java实现哈夫曼压缩与解压缩的方法

2019-11-26 08:30:46

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一哈夫曼树以及文件压缩原理：

1.哈夫曼树：

给定N个权值作为N个叶子结点，构造一棵二叉树，若该树的带权路径长度达到最小，称这样的二叉树为最优二叉树，也称为哈夫曼树。哈夫曼树是带权路径长度最短的树，权值较大的结点离根较近（频率越高的结点离根越进）。

以下数组为例，构建哈夫曼树

int a[] = {0,1,2,3,4,5,6,7,8}

我们可以发现以下规律

1：9个数构成的哈夫曼树一共有17个结点，也就是可以n个数可以生产2*n-1个结点

2：数字越大的数离根节点越近，越小的数离根节点越近。

2.如何利用haffman编码实现文件压缩：

比如abc.txt文件中有以下字符：aaaabbbccde，

1.进行字符统计

aaaabbbccde a : 4次b : 3次c : 2次d : 1次e : 1次

2.用统计结果构建哈夫曼树

3.用哈夫曼树生成哈夫曼编码（从根结点开始，路径左边记为0，右边记为1）：

a的编码：1b的编码:01c的编码:000d的编码:0011e的编码:0010

4.哈夫曼编码代替字符，进行压缩。

源文件内容为：aaaabbbccde
将源文件用对应的哈夫曼编码（haffman code）替换，则有：11110101 01000000 00110010 （总共3个字节）

由此可见，源文件一共有11个字符，占11字节的内存，但是经过用haffman code替换之后，只占3个字节，这样就能达到压缩的目的

二主要技术点：

1.哈夫曼树算法（哈夫曼压缩的基本算法）

2.哈希算法（字符统计时候会用到，也可以直接用HashMap统计）

3.位运算（涉及到将指定位，置0或置1）

4.java文件操作，以及缓冲操作。

5.存储模式（大端存储，小端存储，能看懂文件16进制的形式）

7.设置压缩密码，解压输入密码解压（小编自己加的内容）

三实现过程：

以上述aaaabbbccde为例

1.字符统计：

public class FreqHuf {	public static int BUFFER_SIZE = 1 << 18;	int freq[] = new int[256];	File file;	int count;	List<HuffmanFreq> list;		FreqHuf(String pathname) throws Exception {		list = new ArrayList<>();		this.file = new File(pathname);		if(!file.exists()){			throw new Exception("文件不存在");		}		System.out.println("进行字符统计中");		CensusChar();		System.out.println("字符统计完毕");	}		public void CensusChar() throws IOException{		int intchar;		FileInputStream fis = new FileInputStream(file);		System.out.println("统计中"); //这种统计处理方案，速度极慢，不建议使用，以下采用缓存读数据。//		while((intchar = fis.read()) != -1){//			freq[intchar]++;//		} 		//这里采用缓存机制，一次读1 << 18个字节，大大提高效率。		byte[] bytes = new byte[BUFFER_SIZE];		while((intchar = fis.read(bytes))!= -1){			for(int i = 0; i < intchar;i++){				int temp = bytes[i]& 0xff;				freq[temp]++;			}		}								fis.close();				for(int i = 0; i < 256; i++){			if(freq[i] != 0){				this.count++;			}		}				int index = 0;		for(int i = 0; i < 256; i++){			if(freq[i] != 0){				HuffmanFreq huffman = new HuffmanFreq();				huffman.character = (char)i;				huffman.freq = freq[i];				list.add(index, huffman);			}		}	}}

//统计每个字符和其频率的类public class HuffmanFreq {	char character;	int freq;		HuffmanFreq() {	}		HuffmanFreq(int character,int freq) {		this.character = (char)character;		this.freq = freq;	} 	char getCharacter() {		return character;	} 	void setCharacter(int character) {		this.character = (char)character;	} 	int getFreq() {		return freq;	} 	void setFreq(int freq) {		this.freq = freq;	}		byte[] infoToByte(){		byte[] bt = new byte[6];				byte[] b1 = ByteAnd8Types.charToByte(character);		for(int i= 0; i < b1.length;i++){			bt[i] = b1[i];		}				byte[] b2 = ByteAnd8Types.intToBytes2(freq);		int index = 2;		for(int i= 0; i < b2.length;i++){			bt[index++] = b2[i];		}				return bt;	} 	@Override	public String toString() {		return "Huffman [character=" + character + ", freq=" + freq + "]";	}}

2.用统计结果构建哈夫曼树：

//treeSize为总节点数private void creatTree(int treeSize){		int temp;		treeList = new ArrayList<HuffTreeNode>();		for(int i = 0; i < treeSize; i++){			HuffTreeNode node = new HuffTreeNode();			treeList.add(i, node);		}				for(int i = 0; i < charCount; i++){			HuffTreeNode node = treeList.get(i);			node.freq.freq = charList.get(i).getFreq();			node.freq.character = charList.get(i).getCharacter();			node.left = -1;			node.right = -1;			node.use = 0;		}				for(int i = charCount; i < treeSize; i++){			int index = i;			HuffTreeNode node = treeList.get(i);			node.use = 0;			node.freq.character = '#';			node.right = searchmin(index);			node.left = searchmin(index);			node.freq.freq = treeList.get(node.right).freq.freq + treeList.get(node.left).freq.freq;			temp = searchmin(++index);			if(temp == -1){				break;			}			treeList.get(temp).use = 0;		}	}		private int searchmin(int count){		int minindex = -1;				for(int i = 0; i < count; i++){			if(treeList.get(i).use == 0){				minindex = i;				break;			}		}		if(minindex == -1){			return -1;		}		for(int i = 0; i < count; i++){			if((treeList.get(i).freq.freq <= treeList.get(minindex).freq.freq) && treeList.get(i).use == 0){				minindex = i;			}		}		treeList.get(minindex).use = 1;		return minindex;	}

3.用哈夫曼树生成哈夫曼编码（从根结点开始，路径左边记为0，右边记为1）：

  private void bulidhuftreecode(int root, String str){		if(treeList.get(root).getLeft() != -1 && treeList.get(root).getRight() != -1){			bulidhuftreecode(treeList.get(root).getLeft(), str+"0");			bulidhuftreecode(treeList.get(root).getRight(), str + "1");		}		else{			treeList.get(root).code = str;		}		}

4.哈夫曼编码代替字符，进行压缩，压缩前首先要将文件头（文件标志，字符数量，最后一个字节有效位，密码）字符和其频率的那张表格写入文件，以便于解压缩

public void creatCodeFile(String path) throws Exception{		byte value = 0;		int index = 0;		int arr[] = new int[256];		int intchar;				for(int i = 0; i < charCount; i++){			arr[treeList.get(i).freq.character] = i;					}		File file = new File(path);    if(!file.exists()){       if(!file.createNewFile()){      	 throw new Exception("创建文件失败");       }    }		int count = charList.size();		HuffmanHead head = new HuffmanHead(count, howlongchar(count), password);        //将文件头信息写入文件		this.write = new RandomAccessFile(file, "rw");		write.write(head.InfoToByte());        //将字符及其频率的表写入文件		for(HuffmanFreq freq : charList){			byte[] bt = freq.infoToByte();			write.write(bt);		}		//将字符用哈夫曼编码进行压缩，这里读写都是采用缓存机制		byte[] readBuffer = new byte[BUFFER_SIZE];		while((intchar = read.read(readBuffer))!= -1){			ProgressBar.SetCurrent(read.getFilePointer());			for(int i = 0; i < intchar;i++){				int temp = readBuffer[i]& 0xff; 				String code = treeList.get(arr[temp]).code;				char[] chars = code.toCharArray();								for(int j = 0; j < chars.length; j++){					if(chars[j] == '0'){						value = CLR_BYTE(value, index);					}					if(chars[j] == '1'){						value = SET_BYTE(value, index);					}					if(++index >= 8){						index = 0;						writeInBuffer(value);					}				}			}		}		//此方法速度较慢//		while((intchar = is.read()) != -1){//			String code = treeList.get(arr[intchar]).code;//			char[] chars = code.toCharArray();//			//			for(int i = 0; i < chars.length; i++){//				if(chars[i] == '0'){//					value = CLR_BYTE(value, index);//				}//				if(chars[i] == '1'){//					value = SET_BYTE(value, index);//				}//				if(++index >= 8){//					index = 0;//					oos.write(value);//				}//			}//		}		if(index != 0){			writeInBuffer(value);		}	  byte[] Data = Arrays.copyOfRange(writeBuffer, 0, writeBufferSize);	  write.write(Data);	  write.close();		read.close();	}    //指定位，置1    byte SET_BYTE(byte value, int index){		return (value) |= (1 << ((index) ^ 7));	}	    //指定位，置0	byte CLR_BYTE(byte value, int index){ 		return (value) &= (~(1 << ((index) ^ 7)));	}    //判断指定位是否为0,0为false,1为true	boolean GET_BYTE(byte value, int index){ 		return ((value) & (1 << ((index) ^ 7))) != 0;	}

如果一个字节一个字节往文件里写，速度会极慢，为了提高效率，写也采用缓存，先写到缓存区，缓存区满了后写入文件，

    private void writeInBuffer(byte value) throws Exception {		if(writeBufferSize < BUFFER_SIZE){			writeBuffer[writeBufferSize] = value;			if(++writeBufferSize >= BUFFER_SIZE){				write.write(writeBuffer);				writeBufferSize = 0;			}		} else{			throw new Exception("写入文件出错");		}	}

到这里压缩就完成了，以下为解压缩方法

1.从写入文件中的字符统计的表读出放入list里

public void init() throws Exception{		char isHUf = read.readChar();        //验证文件头信息		if(isHUf != '哈'){			throw new Exception("该文件不是HUFFMAN压缩文件");		}		this.charCount = read.readChar();		this.treeSize = 2*charCount -1;		this.lastIndex = read.readChar();		int password = read.readInt();		if(password != this.password.hashCode()){			System.out.println("密码错误");		} else{			System.out.println("密码正确，正在解压");		}		        //从文件中将字符统计的表读出		byte[] buffer = new byte[charCount * 6];		read.seek(10);		read.read(buffer, 0, charCount * 6);		ProgressBar.SetCurrent(read.getFilePointer());		for(int i = 0; i < buffer.length; i+=6){			byte[] buff = Arrays.copyOfRange(buffer, i, i+2);			ByteBuffer bb = ByteBuffer.allocate (buff.length);		  bb.put (buff);		  bb.flip ();		  CharBuffer cb = cs.decode (bb);		  byte[] buff1 = Arrays.copyOfRange(buffer, i+2, i+6);		  int size = ByteAnd8Types.bytesToInt2(buff1, 0);		  HuffmanFreq freq = new HuffmanFreq(cb.array()[0], size);		  charList.add(freq);		}	}

2.用统计结果构建哈夫曼树（和以上代码一样）

3.用哈夫曼树生成哈夫曼编码（从根结点开始，路径左边记为0，右边记为1）（和以上代码一样）

4.遍历文件每个字节，根据哈夫曼编码找到对应的字符，将字符写入新文件

 public void creatsourcefile(String pathname) throws Exception{		int root = treeList.size() - 1;		int fininsh = 1;		long len;		File file = new File(pathname);		if(!file.exists()){			 if(!file.createNewFile()){				 throw new Exception("创建文件失败");	     }		}		write = new RandomAccessFile(file, "rw");				int intchar;		byte[] bytes = new byte[1<<18];		int index = 0;		while((intchar = read.read(bytes))!= -1){			len = read.getFilePointer();			ProgressBar.SetCurrent(len);			for(int i = 0; i < intchar;i++){				for(;index < 8 && fininsh != 0;){					if(GET_BYTE(bytes[i], index)){						root = treeList.get(root).right;					} else{						root = treeList.get(root).left;					}					if(treeList.get(root).right== -1 && treeList.get(root).left == -1){						byte temp = (byte)treeList.get(root).freq.character;						writeInBuffer(temp);						root = treeList.size() - 1;					}					index++;					if(len == this.goalfilelenth && i == intchar-1){						if(index >= this.lastIndex){							fininsh = 0;						}					}				}				index = 0;			}		}		byte[] Data = Arrays.copyOfRange(writeBuffer, 0, writeBufferSize);		write.write(Data);		write.close();		write.close();		read.close();	}

四运行展示：