#include <processor.h>
Inheritance diagram for EMAN::BilateralProcessor:
Public Member Functions | |
void | process_inplace (EMData *image) |
To process an image in-place. | |
string | get_name () const |
Get the processor's name. | |
string | get_desc () const |
Get the descrition of this specific processor. | |
TypeDict | get_param_types () const |
Get processor parameter information in a dictionary. | |
Static Public Member Functions | |
static Processor * | NEW () |
Static Public Attributes | |
static const string | NAME = "bilateral" |
Bilateral processing does non-linear weighted averaging processing within a certain window.
distance_sigma | means how large the voxel has impact on its neighbors in spatial domain. The larger it is, the more blurry the resulting image. | |
value_sigma | eans how large the voxel has impact on its in range domain. The larger it is, the more blurry the resulting image. | |
niter | how many times to apply this processing on your data. | |
half_width | processing window size = (2 * half_widthh + 1) ^ 3. |
Definition at line 4033 of file processor.h.
string EMAN::BilateralProcessor::get_desc | ( | ) | const [inline, virtual] |
Get the descrition of this specific processor.
This function must be overwritten by a subclass.
Implements EMAN::Processor.
Definition at line 4042 of file processor.h.
04043 { 04044 return "Bilateral processing on 2D or 3D volume data. Bilateral processing does non-linear weighted averaging processing within a certain window. "; 04045 }
string EMAN::BilateralProcessor::get_name | ( | ) | const [inline, virtual] |
Get the processor's name.
Each processor is identified by a unique name.
Implements EMAN::Processor.
Definition at line 4037 of file processor.h.
References NAME.
04038 { 04039 return NAME; 04040 }
TypeDict EMAN::BilateralProcessor::get_param_types | ( | ) | const [inline, virtual] |
Get processor parameter information in a dictionary.
Each parameter has one record in the dictionary. Each record contains its name, data-type, and description.
Reimplemented from EMAN::Processor.
Definition at line 4052 of file processor.h.
References EMAN::EMObject::FLOAT, EMAN::EMObject::INT, and EMAN::TypeDict::put().
04053 { 04054 TypeDict d; 04055 d.put("distance_sigma", EMObject::FLOAT, "means how large the voxel has impact on its neighbors in spatial domain. The larger it is, the more blurry the resulting image."); 04056 d.put("value_sigma", EMObject::FLOAT, "means how large the voxel has impact on its in range domain. The larger it is, the more blurry the resulting image."); 04057 d.put("niter", EMObject::INT, "how many times to apply this processing on your data."); 04058 d.put("half_width", EMObject::INT, "processing window size = (2 * half_widthh + 1) ^ 3."); 04059 return d; 04060 }
static Processor* EMAN::BilateralProcessor::NEW | ( | ) | [inline, static] |
void BilateralProcessor::process_inplace | ( | EMData * | image | ) | [virtual] |
To process an image in-place.
For those processors which can only be processed out-of-place, override this function to just print out some error message to remind user call the out-of-place version.
image | The image to be processed. |
Implements EMAN::Processor.
Definition at line 3948 of file processor.cpp.
References EMAN::EMData::get_attr(), EMAN::EMData::get_data(), EMAN::EMData::get_xsize(), EMAN::EMData::get_ysize(), EMAN::EMData::get_zsize(), LOGWARN, EMAN::Processor::params, square, and EMAN::EMData::update().
03949 { 03950 if (!image) { 03951 LOGWARN("NULL Image"); 03952 return; 03953 } 03954 03955 float distance_sigma = params["distance_sigma"]; 03956 float value_sigma = params["value_sigma"]; 03957 int max_iter = params["niter"]; 03958 int half_width = params["half_width"]; 03959 03960 if (half_width < distance_sigma) { 03961 LOGWARN("localwidth(=%d) should be larger than distance_sigma=(%f)\n", 03962 half_width, distance_sigma); 03963 } 03964 03965 distance_sigma *= distance_sigma; 03966 03967 float image_sigma = image->get_attr("sigma"); 03968 if (image_sigma > value_sigma) { 03969 LOGWARN("image sigma(=%f) should be smaller than value_sigma=(%f)\n", 03970 image_sigma, value_sigma); 03971 } 03972 value_sigma *= value_sigma; 03973 03974 int nx = image->get_xsize(); 03975 int ny = image->get_ysize(); 03976 int nz = image->get_zsize(); 03977 03978 if(nz==1) { //for 2D image 03979 int width=nx, height=ny; 03980 03981 int i,j,m,n; 03982 03983 float tempfloat1,tempfloat2,tempfloat3; 03984 int index1,index2,index; 03985 int Iter; 03986 int tempint1,tempint3; 03987 03988 tempint1=width; 03989 tempint3=width+2*half_width; 03990 03991 float* mask=(float*)calloc((2*half_width+1)*(2*half_width+1),sizeof(float)); 03992 float* OrgImg=(float*)calloc((2*half_width+width)*(2*half_width+height),sizeof(float)); 03993 float* NewImg=image->get_data(); 03994 03995 for(m=-(half_width);m<=half_width;m++) 03996 for(n=-(half_width);n<=half_width;n++) { 03997 index=(m+half_width)*(2*half_width+1)+(n+half_width); 03998 mask[index]=exp((float)(-(m*m+n*n)/distance_sigma/2.0)); 03999 } 04000 04001 //printf("entering bilateral filtering process \n"); 04002 04003 Iter=0; 04004 while(Iter<max_iter) { 04005 for(i=0;i<height;i++) 04006 for(j=0;j<width;j++) { 04007 index1=(i+half_width)*tempint3+(j+half_width); 04008 index2=i*tempint1+j; 04009 OrgImg[index1]=NewImg[index2]; 04010 } 04011 04012 // Mirror Padding 04013 for(i=0;i<height;i++){ 04014 for(j=0;j<half_width;j++) OrgImg[(i+half_width)*tempint3+(j)]=OrgImg[(i+half_width)*tempint3+(2*half_width-j)]; 04015 for(j=0;j<half_width;j++) OrgImg[(i+half_width)*tempint3+(j+width+half_width)]=OrgImg[(i+half_width)*tempint3+(width+half_width-j-2)]; 04016 } 04017 for(i=0;i<half_width;i++){ 04018 for(j=0;j<(width+2*half_width);j++) OrgImg[i*tempint3+j]=OrgImg[(2*half_width-i)*tempint3+j]; 04019 for(j=0;j<(width+2*half_width);j++) OrgImg[(i+height+half_width)*tempint3+j]=OrgImg[(height+half_width-2-i)*tempint3+j]; 04020 } 04021 04022 //printf("finish mirror padding process \n"); 04023 //now mirror padding have been done 04024 04025 for(i=0;i<height;i++){ 04026 //printf("now processing the %d th row \n",i); 04027 for(j=0;j<width;j++){ 04028 tempfloat1=0.0; tempfloat2=0.0; 04029 for(m=-(half_width);m<=half_width;m++) 04030 for(n=-(half_width);n<=half_width;n++){ 04031 index =(m+half_width)*(2*half_width+1)+(n+half_width); 04032 index1=(i+half_width)*tempint3+(j+half_width); 04033 index2=(i+half_width+m)*tempint3+(j+half_width+n); 04034 tempfloat3=(OrgImg[index1]-OrgImg[index2])*(OrgImg[index1]-OrgImg[index2]); 04035 04036 tempfloat3=mask[index]*(1.0f/(1+tempfloat3/value_sigma)); // Lorentz kernel 04037 //tempfloat3=mask[index]*exp(tempfloat3/Sigma2/(-2.0)); // Guassian kernel 04038 tempfloat1+=tempfloat3; 04039 04040 tempfloat2+=tempfloat3*OrgImg[(i+half_width+m)*tempint3+(j+half_width+n)]; 04041 } 04042 NewImg[i*width+j]=tempfloat2/tempfloat1; 04043 } 04044 } 04045 Iter++; 04046 } 04047 04048 //printf("have finished %d th iteration\n ",Iter); 04049 // doneData(); 04050 free(mask); 04051 free(OrgImg); 04052 // end of BilaFilter routine 04053 04054 } 04055 else { //3D case 04056 int width = nx; 04057 int height = ny; 04058 int slicenum = nz; 04059 04060 int slice_size = width * height; 04061 int new_width = width + 2 * half_width; 04062 int new_slice_size = (width + 2 * half_width) * (height + 2 * half_width); 04063 04064 int width1 = 2 * half_width + 1; 04065 int mask_size = width1 * width1; 04066 int old_img_size = (2 * half_width + width) * (2 * half_width + height); 04067 04068 int zstart = -half_width; 04069 int zend = -half_width; 04070 int is_3d = 0; 04071 if (nz > 1) { 04072 mask_size *= width1; 04073 old_img_size *= (2 * half_width + slicenum); 04074 zend = half_width; 04075 is_3d = 1; 04076 } 04077 04078 float *mask = (float *) calloc(mask_size, sizeof(float)); 04079 float *old_img = (float *) calloc(old_img_size, sizeof(float)); 04080 04081 float *new_img = image->get_data(); 04082 04083 for (int p = zstart; p <= zend; p++) { 04084 int cur_p = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 04085 04086 for (int m = -half_width; m <= half_width; m++) { 04087 int cur_m = (m + half_width) * (2 * half_width + 1) + half_width; 04088 04089 for (int n = -half_width; n <= half_width; n++) { 04090 int l = cur_p + cur_m + n; 04091 mask[l] = exp((float) (-(m * m + n * n + p * p * is_3d) / distance_sigma / 2.0f)); 04092 } 04093 } 04094 } 04095 04096 int iter = 0; 04097 while (iter < max_iter) { 04098 for (int k = 0; k < slicenum; k++) { 04099 int cur_k1 = (k + half_width) * new_slice_size * is_3d; 04100 int cur_k2 = k * slice_size; 04101 04102 for (int i = 0; i < height; i++) { 04103 int cur_i1 = (i + half_width) * new_width; 04104 int cur_i2 = i * width; 04105 04106 for (int j = 0; j < width; j++) { 04107 int k1 = cur_k1 + cur_i1 + (j + half_width); 04108 int k2 = cur_k2 + cur_i2 + j; 04109 old_img[k1] = new_img[k2]; 04110 } 04111 } 04112 } 04113 04114 for (int k = 0; k < slicenum; k++) { 04115 int cur_k = (k + half_width) * new_slice_size * is_3d; 04116 04117 for (int i = 0; i < height; i++) { 04118 int cur_i = (i + half_width) * new_width; 04119 04120 for (int j = 0; j < half_width; j++) { 04121 int k1 = cur_k + cur_i + j; 04122 int k2 = cur_k + cur_i + (2 * half_width - j); 04123 old_img[k1] = old_img[k2]; 04124 } 04125 04126 for (int j = 0; j < half_width; j++) { 04127 int k1 = cur_k + cur_i + (width + half_width + j); 04128 int k2 = cur_k + cur_i + (width + half_width - j - 2); 04129 old_img[k1] = old_img[k2]; 04130 } 04131 } 04132 04133 04134 for (int i = 0; i < half_width; i++) { 04135 int i2 = i * new_width; 04136 int i3 = (2 * half_width - i) * new_width; 04137 for (int j = 0; j < (width + 2 * half_width); j++) { 04138 int k1 = cur_k + i2 + j; 04139 int k2 = cur_k + i3 + j; 04140 old_img[k1] = old_img[k2]; 04141 } 04142 04143 i2 = (height + half_width + i) * new_width; 04144 i3 = (height + half_width - 2 - i) * new_width; 04145 for (int j = 0; j < (width + 2 * half_width); j++) { 04146 int k1 = cur_k + i2 + j; 04147 int k2 = cur_k + i3 + j; 04148 old_img[k1] = old_img[k2]; 04149 } 04150 } 04151 } 04152 04153 size_t idx; 04154 for (int k = 0; k < slicenum; k++) { 04155 int cur_k = (k + half_width) * new_slice_size; 04156 04157 for (int i = 0; i < height; i++) { 04158 int cur_i = (i + half_width) * new_width; 04159 04160 for (int j = 0; j < width; j++) { 04161 float f1 = 0; 04162 float f2 = 0; 04163 int k1 = cur_k + cur_i + (j + half_width); 04164 04165 for (int p = zstart; p <= zend; p++) { 04166 int cur_p1 = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 04167 int cur_p2 = (k + half_width + p) * new_slice_size; 04168 04169 for (int m = -half_width; m <= half_width; m++) { 04170 int cur_m1 = (m + half_width) * (2 * half_width + 1); 04171 int cur_m2 = cur_p2 + cur_i + m * new_width + j + half_width; 04172 04173 for (int n = -half_width; n <= half_width; n++) { 04174 int k = cur_p1 + cur_m1 + (n + half_width); 04175 int k2 = cur_m2 + n; 04176 float f3 = Util::square(old_img[k1] - old_img[k2]); 04177 04178 f3 = mask[k] * (1.0f / (1 + f3 / value_sigma)); 04179 f1 += f3; 04180 int l1 = cur_m2 + n; 04181 f2 += f3 * old_img[l1]; 04182 } 04183 04184 idx = k * height * width + i * width + j; 04185 new_img[idx] = f2 / f1; 04186 } 04187 } 04188 } 04189 } 04190 } 04191 iter++; 04192 } 04193 if( mask ) { 04194 free(mask); 04195 mask = 0; 04196 } 04197 04198 if( old_img ) { 04199 free(old_img); 04200 old_img = 0; 04201 } 04202 } 04203 04204 image->update(); 04205 }
const string BilateralProcessor::NAME = "bilateral" [static] |