#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 | |
Processor * | NEW () |
Static Public Attributes | |
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 3993 of file processor.h.
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Get the descrition of this specific processor. This function must be overwritten by a subclass.
Implements EMAN::Processor. Definition at line 4002 of file processor.h. 04003 { 04004 return "Bilateral processing on 2D or 3D volume data. Bilateral processing does non-linear weighted averaging processing within a certain window. "; 04005 }
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Get the processor's name. Each processor is identified by a unique name.
Implements EMAN::Processor. Definition at line 3997 of file processor.h. 03998 {
03999 return NAME;
04000 }
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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 4012 of file processor.h. References EMAN::TypeDict::put(). 04013 { 04014 TypeDict d; 04015 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."); 04016 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."); 04017 d.put("niter", EMObject::INT, "how many times to apply this processing on your data."); 04018 d.put("half_width", EMObject::INT, "processing window size = (2 * half_widthh + 1) ^ 3."); 04019 return d; 04020 }
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Definition at line 4007 of file processor.h. 04008 { 04009 return new BilateralProcessor(); 04010 }
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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.
Implements EMAN::Processor. Definition at line 3810 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, nx, ny, square, and EMAN::EMData::update(). 03811 { 03812 if (!image) { 03813 LOGWARN("NULL Image"); 03814 return; 03815 } 03816 03817 float distance_sigma = params["distance_sigma"]; 03818 float value_sigma = params["value_sigma"]; 03819 int max_iter = params["niter"]; 03820 int half_width = params["half_width"]; 03821 03822 if (half_width < distance_sigma) { 03823 LOGWARN("localwidth(=%d) should be larger than distance_sigma=(%f)\n", 03824 half_width, distance_sigma); 03825 } 03826 03827 distance_sigma *= distance_sigma; 03828 03829 float image_sigma = image->get_attr("sigma"); 03830 if (image_sigma > value_sigma) { 03831 LOGWARN("image sigma(=%f) should be smaller than value_sigma=(%f)\n", 03832 image_sigma, value_sigma); 03833 } 03834 value_sigma *= value_sigma; 03835 03836 int nx = image->get_xsize(); 03837 int ny = image->get_ysize(); 03838 int nz = image->get_zsize(); 03839 03840 if(nz==1) { //for 2D image 03841 int width=nx, height=ny; 03842 03843 int i,j,m,n; 03844 03845 float tempfloat1,tempfloat2,tempfloat3; 03846 int index1,index2,index; 03847 int Iter; 03848 int tempint1,tempint3; 03849 03850 tempint1=width; 03851 tempint3=width+2*half_width; 03852 03853 float* mask=(float*)calloc((2*half_width+1)*(2*half_width+1),sizeof(float)); 03854 float* OrgImg=(float*)calloc((2*half_width+width)*(2*half_width+height),sizeof(float)); 03855 float* NewImg=image->get_data(); 03856 03857 for(m=-(half_width);m<=half_width;m++) 03858 for(n=-(half_width);n<=half_width;n++) { 03859 index=(m+half_width)*(2*half_width+1)+(n+half_width); 03860 mask[index]=exp((float)(-(m*m+n*n)/distance_sigma/2.0)); 03861 } 03862 03863 //printf("entering bilateral filtering process \n"); 03864 03865 Iter=0; 03866 while(Iter<max_iter) { 03867 for(i=0;i<height;i++) 03868 for(j=0;j<width;j++) { 03869 index1=(i+half_width)*tempint3+(j+half_width); 03870 index2=i*tempint1+j; 03871 OrgImg[index1]=NewImg[index2]; 03872 } 03873 03874 // Mirror Padding 03875 for(i=0;i<height;i++){ 03876 for(j=0;j<half_width;j++) OrgImg[(i+half_width)*tempint3+(j)]=OrgImg[(i+half_width)*tempint3+(2*half_width-j)]; 03877 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)]; 03878 } 03879 for(i=0;i<half_width;i++){ 03880 for(j=0;j<(width+2*half_width);j++) OrgImg[i*tempint3+j]=OrgImg[(2*half_width-i)*tempint3+j]; 03881 for(j=0;j<(width+2*half_width);j++) OrgImg[(i+height+half_width)*tempint3+j]=OrgImg[(height+half_width-2-i)*tempint3+j]; 03882 } 03883 03884 //printf("finish mirror padding process \n"); 03885 //now mirror padding have been done 03886 03887 for(i=0;i<height;i++){ 03888 //printf("now processing the %d th row \n",i); 03889 for(j=0;j<width;j++){ 03890 tempfloat1=0.0; tempfloat2=0.0; 03891 for(m=-(half_width);m<=half_width;m++) 03892 for(n=-(half_width);n<=half_width;n++){ 03893 index =(m+half_width)*(2*half_width+1)+(n+half_width); 03894 index1=(i+half_width)*tempint3+(j+half_width); 03895 index2=(i+half_width+m)*tempint3+(j+half_width+n); 03896 tempfloat3=(OrgImg[index1]-OrgImg[index2])*(OrgImg[index1]-OrgImg[index2]); 03897 03898 tempfloat3=mask[index]*(1.0f/(1+tempfloat3/value_sigma)); // Lorentz kernel 03899 //tempfloat3=mask[index]*exp(tempfloat3/Sigma2/(-2.0)); // Guassian kernel 03900 tempfloat1+=tempfloat3; 03901 03902 tempfloat2+=tempfloat3*OrgImg[(i+half_width+m)*tempint3+(j+half_width+n)]; 03903 } 03904 NewImg[i*width+j]=tempfloat2/tempfloat1; 03905 } 03906 } 03907 Iter++; 03908 } 03909 03910 //printf("have finished %d th iteration\n ",Iter); 03911 // doneData(); 03912 free(mask); 03913 free(OrgImg); 03914 // end of BilaFilter routine 03915 03916 } 03917 else { //3D case 03918 int width = nx; 03919 int height = ny; 03920 int slicenum = nz; 03921 03922 int slice_size = width * height; 03923 int new_width = width + 2 * half_width; 03924 int new_slice_size = (width + 2 * half_width) * (height + 2 * half_width); 03925 03926 int width1 = 2 * half_width + 1; 03927 int mask_size = width1 * width1; 03928 int old_img_size = (2 * half_width + width) * (2 * half_width + height); 03929 03930 int zstart = -half_width; 03931 int zend = -half_width; 03932 int is_3d = 0; 03933 if (nz > 1) { 03934 mask_size *= width1; 03935 old_img_size *= (2 * half_width + slicenum); 03936 zend = half_width; 03937 is_3d = 1; 03938 } 03939 03940 float *mask = (float *) calloc(mask_size, sizeof(float)); 03941 float *old_img = (float *) calloc(old_img_size, sizeof(float)); 03942 03943 float *new_img = image->get_data(); 03944 03945 for (int p = zstart; p <= zend; p++) { 03946 int cur_p = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 03947 03948 for (int m = -half_width; m <= half_width; m++) { 03949 int cur_m = (m + half_width) * (2 * half_width + 1) + half_width; 03950 03951 for (int n = -half_width; n <= half_width; n++) { 03952 int l = cur_p + cur_m + n; 03953 mask[l] = exp((float) (-(m * m + n * n + p * p * is_3d) / distance_sigma / 2.0f)); 03954 } 03955 } 03956 } 03957 03958 int iter = 0; 03959 while (iter < max_iter) { 03960 for (int k = 0; k < slicenum; k++) { 03961 int cur_k1 = (k + half_width) * new_slice_size * is_3d; 03962 int cur_k2 = k * slice_size; 03963 03964 for (int i = 0; i < height; i++) { 03965 int cur_i1 = (i + half_width) * new_width; 03966 int cur_i2 = i * width; 03967 03968 for (int j = 0; j < width; j++) { 03969 int k1 = cur_k1 + cur_i1 + (j + half_width); 03970 int k2 = cur_k2 + cur_i2 + j; 03971 old_img[k1] = new_img[k2]; 03972 } 03973 } 03974 } 03975 03976 for (int k = 0; k < slicenum; k++) { 03977 int cur_k = (k + half_width) * new_slice_size * is_3d; 03978 03979 for (int i = 0; i < height; i++) { 03980 int cur_i = (i + half_width) * new_width; 03981 03982 for (int j = 0; j < half_width; j++) { 03983 int k1 = cur_k + cur_i + j; 03984 int k2 = cur_k + cur_i + (2 * half_width - j); 03985 old_img[k1] = old_img[k2]; 03986 } 03987 03988 for (int j = 0; j < half_width; j++) { 03989 int k1 = cur_k + cur_i + (width + half_width + j); 03990 int k2 = cur_k + cur_i + (width + half_width - j - 2); 03991 old_img[k1] = old_img[k2]; 03992 } 03993 } 03994 03995 03996 for (int i = 0; i < half_width; i++) { 03997 int i2 = i * new_width; 03998 int i3 = (2 * half_width - i) * new_width; 03999 for (int j = 0; j < (width + 2 * half_width); j++) { 04000 int k1 = cur_k + i2 + j; 04001 int k2 = cur_k + i3 + j; 04002 old_img[k1] = old_img[k2]; 04003 } 04004 04005 i2 = (height + half_width + i) * new_width; 04006 i3 = (height + half_width - 2 - i) * new_width; 04007 for (int j = 0; j < (width + 2 * half_width); j++) { 04008 int k1 = cur_k + i2 + j; 04009 int k2 = cur_k + i3 + j; 04010 old_img[k1] = old_img[k2]; 04011 } 04012 } 04013 } 04014 04015 size_t idx; 04016 for (int k = 0; k < slicenum; k++) { 04017 int cur_k = (k + half_width) * new_slice_size; 04018 04019 for (int i = 0; i < height; i++) { 04020 int cur_i = (i + half_width) * new_width; 04021 04022 for (int j = 0; j < width; j++) { 04023 float f1 = 0; 04024 float f2 = 0; 04025 int k1 = cur_k + cur_i + (j + half_width); 04026 04027 for (int p = zstart; p <= zend; p++) { 04028 int cur_p1 = (p + half_width) * (2 * half_width + 1) * (2 * half_width + 1); 04029 int cur_p2 = (k + half_width + p) * new_slice_size; 04030 04031 for (int m = -half_width; m <= half_width; m++) { 04032 int cur_m1 = (m + half_width) * (2 * half_width + 1); 04033 int cur_m2 = cur_p2 + cur_i + m * new_width + j + half_width; 04034 04035 for (int n = -half_width; n <= half_width; n++) { 04036 int k = cur_p1 + cur_m1 + (n + half_width); 04037 int k2 = cur_m2 + n; 04038 float f3 = Util::square(old_img[k1] - old_img[k2]); 04039 04040 f3 = mask[k] * (1.0f / (1 + f3 / value_sigma)); 04041 f1 += f3; 04042 int l1 = cur_m2 + n; 04043 f2 += f3 * old_img[l1]; 04044 } 04045 04046 idx = k * height * width + i * width + j; 04047 new_img[idx] = f2 / f1; 04048 } 04049 } 04050 } 04051 } 04052 } 04053 iter++; 04054 } 04055 if( mask ) { 04056 free(mask); 04057 mask = 0; 04058 } 04059 04060 if( old_img ) { 04061 free(old_img); 04062 old_img = 0; 04063 } 04064 } 04065 04066 image->update(); 04067 }
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Definition at line 141 of file processor.cpp. |