均匀LBP纹理特征

[code=language-java]package com.imageretrieval.features;

/**
* 均匀LBP纹理特征<br>
* 参考链接：http://blog.csdn.net/lu597203933/article/details/17184503
* @author VenyoWang
*
*/
public class UniformLBP {

public static void main(String[] args) {
int[] vector = getFeatureVector("");
int[] vector1 = getFeatureVector("");
System.out.println(calculateSimilarity(vector, vector1));
}

public static int[] getFeatureVector(String imagePath){
// 获取灰度矩阵
int[][] grayMatrix = getGrayPixel(imagePath, 200, 200);

// 为特征值(0-255)分组(降维)
int[] groupNums = groupFeatureValue();

// 遍历像素点，计算其特征值，并确定其分组，并进行分组统计
int[] vector = new int[59];
for(int i = 1; i < grayMatrix.length - 1; i++){
for(int j = 1; j < grayMatrix[0].length - 1; j++){
int center = grayMatrix[i][j];
int feature = 0;
feature = grayMatrix[i - 1][j - 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i][j - 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i + 1][j - 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i + 1][j]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i + 1][j + 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i][j + 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i - 1][j + 1]>=center ? (feature<<1)+1 : (feature<<1);
feature = grayMatrix[i - 1][j]>=center ? (feature<<1)+1 : (feature<<1);
feature = getMinFeature(feature);
vector[groupNums[feature]]++;
}
}

return vector;
}

/**
* 对256个特征值进行分组<br>
* groupNums的下标为特征值，value为组号
* @return
*/
private static int[] groupFeatureValue(){
int[] groupNums = new int[256];
int num = 1;
for(int i = 0; i <= 255; i++){
if(getHopCount(i) <= 2){
groupNums[i] = num;
num++;
}
}
return groupNums;
}

/**
* 计算跳变次数
* @param i
* @return
*/
private static int getHopCount(int i)
{
int[] a = new int[8];
int cnt = 0;
int k = 7;
while(i > 0)
{
a[k] = i & 1;
i = i >> 1;
--k;
}
for(k = 0; k < 7; k++)
{
if(a[k] != a[k+1])
{
++cnt;
}
}
if(a[0] != a[7])
{
++cnt;
}
return cnt;
}

/**
* 旋转不变性<br>
* 此处的feature的二进制位数固定为8
* @param feature
* @return
*/
private static int getMinFeature(int feature){
int minFeature = feature;
for(int i = 0; i < 7; i++){
// 循环右移一位
feature = (feature>>1 | feature<<7) & 0xff;
if(feature < minFeature) minFeature = feature;
}

return minFeature;
}

public static double calculateSimilarity(int[] vector, int[] vector1) {
double len = 0, len1 = 0, numerator = 0;
for (int i = 0; i < vector.length; i++) {
len += Math.pow(vector[i], 2);
len1 += Math.pow(vector1[i], 2);
numerator += vector[i] * vector1[i];
}
len = Math.sqrt(len);
len1 = Math.sqrt(len1);

return numerator / (len * len1);
}

public static int[][] getGrayPixel(String imagePath, int width, int height) {
BufferedImage bi = null;
try {
bi = resizeImage(imagePath, width, height, BufferedImage.TYPE_INT_RGB);
} catch (Exception e) {
e.printStackTrace();
return null;
}
int minx = bi.getMinX();
int miny = bi.getMinY();
int[][] matrix = new int[width - minx][height - miny];
for (int i = minx; i < width; i++) {
for (int j = miny; j < height; j++) {
int pixel = bi.getRGB(i, j);
int red = (pixel & 0xff0000) >> 16;
int green = (pixel & 0xff00) >> 8;
int blue = (pixel & 0xff);
int gray = (int) (red * 0.3 + green * 0.59 + blue * 0.11);
matrix[i][j] = gray;
}
}
return matrix;
}

public static BufferedImage resizeImage(String srcImgPath, int width, int height, int imageType)
throws IOException {
File srcFile = new File(srcImgPath);
BufferedImage srcImg = ImageIO.read(srcFile);
BufferedImage buffImg = null;
buffImg = new BufferedImage(width, height, imageType);
buffImg.getGraphics().drawImage(srcImg.getScaledInstance(width, height, Image.SCALE_SMOOTH), 0, 0, null);
return buffImg;
}
}