wavelet.c

包含了多个matlab编程在图像中加入水印的处理代码

	  	free_image(tempi);
	    	  
		if (i!=(level-1))
		{
	    		tempi=new_image(width,height);
	    		copy_into_image(tempi,coarsei,0,0);
	    		free_image(coarsei);
	    		/*if i=level coarsei is inserted into the image tree
	    		  so we should not free coarsei on level-1*/
	 	}
	 	
		temp_tree=temp_tree->coarse;
		
  	}

 	temp_tree->image=coarsei;
	return ret_tree;

	error:
        	err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));	
	  	return NULL;		
}

static Image_tree wavelettransform__wp(Image original, int current_level, int level, FilterGH *flt, enum FilterMethod method)
{	
  int i, width, height, min, max_level, e;
  Image coarse_image,horizontal_image,vertical_image,diagonal_image,temp_image;
  Image_tree return_tree, temp_tree;
			
  width = original->width;
  height = original->height;
	
  temp_image = new_image(width, height);
  if (!temp_image) goto error;
	
  copy_into_image(temp_image, original, 0, 0);
        	
  temp_tree = return_tree = new_image_tree();
  if (!return_tree) goto error;

  temp_tree->level = current_level;
	
  min = original->width;
  if (original->height < min) min = original->height;
  max_level = log(min) / log(2) - 2;
  if (max_level < level) level = max_level;

  if (current_level >= level) {
    return_tree->image = temp_image;
    return return_tree;
  }

  for (i = current_level; i < level; i++) {
    width = (width + 1) / 2;
    height = (height + 1) / 2;
	
    coarse_image = new_image(width, height);
    horizontal_image = new_image(width, height);
    vertical_image = new_image(width, height);
    diagonal_image = new_image(width, height);

    if (!coarse_image || !horizontal_image || 
        !vertical_image || !diagonal_image) goto error;

    e = decomposition(temp_image, coarse_image, horizontal_image, 
                                  vertical_image, diagonal_image,
	                          flt[i]->g, flt[i]->h, method);
    if (!e) return NULL;	                       
		                       
    temp_tree->coarse = new_image_tree();
    temp_tree->coarse->level = i+1;
    temp_tree->horizontal = wavelettransform__wp(horizontal_image, i+1, level, flt, method);
    temp_tree->vertical = wavelettransform__wp(vertical_image, i+1, level, flt, method);
    temp_tree->diagonal = wavelettransform__wp(diagonal_image, i+1, level, flt, method);
		
    free_image(horizontal_image);
    free_image(vertical_image);
    free_image(diagonal_image);
    free_image(temp_image);
	    	  
    if (i != (level - 1)) {
      temp_image = new_image(width, height);
      copy_into_image(temp_image, coarse_image, 0, 0);
      free_image(coarse_image);
    }
	 	
    temp_tree = temp_tree->coarse;
  }

 temp_tree->image = coarse_image;
 return return_tree;

  error:
    err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));	
    return NULL;		
}

extern Image_tree wavelettransform_wp(Image original, int level, FilterGH *flt, enum FilterMethod method) {
  wavelettransform__wp(original, 0, level, flt, method);
}


/************************************************************************/
/*	Functionname: best_basis					*/
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*	    	original: Image to be transformed			*/
/*		level: best basis selection down to this level		*/
/*		flt: transform with filters flt[0..level]		*/
/*		method: transform with filter method			*/
/*		cost: carry best basis selection out with this costfunc */
/*		epsilon: limit for threshold method			*/
/* -------------------------------------------------------------------- */
/* 	Description: carries best basis and near best basis selection	*/
/*			out						*/
/************************************************************************/
extern Image_tree best_basis(Image original,int level,FilterGH *flt,
				enum FilterMethod method,enum Information_Cost cost,double epsilon)

{       Image_tree tree;
	Image img;
	int min,max_level,e;

	tree=new_image_tree();
	if(!tree) goto error;
	
	img=new_image(original->width,original->height);
	if(!img) goto error;
	
        copy_into_image(img,original,0,0);
        
        tree->image=img;	

	min=original->width;
	if (original->height<min) min=original->height;
	max_level=log10((float) min/best_basis_min)/log10(2);
	if (max_level>level) max_level=level;
	
	e=compute_best(tree,0,max_level,flt,method,cost,epsilon);

	if (!tree->image) free(img);

        return tree;

	error:
        	err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));	
	  	return NULL;	        

}
/************************************************************************/
/*	Functionname: best_level_selection				*/
/* -------------------------------------------------------------------- */
/*	Parameter: 							*/
/*		original: Image that should be transformed  		*/
/*	        maxlevel: transform down to level                 	*/
/*		flt: transform with filters flt[0..level]		*/
/*		method: transform with filter method			*/
/*		cost: carry best basis selection out with this costfunc */
/*		epsilon: limit for threshold method			*/
/* -------------------------------------------------------------------- */
/* 	Description: Carries out the best level selection		*/
/*	                           		           		*/
/************************************************************************/
extern Image_tree best_level(Image original,int maxlevel,int *bestlevel,FilterGH *flt,enum FilterMethod method,
				enum Information_Cost cost,double epsilon)
{	Image_tree tree;
	Image img;
	double *entropies,min;
	int best=0,i,e;

	img=new_image(original->width,original->height);
       	copy_into_image(img,original,0,0);
	
	tree=new_image_tree();
	tree->image=img;
	
	entropies=(double *)calloc(maxlevel+1,sizeof(double));
	if(!entropies) goto error;

	/* decompose down to maxlevel */
	e=decompose_all(tree,maxlevel,flt,method,cost,epsilon);
	if (!e) return NULL;

	/* compute costs of each level and store it in entropies array*/
	compute_levels(tree,entropies,cost,epsilon);

	min=entropies[0];
	for (i=1;i<=maxlevel;i++)
	{
		if (entropies[i]<min)
		{
			min=entropies[i];
			best=i;
		}
	}

	/* free all other levels */
	free_levels(tree,best);

	*bestlevel=best;
	
	return tree;

	error:
        	err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));	
	  	return NULL;	
}

/************************************************************************/
/*	Functionname: compute_best					*/
/* -------------------------------------------------------------------- */
/*	Parameter: 							*/
/*		img: Image that should be transformed	 		*/
/*	        level: transform level	                		*/
/*		max_level: transform to maximum level			*/
/*		flt: transform with filters flt[0..level]		*/
/*		method: transform with filter method			*/
/*		cost: carry best basis selection out with this costfunc */
/*		epsilon: limit for threshold method			*/
/* -------------------------------------------------------------------- */
/* 	Description: Carries out the best basis selection (recursivly)  */
/*	             (is needed by the waveletpacket procedure)		*/
/************************************************************************/
static int compute_best(Image_tree tree,int level,int max_level,FilterGH *flt,
                        enum FilterMethod method,enum Information_Cost cost,double epsilon)

{ 	Image coarse,horizontal,vertical,diagonal;
        int e,width,height;
	double sum;
	
	tree->level=level;
		
	/* non additive cost function*/
	if (cost>=shanon) 
	{
		tree->entropy=compute_non_additive(tree,tree->image->size,cost,epsilon,0);
	}
	/*additive cost function*/
	else 	tree->entropy=compute_entropy(tree->image,cost,epsilon);
	
	if (level<max_level) {
        	width=(tree->image->width+1)/2;
        	height=(tree->image->height+1)/2;
          
	  	tree->coarse=new_image_tree();
	  	tree->horizontal=new_image_tree();
		tree->vertical=new_image_tree();
	  	tree->diagonal=new_image_tree();

          	coarse=new_image(width,height);	
          	horizontal=new_image(width,height);	
          	vertical=new_image(width,height);
          	diagonal=new_image(width,height);
		if(!coarse||!vertical||!horizontal||!diagonal) goto error;          	

	        e=decomposition(tree->image,coarse,horizontal,vertical,diagonal,flt[0]->g,flt[0]->h,method);
	        if (!e) return 0;

	        tree->coarse->image=coarse;
	        tree->horizontal->image=horizontal;
	        tree->vertical->image=vertical;
	        tree->diagonal->image=diagonal;
	        
          	e=compute_best(tree->coarse,level+1,max_level,flt+1,method,cost,epsilon);
          	e=compute_best(tree->horizontal,level+1,max_level,flt+1,method,cost,epsilon);
          	e=compute_best(tree->vertical,level+1,max_level,flt+1,method,cost,epsilon);
          	e=compute_best(tree->diagonal,level+1,max_level,flt+1,method,cost,epsilon);
          	if (!e) return 0;
	  
		/*going back in recursion*/

		if (cost>=shanon) {
			 sum=compute_non_additive(tree,tree->image->size,cost,epsilon,1);
		}
		else sum=(tree->coarse->entropy)+(tree->horizontal->entropy)
			+(tree->vertical->entropy)+(tree->diagonal->entropy);
			
	  	if (tree->entropy>sum)
	  	{
	  		tree->entropy=sum;	     		
	      		free_image(tree->image);	/* take down tree */
	      		tree->image=NULL;
	      		
	  	}
	  	else
	  	{   				/* delete the tree downwards */
         		free_image_tree(tree->coarse);
              		free_image_tree(tree->horizontal);
              		free_image_tree(tree->vertical);
              		free_image_tree(tree->diagonal);                			
              			
              		tree->coarse=tree->vertical=tree->horizontal=tree->diagonal=NULL;	    		
	  	}
	}

	return 1;

	error:
        	err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));
        	return 0;
	  		
}

/************************************************************************/
/*	Functionname:		decompose_all                         */
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*            	tree: Image tree to be decomposed			*/
/*           	maxlevel: decompose down to level	                */
/*		flt: transform with filters flt[0..maxlevel]		*/
/*		method: transform with filter method			*/
/*		cost: cost function for entropy computing		*/
/*		epsilon: limit for threshold method			*/
/* -------------------------------------------------------------------- */
/* 	Description: whole decompositing down to maxlevel          	*/
/*		The original image must be in tree->image		*/
/************************************************************************/
extern int decompose_all(Image_tree tree,int maxlevel,FilterGH *flt,enum FilterMethod method,
				enum Information_Cost cost,double epsilon)
{	
	Image original,coarse,horizontal,vertical,diagonal;
	int e,width,height,level;
	
	if (tree->level<maxlevel)
	{
		tree->coarse=new_image_tree();
		tree->horizontal=new_image_tree();
		tree->vertical=new_image_tree();
		tree->diagonal=new_image_tree();

		original=tree->image;
		width=(original->width+1)/2;
		height=(original->height+1)/2;
		level=tree->level;
		
		coarse=new_image(width,height);
		horizontal=new_image(width,height);
		vertical=new_image(width,height);
		diagonal=new_image(width,height);
		if(!coarse||!vertical||!horizontal||!diagonal) goto error;          	
		
		
		e=decomposition(tree->image,coarse,horizontal,vertical,diagonal,flt[0]->g,flt[0]->h,method);
		if (!e) return 0;

		tree->coarse->image=coarse;
		tree->horizontal->image=horizontal;
		tree->vertical->image=vertical;
		tree->diagonal->image=diagonal;

		tree->coarse->entropy=compute_entropy(coarse,cost,epsilon);
		tree->horizontal->entropy=compute_entropy(horizontal,cost,epsilon);
		tree->vertical->entropy=compute_entropy(vertical,cost,epsilon);
		tree->diagonal->entropy=compute_entropy(diagonal,cost,epsilon);
		
		tree->coarse->level=tree->horizontal->level=
			tree->vertical->level=tree->diagonal->level=level+1;

		e=decompose_all(tree->coarse,maxlevel,flt+1,method,cost,epsilon);
		e=decompose_all(tree->horizontal,maxlevel,flt+1,method,cost,epsilon);
		e=decompose_all(tree->vertical,maxlevel,flt+1,method,cost,epsilon);
		e=decompose_all(tree->diagonal,maxlevel,flt+1,method,cost,epsilon);
		if (!e) return 0;
		
	}

	return 1;
	
	error:
        	err_SimpleMessage(err_GetErrorMessage(Error_NotEnoughMemory));	
		return 0;	  		
}

/************************************************************************/
/*	Functionname:		compute_levels                          */
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*            	tree: Image tree where the entropy should be computed	*/
/*           	entropies : array for entropy          	                */
/*		cost: carry best basis selection out with this costfunc */
/*		epsilon: limit for threshold method			*/
/* -------------------------------------------------------------------- */
/* 	Description: Compute the entropies of all decomposition	levels	*/
/************************************************************************/
static compute_levels(Image_tree tree,double *entropies,enum Information_Cost cost,double epsilon)
{	
	if (tree->image){
		entropies[tree->level]+=compute_entropy(tree->image,cost,epsilon);
	}
	if (tree->coarse) compute_levels(tree->coarse,entropies,cost,epsilon);
	if (tree->horizontal) compute_levels(tree->horizontal,entropies,cost,epsilon);
	if (tree->vertical) compute_levels(tree->vertical,entropies,cost,epsilon);
	if (tree->diagonal) compute_levels(tree->diagonal,entropies,cost,epsilon);

}

/************************************************************************/
/*	Functionname:		free_levels	                        */
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*            	tree: Image tree which should be cleaned		*/
/*           	best: best level	         	                */
/* -------------------------------------------------------------------- */
/* 	Description: clean the image tree except the best level      	*/
/************************************************************************/
static free_levels(Image_tree tree,int best)
{
	if (tree->level<best)
	{
		free_image(tree->image);
		tree->image=NULL;
		free_levels(tree->coarse,best);
		free_levels(tree->horizontal,best);
		free_levels(tree->vertical,best);
		free_levels(tree->diagonal,best);
	}
	else
	{
		if (tree->coarse)
		{
			free_image_tree(tree->coarse);
			free_image_tree(tree->horizontal);
			free_image_tree(tree->vertical);
			free_image_tree(tree->diagonal);
			tree->coarse=tree->horizontal=tree->vertical=tree->diagonal=NULL;
		}
	}
}
		
/************************************************************************/
/*	Functionname:		decompose_to_level                       */
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*            	original: original image		 		*/
/*           	level: decompose to level	 	                */
/*		flt: decompos with filters[0..level]                   	*/
/*		method: transform with filter method			*/
/* -------------------------------------------------------------------- */
/* 	Description: Decomposes an image to an certain level and stores	*/
/*	     only this level in the returned quadtree      		*/
/************************************************************************/
extern Image_tree decompose_to_level(Image original,int level,FilterGH *flt,enum FilterMethod method)
{	Image_tree tree;
	int e;
	
	tree=new_image_tree();
	tree->image=original;
	
	e=decompose_all(tree,level,flt,method,entropy,1);
	if (!e) return NULL;
	
	free_levels(tree,level);
	
	return tree;

}
	
/************************************************************************/
/*	Functionname:		decomposition                           */
/* -------------------------------------------------------------------- */
/*	Parameter:							*/
/*            	t_img: Image which should be decomposed  		*/
/*           	coarse,horizontal,vertical,diagonal: 	                */
/*			decomposed images                             	*/
/*		method: transform with filter method			*/
/*            	g,h: the transformation is carried out with these filters*/
/* -------------------------------------------------------------------- */
/* 	Description: This carries out one wavelettransformation		*/
/*	     using waveletfilters.            				*/
/************************************************************************/

static int decomposition(Image t_img,Image coarse,Image horizontal,Image vertical,
                               Image diagonal,Filter g,Filter h,enum FilterMethod method)
{ 	Image temp1;
	
 	/*coarse*/	
	temp1=new_image(coarse->width,t_img->height);
	if(!temp1) goto error;          	
        convolute_lines(temp1,t_img,h,method);