From c76cbbefa26910c82dfd3585f8f999ee5784254b Mon Sep 17 00:00:00 2001
From: KKSU <15274802129@163.com>
Date: Wed, 28 Aug 2024 10:16:48 +0800
Subject: [PATCH] 新增功能操作按钮，和更新了数据库数据
---
src/main/java/cc/mrbird/febs/mall/test/FingerPrint.java | 634 ++++++++++++++++++++++++++++----------------------------
1 files changed, 317 insertions(+), 317 deletions(-)
diff --git a/src/main/java/cc/mrbird/febs/mall/test/FingerPrint.java b/src/main/java/cc/mrbird/febs/mall/test/FingerPrint.java
index 788a348..a73b2f8 100644
--- a/src/main/java/cc/mrbird/febs/mall/test/FingerPrint.java
+++ b/src/main/java/cc/mrbird/febs/mall/test/FingerPrint.java
@@ -1,317 +1,317 @@
-package cc.mrbird.febs.mall.test;
-
-import javax.imageio.ImageIO;
-import java.awt.*;
-import java.awt.color.ColorSpace;
-import java.awt.image.BufferedImage;
-import java.awt.image.ColorConvertOp;
-import java.io.File;
-import java.io.IOException;
-import java.util.Arrays;
-
-/**
- * 图片是采用phash算法,一共分为四步吧.
- *
- * 1.将图片缩放到16*16大小,这是我们选择的合适的大小,假如宽高不一样,直接将其压到16*16,去掉细节,只保留宏观;
- *
- * 2.图片一共是16*16的,共256个像素,我们将图片进行灰度化,灰度化就是只有黑白灰三种,从白到黑,一共分了255层;
- *
- * 3.灰度化之后将图片进行DCT转换(离散余弦变化),因为为了识别有的图片旋转,这个DCT转换是将图片进行了一种压缩算法;
- *
- * 4.我们对这个算法进行了优化,因为之前是计算像素的均值,我们为了更准确,我们取RGB,rgb一共分为255个像素,我们将255个像素分为16段,如果像素大于0-16记为0,17到32记为1,直到255,这样就得到255位的二进制,这就是这张图片的指纹码.
- *
- * 得到唯一标识的指纹码之后怎么去计算像素度呢?
- *
- * 通过汉明距离比较两个二进制距离,如果距离小于<10的话,我们就判定两张图片相似.如果两个指纹码(二进制)一模一样,我们就判定两个是一张图片,或者类似;
- */
-/**
- * 视频相似度算法:
- * 视频的话我们是通过ffmpeg(ff am pig),它是一个专门处理视频的框架,可以从视频中按针提取图片.然后就按照图片的相似度取对比了...
- */
-
-/**
- * https://blog.csdn.net/weixin_34095889/article/details/91923072?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522171652008316800182787012%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=171652008316800182787012&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-4-91923072-null-null.142^v100^pc_search_result_base8&utm_term=java%20%E6%8A%8A%E5%9B%BE%E7%89%87%E8%BD%AC%E6%8D%A2%E6%88%90%E9%BB%91%E7%99%BD%E7%81%B0%E5%83%8F%E7%B4%A0%E9%A3%8E%E6%A0%BC&spm=1018.2226.3001.4187
- * 均值哈希实现图像指纹比较
- */
-public class FingerPrint {
-
- public static void main(String[] args) {
- FingerPrint fp1 = null;
- FingerPrint fp2 = null;
- try {
- fp1 = new FingerPrint(ImageIO.read(new File("D:\\image\\F1.png")));
- fp2 = new FingerPrint(ImageIO.read(new File("D:\\image\\Z1.jpg")));
- } catch (IOException e) {
- e.printStackTrace();
- }
- System.out.println(fp1.toString(false));
- System.out.println(fp2.toString(false));
- System.out.printf("sim=%f",fp1.compare(fp2));
- }
-
- /**
- * 图像指纹的尺寸,将图像resize到指定的尺寸，来计算哈希数组
- */
- private static final int HASH_SIZE=16;
- /**
- * 保存图像指纹的二值化矩阵
- */
- private final byte[] binaryzationMatrix;
- public FingerPrint(byte[] hashValue) {
- if(hashValue.length!=HASH_SIZE*HASH_SIZE)
- throw new IllegalArgumentException(String.format("length of hashValue must be %d",HASH_SIZE*HASH_SIZE ));
- this.binaryzationMatrix=hashValue;
- }
- public FingerPrint(String hashValue) {
- this(toBytes(hashValue));
- }
- public FingerPrint (BufferedImage src){
- this(hashValue(src));
- }
- private static byte[] hashValue(BufferedImage src){
- BufferedImage hashImage = resize(src,HASH_SIZE,HASH_SIZE);
- byte[] matrixGray = (byte[]) toGray(hashImage).getData().getDataElements(0, 0, HASH_SIZE, HASH_SIZE, null);
- return binaryzation(matrixGray);
- }
- /**
- * 从压缩格式指纹创建{@link FingerPrint}对象
- * @param compactValue
- * @return
- */
- public static FingerPrint createFromCompact(byte[] compactValue){
- return new FingerPrint(uncompact(compactValue));
- }
-
- public static boolean validHashValue(byte[] hashValue){
- if(hashValue.length!=HASH_SIZE)
- return false;
- for(byte b:hashValue){
- if(0!=b&&1!=b)return false;
- }
- return true;
- }
- public static boolean validHashValue(String hashValue){
- if(hashValue.length()!=HASH_SIZE)
- return false;
- for(int i=0;i<hashValue.length();++i){
- if('0'!=hashValue.charAt(i)&&'1'!=hashValue.charAt(i))return false;
- }
- return true;
- }
- public byte[] compact(){
- return compact(binaryzationMatrix);
- }
-
- /**
- * 指纹数据按位压缩
- * @param hashValue
- * @return
- */
- private static byte[] compact(byte[] hashValue){
- byte[] result=new byte[(hashValue.length+7)>>3];
- byte b=0;
- for(int i=0;i<hashValue.length;++i){
- if(0==(i&7)){
- b=0;
- }
- if(1==hashValue[i]){
- b|=1<<(i&7);
- }else if(hashValue[i]!=0)
- throw new IllegalArgumentException("invalid hashValue,every element must be 0 or 1");
- if(7==(i&7)||i==hashValue.length-1){
- result[i>>3]=b;
- }
- }
- return result;
- }
-
- /**
- * 压缩格式的指纹解压缩
- * @param compactValue
- * @return
- */
- private static byte[] uncompact(byte[] compactValue){
- byte[] result=new byte[compactValue.length<<3];
- for(int i=0;i<result.length;++i){
- if((compactValue[i>>3]&(1<<(i&7)))==0)
- result[i]=0;
- else
- result[i]=1;
- }
- return result;
- }
- /**
- * 字符串类型的指纹数据转为字节数组
- * @param hashValue
- * @return
- */
- private static byte[] toBytes(String hashValue){
- hashValue=hashValue.replaceAll("\\s", "");
- byte[] result=new byte[hashValue.length()];
- for(int i=0;i<result.length;++i){
- char c = hashValue.charAt(i);
- if('0'==c)
- result[i]=0;
- else if('1'==c)
- result[i]=1;
- else
- throw new IllegalArgumentException("invalid hashValue String");
- }
- return result;
- }
- /**
- * 缩放图像到指定尺寸
- * @param src
- * @param width
- * @param height
- * @return
- */
- private static BufferedImage resize(Image src,int width,int height){
- BufferedImage result = new BufferedImage(width, height,
- BufferedImage.TYPE_3BYTE_BGR);
- Graphics g = result.getGraphics();
- try{
- g.drawImage(src.getScaledInstance(width, height, Image.SCALE_SMOOTH), 0, 0, null);
- }finally{
- g.dispose();
- }
- return result;
- }
- /**
- * 计算均值
- * @param src
- * @return
- */
- private static int mean(byte[] src){
- long sum=0;
- // 将数组元素转为无符号整数
- for(byte b:src)sum+=(long)b&0xff;
- return (int) (Math.round((float)sum/src.length));
- }
- /**
- * 二值化处理
- * @param src
- * @return
- */
- private static byte[] binaryzation(byte[]src){
- byte[] dst = src.clone();
- int mean=mean(src);
- for(int i=0;i<dst.length;++i){
- // 将数组元素转为无符号整数再比较
- dst[i]=(byte) (((int)dst[i]&0xff)>=mean?1:0);
- }
- return dst;
-
- }
- /**
- * 转灰度图像
- * @param src
- * @return
- */
- private static BufferedImage toGray(BufferedImage src){
- if(src.getType()==BufferedImage.TYPE_BYTE_GRAY){
- return src;
- }else{
- // 图像转灰
- BufferedImage grayImage = new BufferedImage(src.getWidth(), src.getHeight(),
- BufferedImage.TYPE_BYTE_GRAY);
- new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null).filter(src, grayImage);
- return grayImage;
- }
- }
-
- @Override
- public String toString() {
- return toString(true);
- }
- /**
- * @param multiLine 是否分行
- * @return
- */
- public String toString(boolean multiLine) {
- StringBuffer buffer=new StringBuffer();
- int count=0;
- for(byte b:this.binaryzationMatrix){
- buffer.append(0==b?'0':'1');
- if(multiLine&&++count%HASH_SIZE==0)
- buffer.append('\n');
- }
- return buffer.toString();
- }
- @Override
- public boolean equals(Object obj) {
- if(obj instanceof FingerPrint){
- return Arrays.equals(this.binaryzationMatrix,((FingerPrint)obj).binaryzationMatrix);
- }else
- return super.equals(obj);
- }
-
- /**
- * 与指定的压缩格式指纹比较相似度
- * @param compactValue
- * @return
- * @see #compare(FingerPrint)
- */
- public float compareCompact(byte[] compactValue){
- return compare(createFromCompact(compactValue));
- }
- /**
- * @param hashValue
- * @return
- * @see #compare(FingerPrint)
- */
- public float compare(String hashValue){
- return compare(new FingerPrint(hashValue));
- }
- /**
- * 与指定的指纹比较相似度
- * @param hashValue
- * @return
- * @see #compare(FingerPrint)
- */
- public float compare(byte[] hashValue){
- return compare(new FingerPrint(hashValue));
- }
- /**
- * 与指定图像比较相似度
- * @param image2
- * @return
- * @see #compare(FingerPrint)
- */
- public float compare(BufferedImage image2){
- return compare(new FingerPrint(image2));
- }
- /**
- * 比较指纹相似度
- * @param src
- * @return
- * @see #compare(byte[], byte[])
- */
- public float compare(FingerPrint src){
- if(src.binaryzationMatrix.length!=this.binaryzationMatrix.length)
- throw new IllegalArgumentException("length of hashValue is mismatch");
- return compare(binaryzationMatrix,src.binaryzationMatrix);
- }
- /**
- * 判断两个数组相似度，数组长度必须一致否则抛出异常
- * @param f1
- * @param f2
- * @return 返回相似度(0.0~1.0)
- */
- private static float compare(byte[] f1,byte[] f2){
- if(f1.length!=f2.length)
- throw new IllegalArgumentException("mismatch FingerPrint length");
- int sameCount=0;
- for(int i=0;i<f1.length;++i){
- if(f1[i]==f2[i])++sameCount;
- }
- return (float)sameCount/f1.length;
- }
- public static float compareCompact(byte[] f1,byte[] f2){
- return compare(uncompact(f1),uncompact(f2));
- }
- public static float compare(BufferedImage image1,BufferedImage image2){
- return new FingerPrint(image1).compare(new FingerPrint(image2));
- }
-
-}
+//package cc.mrbird.febs.mall.test;
+//
+//import javax.imageio.ImageIO;
+//import java.awt.*;
+//import java.awt.color.ColorSpace;
+//import java.awt.image.BufferedImage;
+//import java.awt.image.ColorConvertOp;
+//import java.io.File;
+//import java.io.IOException;
+//import java.util.Arrays;
+//
+///**
+// * 图片是采用phash算法,一共分为四步吧.
+// *
+// * 1.将图片缩放到16*16大小,这是我们选择的合适的大小,假如宽高不一样,直接将其压到16*16,去掉细节,只保留宏观;
+// *
+// * 2.图片一共是16*16的,共256个像素,我们将图片进行灰度化,灰度化就是只有黑白灰三种,从白到黑,一共分了255层;
+// *
+// * 3.灰度化之后将图片进行DCT转换(离散余弦变化),因为为了识别有的图片旋转,这个DCT转换是将图片进行了一种压缩算法;
+// *
+// * 4.我们对这个算法进行了优化,因为之前是计算像素的均值,我们为了更准确,我们取RGB,rgb一共分为255个像素,我们将255个像素分为16段,如果像素大于0-16记为0,17到32记为1,直到255,这样就得到255位的二进制,这就是这张图片的指纹码.
+// *
+// * 得到唯一标识的指纹码之后怎么去计算像素度呢?
+// *
+// * 通过汉明距离比较两个二进制距离,如果距离小于<10的话,我们就判定两张图片相似.如果两个指纹码(二进制)一模一样,我们就判定两个是一张图片,或者类似;
+// */
+///**
+// * 视频相似度算法:
+// * 视频的话我们是通过ffmpeg(ff am pig),它是一个专门处理视频的框架,可以从视频中按针提取图片.然后就按照图片的相似度取对比了...
+// */
+//
+///**
+// * https://blog.csdn.net/weixin_34095889/article/details/91923072?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522171652008316800182787012%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=171652008316800182787012&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-4-91923072-null-null.142^v100^pc_search_result_base8&utm_term=java%20%E6%8A%8A%E5%9B%BE%E7%89%87%E8%BD%AC%E6%8D%A2%E6%88%90%E9%BB%91%E7%99%BD%E7%81%B0%E5%83%8F%E7%B4%A0%E9%A3%8E%E6%A0%BC&spm=1018.2226.3001.4187
+// * 均值哈希实现图像指纹比较
+// */
+//public class FingerPrint {
+//
+// public static void main(String[] args) {
+// FingerPrint fp1 = null;
+// FingerPrint fp2 = null;
+// try {
+// fp1 = new FingerPrint(ImageIO.read(new File("D:\\image\\F1.png")));
+// fp2 = new FingerPrint(ImageIO.read(new File("D:\\image\\Z1.jpg")));
+// } catch (IOException e) {
+// e.printStackTrace();
+// }
+// System.out.println(fp1.toString(false));
+// System.out.println(fp2.toString(false));
+// System.out.printf("sim=%f",fp1.compare(fp2));
+// }
+//
+// /**
+// * 图像指纹的尺寸,将图像resize到指定的尺寸，来计算哈希数组
+// */
+// private static final int HASH_SIZE=16;
+// /**
+// * 保存图像指纹的二值化矩阵
+// */
+// private final byte[] binaryzationMatrix;
+// public FingerPrint(byte[] hashValue) {
+// if(hashValue.length!=HASH_SIZE*HASH_SIZE)
+// throw new IllegalArgumentException(String.format("length of hashValue must be %d",HASH_SIZE*HASH_SIZE ));
+// this.binaryzationMatrix=hashValue;
+// }
+// public FingerPrint(String hashValue) {
+// this(toBytes(hashValue));
+// }
+// public FingerPrint (BufferedImage src){
+// this(hashValue(src));
+// }
+// private static byte[] hashValue(BufferedImage src){
+// BufferedImage hashImage = resize(src,HASH_SIZE,HASH_SIZE);
+// byte[] matrixGray = (byte[]) toGray(hashImage).getData().getDataElements(0, 0, HASH_SIZE, HASH_SIZE, null);
+// return binaryzation(matrixGray);
+// }
+// /**
+// * 从压缩格式指纹创建{@link FingerPrint}对象
+// * @param compactValue
+// * @return
+// */
+// public static FingerPrint createFromCompact(byte[] compactValue){
+// return new FingerPrint(uncompact(compactValue));
+// }
+//
+// public static boolean validHashValue(byte[] hashValue){
+// if(hashValue.length!=HASH_SIZE)
+// return false;
+// for(byte b:hashValue){
+// if(0!=b&&1!=b)return false;
+// }
+// return true;
+// }
+// public static boolean validHashValue(String hashValue){
+// if(hashValue.length()!=HASH_SIZE)
+// return false;
+// for(int i=0;i<hashValue.length();++i){
+// if('0'!=hashValue.charAt(i)&&'1'!=hashValue.charAt(i))return false;
+// }
+// return true;
+// }
+// public byte[] compact(){
+// return compact(binaryzationMatrix);
+// }
+//
+// /**
+// * 指纹数据按位压缩
+// * @param hashValue
+// * @return
+// */
+// private static byte[] compact(byte[] hashValue){
+// byte[] result=new byte[(hashValue.length+7)>>3];
+// byte b=0;
+// for(int i=0;i<hashValue.length;++i){
+// if(0==(i&7)){
+// b=0;
+// }
+// if(1==hashValue[i]){
+// b|=1<<(i&7);
+// }else if(hashValue[i]!=0)
+// throw new IllegalArgumentException("invalid hashValue,every element must be 0 or 1");
+// if(7==(i&7)||i==hashValue.length-1){
+// result[i>>3]=b;
+// }
+// }
+// return result;
+// }
+//
+// /**
+// * 压缩格式的指纹解压缩
+// * @param compactValue
+// * @return
+// */
+// private static byte[] uncompact(byte[] compactValue){
+// byte[] result=new byte[compactValue.length<<3];
+// for(int i=0;i<result.length;++i){
+// if((compactValue[i>>3]&(1<<(i&7)))==0)
+// result[i]=0;
+// else
+// result[i]=1;
+// }
+// return result;
+// }
+// /**
+// * 字符串类型的指纹数据转为字节数组
+// * @param hashValue
+// * @return
+// */
+// private static byte[] toBytes(String hashValue){
+// hashValue=hashValue.replaceAll("\\s", "");
+// byte[] result=new byte[hashValue.length()];
+// for(int i=0;i<result.length;++i){
+// char c = hashValue.charAt(i);
+// if('0'==c)
+// result[i]=0;
+// else if('1'==c)
+// result[i]=1;
+// else
+// throw new IllegalArgumentException("invalid hashValue String");
+// }
+// return result;
+// }
+// /**
+// * 缩放图像到指定尺寸
+// * @param src
+// * @param width
+// * @param height
+// * @return
+// */
+// private static BufferedImage resize(Image src,int width,int height){
+// BufferedImage result = new BufferedImage(width, height,
+// BufferedImage.TYPE_3BYTE_BGR);
+// Graphics g = result.getGraphics();
+// try{
+// g.drawImage(src.getScaledInstance(width, height, Image.SCALE_SMOOTH), 0, 0, null);
+// }finally{
+// g.dispose();
+// }
+// return result;
+// }
+// /**
+// * 计算均值
+// * @param src
+// * @return
+// */
+// private static int mean(byte[] src){
+// long sum=0;
+// // 将数组元素转为无符号整数
+// for(byte b:src)sum+=(long)b&0xff;
+// return (int) (Math.round((float)sum/src.length));
+// }
+// /**
+// * 二值化处理
+// * @param src
+// * @return
+// */
+// private static byte[] binaryzation(byte[]src){
+// byte[] dst = src.clone();
+// int mean=mean(src);
+// for(int i=0;i<dst.length;++i){
+// // 将数组元素转为无符号整数再比较
+// dst[i]=(byte) (((int)dst[i]&0xff)>=mean?1:0);
+// }
+// return dst;
+//
+// }
+// /**
+// * 转灰度图像
+// * @param src
+// * @return
+// */
+// private static BufferedImage toGray(BufferedImage src){
+// if(src.getType()==BufferedImage.TYPE_BYTE_GRAY){
+// return src;
+// }else{
+// // 图像转灰
+// BufferedImage grayImage = new BufferedImage(src.getWidth(), src.getHeight(),
+// BufferedImage.TYPE_BYTE_GRAY);
+// new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null).filter(src, grayImage);
+// return grayImage;
+// }
+// }
+//
+// @Override
+// public String toString() {
+// return toString(true);
+// }
+// /**
+// * @param multiLine 是否分行
+// * @return
+// */
+// public String toString(boolean multiLine) {
+// StringBuffer buffer=new StringBuffer();
+// int count=0;
+// for(byte b:this.binaryzationMatrix){
+// buffer.append(0==b?'0':'1');
+// if(multiLine&&++count%HASH_SIZE==0)
+// buffer.append('\n');
+// }
+// return buffer.toString();
+// }
+// @Override
+// public boolean equals(Object obj) {
+// if(obj instanceof FingerPrint){
+// return Arrays.equals(this.binaryzationMatrix,((FingerPrint)obj).binaryzationMatrix);
+// }else
+// return super.equals(obj);
+// }
+//
+// /**
+// * 与指定的压缩格式指纹比较相似度
+// * @param compactValue
+// * @return
+// * @see #compare(FingerPrint)
+// */
+// public float compareCompact(byte[] compactValue){
+// return compare(createFromCompact(compactValue));
+// }
+// /**
+// * @param hashValue
+// * @return
+// * @see #compare(FingerPrint)
+// */
+// public float compare(String hashValue){
+// return compare(new FingerPrint(hashValue));
+// }
+// /**
+// * 与指定的指纹比较相似度
+// * @param hashValue
+// * @return
+// * @see #compare(FingerPrint)
+// */
+// public float compare(byte[] hashValue){
+// return compare(new FingerPrint(hashValue));
+// }
+// /**
+// * 与指定图像比较相似度
+// * @param image2
+// * @return
+// * @see #compare(FingerPrint)
+// */
+// public float compare(BufferedImage image2){
+// return compare(new FingerPrint(image2));
+// }
+// /**
+// * 比较指纹相似度
+// * @param src
+// * @return
+// * @see #compare(byte[], byte[])
+// */
+// public float compare(FingerPrint src){
+// if(src.binaryzationMatrix.length!=this.binaryzationMatrix.length)
+// throw new IllegalArgumentException("length of hashValue is mismatch");
+// return compare(binaryzationMatrix,src.binaryzationMatrix);
+// }
+// /**
+// * 判断两个数组相似度，数组长度必须一致否则抛出异常
+// * @param f1
+// * @param f2
+// * @return 返回相似度(0.0~1.0)
+// */
+// private static float compare(byte[] f1,byte[] f2){
+// if(f1.length!=f2.length)
+// throw new IllegalArgumentException("mismatch FingerPrint length");
+// int sameCount=0;
+// for(int i=0;i<f1.length;++i){
+// if(f1[i]==f2[i])++sameCount;
+// }
+// return (float)sameCount/f1.length;
+// }
+// public static float compareCompact(byte[] f1,byte[] f2){
+// return compare(uncompact(f1),uncompact(f2));
+// }
+// public static float compare(BufferedImage image1,BufferedImage image2){
+// return new FingerPrint(image1).compare(new FingerPrint(image2));
+// }
+//
+//}
--
Gitblit v1.9.1