morphology.c revision 8693d3f324310fc2ca933cd239aa053d0651e0b6
1/* 2%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3% % 4% % 5% % 6% M M OOO RRRR PPPP H H OOO L OOO GGGG Y Y % 7% MM MM O O R R P P H H O O L O O G Y Y % 8% M M M O O RRRR PPPP HHHHH O O L O O G GGG Y % 9% M M O O R R P H H O O L O O G G Y % 10% M M OOO R R P H H OOO LLLLL OOO GGG Y % 11% % 12% % 13% MagickCore Morphology Methods % 14% % 15% Software Design % 16% Anthony Thyssen % 17% January 2010 % 18% % 19% % 20% Copyright 1999-2013 ImageMagick Studio LLC, a non-profit organization % 21% dedicated to making software imaging solutions freely available. % 22% % 23% You may not use this file except in compliance with the License. You may % 24% obtain a copy of the License at % 25% % 26% http://www.imagemagick.org/script/license.php % 27% % 28% Unless required by applicable law or agreed to in writing, software % 29% distributed under the License is distributed on an "AS IS" BASIS, % 30% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. % 31% See the License for the specific language governing permissions and % 32% limitations under the License. % 33% % 34%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 35% 36% Morpology is the the application of various kernels, of any size and even 37% shape, to a image in various ways (typically binary, but not always). 38% 39% Convolution (weighted sum or average) is just one specific type of 40% morphology. Just one that is very common for image bluring and sharpening 41% effects. Not only 2D Gaussian blurring, but also 2-pass 1D Blurring. 42% 43% This module provides not only a general morphology function, and the ability 44% to apply more advanced or iterative morphologies, but also functions for the 45% generation of many different types of kernel arrays from user supplied 46% arguments. Prehaps even the generation of a kernel from a small image. 47*/ 48 49/* 50 Include declarations. 51*/ 52#include "MagickCore/studio.h" 53#include "MagickCore/artifact.h" 54#include "MagickCore/cache-view.h" 55#include "MagickCore/color-private.h" 56#include "MagickCore/enhance.h" 57#include "MagickCore/exception.h" 58#include "MagickCore/exception-private.h" 59#include "MagickCore/gem.h" 60#include "MagickCore/gem-private.h" 61#include "MagickCore/hashmap.h" 62#include "MagickCore/image.h" 63#include "MagickCore/image-private.h" 64#include "MagickCore/list.h" 65#include "MagickCore/magick.h" 66#include "MagickCore/memory_.h" 67#include "MagickCore/memory-private.h" 68#include "MagickCore/monitor-private.h" 69#include "MagickCore/morphology.h" 70#include "MagickCore/morphology-private.h" 71#include "MagickCore/option.h" 72#include "MagickCore/pixel-accessor.h" 73#include "MagickCore/pixel-private.h" 74#include "MagickCore/prepress.h" 75#include "MagickCore/quantize.h" 76#include "MagickCore/resource_.h" 77#include "MagickCore/registry.h" 78#include "MagickCore/semaphore.h" 79#include "MagickCore/splay-tree.h" 80#include "MagickCore/statistic.h" 81#include "MagickCore/string_.h" 82#include "MagickCore/string-private.h" 83#include "MagickCore/thread-private.h" 84#include "MagickCore/token.h" 85#include "MagickCore/utility.h" 86#include "MagickCore/utility-private.h" 87 88/* 89 Other global definitions used by module. 90*/ 91static inline double MagickMin(const double x,const double y) 92{ 93 return( x < y ? x : y); 94} 95static inline double MagickMax(const double x,const double y) 96{ 97 return( x > y ? x : y); 98} 99#define Minimize(assign,value) assign=MagickMin(assign,value) 100#define Maximize(assign,value) assign=MagickMax(assign,value) 101 102/* Integer Factorial Function - for a Binomial kernel */ 103#if 1 104static inline size_t fact(size_t n) 105{ 106 size_t f,l; 107 for(f=1, l=2; l <= n; f=f*l, l++); 108 return(f); 109} 110#elif 1 /* glibc floating point alternatives */ 111#define fact(n) ((size_t)tgamma((double)n+1)) 112#else 113#define fact(n) ((size_t)lgamma((double)n+1)) 114#endif 115 116 117/* Currently these are only internal to this module */ 118static void 119 CalcKernelMetaData(KernelInfo *), 120 ExpandMirrorKernelInfo(KernelInfo *), 121 ExpandRotateKernelInfo(KernelInfo *, const double), 122 RotateKernelInfo(KernelInfo *, double); 123 124 125/* Quick function to find last kernel in a kernel list */ 126static inline KernelInfo *LastKernelInfo(KernelInfo *kernel) 127{ 128 while (kernel->next != (KernelInfo *) NULL) 129 kernel = kernel->next; 130 return(kernel); 131} 132 133/* 134%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 135% % 136% % 137% % 138% A c q u i r e K e r n e l I n f o % 139% % 140% % 141% % 142%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 143% 144% AcquireKernelInfo() takes the given string (generally supplied by the 145% user) and converts it into a Morphology/Convolution Kernel. This allows 146% users to specify a kernel from a number of pre-defined kernels, or to fully 147% specify their own kernel for a specific Convolution or Morphology 148% Operation. 149% 150% The kernel so generated can be any rectangular array of floating point 151% values (doubles) with the 'control point' or 'pixel being affected' 152% anywhere within that array of values. 153% 154% Previously IM was restricted to a square of odd size using the exact 155% center as origin, this is no longer the case, and any rectangular kernel 156% with any value being declared the origin. This in turn allows the use of 157% highly asymmetrical kernels. 158% 159% The floating point values in the kernel can also include a special value 160% known as 'nan' or 'not a number' to indicate that this value is not part 161% of the kernel array. This allows you to shaped the kernel within its 162% rectangular area. That is 'nan' values provide a 'mask' for the kernel 163% shape. However at least one non-nan value must be provided for correct 164% working of a kernel. 165% 166% The returned kernel should be freed using the DestroyKernelInfo() when you 167% are finished with it. Do not free this memory yourself. 168% 169% Input kernel defintion strings can consist of any of three types. 170% 171% "name:args[[@><]" 172% Select from one of the built in kernels, using the name and 173% geometry arguments supplied. See AcquireKernelBuiltIn() 174% 175% "WxH[+X+Y][@><]:num, num, num ..." 176% a kernel of size W by H, with W*H floating point numbers following. 177% the 'center' can be optionally be defined at +X+Y (such that +0+0 178% is top left corner). If not defined the pixel in the center, for 179% odd sizes, or to the immediate top or left of center for even sizes 180% is automatically selected. 181% 182% "num, num, num, num, ..." 183% list of floating point numbers defining an 'old style' odd sized 184% square kernel. At least 9 values should be provided for a 3x3 185% square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc. 186% Values can be space or comma separated. This is not recommended. 187% 188% You can define a 'list of kernels' which can be used by some morphology 189% operators A list is defined as a semi-colon separated list kernels. 190% 191% " kernel ; kernel ; kernel ; " 192% 193% Any extra ';' characters, at start, end or between kernel defintions are 194% simply ignored. 195% 196% The special flags will expand a single kernel, into a list of rotated 197% kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree 198% cyclic rotations, while a '>' will generate a list of 90-degree rotations. 199% The '<' also exands using 90-degree rotates, but giving a 180-degree 200% reflected kernel before the +/- 90-degree rotations, which can be important 201% for Thinning operations. 202% 203% Note that 'name' kernels will start with an alphabetic character while the 204% new kernel specification has a ':' character in its specification string. 205% If neither is the case, it is assumed an old style of a simple list of 206% numbers generating a odd-sized square kernel has been given. 207% 208% The format of the AcquireKernal method is: 209% 210% KernelInfo *AcquireKernelInfo(const char *kernel_string) 211% 212% A description of each parameter follows: 213% 214% o kernel_string: the Morphology/Convolution kernel wanted. 215% 216*/ 217 218/* This was separated so that it could be used as a separate 219** array input handling function, such as for -color-matrix 220*/ 221static KernelInfo *ParseKernelArray(const char *kernel_string) 222{ 223 KernelInfo 224 *kernel; 225 226 char 227 token[MaxTextExtent]; 228 229 const char 230 *p, 231 *end; 232 233 register ssize_t 234 i; 235 236 double 237 nan = sqrt((double)-1.0); /* Special Value : Not A Number */ 238 239 MagickStatusType 240 flags; 241 242 GeometryInfo 243 args; 244 245 kernel=(KernelInfo *) AcquireQuantumMemory(1,sizeof(*kernel)); 246 if (kernel == (KernelInfo *)NULL) 247 return(kernel); 248 (void) ResetMagickMemory(kernel,0,sizeof(*kernel)); 249 kernel->minimum = kernel->maximum = kernel->angle = 0.0; 250 kernel->negative_range = kernel->positive_range = 0.0; 251 kernel->type = UserDefinedKernel; 252 kernel->next = (KernelInfo *) NULL; 253 kernel->signature = MagickSignature; 254 if (kernel_string == (const char *) NULL) 255 return(kernel); 256 257 /* find end of this specific kernel definition string */ 258 end = strchr(kernel_string, ';'); 259 if ( end == (char *) NULL ) 260 end = strchr(kernel_string, '\0'); 261 262 /* clear flags - for Expanding kernel lists thorugh rotations */ 263 flags = NoValue; 264 265 /* Has a ':' in argument - New user kernel specification 266 FUTURE: this split on ':' could be done by StringToken() 267 */ 268 p = strchr(kernel_string, ':'); 269 if ( p != (char *) NULL && p < end) 270 { 271 /* ParseGeometry() needs the geometry separated! -- Arrgghh */ 272 memcpy(token, kernel_string, (size_t) (p-kernel_string)); 273 token[p-kernel_string] = '\0'; 274 SetGeometryInfo(&args); 275 flags = ParseGeometry(token, &args); 276 277 /* Size handling and checks of geometry settings */ 278 if ( (flags & WidthValue) == 0 ) /* if no width then */ 279 args.rho = args.sigma; /* then width = height */ 280 if ( args.rho < 1.0 ) /* if width too small */ 281 args.rho = 1.0; /* then width = 1 */ 282 if ( args.sigma < 1.0 ) /* if height too small */ 283 args.sigma = args.rho; /* then height = width */ 284 kernel->width = (size_t)args.rho; 285 kernel->height = (size_t)args.sigma; 286 287 /* Offset Handling and Checks */ 288 if ( args.xi < 0.0 || args.psi < 0.0 ) 289 return(DestroyKernelInfo(kernel)); 290 kernel->x = ((flags & XValue)!=0) ? (ssize_t)args.xi 291 : (ssize_t) (kernel->width-1)/2; 292 kernel->y = ((flags & YValue)!=0) ? (ssize_t)args.psi 293 : (ssize_t) (kernel->height-1)/2; 294 if ( kernel->x >= (ssize_t) kernel->width || 295 kernel->y >= (ssize_t) kernel->height ) 296 return(DestroyKernelInfo(kernel)); 297 298 p++; /* advance beyond the ':' */ 299 } 300 else 301 { /* ELSE - Old old specification, forming odd-square kernel */ 302 /* count up number of values given */ 303 p=(const char *) kernel_string; 304 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\'')) 305 p++; /* ignore "'" chars for convolve filter usage - Cristy */ 306 for (i=0; p < end; i++) 307 { 308 GetMagickToken(p,&p,token); 309 if (*token == ',') 310 GetMagickToken(p,&p,token); 311 } 312 /* set the size of the kernel - old sized square */ 313 kernel->width = kernel->height= (size_t) sqrt((double) i+1.0); 314 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 315 p=(const char *) kernel_string; 316 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\'')) 317 p++; /* ignore "'" chars for convolve filter usage - Cristy */ 318 } 319 320 /* Read in the kernel values from rest of input string argument */ 321 kernel->values=(MagickRealType *) MagickAssumeAligned(AcquireAlignedMemory( 322 kernel->width,kernel->height*sizeof(*kernel->values))); 323 if (kernel->values == (MagickRealType *) NULL) 324 return(DestroyKernelInfo(kernel)); 325 kernel->minimum = +MagickHuge; 326 kernel->maximum = -MagickHuge; 327 kernel->negative_range = kernel->positive_range = 0.0; 328 for (i=0; (i < (ssize_t) (kernel->width*kernel->height)) && (p < end); i++) 329 { 330 GetMagickToken(p,&p,token); 331 if (*token == ',') 332 GetMagickToken(p,&p,token); 333 if ( LocaleCompare("nan",token) == 0 334 || LocaleCompare("-",token) == 0 ) { 335 kernel->values[i] = nan; /* this value is not part of neighbourhood */ 336 } 337 else { 338 kernel->values[i] = StringToDouble(token,(char **) NULL); 339 ( kernel->values[i] < 0) 340 ? ( kernel->negative_range += kernel->values[i] ) 341 : ( kernel->positive_range += kernel->values[i] ); 342 Minimize(kernel->minimum, kernel->values[i]); 343 Maximize(kernel->maximum, kernel->values[i]); 344 } 345 } 346 347 /* sanity check -- no more values in kernel definition */ 348 GetMagickToken(p,&p,token); 349 if ( *token != '\0' && *token != ';' && *token != '\'' ) 350 return(DestroyKernelInfo(kernel)); 351 352#if 0 353 /* this was the old method of handling a incomplete kernel */ 354 if ( i < (ssize_t) (kernel->width*kernel->height) ) { 355 Minimize(kernel->minimum, kernel->values[i]); 356 Maximize(kernel->maximum, kernel->values[i]); 357 for ( ; i < (ssize_t) (kernel->width*kernel->height); i++) 358 kernel->values[i]=0.0; 359 } 360#else 361 /* Number of values for kernel was not enough - Report Error */ 362 if ( i < (ssize_t) (kernel->width*kernel->height) ) 363 return(DestroyKernelInfo(kernel)); 364#endif 365 366 /* check that we recieved at least one real (non-nan) value! */ 367 if ( kernel->minimum == MagickHuge ) 368 return(DestroyKernelInfo(kernel)); 369 370 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel size */ 371 ExpandRotateKernelInfo(kernel, 45.0); /* cyclic rotate 3x3 kernels */ 372 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */ 373 ExpandRotateKernelInfo(kernel, 90.0); /* 90 degree rotate of kernel */ 374 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */ 375 ExpandMirrorKernelInfo(kernel); /* 90 degree mirror rotate */ 376 377 return(kernel); 378} 379 380static KernelInfo *ParseKernelName(const char *kernel_string) 381{ 382 char 383 token[MaxTextExtent]; 384 385 const char 386 *p, 387 *end; 388 389 GeometryInfo 390 args; 391 392 KernelInfo 393 *kernel; 394 395 MagickStatusType 396 flags; 397 398 ssize_t 399 type; 400 401 /* Parse special 'named' kernel */ 402 GetMagickToken(kernel_string,&p,token); 403 type=ParseCommandOption(MagickKernelOptions,MagickFalse,token); 404 if ( type < 0 || type == UserDefinedKernel ) 405 return((KernelInfo *)NULL); /* not a valid named kernel */ 406 407 while (((isspace((int) ((unsigned char) *p)) != 0) || 408 (*p == ',') || (*p == ':' )) && (*p != '\0') && (*p != ';')) 409 p++; 410 411 end = strchr(p, ';'); /* end of this kernel defintion */ 412 if ( end == (char *) NULL ) 413 end = strchr(p, '\0'); 414 415 /* ParseGeometry() needs the geometry separated! -- Arrgghh */ 416 memcpy(token, p, (size_t) (end-p)); 417 token[end-p] = '\0'; 418 SetGeometryInfo(&args); 419 flags = ParseGeometry(token, &args); 420 421#if 0 422 /* For Debugging Geometry Input */ 423 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n", 424 flags, args.rho, args.sigma, args.xi, args.psi ); 425#endif 426 427 /* special handling of missing values in input string */ 428 switch( type ) { 429 /* Shape Kernel Defaults */ 430 case UnityKernel: 431 if ( (flags & WidthValue) == 0 ) 432 args.rho = 1.0; /* Default scale = 1.0, zero is valid */ 433 break; 434 case SquareKernel: 435 case DiamondKernel: 436 case OctagonKernel: 437 case DiskKernel: 438 case PlusKernel: 439 case CrossKernel: 440 if ( (flags & HeightValue) == 0 ) 441 args.sigma = 1.0; /* Default scale = 1.0, zero is valid */ 442 break; 443 case RingKernel: 444 if ( (flags & XValue) == 0 ) 445 args.xi = 1.0; /* Default scale = 1.0, zero is valid */ 446 break; 447 case RectangleKernel: /* Rectangle - set size defaults */ 448 if ( (flags & WidthValue) == 0 ) /* if no width then */ 449 args.rho = args.sigma; /* then width = height */ 450 if ( args.rho < 1.0 ) /* if width too small */ 451 args.rho = 3; /* then width = 3 */ 452 if ( args.sigma < 1.0 ) /* if height too small */ 453 args.sigma = args.rho; /* then height = width */ 454 if ( (flags & XValue) == 0 ) /* center offset if not defined */ 455 args.xi = (double)(((ssize_t)args.rho-1)/2); 456 if ( (flags & YValue) == 0 ) 457 args.psi = (double)(((ssize_t)args.sigma-1)/2); 458 break; 459 /* Distance Kernel Defaults */ 460 case ChebyshevKernel: 461 case ManhattanKernel: 462 case OctagonalKernel: 463 case EuclideanKernel: 464 if ( (flags & HeightValue) == 0 ) /* no distance scale */ 465 args.sigma = 100.0; /* default distance scaling */ 466 else if ( (flags & AspectValue ) != 0 ) /* '!' flag */ 467 args.sigma = QuantumRange/(args.sigma+1); /* maximum pixel distance */ 468 else if ( (flags & PercentValue ) != 0 ) /* '%' flag */ 469 args.sigma *= QuantumRange/100.0; /* percentage of color range */ 470 break; 471 default: 472 break; 473 } 474 475 kernel = AcquireKernelBuiltIn((KernelInfoType)type, &args); 476 if ( kernel == (KernelInfo *) NULL ) 477 return(kernel); 478 479 /* global expand to rotated kernel list - only for single kernels */ 480 if ( kernel->next == (KernelInfo *) NULL ) { 481 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel args */ 482 ExpandRotateKernelInfo(kernel, 45.0); 483 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */ 484 ExpandRotateKernelInfo(kernel, 90.0); 485 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */ 486 ExpandMirrorKernelInfo(kernel); 487 } 488 489 return(kernel); 490} 491 492MagickExport KernelInfo *AcquireKernelInfo(const char *kernel_string) 493{ 494 495 KernelInfo 496 *kernel, 497 *new_kernel; 498 499 char 500 token[MaxTextExtent]; 501 502 const char 503 *p; 504 505 size_t 506 kernel_number; 507 508 if (kernel_string == (const char *) NULL) 509 return(ParseKernelArray(kernel_string)); 510 p = kernel_string; 511 kernel = NULL; 512 kernel_number = 0; 513 514 while ( GetMagickToken(p,NULL,token), *token != '\0' ) { 515 516 /* ignore extra or multiple ';' kernel separators */ 517 if ( *token != ';' ) { 518 519 /* tokens starting with alpha is a Named kernel */ 520 if (isalpha((int) *token) != 0) 521 new_kernel = ParseKernelName(p); 522 else /* otherwise a user defined kernel array */ 523 new_kernel = ParseKernelArray(p); 524 525 /* Error handling -- this is not proper error handling! */ 526 if ( new_kernel == (KernelInfo *) NULL ) { 527 (void) FormatLocaleFile(stderr,"Failed to parse kernel number #%.20g\n", 528 (double) kernel_number); 529 if ( kernel != (KernelInfo *) NULL ) 530 kernel=DestroyKernelInfo(kernel); 531 return((KernelInfo *) NULL); 532 } 533 534 /* initialise or append the kernel list */ 535 if ( kernel == (KernelInfo *) NULL ) 536 kernel = new_kernel; 537 else 538 LastKernelInfo(kernel)->next = new_kernel; 539 } 540 541 /* look for the next kernel in list */ 542 p = strchr(p, ';'); 543 if ( p == (char *) NULL ) 544 break; 545 p++; 546 547 } 548 return(kernel); 549} 550 551 552/* 553%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 554% % 555% % 556% % 557% A c q u i r e K e r n e l B u i l t I n % 558% % 559% % 560% % 561%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 562% 563% AcquireKernelBuiltIn() returned one of the 'named' built-in types of 564% kernels used for special purposes such as gaussian blurring, skeleton 565% pruning, and edge distance determination. 566% 567% They take a KernelType, and a set of geometry style arguments, which were 568% typically decoded from a user supplied string, or from a more complex 569% Morphology Method that was requested. 570% 571% The format of the AcquireKernalBuiltIn method is: 572% 573% KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type, 574% const GeometryInfo args) 575% 576% A description of each parameter follows: 577% 578% o type: the pre-defined type of kernel wanted 579% 580% o args: arguments defining or modifying the kernel 581% 582% Convolution Kernels 583% 584% Unity 585% The a No-Op or Scaling single element kernel. 586% 587% Gaussian:{radius},{sigma} 588% Generate a two-dimensional gaussian kernel, as used by -gaussian. 589% The sigma for the curve is required. The resulting kernel is 590% normalized, 591% 592% If 'sigma' is zero, you get a single pixel on a field of zeros. 593% 594% NOTE: that the 'radius' is optional, but if provided can limit (clip) 595% the final size of the resulting kernel to a square 2*radius+1 in size. 596% The radius should be at least 2 times that of the sigma value, or 597% sever clipping and aliasing may result. If not given or set to 0 the 598% radius will be determined so as to produce the best minimal error 599% result, which is usally much larger than is normally needed. 600% 601% LoG:{radius},{sigma} 602% "Laplacian of a Gaussian" or "Mexician Hat" Kernel. 603% The supposed ideal edge detection, zero-summing kernel. 604% 605% An alturnative to this kernel is to use a "DoG" with a sigma ratio of 606% approx 1.6 (according to wikipedia). 607% 608% DoG:{radius},{sigma1},{sigma2} 609% "Difference of Gaussians" Kernel. 610% As "Gaussian" but with a gaussian produced by 'sigma2' subtracted 611% from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1. 612% The result is a zero-summing kernel. 613% 614% Blur:{radius},{sigma}[,{angle}] 615% Generates a 1 dimensional or linear gaussian blur, at the angle given 616% (current restricted to orthogonal angles). If a 'radius' is given the 617% kernel is clipped to a width of 2*radius+1. Kernel can be rotated 618% by a 90 degree angle. 619% 620% If 'sigma' is zero, you get a single pixel on a field of zeros. 621% 622% Note that two convolutions with two "Blur" kernels perpendicular to 623% each other, is equivalent to a far larger "Gaussian" kernel with the 624% same sigma value, However it is much faster to apply. This is how the 625% "-blur" operator actually works. 626% 627% Comet:{width},{sigma},{angle} 628% Blur in one direction only, much like how a bright object leaves 629% a comet like trail. The Kernel is actually half a gaussian curve, 630% Adding two such blurs in opposite directions produces a Blur Kernel. 631% Angle can be rotated in multiples of 90 degrees. 632% 633% Note that the first argument is the width of the kernel and not the 634% radius of the kernel. 635% 636% Binomial:[{radius}] 637% Generate a discrete kernel using a 2 dimentional Pascel's Triangle 638% of values. Used for special forma of image filters. 639% 640% # Still to be implemented... 641% # 642% # Filter2D 643% # Filter1D 644% # Set kernel values using a resize filter, and given scale (sigma) 645% # Cylindrical or Linear. Is this possible with an image? 646% # 647% 648% Named Constant Convolution Kernels 649% 650% All these are unscaled, zero-summing kernels by default. As such for 651% non-HDRI version of ImageMagick some form of normalization, user scaling, 652% and biasing the results is recommended, to prevent the resulting image 653% being 'clipped'. 654% 655% The 3x3 kernels (most of these) can be circularly rotated in multiples of 656% 45 degrees to generate the 8 angled varients of each of the kernels. 657% 658% Laplacian:{type} 659% Discrete Lapacian Kernels, (without normalization) 660% Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood) 661% Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood) 662% Type 2 : 3x3 with center:4 edge:1 corner:-2 663% Type 3 : 3x3 with center:4 edge:-2 corner:1 664% Type 5 : 5x5 laplacian 665% Type 7 : 7x7 laplacian 666% Type 15 : 5x5 LoG (sigma approx 1.4) 667% Type 19 : 9x9 LoG (sigma approx 1.4) 668% 669% Sobel:{angle} 670% Sobel 'Edge' convolution kernel (3x3) 671% | -1, 0, 1 | 672% | -2, 0,-2 | 673% | -1, 0, 1 | 674% 675% Roberts:{angle} 676% Roberts convolution kernel (3x3) 677% | 0, 0, 0 | 678% | -1, 1, 0 | 679% | 0, 0, 0 | 680% 681% Prewitt:{angle} 682% Prewitt Edge convolution kernel (3x3) 683% | -1, 0, 1 | 684% | -1, 0, 1 | 685% | -1, 0, 1 | 686% 687% Compass:{angle} 688% Prewitt's "Compass" convolution kernel (3x3) 689% | -1, 1, 1 | 690% | -1,-2, 1 | 691% | -1, 1, 1 | 692% 693% Kirsch:{angle} 694% Kirsch's "Compass" convolution kernel (3x3) 695% | -3,-3, 5 | 696% | -3, 0, 5 | 697% | -3,-3, 5 | 698% 699% FreiChen:{angle} 700% Frei-Chen Edge Detector is based on a kernel that is similar to 701% the Sobel Kernel, but is designed to be isotropic. That is it takes 702% into account the distance of the diagonal in the kernel. 703% 704% | 1, 0, -1 | 705% | sqrt(2), 0, -sqrt(2) | 706% | 1, 0, -1 | 707% 708% FreiChen:{type},{angle} 709% 710% Frei-Chen Pre-weighted kernels... 711% 712% Type 0: default un-nomalized version shown above. 713% 714% Type 1: Orthogonal Kernel (same as type 11 below) 715% | 1, 0, -1 | 716% | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) 717% | 1, 0, -1 | 718% 719% Type 2: Diagonal form of Kernel... 720% | 1, sqrt(2), 0 | 721% | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) 722% | 0, -sqrt(2) -1 | 723% 724% However this kernel is als at the heart of the FreiChen Edge Detection 725% Process which uses a set of 9 specially weighted kernel. These 9 726% kernels not be normalized, but directly applied to the image. The 727% results is then added together, to produce the intensity of an edge in 728% a specific direction. The square root of the pixel value can then be 729% taken as the cosine of the edge, and at least 2 such runs at 90 degrees 730% from each other, both the direction and the strength of the edge can be 731% determined. 732% 733% Type 10: All 9 of the following pre-weighted kernels... 734% 735% Type 11: | 1, 0, -1 | 736% | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) 737% | 1, 0, -1 | 738% 739% Type 12: | 1, sqrt(2), 1 | 740% | 0, 0, 0 | / 2*sqrt(2) 741% | 1, sqrt(2), 1 | 742% 743% Type 13: | sqrt(2), -1, 0 | 744% | -1, 0, 1 | / 2*sqrt(2) 745% | 0, 1, -sqrt(2) | 746% 747% Type 14: | 0, 1, -sqrt(2) | 748% | -1, 0, 1 | / 2*sqrt(2) 749% | sqrt(2), -1, 0 | 750% 751% Type 15: | 0, -1, 0 | 752% | 1, 0, 1 | / 2 753% | 0, -1, 0 | 754% 755% Type 16: | 1, 0, -1 | 756% | 0, 0, 0 | / 2 757% | -1, 0, 1 | 758% 759% Type 17: | 1, -2, 1 | 760% | -2, 4, -2 | / 6 761% | -1, -2, 1 | 762% 763% Type 18: | -2, 1, -2 | 764% | 1, 4, 1 | / 6 765% | -2, 1, -2 | 766% 767% Type 19: | 1, 1, 1 | 768% | 1, 1, 1 | / 3 769% | 1, 1, 1 | 770% 771% The first 4 are for edge detection, the next 4 are for line detection 772% and the last is to add a average component to the results. 773% 774% Using a special type of '-1' will return all 9 pre-weighted kernels 775% as a multi-kernel list, so that you can use them directly (without 776% normalization) with the special "-set option:morphology:compose Plus" 777% setting to apply the full FreiChen Edge Detection Technique. 778% 779% If 'type' is large it will be taken to be an actual rotation angle for 780% the default FreiChen (type 0) kernel. As such FreiChen:45 will look 781% like a Sobel:45 but with 'sqrt(2)' instead of '2' values. 782% 783% WARNING: The above was layed out as per 784% http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf 785% But rotated 90 degrees so direction is from left rather than the top. 786% I have yet to find any secondary confirmation of the above. The only 787% other source found was actual source code at 788% http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf 789% Neigher paper defineds the kernels in a way that looks locical or 790% correct when taken as a whole. 791% 792% Boolean Kernels 793% 794% Diamond:[{radius}[,{scale}]] 795% Generate a diamond shaped kernel with given radius to the points. 796% Kernel size will again be radius*2+1 square and defaults to radius 1, 797% generating a 3x3 kernel that is slightly larger than a square. 798% 799% Square:[{radius}[,{scale}]] 800% Generate a square shaped kernel of size radius*2+1, and defaulting 801% to a 3x3 (radius 1). 802% 803% Octagon:[{radius}[,{scale}]] 804% Generate octagonal shaped kernel of given radius and constant scale. 805% Default radius is 3 producing a 7x7 kernel. A radius of 1 will result 806% in "Diamond" kernel. 807% 808% Disk:[{radius}[,{scale}]] 809% Generate a binary disk, thresholded at the radius given, the radius 810% may be a float-point value. Final Kernel size is floor(radius)*2+1 811% square. A radius of 5.3 is the default. 812% 813% NOTE: That a low radii Disk kernels produce the same results as 814% many of the previously defined kernels, but differ greatly at larger 815% radii. Here is a table of equivalences... 816% "Disk:1" => "Diamond", "Octagon:1", or "Cross:1" 817% "Disk:1.5" => "Square" 818% "Disk:2" => "Diamond:2" 819% "Disk:2.5" => "Octagon" 820% "Disk:2.9" => "Square:2" 821% "Disk:3.5" => "Octagon:3" 822% "Disk:4.5" => "Octagon:4" 823% "Disk:5.4" => "Octagon:5" 824% "Disk:6.4" => "Octagon:6" 825% All other Disk shapes are unique to this kernel, but because a "Disk" 826% is more circular when using a larger radius, using a larger radius is 827% preferred over iterating the morphological operation. 828% 829% Rectangle:{geometry} 830% Simply generate a rectangle of 1's with the size given. You can also 831% specify the location of the 'control point', otherwise the closest 832% pixel to the center of the rectangle is selected. 833% 834% Properly centered and odd sized rectangles work the best. 835% 836% Symbol Dilation Kernels 837% 838% These kernel is not a good general morphological kernel, but is used 839% more for highlighting and marking any single pixels in an image using, 840% a "Dilate" method as appropriate. 841% 842% For the same reasons iterating these kernels does not produce the 843% same result as using a larger radius for the symbol. 844% 845% Plus:[{radius}[,{scale}]] 846% Cross:[{radius}[,{scale}]] 847% Generate a kernel in the shape of a 'plus' or a 'cross' with 848% a each arm the length of the given radius (default 2). 849% 850% NOTE: "plus:1" is equivalent to a "Diamond" kernel. 851% 852% Ring:{radius1},{radius2}[,{scale}] 853% A ring of the values given that falls between the two radii. 854% Defaults to a ring of approximataly 3 radius in a 7x7 kernel. 855% This is the 'edge' pixels of the default "Disk" kernel, 856% More specifically, "Ring" -> "Ring:2.5,3.5,1.0" 857% 858% Hit and Miss Kernels 859% 860% Peak:radius1,radius2 861% Find any peak larger than the pixels the fall between the two radii. 862% The default ring of pixels is as per "Ring". 863% Edges 864% Find flat orthogonal edges of a binary shape 865% Corners 866% Find 90 degree corners of a binary shape 867% Diagonals:type 868% A special kernel to thin the 'outside' of diagonals 869% LineEnds:type 870% Find end points of lines (for pruning a skeletion) 871% Two types of lines ends (default to both) can be searched for 872% Type 0: All line ends 873% Type 1: single kernel for 4-conneected line ends 874% Type 2: single kernel for simple line ends 875% LineJunctions 876% Find three line junctions (within a skeletion) 877% Type 0: all line junctions 878% Type 1: Y Junction kernel 879% Type 2: Diagonal T Junction kernel 880% Type 3: Orthogonal T Junction kernel 881% Type 4: Diagonal X Junction kernel 882% Type 5: Orthogonal + Junction kernel 883% Ridges:type 884% Find single pixel ridges or thin lines 885% Type 1: Fine single pixel thick lines and ridges 886% Type 2: Find two pixel thick lines and ridges 887% ConvexHull 888% Octagonal Thickening Kernel, to generate convex hulls of 45 degrees 889% Skeleton:type 890% Traditional skeleton generating kernels. 891% Type 1: Tradional Skeleton kernel (4 connected skeleton) 892% Type 2: HIPR2 Skeleton kernel (8 connected skeleton) 893% Type 3: Thinning skeleton based on a ressearch paper by 894% Dan S. Bloomberg (Default Type) 895% ThinSE:type 896% A huge variety of Thinning Kernels designed to preserve conectivity. 897% many other kernel sets use these kernels as source definitions. 898% Type numbers are 41-49, 81-89, 481, and 482 which are based on 899% the super and sub notations used in the source research paper. 900% 901% Distance Measuring Kernels 902% 903% Different types of distance measuring methods, which are used with the 904% a 'Distance' morphology method for generating a gradient based on 905% distance from an edge of a binary shape, though there is a technique 906% for handling a anti-aliased shape. 907% 908% See the 'Distance' Morphological Method, for information of how it is 909% applied. 910% 911% Chebyshev:[{radius}][x{scale}[%!]] 912% Chebyshev Distance (also known as Tchebychev or Chessboard distance) 913% is a value of one to any neighbour, orthogonal or diagonal. One why 914% of thinking of it is the number of squares a 'King' or 'Queen' in 915% chess needs to traverse reach any other position on a chess board. 916% It results in a 'square' like distance function, but one where 917% diagonals are given a value that is closer than expected. 918% 919% Manhattan:[{radius}][x{scale}[%!]] 920% Manhattan Distance (also known as Rectilinear, City Block, or the Taxi 921% Cab distance metric), it is the distance needed when you can only 922% travel in horizontal or vertical directions only. It is the 923% distance a 'Rook' in chess would have to travel, and results in a 924% diamond like distances, where diagonals are further than expected. 925% 926% Octagonal:[{radius}][x{scale}[%!]] 927% An interleving of Manhatten and Chebyshev metrics producing an 928% increasing octagonally shaped distance. Distances matches those of 929% the "Octagon" shaped kernel of the same radius. The minimum radius 930% and default is 2, producing a 5x5 kernel. 931% 932% Euclidean:[{radius}][x{scale}[%!]] 933% Euclidean distance is the 'direct' or 'as the crow flys' distance. 934% However by default the kernel size only has a radius of 1, which 935% limits the distance to 'Knight' like moves, with only orthogonal and 936% diagonal measurements being correct. As such for the default kernel 937% you will get octagonal like distance function. 938% 939% However using a larger radius such as "Euclidean:4" you will get a 940% much smoother distance gradient from the edge of the shape. Especially 941% if the image is pre-processed to include any anti-aliasing pixels. 942% Of course a larger kernel is slower to use, and not always needed. 943% 944% The first three Distance Measuring Kernels will only generate distances 945% of exact multiples of {scale} in binary images. As such you can use a 946% scale of 1 without loosing any information. However you also need some 947% scaling when handling non-binary anti-aliased shapes. 948% 949% The "Euclidean" Distance Kernel however does generate a non-integer 950% fractional results, and as such scaling is vital even for binary shapes. 951% 952*/ 953 954MagickExport KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type, 955 const GeometryInfo *args) 956{ 957 KernelInfo 958 *kernel; 959 960 register ssize_t 961 i; 962 963 register ssize_t 964 u, 965 v; 966 967 double 968 nan = sqrt((double)-1.0); /* Special Value : Not A Number */ 969 970 /* Generate a new empty kernel if needed */ 971 kernel=(KernelInfo *) NULL; 972 switch(type) { 973 case UndefinedKernel: /* These should not call this function */ 974 case UserDefinedKernel: 975 assert("Should not call this function" != (char *)NULL); 976 break; 977 case LaplacianKernel: /* Named Descrete Convolution Kernels */ 978 case SobelKernel: /* these are defined using other kernels */ 979 case RobertsKernel: 980 case PrewittKernel: 981 case CompassKernel: 982 case KirschKernel: 983 case FreiChenKernel: 984 case EdgesKernel: /* Hit and Miss kernels */ 985 case CornersKernel: 986 case DiagonalsKernel: 987 case LineEndsKernel: 988 case LineJunctionsKernel: 989 case RidgesKernel: 990 case ConvexHullKernel: 991 case SkeletonKernel: 992 case ThinSEKernel: 993 break; /* A pre-generated kernel is not needed */ 994#if 0 995 /* set to 1 to do a compile-time check that we haven't missed anything */ 996 case UnityKernel: 997 case GaussianKernel: 998 case DoGKernel: 999 case LoGKernel: 1000 case BlurKernel: 1001 case CometKernel: 1002 case BinomialKernel: 1003 case DiamondKernel: 1004 case SquareKernel: 1005 case RectangleKernel: 1006 case OctagonKernel: 1007 case DiskKernel: 1008 case PlusKernel: 1009 case CrossKernel: 1010 case RingKernel: 1011 case PeaksKernel: 1012 case ChebyshevKernel: 1013 case ManhattanKernel: 1014 case OctangonalKernel: 1015 case EuclideanKernel: 1016#else 1017 default: 1018#endif 1019 /* Generate the base Kernel Structure */ 1020 kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel)); 1021 if (kernel == (KernelInfo *) NULL) 1022 return(kernel); 1023 (void) ResetMagickMemory(kernel,0,sizeof(*kernel)); 1024 kernel->minimum = kernel->maximum = kernel->angle = 0.0; 1025 kernel->negative_range = kernel->positive_range = 0.0; 1026 kernel->type = type; 1027 kernel->next = (KernelInfo *) NULL; 1028 kernel->signature = MagickSignature; 1029 break; 1030 } 1031 1032 switch(type) { 1033 /* 1034 Convolution Kernels 1035 */ 1036 case UnityKernel: 1037 { 1038 kernel->height = kernel->width = (size_t) 1; 1039 kernel->x = kernel->y = (ssize_t) 0; 1040 kernel->values=(MagickRealType *) MagickAssumeAligned( 1041 AcquireAlignedMemory(1,sizeof(*kernel->values))); 1042 if (kernel->values == (MagickRealType *) NULL) 1043 return(DestroyKernelInfo(kernel)); 1044 kernel->maximum = kernel->values[0] = args->rho; 1045 break; 1046 } 1047 break; 1048 case GaussianKernel: 1049 case DoGKernel: 1050 case LoGKernel: 1051 { double 1052 sigma = fabs(args->sigma), 1053 sigma2 = fabs(args->xi), 1054 A, B, R; 1055 1056 if ( args->rho >= 1.0 ) 1057 kernel->width = (size_t)args->rho*2+1; 1058 else if ( (type != DoGKernel) || (sigma >= sigma2) ) 1059 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma); 1060 else 1061 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma2); 1062 kernel->height = kernel->width; 1063 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1064 kernel->values=(MagickRealType *) MagickAssumeAligned( 1065 AcquireAlignedMemory(kernel->width,kernel->height* 1066 sizeof(*kernel->values))); 1067 if (kernel->values == (MagickRealType *) NULL) 1068 return(DestroyKernelInfo(kernel)); 1069 1070 /* WARNING: The following generates a 'sampled gaussian' kernel. 1071 * What we really want is a 'discrete gaussian' kernel. 1072 * 1073 * How to do this is I don't know, but appears to be basied on the 1074 * Error Function 'erf()' (intergral of a gaussian) 1075 */ 1076 1077 if ( type == GaussianKernel || type == DoGKernel ) 1078 { /* Calculate a Gaussian, OR positive half of a DoG */ 1079 if ( sigma > MagickEpsilon ) 1080 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */ 1081 B = (double) (1.0/(Magick2PI*sigma*sigma)); 1082 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1083 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1084 kernel->values[i] = exp(-((double)(u*u+v*v))*A)*B; 1085 } 1086 else /* limiting case - a unity (normalized Dirac) kernel */ 1087 { (void) ResetMagickMemory(kernel->values,0, (size_t) 1088 kernel->width*kernel->height*sizeof(*kernel->values)); 1089 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1090 } 1091 } 1092 1093 if ( type == DoGKernel ) 1094 { /* Subtract a Negative Gaussian for "Difference of Gaussian" */ 1095 if ( sigma2 > MagickEpsilon ) 1096 { sigma = sigma2; /* simplify loop expressions */ 1097 A = 1.0/(2.0*sigma*sigma); 1098 B = (double) (1.0/(Magick2PI*sigma*sigma)); 1099 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1100 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1101 kernel->values[i] -= exp(-((double)(u*u+v*v))*A)*B; 1102 } 1103 else /* limiting case - a unity (normalized Dirac) kernel */ 1104 kernel->values[kernel->x+kernel->y*kernel->width] -= 1.0; 1105 } 1106 1107 if ( type == LoGKernel ) 1108 { /* Calculate a Laplacian of a Gaussian - Or Mexician Hat */ 1109 if ( sigma > MagickEpsilon ) 1110 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */ 1111 B = (double) (1.0/(MagickPI*sigma*sigma*sigma*sigma)); 1112 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1113 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1114 { R = ((double)(u*u+v*v))*A; 1115 kernel->values[i] = (1-R)*exp(-R)*B; 1116 } 1117 } 1118 else /* special case - generate a unity kernel */ 1119 { (void) ResetMagickMemory(kernel->values,0, (size_t) 1120 kernel->width*kernel->height*sizeof(*kernel->values)); 1121 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1122 } 1123 } 1124 1125 /* Note the above kernels may have been 'clipped' by a user defined 1126 ** radius, producing a smaller (darker) kernel. Also for very small 1127 ** sigma's (> 0.1) the central value becomes larger than one, and thus 1128 ** producing a very bright kernel. 1129 ** 1130 ** Normalization will still be needed. 1131 */ 1132 1133 /* Normalize the 2D Gaussian Kernel 1134 ** 1135 ** NB: a CorrelateNormalize performs a normal Normalize if 1136 ** there are no negative values. 1137 */ 1138 CalcKernelMetaData(kernel); /* the other kernel meta-data */ 1139 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue); 1140 1141 break; 1142 } 1143 case BlurKernel: 1144 { double 1145 sigma = fabs(args->sigma), 1146 alpha, beta; 1147 1148 if ( args->rho >= 1.0 ) 1149 kernel->width = (size_t)args->rho*2+1; 1150 else 1151 kernel->width = GetOptimalKernelWidth1D(args->rho,sigma); 1152 kernel->height = 1; 1153 kernel->x = (ssize_t) (kernel->width-1)/2; 1154 kernel->y = 0; 1155 kernel->negative_range = kernel->positive_range = 0.0; 1156 kernel->values=(MagickRealType *) MagickAssumeAligned( 1157 AcquireAlignedMemory(kernel->width,kernel->height* 1158 sizeof(*kernel->values))); 1159 if (kernel->values == (MagickRealType *) NULL) 1160 return(DestroyKernelInfo(kernel)); 1161 1162#if 1 1163#define KernelRank 3 1164 /* Formula derived from GetBlurKernel() in "effect.c" (plus bug fix). 1165 ** It generates a gaussian 3 times the width, and compresses it into 1166 ** the expected range. This produces a closer normalization of the 1167 ** resulting kernel, especially for very low sigma values. 1168 ** As such while wierd it is prefered. 1169 ** 1170 ** I am told this method originally came from Photoshop. 1171 ** 1172 ** A properly normalized curve is generated (apart from edge clipping) 1173 ** even though we later normalize the result (for edge clipping) 1174 ** to allow the correct generation of a "Difference of Blurs". 1175 */ 1176 1177 /* initialize */ 1178 v = (ssize_t) (kernel->width*KernelRank-1)/2; /* start/end points to fit range */ 1179 (void) ResetMagickMemory(kernel->values,0, (size_t) 1180 kernel->width*kernel->height*sizeof(*kernel->values)); 1181 /* Calculate a Positive 1D Gaussian */ 1182 if ( sigma > MagickEpsilon ) 1183 { sigma *= KernelRank; /* simplify loop expressions */ 1184 alpha = 1.0/(2.0*sigma*sigma); 1185 beta= (double) (1.0/(MagickSQ2PI*sigma )); 1186 for ( u=-v; u <= v; u++) { 1187 kernel->values[(u+v)/KernelRank] += 1188 exp(-((double)(u*u))*alpha)*beta; 1189 } 1190 } 1191 else /* special case - generate a unity kernel */ 1192 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1193#else 1194 /* Direct calculation without curve averaging 1195 This is equivelent to a KernelRank of 1 */ 1196 1197 /* Calculate a Positive Gaussian */ 1198 if ( sigma > MagickEpsilon ) 1199 { alpha = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */ 1200 beta = 1.0/(MagickSQ2PI*sigma); 1201 for ( i=0, u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1202 kernel->values[i] = exp(-((double)(u*u))*alpha)*beta; 1203 } 1204 else /* special case - generate a unity kernel */ 1205 { (void) ResetMagickMemory(kernel->values,0, (size_t) 1206 kernel->width*kernel->height*sizeof(*kernel->values)); 1207 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1208 } 1209#endif 1210 /* Note the above kernel may have been 'clipped' by a user defined 1211 ** radius, producing a smaller (darker) kernel. Also for very small 1212 ** sigma's (> 0.1) the central value becomes larger than one, as a 1213 ** result of not generating a actual 'discrete' kernel, and thus 1214 ** producing a very bright 'impulse'. 1215 ** 1216 ** Becuase of these two factors Normalization is required! 1217 */ 1218 1219 /* Normalize the 1D Gaussian Kernel 1220 ** 1221 ** NB: a CorrelateNormalize performs a normal Normalize if 1222 ** there are no negative values. 1223 */ 1224 CalcKernelMetaData(kernel); /* the other kernel meta-data */ 1225 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue); 1226 1227 /* rotate the 1D kernel by given angle */ 1228 RotateKernelInfo(kernel, args->xi ); 1229 break; 1230 } 1231 case CometKernel: 1232 { double 1233 sigma = fabs(args->sigma), 1234 A; 1235 1236 if ( args->rho < 1.0 ) 1237 kernel->width = (GetOptimalKernelWidth1D(args->rho,sigma)-1)/2+1; 1238 else 1239 kernel->width = (size_t)args->rho; 1240 kernel->x = kernel->y = 0; 1241 kernel->height = 1; 1242 kernel->negative_range = kernel->positive_range = 0.0; 1243 kernel->values=(MagickRealType *) MagickAssumeAligned( 1244 AcquireAlignedMemory(kernel->width,kernel->height* 1245 sizeof(*kernel->values))); 1246 if (kernel->values == (MagickRealType *) NULL) 1247 return(DestroyKernelInfo(kernel)); 1248 1249 /* A comet blur is half a 1D gaussian curve, so that the object is 1250 ** blurred in one direction only. This may not be quite the right 1251 ** curve to use so may change in the future. The function must be 1252 ** normalised after generation, which also resolves any clipping. 1253 ** 1254 ** As we are normalizing and not subtracting gaussians, 1255 ** there is no need for a divisor in the gaussian formula 1256 ** 1257 ** It is less comples 1258 */ 1259 if ( sigma > MagickEpsilon ) 1260 { 1261#if 1 1262#define KernelRank 3 1263 v = (ssize_t) kernel->width*KernelRank; /* start/end points */ 1264 (void) ResetMagickMemory(kernel->values,0, (size_t) 1265 kernel->width*sizeof(*kernel->values)); 1266 sigma *= KernelRank; /* simplify the loop expression */ 1267 A = 1.0/(2.0*sigma*sigma); 1268 /* B = 1.0/(MagickSQ2PI*sigma); */ 1269 for ( u=0; u < v; u++) { 1270 kernel->values[u/KernelRank] += 1271 exp(-((double)(u*u))*A); 1272 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */ 1273 } 1274 for (i=0; i < (ssize_t) kernel->width; i++) 1275 kernel->positive_range += kernel->values[i]; 1276#else 1277 A = 1.0/(2.0*sigma*sigma); /* simplify the loop expression */ 1278 /* B = 1.0/(MagickSQ2PI*sigma); */ 1279 for ( i=0; i < (ssize_t) kernel->width; i++) 1280 kernel->positive_range += 1281 kernel->values[i] = exp(-((double)(i*i))*A); 1282 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */ 1283#endif 1284 } 1285 else /* special case - generate a unity kernel */ 1286 { (void) ResetMagickMemory(kernel->values,0, (size_t) 1287 kernel->width*kernel->height*sizeof(*kernel->values)); 1288 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1289 kernel->positive_range = 1.0; 1290 } 1291 1292 kernel->minimum = 0.0; 1293 kernel->maximum = kernel->values[0]; 1294 kernel->negative_range = 0.0; 1295 1296 ScaleKernelInfo(kernel, 1.0, NormalizeValue); /* Normalize */ 1297 RotateKernelInfo(kernel, args->xi); /* Rotate by angle */ 1298 break; 1299 } 1300 case BinomialKernel: 1301 { 1302 size_t 1303 order_f; 1304 1305 if (args->rho < 1.0) 1306 kernel->width = kernel->height = 3; /* default radius = 1 */ 1307 else 1308 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 1309 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1310 1311 order_f = fact(kernel->width-1); 1312 1313 kernel->values=(MagickRealType *) MagickAssumeAligned( 1314 AcquireAlignedMemory(kernel->width,kernel->height* 1315 sizeof(*kernel->values))); 1316 if (kernel->values == (MagickRealType *) NULL) 1317 return(DestroyKernelInfo(kernel)); 1318 1319 /* set all kernel values within diamond area to scale given */ 1320 for ( i=0, v=0; v < (ssize_t)kernel->height; v++) 1321 { size_t 1322 alpha = order_f / ( fact((size_t) v) * fact(kernel->height-v-1) ); 1323 for ( u=0; u < (ssize_t)kernel->width; u++, i++) 1324 kernel->positive_range += kernel->values[i] = (double) 1325 (alpha * order_f / ( fact((size_t) u) * fact(kernel->height-u-1) )); 1326 } 1327 kernel->minimum = 1.0; 1328 kernel->maximum = kernel->values[kernel->x+kernel->y*kernel->width]; 1329 kernel->negative_range = 0.0; 1330 break; 1331 } 1332 1333 /* 1334 Convolution Kernels - Well Known Named Constant Kernels 1335 */ 1336 case LaplacianKernel: 1337 { switch ( (int) args->rho ) { 1338 case 0: 1339 default: /* laplacian square filter -- default */ 1340 kernel=ParseKernelArray("3: -1,-1,-1 -1,8,-1 -1,-1,-1"); 1341 break; 1342 case 1: /* laplacian diamond filter */ 1343 kernel=ParseKernelArray("3: 0,-1,0 -1,4,-1 0,-1,0"); 1344 break; 1345 case 2: 1346 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2"); 1347 break; 1348 case 3: 1349 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 1,-2,1"); 1350 break; 1351 case 5: /* a 5x5 laplacian */ 1352 kernel=ParseKernelArray( 1353 "5: -4,-1,0,-1,-4 -1,2,3,2,-1 0,3,4,3,0 -1,2,3,2,-1 -4,-1,0,-1,-4"); 1354 break; 1355 case 7: /* a 7x7 laplacian */ 1356 kernel=ParseKernelArray( 1357 "7:-10,-5,-2,-1,-2,-5,-10 -5,0,3,4,3,0,-5 -2,3,6,7,6,3,-2 -1,4,7,8,7,4,-1 -2,3,6,7,6,3,-2 -5,0,3,4,3,0,-5 -10,-5,-2,-1,-2,-5,-10" ); 1358 break; 1359 case 15: /* a 5x5 LoG (sigma approx 1.4) */ 1360 kernel=ParseKernelArray( 1361 "5: 0,0,-1,0,0 0,-1,-2,-1,0 -1,-2,16,-2,-1 0,-1,-2,-1,0 0,0,-1,0,0"); 1362 break; 1363 case 19: /* a 9x9 LoG (sigma approx 1.4) */ 1364 /* http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue1/IJIP-15.pdf */ 1365 kernel=ParseKernelArray( 1366 "9: 0,-1,-1,-2,-2,-2,-1,-1,0 -1,-2,-4,-5,-5,-5,-4,-2,-1 -1,-4,-5,-3,-0,-3,-5,-4,-1 -2,-5,-3,12,24,12,-3,-5,-2 -2,-5,-0,24,40,24,-0,-5,-2 -2,-5,-3,12,24,12,-3,-5,-2 -1,-4,-5,-3,-0,-3,-5,-4,-1 -1,-2,-4,-5,-5,-5,-4,-2,-1 0,-1,-1,-2,-2,-2,-1,-1,0"); 1367 break; 1368 } 1369 if (kernel == (KernelInfo *) NULL) 1370 return(kernel); 1371 kernel->type = type; 1372 break; 1373 } 1374 case SobelKernel: 1375 { /* Simple Sobel Kernel */ 1376 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1"); 1377 if (kernel == (KernelInfo *) NULL) 1378 return(kernel); 1379 kernel->type = type; 1380 RotateKernelInfo(kernel, args->rho); 1381 break; 1382 } 1383 case RobertsKernel: 1384 { 1385 kernel=ParseKernelArray("3: 0,0,0 1,-1,0 0,0,0"); 1386 if (kernel == (KernelInfo *) NULL) 1387 return(kernel); 1388 kernel->type = type; 1389 RotateKernelInfo(kernel, args->rho); 1390 break; 1391 } 1392 case PrewittKernel: 1393 { 1394 kernel=ParseKernelArray("3: 1,0,-1 1,0,-1 1,0,-1"); 1395 if (kernel == (KernelInfo *) NULL) 1396 return(kernel); 1397 kernel->type = type; 1398 RotateKernelInfo(kernel, args->rho); 1399 break; 1400 } 1401 case CompassKernel: 1402 { 1403 kernel=ParseKernelArray("3: 1,1,-1 1,-2,-1 1,1,-1"); 1404 if (kernel == (KernelInfo *) NULL) 1405 return(kernel); 1406 kernel->type = type; 1407 RotateKernelInfo(kernel, args->rho); 1408 break; 1409 } 1410 case KirschKernel: 1411 { 1412 kernel=ParseKernelArray("3: 5,-3,-3 5,0,-3 5,-3,-3"); 1413 if (kernel == (KernelInfo *) NULL) 1414 return(kernel); 1415 kernel->type = type; 1416 RotateKernelInfo(kernel, args->rho); 1417 break; 1418 } 1419 case FreiChenKernel: 1420 /* Direction is set to be left to right positive */ 1421 /* http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf -- RIGHT? */ 1422 /* http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf -- WRONG? */ 1423 { switch ( (int) args->rho ) { 1424 default: 1425 case 0: 1426 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1"); 1427 if (kernel == (KernelInfo *) NULL) 1428 return(kernel); 1429 kernel->type = type; 1430 kernel->values[3] = +(MagickRealType) MagickSQ2; 1431 kernel->values[5] = -(MagickRealType) MagickSQ2; 1432 CalcKernelMetaData(kernel); /* recalculate meta-data */ 1433 break; 1434 case 2: 1435 kernel=ParseKernelArray("3: 1,2,0 2,0,-2 0,-2,-1"); 1436 if (kernel == (KernelInfo *) NULL) 1437 return(kernel); 1438 kernel->type = type; 1439 kernel->values[1] = kernel->values[3]= +(MagickRealType) MagickSQ2; 1440 kernel->values[5] = kernel->values[7]= -(MagickRealType) MagickSQ2; 1441 CalcKernelMetaData(kernel); /* recalculate meta-data */ 1442 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue); 1443 break; 1444 case 10: 1445 kernel=AcquireKernelInfo("FreiChen:11;FreiChen:12;FreiChen:13;FreiChen:14;FreiChen:15;FreiChen:16;FreiChen:17;FreiChen:18;FreiChen:19"); 1446 if (kernel == (KernelInfo *) NULL) 1447 return(kernel); 1448 break; 1449 case 1: 1450 case 11: 1451 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1"); 1452 if (kernel == (KernelInfo *) NULL) 1453 return(kernel); 1454 kernel->type = type; 1455 kernel->values[3] = +(MagickRealType) MagickSQ2; 1456 kernel->values[5] = -(MagickRealType) MagickSQ2; 1457 CalcKernelMetaData(kernel); /* recalculate meta-data */ 1458 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue); 1459 break; 1460 case 12: 1461 kernel=ParseKernelArray("3: 1,2,1 0,0,0 1,2,1"); 1462 if (kernel == (KernelInfo *) NULL) 1463 return(kernel); 1464 kernel->type = type; 1465 kernel->values[1] = +(MagickRealType) MagickSQ2; 1466 kernel->values[7] = +(MagickRealType) MagickSQ2; 1467 CalcKernelMetaData(kernel); 1468 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue); 1469 break; 1470 case 13: 1471 kernel=ParseKernelArray("3: 2,-1,0 -1,0,1 0,1,-2"); 1472 if (kernel == (KernelInfo *) NULL) 1473 return(kernel); 1474 kernel->type = type; 1475 kernel->values[0] = +(MagickRealType) MagickSQ2; 1476 kernel->values[8] = -(MagickRealType) MagickSQ2; 1477 CalcKernelMetaData(kernel); 1478 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue); 1479 break; 1480 case 14: 1481 kernel=ParseKernelArray("3: 0,1,-2 -1,0,1 2,-1,0"); 1482 if (kernel == (KernelInfo *) NULL) 1483 return(kernel); 1484 kernel->type = type; 1485 kernel->values[2] = -(MagickRealType) MagickSQ2; 1486 kernel->values[6] = +(MagickRealType) MagickSQ2; 1487 CalcKernelMetaData(kernel); 1488 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue); 1489 break; 1490 case 15: 1491 kernel=ParseKernelArray("3: 0,-1,0 1,0,1 0,-1,0"); 1492 if (kernel == (KernelInfo *) NULL) 1493 return(kernel); 1494 kernel->type = type; 1495 ScaleKernelInfo(kernel, 1.0/2.0, NoValue); 1496 break; 1497 case 16: 1498 kernel=ParseKernelArray("3: 1,0,-1 0,0,0 -1,0,1"); 1499 if (kernel == (KernelInfo *) NULL) 1500 return(kernel); 1501 kernel->type = type; 1502 ScaleKernelInfo(kernel, 1.0/2.0, NoValue); 1503 break; 1504 case 17: 1505 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 -1,-2,1"); 1506 if (kernel == (KernelInfo *) NULL) 1507 return(kernel); 1508 kernel->type = type; 1509 ScaleKernelInfo(kernel, 1.0/6.0, NoValue); 1510 break; 1511 case 18: 1512 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2"); 1513 if (kernel == (KernelInfo *) NULL) 1514 return(kernel); 1515 kernel->type = type; 1516 ScaleKernelInfo(kernel, 1.0/6.0, NoValue); 1517 break; 1518 case 19: 1519 kernel=ParseKernelArray("3: 1,1,1 1,1,1 1,1,1"); 1520 if (kernel == (KernelInfo *) NULL) 1521 return(kernel); 1522 kernel->type = type; 1523 ScaleKernelInfo(kernel, 1.0/3.0, NoValue); 1524 break; 1525 } 1526 if ( fabs(args->sigma) >= MagickEpsilon ) 1527 /* Rotate by correctly supplied 'angle' */ 1528 RotateKernelInfo(kernel, args->sigma); 1529 else if ( args->rho > 30.0 || args->rho < -30.0 ) 1530 /* Rotate by out of bounds 'type' */ 1531 RotateKernelInfo(kernel, args->rho); 1532 break; 1533 } 1534 1535 /* 1536 Boolean or Shaped Kernels 1537 */ 1538 case DiamondKernel: 1539 { 1540 if (args->rho < 1.0) 1541 kernel->width = kernel->height = 3; /* default radius = 1 */ 1542 else 1543 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 1544 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1545 1546 kernel->values=(MagickRealType *) MagickAssumeAligned( 1547 AcquireAlignedMemory(kernel->width,kernel->height* 1548 sizeof(*kernel->values))); 1549 if (kernel->values == (MagickRealType *) NULL) 1550 return(DestroyKernelInfo(kernel)); 1551 1552 /* set all kernel values within diamond area to scale given */ 1553 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1554 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1555 if ( (labs((long) u)+labs((long) v)) <= (long) kernel->x) 1556 kernel->positive_range += kernel->values[i] = args->sigma; 1557 else 1558 kernel->values[i] = nan; 1559 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */ 1560 break; 1561 } 1562 case SquareKernel: 1563 case RectangleKernel: 1564 { double 1565 scale; 1566 if ( type == SquareKernel ) 1567 { 1568 if (args->rho < 1.0) 1569 kernel->width = kernel->height = 3; /* default radius = 1 */ 1570 else 1571 kernel->width = kernel->height = (size_t) (2*args->rho+1); 1572 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1573 scale = args->sigma; 1574 } 1575 else { 1576 /* NOTE: user defaults set in "AcquireKernelInfo()" */ 1577 if ( args->rho < 1.0 || args->sigma < 1.0 ) 1578 return(DestroyKernelInfo(kernel)); /* invalid args given */ 1579 kernel->width = (size_t)args->rho; 1580 kernel->height = (size_t)args->sigma; 1581 if ( args->xi < 0.0 || args->xi > (double)kernel->width || 1582 args->psi < 0.0 || args->psi > (double)kernel->height ) 1583 return(DestroyKernelInfo(kernel)); /* invalid args given */ 1584 kernel->x = (ssize_t) args->xi; 1585 kernel->y = (ssize_t) args->psi; 1586 scale = 1.0; 1587 } 1588 kernel->values=(MagickRealType *) MagickAssumeAligned( 1589 AcquireAlignedMemory(kernel->width,kernel->height* 1590 sizeof(*kernel->values))); 1591 if (kernel->values == (MagickRealType *) NULL) 1592 return(DestroyKernelInfo(kernel)); 1593 1594 /* set all kernel values to scale given */ 1595 u=(ssize_t) (kernel->width*kernel->height); 1596 for ( i=0; i < u; i++) 1597 kernel->values[i] = scale; 1598 kernel->minimum = kernel->maximum = scale; /* a flat shape */ 1599 kernel->positive_range = scale*u; 1600 break; 1601 } 1602 case OctagonKernel: 1603 { 1604 if (args->rho < 1.0) 1605 kernel->width = kernel->height = 5; /* default radius = 2 */ 1606 else 1607 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 1608 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1609 1610 kernel->values=(MagickRealType *) MagickAssumeAligned( 1611 AcquireAlignedMemory(kernel->width,kernel->height* 1612 sizeof(*kernel->values))); 1613 if (kernel->values == (MagickRealType *) NULL) 1614 return(DestroyKernelInfo(kernel)); 1615 1616 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1617 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1618 if ( (labs((long) u)+labs((long) v)) <= 1619 ((long)kernel->x + (long)(kernel->x/2)) ) 1620 kernel->positive_range += kernel->values[i] = args->sigma; 1621 else 1622 kernel->values[i] = nan; 1623 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */ 1624 break; 1625 } 1626 case DiskKernel: 1627 { 1628 ssize_t 1629 limit = (ssize_t)(args->rho*args->rho); 1630 1631 if (args->rho < 0.4) /* default radius approx 4.3 */ 1632 kernel->width = kernel->height = 9L, limit = 18L; 1633 else 1634 kernel->width = kernel->height = (size_t)fabs(args->rho)*2+1; 1635 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1636 1637 kernel->values=(MagickRealType *) MagickAssumeAligned( 1638 AcquireAlignedMemory(kernel->width,kernel->height* 1639 sizeof(*kernel->values))); 1640 if (kernel->values == (MagickRealType *) NULL) 1641 return(DestroyKernelInfo(kernel)); 1642 1643 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1644 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1645 if ((u*u+v*v) <= limit) 1646 kernel->positive_range += kernel->values[i] = args->sigma; 1647 else 1648 kernel->values[i] = nan; 1649 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */ 1650 break; 1651 } 1652 case PlusKernel: 1653 { 1654 if (args->rho < 1.0) 1655 kernel->width = kernel->height = 5; /* default radius 2 */ 1656 else 1657 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 1658 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1659 1660 kernel->values=(MagickRealType *) MagickAssumeAligned( 1661 AcquireAlignedMemory(kernel->width,kernel->height* 1662 sizeof(*kernel->values))); 1663 if (kernel->values == (MagickRealType *) NULL) 1664 return(DestroyKernelInfo(kernel)); 1665 1666 /* set all kernel values along axises to given scale */ 1667 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1668 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1669 kernel->values[i] = (u == 0 || v == 0) ? args->sigma : nan; 1670 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */ 1671 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0); 1672 break; 1673 } 1674 case CrossKernel: 1675 { 1676 if (args->rho < 1.0) 1677 kernel->width = kernel->height = 5; /* default radius 2 */ 1678 else 1679 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 1680 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1681 1682 kernel->values=(MagickRealType *) MagickAssumeAligned( 1683 AcquireAlignedMemory(kernel->width,kernel->height* 1684 sizeof(*kernel->values))); 1685 if (kernel->values == (MagickRealType *) NULL) 1686 return(DestroyKernelInfo(kernel)); 1687 1688 /* set all kernel values along axises to given scale */ 1689 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 1690 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1691 kernel->values[i] = (u == v || u == -v) ? args->sigma : nan; 1692 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */ 1693 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0); 1694 break; 1695 } 1696 /* 1697 HitAndMiss Kernels 1698 */ 1699 case RingKernel: 1700 case PeaksKernel: 1701 { 1702 ssize_t 1703 limit1, 1704 limit2, 1705 scale; 1706 1707 if (args->rho < args->sigma) 1708 { 1709 kernel->width = ((size_t)args->sigma)*2+1; 1710 limit1 = (ssize_t)(args->rho*args->rho); 1711 limit2 = (ssize_t)(args->sigma*args->sigma); 1712 } 1713 else 1714 { 1715 kernel->width = ((size_t)args->rho)*2+1; 1716 limit1 = (ssize_t)(args->sigma*args->sigma); 1717 limit2 = (ssize_t)(args->rho*args->rho); 1718 } 1719 if ( limit2 <= 0 ) 1720 kernel->width = 7L, limit1 = 7L, limit2 = 11L; 1721 1722 kernel->height = kernel->width; 1723 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 1724 kernel->values=(MagickRealType *) MagickAssumeAligned( 1725 AcquireAlignedMemory(kernel->width,kernel->height* 1726 sizeof(*kernel->values))); 1727 if (kernel->values == (MagickRealType *) NULL) 1728 return(DestroyKernelInfo(kernel)); 1729 1730 /* set a ring of points of 'scale' ( 0.0 for PeaksKernel ) */ 1731 scale = (ssize_t) (( type == PeaksKernel) ? 0.0 : args->xi); 1732 for ( i=0, v= -kernel->y; v <= (ssize_t)kernel->y; v++) 1733 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 1734 { ssize_t radius=u*u+v*v; 1735 if (limit1 < radius && radius <= limit2) 1736 kernel->positive_range += kernel->values[i] = (double) scale; 1737 else 1738 kernel->values[i] = nan; 1739 } 1740 kernel->minimum = kernel->maximum = (double) scale; 1741 if ( type == PeaksKernel ) { 1742 /* set the central point in the middle */ 1743 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0; 1744 kernel->positive_range = 1.0; 1745 kernel->maximum = 1.0; 1746 } 1747 break; 1748 } 1749 case EdgesKernel: 1750 { 1751 kernel=AcquireKernelInfo("ThinSE:482"); 1752 if (kernel == (KernelInfo *) NULL) 1753 return(kernel); 1754 kernel->type = type; 1755 ExpandMirrorKernelInfo(kernel); /* mirror expansion of kernels */ 1756 break; 1757 } 1758 case CornersKernel: 1759 { 1760 kernel=AcquireKernelInfo("ThinSE:87"); 1761 if (kernel == (KernelInfo *) NULL) 1762 return(kernel); 1763 kernel->type = type; 1764 ExpandRotateKernelInfo(kernel, 90.0); /* Expand 90 degree rotations */ 1765 break; 1766 } 1767 case DiagonalsKernel: 1768 { 1769 switch ( (int) args->rho ) { 1770 case 0: 1771 default: 1772 { KernelInfo 1773 *new_kernel; 1774 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-"); 1775 if (kernel == (KernelInfo *) NULL) 1776 return(kernel); 1777 kernel->type = type; 1778 new_kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-"); 1779 if (new_kernel == (KernelInfo *) NULL) 1780 return(DestroyKernelInfo(kernel)); 1781 new_kernel->type = type; 1782 LastKernelInfo(kernel)->next = new_kernel; 1783 ExpandMirrorKernelInfo(kernel); 1784 return(kernel); 1785 } 1786 case 1: 1787 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-"); 1788 break; 1789 case 2: 1790 kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-"); 1791 break; 1792 } 1793 if (kernel == (KernelInfo *) NULL) 1794 return(kernel); 1795 kernel->type = type; 1796 RotateKernelInfo(kernel, args->sigma); 1797 break; 1798 } 1799 case LineEndsKernel: 1800 { /* Kernels for finding the end of thin lines */ 1801 switch ( (int) args->rho ) { 1802 case 0: 1803 default: 1804 /* set of kernels to find all end of lines */ 1805 return(AcquireKernelInfo("LineEnds:1>;LineEnds:2>")); 1806 case 1: 1807 /* kernel for 4-connected line ends - no rotation */ 1808 kernel=ParseKernelArray("3: 0,0,- 0,1,1 0,0,-"); 1809 break; 1810 case 2: 1811 /* kernel to add for 8-connected lines - no rotation */ 1812 kernel=ParseKernelArray("3: 0,0,0 0,1,0 0,0,1"); 1813 break; 1814 case 3: 1815 /* kernel to add for orthogonal line ends - does not find corners */ 1816 kernel=ParseKernelArray("3: 0,0,0 0,1,1 0,0,0"); 1817 break; 1818 case 4: 1819 /* traditional line end - fails on last T end */ 1820 kernel=ParseKernelArray("3: 0,0,0 0,1,- 0,0,-"); 1821 break; 1822 } 1823 if (kernel == (KernelInfo *) NULL) 1824 return(kernel); 1825 kernel->type = type; 1826 RotateKernelInfo(kernel, args->sigma); 1827 break; 1828 } 1829 case LineJunctionsKernel: 1830 { /* kernels for finding the junctions of multiple lines */ 1831 switch ( (int) args->rho ) { 1832 case 0: 1833 default: 1834 /* set of kernels to find all line junctions */ 1835 return(AcquireKernelInfo("LineJunctions:1@;LineJunctions:2>")); 1836 case 1: 1837 /* Y Junction */ 1838 kernel=ParseKernelArray("3: 1,-,1 -,1,- -,1,-"); 1839 break; 1840 case 2: 1841 /* Diagonal T Junctions */ 1842 kernel=ParseKernelArray("3: 1,-,- -,1,- 1,-,1"); 1843 break; 1844 case 3: 1845 /* Orthogonal T Junctions */ 1846 kernel=ParseKernelArray("3: -,-,- 1,1,1 -,1,-"); 1847 break; 1848 case 4: 1849 /* Diagonal X Junctions */ 1850 kernel=ParseKernelArray("3: 1,-,1 -,1,- 1,-,1"); 1851 break; 1852 case 5: 1853 /* Orthogonal X Junctions - minimal diamond kernel */ 1854 kernel=ParseKernelArray("3: -,1,- 1,1,1 -,1,-"); 1855 break; 1856 } 1857 if (kernel == (KernelInfo *) NULL) 1858 return(kernel); 1859 kernel->type = type; 1860 RotateKernelInfo(kernel, args->sigma); 1861 break; 1862 } 1863 case RidgesKernel: 1864 { /* Ridges - Ridge finding kernels */ 1865 KernelInfo 1866 *new_kernel; 1867 switch ( (int) args->rho ) { 1868 case 1: 1869 default: 1870 kernel=ParseKernelArray("3x1:0,1,0"); 1871 if (kernel == (KernelInfo *) NULL) 1872 return(kernel); 1873 kernel->type = type; 1874 ExpandRotateKernelInfo(kernel, 90.0); /* 2 rotated kernels (symmetrical) */ 1875 break; 1876 case 2: 1877 kernel=ParseKernelArray("4x1:0,1,1,0"); 1878 if (kernel == (KernelInfo *) NULL) 1879 return(kernel); 1880 kernel->type = type; 1881 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotated kernels */ 1882 1883 /* Kernels to find a stepped 'thick' line, 4 rotates + mirrors */ 1884 /* Unfortunatally we can not yet rotate a non-square kernel */ 1885 /* But then we can't flip a non-symetrical kernel either */ 1886 new_kernel=ParseKernelArray("4x3+1+1:0,1,1,- -,1,1,- -,1,1,0"); 1887 if (new_kernel == (KernelInfo *) NULL) 1888 return(DestroyKernelInfo(kernel)); 1889 new_kernel->type = type; 1890 LastKernelInfo(kernel)->next = new_kernel; 1891 new_kernel=ParseKernelArray("4x3+2+1:0,1,1,- -,1,1,- -,1,1,0"); 1892 if (new_kernel == (KernelInfo *) NULL) 1893 return(DestroyKernelInfo(kernel)); 1894 new_kernel->type = type; 1895 LastKernelInfo(kernel)->next = new_kernel; 1896 new_kernel=ParseKernelArray("4x3+1+1:-,1,1,0 -,1,1,- 0,1,1,-"); 1897 if (new_kernel == (KernelInfo *) NULL) 1898 return(DestroyKernelInfo(kernel)); 1899 new_kernel->type = type; 1900 LastKernelInfo(kernel)->next = new_kernel; 1901 new_kernel=ParseKernelArray("4x3+2+1:-,1,1,0 -,1,1,- 0,1,1,-"); 1902 if (new_kernel == (KernelInfo *) NULL) 1903 return(DestroyKernelInfo(kernel)); 1904 new_kernel->type = type; 1905 LastKernelInfo(kernel)->next = new_kernel; 1906 new_kernel=ParseKernelArray("3x4+1+1:0,-,- 1,1,1 1,1,1 -,-,0"); 1907 if (new_kernel == (KernelInfo *) NULL) 1908 return(DestroyKernelInfo(kernel)); 1909 new_kernel->type = type; 1910 LastKernelInfo(kernel)->next = new_kernel; 1911 new_kernel=ParseKernelArray("3x4+1+2:0,-,- 1,1,1 1,1,1 -,-,0"); 1912 if (new_kernel == (KernelInfo *) NULL) 1913 return(DestroyKernelInfo(kernel)); 1914 new_kernel->type = type; 1915 LastKernelInfo(kernel)->next = new_kernel; 1916 new_kernel=ParseKernelArray("3x4+1+1:-,-,0 1,1,1 1,1,1 0,-,-"); 1917 if (new_kernel == (KernelInfo *) NULL) 1918 return(DestroyKernelInfo(kernel)); 1919 new_kernel->type = type; 1920 LastKernelInfo(kernel)->next = new_kernel; 1921 new_kernel=ParseKernelArray("3x4+1+2:-,-,0 1,1,1 1,1,1 0,-,-"); 1922 if (new_kernel == (KernelInfo *) NULL) 1923 return(DestroyKernelInfo(kernel)); 1924 new_kernel->type = type; 1925 LastKernelInfo(kernel)->next = new_kernel; 1926 break; 1927 } 1928 break; 1929 } 1930 case ConvexHullKernel: 1931 { 1932 KernelInfo 1933 *new_kernel; 1934 /* first set of 8 kernels */ 1935 kernel=ParseKernelArray("3: 1,1,- 1,0,- 1,-,0"); 1936 if (kernel == (KernelInfo *) NULL) 1937 return(kernel); 1938 kernel->type = type; 1939 ExpandRotateKernelInfo(kernel, 90.0); 1940 /* append the mirror versions too - no flip function yet */ 1941 new_kernel=ParseKernelArray("3: 1,1,1 1,0,- -,-,0"); 1942 if (new_kernel == (KernelInfo *) NULL) 1943 return(DestroyKernelInfo(kernel)); 1944 new_kernel->type = type; 1945 ExpandRotateKernelInfo(new_kernel, 90.0); 1946 LastKernelInfo(kernel)->next = new_kernel; 1947 break; 1948 } 1949 case SkeletonKernel: 1950 { 1951 switch ( (int) args->rho ) { 1952 case 1: 1953 default: 1954 /* Traditional Skeleton... 1955 ** A cyclically rotated single kernel 1956 */ 1957 kernel=AcquireKernelInfo("ThinSE:482"); 1958 if (kernel == (KernelInfo *) NULL) 1959 return(kernel); 1960 kernel->type = type; 1961 ExpandRotateKernelInfo(kernel, 45.0); /* 8 rotations */ 1962 break; 1963 case 2: 1964 /* HIPR Variation of the cyclic skeleton 1965 ** Corners of the traditional method made more forgiving, 1966 ** but the retain the same cyclic order. 1967 */ 1968 kernel=AcquireKernelInfo("ThinSE:482; ThinSE:87x90;"); 1969 if (kernel == (KernelInfo *) NULL) 1970 return(kernel); 1971 if (kernel->next == (KernelInfo *) NULL) 1972 return(DestroyKernelInfo(kernel)); 1973 kernel->type = type; 1974 kernel->next->type = type; 1975 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotations of the 2 kernels */ 1976 break; 1977 case 3: 1978 /* Dan Bloomberg Skeleton, from his paper on 3x3 thinning SE's 1979 ** "Connectivity-Preserving Morphological Image Thransformations" 1980 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers, 1981 ** http://www.leptonica.com/papers/conn.pdf 1982 */ 1983 kernel=AcquireKernelInfo( 1984 "ThinSE:41; ThinSE:42; ThinSE:43"); 1985 if (kernel == (KernelInfo *) NULL) 1986 return(kernel); 1987 kernel->type = type; 1988 kernel->next->type = type; 1989 kernel->next->next->type = type; 1990 ExpandMirrorKernelInfo(kernel); /* 12 kernels total */ 1991 break; 1992 } 1993 break; 1994 } 1995 case ThinSEKernel: 1996 { /* Special kernels for general thinning, while preserving connections 1997 ** "Connectivity-Preserving Morphological Image Thransformations" 1998 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers, 1999 ** http://www.leptonica.com/papers/conn.pdf 2000 ** And 2001 ** http://tpgit.github.com/Leptonica/ccthin_8c_source.html 2002 ** 2003 ** Note kernels do not specify the origin pixel, allowing them 2004 ** to be used for both thickening and thinning operations. 2005 */ 2006 switch ( (int) args->rho ) { 2007 /* SE for 4-connected thinning */ 2008 case 41: /* SE_4_1 */ 2009 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,-,1"); 2010 break; 2011 case 42: /* SE_4_2 */ 2012 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,0,-"); 2013 break; 2014 case 43: /* SE_4_3 */ 2015 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,-,1"); 2016 break; 2017 case 44: /* SE_4_4 */ 2018 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,-"); 2019 break; 2020 case 45: /* SE_4_5 */ 2021 kernel=ParseKernelArray("3: -,0,1 0,-,1 -,0,-"); 2022 break; 2023 case 46: /* SE_4_6 */ 2024 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,1"); 2025 break; 2026 case 47: /* SE_4_7 */ 2027 kernel=ParseKernelArray("3: -,1,1 0,-,1 -,0,-"); 2028 break; 2029 case 48: /* SE_4_8 */ 2030 kernel=ParseKernelArray("3: -,-,1 0,-,1 0,-,1"); 2031 break; 2032 case 49: /* SE_4_9 */ 2033 kernel=ParseKernelArray("3: 0,-,1 0,-,1 -,-,1"); 2034 break; 2035 /* SE for 8-connected thinning - negatives of the above */ 2036 case 81: /* SE_8_0 */ 2037 kernel=ParseKernelArray("3: -,1,- 0,-,1 -,1,-"); 2038 break; 2039 case 82: /* SE_8_2 */ 2040 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,-,-"); 2041 break; 2042 case 83: /* SE_8_3 */ 2043 kernel=ParseKernelArray("3: 0,-,- 0,-,1 -,1,-"); 2044 break; 2045 case 84: /* SE_8_4 */ 2046 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,-"); 2047 break; 2048 case 85: /* SE_8_5 */ 2049 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,-"); 2050 break; 2051 case 86: /* SE_8_6 */ 2052 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,1"); 2053 break; 2054 case 87: /* SE_8_7 */ 2055 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,0,-"); 2056 break; 2057 case 88: /* SE_8_8 */ 2058 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,1,-"); 2059 break; 2060 case 89: /* SE_8_9 */ 2061 kernel=ParseKernelArray("3: 0,1,- 0,-,1 -,1,-"); 2062 break; 2063 /* Special combined SE kernels */ 2064 case 423: /* SE_4_2 , SE_4_3 Combined Kernel */ 2065 kernel=ParseKernelArray("3: -,-,1 0,-,- -,0,-"); 2066 break; 2067 case 823: /* SE_8_2 , SE_8_3 Combined Kernel */ 2068 kernel=ParseKernelArray("3: -,1,- -,-,1 0,-,-"); 2069 break; 2070 case 481: /* SE_48_1 - General Connected Corner Kernel */ 2071 kernel=ParseKernelArray("3: -,1,1 0,-,1 0,0,-"); 2072 break; 2073 default: 2074 case 482: /* SE_48_2 - General Edge Kernel */ 2075 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,1"); 2076 break; 2077 } 2078 if (kernel == (KernelInfo *) NULL) 2079 return(kernel); 2080 kernel->type = type; 2081 RotateKernelInfo(kernel, args->sigma); 2082 break; 2083 } 2084 /* 2085 Distance Measuring Kernels 2086 */ 2087 case ChebyshevKernel: 2088 { 2089 if (args->rho < 1.0) 2090 kernel->width = kernel->height = 3; /* default radius = 1 */ 2091 else 2092 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 2093 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 2094 2095 kernel->values=(MagickRealType *) MagickAssumeAligned( 2096 AcquireAlignedMemory(kernel->width,kernel->height* 2097 sizeof(*kernel->values))); 2098 if (kernel->values == (MagickRealType *) NULL) 2099 return(DestroyKernelInfo(kernel)); 2100 2101 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 2102 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 2103 kernel->positive_range += ( kernel->values[i] = 2104 args->sigma*MagickMax(fabs((double)u),fabs((double)v)) ); 2105 kernel->maximum = kernel->values[0]; 2106 break; 2107 } 2108 case ManhattanKernel: 2109 { 2110 if (args->rho < 1.0) 2111 kernel->width = kernel->height = 3; /* default radius = 1 */ 2112 else 2113 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 2114 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 2115 2116 kernel->values=(MagickRealType *) MagickAssumeAligned( 2117 AcquireAlignedMemory(kernel->width,kernel->height* 2118 sizeof(*kernel->values))); 2119 if (kernel->values == (MagickRealType *) NULL) 2120 return(DestroyKernelInfo(kernel)); 2121 2122 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 2123 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 2124 kernel->positive_range += ( kernel->values[i] = 2125 args->sigma*(labs((long) u)+labs((long) v)) ); 2126 kernel->maximum = kernel->values[0]; 2127 break; 2128 } 2129 case OctagonalKernel: 2130 { 2131 if (args->rho < 2.0) 2132 kernel->width = kernel->height = 5; /* default/minimum radius = 2 */ 2133 else 2134 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 2135 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 2136 2137 kernel->values=(MagickRealType *) MagickAssumeAligned( 2138 AcquireAlignedMemory(kernel->width,kernel->height* 2139 sizeof(*kernel->values))); 2140 if (kernel->values == (MagickRealType *) NULL) 2141 return(DestroyKernelInfo(kernel)); 2142 2143 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 2144 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 2145 { 2146 double 2147 r1 = MagickMax(fabs((double)u),fabs((double)v)), 2148 r2 = floor((double)(labs((long)u)+labs((long)v)+1)/1.5); 2149 kernel->positive_range += kernel->values[i] = 2150 args->sigma*MagickMax(r1,r2); 2151 } 2152 kernel->maximum = kernel->values[0]; 2153 break; 2154 } 2155 case EuclideanKernel: 2156 { 2157 if (args->rho < 1.0) 2158 kernel->width = kernel->height = 3; /* default radius = 1 */ 2159 else 2160 kernel->width = kernel->height = ((size_t)args->rho)*2+1; 2161 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2; 2162 2163 kernel->values=(MagickRealType *) MagickAssumeAligned( 2164 AcquireAlignedMemory(kernel->width,kernel->height* 2165 sizeof(*kernel->values))); 2166 if (kernel->values == (MagickRealType *) NULL) 2167 return(DestroyKernelInfo(kernel)); 2168 2169 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++) 2170 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++) 2171 kernel->positive_range += ( kernel->values[i] = 2172 args->sigma*sqrt((double)(u*u+v*v)) ); 2173 kernel->maximum = kernel->values[0]; 2174 break; 2175 } 2176 default: 2177 { 2178 /* No-Op Kernel - Basically just a single pixel on its own */ 2179 kernel=ParseKernelArray("1:1"); 2180 if (kernel == (KernelInfo *) NULL) 2181 return(kernel); 2182 kernel->type = UndefinedKernel; 2183 break; 2184 } 2185 break; 2186 } 2187 return(kernel); 2188} 2189 2190/* 2191%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2192% % 2193% % 2194% % 2195% C l o n e K e r n e l I n f o % 2196% % 2197% % 2198% % 2199%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2200% 2201% CloneKernelInfo() creates a new clone of the given Kernel List so that its 2202% can be modified without effecting the original. The cloned kernel should 2203% be destroyed using DestoryKernelInfo() when no longer needed. 2204% 2205% The format of the CloneKernelInfo method is: 2206% 2207% KernelInfo *CloneKernelInfo(const KernelInfo *kernel) 2208% 2209% A description of each parameter follows: 2210% 2211% o kernel: the Morphology/Convolution kernel to be cloned 2212% 2213*/ 2214MagickExport KernelInfo *CloneKernelInfo(const KernelInfo *kernel) 2215{ 2216 register ssize_t 2217 i; 2218 2219 KernelInfo 2220 *new_kernel; 2221 2222 assert(kernel != (KernelInfo *) NULL); 2223 new_kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel)); 2224 if (new_kernel == (KernelInfo *) NULL) 2225 return(new_kernel); 2226 *new_kernel=(*kernel); /* copy values in structure */ 2227 2228 /* replace the values with a copy of the values */ 2229 new_kernel->values=(MagickRealType *) MagickAssumeAligned( 2230 AcquireAlignedMemory(kernel->width,kernel->height*sizeof(*kernel->values))); 2231 if (new_kernel->values == (MagickRealType *) NULL) 2232 return(DestroyKernelInfo(new_kernel)); 2233 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++) 2234 new_kernel->values[i]=kernel->values[i]; 2235 2236 /* Also clone the next kernel in the kernel list */ 2237 if ( kernel->next != (KernelInfo *) NULL ) { 2238 new_kernel->next = CloneKernelInfo(kernel->next); 2239 if ( new_kernel->next == (KernelInfo *) NULL ) 2240 return(DestroyKernelInfo(new_kernel)); 2241 } 2242 2243 return(new_kernel); 2244} 2245 2246/* 2247%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2248% % 2249% % 2250% % 2251% D e s t r o y K e r n e l I n f o % 2252% % 2253% % 2254% % 2255%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2256% 2257% DestroyKernelInfo() frees the memory used by a Convolution/Morphology 2258% kernel. 2259% 2260% The format of the DestroyKernelInfo method is: 2261% 2262% KernelInfo *DestroyKernelInfo(KernelInfo *kernel) 2263% 2264% A description of each parameter follows: 2265% 2266% o kernel: the Morphology/Convolution kernel to be destroyed 2267% 2268*/ 2269MagickExport KernelInfo *DestroyKernelInfo(KernelInfo *kernel) 2270{ 2271 assert(kernel != (KernelInfo *) NULL); 2272 if ( kernel->next != (KernelInfo *) NULL ) 2273 kernel->next=DestroyKernelInfo(kernel->next); 2274 kernel->values=(MagickRealType *) RelinquishAlignedMemory(kernel->values); 2275 kernel=(KernelInfo *) RelinquishMagickMemory(kernel); 2276 return(kernel); 2277} 2278 2279/* 2280%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2281% % 2282% % 2283% % 2284+ E x p a n d M i r r o r K e r n e l I n f o % 2285% % 2286% % 2287% % 2288%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2289% 2290% ExpandMirrorKernelInfo() takes a single kernel, and expands it into a 2291% sequence of 90-degree rotated kernels but providing a reflected 180 2292% rotatation, before the -/+ 90-degree rotations. 2293% 2294% This special rotation order produces a better, more symetrical thinning of 2295% objects. 2296% 2297% The format of the ExpandMirrorKernelInfo method is: 2298% 2299% void ExpandMirrorKernelInfo(KernelInfo *kernel) 2300% 2301% A description of each parameter follows: 2302% 2303% o kernel: the Morphology/Convolution kernel 2304% 2305% This function is only internel to this module, as it is not finalized, 2306% especially with regard to non-orthogonal angles, and rotation of larger 2307% 2D kernels. 2308*/ 2309 2310#if 0 2311static void FlopKernelInfo(KernelInfo *kernel) 2312 { /* Do a Flop by reversing each row. */ 2313 size_t 2314 y; 2315 register ssize_t 2316 x,r; 2317 register double 2318 *k,t; 2319 2320 for ( y=0, k=kernel->values; y < kernel->height; y++, k+=kernel->width) 2321 for ( x=0, r=kernel->width-1; x<kernel->width/2; x++, r--) 2322 t=k[x], k[x]=k[r], k[r]=t; 2323 2324 kernel->x = kernel->width - kernel->x - 1; 2325 angle = fmod(angle+180.0, 360.0); 2326 } 2327#endif 2328 2329static void ExpandMirrorKernelInfo(KernelInfo *kernel) 2330{ 2331 KernelInfo 2332 *clone, 2333 *last; 2334 2335 last = kernel; 2336 2337 clone = CloneKernelInfo(last); 2338 RotateKernelInfo(clone, 180); /* flip */ 2339 LastKernelInfo(last)->next = clone; 2340 last = clone; 2341 2342 clone = CloneKernelInfo(last); 2343 RotateKernelInfo(clone, 90); /* transpose */ 2344 LastKernelInfo(last)->next = clone; 2345 last = clone; 2346 2347 clone = CloneKernelInfo(last); 2348 RotateKernelInfo(clone, 180); /* flop */ 2349 LastKernelInfo(last)->next = clone; 2350 2351 return; 2352} 2353 2354/* 2355%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2356% % 2357% % 2358% % 2359+ E x p a n d R o t a t e K e r n e l I n f o % 2360% % 2361% % 2362% % 2363%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2364% 2365% ExpandRotateKernelInfo() takes a kernel list, and expands it by rotating 2366% incrementally by the angle given, until the kernel repeats. 2367% 2368% WARNING: 45 degree rotations only works for 3x3 kernels. 2369% While 90 degree roatations only works for linear and square kernels 2370% 2371% The format of the ExpandRotateKernelInfo method is: 2372% 2373% void ExpandRotateKernelInfo(KernelInfo *kernel, double angle) 2374% 2375% A description of each parameter follows: 2376% 2377% o kernel: the Morphology/Convolution kernel 2378% 2379% o angle: angle to rotate in degrees 2380% 2381% This function is only internel to this module, as it is not finalized, 2382% especially with regard to non-orthogonal angles, and rotation of larger 2383% 2D kernels. 2384*/ 2385 2386/* Internal Routine - Return true if two kernels are the same */ 2387static MagickBooleanType SameKernelInfo(const KernelInfo *kernel1, 2388 const KernelInfo *kernel2) 2389{ 2390 register size_t 2391 i; 2392 2393 /* check size and origin location */ 2394 if ( kernel1->width != kernel2->width 2395 || kernel1->height != kernel2->height 2396 || kernel1->x != kernel2->x 2397 || kernel1->y != kernel2->y ) 2398 return MagickFalse; 2399 2400 /* check actual kernel values */ 2401 for (i=0; i < (kernel1->width*kernel1->height); i++) { 2402 /* Test for Nan equivalence */ 2403 if ( IsNaN(kernel1->values[i]) && !IsNaN(kernel2->values[i]) ) 2404 return MagickFalse; 2405 if ( IsNaN(kernel2->values[i]) && !IsNaN(kernel1->values[i]) ) 2406 return MagickFalse; 2407 /* Test actual values are equivalent */ 2408 if ( fabs(kernel1->values[i] - kernel2->values[i]) >= MagickEpsilon ) 2409 return MagickFalse; 2410 } 2411 2412 return MagickTrue; 2413} 2414 2415static void ExpandRotateKernelInfo(KernelInfo *kernel, const double angle) 2416{ 2417 KernelInfo 2418 *clone, 2419 *last; 2420 2421 last = kernel; 2422 while(1) { 2423 clone = CloneKernelInfo(last); 2424 RotateKernelInfo(clone, angle); 2425 if ( SameKernelInfo(kernel, clone) == MagickTrue ) 2426 break; 2427 LastKernelInfo(last)->next = clone; 2428 last = clone; 2429 } 2430 clone = DestroyKernelInfo(clone); /* kernel has repeated - junk the clone */ 2431 return; 2432} 2433 2434/* 2435%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2436% % 2437% % 2438% % 2439+ C a l c M e t a K e r n a l I n f o % 2440% % 2441% % 2442% % 2443%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2444% 2445% CalcKernelMetaData() recalculate the KernelInfo meta-data of this kernel only, 2446% using the kernel values. This should only ne used if it is not possible to 2447% calculate that meta-data in some easier way. 2448% 2449% It is important that the meta-data is correct before ScaleKernelInfo() is 2450% used to perform kernel normalization. 2451% 2452% The format of the CalcKernelMetaData method is: 2453% 2454% void CalcKernelMetaData(KernelInfo *kernel, const double scale ) 2455% 2456% A description of each parameter follows: 2457% 2458% o kernel: the Morphology/Convolution kernel to modify 2459% 2460% WARNING: Minimum and Maximum values are assumed to include zero, even if 2461% zero is not part of the kernel (as in Gaussian Derived kernels). This 2462% however is not true for flat-shaped morphological kernels. 2463% 2464% WARNING: Only the specific kernel pointed to is modified, not a list of 2465% multiple kernels. 2466% 2467% This is an internal function and not expected to be useful outside this 2468% module. This could change however. 2469*/ 2470static void CalcKernelMetaData(KernelInfo *kernel) 2471{ 2472 register size_t 2473 i; 2474 2475 kernel->minimum = kernel->maximum = 0.0; 2476 kernel->negative_range = kernel->positive_range = 0.0; 2477 for (i=0; i < (kernel->width*kernel->height); i++) 2478 { 2479 if ( fabs(kernel->values[i]) < MagickEpsilon ) 2480 kernel->values[i] = 0.0; 2481 ( kernel->values[i] < 0) 2482 ? ( kernel->negative_range += kernel->values[i] ) 2483 : ( kernel->positive_range += kernel->values[i] ); 2484 Minimize(kernel->minimum, kernel->values[i]); 2485 Maximize(kernel->maximum, kernel->values[i]); 2486 } 2487 2488 return; 2489} 2490 2491/* 2492%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2493% % 2494% % 2495% % 2496% M o r p h o l o g y A p p l y % 2497% % 2498% % 2499% % 2500%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2501% 2502% MorphologyApply() applies a morphological method, multiple times using 2503% a list of multiple kernels. This is the method that should be called by 2504% other 'operators' that internally use morphology operations as part of 2505% their processing. 2506% 2507% It is basically equivalent to as MorphologyImage() (see below) but without 2508% any user controls. This allows internel programs to use this method to 2509% perform a specific task without possible interference by any API user 2510% supplied settings. 2511% 2512% It is MorphologyImage() task to extract any such user controls, and 2513% pass them to this function for processing. 2514% 2515% More specifically all given kernels should already be scaled, normalised, 2516% and blended appropriatally before being parred to this routine. The 2517% appropriate bias, and compose (typically 'UndefinedComposeOp') given. 2518% 2519% The format of the MorphologyApply method is: 2520% 2521% Image *MorphologyApply(const Image *image,MorphologyMethod method, 2522% const ssize_t iterations,const KernelInfo *kernel, 2523% const CompositeMethod compose,const double bias, 2524% ExceptionInfo *exception) 2525% 2526% A description of each parameter follows: 2527% 2528% o image: the source image 2529% 2530% o method: the morphology method to be applied. 2531% 2532% o iterations: apply the operation this many times (or no change). 2533% A value of -1 means loop until no change found. 2534% How this is applied may depend on the morphology method. 2535% Typically this is a value of 1. 2536% 2537% o channel: the channel type. 2538% 2539% o kernel: An array of double representing the morphology kernel. 2540% 2541% o compose: How to handle or merge multi-kernel results. 2542% If 'UndefinedCompositeOp' use default for the Morphology method. 2543% If 'NoCompositeOp' force image to be re-iterated by each kernel. 2544% Otherwise merge the results using the compose method given. 2545% 2546% o bias: Convolution Output Bias. 2547% 2548% o exception: return any errors or warnings in this structure. 2549% 2550*/ 2551static ssize_t MorphologyPrimitive(const Image *image,Image *morphology_image, 2552 const MorphologyMethod method,const KernelInfo *kernel,const double bias, 2553 ExceptionInfo *exception) 2554{ 2555#define MorphologyTag "Morphology/Image" 2556 2557 CacheView 2558 *image_view, 2559 *morphology_view; 2560 2561 OffsetInfo 2562 offset; 2563 2564 ssize_t 2565 y; 2566 2567 size_t 2568 width, 2569 changed; 2570 2571 MagickBooleanType 2572 status; 2573 2574 MagickOffsetType 2575 progress; 2576 2577 assert(image != (Image *) NULL); 2578 assert(image->signature == MagickSignature); 2579 assert(morphology_image != (Image *) NULL); 2580 assert(morphology_image->signature == MagickSignature); 2581 assert(kernel != (KernelInfo *) NULL); 2582 assert(kernel->signature == MagickSignature); 2583 assert(exception != (ExceptionInfo *) NULL); 2584 assert(exception->signature == MagickSignature); 2585 status=MagickTrue; 2586 changed=0; 2587 progress=0; 2588 image_view=AcquireVirtualCacheView(image,exception); 2589 morphology_view=AcquireAuthenticCacheView(morphology_image,exception); 2590 width=image->columns+kernel->width-1; 2591 switch (method) 2592 { 2593 case ConvolveMorphology: 2594 case DilateMorphology: 2595 case DilateIntensityMorphology: 2596 case IterativeDistanceMorphology: 2597 { 2598 /* 2599 Kernel needs to used with reflection about origin. 2600 */ 2601 offset.x=(ssize_t) kernel->width-kernel->x-1; 2602 offset.y=(ssize_t) kernel->height-kernel->y-1; 2603 break; 2604 } 2605 case ErodeMorphology: 2606 case ErodeIntensityMorphology: 2607 case HitAndMissMorphology: 2608 case ThinningMorphology: 2609 case ThickenMorphology: 2610 { 2611 offset.x=kernel->x; 2612 offset.y=kernel->y; 2613 break; 2614 } 2615 default: 2616 { 2617 assert("Not a Primitive Morphology Method" != (char *) NULL); 2618 break; 2619 } 2620 } 2621 if ((method == ConvolveMorphology) && (kernel->width == 1)) 2622 { 2623 register ssize_t 2624 x; 2625 2626 /* 2627 Special handling (for speed) of vertical (blur) kernels. This performs 2628 its handling in columns rather than in rows. This is only done 2629 for convolve as it is the only method that generates very large 1-D 2630 vertical kernels (such as a 'BlurKernel') 2631 */ 2632#if defined(MAGICKCORE_OPENMP_SUPPORT) 2633 #pragma omp parallel for schedule(static,4) shared(progress,status) \ 2634 magick_threads(image,morphology_image,image->columns,1) 2635#endif 2636 for (x=0; x < (ssize_t) image->columns; x++) 2637 { 2638 register const Quantum 2639 *restrict p; 2640 2641 register Quantum 2642 *restrict q; 2643 2644 register ssize_t 2645 y; 2646 2647 ssize_t 2648 center; 2649 2650 if (status == MagickFalse) 2651 continue; 2652 p=GetCacheViewVirtualPixels(image_view,x,-offset.y,1,image->rows+ 2653 kernel->height-1,exception); 2654 q=GetCacheViewAuthenticPixels(morphology_view,x,0,1, 2655 morphology_image->rows,exception); 2656 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL)) 2657 { 2658 status=MagickFalse; 2659 continue; 2660 } 2661 center=(ssize_t) GetPixelChannels(image)*offset.y; 2662 for (y=0; y < (ssize_t) image->rows; y++) 2663 { 2664 register ssize_t 2665 i; 2666 2667 for (i=0; i < (ssize_t) GetPixelChannels(image); i++) 2668 { 2669 double 2670 alpha, 2671 gamma, 2672 pixel; 2673 2674 PixelChannel 2675 channel; 2676 2677 PixelTrait 2678 morphology_traits, 2679 traits; 2680 2681 register const MagickRealType 2682 *restrict k; 2683 2684 register const Quantum 2685 *restrict pixels; 2686 2687 register ssize_t 2688 u; 2689 2690 ssize_t 2691 v; 2692 2693 channel=GetPixelChannelChannel(image,i); 2694 traits=GetPixelChannelTraits(image,channel); 2695 morphology_traits=GetPixelChannelTraits(morphology_image,channel); 2696 if ((traits == UndefinedPixelTrait) || 2697 (morphology_traits == UndefinedPixelTrait)) 2698 continue; 2699 if (((morphology_traits & CopyPixelTrait) != 0) || 2700 (GetPixelMask(image,p+center) != 0)) 2701 { 2702 SetPixelChannel(morphology_image,channel,p[center+i],q); 2703 continue; 2704 } 2705 k=(&kernel->values[kernel->width*kernel->height-1]); 2706 pixels=p; 2707 pixel=bias; 2708 gamma=0.0; 2709 if ((morphology_traits & BlendPixelTrait) == 0) 2710 { 2711 /* 2712 No alpha blending. 2713 */ 2714 for (v=0; v < (ssize_t) kernel->height; v++) 2715 { 2716 for (u=0; u < (ssize_t) kernel->width; u++) 2717 { 2718 if (IsNaN(*k) != MagickFalse) 2719 continue; 2720 pixel+=(*k)*pixels[i]; 2721 gamma+=(*k); 2722 k--; 2723 pixels+=GetPixelChannels(image); 2724 } 2725 } 2726 gamma=PerceptibleReciprocal(gamma); 2727 pixel*=gamma; 2728 if (fabs(pixel-p[center+i]) > MagickEpsilon) 2729 changed++; 2730 SetPixelChannel(morphology_image,channel,ClampToQuantum(pixel), 2731 q); 2732 continue; 2733 } 2734 /* 2735 Alpha blending. 2736 */ 2737 for (v=0; v < (ssize_t) kernel->width; v++) 2738 { 2739 for (u=0; u < (ssize_t) kernel->width; u++) 2740 { 2741 if (IsNaN(*k) != MagickFalse) 2742 continue; 2743 alpha=(double) (QuantumScale*GetPixelAlpha(image,pixels)); 2744 pixel+=(*k)*alpha*pixels[i]; 2745 gamma+=(*k)*alpha; 2746 k--; 2747 pixels+=GetPixelChannels(image); 2748 } 2749 } 2750 gamma=PerceptibleReciprocal(gamma); 2751 pixel*=gamma; 2752 if (fabs(pixel-p[center+i]) > MagickEpsilon) 2753 changed++; 2754 SetPixelChannel(morphology_image,channel,ClampToQuantum(pixel),q); 2755 } 2756 p+=GetPixelChannels(image); 2757 q+=GetPixelChannels(morphology_image); 2758 } 2759 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse) 2760 status=MagickFalse; 2761 if (image->progress_monitor != (MagickProgressMonitor) NULL) 2762 { 2763 MagickBooleanType 2764 proceed; 2765 2766#if defined(MAGICKCORE_OPENMP_SUPPORT) 2767 #pragma omp critical (MagickCore_MorphologyImage) 2768#endif 2769 proceed=SetImageProgress(image,MorphologyTag,progress++, 2770 image->rows); 2771 if (proceed == MagickFalse) 2772 status=MagickFalse; 2773 } 2774 } 2775 morphology_image->type=image->type; 2776 morphology_view=DestroyCacheView(morphology_view); 2777 image_view=DestroyCacheView(image_view); 2778 return(status ? (ssize_t) changed : 0); 2779 } 2780 /* 2781 Normal handling of horizontal or rectangular kernels (row by row). 2782 */ 2783#if defined(MAGICKCORE_OPENMP_SUPPORT) 2784 #pragma omp parallel for schedule(static,4) shared(progress,status) \ 2785 magick_threads(image,morphology_image,image->rows,1) 2786#endif 2787 for (y=0; y < (ssize_t) image->rows; y++) 2788 { 2789 register const Quantum 2790 *restrict p; 2791 2792 register Quantum 2793 *restrict q; 2794 2795 register ssize_t 2796 x; 2797 2798 ssize_t 2799 center; 2800 2801 if (status == MagickFalse) 2802 continue; 2803 p=GetCacheViewVirtualPixels(image_view,-offset.x,y-offset.y,width, 2804 kernel->height,exception); 2805 q=GetCacheViewAuthenticPixels(morphology_view,0,y,morphology_image->columns, 2806 1,exception); 2807 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL)) 2808 { 2809 status=MagickFalse; 2810 continue; 2811 } 2812 center=(ssize_t) (GetPixelChannels(image)*width*offset.y+ 2813 GetPixelChannels(image)*offset.x); 2814 for (x=0; x < (ssize_t) image->columns; x++) 2815 { 2816 register ssize_t 2817 i; 2818 2819 for (i=0; i < (ssize_t) GetPixelChannels(image); i++) 2820 { 2821 double 2822 alpha, 2823 gamma, 2824 maximum, 2825 minimum, 2826 pixel; 2827 2828 PixelChannel 2829 channel; 2830 2831 PixelTrait 2832 morphology_traits, 2833 traits; 2834 2835 register const MagickRealType 2836 *restrict k; 2837 2838 register const Quantum 2839 *restrict pixels; 2840 2841 register ssize_t 2842 u; 2843 2844 ssize_t 2845 v; 2846 2847 channel=GetPixelChannelChannel(image,i); 2848 traits=GetPixelChannelTraits(image,channel); 2849 morphology_traits=GetPixelChannelTraits(morphology_image,channel); 2850 if ((traits == UndefinedPixelTrait) || 2851 (morphology_traits == UndefinedPixelTrait)) 2852 continue; 2853 if (((morphology_traits & CopyPixelTrait) != 0) || 2854 (GetPixelMask(image,p+center) != 0)) 2855 { 2856 SetPixelChannel(morphology_image,channel,p[center+i],q); 2857 continue; 2858 } 2859 pixels=p; 2860 maximum=0.0; 2861 minimum=(double) QuantumRange; 2862 switch (method) 2863 { 2864 case ConvolveMorphology: pixel=bias; break; 2865 case HitAndMissMorphology: pixel=(double) QuantumRange; break; 2866 case ThinningMorphology: pixel=(double) QuantumRange; break; 2867 case ThickenMorphology: pixel=(double) QuantumRange; break; 2868 case ErodeMorphology: pixel=(double) QuantumRange; break; 2869 case DilateMorphology: pixel=0.0; break; 2870 case ErodeIntensityMorphology: 2871 case DilateIntensityMorphology: 2872 case IterativeDistanceMorphology: 2873 { 2874 pixel=(double) p[center+i]; 2875 break; 2876 } 2877 default: pixel=0; break; 2878 } 2879 gamma=0.0; 2880 switch (method) 2881 { 2882 case ConvolveMorphology: 2883 { 2884 /* 2885 Weighted Average of pixels using reflected kernel 2886 2887 For correct working of this operation for asymetrical 2888 kernels, the kernel needs to be applied in its reflected form. 2889 That is its values needs to be reversed. 2890 2891 Correlation is actually the same as this but without reflecting 2892 the kernel, and thus 'lower-level' that Convolution. However 2893 as Convolution is the more common method used, and it does not 2894 really cost us much in terms of processing to use a reflected 2895 kernel, so it is Convolution that is implemented. 2896 2897 Correlation will have its kernel reflected before calling 2898 this function to do a Convolve. 2899 2900 For more details of Correlation vs Convolution see 2901 http://www.cs.umd.edu/~djacobs/CMSC426/Convolution.pdf 2902 */ 2903 k=(&kernel->values[kernel->width*kernel->height-1]); 2904 if ((morphology_traits & BlendPixelTrait) == 0) 2905 { 2906 /* 2907 No alpha blending. 2908 */ 2909 for (v=0; v < (ssize_t) kernel->height; v++) 2910 { 2911 for (u=0; u < (ssize_t) kernel->width; u++) 2912 { 2913 if (IsNaN(*k) == MagickFalse) 2914 { 2915 pixel+=(*k)*pixels[i]; 2916 gamma+=(*k); 2917 } 2918 k--; 2919 pixels+=GetPixelChannels(image); 2920 } 2921 pixels+=image->columns*GetPixelChannels(image); 2922 } 2923 gamma=PerceptibleReciprocal(gamma); 2924 pixel*=gamma; 2925 break; 2926 } 2927 /* 2928 Alpha blending. 2929 */ 2930 for (v=0; v < (ssize_t) kernel->width; v++) 2931 { 2932 for (u=0; u < (ssize_t) kernel->width; u++) 2933 { 2934 if (IsNaN(*k) == MagickFalse) 2935 { 2936 alpha=(double) (QuantumScale*GetPixelAlpha(image,pixels)); 2937 pixel+=(*k)*alpha*pixels[i]; 2938 gamma+=(*k)*alpha; 2939 } 2940 k--; 2941 pixels+=GetPixelChannels(image); 2942 } 2943 pixels+=image->columns*GetPixelChannels(image); 2944 } 2945 gamma=PerceptibleReciprocal(gamma); 2946 pixel*=gamma; 2947 break; 2948 } 2949 case ErodeMorphology: 2950 { 2951 /* 2952 Minimum value within kernel neighbourhood. 2953 2954 The kernel is not reflected for this operation. In normal 2955 Greyscale Morphology, the kernel value should be added 2956 to the real value, this is currently not done, due to the 2957 nature of the boolean kernels being used. 2958 */ 2959 k=kernel->values; 2960 for (v=0; v < (ssize_t) kernel->height; v++) 2961 { 2962 for (u=0; u < (ssize_t) kernel->width; u++) 2963 { 2964 if ((IsNaN(*k) == MagickFalse) && (*k >= 0.5)) 2965 { 2966 if ((double) pixels[i] < pixel) 2967 pixel=(double) pixels[i]; 2968 } 2969 k++; 2970 pixels+=GetPixelChannels(image); 2971 } 2972 pixels+=image->columns*GetPixelChannels(image); 2973 } 2974 break; 2975 } 2976 case DilateMorphology: 2977 { 2978 /* 2979 Maximum value within kernel neighbourhood. 2980 2981 For correct working of this operation for asymetrical kernels, 2982 the kernel needs to be applied in its reflected form. That is 2983 its values needs to be reversed. 2984 2985 In normal Greyscale Morphology, the kernel value should be 2986 added to the real value, this is currently not done, due to the 2987 nature of the boolean kernels being used. 2988 */ 2989 k=(&kernel->values[kernel->width*kernel->height-1]); 2990 for (v=0; v < (ssize_t) kernel->height; v++) 2991 { 2992 for (u=0; u < (ssize_t) kernel->width; u++) 2993 { 2994 if ((IsNaN(*k) == MagickFalse) && (*k > 0.5)) 2995 { 2996 if ((double) pixels[i] > pixel) 2997 pixel=(double) pixels[i]; 2998 } 2999 k--; 3000 pixels+=GetPixelChannels(image); 3001 } 3002 pixels+=image->columns*GetPixelChannels(image); 3003 } 3004 break; 3005 } 3006 case HitAndMissMorphology: 3007 case ThinningMorphology: 3008 case ThickenMorphology: 3009 { 3010 /* 3011 Minimum of foreground pixel minus maxumum of background pixels. 3012 3013 The kernel is not reflected for this operation, and consists 3014 of both foreground and background pixel neighbourhoods, 0.0 for 3015 background, and 1.0 for foreground with either Nan or 0.5 values 3016 for don't care. 3017 3018 This never produces a meaningless negative result. Such results 3019 cause Thinning/Thicken to not work correctly when used against a 3020 greyscale image. 3021 */ 3022 k=kernel->values; 3023 for (v=0; v < (ssize_t) kernel->height; v++) 3024 { 3025 for (u=0; u < (ssize_t) kernel->width; u++) 3026 { 3027 if (IsNaN(*k) == MagickFalse) 3028 { 3029 if (*k > 0.7) 3030 { 3031 if ((double) pixels[i] < pixel) 3032 pixel=(double) pixels[i]; 3033 } 3034 else 3035 if (*k < 0.3) 3036 { 3037 if ((double) pixels[i] > maximum) 3038 maximum=(double) pixels[i]; 3039 } 3040 } 3041 k++; 3042 pixels+=GetPixelChannels(image); 3043 } 3044 pixels+=image->columns*GetPixelChannels(image); 3045 } 3046 pixel-=maximum; 3047 if (pixel < 0.0) 3048 pixel=0.0; 3049 if (method == ThinningMorphology) 3050 pixel=(double) p[center+i]-pixel; 3051 else 3052 if (method == ThickenMorphology) 3053 pixel+=(double) p[center+i]+pixel; 3054 break; 3055 } 3056 case ErodeIntensityMorphology: 3057 { 3058 /* 3059 Select pixel with minimum intensity within kernel neighbourhood. 3060 3061 The kernel is not reflected for this operation. 3062 */ 3063 k=kernel->values; 3064 for (v=0; v < (ssize_t) kernel->height; v++) 3065 { 3066 for (u=0; u < (ssize_t) kernel->width; u++) 3067 { 3068 if ((IsNaN(*k) == MagickFalse) && (*k >= 0.5)) 3069 { 3070 if (GetPixelIntensity(image,pixels) < minimum) 3071 { 3072 pixel=(double) pixels[i]; 3073 minimum=GetPixelIntensity(image,pixels); 3074 } 3075 } 3076 k++; 3077 pixels+=GetPixelChannels(image); 3078 } 3079 pixels+=image->columns*GetPixelChannels(image); 3080 } 3081 break; 3082 } 3083 case DilateIntensityMorphology: 3084 { 3085 /* 3086 Select pixel with maximum intensity within kernel neighbourhood. 3087 3088 The kernel is not reflected for this operation. 3089 */ 3090 k=(&kernel->values[kernel->width*kernel->height-1]); 3091 for (v=0; v < (ssize_t) kernel->height; v++) 3092 { 3093 for (u=0; u < (ssize_t) kernel->width; u++) 3094 { 3095 if ((IsNaN(*k) == MagickFalse) && (*k >= 0.5)) 3096 { 3097 if (GetPixelIntensity(image,pixels) > maximum) 3098 { 3099 pixel=(double) pixels[i]; 3100 maximum=GetPixelIntensity(image,pixels); 3101 } 3102 } 3103 k--; 3104 pixels+=GetPixelChannels(image); 3105 } 3106 pixels+=image->columns*GetPixelChannels(image); 3107 } 3108 break; 3109 } 3110 case IterativeDistanceMorphology: 3111 { 3112 /* 3113 Compute th iterative distance from black edge of a white image 3114 shape. Essentually white values are decreased to the smallest 3115 'distance from edge' it can find. 3116 3117 It works by adding kernel values to the neighbourhood, and and 3118 select the minimum value found. The kernel is rotated before 3119 use, so kernel distances match resulting distances, when a user 3120 provided asymmetric kernel is applied. 3121 3122 This code is nearly identical to True GrayScale Morphology but 3123 not quite. 3124 3125 GreyDilate Kernel values added, maximum value found Kernel is 3126 rotated before use. 3127 3128 GrayErode: Kernel values subtracted and minimum value found No 3129 kernel rotation used. 3130 3131 Note the the Iterative Distance method is essentially a 3132 GrayErode, but with negative kernel values, and kernel rotation 3133 applied. 3134 */ 3135 k=(&kernel->values[kernel->width*kernel->height-1]); 3136 for (v=0; v < (ssize_t) kernel->height; v++) 3137 { 3138 for (u=0; u < (ssize_t) kernel->width; u++) 3139 { 3140 if (IsNaN(*k) == MagickFalse) 3141 { 3142 if ((pixels[i]+(*k)) < pixel) 3143 pixel=(double) pixels[i]+(*k); 3144 } 3145 k--; 3146 pixels+=GetPixelChannels(image); 3147 } 3148 pixels+=image->columns*GetPixelChannels(image); 3149 } 3150 break; 3151 } 3152 case UndefinedMorphology: 3153 default: 3154 break; 3155 } 3156 if (fabs(pixel-p[center+i]) > MagickEpsilon) 3157 changed++; 3158 SetPixelChannel(morphology_image,channel,ClampToQuantum(pixel),q); 3159 } 3160 p+=GetPixelChannels(image); 3161 q+=GetPixelChannels(morphology_image); 3162 } 3163 if ( SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse) 3164 status=MagickFalse; 3165 if (image->progress_monitor != (MagickProgressMonitor) NULL) 3166 { 3167 MagickBooleanType 3168 proceed; 3169 3170#if defined(MAGICKCORE_OPENMP_SUPPORT) 3171 #pragma omp critical (MagickCore_MorphologyImage) 3172#endif 3173 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows); 3174 if (proceed == MagickFalse) 3175 status=MagickFalse; 3176 } 3177 } 3178 morphology_view=DestroyCacheView(morphology_view); 3179 image_view=DestroyCacheView(image_view); 3180 return(status ? (ssize_t)changed : -1); 3181} 3182 3183/* 3184 This is almost identical to the MorphologyPrimative() function above, but 3185 applies the primitive directly to the actual image using two passes, once in 3186 each direction, with the results of the previous (and current) row being 3187 re-used. 3188 3189 That is after each row is 'Sync'ed' into the image, the next row makes use of 3190 those values as part of the calculation of the next row. It repeats, but 3191 going in the oppisite (bottom-up) direction. 3192 3193 Because of this 're-use of results' this function can not make use of multi- 3194 threaded, parellel processing. 3195*/ 3196static ssize_t MorphologyPrimitiveDirect(Image *image, 3197 const MorphologyMethod method,const KernelInfo *kernel, 3198 ExceptionInfo *exception) 3199{ 3200 CacheView 3201 *morphology_view, 3202 *image_view; 3203 3204 MagickBooleanType 3205 status; 3206 3207 MagickOffsetType 3208 progress; 3209 3210 OffsetInfo 3211 offset; 3212 3213 size_t 3214 width, 3215 changed; 3216 3217 ssize_t 3218 y; 3219 3220 assert(image != (Image *) NULL); 3221 assert(image->signature == MagickSignature); 3222 assert(kernel != (KernelInfo *) NULL); 3223 assert(kernel->signature == MagickSignature); 3224 assert(exception != (ExceptionInfo *) NULL); 3225 assert(exception->signature == MagickSignature); 3226 status=MagickTrue; 3227 changed=0; 3228 progress=0; 3229 switch(method) 3230 { 3231 case DistanceMorphology: 3232 case VoronoiMorphology: 3233 { 3234 /* 3235 Kernel reflected about origin. 3236 */ 3237 offset.x=(ssize_t) kernel->width-kernel->x-1; 3238 offset.y=(ssize_t) kernel->height-kernel->y-1; 3239 break; 3240 } 3241 default: 3242 { 3243 offset.x=kernel->x; 3244 offset.y=kernel->y; 3245 break; 3246 } 3247 } 3248 /* 3249 Two views into same image, do not thread. 3250 */ 3251 image_view=AcquireVirtualCacheView(image,exception); 3252 morphology_view=AcquireAuthenticCacheView(image,exception); 3253 width=image->columns+kernel->width-1; 3254 for (y=0; y < (ssize_t) image->rows; y++) 3255 { 3256 register const Quantum 3257 *restrict p; 3258 3259 register Quantum 3260 *restrict q; 3261 3262 register ssize_t 3263 x; 3264 3265 /* 3266 Read virtual pixels, and authentic pixels, from the same image! We read 3267 using virtual to get virtual pixel handling, but write back into the same 3268 image. 3269 3270 Only top half of kernel is processed as we do a single pass downward 3271 through the image iterating the distance function as we go. 3272 */ 3273 if (status == MagickFalse) 3274 break; 3275 p=GetCacheViewVirtualPixels(image_view,-offset.x,y-offset.y,width,(size_t) 3276 offset.y+1,exception); 3277 q=GetCacheViewAuthenticPixels(morphology_view,0,y,image->columns,1, 3278 exception); 3279 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL)) 3280 status=MagickFalse; 3281 if (status == MagickFalse) 3282 break; 3283 for (x=0; x < (ssize_t) image->columns; x++) 3284 { 3285 register ssize_t 3286 i; 3287 3288 for (i=0; i < (ssize_t) GetPixelChannels(image); i++) 3289 { 3290 double 3291 pixel; 3292 3293 PixelTrait 3294 traits; 3295 3296 register const MagickRealType 3297 *restrict k; 3298 3299 register const Quantum 3300 *restrict pixels; 3301 3302 register ssize_t 3303 u; 3304 3305 ssize_t 3306 v; 3307 3308 traits=GetPixelChannelTraits(image,i); 3309 if (traits == UndefinedPixelTrait) 3310 continue; 3311 if (((traits & CopyPixelTrait) != 0) || (GetPixelMask(image,p) != 0)) 3312 continue; 3313 pixels=p; 3314 pixel=(double) q[i]; 3315 switch (method) 3316 { 3317 case DistanceMorphology: 3318 { 3319 k=(&kernel->values[kernel->width*kernel->height-1]); 3320 for (v=0; v <= offset.y; v++) 3321 { 3322 for (u=0; u < (ssize_t) kernel->width; u++) 3323 { 3324 if (IsNaN(*k) == MagickFalse) 3325 { 3326 if ((pixels[i]+(*k)) < pixel) 3327 pixel=(double) pixels[i]+(*k); 3328 } 3329 k--; 3330 pixels+=GetPixelChannels(image); 3331 } 3332 pixels+=width*GetPixelChannels(image); 3333 } 3334 k=(&kernel->values[kernel->width*(kernel->y+1)-1]); 3335 pixels=q-offset.x*GetPixelChannels(image); 3336 for (u=0; u < offset.x; u++) 3337 { 3338 if ((IsNaN(*k) == MagickFalse) && ((x+u-offset.x) >= 0)) 3339 { 3340 if ((pixels[i]+(*k)) < pixel) 3341 pixel=(double) pixels[i]+(*k); 3342 } 3343 k--; 3344 pixels+=GetPixelChannels(image); 3345 } 3346 break; 3347 } 3348 case VoronoiMorphology: 3349 { 3350 k=(&kernel->values[kernel->width*kernel->height-1]); 3351 for (v=0; v < offset.y; v++) 3352 { 3353 for (u=0; u < (ssize_t) kernel->width; u++) 3354 { 3355 if (IsNaN(*k) == MagickFalse) 3356 { 3357 if ((pixels[i]+(*k)) < pixel) 3358 pixel=(double) pixels[i]+(*k); 3359 } 3360 k--; 3361 pixels+=GetPixelChannels(image); 3362 } 3363 pixels+=width*GetPixelChannels(image); 3364 } 3365 k=(&kernel->values[kernel->width*(kernel->y+1)-1]); 3366 pixels=q-offset.x*GetPixelChannels(image); 3367 for (u=0; u < offset.x; u++) 3368 { 3369 if ((IsNaN(*k) == MagickFalse) && ((x+u-offset.x) >= 0)) 3370 { 3371 if ((pixels[i]+(*k)) < pixel) 3372 pixel=(double) pixels[i]+(*k); 3373 } 3374 k--; 3375 pixels+=GetPixelChannels(image); 3376 } 3377 break; 3378 } 3379 default: 3380 break; 3381 } 3382 if (fabs(pixel-q[i]) > MagickEpsilon) 3383 changed++; 3384 q[i]=ClampToQuantum(pixel); 3385 } 3386 p+=GetPixelChannels(image); 3387 q+=GetPixelChannels(image); 3388 } 3389 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse) 3390 status=MagickFalse; 3391 if (image->progress_monitor != (MagickProgressMonitor) NULL) 3392 { 3393 MagickBooleanType 3394 proceed; 3395 3396#if defined(MAGICKCORE_OPENMP_SUPPORT) 3397 #pragma omp critical (MagickCore_MorphologyImage) 3398#endif 3399 proceed=SetImageProgress(image,MorphologyTag,progress++,2*image->rows); 3400 if (proceed == MagickFalse) 3401 status=MagickFalse; 3402 } 3403 } 3404 morphology_view=DestroyCacheView(morphology_view); 3405 image_view=DestroyCacheView(image_view); 3406 /* 3407 Do the reverse pass through the image. 3408 */ 3409 image_view=AcquireVirtualCacheView(image,exception); 3410 morphology_view=AcquireAuthenticCacheView(image,exception); 3411 for (y=(ssize_t) image->rows-1; y >= 0; y--) 3412 { 3413 register const Quantum 3414 *restrict p; 3415 3416 register Quantum 3417 *restrict q; 3418 3419 register ssize_t 3420 x; 3421 3422 /* 3423 Read virtual pixels, and authentic pixels, from the same image. We 3424 read using virtual to get virtual pixel handling, but write back 3425 into the same image. 3426 3427 Only the bottom half of the kernel is processed as we up the image. 3428 */ 3429 if (status == MagickFalse) 3430 break; 3431 p=GetCacheViewVirtualPixels(image_view,-offset.x,y,width,(size_t) 3432 kernel->y+1,exception); 3433 q=GetCacheViewAuthenticPixels(morphology_view,0,y,image->columns,1, 3434 exception); 3435 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL)) 3436 status=MagickFalse; 3437 if (status == MagickFalse) 3438 break; 3439 p+=(image->columns-1)*GetPixelChannels(image); 3440 q+=(image->columns-1)*GetPixelChannels(image); 3441 for (x=(ssize_t) image->columns-1; x >= 0; x--) 3442 { 3443 register ssize_t 3444 i; 3445 3446 for (i=0; i < (ssize_t) GetPixelChannels(image); i++) 3447 { 3448 double 3449 pixel; 3450 3451 PixelTrait 3452 traits; 3453 3454 register const MagickRealType 3455 *restrict k; 3456 3457 register const Quantum 3458 *restrict pixels; 3459 3460 register ssize_t 3461 u; 3462 3463 ssize_t 3464 v; 3465 3466 traits=GetPixelChannelTraits(image,i); 3467 if (traits == UndefinedPixelTrait) 3468 continue; 3469 if (((traits & CopyPixelTrait) != 0) || (GetPixelMask(image,p) != 0)) 3470 continue; 3471 pixels=p; 3472 pixel=(double) q[i]; 3473 switch (method) 3474 { 3475 case DistanceMorphology: 3476 { 3477 k=(&kernel->values[kernel->width*(kernel->y+1)-1]); 3478 for (v=offset.y; v < (ssize_t) kernel->height; v++) 3479 { 3480 for (u=0; u < (ssize_t) kernel->width; u++) 3481 { 3482 if (IsNaN(*k) == MagickFalse) 3483 { 3484 if ((pixels[i]+(*k)) < pixel) 3485 pixel=(double) pixels[i]+(*k); 3486 } 3487 k--; 3488 pixels+=GetPixelChannels(image); 3489 } 3490 pixels+=width*GetPixelChannels(image); 3491 } 3492 k=(&kernel->values[kernel->width*kernel->y+kernel->x-1]); 3493 pixels=q-offset.x*GetPixelChannels(image); 3494 for (u=offset.x+1; u < (ssize_t) kernel->width; u++) 3495 { 3496 if ((IsNaN(*k) == MagickFalse) && 3497 ((x+u-offset.x) < (ssize_t) image->columns)) 3498 { 3499 if ((pixels[i]+(*k)) < pixel) 3500 pixel=(double) pixels[i]+(*k); 3501 } 3502 k--; 3503 pixels+=GetPixelChannels(image); 3504 } 3505 break; 3506 } 3507 case VoronoiMorphology: 3508 { 3509 k=(&kernel->values[kernel->width*(kernel->y+1)-1]); 3510 for (v=offset.y; v < (ssize_t) kernel->height; v++) 3511 { 3512 for (u=0; u < (ssize_t) kernel->width; u++) 3513 { 3514 if (IsNaN(*k) == MagickFalse) 3515 { 3516 if ((pixels[i]+(*k)) < pixel) 3517 pixel=(double) pixels[i]+(*k); 3518 } 3519 k--; 3520 pixels+=GetPixelChannels(image); 3521 } 3522 pixels+=width*GetPixelChannels(image); 3523 } 3524 k=(&kernel->values[kernel->width*(kernel->y+1)-1]); 3525 pixels=q-offset.x*GetPixelChannels(image); 3526 for (u=offset.x+1; u < (ssize_t) kernel->width; u++) 3527 { 3528 if ((IsNaN(*k) == MagickFalse) && 3529 ((x+u-offset.x) < (ssize_t) image->columns)) 3530 { 3531 if ((pixels[i]+(*k)) < pixel) 3532 pixel=(double) pixels[i]+(*k); 3533 } 3534 k--; 3535 pixels+=GetPixelChannels(image); 3536 } 3537 break; 3538 } 3539 default: 3540 break; 3541 } 3542 if (fabs(pixel-q[i]) > MagickEpsilon) 3543 changed++; 3544 q[i]=ClampToQuantum(pixel); 3545 } 3546 p-=GetPixelChannels(image); 3547 q-=GetPixelChannels(image); 3548 } 3549 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse) 3550 status=MagickFalse; 3551 if (image->progress_monitor != (MagickProgressMonitor) NULL) 3552 { 3553 MagickBooleanType 3554 proceed; 3555 3556#if defined(MAGICKCORE_OPENMP_SUPPORT) 3557 #pragma omp critical (MagickCore_MorphologyImage) 3558#endif 3559 proceed=SetImageProgress(image,MorphologyTag,progress++,2*image->rows); 3560 if (proceed == MagickFalse) 3561 status=MagickFalse; 3562 } 3563 } 3564 morphology_view=DestroyCacheView(morphology_view); 3565 image_view=DestroyCacheView(image_view); 3566 return(status ? (ssize_t) changed : -1); 3567} 3568 3569/* 3570 Apply a Morphology by calling one of the above low level primitive 3571 application functions. This function handles any iteration loops, 3572 composition or re-iteration of results, and compound morphology methods that 3573 is based on multiple low-level (staged) morphology methods. 3574 3575 Basically this provides the complex glue between the requested morphology 3576 method and raw low-level implementation (above). 3577*/ 3578MagickPrivate Image *MorphologyApply(const Image *image, 3579 const MorphologyMethod method, const ssize_t iterations, 3580 const KernelInfo *kernel, const CompositeOperator compose,const double bias, 3581 ExceptionInfo *exception) 3582{ 3583 CompositeOperator 3584 curr_compose; 3585 3586 Image 3587 *curr_image, /* Image we are working with or iterating */ 3588 *work_image, /* secondary image for primitive iteration */ 3589 *save_image, /* saved image - for 'edge' method only */ 3590 *rslt_image; /* resultant image - after multi-kernel handling */ 3591 3592 KernelInfo 3593 *reflected_kernel, /* A reflected copy of the kernel (if needed) */ 3594 *norm_kernel, /* the current normal un-reflected kernel */ 3595 *rflt_kernel, /* the current reflected kernel (if needed) */ 3596 *this_kernel; /* the kernel being applied */ 3597 3598 MorphologyMethod 3599 primitive; /* the current morphology primitive being applied */ 3600 3601 CompositeOperator 3602 rslt_compose; /* multi-kernel compose method for results to use */ 3603 3604 MagickBooleanType 3605 special, /* do we use a direct modify function? */ 3606 verbose; /* verbose output of results */ 3607 3608 size_t 3609 method_loop, /* Loop 1: number of compound method iterations (norm 1) */ 3610 method_limit, /* maximum number of compound method iterations */ 3611 kernel_number, /* Loop 2: the kernel number being applied */ 3612 stage_loop, /* Loop 3: primitive loop for compound morphology */ 3613 stage_limit, /* how many primitives are in this compound */ 3614 kernel_loop, /* Loop 4: iterate the kernel over image */ 3615 kernel_limit, /* number of times to iterate kernel */ 3616 count, /* total count of primitive steps applied */ 3617 kernel_changed, /* total count of changed using iterated kernel */ 3618 method_changed; /* total count of changed over method iteration */ 3619 3620 ssize_t 3621 changed; /* number pixels changed by last primitive operation */ 3622 3623 char 3624 v_info[80]; 3625 3626 assert(image != (Image *) NULL); 3627 assert(image->signature == MagickSignature); 3628 assert(kernel != (KernelInfo *) NULL); 3629 assert(kernel->signature == MagickSignature); 3630 assert(exception != (ExceptionInfo *) NULL); 3631 assert(exception->signature == MagickSignature); 3632 3633 count = 0; /* number of low-level morphology primitives performed */ 3634 if ( iterations == 0 ) 3635 return((Image *)NULL); /* null operation - nothing to do! */ 3636 3637 kernel_limit = (size_t) iterations; 3638 if ( iterations < 0 ) /* negative interations = infinite (well alomst) */ 3639 kernel_limit = image->columns>image->rows ? image->columns : image->rows; 3640 3641 verbose = IsStringTrue(GetImageArtifact(image,"verbose")); 3642 3643 /* initialise for cleanup */ 3644 curr_image = (Image *) image; 3645 curr_compose = image->compose; 3646 (void) curr_compose; 3647 work_image = save_image = rslt_image = (Image *) NULL; 3648 reflected_kernel = (KernelInfo *) NULL; 3649 3650 /* Initialize specific methods 3651 * + which loop should use the given iteratations 3652 * + how many primitives make up the compound morphology 3653 * + multi-kernel compose method to use (by default) 3654 */ 3655 method_limit = 1; /* just do method once, unless otherwise set */ 3656 stage_limit = 1; /* assume method is not a compound */ 3657 special = MagickFalse; /* assume it is NOT a direct modify primitive */ 3658 rslt_compose = compose; /* and we are composing multi-kernels as given */ 3659 switch( method ) { 3660 case SmoothMorphology: /* 4 primitive compound morphology */ 3661 stage_limit = 4; 3662 break; 3663 case OpenMorphology: /* 2 primitive compound morphology */ 3664 case OpenIntensityMorphology: 3665 case TopHatMorphology: 3666 case CloseMorphology: 3667 case CloseIntensityMorphology: 3668 case BottomHatMorphology: 3669 case EdgeMorphology: 3670 stage_limit = 2; 3671 break; 3672 case HitAndMissMorphology: 3673 rslt_compose = LightenCompositeOp; /* Union of multi-kernel results */ 3674 /* FALL THUR */ 3675 case ThinningMorphology: 3676 case ThickenMorphology: 3677 method_limit = kernel_limit; /* iterate the whole method */ 3678 kernel_limit = 1; /* do not do kernel iteration */ 3679 break; 3680 case DistanceMorphology: 3681 case VoronoiMorphology: 3682 special = MagickTrue; /* use special direct primative */ 3683 break; 3684 default: 3685 break; 3686 } 3687 3688 /* Apply special methods with special requirments 3689 ** For example, single run only, or post-processing requirements 3690 */ 3691 if ( special == MagickTrue ) 3692 { 3693 rslt_image=CloneImage(image,0,0,MagickTrue,exception); 3694 if (rslt_image == (Image *) NULL) 3695 goto error_cleanup; 3696 if (SetImageStorageClass(rslt_image,DirectClass,exception) == MagickFalse) 3697 goto error_cleanup; 3698 3699 changed = MorphologyPrimitiveDirect(rslt_image, method, 3700 kernel, exception); 3701 3702 if ( IfMagickTrue(verbose) ) 3703 (void) (void) FormatLocaleFile(stderr, 3704 "%s:%.20g.%.20g #%.20g => Changed %.20g\n", 3705 CommandOptionToMnemonic(MagickMorphologyOptions, method), 3706 1.0,0.0,1.0, (double) changed); 3707 3708 if ( changed < 0 ) 3709 goto error_cleanup; 3710 3711 if ( method == VoronoiMorphology ) { 3712 /* Preserve the alpha channel of input image - but turned off */ 3713 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel, 3714 exception); 3715 (void) CompositeImage(rslt_image,image,CopyAlphaCompositeOp, 3716 MagickTrue,0,0,exception); 3717 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel, 3718 exception); 3719 } 3720 goto exit_cleanup; 3721 } 3722 3723 /* Handle user (caller) specified multi-kernel composition method */ 3724 if ( compose != UndefinedCompositeOp ) 3725 rslt_compose = compose; /* override default composition for method */ 3726 if ( rslt_compose == UndefinedCompositeOp ) 3727 rslt_compose = NoCompositeOp; /* still not defined! Then re-iterate */ 3728 3729 /* Some methods require a reflected kernel to use with primitives. 3730 * Create the reflected kernel for those methods. */ 3731 switch ( method ) { 3732 case CorrelateMorphology: 3733 case CloseMorphology: 3734 case CloseIntensityMorphology: 3735 case BottomHatMorphology: 3736 case SmoothMorphology: 3737 reflected_kernel = CloneKernelInfo(kernel); 3738 if (reflected_kernel == (KernelInfo *) NULL) 3739 goto error_cleanup; 3740 RotateKernelInfo(reflected_kernel,180); 3741 break; 3742 default: 3743 break; 3744 } 3745 3746 /* Loops around more primitive morpholgy methods 3747 ** erose, dilate, open, close, smooth, edge, etc... 3748 */ 3749 /* Loop 1: iterate the compound method */ 3750 method_loop = 0; 3751 method_changed = 1; 3752 while ( method_loop < method_limit && method_changed > 0 ) { 3753 method_loop++; 3754 method_changed = 0; 3755 3756 /* Loop 2: iterate over each kernel in a multi-kernel list */ 3757 norm_kernel = (KernelInfo *) kernel; 3758 this_kernel = (KernelInfo *) kernel; 3759 rflt_kernel = reflected_kernel; 3760 3761 kernel_number = 0; 3762 while ( norm_kernel != NULL ) { 3763 3764 /* Loop 3: Compound Morphology Staging - Select Primative to apply */ 3765 stage_loop = 0; /* the compound morphology stage number */ 3766 while ( stage_loop < stage_limit ) { 3767 stage_loop++; /* The stage of the compound morphology */ 3768 3769 /* Select primitive morphology for this stage of compound method */ 3770 this_kernel = norm_kernel; /* default use unreflected kernel */ 3771 primitive = method; /* Assume method is a primitive */ 3772 switch( method ) { 3773 case ErodeMorphology: /* just erode */ 3774 case EdgeInMorphology: /* erode and image difference */ 3775 primitive = ErodeMorphology; 3776 break; 3777 case DilateMorphology: /* just dilate */ 3778 case EdgeOutMorphology: /* dilate and image difference */ 3779 primitive = DilateMorphology; 3780 break; 3781 case OpenMorphology: /* erode then dialate */ 3782 case TopHatMorphology: /* open and image difference */ 3783 primitive = ErodeMorphology; 3784 if ( stage_loop == 2 ) 3785 primitive = DilateMorphology; 3786 break; 3787 case OpenIntensityMorphology: 3788 primitive = ErodeIntensityMorphology; 3789 if ( stage_loop == 2 ) 3790 primitive = DilateIntensityMorphology; 3791 break; 3792 case CloseMorphology: /* dilate, then erode */ 3793 case BottomHatMorphology: /* close and image difference */ 3794 this_kernel = rflt_kernel; /* use the reflected kernel */ 3795 primitive = DilateMorphology; 3796 if ( stage_loop == 2 ) 3797 primitive = ErodeMorphology; 3798 break; 3799 case CloseIntensityMorphology: 3800 this_kernel = rflt_kernel; /* use the reflected kernel */ 3801 primitive = DilateIntensityMorphology; 3802 if ( stage_loop == 2 ) 3803 primitive = ErodeIntensityMorphology; 3804 break; 3805 case SmoothMorphology: /* open, close */ 3806 switch ( stage_loop ) { 3807 case 1: /* start an open method, which starts with Erode */ 3808 primitive = ErodeMorphology; 3809 break; 3810 case 2: /* now Dilate the Erode */ 3811 primitive = DilateMorphology; 3812 break; 3813 case 3: /* Reflect kernel a close */ 3814 this_kernel = rflt_kernel; /* use the reflected kernel */ 3815 primitive = DilateMorphology; 3816 break; 3817 case 4: /* Finish the Close */ 3818 this_kernel = rflt_kernel; /* use the reflected kernel */ 3819 primitive = ErodeMorphology; 3820 break; 3821 } 3822 break; 3823 case EdgeMorphology: /* dilate and erode difference */ 3824 primitive = DilateMorphology; 3825 if ( stage_loop == 2 ) { 3826 save_image = curr_image; /* save the image difference */ 3827 curr_image = (Image *) image; 3828 primitive = ErodeMorphology; 3829 } 3830 break; 3831 case CorrelateMorphology: 3832 /* A Correlation is a Convolution with a reflected kernel. 3833 ** However a Convolution is a weighted sum using a reflected 3834 ** kernel. It may seem stange to convert a Correlation into a 3835 ** Convolution as the Correlation is the simplier method, but 3836 ** Convolution is much more commonly used, and it makes sense to 3837 ** implement it directly so as to avoid the need to duplicate the 3838 ** kernel when it is not required (which is typically the 3839 ** default). 3840 */ 3841 this_kernel = rflt_kernel; /* use the reflected kernel */ 3842 primitive = ConvolveMorphology; 3843 break; 3844 default: 3845 break; 3846 } 3847 assert( this_kernel != (KernelInfo *) NULL ); 3848 3849 /* Extra information for debugging compound operations */ 3850 if ( IfMagickTrue(verbose) ) { 3851 if ( stage_limit > 1 ) 3852 (void) FormatLocaleString(v_info,MaxTextExtent,"%s:%.20g.%.20g -> ", 3853 CommandOptionToMnemonic(MagickMorphologyOptions,method),(double) 3854 method_loop,(double) stage_loop); 3855 else if ( primitive != method ) 3856 (void) FormatLocaleString(v_info, MaxTextExtent, "%s:%.20g -> ", 3857 CommandOptionToMnemonic(MagickMorphologyOptions, method),(double) 3858 method_loop); 3859 else 3860 v_info[0] = '\0'; 3861 } 3862 3863 /* Loop 4: Iterate the kernel with primitive */ 3864 kernel_loop = 0; 3865 kernel_changed = 0; 3866 changed = 1; 3867 while ( kernel_loop < kernel_limit && changed > 0 ) { 3868 kernel_loop++; /* the iteration of this kernel */ 3869 3870 /* Create a clone as the destination image, if not yet defined */ 3871 if ( work_image == (Image *) NULL ) 3872 { 3873 work_image=CloneImage(image,0,0,MagickTrue,exception); 3874 if (work_image == (Image *) NULL) 3875 goto error_cleanup; 3876 if (SetImageStorageClass(work_image,DirectClass,exception) == MagickFalse) 3877 goto error_cleanup; 3878 /* work_image->type=image->type; ??? */ 3879 } 3880 3881 /* APPLY THE MORPHOLOGICAL PRIMITIVE (curr -> work) */ 3882 count++; 3883 changed = MorphologyPrimitive(curr_image, work_image, primitive, 3884 this_kernel, bias, exception); 3885 3886 if ( IfMagickTrue(verbose) ) { 3887 if ( kernel_loop > 1 ) 3888 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line from previous */ 3889 (void) (void) FormatLocaleFile(stderr, 3890 "%s%s%s:%.20g.%.20g #%.20g => Changed %.20g", 3891 v_info,CommandOptionToMnemonic(MagickMorphologyOptions, 3892 primitive),(this_kernel == rflt_kernel ) ? "*" : "", 3893 (double) (method_loop+kernel_loop-1),(double) kernel_number, 3894 (double) count,(double) changed); 3895 } 3896 if ( changed < 0 ) 3897 goto error_cleanup; 3898 kernel_changed += changed; 3899 method_changed += changed; 3900 3901 /* prepare next loop */ 3902 { Image *tmp = work_image; /* swap images for iteration */ 3903 work_image = curr_image; 3904 curr_image = tmp; 3905 } 3906 if ( work_image == image ) 3907 work_image = (Image *) NULL; /* replace input 'image' */ 3908 3909 } /* End Loop 4: Iterate the kernel with primitive */ 3910 3911 if ( IfMagickTrue(verbose) && kernel_changed != (size_t)changed ) 3912 (void) FormatLocaleFile(stderr, " Total %.20g",(double) kernel_changed); 3913 if ( IfMagickTrue(verbose) && stage_loop < stage_limit ) 3914 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line before looping */ 3915 3916#if 0 3917 (void) FormatLocaleFile(stderr, "--E-- image=0x%lx\n", (unsigned long)image); 3918 (void) FormatLocaleFile(stderr, " curr =0x%lx\n", (unsigned long)curr_image); 3919 (void) FormatLocaleFile(stderr, " work =0x%lx\n", (unsigned long)work_image); 3920 (void) FormatLocaleFile(stderr, " save =0x%lx\n", (unsigned long)save_image); 3921 (void) FormatLocaleFile(stderr, " union=0x%lx\n", (unsigned long)rslt_image); 3922#endif 3923 3924 } /* End Loop 3: Primative (staging) Loop for Coumpound Methods */ 3925 3926 /* Final Post-processing for some Compound Methods 3927 ** 3928 ** The removal of any 'Sync' channel flag in the Image Compositon 3929 ** below ensures the methematical compose method is applied in a 3930 ** purely mathematical way, and only to the selected channels. 3931 ** Turn off SVG composition 'alpha blending'. 3932 */ 3933 switch( method ) { 3934 case EdgeOutMorphology: 3935 case EdgeInMorphology: 3936 case TopHatMorphology: 3937 case BottomHatMorphology: 3938 if ( IfMagickTrue(verbose) ) 3939 (void) FormatLocaleFile(stderr, 3940 "\n%s: Difference with original image",CommandOptionToMnemonic( 3941 MagickMorphologyOptions, method) ); 3942 (void) CompositeImage(curr_image,image,DifferenceCompositeOp, 3943 MagickTrue,0,0,exception); 3944 break; 3945 case EdgeMorphology: 3946 if ( IfMagickTrue(verbose) ) 3947 (void) FormatLocaleFile(stderr, 3948 "\n%s: Difference of Dilate and Erode",CommandOptionToMnemonic( 3949 MagickMorphologyOptions, method) ); 3950 (void) CompositeImage(curr_image,save_image,DifferenceCompositeOp, 3951 MagickTrue,0,0,exception); 3952 save_image = DestroyImage(save_image); /* finished with save image */ 3953 break; 3954 default: 3955 break; 3956 } 3957 3958 /* multi-kernel handling: re-iterate, or compose results */ 3959 if ( kernel->next == (KernelInfo *) NULL ) 3960 rslt_image = curr_image; /* just return the resulting image */ 3961 else if ( rslt_compose == NoCompositeOp ) 3962 { if ( IfMagickTrue(verbose) ) { 3963 if ( this_kernel->next != (KernelInfo *) NULL ) 3964 (void) FormatLocaleFile(stderr, " (re-iterate)"); 3965 else 3966 (void) FormatLocaleFile(stderr, " (done)"); 3967 } 3968 rslt_image = curr_image; /* return result, and re-iterate */ 3969 } 3970 else if ( rslt_image == (Image *) NULL) 3971 { if ( IfMagickTrue(verbose) ) 3972 (void) FormatLocaleFile(stderr, " (save for compose)"); 3973 rslt_image = curr_image; 3974 curr_image = (Image *) image; /* continue with original image */ 3975 } 3976 else 3977 { /* Add the new 'current' result to the composition 3978 ** 3979 ** The removal of any 'Sync' channel flag in the Image Compositon 3980 ** below ensures the methematical compose method is applied in a 3981 ** purely mathematical way, and only to the selected channels. 3982 ** IE: Turn off SVG composition 'alpha blending'. 3983 */ 3984 if ( IfMagickTrue(verbose) ) 3985 (void) FormatLocaleFile(stderr, " (compose \"%s\")", 3986 CommandOptionToMnemonic(MagickComposeOptions, rslt_compose) ); 3987 (void) CompositeImage(rslt_image,curr_image,rslt_compose,MagickTrue, 3988 0,0,exception); 3989 curr_image = DestroyImage(curr_image); 3990 curr_image = (Image *) image; /* continue with original image */ 3991 } 3992 if ( IfMagickTrue(verbose) ) 3993 (void) FormatLocaleFile(stderr, "\n"); 3994 3995 /* loop to the next kernel in a multi-kernel list */ 3996 norm_kernel = norm_kernel->next; 3997 if ( rflt_kernel != (KernelInfo *) NULL ) 3998 rflt_kernel = rflt_kernel->next; 3999 kernel_number++; 4000 } /* End Loop 2: Loop over each kernel */ 4001 4002 } /* End Loop 1: compound method interation */ 4003 4004 goto exit_cleanup; 4005 4006 /* Yes goto's are bad, but it makes cleanup lot more efficient */ 4007error_cleanup: 4008 if ( curr_image == rslt_image ) 4009 curr_image = (Image *) NULL; 4010 if ( rslt_image != (Image *) NULL ) 4011 rslt_image = DestroyImage(rslt_image); 4012exit_cleanup: 4013 if ( curr_image == rslt_image || curr_image == image ) 4014 curr_image = (Image *) NULL; 4015 if ( curr_image != (Image *) NULL ) 4016 curr_image = DestroyImage(curr_image); 4017 if ( work_image != (Image *) NULL ) 4018 work_image = DestroyImage(work_image); 4019 if ( save_image != (Image *) NULL ) 4020 save_image = DestroyImage(save_image); 4021 if ( reflected_kernel != (KernelInfo *) NULL ) 4022 reflected_kernel = DestroyKernelInfo(reflected_kernel); 4023 return(rslt_image); 4024} 4025 4026 4027/* 4028%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4029% % 4030% % 4031% % 4032% M o r p h o l o g y I m a g e % 4033% % 4034% % 4035% % 4036%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4037% 4038% MorphologyImage() applies a user supplied kernel to the image 4039% according to the given mophology method. 4040% 4041% This function applies any and all user defined settings before calling 4042% the above internal function MorphologyApply(). 4043% 4044% User defined settings include... 4045% * Output Bias for Convolution and correlation ("-define convolve:bias=??") 4046% * Kernel Scale/normalize settings ("-define convolve:scale=??") 4047% This can also includes the addition of a scaled unity kernel. 4048% * Show Kernel being applied ("-define showkernel=1") 4049% 4050% Other operators that do not want user supplied options interfering, 4051% especially "convolve:bias" and "showkernel" should use MorphologyApply() 4052% directly. 4053% 4054% The format of the MorphologyImage method is: 4055% 4056% Image *MorphologyImage(const Image *image,MorphologyMethod method, 4057% const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception) 4058% 4059% A description of each parameter follows: 4060% 4061% o image: the image. 4062% 4063% o method: the morphology method to be applied. 4064% 4065% o iterations: apply the operation this many times (or no change). 4066% A value of -1 means loop until no change found. 4067% How this is applied may depend on the morphology method. 4068% Typically this is a value of 1. 4069% 4070% o kernel: An array of double representing the morphology kernel. 4071% Warning: kernel may be normalized for the Convolve method. 4072% 4073% o exception: return any errors or warnings in this structure. 4074% 4075*/ 4076MagickExport Image *MorphologyImage(const Image *image, 4077 const MorphologyMethod method,const ssize_t iterations, 4078 const KernelInfo *kernel,ExceptionInfo *exception) 4079{ 4080 KernelInfo 4081 *curr_kernel; 4082 4083 CompositeOperator 4084 compose; 4085 4086 Image 4087 *morphology_image; 4088 4089 double 4090 bias; 4091 4092 curr_kernel = (KernelInfo *) kernel; 4093 bias=0.0; 4094 compose = UndefinedCompositeOp; /* use default for method */ 4095 4096 /* Apply Convolve/Correlate Normalization and Scaling Factors. 4097 * This is done BEFORE the ShowKernelInfo() function is called so that 4098 * users can see the results of the 'option:convolve:scale' option. 4099 */ 4100 if ( method == ConvolveMorphology || method == CorrelateMorphology ) { 4101 const char 4102 *artifact; 4103 4104 /* Get the bias value as it will be needed */ 4105 artifact = GetImageArtifact(image,"convolve:bias"); 4106 if ( artifact != (const char *) NULL) { 4107 if (IfMagickFalse(IsGeometry(artifact))) 4108 (void) ThrowMagickException(exception,GetMagickModule(), 4109 OptionWarning,"InvalidSetting","'%s' '%s'", 4110 "convolve:bias",artifact); 4111 else 4112 bias=StringToDoubleInterval(artifact,(double) QuantumRange+1.0); 4113 } 4114 4115 /* Scale kernel according to user wishes */ 4116 artifact = GetImageArtifact(image,"convolve:scale"); 4117 if ( artifact != (const char *)NULL ) { 4118 if (IfMagickFalse(IsGeometry(artifact))) 4119 (void) ThrowMagickException(exception,GetMagickModule(), 4120 OptionWarning,"InvalidSetting","'%s' '%s'", 4121 "convolve:scale",artifact); 4122 else { 4123 if ( curr_kernel == kernel ) 4124 curr_kernel = CloneKernelInfo(kernel); 4125 if (curr_kernel == (KernelInfo *) NULL) 4126 return((Image *) NULL); 4127 ScaleGeometryKernelInfo(curr_kernel, artifact); 4128 } 4129 } 4130 } 4131 4132 /* display the (normalized) kernel via stderr */ 4133 if ( IfStringTrue(GetImageArtifact(image,"showkernel")) 4134 || IfStringTrue(GetImageArtifact(image,"convolve:showkernel")) 4135 || IfStringTrue(GetImageArtifact(image,"morphology:showkernel")) ) 4136 ShowKernelInfo(curr_kernel); 4137 4138 /* Override the default handling of multi-kernel morphology results 4139 * If 'Undefined' use the default method 4140 * If 'None' (default for 'Convolve') re-iterate previous result 4141 * Otherwise merge resulting images using compose method given. 4142 * Default for 'HitAndMiss' is 'Lighten'. 4143 */ 4144 { const char 4145 *artifact; 4146 ssize_t 4147 parse; 4148 4149 artifact = GetImageArtifact(image,"morphology:compose"); 4150 if ( artifact != (const char *) NULL) { 4151 parse=ParseCommandOption(MagickComposeOptions, 4152 MagickFalse,artifact); 4153 if ( parse < 0 ) 4154 (void) ThrowMagickException(exception,GetMagickModule(), 4155 OptionWarning,"UnrecognizedComposeOperator","'%s' '%s'", 4156 "morphology:compose",artifact); 4157 else 4158 compose=(CompositeOperator)parse; 4159 } 4160 } 4161 /* Apply the Morphology */ 4162 morphology_image = MorphologyApply(image,method,iterations, 4163 curr_kernel,compose,bias,exception); 4164 4165 /* Cleanup and Exit */ 4166 if ( curr_kernel != kernel ) 4167 curr_kernel=DestroyKernelInfo(curr_kernel); 4168 return(morphology_image); 4169} 4170 4171/* 4172%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4173% % 4174% % 4175% % 4176+ R o t a t e K e r n e l I n f o % 4177% % 4178% % 4179% % 4180%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4181% 4182% RotateKernelInfo() rotates the kernel by the angle given. 4183% 4184% Currently it is restricted to 90 degree angles, of either 1D kernels 4185% or square kernels. And 'circular' rotations of 45 degrees for 3x3 kernels. 4186% It will ignore usless rotations for specific 'named' built-in kernels. 4187% 4188% The format of the RotateKernelInfo method is: 4189% 4190% void RotateKernelInfo(KernelInfo *kernel, double angle) 4191% 4192% A description of each parameter follows: 4193% 4194% o kernel: the Morphology/Convolution kernel 4195% 4196% o angle: angle to rotate in degrees 4197% 4198% This function is currently internal to this module only, but can be exported 4199% to other modules if needed. 4200*/ 4201static void RotateKernelInfo(KernelInfo *kernel, double angle) 4202{ 4203 /* angle the lower kernels first */ 4204 if ( kernel->next != (KernelInfo *) NULL) 4205 RotateKernelInfo(kernel->next, angle); 4206 4207 /* WARNING: Currently assumes the kernel (rightly) is horizontally symetrical 4208 ** 4209 ** TODO: expand beyond simple 90 degree rotates, flips and flops 4210 */ 4211 4212 /* Modulus the angle */ 4213 angle = fmod(angle, 360.0); 4214 if ( angle < 0 ) 4215 angle += 360.0; 4216 4217 if ( 337.5 < angle || angle <= 22.5 ) 4218 return; /* Near zero angle - no change! - At least not at this time */ 4219 4220 /* Handle special cases */ 4221 switch (kernel->type) { 4222 /* These built-in kernels are cylindrical kernels, rotating is useless */ 4223 case GaussianKernel: 4224 case DoGKernel: 4225 case LoGKernel: 4226 case DiskKernel: 4227 case PeaksKernel: 4228 case LaplacianKernel: 4229 case ChebyshevKernel: 4230 case ManhattanKernel: 4231 case EuclideanKernel: 4232 return; 4233 4234 /* These may be rotatable at non-90 angles in the future */ 4235 /* but simply rotating them in multiples of 90 degrees is useless */ 4236 case SquareKernel: 4237 case DiamondKernel: 4238 case PlusKernel: 4239 case CrossKernel: 4240 return; 4241 4242 /* These only allows a +/-90 degree rotation (by transpose) */ 4243 /* A 180 degree rotation is useless */ 4244 case BlurKernel: 4245 if ( 135.0 < angle && angle <= 225.0 ) 4246 return; 4247 if ( 225.0 < angle && angle <= 315.0 ) 4248 angle -= 180; 4249 break; 4250 4251 default: 4252 break; 4253 } 4254 /* Attempt rotations by 45 degrees -- 3x3 kernels only */ 4255 if ( 22.5 < fmod(angle,90.0) && fmod(angle,90.0) <= 67.5 ) 4256 { 4257 if ( kernel->width == 3 && kernel->height == 3 ) 4258 { /* Rotate a 3x3 square by 45 degree angle */ 4259 double t = kernel->values[0]; 4260 kernel->values[0] = kernel->values[3]; 4261 kernel->values[3] = kernel->values[6]; 4262 kernel->values[6] = kernel->values[7]; 4263 kernel->values[7] = kernel->values[8]; 4264 kernel->values[8] = kernel->values[5]; 4265 kernel->values[5] = kernel->values[2]; 4266 kernel->values[2] = kernel->values[1]; 4267 kernel->values[1] = t; 4268 /* rotate non-centered origin */ 4269 if ( kernel->x != 1 || kernel->y != 1 ) { 4270 ssize_t x,y; 4271 x = (ssize_t) kernel->x-1; 4272 y = (ssize_t) kernel->y-1; 4273 if ( x == y ) x = 0; 4274 else if ( x == 0 ) x = -y; 4275 else if ( x == -y ) y = 0; 4276 else if ( y == 0 ) y = x; 4277 kernel->x = (ssize_t) x+1; 4278 kernel->y = (ssize_t) y+1; 4279 } 4280 angle = fmod(angle+315.0, 360.0); /* angle reduced 45 degrees */ 4281 kernel->angle = fmod(kernel->angle+45.0, 360.0); 4282 } 4283 else 4284 perror("Unable to rotate non-3x3 kernel by 45 degrees"); 4285 } 4286 if ( 45.0 < fmod(angle, 180.0) && fmod(angle,180.0) <= 135.0 ) 4287 { 4288 if ( kernel->width == 1 || kernel->height == 1 ) 4289 { /* Do a transpose of a 1 dimensional kernel, 4290 ** which results in a fast 90 degree rotation of some type. 4291 */ 4292 ssize_t 4293 t; 4294 t = (ssize_t) kernel->width; 4295 kernel->width = kernel->height; 4296 kernel->height = (size_t) t; 4297 t = kernel->x; 4298 kernel->x = kernel->y; 4299 kernel->y = t; 4300 if ( kernel->width == 1 ) { 4301 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */ 4302 kernel->angle = fmod(kernel->angle+90.0, 360.0); 4303 } else { 4304 angle = fmod(angle+90.0, 360.0); /* angle increased 90 degrees */ 4305 kernel->angle = fmod(kernel->angle+270.0, 360.0); 4306 } 4307 } 4308 else if ( kernel->width == kernel->height ) 4309 { /* Rotate a square array of values by 90 degrees */ 4310 { register ssize_t 4311 i,j,x,y; 4312 4313 register MagickRealType 4314 *k,t; 4315 4316 k=kernel->values; 4317 for( i=0, x=(ssize_t) kernel->width-1; i<=x; i++, x--) 4318 for( j=0, y=(ssize_t) kernel->height-1; j<y; j++, y--) 4319 { t = k[i+j*kernel->width]; 4320 k[i+j*kernel->width] = k[j+x*kernel->width]; 4321 k[j+x*kernel->width] = k[x+y*kernel->width]; 4322 k[x+y*kernel->width] = k[y+i*kernel->width]; 4323 k[y+i*kernel->width] = t; 4324 } 4325 } 4326 /* rotate the origin - relative to center of array */ 4327 { register ssize_t x,y; 4328 x = (ssize_t) (kernel->x*2-kernel->width+1); 4329 y = (ssize_t) (kernel->y*2-kernel->height+1); 4330 kernel->x = (ssize_t) ( -y +(ssize_t) kernel->width-1)/2; 4331 kernel->y = (ssize_t) ( +x +(ssize_t) kernel->height-1)/2; 4332 } 4333 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */ 4334 kernel->angle = fmod(kernel->angle+90.0, 360.0); 4335 } 4336 else 4337 perror("Unable to rotate a non-square, non-linear kernel 90 degrees"); 4338 } 4339 if ( 135.0 < angle && angle <= 225.0 ) 4340 { 4341 /* For a 180 degree rotation - also know as a reflection 4342 * This is actually a very very common operation! 4343 * Basically all that is needed is a reversal of the kernel data! 4344 * And a reflection of the origon 4345 */ 4346 double 4347 t; 4348 4349 register MagickRealType 4350 *k; 4351 4352 ssize_t 4353 i, 4354 j; 4355 4356 k=kernel->values; 4357 j=(ssize_t) (kernel->width*kernel->height-1); 4358 for (i=0; i < j; i++, j--) 4359 t=k[i], k[i]=k[j], k[j]=t; 4360 4361 kernel->x = (ssize_t) kernel->width - kernel->x - 1; 4362 kernel->y = (ssize_t) kernel->height - kernel->y - 1; 4363 angle = fmod(angle-180.0, 360.0); /* angle+180 degrees */ 4364 kernel->angle = fmod(kernel->angle+180.0, 360.0); 4365 } 4366 /* At this point angle should at least between -45 (315) and +45 degrees 4367 * In the future some form of non-orthogonal angled rotates could be 4368 * performed here, posibily with a linear kernel restriction. 4369 */ 4370 4371 return; 4372} 4373 4374/* 4375%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4376% % 4377% % 4378% % 4379% S c a l e G e o m e t r y K e r n e l I n f o % 4380% % 4381% % 4382% % 4383%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4384% 4385% ScaleGeometryKernelInfo() takes a geometry argument string, typically 4386% provided as a "-set option:convolve:scale {geometry}" user setting, 4387% and modifies the kernel according to the parsed arguments of that setting. 4388% 4389% The first argument (and any normalization flags) are passed to 4390% ScaleKernelInfo() to scale/normalize the kernel. The second argument 4391% is then passed to UnityAddKernelInfo() to add a scled unity kernel 4392% into the scaled/normalized kernel. 4393% 4394% The format of the ScaleGeometryKernelInfo method is: 4395% 4396% void ScaleGeometryKernelInfo(KernelInfo *kernel, 4397% const double scaling_factor,const MagickStatusType normalize_flags) 4398% 4399% A description of each parameter follows: 4400% 4401% o kernel: the Morphology/Convolution kernel to modify 4402% 4403% o geometry: 4404% The geometry string to parse, typically from the user provided 4405% "-set option:convolve:scale {geometry}" setting. 4406% 4407*/ 4408MagickExport void ScaleGeometryKernelInfo (KernelInfo *kernel, 4409 const char *geometry) 4410{ 4411 MagickStatusType 4412 flags; 4413 4414 GeometryInfo 4415 args; 4416 4417 SetGeometryInfo(&args); 4418 flags = ParseGeometry(geometry, &args); 4419 4420#if 0 4421 /* For Debugging Geometry Input */ 4422 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n", 4423 flags, args.rho, args.sigma, args.xi, args.psi ); 4424#endif 4425 4426 if ( (flags & PercentValue) != 0 ) /* Handle Percentage flag*/ 4427 args.rho *= 0.01, args.sigma *= 0.01; 4428 4429 if ( (flags & RhoValue) == 0 ) /* Set Defaults for missing args */ 4430 args.rho = 1.0; 4431 if ( (flags & SigmaValue) == 0 ) 4432 args.sigma = 0.0; 4433 4434 /* Scale/Normalize the input kernel */ 4435 ScaleKernelInfo(kernel, args.rho, (GeometryFlags) flags); 4436 4437 /* Add Unity Kernel, for blending with original */ 4438 if ( (flags & SigmaValue) != 0 ) 4439 UnityAddKernelInfo(kernel, args.sigma); 4440 4441 return; 4442} 4443/* 4444%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4445% % 4446% % 4447% % 4448% S c a l e K e r n e l I n f o % 4449% % 4450% % 4451% % 4452%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4453% 4454% ScaleKernelInfo() scales the given kernel list by the given amount, with or 4455% without normalization of the sum of the kernel values (as per given flags). 4456% 4457% By default (no flags given) the values within the kernel is scaled 4458% directly using given scaling factor without change. 4459% 4460% If either of the two 'normalize_flags' are given the kernel will first be 4461% normalized and then further scaled by the scaling factor value given. 4462% 4463% Kernel normalization ('normalize_flags' given) is designed to ensure that 4464% any use of the kernel scaling factor with 'Convolve' or 'Correlate' 4465% morphology methods will fall into -1.0 to +1.0 range. Note that for 4466% non-HDRI versions of IM this may cause images to have any negative results 4467% clipped, unless some 'bias' is used. 4468% 4469% More specifically. Kernels which only contain positive values (such as a 4470% 'Gaussian' kernel) will be scaled so that those values sum to +1.0, 4471% ensuring a 0.0 to +1.0 output range for non-HDRI images. 4472% 4473% For Kernels that contain some negative values, (such as 'Sharpen' kernels) 4474% the kernel will be scaled by the absolute of the sum of kernel values, so 4475% that it will generally fall within the +/- 1.0 range. 4476% 4477% For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel 4478% will be scaled by just the sum of the postive values, so that its output 4479% range will again fall into the +/- 1.0 range. 4480% 4481% For special kernels designed for locating shapes using 'Correlate', (often 4482% only containing +1 and -1 values, representing foreground/brackground 4483% matching) a special normalization method is provided to scale the positive 4484% values separately to those of the negative values, so the kernel will be 4485% forced to become a zero-sum kernel better suited to such searches. 4486% 4487% WARNING: Correct normalization of the kernel assumes that the '*_range' 4488% attributes within the kernel structure have been correctly set during the 4489% kernels creation. 4490% 4491% NOTE: The values used for 'normalize_flags' have been selected specifically 4492% to match the use of geometry options, so that '!' means NormalizeValue, '^' 4493% means CorrelateNormalizeValue. All other GeometryFlags values are ignored. 4494% 4495% The format of the ScaleKernelInfo method is: 4496% 4497% void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor, 4498% const MagickStatusType normalize_flags ) 4499% 4500% A description of each parameter follows: 4501% 4502% o kernel: the Morphology/Convolution kernel 4503% 4504% o scaling_factor: 4505% multiply all values (after normalization) by this factor if not 4506% zero. If the kernel is normalized regardless of any flags. 4507% 4508% o normalize_flags: 4509% GeometryFlags defining normalization method to use. 4510% specifically: NormalizeValue, CorrelateNormalizeValue, 4511% and/or PercentValue 4512% 4513*/ 4514MagickExport void ScaleKernelInfo(KernelInfo *kernel, 4515 const double scaling_factor,const GeometryFlags normalize_flags) 4516{ 4517 register ssize_t 4518 i; 4519 4520 register double 4521 pos_scale, 4522 neg_scale; 4523 4524 /* do the other kernels in a multi-kernel list first */ 4525 if ( kernel->next != (KernelInfo *) NULL) 4526 ScaleKernelInfo(kernel->next, scaling_factor, normalize_flags); 4527 4528 /* Normalization of Kernel */ 4529 pos_scale = 1.0; 4530 if ( (normalize_flags&NormalizeValue) != 0 ) { 4531 if ( fabs(kernel->positive_range + kernel->negative_range) >= MagickEpsilon ) 4532 /* non-zero-summing kernel (generally positive) */ 4533 pos_scale = fabs(kernel->positive_range + kernel->negative_range); 4534 else 4535 /* zero-summing kernel */ 4536 pos_scale = kernel->positive_range; 4537 } 4538 /* Force kernel into a normalized zero-summing kernel */ 4539 if ( (normalize_flags&CorrelateNormalizeValue) != 0 ) { 4540 pos_scale = ( fabs(kernel->positive_range) >= MagickEpsilon ) 4541 ? kernel->positive_range : 1.0; 4542 neg_scale = ( fabs(kernel->negative_range) >= MagickEpsilon ) 4543 ? -kernel->negative_range : 1.0; 4544 } 4545 else 4546 neg_scale = pos_scale; 4547 4548 /* finialize scaling_factor for positive and negative components */ 4549 pos_scale = scaling_factor/pos_scale; 4550 neg_scale = scaling_factor/neg_scale; 4551 4552 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++) 4553 if ( ! IsNaN(kernel->values[i]) ) 4554 kernel->values[i] *= (kernel->values[i] >= 0) ? pos_scale : neg_scale; 4555 4556 /* convolution output range */ 4557 kernel->positive_range *= pos_scale; 4558 kernel->negative_range *= neg_scale; 4559 /* maximum and minimum values in kernel */ 4560 kernel->maximum *= (kernel->maximum >= 0.0) ? pos_scale : neg_scale; 4561 kernel->minimum *= (kernel->minimum >= 0.0) ? pos_scale : neg_scale; 4562 4563 /* swap kernel settings if user's scaling factor is negative */ 4564 if ( scaling_factor < MagickEpsilon ) { 4565 double t; 4566 t = kernel->positive_range; 4567 kernel->positive_range = kernel->negative_range; 4568 kernel->negative_range = t; 4569 t = kernel->maximum; 4570 kernel->maximum = kernel->minimum; 4571 kernel->minimum = 1; 4572 } 4573 4574 return; 4575} 4576 4577/* 4578%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4579% % 4580% % 4581% % 4582% S h o w K e r n e l I n f o % 4583% % 4584% % 4585% % 4586%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4587% 4588% ShowKernelInfo() outputs the details of the given kernel defination to 4589% standard error, generally due to a users 'showkernel' option request. 4590% 4591% The format of the ShowKernel method is: 4592% 4593% void ShowKernelInfo(const KernelInfo *kernel) 4594% 4595% A description of each parameter follows: 4596% 4597% o kernel: the Morphology/Convolution kernel 4598% 4599*/ 4600MagickPrivate void ShowKernelInfo(const KernelInfo *kernel) 4601{ 4602 const KernelInfo 4603 *k; 4604 4605 size_t 4606 c, i, u, v; 4607 4608 for (c=0, k=kernel; k != (KernelInfo *) NULL; c++, k=k->next ) { 4609 4610 (void) FormatLocaleFile(stderr, "Kernel"); 4611 if ( kernel->next != (KernelInfo *) NULL ) 4612 (void) FormatLocaleFile(stderr, " #%lu", (unsigned long) c ); 4613 (void) FormatLocaleFile(stderr, " \"%s", 4614 CommandOptionToMnemonic(MagickKernelOptions, k->type) ); 4615 if ( fabs(k->angle) >= MagickEpsilon ) 4616 (void) FormatLocaleFile(stderr, "@%lg", k->angle); 4617 (void) FormatLocaleFile(stderr, "\" of size %lux%lu%+ld%+ld",(unsigned long) 4618 k->width,(unsigned long) k->height,(long) k->x,(long) k->y); 4619 (void) FormatLocaleFile(stderr, 4620 " with values from %.*lg to %.*lg\n", 4621 GetMagickPrecision(), k->minimum, 4622 GetMagickPrecision(), k->maximum); 4623 (void) FormatLocaleFile(stderr, "Forming a output range from %.*lg to %.*lg", 4624 GetMagickPrecision(), k->negative_range, 4625 GetMagickPrecision(), k->positive_range); 4626 if ( fabs(k->positive_range+k->negative_range) < MagickEpsilon ) 4627 (void) FormatLocaleFile(stderr, " (Zero-Summing)\n"); 4628 else if ( fabs(k->positive_range+k->negative_range-1.0) < MagickEpsilon ) 4629 (void) FormatLocaleFile(stderr, " (Normalized)\n"); 4630 else 4631 (void) FormatLocaleFile(stderr, " (Sum %.*lg)\n", 4632 GetMagickPrecision(), k->positive_range+k->negative_range); 4633 for (i=v=0; v < k->height; v++) { 4634 (void) FormatLocaleFile(stderr, "%2lu:", (unsigned long) v ); 4635 for (u=0; u < k->width; u++, i++) 4636 if ( IsNaN(k->values[i]) ) 4637 (void) FormatLocaleFile(stderr," %*s", GetMagickPrecision()+3, "nan"); 4638 else 4639 (void) FormatLocaleFile(stderr," %*.*lg", GetMagickPrecision()+3, 4640 GetMagickPrecision(), (double) k->values[i]); 4641 (void) FormatLocaleFile(stderr,"\n"); 4642 } 4643 } 4644} 4645 4646/* 4647%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4648% % 4649% % 4650% % 4651% U n i t y A d d K e r n a l I n f o % 4652% % 4653% % 4654% % 4655%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4656% 4657% UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel 4658% to the given pre-scaled and normalized Kernel. This in effect adds that 4659% amount of the original image into the resulting convolution kernel. This 4660% value is usually provided by the user as a percentage value in the 4661% 'convolve:scale' setting. 4662% 4663% The resulting effect is to convert the defined kernels into blended 4664% soft-blurs, unsharp kernels or into sharpening kernels. 4665% 4666% The format of the UnityAdditionKernelInfo method is: 4667% 4668% void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale ) 4669% 4670% A description of each parameter follows: 4671% 4672% o kernel: the Morphology/Convolution kernel 4673% 4674% o scale: 4675% scaling factor for the unity kernel to be added to 4676% the given kernel. 4677% 4678*/ 4679MagickExport void UnityAddKernelInfo(KernelInfo *kernel, 4680 const double scale) 4681{ 4682 /* do the other kernels in a multi-kernel list first */ 4683 if ( kernel->next != (KernelInfo *) NULL) 4684 UnityAddKernelInfo(kernel->next, scale); 4685 4686 /* Add the scaled unity kernel to the existing kernel */ 4687 kernel->values[kernel->x+kernel->y*kernel->width] += scale; 4688 CalcKernelMetaData(kernel); /* recalculate the meta-data */ 4689 4690 return; 4691} 4692 4693/* 4694%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4695% % 4696% % 4697% % 4698% Z e r o K e r n e l N a n s % 4699% % 4700% % 4701% % 4702%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4703% 4704% ZeroKernelNans() replaces any special 'nan' value that may be present in 4705% the kernel with a zero value. This is typically done when the kernel will 4706% be used in special hardware (GPU) convolution processors, to simply 4707% matters. 4708% 4709% The format of the ZeroKernelNans method is: 4710% 4711% void ZeroKernelNans (KernelInfo *kernel) 4712% 4713% A description of each parameter follows: 4714% 4715% o kernel: the Morphology/Convolution kernel 4716% 4717*/ 4718MagickPrivate void ZeroKernelNans(KernelInfo *kernel) 4719{ 4720 register size_t 4721 i; 4722 4723 /* do the other kernels in a multi-kernel list first */ 4724 if ( kernel->next != (KernelInfo *) NULL) 4725 ZeroKernelNans(kernel->next); 4726 4727 for (i=0; i < (kernel->width*kernel->height); i++) 4728 if ( IsNaN(kernel->values[i]) ) 4729 kernel->values[i] = 0.0; 4730 4731 return; 4732} 4733