#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 = "filter.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 3996 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 4005 of file processor.h.
04006 { 04007 return "Bilateral processing on 2D or 3D volume data. Bilateral processing does non-linear weighted averaging processing within a certain window. "; 04008 }
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 4000 of file processor.h.
References NAME.
04001 { 04002 return NAME; 04003 }
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 4015 of file processor.h.
References EMAN::EMObject::FLOAT, EMAN::EMObject::INT, and EMAN::TypeDict::put().
04016 { 04017 TypeDict d; 04018 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."); 04019 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."); 04020 d.put("niter", EMObject::INT, "how many times to apply this processing on your data."); 04021 d.put("half_width", EMObject::INT, "processing window size = (2 * half_widthh + 1) ^ 3."); 04022 return d; 04023 }
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 3895 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().
03896 { 03897 if (!image) { 03898 LOGWARN("NULL Image"); 03899 return; 03900 } 03901 03902 float distance_sigma = params["distance_sigma"]; 03903 float value_sigma = params["value_sigma"]; 03904 int max_iter = params["niter"]; 03905 int half_width = params["half_width"]; 03906 03907 if (half_width < distance_sigma) { 03908 LOGWARN("localwidth(=%d) should be larger than distance_sigma=(%f)\n", 03909 half_width, distance_sigma); 03910 } 03911 03912 distance_sigma *= distance_sigma; 03913 03914 float image_sigma = image->get_attr("sigma"); 03915 if (image_sigma > value_sigma) { 03916 LOGWARN("image sigma(=%f) should be smaller than value_sigma=(%f)\n", 03917 image_sigma, value_sigma); 03918 } 03919 value_sigma *= value_sigma; 03920 03921 int nx = image->get_xsize(); 03922 int ny = image->get_ysize(); 03923 int nz = image->get_zsize(); 03924 03925 if(nz==1) { //for 2D image 03926 int width=nx, height=ny; 03927 03928 int i,j,m,n; 03929 03930 float tempfloat1,tempfloat2,tempfloat3; 03931 int index1,index2,index; 03932 int Iter; 03933 int tempint1,tempint3; 03934 03935 tempint1=width; 03936 tempint3=width+2*half_width; 03937 03938 float* mask=(float*)calloc((2*half_width+1)*(2*half_width+1),sizeof(float)); 03939 float* OrgImg=(float*)calloc((2*half_width+width)*(2*half_width+height),sizeof(float)); 03940 float* NewImg=image->get_data(); 03941 03942 for(m=-(half_width);m<=half_width;m++) 03943 for(n=-(half_width);n<=half_width;n++) { 03944 index=(m+half_width)*(2*half_width+1)+(n+half_width); 03945 mask[index]=exp((float)(-(m*m+n*n)/distance_sigma/2.0)); 03946 } 03947 03948 //printf("entering bilateral filtering process \n"); 03949 03950 Iter=0; 03951 while(Iter<max_iter) { 03952 for(i=0;i<height;i++) 03953 for(j=0;j<width;j++) { 03954 index1=(i+half_width)*tempint3+(j+half_width); 03955 index2=i*tempint1+j; 03956 OrgImg[index1]=NewImg[index2]; 03957 } 03958 03959 // Mirror Padding 03960 for(i=0;i<height;i++){ 03961 for(j=0;j<half_width;j++) OrgImg[(i+half_width)*tempint3+(j)]=OrgImg[(i+half_width)*tempint3+(2*half_width-j)]; 03962 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)]; 03963 } 03964 for(i=0;i<half_width;i++){ 03965 for(j=0;j<(width+2*half_width);j++) OrgImg[i*tempint3+j]=OrgImg[(2*half_width-i)*tempint3+j]; 03966 for(j=0;j<(width+2*half_width);j++) OrgImg[(i+height+half_width)*tempint3+j]=OrgImg[(height+half_width-2-i)*tempint3+j]; 03967 } 03968 03969 //printf("finish mirror padding process \n"); 03970 //now mirror padding have been done 03971 03972 for(i=0;i<height;i++){ 03973 //printf("now processing the %d th row \n",i); 03974 for(j=0;j<width;j++){ 03975 tempfloat1=0.0; tempfloat2=0.0; 03976 for(m=-(half_width);m<=half_width;m++) 03977 for(n=-(half_width);n<=half_width;n++){ 03978 index =(m+half_width)*(2*half_width+1)+(n+half_width); 03979 index1=(i+half_width)*tempint3+(j+half_width); 03980 index2=(i+half_width+m)*tempint3+(j+half_width+n); 03981 tempfloat3=(OrgImg[index1]-OrgImg[index2])*(OrgImg[index1]-OrgImg[index2]); 03982 03983 tempfloat3=mask[index]*(1.0f/(1+tempfloat3/value_sigma)); // Lorentz kernel 03984 //tempfloat3=mask[index]*exp(tempfloat3/Sigma2/(-2.0)); // Guassian kernel 03985 tempfloat1+=tempfloat3; 03986 03987 tempfloat2+=tempfloat3*OrgImg[(i+half_width+m)*tempint3+(j+half_width+n)]; 03988 } 03989 NewImg[i*width+j]=tempfloat2/tempfloat1; 03990 } 03991 } 03992 Iter++; 03993 } 03994 03995 //printf("have finished %d th iteration\n ",Iter); 03996 // doneData(); 03997 free(mask); 03998 free(OrgImg); 03999 // end of BilaFilter routine 04000 04001 } 04002 else { //3D case 04003 int width = nx; 04004 int height = ny; 04005 int slicenum = nz; 04006 04007 int slice_size = width * height; 04008 int new_width = width + 2 * half_width; 04009 int new_slice_size = (width + 2 * half_width) * (height + 2 * half_width); 04010 04011 int width1 = 2 * half_width + 1; 04012 int mask_size = width1 * width1; 04013 int old_img_size = (2 * half_width + width) * (2 * half_width + height); 04014 04015 int zstart = -half_width; 04016 int zend = -half_width; 04017 int is_3d = 0; 04018 if (nz > 1) { 04019 mask_size *= width1; 04020 old_img_size *= (2 * half_width + slicenum); 04021 zend = half_width; 04022 is_3d = 1; 04023 } 04024 04025 float *mask = (float *) calloc(mask_size, sizeof(float)); 04026 float *old_img = (float *) calloc(old_img_size, sizeof(float)); 04027 04028 float *new_img = image->get_data(); 04029 04030 for (int p = zstart; p <= zend; p++) { 04031 int cur_p = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 04032 04033 for (int m = -half_width; m <= half_width; m++) { 04034 int cur_m = (m + half_width) * (2 * half_width + 1) + half_width; 04035 04036 for (int n = -half_width; n <= half_width; n++) { 04037 int l = cur_p + cur_m + n; 04038 mask[l] = exp((float) (-(m * m + n * n + p * p * is_3d) / distance_sigma / 2.0f)); 04039 } 04040 } 04041 } 04042 04043 int iter = 0; 04044 while (iter < max_iter) { 04045 for (int k = 0; k < slicenum; k++) { 04046 size_t cur_k1 = (size_t)(k + half_width) * new_slice_size * is_3d; 04047 int cur_k2 = k * slice_size; 04048 04049 for (int i = 0; i < height; i++) { 04050 int cur_i1 = (i + half_width) * new_width; 04051 int cur_i2 = i * width; 04052 04053 for (int j = 0; j < width; j++) { 04054 size_t k1 = cur_k1 + cur_i1 + (j + half_width); 04055 int k2 = cur_k2 + cur_i2 + j; 04056 old_img[k1] = new_img[k2]; 04057 } 04058 } 04059 } 04060 04061 for (int k = 0; k < slicenum; k++) { 04062 size_t cur_k = (k + half_width) * new_slice_size * is_3d; 04063 04064 for (int i = 0; i < height; i++) { 04065 int cur_i = (i + half_width) * new_width; 04066 04067 for (int j = 0; j < half_width; j++) { 04068 size_t k1 = cur_k + cur_i + j; 04069 size_t k2 = cur_k + cur_i + (2 * half_width - j); 04070 old_img[k1] = old_img[k2]; 04071 } 04072 04073 for (int j = 0; j < half_width; j++) { 04074 size_t k1 = cur_k + cur_i + (width + half_width + j); 04075 size_t k2 = cur_k + cur_i + (width + half_width - j - 2); 04076 old_img[k1] = old_img[k2]; 04077 } 04078 } 04079 04080 04081 for (int i = 0; i < half_width; i++) { 04082 int i2 = i * new_width; 04083 int i3 = (2 * half_width - i) * new_width; 04084 for (int j = 0; j < (width + 2 * half_width); j++) { 04085 size_t k1 = cur_k + i2 + j; 04086 size_t k2 = cur_k + i3 + j; 04087 old_img[k1] = old_img[k2]; 04088 } 04089 04090 i2 = (height + half_width + i) * new_width; 04091 i3 = (height + half_width - 2 - i) * new_width; 04092 for (int j = 0; j < (width + 2 * half_width); j++) { 04093 size_t k1 = cur_k + i2 + j; 04094 size_t k2 = cur_k + i3 + j; 04095 old_img[k1] = old_img[k2]; 04096 } 04097 } 04098 } 04099 04100 size_t idx; 04101 for (int k = 0; k < slicenum; k++) { 04102 size_t cur_k = (k + half_width) * new_slice_size; 04103 04104 for (int i = 0; i < height; i++) { 04105 int cur_i = (i + half_width) * new_width; 04106 04107 for (int j = 0; j < width; j++) { 04108 float f1 = 0; 04109 float f2 = 0; 04110 size_t k1 = cur_k + cur_i + (j + half_width); 04111 04112 for (int p = zstart; p <= zend; p++) { 04113 size_t cur_p1 = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 04114 size_t cur_p2 = (k + half_width + p) * new_slice_size; 04115 04116 for (int m = -half_width; m <= half_width; m++) { 04117 size_t cur_m1 = (m + half_width) * (2 * half_width + 1); 04118 size_t cur_m2 = cur_p2 + cur_i + m * new_width + j + half_width; 04119 04120 for (int n = -half_width; n <= half_width; n++) { 04121 size_t k = cur_p1 + cur_m1 + (n + half_width); 04122 size_t k2 = cur_m2 + n; 04123 float f3 = Util::square(old_img[k1] - old_img[k2]); 04124 04125 f3 = mask[k] * (1.0f / (1 + f3 / value_sigma)); 04126 f1 += f3; 04127 size_t l1 = cur_m2 + n; 04128 f2 += f3 * old_img[l1]; 04129 } 04130 04131 idx = (size_t)k * height * width + i * width + j; 04132 new_img[idx] = f2 / f1; 04133 } 04134 } 04135 } 04136 } 04137 } 04138 iter++; 04139 } 04140 if( mask ) { 04141 free(mask); 04142 mask = 0; 04143 } 04144 04145 if( old_img ) { 04146 free(old_img); 04147 old_img = 0; 04148 } 04149 } 04150 04151 image->update(); 04152 }
const string BilateralProcessor::NAME = "filter.bilateral" [static] |