#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 272 of file cmp.h.
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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 407 of file cmp.cpp. References dm, dot_cmp_cuda(), 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(). 00408 { 00409 ENTERFUNC; 00410 00411 validate_input_args(image, with); 00412 00413 int normalize = params.set_default("normalize", 0); 00414 float negative = (float)params.set_default("negative", 1); 00415 if (negative) negative=-1.0; else negative=1.0; 00416 #ifdef EMAN2_USING_CUDA // SO far only works for real images I put CUDA first to avoid running non CUDA overhead (calls to getdata are expensive!!!!) 00417 if(image->is_complex() && with->is_complex()) { 00418 } else { 00419 if (image->getcudarwdata() && with->getcudarwdata()) { 00420 //cout << "CUDA dot cmp" << endl; 00421 float* maskdata = 0; 00422 bool has_mask = false; 00423 EMData* mask = 0; 00424 if (params.has_key("mask")) { 00425 mask = params["mask"]; 00426 if(mask!=0) {has_mask=true;} 00427 } 00428 if(has_mask && !mask->getcudarwdata()){ 00429 mask->copy_to_cuda(); 00430 maskdata = mask->getcudarwdata(); 00431 } 00432 00433 float result = dot_cmp_cuda(image->getcudarwdata(), with->getcudarwdata(), maskdata, image->get_xsize(), image->get_ysize(), image->get_zsize()); 00434 result *= negative; 00435 00436 return result; 00437 00438 } 00439 } 00440 #endif 00441 const float *const x_data = image->get_const_data(); 00442 const float *const y_data = with->get_const_data(); 00443 00444 double result = 0.; 00445 long n = 0; 00446 if(image->is_complex() && with->is_complex()) { 00447 // Implemented by PAP 01/09/06 - please do not change. If in doubts, write/call me. 00448 int nx = with->get_xsize(); 00449 int ny = with->get_ysize(); 00450 int nz = with->get_zsize(); 00451 nx = (nx - 2 + with->is_fftodd()); // nx is the real-space size of the input image 00452 int lsd2 = (nx + 2 - nx%2) ; // Extended x-dimension of the complex image 00453 00454 int ixb = 2*((nx+1)%2); 00455 int iyb = ny%2; 00456 // 00457 if(nz == 1) { 00458 // it looks like it could work in 3D, but does not 00459 for ( int iz = 0; iz <= nz-1; ++iz) { 00460 double part = 0.; 00461 for ( int iy = 0; iy <= ny-1; ++iy) { 00462 for ( int ix = 2; ix <= lsd2 - 1 - ixb; ++ix) { 00463 size_t ii = ix + (iy + iz * ny)* lsd2; 00464 part += x_data[ii] * double(y_data[ii]); 00465 } 00466 } 00467 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00468 size_t ii = (iy + iz * ny)* lsd2; 00469 part += x_data[ii] * double(y_data[ii]); 00470 part += x_data[ii+1] * double(y_data[ii+1]); 00471 } 00472 if(nx%2 == 0) { 00473 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00474 size_t ii = lsd2 - 2 + (iy + iz * ny)* lsd2; 00475 part += x_data[ii] * double(y_data[ii]); 00476 part += x_data[ii+1] * double(y_data[ii+1]); 00477 } 00478 00479 } 00480 part *= 2; 00481 part += x_data[0] * double(y_data[0]); 00482 if(ny%2 == 0) { 00483 size_t ii = (ny/2 + iz * ny)* lsd2; 00484 part += x_data[ii] * double(y_data[ii]); 00485 } 00486 if(nx%2 == 0) { 00487 size_t ii = lsd2 - 2 + (0 + iz * ny)* lsd2; 00488 part += x_data[ii] * double(y_data[ii]); 00489 if(ny%2 == 0) { 00490 int ii = lsd2 - 2 +(ny/2 + iz * ny)* lsd2; 00491 part += x_data[ii] * double(y_data[ii]); 00492 } 00493 } 00494 result += part; 00495 } 00496 if( normalize ) { 00497 // it looks like it could work in 3D, but does not 00498 double square_sum1 = 0., square_sum2 = 0.; 00499 for ( int iz = 0; iz <= nz-1; ++iz) { 00500 for ( int iy = 0; iy <= ny-1; ++iy) { 00501 for ( int ix = 2; ix <= lsd2 - 1 - ixb; ++ix) { 00502 size_t ii = ix + (iy + iz * ny)* lsd2; 00503 square_sum1 += x_data[ii] * double(x_data[ii]); 00504 square_sum2 += y_data[ii] * double(y_data[ii]); 00505 } 00506 } 00507 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00508 size_t ii = (iy + iz * ny)* lsd2; 00509 square_sum1 += x_data[ii] * double(x_data[ii]); 00510 square_sum1 += x_data[ii+1] * double(x_data[ii+1]); 00511 square_sum2 += y_data[ii] * double(y_data[ii]); 00512 square_sum2 += y_data[ii+1] * double(y_data[ii+1]); 00513 } 00514 if(nx%2 == 0) { 00515 for ( int iy = 1; iy <= ny/2-1 + iyb; ++iy) { 00516 size_t ii = lsd2 - 2 + (iy + iz * ny)* lsd2; 00517 square_sum1 += x_data[ii] * double(x_data[ii]); 00518 square_sum1 += x_data[ii+1] * double(x_data[ii+1]); 00519 square_sum2 += y_data[ii] * double(y_data[ii]); 00520 square_sum2 += y_data[ii+1] * double(y_data[ii+1]); 00521 } 00522 00523 } 00524 square_sum1 *= 2; 00525 square_sum1 += x_data[0] * double(x_data[0]); 00526 square_sum2 *= 2; 00527 square_sum2 += y_data[0] * double(y_data[0]); 00528 if(ny%2 == 0) { 00529 int ii = (ny/2 + iz * ny)* lsd2; 00530 square_sum1 += x_data[ii] * double(x_data[ii]); 00531 square_sum2 += y_data[ii] * double(y_data[ii]); 00532 } 00533 if(nx%2 == 0) { 00534 int ii = lsd2 - 2 + (0 + iz * ny)* lsd2; 00535 square_sum1 += x_data[ii] * double(x_data[ii]); 00536 square_sum2 += y_data[ii] * double(y_data[ii]); 00537 if(ny%2 == 0) { 00538 int ii = lsd2 - 2 +(ny/2 + iz * ny)* lsd2; 00539 square_sum1 += x_data[ii] * double(x_data[ii]); 00540 square_sum2 += y_data[ii] * double(y_data[ii]); 00541 } 00542 } 00543 } 00544 result /= sqrt(square_sum1*square_sum2); 00545 } else result /= ((float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz); 00546 00547 } else { //This 3D code is incorrect, but it is the best I can do now 01/09/06 PAP 00548 int ky, kz; 00549 int ny2 = ny/2; int nz2 = nz/2; 00550 for ( int iz = 0; iz <= nz-1; ++iz) { 00551 if(iz>nz2) kz=iz-nz; else kz=iz; 00552 for ( int iy = 0; iy <= ny-1; ++iy) { 00553 if(iy>ny2) ky=iy-ny; else ky=iy; 00554 for ( int ix = 0; ix <= lsd2-1; ++ix) { 00555 // Skip Friedel related values 00556 if(ix>0 || (kz>=0 && (ky>=0 || kz!=0))) { 00557 size_t ii = ix + (iy + iz * ny)* (size_t)lsd2; 00558 result += x_data[ii] * double(y_data[ii]); 00559 } 00560 } 00561 } 00562 } 00563 if( normalize ) { 00564 // still incorrect 00565 double square_sum1 = 0., square_sum2 = 0.; 00566 int ky, kz; 00567 int ny2 = ny/2; int nz2 = nz/2; 00568 for ( int iz = 0; iz <= nz-1; ++iz) { 00569 if(iz>nz2) kz=iz-nz; else kz=iz; 00570 for ( int iy = 0; iy <= ny-1; ++iy) { 00571 if(iy>ny2) ky=iy-ny; else ky=iy; 00572 for ( int ix = 0; ix <= lsd2-1; ++ix) { 00573 // Skip Friedel related values 00574 if(ix>0 || (kz>=0 && (ky>=0 || kz!=0))) { 00575 size_t ii = ix + (iy + iz * ny)* (size_t)lsd2; 00576 square_sum1 += x_data[ii] * double(x_data[ii]); 00577 square_sum2 += y_data[ii] * double(y_data[ii]); 00578 } 00579 } 00580 } 00581 } 00582 result /= sqrt(square_sum1*square_sum2); 00583 } else result /= ((float)nx*(float)ny*(float)nz*(float)nx*(float)ny*(float)nz/2); 00584 } 00585 } else { 00586 00587 size_t totsize = (size_t)image->get_xsize() * image->get_ysize() * image->get_zsize(); 00588 00589 double square_sum1 = 0., square_sum2 = 0.; 00590 00591 if (params.has_key("mask")) { 00592 EMData* mask; 00593 mask = params["mask"]; 00594 const float *const dm = mask->get_const_data(); 00595 if (normalize) { 00596 for (size_t i = 0; i < totsize; i++) { 00597 if (dm[i] > 0.5) { 00598 square_sum1 += x_data[i]*double(x_data[i]); 00599 square_sum2 += y_data[i]*double(y_data[i]); 00600 result += x_data[i]*double(y_data[i]); 00601 } 00602 } 00603 } else { 00604 for (size_t i = 0; i < totsize; i++) { 00605 if (dm[i] > 0.5) { 00606 result += x_data[i]*double(y_data[i]); 00607 n++; 00608 } 00609 } 00610 } 00611 } else { 00612 // this little bit of manual loop unrolling makes the dot product as fast as sqeuclidean with -O2 00613 for (size_t i=0; i<totsize; i++) result+=x_data[i]*y_data[i]; 00614 00615 if (normalize) { 00616 square_sum1 = image->get_attr("square_sum"); 00617 square_sum2 = with->get_attr("square_sum"); 00618 } else n = totsize; 00619 } 00620 if (normalize) result /= (sqrt(square_sum1*square_sum2)); else result /= n; 00621 } 00622 00623 00624 EXITFUNC; 00625 return (float) (negative*result); 00626 }
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Implements EMAN::Cmp. Definition at line 282 of file cmp.h. 00283 { 00284 return "Dot product (default -1 * dot product)"; 00285 }
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Get the Cmp's name. Each Cmp is identified by a unique name.
Implements EMAN::Cmp. Definition at line 277 of file cmp.h. 00278 {
00279 return NAME;
00280 }
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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 292 of file cmp.h. References EMAN::TypeDict::put(). 00293 { 00294 TypeDict d; 00295 d.put("negative", EMObject::INT, "If set, returns -1 * dot product. Set by default so smaller is better"); 00296 d.put("normalize", EMObject::INT, "If set, returns normalized dot product (cosine of the angle) -1.0 - 1.0."); 00297 d.put("mask", EMObject::EMDATA, "image mask"); 00298 return d; 00299 }
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Definition at line 287 of file cmp.h. 00288 { 00289 return new DotCmp(); 00290 }
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