private static void trainCascadeAdaBoost() {
int imgSize = 30;
double eachDR = 0.99, eachFAR = 0.5, finalFAR = 0;
String root = "/home/hadoop/ProgramDatas/MLStudy/FaceDection/";
// String root = "E:/TestDatas/MLStudy/FaceDection/";
String dataFile = root + "train_data_2.txt";
String modelFile = root + "CascadeAdaboost_model.txt";
String misclassificationFile = root + "mis_classifications.txt";
String sparkAppName = "Viola-Jones Train";
String sparkMaster = "spark://localhost:7077";
int sparkCores = 60;
String sparkJars = "/home/hadoop/violajones.jar";
checkMemoryInfo();
System.out.println("initing feature templates...");
List<FeatureTemplate> templates = FeatureTemplate.initFeaTemplates();
System.out.println("initing features...");
List<HaarLikeFeature> features = HaarLikeFeature.initFeatures(imgSize, imgSize, templates);
System.out.println("loading train datas...");
Map<Integer, List<IntegralImage>> trainDatas =
FileUtils.loadTrainDatasSeparate(dataFile, imgSize, imgSize);
List<IntegralImage> posDatas = trainDatas.get(1);
List<IntegralImage> negDatas = trainDatas.get(0);
SparkConf sparkConf =
new SparkConf()
.setMaster(sparkMaster)
.setAppName(sparkAppName)
.set("spark.executor.memory", "3g");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
sc.addJar(sparkJars);
sc.setLogLevel("WARN");
System.out.println("training cascade adaboost...");
CascadeAdaBoost cascade = new CascadeAdaBoost(posDatas, negDatas, features);
CascadeClassifier classifier =
cascade.train(sc, sparkCores, eachDR, eachFAR, finalFAR, modelFile, misclassificationFile);
System.out.println("exporting model...");
FileUtils.exportFile(modelFile, classifier.exportModel());
sc.close();
}
private static void trainAdaBoost() {
int imgSize = 24;
int T = 300;
// String root = "/home/hadoop/ProgramDatas/MLStudy/FaceDection/";
String root = "E:/TestDatas/MLStudy/FaceDection/";
String dataFile = root + "train_data.txt";
String modelFile = root + "adaboost_model.txt";
String sparkAppName = "Viola-Jones Train";
String sparkMaster = "spark://localhost:7077";
int sparkCores = 60;
String sparkJars = "/home/hadoop/violajones.jar";
checkMemoryInfo();
System.out.println("initing feature templates...");
List<FeatureTemplate> templates = FeatureTemplate.initFeaTemplates();
System.out.println("initing features...");
List<HaarLikeFeature> features = HaarLikeFeature.initFeatures(imgSize, imgSize, templates);
System.out.println("loading train datas...");
List<IntegralImage> trainDatas = FileUtils.loadTrainDatas(dataFile, imgSize, imgSize);
Collections.shuffle(trainDatas);
SparkConf sparkConf =
new SparkConf()
.setMaster(sparkMaster)
.setAppName(sparkAppName)
.set("spark.executor.memory", "2g");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
sc.addJar(sparkJars);
sc.setLogLevel("WARN");
System.out.println("training adaboost...");
AdaBoost adaBoost = new AdaBoost(trainDatas, features);
Map<HaarLikeFeature, Double> classifiers = adaBoost.train(sc, sparkCores, T);
System.out.println("exporting model...");
List<String> model = new ArrayList<String>();
for (Entry<HaarLikeFeature, Double> item : classifiers.entrySet()) {
model.add(item.getKey().toStringWithWeight(item.getValue()));
}
FileUtils.exportFile(modelFile, model);
System.out.println("viola jones training success!");
sc.close();
}
