：{2}M\r\n",
maxSampleNum, weakNum, MaxMemoryUse / 1024 / 1024);
DebugMsg.AddMessage(msg, 0);
}
}
//如果已有分类器，计算当前的性能
if (_classifiers != null && _classifiers.Length > 0)
{
PredictResult result = EvaluateErrorRate(validateSamples);
hitRate = result.HitRate;
falsePositiveRate = result.FalsePositiveRate;
stageCount = _classifiers.Length;
stageClassifier.AddRange(_classifiers);
negSamples = CreateNextSamples(allNegSamples, falsePositiveRate, maxSampleNum);
if (DebugMsg.Debug)
{
string msg = string.Format("载入分类器完成，层数：{0}，当前性能为：\r\n检测率：\t{1:P5}，\t误检率：\t{2:P5}\r\n------\r\n",
stageCount, hitRate, falsePositiveRate);
DebugMsg.AddMessage(msg, 0);
}
}
else
{
negSamples = allNegSamples.GetNegSamples(maxSampleNum);
}
while (falsePositiveRate > targetFalsePositiveRate && negSamples.Count != 0 && posSamples.Count != 0)
{
stageCount++;
if (DebugMsg.Debug)
{
string msg = string.Format("--------------------\r\n开始训练第{0}级分类器，使用的数量为：\r\n正样本：\t{1}\t负样本：\t{2}\r\n目标检测率：\t{3:P5}\t目标误检率：\t{4:P5}\r\n",
stageCount, posSamples.Count, negSamples.Count, minHitRate, maxFalsePositiveRate);
DebugMsg.AddMessage(msg, 0);
}
StageClassifier currentStage = new StageClassifier(stageCount);
PredictResult result = currentStage.Train(posSamples, negSamples, validateSamples,
maxFalsePositiveRate, minHitRate);
falsePositiveRate *= result.FalsePositiveRate;
hitRate *= result.HitRate;
stageClassifier.Add(currentStage);
_classifiers = stageClassifier.ToArray();
if (DebugMsg.Debug)
{
string msg = string.Format("------\r\n第{0}级分类器训练结束，当前性能为：\r\n检测率：\t{1:P5}，\t误检率：\t{2:P5}\r\n弱分类器数量：{3}\r\n目前训练时间总计：{4}\r\n",
stageCount, hitRate, falsePositiveRate, currentStage.WeakClassifierCount, DebugMsg.stopwatch.Elapsed.ToString());
DebugMsg.AddMessage(msg, 0);
}
if (falsePositiveRate > targetFalsePositiveRate)
{
//posSamples = this.CreateNextSamples(posSamples);
validateSamples = this.GetPositivePredictedSamples(validateSamples);
negSamples = CreateNextSamples(allNegSamples, falsePositiveRate, maxSampleNum);
}
this.Save(string.Format(@"D:\ccc{0}.xml", stageCount.ToString()));
}
if (DebugMsg.Debug)
{
DebugMsg.stopwatch.Stop();
string msg = string.Format("\r\n--------------------\r\n全部训练完成，训练时间总计：{0}\r\n",
DebugMsg.stopwatch.Elapsed.ToString());
DebugMsg.AddMessage(msg, 0);
}
this.Save(@"D:\ccc.xml");
}