#include <cmp.h>
Inheritance diagram for EMAN::SqEuclideanCmp:
Public Member Functions | |
SqEuclideanCmp () | |
float | cmp (EMData *image, EMData *with) const |
To compare 'image' with another image passed in through its parameters. | |
string | get_name () const |
Get the Cmp's name. | |
string | get_desc () const |
TypeDict | get_param_types () const |
Get Cmp parameter information in a dictionary. | |
Static Public Member Functions | |
Cmp * | NEW () |
Static Public Attributes | |
const string | NAME = "sqeuclidean" |
Definition at line 192 of file cmp.h.
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Definition at line 195 of file cmp.h. 00195 {}
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To compare 'image' with another image passed in through its parameters. An optional transformation may be used to transform the 2 images.
Implements EMAN::Cmp. Definition at line 151 of file cmp.cpp. References dm, EMAN::EMData::get_attr(), EMAN::EMData::get_const_data(), EMAN::EMData::get_xsize(), EMAN::EMData::get_ysize(), EMAN::EMData::get_zsize(), EMAN::EMData::has_attr(), EMAN::Dict::has_key(), EMAN::EMData::is_complex(), EMAN::EMData::is_fftodd(), nx, ny, EMAN::EMData::process(), EMAN::EMData::set_attr(), EMAN::Dict::set_default(), and EMAN::Cmp::validate_input_args(). 00152 { 00153 ENTERFUNC; 00154 EMData *with = withorig; 00155 validate_input_args(image, with); 00156 00157 int zeromask = params.set_default("zeromask",0); 00158 int normto = params.set_default("normto",0); 00159 00160 if (normto) { 00161 with = withorig->process("normalize.toimage",Dict("to",image)); 00162 with->set_attr("deleteme",1); 00163 if ((float)(with->get_attr("norm_mult"))<=0) { // This means the normalization inverted the image, a clear probablity of noise bias, so we undo the normalization 00164 delete with; 00165 with=withorig; 00166 } 00167 } 00168 00169 const float *const y_data = with->get_const_data(); 00170 const float *const x_data = image->get_const_data(); 00171 double result = 0.; 00172 float n = 0.0f; 00173 if(image->is_complex() && with->is_complex()) { 00174 // Implemented by PAP 01/09/06 - please do not change. If in doubts, write/call me. 00175 int nx = with->get_xsize(); 00176 int ny = with->get_ysize(); 00177 int nz = with->get_zsize(); 00178 nx = (nx - 2 + with->is_fftodd()); // nx is the real-space size of the input image 00179 int lsd2 = (nx + 2 - nx%2) ; // Extended x-dimension of the complex image 00180 00181 int ixb = 2*((nx+1)%2); 00182 int iyb = ny%2; 00183 // 00184 if(nz == 1) { 00185 // it looks like it could work in 3D, but it is not, really. 00186 for ( int iz = 0; iz <= nz-1; iz++) { 00187 double part = 0.; 00188 for ( int iy = 0; iy <= ny-1; iy++) { 00189 for ( int ix = 2; ix <= lsd2 - 1 - ixb; ix++) { 00190 size_t ii = ix + (iy + iz * ny)* lsd2; 00191 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00192 } 00193 } 00194 for ( int iy = 1; iy <= ny/2-1 + iyb; iy++) { 00195 size_t ii = (iy + iz * ny)* lsd2; 00196 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00197 part += (x_data[ii+1] - y_data[ii+1])*double(x_data[ii+1] - y_data[ii+1]); 00198 } 00199 if(nx%2 == 0) { 00200 for ( int iy = 1; iy <= ny/2-1 + iyb; iy++) { 00201 size_t ii = lsd2 - 2 + (iy + iz * ny)* lsd2; 00202 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00203 part += (x_data[ii+1] - y_data[ii+1])*double(x_data[ii+1] - y_data[ii+1]); 00204 } 00205 00206 } 00207 part *= 2; 00208 part += (x_data[0] - y_data[0])*double(x_data[0] - y_data[0]); 00209 if(ny%2 == 0) { 00210 int ii = (ny/2 + iz * ny)* lsd2; 00211 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00212 } 00213 if(nx%2 == 0) { 00214 int ii = lsd2 - 2 + (0 + iz * ny)* lsd2; 00215 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00216 if(ny%2 == 0) { 00217 int ii = lsd2 - 2 +(ny/2 + iz * ny)* lsd2; 00218 part += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00219 } 00220 } 00221 result += part; 00222 } 00223 n = (float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz; 00224 00225 }else{ //This 3D code is incorrect, but it is the best I can do now 01/09/06 PAP 00226 int ky, kz; 00227 int ny2 = ny/2; int nz2 = nz/2; 00228 for ( int iz = 0; iz <= nz-1; iz++) { 00229 if(iz>nz2) kz=iz-nz; else kz=iz; 00230 for ( int iy = 0; iy <= ny-1; iy++) { 00231 if(iy>ny2) ky=iy-ny; else ky=iy; 00232 for ( int ix = 0; ix <= lsd2-1; ix++) { 00233 // Skip Friedel related values 00234 if(ix>0 || (kz>=0 && (ky>=0 || kz!=0))) { 00235 size_t ii = ix + (iy + iz * ny)* lsd2; 00236 result += (x_data[ii] - y_data[ii])*double(x_data[ii] - y_data[ii]); 00237 } 00238 } 00239 } 00240 } 00241 n = ((float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz)/2.0f; 00242 } 00243 } else { // real space 00244 size_t totsize = image->get_xsize()*image->get_ysize()*image->get_zsize(); 00245 if (params.has_key("mask")) { 00246 EMData* mask; 00247 mask = params["mask"]; 00248 const float *const dm = mask->get_const_data(); 00249 for (size_t i = 0; i < totsize; i++) { 00250 if (dm[i] > 0.5) { 00251 double temp = x_data[i]- y_data[i]; 00252 result += temp*temp; 00253 n++; 00254 } 00255 } 00256 } 00257 else if (zeromask) { 00258 n=0; 00259 for (size_t i = 0; i < totsize; i++) { 00260 if (x_data[i]==0 || y_data[i]==0) continue; 00261 double temp = x_data[i]- y_data[i]; 00262 result += temp*temp; 00263 n++; 00264 } 00265 00266 } 00267 else { 00268 for (size_t i = 0; i < totsize; i++) { 00269 double temp = x_data[i]- y_data[i]; 00270 result += temp*temp; 00271 } 00272 n = (float)totsize; 00273 } 00274 } 00275 result/=n; 00276 00277 EXITFUNC; 00278 if (with->has_attr("deleteme")) delete with; 00279 return static_cast<float>(result); 00280 }
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Implements EMAN::Cmp. Definition at line 204 of file cmp.h. 00205 { 00206 return "Squared Euclidean distance (sum(a - b)^2)/n."; 00207 }
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Get the Cmp's name. Each Cmp is identified by a unique name.
Implements EMAN::Cmp. Definition at line 199 of file cmp.h. 00200 {
00201 return NAME;
00202 }
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Get Cmp parameter information in a dictionary. Each parameter has one record in the dictionary. Each record contains its name, data-type, and description.
Implements EMAN::Cmp. Definition at line 214 of file cmp.h. References EMAN::TypeDict::put(). 00215 { 00216 TypeDict d; 00217 d.put("mask", EMObject::EMDATA, "image mask"); 00218 d.put("zeromask", EMObject::INT, "If set, zero pixels in either image will be excluded from the statistics"); 00219 d.put("normto",EMObject::INT,"If set, 'with' is normalized to 'this' before computing the distance"); 00220 return d; 00221 }
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Definition at line 209 of file cmp.h. 00210 { 00211 return new SqEuclideanCmp(); 00212 }
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