#include <cmp.h>
Inheritance diagram for EMAN::DotCmp:
Public Member Functions | |
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 = "dot" |
// Added mask option PAP 04/23/06 For complex images, it does not check r/i vs a/p.
negative | Returns -1 * dot product, default true | |
normalize | Returns normalized dot product -1.0 - 1.0 |
Definition at line 234 of file cmp.h.
|
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 285 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::Dict::has_key(), EMAN::EMData::is_complex(), EMAN::EMData::is_fftodd(), nx, ny, EMAN::Dict::set_default(), sqrt(), and EMAN::Cmp::validate_input_args(). Referenced by EMAN::EMData::dot(). 00286 { 00287 ENTERFUNC; 00288 validate_input_args(image, with); 00289 00290 const float *const x_data = image->get_const_data(); 00291 const float *const y_data = with->get_const_data(); 00292 00293 int normalize = params.set_default("normalize", 0); 00294 float negative = (float)params.set_default("negative", 1); 00295 00296 if (negative) negative=-1.0; else negative=1.0; 00297 double result = 0.; 00298 long n = 0; 00299 if(image->is_complex() && with->is_complex()) { 00300 // Implemented by PAP 01/09/06 - please do not change. If in doubts, write/call me. 00301 int nx = with->get_xsize(); 00302 int ny = with->get_ysize(); 00303 int nz = with->get_zsize(); 00304 nx = (nx - 2 + with->is_fftodd()); // nx is the real-space size of the input image 00305 int lsd2 = (nx + 2 - nx%2) ; // Extended x-dimension of the complex image 00306 00307 int ixb = 2*((nx+1)%2); 00308 int iyb = ny%2; 00309 // 00310 if(nz == 1) { 00311 // it looks like it could work in 3D, but does not 00312 for ( int iz = 0; iz <= nz-1; ++iz) { 00313 double part = 0.; 00314 for ( int iy = 0; iy <= ny-1; ++iy) { 00315 for ( int ix = 2; ix <= lsd2 - 1 - ixb; ++ix) { 00316 size_t ii = ix + (iy + iz * ny)* lsd2; 00317 part += x_data[ii] * double(y_data[ii]); 00318 } 00319 } 00320 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00321 size_t ii = (iy + iz * ny)* lsd2; 00322 part += x_data[ii] * double(y_data[ii]); 00323 part += x_data[ii+1] * double(y_data[ii+1]); 00324 } 00325 if(nx%2 == 0) { 00326 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00327 size_t ii = lsd2 - 2 + (iy + iz * ny)* lsd2; 00328 part += x_data[ii] * double(y_data[ii]); 00329 part += x_data[ii+1] * double(y_data[ii+1]); 00330 } 00331 00332 } 00333 part *= 2; 00334 part += x_data[0] * double(y_data[0]); 00335 if(ny%2 == 0) { 00336 size_t ii = (ny/2 + iz * ny)* lsd2; 00337 part += x_data[ii] * double(y_data[ii]); 00338 } 00339 if(nx%2 == 0) { 00340 size_t ii = lsd2 - 2 + (0 + iz * ny)* lsd2; 00341 part += x_data[ii] * double(y_data[ii]); 00342 if(ny%2 == 0) { 00343 int ii = lsd2 - 2 +(ny/2 + iz * ny)* lsd2; 00344 part += x_data[ii] * double(y_data[ii]); 00345 } 00346 } 00347 result += part; 00348 } 00349 if( normalize ) { 00350 // it looks like it could work in 3D, but does not 00351 double square_sum1 = 0., square_sum2 = 0.; 00352 for ( int iz = 0; iz <= nz-1; ++iz) { 00353 for ( int iy = 0; iy <= ny-1; ++iy) { 00354 for ( int ix = 2; ix <= lsd2 - 1 - ixb; ++ix) { 00355 size_t ii = ix + (iy + iz * ny)* lsd2; 00356 square_sum1 += x_data[ii] * double(x_data[ii]); 00357 square_sum2 += y_data[ii] * double(y_data[ii]); 00358 } 00359 } 00360 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00361 size_t ii = (iy + iz * ny)* lsd2; 00362 square_sum1 += x_data[ii] * double(x_data[ii]); 00363 square_sum1 += x_data[ii+1] * double(x_data[ii+1]); 00364 square_sum2 += y_data[ii] * double(y_data[ii]); 00365 square_sum2 += y_data[ii+1] * double(y_data[ii+1]); 00366 } 00367 if(nx%2 == 0) { 00368 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00369 size_t ii = lsd2 - 2 + (iy + iz * ny)* lsd2; 00370 square_sum1 += x_data[ii] * double(x_data[ii]); 00371 square_sum1 += x_data[ii+1] * double(x_data[ii+1]); 00372 square_sum2 += y_data[ii] * double(y_data[ii]); 00373 square_sum2 += y_data[ii+1] * double(y_data[ii+1]); 00374 } 00375 00376 } 00377 square_sum1 *= 2; 00378 square_sum1 += x_data[0] * double(x_data[0]); 00379 square_sum2 *= 2; 00380 square_sum2 += y_data[0] * double(y_data[0]); 00381 if(ny%2 == 0) { 00382 int ii = (ny/2 + iz * ny)* lsd2; 00383 square_sum1 += x_data[ii] * double(x_data[ii]); 00384 square_sum2 += y_data[ii] * double(y_data[ii]); 00385 } 00386 if(nx%2 == 0) { 00387 int ii = lsd2 - 2 + (0 + iz * ny)* lsd2; 00388 square_sum1 += x_data[ii] * double(x_data[ii]); 00389 square_sum2 += y_data[ii] * double(y_data[ii]); 00390 if(ny%2 == 0) { 00391 int ii = lsd2 - 2 +(ny/2 + iz * ny)* lsd2; 00392 square_sum1 += x_data[ii] * double(x_data[ii]); 00393 square_sum2 += y_data[ii] * double(y_data[ii]); 00394 } 00395 } 00396 } 00397 result /= sqrt(square_sum1*square_sum2); 00398 } else result /= ((float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz); 00399 00400 } else { //This 3D code is incorrect, but it is the best I can do now 01/09/06 PAP 00401 int ky, kz; 00402 int ny2 = ny/2; int nz2 = nz/2; 00403 for ( int iz = 0; iz <= nz-1; ++iz) { 00404 if(iz>nz2) kz=iz-nz; else kz=iz; 00405 for ( int iy = 0; iy <= ny-1; ++iy) { 00406 if(iy>ny2) ky=iy-ny; else ky=iy; 00407 for ( int ix = 0; ix <= lsd2-1; ++ix) { 00408 // Skip Friedel related values 00409 if(ix>0 || (kz>=0 && (ky>=0 || kz!=0))) { 00410 size_t ii = ix + (iy + iz * ny)* lsd2; 00411 result += x_data[ii] * double(y_data[ii]); 00412 } 00413 } 00414 } 00415 } 00416 if( normalize ) { 00417 // still incorrect 00418 double square_sum1 = 0., square_sum2 = 0.; 00419 int ky, kz; 00420 int ny2 = ny/2; int nz2 = nz/2; 00421 for ( int iz = 0; iz <= nz-1; ++iz) { 00422 if(iz>nz2) kz=iz-nz; else kz=iz; 00423 for ( int iy = 0; iy <= ny-1; ++iy) { 00424 if(iy>ny2) ky=iy-ny; else ky=iy; 00425 for ( int ix = 0; ix <= lsd2-1; ++ix) { 00426 // Skip Friedel related values 00427 if(ix>0 || (kz>=0 && (ky>=0 || kz!=0))) { 00428 size_t ii = ix + (iy + iz * ny)* lsd2; 00429 square_sum1 += x_data[ii] * double(x_data[ii]); 00430 square_sum2 += y_data[ii] * double(y_data[ii]); 00431 } 00432 } 00433 } 00434 } 00435 result /= sqrt(square_sum1*square_sum2); 00436 } else result /= ((float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz/2); 00437 } 00438 } else { 00439 size_t totsize = image->get_xsize() * image->get_ysize() * image->get_zsize(); 00440 00441 double square_sum1 = 0., square_sum2 = 0.; 00442 00443 if (params.has_key("mask")) { 00444 EMData* mask; 00445 mask = params["mask"]; 00446 const float *const dm = mask->get_const_data(); 00447 if (normalize) { 00448 for (size_t i = 0; i < totsize; i++) { 00449 if (dm[i] > 0.5) { 00450 square_sum1 += x_data[i]*double(x_data[i]); 00451 square_sum2 += y_data[i]*double(y_data[i]); 00452 result += x_data[i]*double(y_data[i]); 00453 } 00454 } 00455 } else { 00456 for (size_t i = 0; i < totsize; i++) { 00457 if (dm[i] > 0.5) { 00458 result += x_data[i]*double(y_data[i]); 00459 n++; 00460 } 00461 } 00462 } 00463 } else { 00464 // this little bit of manual loop unrolling makes the dot product as fast as sqeuclidean with -O2 00465 for (size_t i=0; i<totsize; i++) result+=x_data[i]*y_data[i]; 00466 00467 if (normalize) { 00468 square_sum1 = image->get_attr("square_sum"); 00469 square_sum2 = with->get_attr("square_sum"); 00470 } else n = totsize; 00471 } 00472 if (normalize) result /= (sqrt(square_sum1*square_sum2)); else result /= n; 00473 } 00474 00475 00476 EXITFUNC; 00477 return (float) (negative*result); 00478 }
|
|
Implements EMAN::Cmp. Definition at line 244 of file cmp.h. 00245 { 00246 return "Dot product (default -1 * dot product)"; 00247 }
|
|
Get the Cmp's name. Each Cmp is identified by a unique name.
Implements EMAN::Cmp. Definition at line 239 of file cmp.h. 00240 {
00241 return NAME;
00242 }
|
|
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 254 of file cmp.h. References EMAN::TypeDict::put(). 00255 { 00256 TypeDict d; 00257 d.put("negative", EMObject::INT, "If set, returns -1 * dot product. Set by default so smaller is better"); 00258 d.put("normalize", EMObject::INT, "If set, returns normalized dot product (cosine of the angle) -1.0 - 1.0."); 00259 d.put("mask", EMObject::EMDATA, "image mask"); 00260 return d; 00261 }
|
|
Definition at line 249 of file cmp.h. 00250 { 00251 return new DotCmp(); 00252 }
|
|
|