public bool Decompress(ref Huffman.HuffmanData data)
{
Node treeTop = createBinaryTree(data.frequency);
createDictionary(treeTop, new List <bool>());
data.uncompressedData = decodeBitArray(new BitArray(data.compressedData), ref treeTop, ref data.sizeOfUncompressedData);
return(true);
}
public bool Compress(ref Huffman.HuffmanData data)
{
byte[] inValue = data.uncompressedData;
data.frequency = frequency(inValue);
Node treeTop = createBinaryTree(data.frequency);
createDictionary(treeTop, new List <bool>());
data.compressedData = storeContentToByteArray(inValue);
data.sizeOfUncompressedData = inValue.Count();
return(true);
}
bool Huffman.IPlugin.Decompress(ref Huffman.HuffmanData hf)
{
try
{
TableFq tf = new TableFq(hf.frequency); // Création de la table de frèquence à partir des données d'entrée
BinaryTree bnTree = new BinaryTree(); // nouvel arbre binaire vide
bnTree.feedTree(tf); // initialisation de l'arbre avec la table de fréquence
bnTree.computeTree(); //génération de l'arbre binaire final à partir des données qu'il contient
HuffmanCode hfCode = new HuffmanCode();
hfCode = bnTree.createHuffman(); //génération d'un code huffman pour chaque byte différent des données d'entrée à l'aide de l'arbre binaire (stockage dans un dictionnaire)
hf.uncompressedData = hfCode.decompress(hf.compressedData, hf.sizeOfUncompressedData); /* décompression à l'aide du code de Huffman. Stockage dans la structure HuffmanData
* qui est échangée entre le plugin et le programme par le biais de MarshalByRefObject */
}
catch (Exception e)
{
return(false); // la décompression a échouée si une exception est levée
}
return(true);
}
static void Main(string[] args)
{
bool Compress(ref HuffmanData data)
{
FrequencyTable frequencyTable = new FrequencyTable();
foreach (byte b in data.uncompressedData)
{
frequencyTable.Add(b);
}
/*
* Creation of the forest which is represented by a list of binary trees.
* We add one BinaryTree containing one Node representing a symbol for all the different possible symbols (byte).
* Each Node will thus have its symbol (byte) and its frequency (int).
*/
Forest forest = new Forest();
foreach (byte b in frequencyTable.Keys)
{
forest.Add(new BinaryTree(new Node(b, frequencyTable[b])));
}
/*
* This function is in charge of getting a unique binary tree from all the binary trees contained in the Forest.
* While the Forest has several trees, we remove the two trees containing the two nodes that have the lowest frequency
* and assign them to the left and right nodes of the root of a new BinaryTree that we finally had to the forest.
*/
BinaryTree binaryTree = forest.GetUniqueTree();
/*
* This is where the Huffman codes are created.
* We go all through our BinaryTree thanks to the classic preorder method.
* When we reach a leaf, we add our code to dictionaries that we'll be using to compress our data.
* A code is represented by a List of Boolean.
*/
forest.Preorder(binaryTree.Root, new Code());
Dictionary <Byte, Code> codeTable = forest.getCodeTable();
Dictionary <Code, Byte> decodeTable = forest.getDecodeTable();
/*
* We get the number of bytes that are required to store our compressed data on a array of bytes.
* To do it, for each symbol (byte), we multiply its number of occurrences in our uncompressed data by the number of Boolean
* used to code the symbol. If the result is divisible by eight, we proceed to the division and this is our seeked number. If not,
* we add one extra byte.
*/
int GetRequiredBytesNumber()
{
int size = 0;
foreach (Byte b in frequencyTable.Keys)
{
size += frequencyTable[b] * codeTable[b].Count;
}
return(size % 8 == 0 ? size / 8 : size / 8 + 1);
}
int RequiredBytesNumber = GetRequiredBytesNumber();
/*
* Here starts the compressing.
*/
Byte[] compressedData = new Byte[RequiredBytesNumber];
/*
* First, for all our symbols in our uncompressed data, let's add their Huffman code to a List of Booleans.
* This List only contains the bits that are very significant according to our Huffman Code.
*/
List <bool> compressedDataBoolean = new List <bool>();
foreach (Byte b in data.uncompressedData)
{
compressedDataBoolean.AddRange(codeTable[b]);
}
int sizeDataUncompressed = compressedDataBoolean.Count;
/*
* This function will put all the booleans contained in our list, in bytes (eight by eight) to store them into
* an array of bytes.
*/
Byte[] BooleanListToByteArray(List <bool> boolList)
{
Byte[] _compressedData = new Byte[RequiredBytesNumber];
Byte curr = 0;
int i = 0, j = 0;
Boolean b;
while (boolList.Count > 0)
{
b = boolList.Last();
boolList.RemoveAt(boolList.Count - 1);
if (i == 0)
{
// If we iterate over a boolean that must be the first to be put in a byte ...
curr = new byte();
curr = 0x0;
}
if (b)
{
// If our boolean is true, then we need to put a one in our byte at the right position.
curr = (Byte)((1 << i) | curr);
}
else
{
// Same if it's false.
curr = (Byte)((0 << i) | curr);
}
if (i == 7)
{
// If we iterate over a boolean that will fill our byte, then we add the byte to our array.
_compressedData[j] = curr;
j++;
i = 0; // We get the 'byte pointer' ready for the next one.
}
else
{
i++;
}
}
// If we didn't get enough booleans to fill the final byte,
// we had the byte to the array (the other bits of the byte are zero by default).
if (i != 0)
{
_compressedData[j] = curr;
}
return(_compressedData);
}
compressedData = BooleanListToByteArray(compressedDataBoolean);
/*
* We need to pass our frequency table as a List<KeyValuePair<Byte, int>>.
* It's required by the struct used by the tool.
*/
List <KeyValuePair <Byte, int> > frequency = frequencyTable.ToList();
data.compressedData = compressedData;
data.frequency = frequency;
data.sizeOfUncompressedData = data.uncompressedData.Length;
return(true);
}
bool Decompress(ref HuffmanData data)
{
/*
* We start by receiving the frequency table already created and we create (again) our forest from it.
* We then re-generate the Huffman codes for each symbol.
*/
Forest forest2 = new Forest();
int necessarybytes = 0;
foreach (KeyValuePair <Byte, int> kvp in data.frequency)
{
forest2.Add(new BinaryTree(new Node(kvp.Key, kvp.Value)));
necessarybytes += kvp.Value;
}
BinaryTree binaryTree2 = forest2.GetUniqueTree();
forest2.Preorder(binaryTree2.Root, new Code());
/*
* To decompress our data, we must parse our compressed data. For an easier writing of the code, We will parse
* it from a BitArray, so that we don't have to jump from byte to byte.
*/
BitArray bitArray = new BitArray(data.compressedData);
/*
* In some cases, the first byte isn't full of significant bits for our Huffman encoding. It means, that the first
* zeros must be ignored (because they're only here to fill the byte). So we need to start parsing after the non-significant
* bits. In order to do that, we first retrieve the number of bits that are significant (sizeDataUncompressed).
* We just need to adjust our index (start) according to that number.
*/
int sizeDataUncompressed = 0;
foreach (KeyValuePair <Byte, int> kvp in data.frequency)
{
sizeDataUncompressed += kvp.Value * forest2.getCodeTable()[kvp.Key].Count;
}
int start = sizeDataUncompressed % 8 == 0 ? 0 : 8 - sizeDataUncompressed % 8;
/*
* This is the function that will parse the full stream of bits, and using the Huffman Table, will get each Symbol.
*/
forest2.ProduceByteArrayNew(bitArray, binaryTree2.Root, necessarybytes, start);
Byte[] finalArray = forest2.GetByteArray();
data.uncompressedData = finalArray;
return(true);
}
/*
* Actual Main instructions used to test the proper functioning of the program.
*/
byte[] testString = System.IO.File.ReadAllBytes(Path.GetDirectoryName(Process.GetCurrentProcess().MainModule.FileName) + @"\..\..\testString.txt");
HuffmanData mainData = new HuffmanData();
mainData.uncompressedData = testString;
Compress(ref mainData);
Decompress(ref mainData);
}