@Test
public void testWithNull() throws Exception {
SimpleFeatureSource source = getFeatureSource(MockData.BASIC_POLYGONS);
SimpleFeatureCollection fc = source.getFeatures();
SimpleFeatureIterator i = fc.features();
try {
SimpleFeature f = (SimpleFeature) i.next();
FeatureTemplate template = new FeatureTemplate();
template.description(f);
// set a value to null
f.setAttribute(1, null);
try {
template.description(f);
} catch (Exception e) {
e.printStackTrace();
fail("template threw exception on null value");
}
} finally {
i.close();
}
}
/**
* Reads the features templates from an input stream and puts them in global map
*
* @param input the input stream from which to read the feature templates
*/
public static void s_readFeatureTemplates(InputStream input) {
try {
ArrayList<FeatureTemplate> features = FeatureTemplateLoader.s_readTemplates(input);
synchronized (s_features) {
for (FeatureTemplate f : features) {
s_features.put(f.getName(), f);
}
}
} catch (IOException e) {
logger.error("IOException while reading device features", e);
} catch (ParsingException e) {
logger.error("Parsing exception while reading device features", e);
}
}
@Test
public void testAlternateLookup() throws Exception {
SimpleFeatureSource source = getFeatureSource(MockData.PRIMITIVEGEOFEATURE);
SimpleFeatureCollection fc = source.getFeatures();
SimpleFeatureIterator features = fc.features();
try {
SimpleFeature f = features.next();
FeatureTemplate template = new FeatureTemplate();
String result = template.template(f, "dummy.ftl", Dummy.class);
assertEquals("dummy", result);
} finally {
features.close();
}
}
@Test
public void testRawValue() throws Exception {
SimpleFeatureSource source = getFeatureSource(MockData.PRIMITIVEGEOFEATURE);
SimpleFeatureCollection fc = source.getFeatures();
SimpleFeatureIterator i = fc.features();
try {
SimpleFeature f = (SimpleFeature) i.next();
FeatureTemplate template = new FeatureTemplate();
try {
template.template(f, "rawValues.ftl", FeatureTemplateTest.class);
} catch (Exception e) {
e.printStackTrace();
throw (e);
}
} finally {
i.close();
}
}
@Test
public void testWithDateAndBoolean() throws Exception {
SimpleFeatureSource source = getFeatureSource(MockData.PRIMITIVEGEOFEATURE);
SimpleFeatureCollection fc = source.getFeatures();
SimpleFeatureIterator i = fc.features();
try {
SimpleFeature f = (SimpleFeature) i.next();
FeatureTemplate template = new FeatureTemplate();
try {
template.description(f);
} catch (Exception e) {
e.printStackTrace();
fail("template threw exception on null value");
}
} finally {
i.close();
}
}
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();
}
