本文整理汇总了Java中net.semanticmetadata.lire.imageanalysis.correlogram.MLuxAutoCorrelogramExtraction类的典型用法代码示例。如果您正苦于以下问题：Java MLuxAutoCorrelogramExtraction类的具体用法？Java MLuxAutoCorrelogramExtraction怎么用？Java MLuxAutoCorrelogramExtraction使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
MLuxAutoCorrelogramExtraction类属于net.semanticmetadata.lire.imageanalysis.correlogram包，在下文中一共展示了MLuxAutoCorrelogramExtraction类的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: AutoColorCorrelogram
import net.semanticmetadata.lire.imageanalysis.correlogram.MLuxAutoCorrelogramExtraction; //导入依赖的package包/类
/**
* Creates a new AutoColorCorrelogram using a maximum L_inf pixel distance for analysis and given mode
*
* @param maxDistance maximum L_inf pixel distance for analysis
* @param mode the mode of calculation (determines the speed of extraction)
*/
public AutoColorCorrelogram(int maxDistance, Mode mode) {
this(DEFAULT_NUMBER_COLORS, null, new MLuxAutoCorrelogramExtraction(mode));
int[] D = new int[maxDistance];
for (int i = 0; i < maxDistance; i++) D[i] = i + 1;
this.distanceSet = D;
}
示例2: testMethodsPerformance
import net.semanticmetadata.lire.imageanalysis.correlogram.MLuxAutoCorrelogramExtraction; //导入依赖的package包/类
public void testMethodsPerformance() throws IOException {
AutoColorCorrelogram[] acc = new AutoColorCorrelogram[4];
int[] D = {1, 3, 5, 7};
int C = 64;
acc[0] = new AutoColorCorrelogram(C, D, new MLuxAutoCorrelogramExtraction(AutoColorCorrelogram.Mode.SuperFast));
acc[1] = new AutoColorCorrelogram(C, D, new MLuxAutoCorrelogramExtraction(AutoColorCorrelogram.Mode.FullNeighbourhood));
acc[2] = new AutoColorCorrelogram(C, D, new NaiveAutoCorrelogramExtraction());
acc[3] = new AutoColorCorrelogram(C, D, DynamicProgrammingAutoCorrelogramExtraction.getInstance());
int[] testSet = {284, 77, 108, 416, 144, 534, 898, 104, 67, 10};
//reads all images
BufferedImage[] image = new BufferedImage[testSet.length];
for (int j = 0; j < testSet.length; j++) {
int id = testSet[j];
String file = testExtensive + "/" + id + ".jpg";
image[j] = ImageIO.read(new FileInputStream(file));
}
for (int i = 0; i < 4; i++) {
long t0 = System.currentTimeMillis();
for (int j = 0; j < testSet.length; j++) {
acc[i].extract(image[j]);
System.out.print(".");
}
long tf = System.currentTimeMillis();
long dt = tf - t0;
double avt = (double) dt / testSet.length;
System.out.printf("Method %d: total time %d, average %f\n", i, dt, avt);
}
}