我正在尝试学习BOW对象分类。我试图实现书中给出的例子＆＃34; Practical OpenCV, Samarth Brahmbhatt＆＃34;第8章（第148页）
svm = notFromFile[category];被删除，结果有误;如果没有，它可以成功使用本书提供的数据集。）bowDescriptorExtractor.Compute(frame_g, kp, img);行的Emgu.CV.World.dll 中，应用程序关闭。它无法处理。我尝试过很多东西，但无法弄明白。任何关于为什么会发生以及如何解决的建议都非常感激。
我正在使用emgucv-windesktop 3.1.0.2504
我的实施：
internal class Categorizer3 : ICategorizer
{
public string Name
{
get
{
return "Categorizer3";
}
}
public bool Train()
{
try
{
initDir();
Feature2D descriptorExtractor;
Feature2D featureDetector;
List<Mat> templates;
BOWKMeansTrainer bowtrainer;
BOWImgDescriptorExtractor bowDescriptorExtractor;
init(out descriptorExtractor, out featureDetector, out templates, out bowtrainer, out bowDescriptorExtractor);
List<Tuple<string, Mat>> train_set;
List<string> category_names;
make_train_set(out train_set, out category_names);
Mat vocab;
build_vocab(descriptorExtractor, featureDetector, templates, bowtrainer, out vocab);
bowDescriptorExtractor.SetVocabulary(vocab);
Dictionary<string, Mat> positive_data;
Dictionary<string, Mat> negative_data;
make_pos_neg(train_set, bowDescriptorExtractor, featureDetector, category_names, out positive_data, out negative_data);
this.train_classifiers(category_names, positive_data, negative_data);
return true;
}
catch (Exception)
{
return false;
}
}
public event TrainedEventHandler Trained;
protected void OnTrained(string fn)
{
if (this.Trained != null)
this.Trained(fn);
}
public Categorizer3()
{
}
private Feature2D create_FeatureDetector()
{
return new SURF(500);
//return new KAZE();
//return new SIFT();
//return new Freak();
}
private BOWImgDescriptorExtractor create_bowDescriptorExtractor(Feature2D descriptorExtractor)
{
LinearIndexParams ip = new LinearIndexParams();
SearchParams sp = new SearchParams();
var descriptorMatcher = new FlannBasedMatcher(ip, sp);
return new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
private void init(out Feature2D descriptorExtractor, out Feature2D featureDetector, out List<Mat> templates, out BOWKMeansTrainer bowtrainer, out BOWImgDescriptorExtractor bowDescriptorExtractor)
{
int clusters = 1000;
featureDetector = create_FeatureDetector();
MCvTermCriteria term = new MCvTermCriteria(10000, 0.0001d);
term.Type = TermCritType.Iter | TermCritType.Eps;
bowtrainer = new BOWKMeansTrainer(clusters, term, 5, Emgu.CV.CvEnum.KMeansInitType.PPCenters);//****
BFMatcher matcher = new BFMatcher(DistanceType.L1);//****
descriptorExtractor = featureDetector;//******
bowDescriptorExtractor = create_bowDescriptorExtractor(descriptorExtractor);
templates = new List<Mat>();
string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\templates";
//string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";
foreach (var filename in Directory.GetFiles(TEMPLATE_FOLDER, "*", SearchOption.AllDirectories))
{
templates.Add(GetMat(filename, true));
this.OnTrained(filename);
}
}
void make_train_set(out List<Tuple<string, Mat>> train_set, out List<string> category_names)
{
string TRAIN_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";
category_names = new List<string>();
train_set = new List<Tuple<string, Mat>>();
foreach (var dir in Directory.GetDirectories(TRAIN_FOLDER))
{
// Get category name from name of the folder
string category = new DirectoryInfo(dir).Name;
category_names.Add(category);
foreach (var filename in Directory.GetFiles(dir))
{
train_set.Add(new Tuple<string, Mat>(category, GetMat(filename, true)));
this.OnTrained(filename);
}
}
}
void build_vocab(Feature2D descriptorExtractor, Feature2D featureDetector, List<Mat> templates, BOWKMeansTrainer bowtrainer, out Mat vocab)
{
Mat vocab_descriptors = new Mat();
foreach (Mat templ in templates)
{
Mat desc = new Mat();
VectorOfKeyPoint kp = new VectorOfKeyPoint(featureDetector.Detect(templ));
descriptorExtractor.Compute(templ, kp, desc);
vocab_descriptors.PushBack(desc);
}
bowtrainer.Add(vocab_descriptors);
vocab = new Mat();
bowtrainer.Cluster(vocab);
string fn = getVocabularyFileName();
using (FileStorage fs = new FileStorage(fn, FileStorage.Mode.Write))
{
fs.Write(vocab, "vocab");
fs.ReleaseAndGetString();
}
}
void make_pos_neg(List<Tuple<string, Mat>> train_set, BOWImgDescriptorExtractor bowDescriptorExtractor, Feature2D featureDetector, List<string> category_names,
out Dictionary<string, Mat> positive_data, out Dictionary<string, Mat> negative_data)
{
positive_data = new Dictionary<string, Mat>();
negative_data = new Dictionary<string, Mat>();
foreach (var tu in train_set)
{
string category = tu.Item1;
Mat im = tu.Item2;
Mat feat = new Mat();
VectorOfKeyPoint kp = new VectorOfKeyPoint(featureDetector.Detect(im));
bowDescriptorExtractor.Compute(im, kp, feat);
for (int cat_index = 0; cat_index < category_names.Count; cat_index++)
{
string check_category = category_names[cat_index];
if (check_category.CompareTo(category) == 0)
{
if (!positive_data.ContainsKey(check_category))
positive_data[check_category] = new Mat();
positive_data[check_category].PushBack(feat);
}
else
{
if (!negative_data.ContainsKey(check_category))
negative_data[check_category] = new Mat();
negative_data[check_category].PushBack(feat);
}
}
}
}
void train_classifiers(List<string> category_names, Dictionary<string, Mat> positive_data, Dictionary<string, Mat> negative_data)
{
for (int i = 0; i < category_names.Count; i++)
{
string category = category_names[i];
// Postive training data has labels 1
Mat train_data = positive_data[category];
Mat train_labels = new Mat(train_data.Rows, 1, DepthType.Cv32S, 1);
{
for (int col = 0; col < train_labels.Cols; col++)
for (int row = 0; row < train_labels.Rows; row++)
train_labels.SetValue(row, col, (int)1);
train_labels.SetTo(new MCvScalar(1));
// Negative training data has labels 0
train_data.PushBack(negative_data[category]);
Mat m = new Mat(negative_data[category].Rows, 1, DepthType.Cv32S, 1);
{
for (int col = 0; col < m.Cols; col++)
for (int row = 0; row < m.Rows; row++)
m.SetValue(row, col, (int)0);
m.SetTo(new MCvScalar(0));
train_labels.PushBack(m);
}
SVM svm = new SVM();
svm.C = 312.5;
svm.Gamma = 0.50625000000000009;
svm.SetKernel(SVM.SvmKernelType.Rbf);
svm.Type = SVM.SvmType.CSvc;
svm.Train(train_data, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, train_labels);
var fn = getSVMFileName(category);
svm.SaveSVMToFile(fn);
notFromFile[category] = svm;
}
}
}
Dictionary<string, SVM> notFromFile = new Dictionary<string, SVM>();//****
private void initDir()
{
var dir = getSaveDir();
if (Directory.Exists(dir))
foreach (var fn in Directory.GetFiles(dir))
File.Delete(fn);
}
private string getSaveDir()
{
string dir = Path.Combine(Path.GetTempPath(), "Dece", "SVMS");
if (!Directory.Exists(dir))
Directory.CreateDirectory(dir);
return dir;
}
private string getSVMFileName(string category)
{
return Path.Combine(getSaveDir(), category + ".svm");
}
private string getVocabularyFileName()
{
return Path.Combine(getSaveDir(), "vocabulary.voc");
}
//[HandleProcessCorruptedStateExceptions]
public IEnumerable<ImageInfo> Categorize(IEnumerable<string> imageFileNames)
{
var featureDetector = create_FeatureDetector();
var bowDescriptorExtractor = create_bowDescriptorExtractor(featureDetector);
Mat vocab = new Mat();
using (var fs = new FileStorage(getVocabularyFileName(), FileStorage.Mode.Read))
fs["vocab"].ReadMat(vocab);
bowDescriptorExtractor.SetVocabulary(vocab);
Dictionary<string, string> svms = new Dictionary<string, string>();
foreach (var xml in Directory.GetFiles(getSaveDir(), "*.svm"))
svms.Add(Path.GetFileNameWithoutExtension(xml), xml);
Dictionary<string, SVM> dic = new Dictionary<string, SVM>();
foreach (var fn in imageFileNames)
{
string scoreTxt = Environment.NewLine;
float score = float.MaxValue;
//float score = float.MinValue;
string cat = "";
try
{
using (Mat frame_g = GetMat(fn, false))
{
using (Mat img = new Mat())
{
VectorOfKeyPoint kp = new VectorOfKeyPoint(featureDetector.Detect(frame_g));
bowDescriptorExtractor.Compute(frame_g, kp, img);
foreach (var category in svms.Keys)
{
SVM svm = null;
if (!dic.ContainsKey(category))
{
string svmFn = svms[category];
svm = new SVM();
svm.LoadSVMFromFile(svmFn);
dic[category] = svm;
}
else
svm = dic[category];
svm = notFromFile[category];//*************
float classVal = svm.Predict(img, null);
float scoreVal = svm.Predict(img, null, 1);
//float signMul = (classVal < 0) == (scoreVal < 0) ? 1f : -1f;
//float score1 = signMul * scoreVal;
scoreTxt += string.Format("{0}-{1}: {2}{3}", category, classVal.ToString(), scoreVal.ToString("N3"), Environment.NewLine);
if (scoreVal < score)
{
score = scoreVal;
cat = category;
}
}
}
}
}
catch (Exception)
{
score = 0f;
cat = "hata";
}
if (string.IsNullOrEmpty(cat))
score = 0f;
yield return new ImageInfo(fn, cat, scoreTxt);
}
}
private static object matLocker = new object();
public Mat GetMat(string fn, bool train)
{
lock (matLocker)
{
var mat = new Mat(fn, ImreadModes.Color);
var mat2 = new Mat();
mat.ConvertTo(mat2, DepthType.Cv8U);
return mat2;
}
}
}