2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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 %
13 % MagickCore Morphology Methods %
20 % Copyright 1999-2018 ImageMagick Studio LLC, a non-profit organization %
21 % dedicated to making software imaging solutions freely available. %
23 % You may not use this file except in compliance with the License. You may %
24 % obtain a copy of the License at %
26 % https://www.imagemagick.org/script/license.php %
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. %
34 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 % Morphology is the application of various kernels, of any size or shape, to an
37 % image in various ways (typically binary, but not always).
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.
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.
52 #include "MagickCore/studio.h"
53 #include "MagickCore/artifact.h"
54 #include "MagickCore/cache-view.h"
55 #include "MagickCore/channel.h"
56 #include "MagickCore/color-private.h"
57 #include "MagickCore/enhance.h"
58 #include "MagickCore/exception.h"
59 #include "MagickCore/exception-private.h"
60 #include "MagickCore/gem.h"
61 #include "MagickCore/gem-private.h"
62 #include "MagickCore/image.h"
63 #include "MagickCore/image-private.h"
64 #include "MagickCore/linked-list.h"
65 #include "MagickCore/list.h"
66 #include "MagickCore/magick.h"
67 #include "MagickCore/memory_.h"
68 #include "MagickCore/memory-private.h"
69 #include "MagickCore/monitor-private.h"
70 #include "MagickCore/morphology.h"
71 #include "MagickCore/morphology-private.h"
72 #include "MagickCore/option.h"
73 #include "MagickCore/pixel-accessor.h"
74 #include "MagickCore/pixel-private.h"
75 #include "MagickCore/prepress.h"
76 #include "MagickCore/quantize.h"
77 #include "MagickCore/resource_.h"
78 #include "MagickCore/registry.h"
79 #include "MagickCore/semaphore.h"
80 #include "MagickCore/splay-tree.h"
81 #include "MagickCore/statistic.h"
82 #include "MagickCore/string_.h"
83 #include "MagickCore/string-private.h"
84 #include "MagickCore/thread-private.h"
85 #include "MagickCore/token.h"
86 #include "MagickCore/utility.h"
87 #include "MagickCore/utility-private.h"
90 Other global definitions used by module.
92 #define Minimize(assign,value) assign=MagickMin(assign,value)
93 #define Maximize(assign,value) assign=MagickMax(assign,value)
95 /* Integer Factorial Function - for a Binomial kernel */
97 static inline size_t fact(size_t n)
100 for(f=1, l=2; l <= n; f=f*l, l++);
103 #elif 1 /* glibc floating point alternatives */
104 #define fact(n) ((size_t)tgamma((double)n+1))
106 #define fact(n) ((size_t)lgamma((double)n+1))
110 /* Currently these are only internal to this module */
112 CalcKernelMetaData(KernelInfo *),
113 ExpandMirrorKernelInfo(KernelInfo *),
114 ExpandRotateKernelInfo(KernelInfo *, const double),
115 RotateKernelInfo(KernelInfo *, double);
118 /* Quick function to find last kernel in a kernel list */
119 static inline KernelInfo *LastKernelInfo(KernelInfo *kernel)
121 while (kernel->next != (KernelInfo *) NULL)
127 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
131 % A c q u i r e K e r n e l I n f o %
135 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
137 % AcquireKernelInfo() takes the given string (generally supplied by the
138 % user) and converts it into a Morphology/Convolution Kernel. This allows
139 % users to specify a kernel from a number of pre-defined kernels, or to fully
140 % specify their own kernel for a specific Convolution or Morphology
143 % The kernel so generated can be any rectangular array of floating point
144 % values (doubles) with the 'control point' or 'pixel being affected'
145 % anywhere within that array of values.
147 % Previously IM was restricted to a square of odd size using the exact
148 % center as origin, this is no longer the case, and any rectangular kernel
149 % with any value being declared the origin. This in turn allows the use of
150 % highly asymmetrical kernels.
152 % The floating point values in the kernel can also include a special value
153 % known as 'nan' or 'not a number' to indicate that this value is not part
154 % of the kernel array. This allows you to shaped the kernel within its
155 % rectangular area. That is 'nan' values provide a 'mask' for the kernel
156 % shape. However at least one non-nan value must be provided for correct
157 % working of a kernel.
159 % The returned kernel should be freed using the DestroyKernelInfo() when you
160 % are finished with it. Do not free this memory yourself.
162 % Input kernel defintion strings can consist of any of three types.
165 % Select from one of the built in kernels, using the name and
166 % geometry arguments supplied. See AcquireKernelBuiltIn()
168 % "WxH[+X+Y][@><]:num, num, num ..."
169 % a kernel of size W by H, with W*H floating point numbers following.
170 % the 'center' can be optionally be defined at +X+Y (such that +0+0
171 % is top left corner). If not defined the pixel in the center, for
172 % odd sizes, or to the immediate top or left of center for even sizes
173 % is automatically selected.
175 % "num, num, num, num, ..."
176 % list of floating point numbers defining an 'old style' odd sized
177 % square kernel. At least 9 values should be provided for a 3x3
178 % square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc.
179 % Values can be space or comma separated. This is not recommended.
181 % You can define a 'list of kernels' which can be used by some morphology
182 % operators A list is defined as a semi-colon separated list kernels.
184 % " kernel ; kernel ; kernel ; "
186 % Any extra ';' characters, at start, end or between kernel defintions are
189 % The special flags will expand a single kernel, into a list of rotated
190 % kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree
191 % cyclic rotations, while a '>' will generate a list of 90-degree rotations.
192 % The '<' also exands using 90-degree rotates, but giving a 180-degree
193 % reflected kernel before the +/- 90-degree rotations, which can be important
194 % for Thinning operations.
196 % Note that 'name' kernels will start with an alphabetic character while the
197 % new kernel specification has a ':' character in its specification string.
198 % If neither is the case, it is assumed an old style of a simple list of
199 % numbers generating a odd-sized square kernel has been given.
201 % The format of the AcquireKernal method is:
203 % KernelInfo *AcquireKernelInfo(const char *kernel_string)
205 % A description of each parameter follows:
207 % o kernel_string: the Morphology/Convolution kernel wanted.
211 /* This was separated so that it could be used as a separate
212 ** array input handling function, such as for -color-matrix
214 static KernelInfo *ParseKernelArray(const char *kernel_string)
220 token[MagickPathExtent];
230 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
238 kernel=(KernelInfo *) AcquireQuantumMemory(1,sizeof(*kernel));
239 if (kernel == (KernelInfo *) NULL)
241 (void) memset(kernel,0,sizeof(*kernel));
242 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
243 kernel->negative_range = kernel->positive_range = 0.0;
244 kernel->type = UserDefinedKernel;
245 kernel->next = (KernelInfo *) NULL;
246 kernel->signature=MagickCoreSignature;
247 if (kernel_string == (const char *) NULL)
250 /* find end of this specific kernel definition string */
251 end = strchr(kernel_string, ';');
252 if ( end == (char *) NULL )
253 end = strchr(kernel_string, '\0');
255 /* clear flags - for Expanding kernel lists thorugh rotations */
258 /* Has a ':' in argument - New user kernel specification
259 FUTURE: this split on ':' could be done by StringToken()
261 p = strchr(kernel_string, ':');
262 if ( p != (char *) NULL && p < end)
264 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
265 memcpy(token, kernel_string, (size_t) (p-kernel_string));
266 token[p-kernel_string] = '\0';
267 SetGeometryInfo(&args);
268 flags = ParseGeometry(token, &args);
270 /* Size handling and checks of geometry settings */
271 if ( (flags & WidthValue) == 0 ) /* if no width then */
272 args.rho = args.sigma; /* then width = height */
273 if ( args.rho < 1.0 ) /* if width too small */
274 args.rho = 1.0; /* then width = 1 */
275 if ( args.sigma < 1.0 ) /* if height too small */
276 args.sigma = args.rho; /* then height = width */
277 kernel->width = (size_t)args.rho;
278 kernel->height = (size_t)args.sigma;
280 /* Offset Handling and Checks */
281 if ( args.xi < 0.0 || args.psi < 0.0 )
282 return(DestroyKernelInfo(kernel));
283 kernel->x = ((flags & XValue)!=0) ? (ssize_t)args.xi
284 : (ssize_t) (kernel->width-1)/2;
285 kernel->y = ((flags & YValue)!=0) ? (ssize_t)args.psi
286 : (ssize_t) (kernel->height-1)/2;
287 if ( kernel->x >= (ssize_t) kernel->width ||
288 kernel->y >= (ssize_t) kernel->height )
289 return(DestroyKernelInfo(kernel));
291 p++; /* advance beyond the ':' */
294 { /* ELSE - Old old specification, forming odd-square kernel */
295 /* count up number of values given */
296 p=(const char *) kernel_string;
297 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
298 p++; /* ignore "'" chars for convolve filter usage - Cristy */
299 for (i=0; p < end; i++)
301 GetNextToken(p,&p,MagickPathExtent,token);
303 GetNextToken(p,&p,MagickPathExtent,token);
305 /* set the size of the kernel - old sized square */
306 kernel->width = kernel->height= (size_t) sqrt((double) i+1.0);
307 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
308 p=(const char *) kernel_string;
309 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
310 p++; /* ignore "'" chars for convolve filter usage - Cristy */
313 /* Read in the kernel values from rest of input string argument */
314 kernel->values=(MagickRealType *) MagickAssumeAligned(AcquireAlignedMemory(
315 kernel->width,kernel->height*sizeof(*kernel->values)));
316 if (kernel->values == (MagickRealType *) NULL)
317 return(DestroyKernelInfo(kernel));
318 kernel->minimum=MagickMaximumValue;
319 kernel->maximum=(-MagickMaximumValue);
320 kernel->negative_range = kernel->positive_range = 0.0;
321 for (i=0; (i < (ssize_t) (kernel->width*kernel->height)) && (p < end); i++)
323 GetNextToken(p,&p,MagickPathExtent,token);
325 GetNextToken(p,&p,MagickPathExtent,token);
326 if ( LocaleCompare("nan",token) == 0
327 || LocaleCompare("-",token) == 0 ) {
328 kernel->values[i] = nan; /* this value is not part of neighbourhood */
331 kernel->values[i] = StringToDouble(token,(char **) NULL);
332 ( kernel->values[i] < 0)
333 ? ( kernel->negative_range += kernel->values[i] )
334 : ( kernel->positive_range += kernel->values[i] );
335 Minimize(kernel->minimum, kernel->values[i]);
336 Maximize(kernel->maximum, kernel->values[i]);
340 /* sanity check -- no more values in kernel definition */
341 GetNextToken(p,&p,MagickPathExtent,token);
342 if ( *token != '\0' && *token != ';' && *token != '\'' )
343 return(DestroyKernelInfo(kernel));
346 /* this was the old method of handling a incomplete kernel */
347 if ( i < (ssize_t) (kernel->width*kernel->height) ) {
348 Minimize(kernel->minimum, kernel->values[i]);
349 Maximize(kernel->maximum, kernel->values[i]);
350 for ( ; i < (ssize_t) (kernel->width*kernel->height); i++)
351 kernel->values[i]=0.0;
354 /* Number of values for kernel was not enough - Report Error */
355 if ( i < (ssize_t) (kernel->width*kernel->height) )
356 return(DestroyKernelInfo(kernel));
359 /* check that we recieved at least one real (non-nan) value! */
360 if (kernel->minimum == MagickMaximumValue)
361 return(DestroyKernelInfo(kernel));
363 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel size */
364 ExpandRotateKernelInfo(kernel, 45.0); /* cyclic rotate 3x3 kernels */
365 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
366 ExpandRotateKernelInfo(kernel, 90.0); /* 90 degree rotate of kernel */
367 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
368 ExpandMirrorKernelInfo(kernel); /* 90 degree mirror rotate */
373 static KernelInfo *ParseKernelName(const char *kernel_string,
374 ExceptionInfo *exception)
377 token[MagickPathExtent];
395 /* Parse special 'named' kernel */
396 GetNextToken(kernel_string,&p,MagickPathExtent,token);
397 type=ParseCommandOption(MagickKernelOptions,MagickFalse,token);
398 if ( type < 0 || type == UserDefinedKernel )
399 return((KernelInfo *) NULL); /* not a valid named kernel */
401 while (((isspace((int) ((unsigned char) *p)) != 0) ||
402 (*p == ',') || (*p == ':' )) && (*p != '\0') && (*p != ';'))
405 end = strchr(p, ';'); /* end of this kernel defintion */
406 if ( end == (char *) NULL )
407 end = strchr(p, '\0');
409 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
410 memcpy(token, p, (size_t) (end-p));
412 SetGeometryInfo(&args);
413 flags = ParseGeometry(token, &args);
416 /* For Debugging Geometry Input */
417 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
418 flags, args.rho, args.sigma, args.xi, args.psi );
421 /* special handling of missing values in input string */
423 /* Shape Kernel Defaults */
425 if ( (flags & WidthValue) == 0 )
426 args.rho = 1.0; /* Default scale = 1.0, zero is valid */
434 if ( (flags & HeightValue) == 0 )
435 args.sigma = 1.0; /* Default scale = 1.0, zero is valid */
438 if ( (flags & XValue) == 0 )
439 args.xi = 1.0; /* Default scale = 1.0, zero is valid */
441 case RectangleKernel: /* Rectangle - set size defaults */
442 if ( (flags & WidthValue) == 0 ) /* if no width then */
443 args.rho = args.sigma; /* then width = height */
444 if ( args.rho < 1.0 ) /* if width too small */
445 args.rho = 3; /* then width = 3 */
446 if ( args.sigma < 1.0 ) /* if height too small */
447 args.sigma = args.rho; /* then height = width */
448 if ( (flags & XValue) == 0 ) /* center offset if not defined */
449 args.xi = (double)(((ssize_t)args.rho-1)/2);
450 if ( (flags & YValue) == 0 )
451 args.psi = (double)(((ssize_t)args.sigma-1)/2);
453 /* Distance Kernel Defaults */
454 case ChebyshevKernel:
455 case ManhattanKernel:
456 case OctagonalKernel:
457 case EuclideanKernel:
458 if ( (flags & HeightValue) == 0 ) /* no distance scale */
459 args.sigma = 100.0; /* default distance scaling */
460 else if ( (flags & AspectValue ) != 0 ) /* '!' flag */
461 args.sigma = QuantumRange/(args.sigma+1); /* maximum pixel distance */
462 else if ( (flags & PercentValue ) != 0 ) /* '%' flag */
463 args.sigma *= QuantumRange/100.0; /* percentage of color range */
469 kernel = AcquireKernelBuiltIn((KernelInfoType)type, &args, exception);
470 if ( kernel == (KernelInfo *) NULL )
473 /* global expand to rotated kernel list - only for single kernels */
474 if ( kernel->next == (KernelInfo *) NULL ) {
475 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel args */
476 ExpandRotateKernelInfo(kernel, 45.0);
477 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
478 ExpandRotateKernelInfo(kernel, 90.0);
479 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
480 ExpandMirrorKernelInfo(kernel);
486 MagickExport KernelInfo *AcquireKernelInfo(const char *kernel_string,
487 ExceptionInfo *exception)
495 token[MagickPathExtent];
500 if (kernel_string == (const char *) NULL)
501 return(ParseKernelArray(kernel_string));
503 kernel_cache=(char *) NULL;
504 if (*kernel_string == '@')
506 kernel_cache=FileToString(kernel_string+1,~0UL,exception);
507 if (kernel_cache == (char *) NULL)
508 return((KernelInfo *) NULL);
509 p=(const char *) kernel_cache;
512 while (GetNextToken(p,(const char **) NULL,MagickPathExtent,token), *token != '\0')
514 /* ignore extra or multiple ';' kernel separators */
517 /* tokens starting with alpha is a Named kernel */
518 if (isalpha((int) ((unsigned char) *token)) != 0)
519 new_kernel=ParseKernelName(p,exception);
520 else /* otherwise a user defined kernel array */
521 new_kernel=ParseKernelArray(p);
523 /* Error handling -- this is not proper error handling! */
524 if (new_kernel == (KernelInfo *) NULL)
526 if (kernel != (KernelInfo *) NULL)
527 kernel=DestroyKernelInfo(kernel);
528 return((KernelInfo *) NULL);
531 /* initialise or append the kernel list */
532 if (kernel == (KernelInfo *) NULL)
535 LastKernelInfo(kernel)->next=new_kernel;
538 /* look for the next kernel in list */
540 if (p == (char *) NULL)
544 if (kernel_cache != (char *) NULL)
545 kernel_cache=DestroyString(kernel_cache);
550 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
554 % A c q u i r e K e r n e l B u i l t I n %
558 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
560 % AcquireKernelBuiltIn() returned one of the 'named' built-in types of
561 % kernels used for special purposes such as gaussian blurring, skeleton
562 % pruning, and edge distance determination.
564 % They take a KernelType, and a set of geometry style arguments, which were
565 % typically decoded from a user supplied string, or from a more complex
566 % Morphology Method that was requested.
568 % The format of the AcquireKernalBuiltIn method is:
570 % KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
571 % const GeometryInfo args)
573 % A description of each parameter follows:
575 % o type: the pre-defined type of kernel wanted
577 % o args: arguments defining or modifying the kernel
579 % Convolution Kernels
582 % The a No-Op or Scaling single element kernel.
584 % Gaussian:{radius},{sigma}
585 % Generate a two-dimensional gaussian kernel, as used by -gaussian.
586 % The sigma for the curve is required. The resulting kernel is
589 % If 'sigma' is zero, you get a single pixel on a field of zeros.
591 % NOTE: that the 'radius' is optional, but if provided can limit (clip)
592 % the final size of the resulting kernel to a square 2*radius+1 in size.
593 % The radius should be at least 2 times that of the sigma value, or
594 % sever clipping and aliasing may result. If not given or set to 0 the
595 % radius will be determined so as to produce the best minimal error
596 % result, which is usally much larger than is normally needed.
598 % LoG:{radius},{sigma}
599 % "Laplacian of a Gaussian" or "Mexician Hat" Kernel.
600 % The supposed ideal edge detection, zero-summing kernel.
602 % An alturnative to this kernel is to use a "DoG" with a sigma ratio of
603 % approx 1.6 (according to wikipedia).
605 % DoG:{radius},{sigma1},{sigma2}
606 % "Difference of Gaussians" Kernel.
607 % As "Gaussian" but with a gaussian produced by 'sigma2' subtracted
608 % from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1.
609 % The result is a zero-summing kernel.
611 % Blur:{radius},{sigma}[,{angle}]
612 % Generates a 1 dimensional or linear gaussian blur, at the angle given
613 % (current restricted to orthogonal angles). If a 'radius' is given the
614 % kernel is clipped to a width of 2*radius+1. Kernel can be rotated
615 % by a 90 degree angle.
617 % If 'sigma' is zero, you get a single pixel on a field of zeros.
619 % Note that two convolutions with two "Blur" kernels perpendicular to
620 % each other, is equivalent to a far larger "Gaussian" kernel with the
621 % same sigma value, However it is much faster to apply. This is how the
622 % "-blur" operator actually works.
624 % Comet:{width},{sigma},{angle}
625 % Blur in one direction only, much like how a bright object leaves
626 % a comet like trail. The Kernel is actually half a gaussian curve,
627 % Adding two such blurs in opposite directions produces a Blur Kernel.
628 % Angle can be rotated in multiples of 90 degrees.
630 % Note that the first argument is the width of the kernel and not the
631 % radius of the kernel.
633 % Binomial:[{radius}]
634 % Generate a discrete kernel using a 2 dimentional Pascel's Triangle
635 % of values. Used for special forma of image filters.
637 % # Still to be implemented...
641 % # Set kernel values using a resize filter, and given scale (sigma)
642 % # Cylindrical or Linear. Is this possible with an image?
645 % Named Constant Convolution Kernels
647 % All these are unscaled, zero-summing kernels by default. As such for
648 % non-HDRI version of ImageMagick some form of normalization, user scaling,
649 % and biasing the results is recommended, to prevent the resulting image
652 % The 3x3 kernels (most of these) can be circularly rotated in multiples of
653 % 45 degrees to generate the 8 angled varients of each of the kernels.
656 % Discrete Lapacian Kernels, (without normalization)
657 % Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood)
658 % Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood)
659 % Type 2 : 3x3 with center:4 edge:1 corner:-2
660 % Type 3 : 3x3 with center:4 edge:-2 corner:1
661 % Type 5 : 5x5 laplacian
662 % Type 7 : 7x7 laplacian
663 % Type 15 : 5x5 LoG (sigma approx 1.4)
664 % Type 19 : 9x9 LoG (sigma approx 1.4)
667 % Sobel 'Edge' convolution kernel (3x3)
673 % Roberts convolution kernel (3x3)
679 % Prewitt Edge convolution kernel (3x3)
685 % Prewitt's "Compass" convolution kernel (3x3)
691 % Kirsch's "Compass" convolution kernel (3x3)
697 % Frei-Chen Edge Detector is based on a kernel that is similar to
698 % the Sobel Kernel, but is designed to be isotropic. That is it takes
699 % into account the distance of the diagonal in the kernel.
702 % | sqrt(2), 0, -sqrt(2) |
705 % FreiChen:{type},{angle}
707 % Frei-Chen Pre-weighted kernels...
709 % Type 0: default un-nomalized version shown above.
711 % Type 1: Orthogonal Kernel (same as type 11 below)
713 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
716 % Type 2: Diagonal form of Kernel...
718 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
721 % However this kernel is als at the heart of the FreiChen Edge Detection
722 % Process which uses a set of 9 specially weighted kernel. These 9
723 % kernels not be normalized, but directly applied to the image. The
724 % results is then added together, to produce the intensity of an edge in
725 % a specific direction. The square root of the pixel value can then be
726 % taken as the cosine of the edge, and at least 2 such runs at 90 degrees
727 % from each other, both the direction and the strength of the edge can be
730 % Type 10: All 9 of the following pre-weighted kernels...
732 % Type 11: | 1, 0, -1 |
733 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
736 % Type 12: | 1, sqrt(2), 1 |
737 % | 0, 0, 0 | / 2*sqrt(2)
740 % Type 13: | sqrt(2), -1, 0 |
741 % | -1, 0, 1 | / 2*sqrt(2)
744 % Type 14: | 0, 1, -sqrt(2) |
745 % | -1, 0, 1 | / 2*sqrt(2)
748 % Type 15: | 0, -1, 0 |
752 % Type 16: | 1, 0, -1 |
756 % Type 17: | 1, -2, 1 |
760 % Type 18: | -2, 1, -2 |
764 % Type 19: | 1, 1, 1 |
768 % The first 4 are for edge detection, the next 4 are for line detection
769 % and the last is to add a average component to the results.
771 % Using a special type of '-1' will return all 9 pre-weighted kernels
772 % as a multi-kernel list, so that you can use them directly (without
773 % normalization) with the special "-set option:morphology:compose Plus"
774 % setting to apply the full FreiChen Edge Detection Technique.
776 % If 'type' is large it will be taken to be an actual rotation angle for
777 % the default FreiChen (type 0) kernel. As such FreiChen:45 will look
778 % like a Sobel:45 but with 'sqrt(2)' instead of '2' values.
780 % WARNING: The above was layed out as per
781 % http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf
782 % But rotated 90 degrees so direction is from left rather than the top.
783 % I have yet to find any secondary confirmation of the above. The only
784 % other source found was actual source code at
785 % http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf
786 % Neigher paper defineds the kernels in a way that looks locical or
787 % correct when taken as a whole.
791 % Diamond:[{radius}[,{scale}]]
792 % Generate a diamond shaped kernel with given radius to the points.
793 % Kernel size will again be radius*2+1 square and defaults to radius 1,
794 % generating a 3x3 kernel that is slightly larger than a square.
796 % Square:[{radius}[,{scale}]]
797 % Generate a square shaped kernel of size radius*2+1, and defaulting
798 % to a 3x3 (radius 1).
800 % Octagon:[{radius}[,{scale}]]
801 % Generate octagonal shaped kernel of given radius and constant scale.
802 % Default radius is 3 producing a 7x7 kernel. A radius of 1 will result
803 % in "Diamond" kernel.
805 % Disk:[{radius}[,{scale}]]
806 % Generate a binary disk, thresholded at the radius given, the radius
807 % may be a float-point value. Final Kernel size is floor(radius)*2+1
808 % square. A radius of 5.3 is the default.
810 % NOTE: That a low radii Disk kernels produce the same results as
811 % many of the previously defined kernels, but differ greatly at larger
812 % radii. Here is a table of equivalences...
813 % "Disk:1" => "Diamond", "Octagon:1", or "Cross:1"
814 % "Disk:1.5" => "Square"
815 % "Disk:2" => "Diamond:2"
816 % "Disk:2.5" => "Octagon"
817 % "Disk:2.9" => "Square:2"
818 % "Disk:3.5" => "Octagon:3"
819 % "Disk:4.5" => "Octagon:4"
820 % "Disk:5.4" => "Octagon:5"
821 % "Disk:6.4" => "Octagon:6"
822 % All other Disk shapes are unique to this kernel, but because a "Disk"
823 % is more circular when using a larger radius, using a larger radius is
824 % preferred over iterating the morphological operation.
826 % Rectangle:{geometry}
827 % Simply generate a rectangle of 1's with the size given. You can also
828 % specify the location of the 'control point', otherwise the closest
829 % pixel to the center of the rectangle is selected.
831 % Properly centered and odd sized rectangles work the best.
833 % Symbol Dilation Kernels
835 % These kernel is not a good general morphological kernel, but is used
836 % more for highlighting and marking any single pixels in an image using,
837 % a "Dilate" method as appropriate.
839 % For the same reasons iterating these kernels does not produce the
840 % same result as using a larger radius for the symbol.
842 % Plus:[{radius}[,{scale}]]
843 % Cross:[{radius}[,{scale}]]
844 % Generate a kernel in the shape of a 'plus' or a 'cross' with
845 % a each arm the length of the given radius (default 2).
847 % NOTE: "plus:1" is equivalent to a "Diamond" kernel.
849 % Ring:{radius1},{radius2}[,{scale}]
850 % A ring of the values given that falls between the two radii.
851 % Defaults to a ring of approximataly 3 radius in a 7x7 kernel.
852 % This is the 'edge' pixels of the default "Disk" kernel,
853 % More specifically, "Ring" -> "Ring:2.5,3.5,1.0"
855 % Hit and Miss Kernels
857 % Peak:radius1,radius2
858 % Find any peak larger than the pixels the fall between the two radii.
859 % The default ring of pixels is as per "Ring".
861 % Find flat orthogonal edges of a binary shape
863 % Find 90 degree corners of a binary shape
865 % A special kernel to thin the 'outside' of diagonals
867 % Find end points of lines (for pruning a skeletion)
868 % Two types of lines ends (default to both) can be searched for
869 % Type 0: All line ends
870 % Type 1: single kernel for 4-conneected line ends
871 % Type 2: single kernel for simple line ends
873 % Find three line junctions (within a skeletion)
874 % Type 0: all line junctions
875 % Type 1: Y Junction kernel
876 % Type 2: Diagonal T Junction kernel
877 % Type 3: Orthogonal T Junction kernel
878 % Type 4: Diagonal X Junction kernel
879 % Type 5: Orthogonal + Junction kernel
881 % Find single pixel ridges or thin lines
882 % Type 1: Fine single pixel thick lines and ridges
883 % Type 2: Find two pixel thick lines and ridges
885 % Octagonal Thickening Kernel, to generate convex hulls of 45 degrees
887 % Traditional skeleton generating kernels.
888 % Type 1: Tradional Skeleton kernel (4 connected skeleton)
889 % Type 2: HIPR2 Skeleton kernel (8 connected skeleton)
890 % Type 3: Thinning skeleton based on a ressearch paper by
891 % Dan S. Bloomberg (Default Type)
893 % A huge variety of Thinning Kernels designed to preserve conectivity.
894 % many other kernel sets use these kernels as source definitions.
895 % Type numbers are 41-49, 81-89, 481, and 482 which are based on
896 % the super and sub notations used in the source research paper.
898 % Distance Measuring Kernels
900 % Different types of distance measuring methods, which are used with the
901 % a 'Distance' morphology method for generating a gradient based on
902 % distance from an edge of a binary shape, though there is a technique
903 % for handling a anti-aliased shape.
905 % See the 'Distance' Morphological Method, for information of how it is
908 % Chebyshev:[{radius}][x{scale}[%!]]
909 % Chebyshev Distance (also known as Tchebychev or Chessboard distance)
910 % is a value of one to any neighbour, orthogonal or diagonal. One why
911 % of thinking of it is the number of squares a 'King' or 'Queen' in
912 % chess needs to traverse reach any other position on a chess board.
913 % It results in a 'square' like distance function, but one where
914 % diagonals are given a value that is closer than expected.
916 % Manhattan:[{radius}][x{scale}[%!]]
917 % Manhattan Distance (also known as Rectilinear, City Block, or the Taxi
918 % Cab distance metric), it is the distance needed when you can only
919 % travel in horizontal or vertical directions only. It is the
920 % distance a 'Rook' in chess would have to travel, and results in a
921 % diamond like distances, where diagonals are further than expected.
923 % Octagonal:[{radius}][x{scale}[%!]]
924 % An interleving of Manhatten and Chebyshev metrics producing an
925 % increasing octagonally shaped distance. Distances matches those of
926 % the "Octagon" shaped kernel of the same radius. The minimum radius
927 % and default is 2, producing a 5x5 kernel.
929 % Euclidean:[{radius}][x{scale}[%!]]
930 % Euclidean distance is the 'direct' or 'as the crow flys' distance.
931 % However by default the kernel size only has a radius of 1, which
932 % limits the distance to 'Knight' like moves, with only orthogonal and
933 % diagonal measurements being correct. As such for the default kernel
934 % you will get octagonal like distance function.
936 % However using a larger radius such as "Euclidean:4" you will get a
937 % much smoother distance gradient from the edge of the shape. Especially
938 % if the image is pre-processed to include any anti-aliasing pixels.
939 % Of course a larger kernel is slower to use, and not always needed.
941 % The first three Distance Measuring Kernels will only generate distances
942 % of exact multiples of {scale} in binary images. As such you can use a
943 % scale of 1 without loosing any information. However you also need some
944 % scaling when handling non-binary anti-aliased shapes.
946 % The "Euclidean" Distance Kernel however does generate a non-integer
947 % fractional results, and as such scaling is vital even for binary shapes.
951 MagickExport KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
952 const GeometryInfo *args,ExceptionInfo *exception)
965 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
967 /* Generate a new empty kernel if needed */
968 kernel=(KernelInfo *) NULL;
970 case UndefinedKernel: /* These should not call this function */
971 case UserDefinedKernel:
972 assert("Should not call this function" != (char *) NULL);
974 case LaplacianKernel: /* Named Descrete Convolution Kernels */
975 case SobelKernel: /* these are defined using other kernels */
981 case EdgesKernel: /* Hit and Miss kernels */
983 case DiagonalsKernel:
985 case LineJunctionsKernel:
987 case ConvexHullKernel:
990 break; /* A pre-generated kernel is not needed */
992 /* set to 1 to do a compile-time check that we haven't missed anything */
1002 case RectangleKernel:
1009 case ChebyshevKernel:
1010 case ManhattanKernel:
1011 case OctangonalKernel:
1012 case EuclideanKernel:
1016 /* Generate the base Kernel Structure */
1017 kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
1018 if (kernel == (KernelInfo *) NULL)
1020 (void) memset(kernel,0,sizeof(*kernel));
1021 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
1022 kernel->negative_range = kernel->positive_range = 0.0;
1023 kernel->type = type;
1024 kernel->next = (KernelInfo *) NULL;
1025 kernel->signature=MagickCoreSignature;
1035 kernel->height = kernel->width = (size_t) 1;
1036 kernel->x = kernel->y = (ssize_t) 0;
1037 kernel->values=(MagickRealType *) MagickAssumeAligned(
1038 AcquireAlignedMemory(1,sizeof(*kernel->values)));
1039 if (kernel->values == (MagickRealType *) NULL)
1040 return(DestroyKernelInfo(kernel));
1041 kernel->maximum = kernel->values[0] = args->rho;
1045 case GaussianKernel:
1049 sigma = fabs(args->sigma),
1050 sigma2 = fabs(args->xi),
1053 if ( args->rho >= 1.0 )
1054 kernel->width = (size_t)args->rho*2+1;
1055 else if ( (type != DoGKernel) || (sigma >= sigma2) )
1056 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma);
1058 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma2);
1059 kernel->height = kernel->width;
1060 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1061 kernel->values=(MagickRealType *) MagickAssumeAligned(
1062 AcquireAlignedMemory(kernel->width,kernel->height*
1063 sizeof(*kernel->values)));
1064 if (kernel->values == (MagickRealType *) NULL)
1065 return(DestroyKernelInfo(kernel));
1067 /* WARNING: The following generates a 'sampled gaussian' kernel.
1068 * What we really want is a 'discrete gaussian' kernel.
1070 * How to do this is I don't know, but appears to be basied on the
1071 * Error Function 'erf()' (intergral of a gaussian)
1074 if ( type == GaussianKernel || type == DoGKernel )
1075 { /* Calculate a Gaussian, OR positive half of a DoG */
1076 if ( sigma > MagickEpsilon )
1077 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1078 B = (double) (1.0/(Magick2PI*sigma*sigma));
1079 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1080 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1081 kernel->values[i] = exp(-((double)(u*u+v*v))*A)*B;
1083 else /* limiting case - a unity (normalized Dirac) kernel */
1084 { (void) memset(kernel->values,0, (size_t)
1085 kernel->width*kernel->height*sizeof(*kernel->values));
1086 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1090 if ( type == DoGKernel )
1091 { /* Subtract a Negative Gaussian for "Difference of Gaussian" */
1092 if ( sigma2 > MagickEpsilon )
1093 { sigma = sigma2; /* simplify loop expressions */
1094 A = 1.0/(2.0*sigma*sigma);
1095 B = (double) (1.0/(Magick2PI*sigma*sigma));
1096 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1097 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1098 kernel->values[i] -= exp(-((double)(u*u+v*v))*A)*B;
1100 else /* limiting case - a unity (normalized Dirac) kernel */
1101 kernel->values[kernel->x+kernel->y*kernel->width] -= 1.0;
1104 if ( type == LoGKernel )
1105 { /* Calculate a Laplacian of a Gaussian - Or Mexician Hat */
1106 if ( sigma > MagickEpsilon )
1107 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1108 B = (double) (1.0/(MagickPI*sigma*sigma*sigma*sigma));
1109 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1110 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1111 { R = ((double)(u*u+v*v))*A;
1112 kernel->values[i] = (1-R)*exp(-R)*B;
1115 else /* special case - generate a unity kernel */
1116 { (void) memset(kernel->values,0, (size_t)
1117 kernel->width*kernel->height*sizeof(*kernel->values));
1118 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1122 /* Note the above kernels may have been 'clipped' by a user defined
1123 ** radius, producing a smaller (darker) kernel. Also for very small
1124 ** sigma's (> 0.1) the central value becomes larger than one, and thus
1125 ** producing a very bright kernel.
1127 ** Normalization will still be needed.
1130 /* Normalize the 2D Gaussian Kernel
1132 ** NB: a CorrelateNormalize performs a normal Normalize if
1133 ** there are no negative values.
1135 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1136 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1142 sigma = fabs(args->sigma),
1145 if ( args->rho >= 1.0 )
1146 kernel->width = (size_t)args->rho*2+1;
1148 kernel->width = GetOptimalKernelWidth1D(args->rho,sigma);
1150 kernel->x = (ssize_t) (kernel->width-1)/2;
1152 kernel->negative_range = kernel->positive_range = 0.0;
1153 kernel->values=(MagickRealType *) MagickAssumeAligned(
1154 AcquireAlignedMemory(kernel->width,kernel->height*
1155 sizeof(*kernel->values)));
1156 if (kernel->values == (MagickRealType *) NULL)
1157 return(DestroyKernelInfo(kernel));
1160 #define KernelRank 3
1161 /* Formula derived from GetBlurKernel() in "effect.c" (plus bug fix).
1162 ** It generates a gaussian 3 times the width, and compresses it into
1163 ** the expected range. This produces a closer normalization of the
1164 ** resulting kernel, especially for very low sigma values.
1165 ** As such while wierd it is prefered.
1167 ** I am told this method originally came from Photoshop.
1169 ** A properly normalized curve is generated (apart from edge clipping)
1170 ** even though we later normalize the result (for edge clipping)
1171 ** to allow the correct generation of a "Difference of Blurs".
1175 v = (ssize_t) (kernel->width*KernelRank-1)/2; /* start/end points to fit range */
1176 (void) memset(kernel->values,0, (size_t)
1177 kernel->width*kernel->height*sizeof(*kernel->values));
1178 /* Calculate a Positive 1D Gaussian */
1179 if ( sigma > MagickEpsilon )
1180 { sigma *= KernelRank; /* simplify loop expressions */
1181 alpha = 1.0/(2.0*sigma*sigma);
1182 beta= (double) (1.0/(MagickSQ2PI*sigma ));
1183 for ( u=-v; u <= v; u++) {
1184 kernel->values[(u+v)/KernelRank] +=
1185 exp(-((double)(u*u))*alpha)*beta;
1188 else /* special case - generate a unity kernel */
1189 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1191 /* Direct calculation without curve averaging
1192 This is equivelent to a KernelRank of 1 */
1194 /* Calculate a Positive Gaussian */
1195 if ( sigma > MagickEpsilon )
1196 { alpha = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1197 beta = 1.0/(MagickSQ2PI*sigma);
1198 for ( i=0, u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1199 kernel->values[i] = exp(-((double)(u*u))*alpha)*beta;
1201 else /* special case - generate a unity kernel */
1202 { (void) memset(kernel->values,0, (size_t)
1203 kernel->width*kernel->height*sizeof(*kernel->values));
1204 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1207 /* Note the above kernel may have been 'clipped' by a user defined
1208 ** radius, producing a smaller (darker) kernel. Also for very small
1209 ** sigma's (> 0.1) the central value becomes larger than one, as a
1210 ** result of not generating a actual 'discrete' kernel, and thus
1211 ** producing a very bright 'impulse'.
1213 ** Becuase of these two factors Normalization is required!
1216 /* Normalize the 1D Gaussian Kernel
1218 ** NB: a CorrelateNormalize performs a normal Normalize if
1219 ** there are no negative values.
1221 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1222 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1224 /* rotate the 1D kernel by given angle */
1225 RotateKernelInfo(kernel, args->xi );
1230 sigma = fabs(args->sigma),
1233 if ( args->rho < 1.0 )
1234 kernel->width = (GetOptimalKernelWidth1D(args->rho,sigma)-1)/2+1;
1236 kernel->width = (size_t)args->rho;
1237 kernel->x = kernel->y = 0;
1239 kernel->negative_range = kernel->positive_range = 0.0;
1240 kernel->values=(MagickRealType *) MagickAssumeAligned(
1241 AcquireAlignedMemory(kernel->width,kernel->height*
1242 sizeof(*kernel->values)));
1243 if (kernel->values == (MagickRealType *) NULL)
1244 return(DestroyKernelInfo(kernel));
1246 /* A comet blur is half a 1D gaussian curve, so that the object is
1247 ** blurred in one direction only. This may not be quite the right
1248 ** curve to use so may change in the future. The function must be
1249 ** normalised after generation, which also resolves any clipping.
1251 ** As we are normalizing and not subtracting gaussians,
1252 ** there is no need for a divisor in the gaussian formula
1254 ** It is less comples
1256 if ( sigma > MagickEpsilon )
1259 #define KernelRank 3
1260 v = (ssize_t) kernel->width*KernelRank; /* start/end points */
1261 (void) memset(kernel->values,0, (size_t)
1262 kernel->width*sizeof(*kernel->values));
1263 sigma *= KernelRank; /* simplify the loop expression */
1264 A = 1.0/(2.0*sigma*sigma);
1265 /* B = 1.0/(MagickSQ2PI*sigma); */
1266 for ( u=0; u < v; u++) {
1267 kernel->values[u/KernelRank] +=
1268 exp(-((double)(u*u))*A);
1269 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1271 for (i=0; i < (ssize_t) kernel->width; i++)
1272 kernel->positive_range += kernel->values[i];
1274 A = 1.0/(2.0*sigma*sigma); /* simplify the loop expression */
1275 /* B = 1.0/(MagickSQ2PI*sigma); */
1276 for ( i=0; i < (ssize_t) kernel->width; i++)
1277 kernel->positive_range +=
1278 kernel->values[i] = exp(-((double)(i*i))*A);
1279 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1282 else /* special case - generate a unity kernel */
1283 { (void) memset(kernel->values,0, (size_t)
1284 kernel->width*kernel->height*sizeof(*kernel->values));
1285 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1286 kernel->positive_range = 1.0;
1289 kernel->minimum = 0.0;
1290 kernel->maximum = kernel->values[0];
1291 kernel->negative_range = 0.0;
1293 ScaleKernelInfo(kernel, 1.0, NormalizeValue); /* Normalize */
1294 RotateKernelInfo(kernel, args->xi); /* Rotate by angle */
1297 case BinomialKernel:
1302 if (args->rho < 1.0)
1303 kernel->width = kernel->height = 3; /* default radius = 1 */
1305 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1306 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1308 order_f = fact(kernel->width-1);
1310 kernel->values=(MagickRealType *) MagickAssumeAligned(
1311 AcquireAlignedMemory(kernel->width,kernel->height*
1312 sizeof(*kernel->values)));
1313 if (kernel->values == (MagickRealType *) NULL)
1314 return(DestroyKernelInfo(kernel));
1316 /* set all kernel values within diamond area to scale given */
1317 for ( i=0, v=0; v < (ssize_t)kernel->height; v++)
1319 alpha = order_f / ( fact((size_t) v) * fact(kernel->height-v-1) );
1320 for ( u=0; u < (ssize_t)kernel->width; u++, i++)
1321 kernel->positive_range += kernel->values[i] = (double)
1322 (alpha * order_f / ( fact((size_t) u) * fact(kernel->height-u-1) ));
1324 kernel->minimum = 1.0;
1325 kernel->maximum = kernel->values[kernel->x+kernel->y*kernel->width];
1326 kernel->negative_range = 0.0;
1331 Convolution Kernels - Well Known Named Constant Kernels
1333 case LaplacianKernel:
1334 { switch ( (int) args->rho ) {
1336 default: /* laplacian square filter -- default */
1337 kernel=ParseKernelArray("3: -1,-1,-1 -1,8,-1 -1,-1,-1");
1339 case 1: /* laplacian diamond filter */
1340 kernel=ParseKernelArray("3: 0,-1,0 -1,4,-1 0,-1,0");
1343 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1346 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 1,-2,1");
1348 case 5: /* a 5x5 laplacian */
1349 kernel=ParseKernelArray(
1350 "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");
1352 case 7: /* a 7x7 laplacian */
1353 kernel=ParseKernelArray(
1354 "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" );
1356 case 15: /* a 5x5 LoG (sigma approx 1.4) */
1357 kernel=ParseKernelArray(
1358 "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");
1360 case 19: /* a 9x9 LoG (sigma approx 1.4) */
1361 /* http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue1/IJIP-15.pdf */
1362 kernel=ParseKernelArray(
1363 "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");
1366 if (kernel == (KernelInfo *) NULL)
1368 kernel->type = type;
1372 { /* Simple Sobel Kernel */
1373 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1374 if (kernel == (KernelInfo *) NULL)
1376 kernel->type = type;
1377 RotateKernelInfo(kernel, args->rho);
1382 kernel=ParseKernelArray("3: 0,0,0 1,-1,0 0,0,0");
1383 if (kernel == (KernelInfo *) NULL)
1385 kernel->type = type;
1386 RotateKernelInfo(kernel, args->rho);
1391 kernel=ParseKernelArray("3: 1,0,-1 1,0,-1 1,0,-1");
1392 if (kernel == (KernelInfo *) NULL)
1394 kernel->type = type;
1395 RotateKernelInfo(kernel, args->rho);
1400 kernel=ParseKernelArray("3: 1,1,-1 1,-2,-1 1,1,-1");
1401 if (kernel == (KernelInfo *) NULL)
1403 kernel->type = type;
1404 RotateKernelInfo(kernel, args->rho);
1409 kernel=ParseKernelArray("3: 5,-3,-3 5,0,-3 5,-3,-3");
1410 if (kernel == (KernelInfo *) NULL)
1412 kernel->type = type;
1413 RotateKernelInfo(kernel, args->rho);
1416 case FreiChenKernel:
1417 /* Direction is set to be left to right positive */
1418 /* http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf -- RIGHT? */
1419 /* http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf -- WRONG? */
1420 { switch ( (int) args->rho ) {
1423 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1424 if (kernel == (KernelInfo *) NULL)
1426 kernel->type = type;
1427 kernel->values[3] = +(MagickRealType) MagickSQ2;
1428 kernel->values[5] = -(MagickRealType) MagickSQ2;
1429 CalcKernelMetaData(kernel); /* recalculate meta-data */
1432 kernel=ParseKernelArray("3: 1,2,0 2,0,-2 0,-2,-1");
1433 if (kernel == (KernelInfo *) NULL)
1435 kernel->type = type;
1436 kernel->values[1] = kernel->values[3]= +(MagickRealType) MagickSQ2;
1437 kernel->values[5] = kernel->values[7]= -(MagickRealType) MagickSQ2;
1438 CalcKernelMetaData(kernel); /* recalculate meta-data */
1439 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1443 kernel=AcquireKernelInfo("FreiChen:11;FreiChen:12;FreiChen:13;FreiChen:14;FreiChen:15;FreiChen:16;FreiChen:17;FreiChen:18;FreiChen:19",exception);
1444 if (kernel == (KernelInfo *) NULL)
1450 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1451 if (kernel == (KernelInfo *) NULL)
1453 kernel->type = type;
1454 kernel->values[3] = +(MagickRealType) MagickSQ2;
1455 kernel->values[5] = -(MagickRealType) MagickSQ2;
1456 CalcKernelMetaData(kernel); /* recalculate meta-data */
1457 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1460 kernel=ParseKernelArray("3: 1,2,1 0,0,0 1,2,1");
1461 if (kernel == (KernelInfo *) NULL)
1463 kernel->type = type;
1464 kernel->values[1] = +(MagickRealType) MagickSQ2;
1465 kernel->values[7] = +(MagickRealType) MagickSQ2;
1466 CalcKernelMetaData(kernel);
1467 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1470 kernel=ParseKernelArray("3: 2,-1,0 -1,0,1 0,1,-2");
1471 if (kernel == (KernelInfo *) NULL)
1473 kernel->type = type;
1474 kernel->values[0] = +(MagickRealType) MagickSQ2;
1475 kernel->values[8] = -(MagickRealType) MagickSQ2;
1476 CalcKernelMetaData(kernel);
1477 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1480 kernel=ParseKernelArray("3: 0,1,-2 -1,0,1 2,-1,0");
1481 if (kernel == (KernelInfo *) NULL)
1483 kernel->type = type;
1484 kernel->values[2] = -(MagickRealType) MagickSQ2;
1485 kernel->values[6] = +(MagickRealType) MagickSQ2;
1486 CalcKernelMetaData(kernel);
1487 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1490 kernel=ParseKernelArray("3: 0,-1,0 1,0,1 0,-1,0");
1491 if (kernel == (KernelInfo *) NULL)
1493 kernel->type = type;
1494 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1497 kernel=ParseKernelArray("3: 1,0,-1 0,0,0 -1,0,1");
1498 if (kernel == (KernelInfo *) NULL)
1500 kernel->type = type;
1501 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1504 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 -1,-2,1");
1505 if (kernel == (KernelInfo *) NULL)
1507 kernel->type = type;
1508 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1511 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1512 if (kernel == (KernelInfo *) NULL)
1514 kernel->type = type;
1515 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1518 kernel=ParseKernelArray("3: 1,1,1 1,1,1 1,1,1");
1519 if (kernel == (KernelInfo *) NULL)
1521 kernel->type = type;
1522 ScaleKernelInfo(kernel, 1.0/3.0, NoValue);
1525 if ( fabs(args->sigma) >= MagickEpsilon )
1526 /* Rotate by correctly supplied 'angle' */
1527 RotateKernelInfo(kernel, args->sigma);
1528 else if ( args->rho > 30.0 || args->rho < -30.0 )
1529 /* Rotate by out of bounds 'type' */
1530 RotateKernelInfo(kernel, args->rho);
1535 Boolean or Shaped Kernels
1539 if (args->rho < 1.0)
1540 kernel->width = kernel->height = 3; /* default radius = 1 */
1542 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1543 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1545 kernel->values=(MagickRealType *) MagickAssumeAligned(
1546 AcquireAlignedMemory(kernel->width,kernel->height*
1547 sizeof(*kernel->values)));
1548 if (kernel->values == (MagickRealType *) NULL)
1549 return(DestroyKernelInfo(kernel));
1551 /* set all kernel values within diamond area to scale given */
1552 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1553 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1554 if ( (labs((long) u)+labs((long) v)) <= (long) kernel->x)
1555 kernel->positive_range += kernel->values[i] = args->sigma;
1557 kernel->values[i] = nan;
1558 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1562 case RectangleKernel:
1565 if ( type == SquareKernel )
1567 if (args->rho < 1.0)
1568 kernel->width = kernel->height = 3; /* default radius = 1 */
1570 kernel->width = kernel->height = (size_t) (2*args->rho+1);
1571 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1572 scale = args->sigma;
1575 /* NOTE: user defaults set in "AcquireKernelInfo()" */
1576 if ( args->rho < 1.0 || args->sigma < 1.0 )
1577 return(DestroyKernelInfo(kernel)); /* invalid args given */
1578 kernel->width = (size_t)args->rho;
1579 kernel->height = (size_t)args->sigma;
1580 if ( args->xi < 0.0 || args->xi > (double)kernel->width ||
1581 args->psi < 0.0 || args->psi > (double)kernel->height )
1582 return(DestroyKernelInfo(kernel)); /* invalid args given */
1583 kernel->x = (ssize_t) args->xi;
1584 kernel->y = (ssize_t) args->psi;
1587 kernel->values=(MagickRealType *) MagickAssumeAligned(
1588 AcquireAlignedMemory(kernel->width,kernel->height*
1589 sizeof(*kernel->values)));
1590 if (kernel->values == (MagickRealType *) NULL)
1591 return(DestroyKernelInfo(kernel));
1593 /* set all kernel values to scale given */
1594 u=(ssize_t) (kernel->width*kernel->height);
1595 for ( i=0; i < u; i++)
1596 kernel->values[i] = scale;
1597 kernel->minimum = kernel->maximum = scale; /* a flat shape */
1598 kernel->positive_range = scale*u;
1603 if (args->rho < 1.0)
1604 kernel->width = kernel->height = 5; /* default radius = 2 */
1606 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1607 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1609 kernel->values=(MagickRealType *) MagickAssumeAligned(
1610 AcquireAlignedMemory(kernel->width,kernel->height*
1611 sizeof(*kernel->values)));
1612 if (kernel->values == (MagickRealType *) NULL)
1613 return(DestroyKernelInfo(kernel));
1615 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1616 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1617 if ( (labs((long) u)+labs((long) v)) <=
1618 ((long)kernel->x + (long)(kernel->x/2)) )
1619 kernel->positive_range += kernel->values[i] = args->sigma;
1621 kernel->values[i] = nan;
1622 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1628 limit = (ssize_t)(args->rho*args->rho);
1630 if (args->rho < 0.4) /* default radius approx 4.3 */
1631 kernel->width = kernel->height = 9L, limit = 18L;
1633 kernel->width = kernel->height = (size_t)fabs(args->rho)*2+1;
1634 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1636 kernel->values=(MagickRealType *) MagickAssumeAligned(
1637 AcquireAlignedMemory(kernel->width,kernel->height*
1638 sizeof(*kernel->values)));
1639 if (kernel->values == (MagickRealType *) NULL)
1640 return(DestroyKernelInfo(kernel));
1642 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1643 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1644 if ((u*u+v*v) <= limit)
1645 kernel->positive_range += kernel->values[i] = args->sigma;
1647 kernel->values[i] = nan;
1648 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1653 if (args->rho < 1.0)
1654 kernel->width = kernel->height = 5; /* default radius 2 */
1656 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1657 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1659 kernel->values=(MagickRealType *) MagickAssumeAligned(
1660 AcquireAlignedMemory(kernel->width,kernel->height*
1661 sizeof(*kernel->values)));
1662 if (kernel->values == (MagickRealType *) NULL)
1663 return(DestroyKernelInfo(kernel));
1665 /* set all kernel values along axises to given scale */
1666 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1667 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1668 kernel->values[i] = (u == 0 || v == 0) ? args->sigma : nan;
1669 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1670 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1675 if (args->rho < 1.0)
1676 kernel->width = kernel->height = 5; /* default radius 2 */
1678 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1679 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1681 kernel->values=(MagickRealType *) MagickAssumeAligned(
1682 AcquireAlignedMemory(kernel->width,kernel->height*
1683 sizeof(*kernel->values)));
1684 if (kernel->values == (MagickRealType *) NULL)
1685 return(DestroyKernelInfo(kernel));
1687 /* set all kernel values along axises to given scale */
1688 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1689 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1690 kernel->values[i] = (u == v || u == -v) ? args->sigma : nan;
1691 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1692 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1706 if (args->rho < args->sigma)
1708 kernel->width = ((size_t)args->sigma)*2+1;
1709 limit1 = (ssize_t)(args->rho*args->rho);
1710 limit2 = (ssize_t)(args->sigma*args->sigma);
1714 kernel->width = ((size_t)args->rho)*2+1;
1715 limit1 = (ssize_t)(args->sigma*args->sigma);
1716 limit2 = (ssize_t)(args->rho*args->rho);
1719 kernel->width = 7L, limit1 = 7L, limit2 = 11L;
1721 kernel->height = kernel->width;
1722 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1723 kernel->values=(MagickRealType *) MagickAssumeAligned(
1724 AcquireAlignedMemory(kernel->width,kernel->height*
1725 sizeof(*kernel->values)));
1726 if (kernel->values == (MagickRealType *) NULL)
1727 return(DestroyKernelInfo(kernel));
1729 /* set a ring of points of 'scale' ( 0.0 for PeaksKernel ) */
1730 scale = (ssize_t) (( type == PeaksKernel) ? 0.0 : args->xi);
1731 for ( i=0, v= -kernel->y; v <= (ssize_t)kernel->y; v++)
1732 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1733 { ssize_t radius=u*u+v*v;
1734 if (limit1 < radius && radius <= limit2)
1735 kernel->positive_range += kernel->values[i] = (double) scale;
1737 kernel->values[i] = nan;
1739 kernel->minimum = kernel->maximum = (double) scale;
1740 if ( type == PeaksKernel ) {
1741 /* set the central point in the middle */
1742 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1743 kernel->positive_range = 1.0;
1744 kernel->maximum = 1.0;
1750 kernel=AcquireKernelInfo("ThinSE:482",exception);
1751 if (kernel == (KernelInfo *) NULL)
1753 kernel->type = type;
1754 ExpandMirrorKernelInfo(kernel); /* mirror expansion of kernels */
1759 kernel=AcquireKernelInfo("ThinSE:87",exception);
1760 if (kernel == (KernelInfo *) NULL)
1762 kernel->type = type;
1763 ExpandRotateKernelInfo(kernel, 90.0); /* Expand 90 degree rotations */
1766 case DiagonalsKernel:
1768 switch ( (int) args->rho ) {
1773 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1774 if (kernel == (KernelInfo *) NULL)
1776 kernel->type = type;
1777 new_kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1778 if (new_kernel == (KernelInfo *) NULL)
1779 return(DestroyKernelInfo(kernel));
1780 new_kernel->type = type;
1781 LastKernelInfo(kernel)->next = new_kernel;
1782 ExpandMirrorKernelInfo(kernel);
1786 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1789 kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1792 if (kernel == (KernelInfo *) NULL)
1794 kernel->type = type;
1795 RotateKernelInfo(kernel, args->sigma);
1798 case LineEndsKernel:
1799 { /* Kernels for finding the end of thin lines */
1800 switch ( (int) args->rho ) {
1803 /* set of kernels to find all end of lines */
1804 return(AcquireKernelInfo("LineEnds:1>;LineEnds:2>",exception));
1806 /* kernel for 4-connected line ends - no rotation */
1807 kernel=ParseKernelArray("3: 0,0,- 0,1,1 0,0,-");
1810 /* kernel to add for 8-connected lines - no rotation */
1811 kernel=ParseKernelArray("3: 0,0,0 0,1,0 0,0,1");
1814 /* kernel to add for orthogonal line ends - does not find corners */
1815 kernel=ParseKernelArray("3: 0,0,0 0,1,1 0,0,0");
1818 /* traditional line end - fails on last T end */
1819 kernel=ParseKernelArray("3: 0,0,0 0,1,- 0,0,-");
1822 if (kernel == (KernelInfo *) NULL)
1824 kernel->type = type;
1825 RotateKernelInfo(kernel, args->sigma);
1828 case LineJunctionsKernel:
1829 { /* kernels for finding the junctions of multiple lines */
1830 switch ( (int) args->rho ) {
1833 /* set of kernels to find all line junctions */
1834 return(AcquireKernelInfo("LineJunctions:1@;LineJunctions:2>",exception));
1837 kernel=ParseKernelArray("3: 1,-,1 -,1,- -,1,-");
1840 /* Diagonal T Junctions */
1841 kernel=ParseKernelArray("3: 1,-,- -,1,- 1,-,1");
1844 /* Orthogonal T Junctions */
1845 kernel=ParseKernelArray("3: -,-,- 1,1,1 -,1,-");
1848 /* Diagonal X Junctions */
1849 kernel=ParseKernelArray("3: 1,-,1 -,1,- 1,-,1");
1852 /* Orthogonal X Junctions - minimal diamond kernel */
1853 kernel=ParseKernelArray("3: -,1,- 1,1,1 -,1,-");
1856 if (kernel == (KernelInfo *) NULL)
1858 kernel->type = type;
1859 RotateKernelInfo(kernel, args->sigma);
1863 { /* Ridges - Ridge finding kernels */
1866 switch ( (int) args->rho ) {
1869 kernel=ParseKernelArray("3x1:0,1,0");
1870 if (kernel == (KernelInfo *) NULL)
1872 kernel->type = type;
1873 ExpandRotateKernelInfo(kernel, 90.0); /* 2 rotated kernels (symmetrical) */
1876 kernel=ParseKernelArray("4x1:0,1,1,0");
1877 if (kernel == (KernelInfo *) NULL)
1879 kernel->type = type;
1880 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotated kernels */
1882 /* Kernels to find a stepped 'thick' line, 4 rotates + mirrors */
1883 /* Unfortunatally we can not yet rotate a non-square kernel */
1884 /* But then we can't flip a non-symetrical kernel either */
1885 new_kernel=ParseKernelArray("4x3+1+1:0,1,1,- -,1,1,- -,1,1,0");
1886 if (new_kernel == (KernelInfo *) NULL)
1887 return(DestroyKernelInfo(kernel));
1888 new_kernel->type = type;
1889 LastKernelInfo(kernel)->next = new_kernel;
1890 new_kernel=ParseKernelArray("4x3+2+1:0,1,1,- -,1,1,- -,1,1,0");
1891 if (new_kernel == (KernelInfo *) NULL)
1892 return(DestroyKernelInfo(kernel));
1893 new_kernel->type = type;
1894 LastKernelInfo(kernel)->next = new_kernel;
1895 new_kernel=ParseKernelArray("4x3+1+1:-,1,1,0 -,1,1,- 0,1,1,-");
1896 if (new_kernel == (KernelInfo *) NULL)
1897 return(DestroyKernelInfo(kernel));
1898 new_kernel->type = type;
1899 LastKernelInfo(kernel)->next = new_kernel;
1900 new_kernel=ParseKernelArray("4x3+2+1:-,1,1,0 -,1,1,- 0,1,1,-");
1901 if (new_kernel == (KernelInfo *) NULL)
1902 return(DestroyKernelInfo(kernel));
1903 new_kernel->type = type;
1904 LastKernelInfo(kernel)->next = new_kernel;
1905 new_kernel=ParseKernelArray("3x4+1+1:0,-,- 1,1,1 1,1,1 -,-,0");
1906 if (new_kernel == (KernelInfo *) NULL)
1907 return(DestroyKernelInfo(kernel));
1908 new_kernel->type = type;
1909 LastKernelInfo(kernel)->next = new_kernel;
1910 new_kernel=ParseKernelArray("3x4+1+2:0,-,- 1,1,1 1,1,1 -,-,0");
1911 if (new_kernel == (KernelInfo *) NULL)
1912 return(DestroyKernelInfo(kernel));
1913 new_kernel->type = type;
1914 LastKernelInfo(kernel)->next = new_kernel;
1915 new_kernel=ParseKernelArray("3x4+1+1:-,-,0 1,1,1 1,1,1 0,-,-");
1916 if (new_kernel == (KernelInfo *) NULL)
1917 return(DestroyKernelInfo(kernel));
1918 new_kernel->type = type;
1919 LastKernelInfo(kernel)->next = new_kernel;
1920 new_kernel=ParseKernelArray("3x4+1+2:-,-,0 1,1,1 1,1,1 0,-,-");
1921 if (new_kernel == (KernelInfo *) NULL)
1922 return(DestroyKernelInfo(kernel));
1923 new_kernel->type = type;
1924 LastKernelInfo(kernel)->next = new_kernel;
1929 case ConvexHullKernel:
1933 /* first set of 8 kernels */
1934 kernel=ParseKernelArray("3: 1,1,- 1,0,- 1,-,0");
1935 if (kernel == (KernelInfo *) NULL)
1937 kernel->type = type;
1938 ExpandRotateKernelInfo(kernel, 90.0);
1939 /* append the mirror versions too - no flip function yet */
1940 new_kernel=ParseKernelArray("3: 1,1,1 1,0,- -,-,0");
1941 if (new_kernel == (KernelInfo *) NULL)
1942 return(DestroyKernelInfo(kernel));
1943 new_kernel->type = type;
1944 ExpandRotateKernelInfo(new_kernel, 90.0);
1945 LastKernelInfo(kernel)->next = new_kernel;
1948 case SkeletonKernel:
1950 switch ( (int) args->rho ) {
1953 /* Traditional Skeleton...
1954 ** A cyclically rotated single kernel
1956 kernel=AcquireKernelInfo("ThinSE:482",exception);
1957 if (kernel == (KernelInfo *) NULL)
1959 kernel->type = type;
1960 ExpandRotateKernelInfo(kernel, 45.0); /* 8 rotations */
1963 /* HIPR Variation of the cyclic skeleton
1964 ** Corners of the traditional method made more forgiving,
1965 ** but the retain the same cyclic order.
1967 kernel=AcquireKernelInfo("ThinSE:482; ThinSE:87x90;",exception);
1968 if (kernel == (KernelInfo *) NULL)
1970 if (kernel->next == (KernelInfo *) NULL)
1971 return(DestroyKernelInfo(kernel));
1972 kernel->type = type;
1973 kernel->next->type = type;
1974 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotations of the 2 kernels */
1977 /* Dan Bloomberg Skeleton, from his paper on 3x3 thinning SE's
1978 ** "Connectivity-Preserving Morphological Image Thransformations"
1979 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1980 ** http://www.leptonica.com/papers/conn.pdf
1982 kernel=AcquireKernelInfo("ThinSE:41; ThinSE:42; ThinSE:43",
1984 if (kernel == (KernelInfo *) NULL)
1986 kernel->type = type;
1987 kernel->next->type = type;
1988 kernel->next->next->type = type;
1989 ExpandMirrorKernelInfo(kernel); /* 12 kernels total */
1995 { /* Special kernels for general thinning, while preserving connections
1996 ** "Connectivity-Preserving Morphological Image Thransformations"
1997 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1998 ** http://www.leptonica.com/papers/conn.pdf
2000 ** http://tpgit.github.com/Leptonica/ccthin_8c_source.html
2002 ** Note kernels do not specify the origin pixel, allowing them
2003 ** to be used for both thickening and thinning operations.
2005 switch ( (int) args->rho ) {
2006 /* SE for 4-connected thinning */
2007 case 41: /* SE_4_1 */
2008 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,-,1");
2010 case 42: /* SE_4_2 */
2011 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,0,-");
2013 case 43: /* SE_4_3 */
2014 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,-,1");
2016 case 44: /* SE_4_4 */
2017 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,-");
2019 case 45: /* SE_4_5 */
2020 kernel=ParseKernelArray("3: -,0,1 0,-,1 -,0,-");
2022 case 46: /* SE_4_6 */
2023 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,1");
2025 case 47: /* SE_4_7 */
2026 kernel=ParseKernelArray("3: -,1,1 0,-,1 -,0,-");
2028 case 48: /* SE_4_8 */
2029 kernel=ParseKernelArray("3: -,-,1 0,-,1 0,-,1");
2031 case 49: /* SE_4_9 */
2032 kernel=ParseKernelArray("3: 0,-,1 0,-,1 -,-,1");
2034 /* SE for 8-connected thinning - negatives of the above */
2035 case 81: /* SE_8_0 */
2036 kernel=ParseKernelArray("3: -,1,- 0,-,1 -,1,-");
2038 case 82: /* SE_8_2 */
2039 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,-,-");
2041 case 83: /* SE_8_3 */
2042 kernel=ParseKernelArray("3: 0,-,- 0,-,1 -,1,-");
2044 case 84: /* SE_8_4 */
2045 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,-");
2047 case 85: /* SE_8_5 */
2048 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,-");
2050 case 86: /* SE_8_6 */
2051 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,1");
2053 case 87: /* SE_8_7 */
2054 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,0,-");
2056 case 88: /* SE_8_8 */
2057 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,1,-");
2059 case 89: /* SE_8_9 */
2060 kernel=ParseKernelArray("3: 0,1,- 0,-,1 -,1,-");
2062 /* Special combined SE kernels */
2063 case 423: /* SE_4_2 , SE_4_3 Combined Kernel */
2064 kernel=ParseKernelArray("3: -,-,1 0,-,- -,0,-");
2066 case 823: /* SE_8_2 , SE_8_3 Combined Kernel */
2067 kernel=ParseKernelArray("3: -,1,- -,-,1 0,-,-");
2069 case 481: /* SE_48_1 - General Connected Corner Kernel */
2070 kernel=ParseKernelArray("3: -,1,1 0,-,1 0,0,-");
2073 case 482: /* SE_48_2 - General Edge Kernel */
2074 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,1");
2077 if (kernel == (KernelInfo *) NULL)
2079 kernel->type = type;
2080 RotateKernelInfo(kernel, args->sigma);
2084 Distance Measuring Kernels
2086 case ChebyshevKernel:
2088 if (args->rho < 1.0)
2089 kernel->width = kernel->height = 3; /* default radius = 1 */
2091 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2092 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2094 kernel->values=(MagickRealType *) MagickAssumeAligned(
2095 AcquireAlignedMemory(kernel->width,kernel->height*
2096 sizeof(*kernel->values)));
2097 if (kernel->values == (MagickRealType *) NULL)
2098 return(DestroyKernelInfo(kernel));
2100 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2101 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2102 kernel->positive_range += ( kernel->values[i] =
2103 args->sigma*MagickMax(fabs((double)u),fabs((double)v)) );
2104 kernel->maximum = kernel->values[0];
2107 case ManhattanKernel:
2109 if (args->rho < 1.0)
2110 kernel->width = kernel->height = 3; /* default radius = 1 */
2112 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2113 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2115 kernel->values=(MagickRealType *) MagickAssumeAligned(
2116 AcquireAlignedMemory(kernel->width,kernel->height*
2117 sizeof(*kernel->values)));
2118 if (kernel->values == (MagickRealType *) NULL)
2119 return(DestroyKernelInfo(kernel));
2121 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2122 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2123 kernel->positive_range += ( kernel->values[i] =
2124 args->sigma*(labs((long) u)+labs((long) v)) );
2125 kernel->maximum = kernel->values[0];
2128 case OctagonalKernel:
2130 if (args->rho < 2.0)
2131 kernel->width = kernel->height = 5; /* default/minimum radius = 2 */
2133 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2134 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2136 kernel->values=(MagickRealType *) MagickAssumeAligned(
2137 AcquireAlignedMemory(kernel->width,kernel->height*
2138 sizeof(*kernel->values)));
2139 if (kernel->values == (MagickRealType *) NULL)
2140 return(DestroyKernelInfo(kernel));
2142 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2143 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2146 r1 = MagickMax(fabs((double)u),fabs((double)v)),
2147 r2 = floor((double)(labs((long)u)+labs((long)v)+1)/1.5);
2148 kernel->positive_range += kernel->values[i] =
2149 args->sigma*MagickMax(r1,r2);
2151 kernel->maximum = kernel->values[0];
2154 case EuclideanKernel:
2156 if (args->rho < 1.0)
2157 kernel->width = kernel->height = 3; /* default radius = 1 */
2159 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2160 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2162 kernel->values=(MagickRealType *) MagickAssumeAligned(
2163 AcquireAlignedMemory(kernel->width,kernel->height*
2164 sizeof(*kernel->values)));
2165 if (kernel->values == (MagickRealType *) NULL)
2166 return(DestroyKernelInfo(kernel));
2168 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2169 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2170 kernel->positive_range += ( kernel->values[i] =
2171 args->sigma*sqrt((double)(u*u+v*v)) );
2172 kernel->maximum = kernel->values[0];
2177 /* No-Op Kernel - Basically just a single pixel on its own */
2178 kernel=ParseKernelArray("1:1");
2179 if (kernel == (KernelInfo *) NULL)
2181 kernel->type = UndefinedKernel;
2190 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2194 % C l o n e K e r n e l I n f o %
2198 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2200 % CloneKernelInfo() creates a new clone of the given Kernel List so that its
2201 % can be modified without effecting the original. The cloned kernel should
2202 % be destroyed using DestoryKernelInfo() when no longer needed.
2204 % The format of the CloneKernelInfo method is:
2206 % KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2208 % A description of each parameter follows:
2210 % o kernel: the Morphology/Convolution kernel to be cloned
2213 MagickExport KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2221 assert(kernel != (KernelInfo *) NULL);
2222 new_kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
2223 if (new_kernel == (KernelInfo *) NULL)
2225 *new_kernel=(*kernel); /* copy values in structure */
2227 /* replace the values with a copy of the values */
2228 new_kernel->values=(MagickRealType *) MagickAssumeAligned(
2229 AcquireAlignedMemory(kernel->width,kernel->height*sizeof(*kernel->values)));
2230 if (new_kernel->values == (MagickRealType *) NULL)
2231 return(DestroyKernelInfo(new_kernel));
2232 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
2233 new_kernel->values[i]=kernel->values[i];
2235 /* Also clone the next kernel in the kernel list */
2236 if ( kernel->next != (KernelInfo *) NULL ) {
2237 new_kernel->next = CloneKernelInfo(kernel->next);
2238 if ( new_kernel->next == (KernelInfo *) NULL )
2239 return(DestroyKernelInfo(new_kernel));
2246 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2250 % D e s t r o y K e r n e l I n f o %
2254 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2256 % DestroyKernelInfo() frees the memory used by a Convolution/Morphology
2259 % The format of the DestroyKernelInfo method is:
2261 % KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2263 % A description of each parameter follows:
2265 % o kernel: the Morphology/Convolution kernel to be destroyed
2268 MagickExport KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2270 assert(kernel != (KernelInfo *) NULL);
2271 if (kernel->next != (KernelInfo *) NULL)
2272 kernel->next=DestroyKernelInfo(kernel->next);
2273 kernel->values=(MagickRealType *) RelinquishAlignedMemory(kernel->values);
2274 kernel=(KernelInfo *) RelinquishMagickMemory(kernel);
2279 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2283 + E x p a n d M i r r o r K e r n e l I n f o %
2287 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2289 % ExpandMirrorKernelInfo() takes a single kernel, and expands it into a
2290 % sequence of 90-degree rotated kernels but providing a reflected 180
2291 % rotatation, before the -/+ 90-degree rotations.
2293 % This special rotation order produces a better, more symetrical thinning of
2296 % The format of the ExpandMirrorKernelInfo method is:
2298 % void ExpandMirrorKernelInfo(KernelInfo *kernel)
2300 % A description of each parameter follows:
2302 % o kernel: the Morphology/Convolution kernel
2304 % This function is only internel to this module, as it is not finalized,
2305 % especially with regard to non-orthogonal angles, and rotation of larger
2310 static void FlopKernelInfo(KernelInfo *kernel)
2311 { /* Do a Flop by reversing each row. */
2319 for ( y=0, k=kernel->values; y < kernel->height; y++, k+=kernel->width)
2320 for ( x=0, r=kernel->width-1; x<kernel->width/2; x++, r--)
2321 t=k[x], k[x]=k[r], k[r]=t;
2323 kernel->x = kernel->width - kernel->x - 1;
2324 angle = fmod(angle+180.0, 360.0);
2328 static void ExpandMirrorKernelInfo(KernelInfo *kernel)
2336 clone = CloneKernelInfo(last);
2337 if (clone == (KernelInfo *) NULL)
2339 RotateKernelInfo(clone, 180); /* flip */
2340 LastKernelInfo(last)->next = clone;
2343 clone = CloneKernelInfo(last);
2344 if (clone == (KernelInfo *) NULL)
2346 RotateKernelInfo(clone, 90); /* transpose */
2347 LastKernelInfo(last)->next = clone;
2350 clone = CloneKernelInfo(last);
2351 if (clone == (KernelInfo *) NULL)
2353 RotateKernelInfo(clone, 180); /* flop */
2354 LastKernelInfo(last)->next = clone;
2360 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2364 + E x p a n d R o t a t e K e r n e l I n f o %
2368 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2370 % ExpandRotateKernelInfo() takes a kernel list, and expands it by rotating
2371 % incrementally by the angle given, until the kernel repeats.
2373 % WARNING: 45 degree rotations only works for 3x3 kernels.
2374 % While 90 degree roatations only works for linear and square kernels
2376 % The format of the ExpandRotateKernelInfo method is:
2378 % void ExpandRotateKernelInfo(KernelInfo *kernel, double angle)
2380 % A description of each parameter follows:
2382 % o kernel: the Morphology/Convolution kernel
2384 % o angle: angle to rotate in degrees
2386 % This function is only internel to this module, as it is not finalized,
2387 % especially with regard to non-orthogonal angles, and rotation of larger
2391 /* Internal Routine - Return true if two kernels are the same */
2392 static MagickBooleanType SameKernelInfo(const KernelInfo *kernel1,
2393 const KernelInfo *kernel2)
2398 /* check size and origin location */
2399 if ( kernel1->width != kernel2->width
2400 || kernel1->height != kernel2->height
2401 || kernel1->x != kernel2->x
2402 || kernel1->y != kernel2->y )
2405 /* check actual kernel values */
2406 for (i=0; i < (kernel1->width*kernel1->height); i++) {
2407 /* Test for Nan equivalence */
2408 if ( IsNaN(kernel1->values[i]) && !IsNaN(kernel2->values[i]) )
2410 if ( IsNaN(kernel2->values[i]) && !IsNaN(kernel1->values[i]) )
2412 /* Test actual values are equivalent */
2413 if ( fabs(kernel1->values[i] - kernel2->values[i]) >= MagickEpsilon )
2420 static void ExpandRotateKernelInfo(KernelInfo *kernel, const double angle)
2427 DisableMSCWarning(4127)
2430 clone_info=CloneKernelInfo(last);
2431 if (clone_info == (KernelInfo *) NULL)
2433 RotateKernelInfo(clone_info,angle);
2434 if (SameKernelInfo(kernel,clone_info) != MagickFalse)
2436 LastKernelInfo(last)->next=clone_info;
2439 if (clone_info != (KernelInfo *) NULL)
2440 clone_info=DestroyKernelInfo(clone_info); /* kernel repeated - junk */
2445 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2449 + C a l c M e t a K e r n a l I n f o %
2453 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2455 % CalcKernelMetaData() recalculate the KernelInfo meta-data of this kernel only,
2456 % using the kernel values. This should only ne used if it is not possible to
2457 % calculate that meta-data in some easier way.
2459 % It is important that the meta-data is correct before ScaleKernelInfo() is
2460 % used to perform kernel normalization.
2462 % The format of the CalcKernelMetaData method is:
2464 % void CalcKernelMetaData(KernelInfo *kernel, const double scale )
2466 % A description of each parameter follows:
2468 % o kernel: the Morphology/Convolution kernel to modify
2470 % WARNING: Minimum and Maximum values are assumed to include zero, even if
2471 % zero is not part of the kernel (as in Gaussian Derived kernels). This
2472 % however is not true for flat-shaped morphological kernels.
2474 % WARNING: Only the specific kernel pointed to is modified, not a list of
2477 % This is an internal function and not expected to be useful outside this
2478 % module. This could change however.
2480 static void CalcKernelMetaData(KernelInfo *kernel)
2485 kernel->minimum = kernel->maximum = 0.0;
2486 kernel->negative_range = kernel->positive_range = 0.0;
2487 for (i=0; i < (kernel->width*kernel->height); i++)
2489 if ( fabs(kernel->values[i]) < MagickEpsilon )
2490 kernel->values[i] = 0.0;
2491 ( kernel->values[i] < 0)
2492 ? ( kernel->negative_range += kernel->values[i] )
2493 : ( kernel->positive_range += kernel->values[i] );
2494 Minimize(kernel->minimum, kernel->values[i]);
2495 Maximize(kernel->maximum, kernel->values[i]);
2502 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2506 % M o r p h o l o g y A p p l y %
2510 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2512 % MorphologyApply() applies a morphological method, multiple times using
2513 % a list of multiple kernels. This is the method that should be called by
2514 % other 'operators' that internally use morphology operations as part of
2517 % It is basically equivalent to as MorphologyImage() (see below) but without
2518 % any user controls. This allows internel programs to use this method to
2519 % perform a specific task without possible interference by any API user
2520 % supplied settings.
2522 % It is MorphologyImage() task to extract any such user controls, and
2523 % pass them to this function for processing.
2525 % More specifically all given kernels should already be scaled, normalised,
2526 % and blended appropriatally before being parred to this routine. The
2527 % appropriate bias, and compose (typically 'UndefinedComposeOp') given.
2529 % The format of the MorphologyApply method is:
2531 % Image *MorphologyApply(const Image *image,MorphologyMethod method,
2532 % const ssize_t iterations,const KernelInfo *kernel,
2533 % const CompositeMethod compose,const double bias,
2534 % ExceptionInfo *exception)
2536 % A description of each parameter follows:
2538 % o image: the source image
2540 % o method: the morphology method to be applied.
2542 % o iterations: apply the operation this many times (or no change).
2543 % A value of -1 means loop until no change found.
2544 % How this is applied may depend on the morphology method.
2545 % Typically this is a value of 1.
2547 % o channel: the channel type.
2549 % o kernel: An array of double representing the morphology kernel.
2551 % o compose: How to handle or merge multi-kernel results.
2552 % If 'UndefinedCompositeOp' use default for the Morphology method.
2553 % If 'NoCompositeOp' force image to be re-iterated by each kernel.
2554 % Otherwise merge the results using the compose method given.
2556 % o bias: Convolution Output Bias.
2558 % o exception: return any errors or warnings in this structure.
2561 static ssize_t MorphologyPrimitive(const Image *image,Image *morphology_image,
2562 const MorphologyMethod method,const KernelInfo *kernel,const double bias,
2563 ExceptionInfo *exception)
2565 #define MorphologyTag "Morphology/Image"
2589 assert(image != (Image *) NULL);
2590 assert(image->signature == MagickCoreSignature);
2591 assert(morphology_image != (Image *) NULL);
2592 assert(morphology_image->signature == MagickCoreSignature);
2593 assert(kernel != (KernelInfo *) NULL);
2594 assert(kernel->signature == MagickCoreSignature);
2595 assert(exception != (ExceptionInfo *) NULL);
2596 assert(exception->signature == MagickCoreSignature);
2599 image_view=AcquireVirtualCacheView(image,exception);
2600 morphology_view=AcquireAuthenticCacheView(morphology_image,exception);
2601 width=image->columns+kernel->width-1;
2606 case ConvolveMorphology:
2607 case DilateMorphology:
2608 case DilateIntensityMorphology:
2609 case IterativeDistanceMorphology:
2612 Kernel needs to used with reflection about origin.
2614 offset.x=(ssize_t) kernel->width-kernel->x-1;
2615 offset.y=(ssize_t) kernel->height-kernel->y-1;
2618 case ErodeMorphology:
2619 case ErodeIntensityMorphology:
2620 case HitAndMissMorphology:
2621 case ThinningMorphology:
2622 case ThickenMorphology:
2630 assert("Not a Primitive Morphology Method" != (char *) NULL);
2635 changes=(size_t *) AcquireQuantumMemory(GetOpenMPMaximumThreads(),
2637 if (changes == (size_t *) NULL)
2638 ThrowFatalException(ResourceLimitFatalError,"MemoryAllocationFailed");
2639 for (j=0; j < (ssize_t) GetOpenMPMaximumThreads(); j++)
2642 if ((method == ConvolveMorphology) && (kernel->width == 1))
2648 Special handling (for speed) of vertical (blur) kernels. This performs
2649 its handling in columns rather than in rows. This is only done
2650 for convolve as it is the only method that generates very large 1-D
2651 vertical kernels (such as a 'BlurKernel')
2653 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2654 #pragma omp parallel for schedule(static) shared(progress,status) \
2655 magick_number_threads(image,morphology_image,image->columns,1)
2657 for (x=0; x < (ssize_t) image->columns; x++)
2660 id = GetOpenMPThreadId();
2662 register const Quantum
2674 if (status == MagickFalse)
2676 p=GetCacheViewVirtualPixels(image_view,x,-offset.y,1,image->rows+
2677 kernel->height-1,exception);
2678 q=GetCacheViewAuthenticPixels(morphology_view,x,0,1,
2679 morphology_image->rows,exception);
2680 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2685 center=(ssize_t) GetPixelChannels(image)*offset.y;
2686 for (r=0; r < (ssize_t) image->rows; r++)
2691 for (i=0; i < (ssize_t) GetPixelChannels(image); i++)
2705 register const MagickRealType
2708 register const Quantum
2709 *magick_restrict pixels;
2717 channel=GetPixelChannelChannel(image,i);
2718 traits=GetPixelChannelTraits(image,channel);
2719 morphology_traits=GetPixelChannelTraits(morphology_image,channel);
2720 if ((traits == UndefinedPixelTrait) ||
2721 (morphology_traits == UndefinedPixelTrait))
2723 if ((traits & CopyPixelTrait) != 0)
2725 SetPixelChannel(morphology_image,channel,p[center+i],q);
2728 k=(&kernel->values[kernel->height-1]);
2733 if ((morphology_traits & BlendPixelTrait) == 0)
2734 for (v=0; v < (ssize_t) kernel->height; v++)
2738 pixel+=(*k)*pixels[i];
2743 pixels+=GetPixelChannels(image);
2746 for (v=0; v < (ssize_t) kernel->height; v++)
2750 alpha=(double) (QuantumScale*GetPixelAlpha(image,pixels));
2751 pixel+=alpha*(*k)*pixels[i];
2756 pixels+=GetPixelChannels(image);
2758 if (fabs(pixel-p[center+i]) > MagickEpsilon)
2760 gamma=PerceptibleReciprocal(gamma);
2762 gamma*=(double) kernel->height/count;
2763 SetPixelChannel(morphology_image,channel,ClampToQuantum(gamma*
2766 p+=GetPixelChannels(image);
2767 q+=GetPixelChannels(morphology_image);
2769 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
2771 if (image->progress_monitor != (MagickProgressMonitor) NULL)
2776 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2777 #pragma omp critical (MagickCore_MorphologyPrimitive)
2779 proceed=SetImageProgress(image,MorphologyTag,progress++,
2781 if (proceed == MagickFalse)
2785 morphology_image->type=image->type;
2786 morphology_view=DestroyCacheView(morphology_view);
2787 image_view=DestroyCacheView(image_view);
2788 for (j=0; j < (ssize_t) GetOpenMPMaximumThreads(); j++)
2789 changed+=changes[j];
2790 changes=(size_t *) RelinquishMagickMemory(changes);
2791 return(status ? (ssize_t) changed : 0);
2794 Normal handling of horizontal or rectangular kernels (row by row).
2796 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2797 #pragma omp parallel for schedule(static) shared(progress,status) \
2798 magick_number_threads(image,morphology_image,image->rows,1)
2800 for (y=0; y < (ssize_t) image->rows; y++)
2803 id = GetOpenMPThreadId();
2805 register const Quantum
2817 if (status == MagickFalse)
2819 p=GetCacheViewVirtualPixels(image_view,-offset.x,y-offset.y,width,
2820 kernel->height,exception);
2821 q=GetCacheViewAuthenticPixels(morphology_view,0,y,morphology_image->columns,
2823 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2828 center=(ssize_t) (GetPixelChannels(image)*width*offset.y+
2829 GetPixelChannels(image)*offset.x);
2830 for (x=0; x < (ssize_t) image->columns; x++)
2835 for (i=0; i < (ssize_t) GetPixelChannels(image); i++)
2852 register const MagickRealType
2855 register const Quantum
2856 *magick_restrict pixels;
2867 channel=GetPixelChannelChannel(image,i);
2868 traits=GetPixelChannelTraits(image,channel);
2869 morphology_traits=GetPixelChannelTraits(morphology_image,channel);
2870 if ((traits == UndefinedPixelTrait) ||
2871 (morphology_traits == UndefinedPixelTrait))
2873 if ((traits & CopyPixelTrait) != 0)
2875 SetPixelChannel(morphology_image,channel,p[center+i],q);
2880 minimum=(double) QuantumRange;
2883 case ConvolveMorphology: pixel=bias; break;
2884 case DilateMorphology:
2885 case ErodeIntensityMorphology:
2892 pixel=(double) p[center+i];
2900 case ConvolveMorphology:
2903 Weighted Average of pixels using reflected kernel
2905 For correct working of this operation for asymetrical kernels,
2906 the kernel needs to be applied in its reflected form. That is
2907 its values needs to be reversed.
2909 Correlation is actually the same as this but without reflecting
2910 the kernel, and thus 'lower-level' that Convolution. However as
2911 Convolution is the more common method used, and it does not
2912 really cost us much in terms of processing to use a reflected
2913 kernel, so it is Convolution that is implemented.
2915 Correlation will have its kernel reflected before calling this
2916 function to do a Convolve.
2918 For more details of Correlation vs Convolution see
2919 http://www.cs.umd.edu/~djacobs/CMSC426/Convolution.pdf
2921 k=(&kernel->values[kernel->width*kernel->height-1]);
2922 if ((morphology_traits & BlendPixelTrait) == 0)
2927 for (v=0; v < (ssize_t) kernel->height; v++)
2929 for (u=0; u < (ssize_t) kernel->width; u++)
2933 pixel+=(*k)*pixels[i];
2937 pixels+=GetPixelChannels(image);
2939 pixels+=(image->columns-1)*GetPixelChannels(image);
2947 for (v=0; v < (ssize_t) kernel->height; v++)
2949 for (u=0; u < (ssize_t) kernel->width; u++)
2953 alpha=(double) (QuantumScale*GetPixelAlpha(image,pixels));
2954 pixel+=alpha*(*k)*pixels[i];
2959 pixels+=GetPixelChannels(image);
2961 pixels+=(image->columns-1)*GetPixelChannels(image);
2965 case ErodeMorphology:
2968 Minimum value within kernel neighbourhood.
2970 The kernel is not reflected for this operation. In normal
2971 Greyscale Morphology, the kernel value should be added
2972 to the real value, this is currently not done, due to the
2973 nature of the boolean kernels being used.
2976 for (v=0; v < (ssize_t) kernel->height; v++)
2978 for (u=0; u < (ssize_t) kernel->width; u++)
2980 if (!IsNaN(*k) && (*k >= 0.5))
2982 if ((double) pixels[i] < pixel)
2983 pixel=(double) pixels[i];
2986 pixels+=GetPixelChannels(image);
2988 pixels+=(image->columns-1)*GetPixelChannels(image);
2992 case DilateMorphology:
2995 Maximum value within kernel neighbourhood.
2997 For correct working of this operation for asymetrical kernels,
2998 the kernel needs to be applied in its reflected form. That is
2999 its values needs to be reversed.
3001 In normal Greyscale Morphology, the kernel value should be
3002 added to the real value, this is currently not done, due to the
3003 nature of the boolean kernels being used.
3005 k=(&kernel->values[kernel->width*kernel->height-1]);
3006 for (v=0; v < (ssize_t) kernel->height; v++)
3008 for (u=0; u < (ssize_t) kernel->width; u++)
3010 if (!IsNaN(*k) && (*k > 0.5))
3012 if ((double) pixels[i] > pixel)
3013 pixel=(double) pixels[i];
3016 pixels+=GetPixelChannels(image);
3018 pixels+=(image->columns-1)*GetPixelChannels(image);
3022 case HitAndMissMorphology:
3023 case ThinningMorphology:
3024 case ThickenMorphology:
3027 Minimum of foreground pixel minus maxumum of background pixels.
3029 The kernel is not reflected for this operation, and consists
3030 of both foreground and background pixel neighbourhoods, 0.0 for
3031 background, and 1.0 for foreground with either Nan or 0.5 values
3034 This never produces a meaningless negative result. Such results
3035 cause Thinning/Thicken to not work correctly when used against a
3039 for (v=0; v < (ssize_t) kernel->height; v++)
3041 for (u=0; u < (ssize_t) kernel->width; u++)
3047 if ((double) pixels[i] < pixel)
3048 pixel=(double) pixels[i];
3053 if ((double) pixels[i] > maximum)
3054 maximum=(double) pixels[i];
3059 pixels+=GetPixelChannels(image);
3061 pixels+=(image->columns-1)*GetPixelChannels(image);
3066 if (method == ThinningMorphology)
3067 pixel=(double) p[center+i]-pixel;
3069 if (method == ThickenMorphology)
3070 pixel+=(double) p[center+i]+pixel;
3073 case ErodeIntensityMorphology:
3076 Select pixel with minimum intensity within kernel neighbourhood.
3078 The kernel is not reflected for this operation.
3081 for (v=0; v < (ssize_t) kernel->height; v++)
3083 for (u=0; u < (ssize_t) kernel->width; u++)
3085 if (!IsNaN(*k) && (*k >= 0.5))
3087 intensity=(double) GetPixelIntensity(image,pixels);
3088 if (intensity < minimum)
3090 pixel=(double) pixels[i];
3096 pixels+=GetPixelChannels(image);
3098 pixels+=(image->columns-1)*GetPixelChannels(image);
3102 case DilateIntensityMorphology:
3105 Select pixel with maximum intensity within kernel neighbourhood.
3107 The kernel is not reflected for this operation.
3109 k=(&kernel->values[kernel->width*kernel->height-1]);
3110 for (v=0; v < (ssize_t) kernel->height; v++)
3112 for (u=0; u < (ssize_t) kernel->width; u++)
3114 if (!IsNaN(*k) && (*k >= 0.5))
3116 intensity=(double) GetPixelIntensity(image,pixels);
3117 if (intensity > maximum)
3119 pixel=(double) pixels[i];
3125 pixels+=GetPixelChannels(image);
3127 pixels+=(image->columns-1)*GetPixelChannels(image);
3131 case IterativeDistanceMorphology:
3134 Compute th iterative distance from black edge of a white image
3135 shape. Essentually white values are decreased to the smallest
3136 'distance from edge' it can find.
3138 It works by adding kernel values to the neighbourhood, and and
3139 select the minimum value found. The kernel is rotated before
3140 use, so kernel distances match resulting distances, when a user
3141 provided asymmetric kernel is applied.
3143 This code is nearly identical to True GrayScale Morphology but
3146 GreyDilate Kernel values added, maximum value found Kernel is
3149 GrayErode: Kernel values subtracted and minimum value found No
3150 kernel rotation used.
3152 Note the the Iterative Distance method is essentially a
3153 GrayErode, but with negative kernel values, and kernel rotation
3156 k=(&kernel->values[kernel->width*kernel->height-1]);
3157 for (v=0; v < (ssize_t) kernel->height; v++)
3159 for (u=0; u < (ssize_t) kernel->width; u++)
3163 if ((pixels[i]+(*k)) < pixel)
3164 pixel=(double) pixels[i]+(*k);
3168 pixels+=GetPixelChannels(image);
3170 pixels+=(image->columns-1)*GetPixelChannels(image);
3174 case UndefinedMorphology:
3178 if (fabs(pixel-p[center+i]) > MagickEpsilon)
3180 gamma=PerceptibleReciprocal(gamma);
3182 gamma*=(double) kernel->height*kernel->width/count;
3183 SetPixelChannel(morphology_image,channel,ClampToQuantum(gamma*pixel),q);
3185 p+=GetPixelChannels(image);
3186 q+=GetPixelChannels(morphology_image);
3188 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
3190 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3195 #if defined(MAGICKCORE_OPENMP_SUPPORT)
3196 #pragma omp critical (MagickCore_MorphologyPrimitive)
3198 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows);
3199 if (proceed == MagickFalse)
3203 morphology_view=DestroyCacheView(morphology_view);
3204 image_view=DestroyCacheView(image_view);
3205 for (j=0; j < (ssize_t) GetOpenMPMaximumThreads(); j++)
3206 changed+=changes[j];
3207 changes=(size_t *) RelinquishMagickMemory(changes);
3208 return(status ? (ssize_t) changed : -1);
3212 This is almost identical to the MorphologyPrimative() function above, but
3213 applies the primitive directly to the actual image using two passes, once in
3214 each direction, with the results of the previous (and current) row being
3217 That is after each row is 'Sync'ed' into the image, the next row makes use of
3218 those values as part of the calculation of the next row. It repeats, but
3219 going in the oppisite (bottom-up) direction.
3221 Because of this 're-use of results' this function can not make use of multi-
3222 threaded, parellel processing.
3224 static ssize_t MorphologyPrimitiveDirect(Image *image,
3225 const MorphologyMethod method,const KernelInfo *kernel,
3226 ExceptionInfo *exception)
3248 assert(image != (Image *) NULL);
3249 assert(image->signature == MagickCoreSignature);
3250 assert(kernel != (KernelInfo *) NULL);
3251 assert(kernel->signature == MagickCoreSignature);
3252 assert(exception != (ExceptionInfo *) NULL);
3253 assert(exception->signature == MagickCoreSignature);
3259 case DistanceMorphology:
3260 case VoronoiMorphology:
3263 Kernel reflected about origin.
3265 offset.x=(ssize_t) kernel->width-kernel->x-1;
3266 offset.y=(ssize_t) kernel->height-kernel->y-1;
3277 Two views into same image, do not thread.
3279 image_view=AcquireVirtualCacheView(image,exception);
3280 morphology_view=AcquireAuthenticCacheView(image,exception);
3281 width=image->columns+kernel->width-1;
3282 for (y=0; y < (ssize_t) image->rows; y++)
3284 register const Quantum
3294 Read virtual pixels, and authentic pixels, from the same image! We read
3295 using virtual to get virtual pixel handling, but write back into the same
3298 Only top half of kernel is processed as we do a single pass downward
3299 through the image iterating the distance function as we go.
3301 if (status == MagickFalse)
3303 p=GetCacheViewVirtualPixels(image_view,-offset.x,y-offset.y,width,(size_t)
3304 offset.y+1,exception);
3305 q=GetCacheViewAuthenticPixels(morphology_view,0,y,image->columns,1,
3307 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3312 for (x=0; x < (ssize_t) image->columns; x++)
3317 for (i=0; i < (ssize_t) GetPixelChannels(image); i++)
3328 register const MagickRealType
3331 register const Quantum
3332 *magick_restrict pixels;
3340 channel=GetPixelChannelChannel(image,i);
3341 traits=GetPixelChannelTraits(image,channel);
3342 if (traits == UndefinedPixelTrait)
3344 if ((traits & CopyPixelTrait) != 0)
3347 pixel=(double) QuantumRange;
3350 case DistanceMorphology:
3352 k=(&kernel->values[kernel->width*kernel->height-1]);
3353 for (v=0; v <= offset.y; v++)
3355 for (u=0; u < (ssize_t) kernel->width; u++)
3359 if ((pixels[i]+(*k)) < pixel)
3360 pixel=(double) pixels[i]+(*k);
3363 pixels+=GetPixelChannels(image);
3365 pixels+=(image->columns-1)*GetPixelChannels(image);
3367 k=(&kernel->values[kernel->width*(kernel->y+1)-1]);
3368 pixels=q-offset.x*GetPixelChannels(image);
3369 for (u=0; u < offset.x; u++)
3371 if (!IsNaN(*k) && ((x+u-offset.x) >= 0))
3373 if ((pixels[i]+(*k)) < pixel)
3374 pixel=(double) pixels[i]+(*k);
3377 pixels+=GetPixelChannels(image);
3381 case VoronoiMorphology:
3383 k=(&kernel->values[kernel->width*kernel->height-1]);
3384 for (v=0; v < offset.y; v++)
3386 for (u=0; u < (ssize_t) kernel->width; u++)
3390 if ((pixels[i]+(*k)) < pixel)
3391 pixel=(double) pixels[i]+(*k);
3394 pixels+=GetPixelChannels(image);
3396 pixels+=(image->columns-1)*GetPixelChannels(image);
3398 k=(&kernel->values[kernel->width*(kernel->y+1)-1]);
3399 pixels=q-offset.x*GetPixelChannels(image);
3400 for (u=0; u < offset.x; u++)
3402 if (!IsNaN(*k) && ((x+u-offset.x) >= 0))
3404 if ((pixels[i]+(*k)) < pixel)
3405 pixel=(double) pixels[i]+(*k);
3408 pixels+=GetPixelChannels(image);
3415 if (fabs(pixel-q[i]) > MagickEpsilon)
3417 q[i]=ClampToQuantum(pixel);
3419 p+=GetPixelChannels(image);
3420 q+=GetPixelChannels(image);
3422 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
3424 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3429 proceed=SetImageProgress(image,MorphologyTag,progress++,2*image->rows);
3430 if (proceed == MagickFalse)
3434 morphology_view=DestroyCacheView(morphology_view);
3435 image_view=DestroyCacheView(image_view);
3437 Do the reverse pass through the image.
3439 image_view=AcquireVirtualCacheView(image,exception);
3440 morphology_view=AcquireAuthenticCacheView(image,exception);
3441 for (y=(ssize_t) image->rows-1; y >= 0; y--)
3443 register const Quantum
3453 Read virtual pixels, and authentic pixels, from the same image. We
3454 read using virtual to get virtual pixel handling, but write back
3455 into the same image.
3457 Only the bottom half of the kernel is processed as we up the image.
3459 if (status == MagickFalse)
3461 p=GetCacheViewVirtualPixels(image_view,-offset.x,y,width,(size_t)
3462 kernel->y+1,exception);
3463 q=GetCacheViewAuthenticPixels(morphology_view,0,y,image->columns,1,
3465 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3470 p+=(image->columns-1)*GetPixelChannels(image);
3471 q+=(image->columns-1)*GetPixelChannels(image);
3472 for (x=(ssize_t) image->columns-1; x >= 0; x--)
3477 for (i=0; i < (ssize_t) GetPixelChannels(image); i++)
3488 register const MagickRealType
3491 register const Quantum
3492 *magick_restrict pixels;
3500 channel=GetPixelChannelChannel(image,i);
3501 traits=GetPixelChannelTraits(image,channel);
3502 if (traits == UndefinedPixelTrait)
3504 if ((traits & CopyPixelTrait) != 0)
3507 pixel=(double) QuantumRange;
3510 case DistanceMorphology:
3512 k=(&kernel->values[kernel->width*(kernel->y+1)-1]);
3513 for (v=offset.y; v < (ssize_t) kernel->height; v++)
3515 for (u=0; u < (ssize_t) kernel->width; u++)
3519 if ((pixels[i]+(*k)) < pixel)
3520 pixel=(double) pixels[i]+(*k);
3523 pixels+=GetPixelChannels(image);
3525 pixels+=(image->columns-1)*GetPixelChannels(image);
3527 k=(&kernel->values[kernel->width*kernel->y+kernel->x-1]);
3529 for (u=offset.x+1; u < (ssize_t) kernel->width; u++)
3531 pixels+=GetPixelChannels(image);
3532 if (!IsNaN(*k) && ((x+u-offset.x) < (ssize_t) image->columns))
3534 if ((pixels[i]+(*k)) < pixel)
3535 pixel=(double) pixels[i]+(*k);
3541 case VoronoiMorphology:
3543 k=(&kernel->values[kernel->width*(kernel->y+1)-1]);
3544 for (v=offset.y; v < (ssize_t) kernel->height; v++)
3546 for (u=0; u < (ssize_t) kernel->width; u++)
3550 if ((pixels[i]+(*k)) < pixel)
3551 pixel=(double) pixels[i]+(*k);
3554 pixels+=GetPixelChannels(image);
3556 pixels+=(image->columns-1)*GetPixelChannels(image);
3558 k=(&kernel->values[kernel->width*(kernel->y+1)-1]);
3560 for (u=offset.x+1; u < (ssize_t) kernel->width; u++)
3562 pixels+=GetPixelChannels(image);
3563 if (!IsNaN(*k) && ((x+u-offset.x) < (ssize_t) image->columns))
3565 if ((pixels[i]+(*k)) < pixel)
3566 pixel=(double) pixels[i]+(*k);
3575 if (fabs(pixel-q[i]) > MagickEpsilon)
3577 q[i]=ClampToQuantum(pixel);
3579 p-=GetPixelChannels(image);
3580 q-=GetPixelChannels(image);
3582 if (SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
3584 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3589 proceed=SetImageProgress(image,MorphologyTag,progress++,2*image->rows);
3590 if (proceed == MagickFalse)
3594 morphology_view=DestroyCacheView(morphology_view);
3595 image_view=DestroyCacheView(image_view);
3596 return(status ? (ssize_t) changed : -1);
3600 Apply a Morphology by calling one of the above low level primitive
3601 application functions. This function handles any iteration loops,
3602 composition or re-iteration of results, and compound morphology methods that
3603 is based on multiple low-level (staged) morphology methods.
3605 Basically this provides the complex glue between the requested morphology
3606 method and raw low-level implementation (above).
3608 MagickPrivate Image *MorphologyApply(const Image *image,
3609 const MorphologyMethod method, const ssize_t iterations,
3610 const KernelInfo *kernel, const CompositeOperator compose,const double bias,
3611 ExceptionInfo *exception)
3617 *curr_image, /* Image we are working with or iterating */
3618 *work_image, /* secondary image for primitive iteration */
3619 *save_image, /* saved image - for 'edge' method only */
3620 *rslt_image; /* resultant image - after multi-kernel handling */
3623 *reflected_kernel, /* A reflected copy of the kernel (if needed) */
3624 *norm_kernel, /* the current normal un-reflected kernel */
3625 *rflt_kernel, /* the current reflected kernel (if needed) */
3626 *this_kernel; /* the kernel being applied */
3629 primitive; /* the current morphology primitive being applied */
3632 rslt_compose; /* multi-kernel compose method for results to use */
3635 special, /* do we use a direct modify function? */
3636 verbose; /* verbose output of results */
3639 method_loop, /* Loop 1: number of compound method iterations (norm 1) */
3640 method_limit, /* maximum number of compound method iterations */
3641 kernel_number, /* Loop 2: the kernel number being applied */
3642 stage_loop, /* Loop 3: primitive loop for compound morphology */
3643 stage_limit, /* how many primitives are in this compound */
3644 kernel_loop, /* Loop 4: iterate the kernel over image */
3645 kernel_limit, /* number of times to iterate kernel */
3646 count, /* total count of primitive steps applied */
3647 kernel_changed, /* total count of changed using iterated kernel */
3648 method_changed; /* total count of changed over method iteration */
3651 changed; /* number pixels changed by last primitive operation */
3654 v_info[MagickPathExtent];
3656 assert(image != (Image *) NULL);
3657 assert(image->signature == MagickCoreSignature);
3658 assert(kernel != (KernelInfo *) NULL);
3659 assert(kernel->signature == MagickCoreSignature);
3660 assert(exception != (ExceptionInfo *) NULL);
3661 assert(exception->signature == MagickCoreSignature);
3663 count = 0; /* number of low-level morphology primitives performed */
3664 if ( iterations == 0 )
3665 return((Image *) NULL); /* null operation - nothing to do! */
3667 kernel_limit = (size_t) iterations;
3668 if ( iterations < 0 ) /* negative interations = infinite (well alomst) */
3669 kernel_limit = image->columns>image->rows ? image->columns : image->rows;
3671 verbose = IsStringTrue(GetImageArtifact(image,"debug"));
3673 /* initialise for cleanup */
3674 curr_image = (Image *) image;
3675 curr_compose = image->compose;
3676 (void) curr_compose;
3677 work_image = save_image = rslt_image = (Image *) NULL;
3678 reflected_kernel = (KernelInfo *) NULL;
3680 /* Initialize specific methods
3681 * + which loop should use the given iteratations
3682 * + how many primitives make up the compound morphology
3683 * + multi-kernel compose method to use (by default)
3685 method_limit = 1; /* just do method once, unless otherwise set */
3686 stage_limit = 1; /* assume method is not a compound */
3687 special = MagickFalse; /* assume it is NOT a direct modify primitive */
3688 rslt_compose = compose; /* and we are composing multi-kernels as given */
3690 case SmoothMorphology: /* 4 primitive compound morphology */
3693 case OpenMorphology: /* 2 primitive compound morphology */
3694 case OpenIntensityMorphology:
3695 case TopHatMorphology:
3696 case CloseMorphology:
3697 case CloseIntensityMorphology:
3698 case BottomHatMorphology:
3699 case EdgeMorphology:
3702 case HitAndMissMorphology:
3703 rslt_compose = LightenCompositeOp; /* Union of multi-kernel results */
3705 case ThinningMorphology:
3706 case ThickenMorphology:
3707 method_limit = kernel_limit; /* iterate the whole method */
3708 kernel_limit = 1; /* do not do kernel iteration */
3710 case DistanceMorphology:
3711 case VoronoiMorphology:
3712 special = MagickTrue; /* use special direct primative */
3718 /* Apply special methods with special requirments
3719 ** For example, single run only, or post-processing requirements
3721 if ( special != MagickFalse )
3723 rslt_image=CloneImage(image,0,0,MagickTrue,exception);
3724 if (rslt_image == (Image *) NULL)
3726 if (SetImageStorageClass(rslt_image,DirectClass,exception) == MagickFalse)
3729 changed=MorphologyPrimitiveDirect(rslt_image,method,kernel,exception);
3731 if (verbose != MagickFalse)
3732 (void) (void) FormatLocaleFile(stderr,
3733 "%s:%.20g.%.20g #%.20g => Changed %.20g\n",
3734 CommandOptionToMnemonic(MagickMorphologyOptions, method),
3735 1.0,0.0,1.0, (double) changed);
3740 if ( method == VoronoiMorphology ) {
3741 /* Preserve the alpha channel of input image - but turned it off */
3742 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3744 (void) CompositeImage(rslt_image,image,CopyAlphaCompositeOp,
3745 MagickTrue,0,0,exception);
3746 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3752 /* Handle user (caller) specified multi-kernel composition method */
3753 if ( compose != UndefinedCompositeOp )
3754 rslt_compose = compose; /* override default composition for method */
3755 if ( rslt_compose == UndefinedCompositeOp )
3756 rslt_compose = NoCompositeOp; /* still not defined! Then re-iterate */
3758 /* Some methods require a reflected kernel to use with primitives.
3759 * Create the reflected kernel for those methods. */
3761 case CorrelateMorphology:
3762 case CloseMorphology:
3763 case CloseIntensityMorphology:
3764 case BottomHatMorphology:
3765 case SmoothMorphology:
3766 reflected_kernel = CloneKernelInfo(kernel);
3767 if (reflected_kernel == (KernelInfo *) NULL)
3769 RotateKernelInfo(reflected_kernel,180);
3775 /* Loops around more primitive morpholgy methods
3776 ** erose, dilate, open, close, smooth, edge, etc...
3778 /* Loop 1: iterate the compound method */
3781 while ( method_loop < method_limit && method_changed > 0 ) {
3785 /* Loop 2: iterate over each kernel in a multi-kernel list */
3786 norm_kernel = (KernelInfo *) kernel;
3787 this_kernel = (KernelInfo *) kernel;
3788 rflt_kernel = reflected_kernel;
3791 while ( norm_kernel != NULL ) {
3793 /* Loop 3: Compound Morphology Staging - Select Primative to apply */
3794 stage_loop = 0; /* the compound morphology stage number */
3795 while ( stage_loop < stage_limit ) {
3796 stage_loop++; /* The stage of the compound morphology */
3798 /* Select primitive morphology for this stage of compound method */
3799 this_kernel = norm_kernel; /* default use unreflected kernel */
3800 primitive = method; /* Assume method is a primitive */
3802 case ErodeMorphology: /* just erode */
3803 case EdgeInMorphology: /* erode and image difference */
3804 primitive = ErodeMorphology;
3806 case DilateMorphology: /* just dilate */
3807 case EdgeOutMorphology: /* dilate and image difference */
3808 primitive = DilateMorphology;
3810 case OpenMorphology: /* erode then dialate */
3811 case TopHatMorphology: /* open and image difference */
3812 primitive = ErodeMorphology;
3813 if ( stage_loop == 2 )
3814 primitive = DilateMorphology;
3816 case OpenIntensityMorphology:
3817 primitive = ErodeIntensityMorphology;
3818 if ( stage_loop == 2 )
3819 primitive = DilateIntensityMorphology;
3821 case CloseMorphology: /* dilate, then erode */
3822 case BottomHatMorphology: /* close and image difference */
3823 this_kernel = rflt_kernel; /* use the reflected kernel */
3824 primitive = DilateMorphology;
3825 if ( stage_loop == 2 )
3826 primitive = ErodeMorphology;
3828 case CloseIntensityMorphology:
3829 this_kernel = rflt_kernel; /* use the reflected kernel */
3830 primitive = DilateIntensityMorphology;
3831 if ( stage_loop == 2 )
3832 primitive = ErodeIntensityMorphology;
3834 case SmoothMorphology: /* open, close */
3835 switch ( stage_loop ) {
3836 case 1: /* start an open method, which starts with Erode */
3837 primitive = ErodeMorphology;
3839 case 2: /* now Dilate the Erode */
3840 primitive = DilateMorphology;
3842 case 3: /* Reflect kernel a close */
3843 this_kernel = rflt_kernel; /* use the reflected kernel */
3844 primitive = DilateMorphology;
3846 case 4: /* Finish the Close */
3847 this_kernel = rflt_kernel; /* use the reflected kernel */
3848 primitive = ErodeMorphology;
3852 case EdgeMorphology: /* dilate and erode difference */
3853 primitive = DilateMorphology;
3854 if ( stage_loop == 2 ) {
3855 save_image = curr_image; /* save the image difference */
3856 curr_image = (Image *) image;
3857 primitive = ErodeMorphology;
3860 case CorrelateMorphology:
3861 /* A Correlation is a Convolution with a reflected kernel.
3862 ** However a Convolution is a weighted sum using a reflected
3863 ** kernel. It may seem stange to convert a Correlation into a
3864 ** Convolution as the Correlation is the simplier method, but
3865 ** Convolution is much more commonly used, and it makes sense to
3866 ** implement it directly so as to avoid the need to duplicate the
3867 ** kernel when it is not required (which is typically the
3870 this_kernel = rflt_kernel; /* use the reflected kernel */
3871 primitive = ConvolveMorphology;
3876 assert( this_kernel != (KernelInfo *) NULL );
3878 /* Extra information for debugging compound operations */
3879 if (verbose != MagickFalse) {
3880 if ( stage_limit > 1 )
3881 (void) FormatLocaleString(v_info,MagickPathExtent,"%s:%.20g.%.20g -> ",
3882 CommandOptionToMnemonic(MagickMorphologyOptions,method),(double)
3883 method_loop,(double) stage_loop);
3884 else if ( primitive != method )
3885 (void) FormatLocaleString(v_info, MagickPathExtent, "%s:%.20g -> ",
3886 CommandOptionToMnemonic(MagickMorphologyOptions, method),(double)
3892 /* Loop 4: Iterate the kernel with primitive */
3896 while ( kernel_loop < kernel_limit && changed > 0 ) {
3897 kernel_loop++; /* the iteration of this kernel */
3899 /* Create a clone as the destination image, if not yet defined */
3900 if ( work_image == (Image *) NULL )
3902 work_image=CloneImage(image,0,0,MagickTrue,exception);
3903 if (work_image == (Image *) NULL)
3905 if (SetImageStorageClass(work_image,DirectClass,exception) == MagickFalse)
3909 /* APPLY THE MORPHOLOGICAL PRIMITIVE (curr -> work) */
3911 changed = MorphologyPrimitive(curr_image, work_image, primitive,
3912 this_kernel, bias, exception);
3913 if (verbose != MagickFalse) {
3914 if ( kernel_loop > 1 )
3915 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line from previous */
3916 (void) (void) FormatLocaleFile(stderr,
3917 "%s%s%s:%.20g.%.20g #%.20g => Changed %.20g",
3918 v_info,CommandOptionToMnemonic(MagickMorphologyOptions,
3919 primitive),(this_kernel == rflt_kernel ) ? "*" : "",
3920 (double) (method_loop+kernel_loop-1),(double) kernel_number,
3921 (double) count,(double) changed);
3925 kernel_changed += changed;
3926 method_changed += changed;
3928 /* prepare next loop */
3929 { Image *tmp = work_image; /* swap images for iteration */
3930 work_image = curr_image;
3933 if ( work_image == image )
3934 work_image = (Image *) NULL; /* replace input 'image' */
3936 } /* End Loop 4: Iterate the kernel with primitive */
3938 if (verbose != MagickFalse && kernel_changed != (size_t)changed)
3939 (void) FormatLocaleFile(stderr, " Total %.20g",(double) kernel_changed);
3940 if (verbose != MagickFalse && stage_loop < stage_limit)
3941 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line before looping */
3944 (void) FormatLocaleFile(stderr, "--E-- image=0x%lx\n", (unsigned long)image);
3945 (void) FormatLocaleFile(stderr, " curr =0x%lx\n", (unsigned long)curr_image);
3946 (void) FormatLocaleFile(stderr, " work =0x%lx\n", (unsigned long)work_image);
3947 (void) FormatLocaleFile(stderr, " save =0x%lx\n", (unsigned long)save_image);
3948 (void) FormatLocaleFile(stderr, " union=0x%lx\n", (unsigned long)rslt_image);
3951 } /* End Loop 3: Primative (staging) Loop for Coumpound Methods */
3953 /* Final Post-processing for some Compound Methods
3955 ** The removal of any 'Sync' channel flag in the Image Compositon
3956 ** below ensures the methematical compose method is applied in a
3957 ** purely mathematical way, and only to the selected channels.
3958 ** Turn off SVG composition 'alpha blending'.
3961 case EdgeOutMorphology:
3962 case EdgeInMorphology:
3963 case TopHatMorphology:
3964 case BottomHatMorphology:
3965 if (verbose != MagickFalse)
3966 (void) FormatLocaleFile(stderr,
3967 "\n%s: Difference with original image",CommandOptionToMnemonic(
3968 MagickMorphologyOptions, method) );
3969 (void) CompositeImage(curr_image,image,DifferenceCompositeOp,
3970 MagickTrue,0,0,exception);
3972 case EdgeMorphology:
3973 if (verbose != MagickFalse)
3974 (void) FormatLocaleFile(stderr,
3975 "\n%s: Difference of Dilate and Erode",CommandOptionToMnemonic(
3976 MagickMorphologyOptions, method) );
3977 (void) CompositeImage(curr_image,save_image,DifferenceCompositeOp,
3978 MagickTrue,0,0,exception);
3979 save_image = DestroyImage(save_image); /* finished with save image */
3985 /* multi-kernel handling: re-iterate, or compose results */
3986 if ( kernel->next == (KernelInfo *) NULL )
3987 rslt_image = curr_image; /* just return the resulting image */
3988 else if ( rslt_compose == NoCompositeOp )
3989 { if (verbose != MagickFalse) {
3990 if ( this_kernel->next != (KernelInfo *) NULL )
3991 (void) FormatLocaleFile(stderr, " (re-iterate)");
3993 (void) FormatLocaleFile(stderr, " (done)");
3995 rslt_image = curr_image; /* return result, and re-iterate */
3997 else if ( rslt_image == (Image *) NULL)
3998 { if (verbose != MagickFalse)
3999 (void) FormatLocaleFile(stderr, " (save for compose)");
4000 rslt_image = curr_image;
4001 curr_image = (Image *) image; /* continue with original image */
4004 { /* Add the new 'current' result to the composition
4006 ** The removal of any 'Sync' channel flag in the Image Compositon
4007 ** below ensures the methematical compose method is applied in a
4008 ** purely mathematical way, and only to the selected channels.
4009 ** IE: Turn off SVG composition 'alpha blending'.
4011 if (verbose != MagickFalse)
4012 (void) FormatLocaleFile(stderr, " (compose \"%s\")",
4013 CommandOptionToMnemonic(MagickComposeOptions, rslt_compose) );
4014 (void) CompositeImage(rslt_image,curr_image,rslt_compose,MagickTrue,
4016 curr_image = DestroyImage(curr_image);
4017 curr_image = (Image *) image; /* continue with original image */
4019 if (verbose != MagickFalse)
4020 (void) FormatLocaleFile(stderr, "\n");
4022 /* loop to the next kernel in a multi-kernel list */
4023 norm_kernel = norm_kernel->next;
4024 if ( rflt_kernel != (KernelInfo *) NULL )
4025 rflt_kernel = rflt_kernel->next;
4027 } /* End Loop 2: Loop over each kernel */
4029 } /* End Loop 1: compound method interation */
4033 /* Yes goto's are bad, but it makes cleanup lot more efficient */
4035 if ( curr_image == rslt_image )
4036 curr_image = (Image *) NULL;
4037 if ( rslt_image != (Image *) NULL )
4038 rslt_image = DestroyImage(rslt_image);
4040 if ( curr_image == rslt_image || curr_image == image )
4041 curr_image = (Image *) NULL;
4042 if ( curr_image != (Image *) NULL )
4043 curr_image = DestroyImage(curr_image);
4044 if ( work_image != (Image *) NULL )
4045 work_image = DestroyImage(work_image);
4046 if ( save_image != (Image *) NULL )
4047 save_image = DestroyImage(save_image);
4048 if ( reflected_kernel != (KernelInfo *) NULL )
4049 reflected_kernel = DestroyKernelInfo(reflected_kernel);
4055 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4059 % M o r p h o l o g y I m a g e %
4063 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4065 % MorphologyImage() applies a user supplied kernel to the image according to
4066 % the given mophology method.
4068 % This function applies any and all user defined settings before calling
4069 % the above internal function MorphologyApply().
4071 % User defined settings include...
4072 % * Output Bias for Convolution and correlation ("-define convolve:bias=??")
4073 % * Kernel Scale/normalize settings ("-define convolve:scale=??")
4074 % This can also includes the addition of a scaled unity kernel.
4075 % * Show Kernel being applied ("-define morphology:showKernel=1")
4077 % Other operators that do not want user supplied options interfering,
4078 % especially "convolve:bias" and "morphology:showKernel" should use
4079 % MorphologyApply() directly.
4081 % The format of the MorphologyImage method is:
4083 % Image *MorphologyImage(const Image *image,MorphologyMethod method,
4084 % const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
4086 % A description of each parameter follows:
4088 % o image: the image.
4090 % o method: the morphology method to be applied.
4092 % o iterations: apply the operation this many times (or no change).
4093 % A value of -1 means loop until no change found.
4094 % How this is applied may depend on the morphology method.
4095 % Typically this is a value of 1.
4097 % o kernel: An array of double representing the morphology kernel.
4098 % Warning: kernel may be normalized for the Convolve method.
4100 % o exception: return any errors or warnings in this structure.
4103 MagickExport Image *MorphologyImage(const Image *image,
4104 const MorphologyMethod method,const ssize_t iterations,
4105 const KernelInfo *kernel,ExceptionInfo *exception)
4122 curr_kernel = (KernelInfo *) kernel;
4124 compose = UndefinedCompositeOp; /* use default for method */
4126 /* Apply Convolve/Correlate Normalization and Scaling Factors.
4127 * This is done BEFORE the ShowKernelInfo() function is called so that
4128 * users can see the results of the 'option:convolve:scale' option.
4130 if ( method == ConvolveMorphology || method == CorrelateMorphology ) {
4131 /* Get the bias value as it will be needed */
4132 artifact = GetImageArtifact(image,"convolve:bias");
4133 if ( artifact != (const char *) NULL) {
4134 if (IsGeometry(artifact) == MagickFalse)
4135 (void) ThrowMagickException(exception,GetMagickModule(),
4136 OptionWarning,"InvalidSetting","'%s' '%s'",
4137 "convolve:bias",artifact);
4139 bias=StringToDoubleInterval(artifact,(double) QuantumRange+1.0);
4142 /* Scale kernel according to user wishes */
4143 artifact = GetImageArtifact(image,"convolve:scale");
4144 if ( artifact != (const char *) NULL ) {
4145 if (IsGeometry(artifact) == MagickFalse)
4146 (void) ThrowMagickException(exception,GetMagickModule(),
4147 OptionWarning,"InvalidSetting","'%s' '%s'",
4148 "convolve:scale",artifact);
4150 if ( curr_kernel == kernel )
4151 curr_kernel = CloneKernelInfo(kernel);
4152 if (curr_kernel == (KernelInfo *) NULL)
4153 return((Image *) NULL);
4154 ScaleGeometryKernelInfo(curr_kernel, artifact);
4159 /* display the (normalized) kernel via stderr */
4160 artifact=GetImageArtifact(image,"morphology:showKernel");
4161 if (IsStringTrue(artifact) != MagickFalse)
4162 ShowKernelInfo(curr_kernel);
4164 /* Override the default handling of multi-kernel morphology results
4165 * If 'Undefined' use the default method
4166 * If 'None' (default for 'Convolve') re-iterate previous result
4167 * Otherwise merge resulting images using compose method given.
4168 * Default for 'HitAndMiss' is 'Lighten'.
4174 artifact = GetImageArtifact(image,"morphology:compose");
4175 if ( artifact != (const char *) NULL) {
4176 parse=ParseCommandOption(MagickComposeOptions,
4177 MagickFalse,artifact);
4179 (void) ThrowMagickException(exception,GetMagickModule(),
4180 OptionWarning,"UnrecognizedComposeOperator","'%s' '%s'",
4181 "morphology:compose",artifact);
4183 compose=(CompositeOperator)parse;
4186 /* Apply the Morphology */
4187 morphology_image = MorphologyApply(image,method,iterations,
4188 curr_kernel,compose,bias,exception);
4190 /* Cleanup and Exit */
4191 if ( curr_kernel != kernel )
4192 curr_kernel=DestroyKernelInfo(curr_kernel);
4193 return(morphology_image);
4197 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4201 + R o t a t e K e r n e l I n f o %
4205 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4207 % RotateKernelInfo() rotates the kernel by the angle given.
4209 % Currently it is restricted to 90 degree angles, of either 1D kernels
4210 % or square kernels. And 'circular' rotations of 45 degrees for 3x3 kernels.
4211 % It will ignore usless rotations for specific 'named' built-in kernels.
4213 % The format of the RotateKernelInfo method is:
4215 % void RotateKernelInfo(KernelInfo *kernel, double angle)
4217 % A description of each parameter follows:
4219 % o kernel: the Morphology/Convolution kernel
4221 % o angle: angle to rotate in degrees
4223 % This function is currently internal to this module only, but can be exported
4224 % to other modules if needed.
4226 static void RotateKernelInfo(KernelInfo *kernel, double angle)
4228 /* angle the lower kernels first */
4229 if ( kernel->next != (KernelInfo *) NULL)
4230 RotateKernelInfo(kernel->next, angle);
4232 /* WARNING: Currently assumes the kernel (rightly) is horizontally symetrical
4234 ** TODO: expand beyond simple 90 degree rotates, flips and flops
4237 /* Modulus the angle */
4238 angle = fmod(angle, 360.0);
4242 if ( 337.5 < angle || angle <= 22.5 )
4243 return; /* Near zero angle - no change! - At least not at this time */
4245 /* Handle special cases */
4246 switch (kernel->type) {
4247 /* These built-in kernels are cylindrical kernels, rotating is useless */
4248 case GaussianKernel:
4253 case LaplacianKernel:
4254 case ChebyshevKernel:
4255 case ManhattanKernel:
4256 case EuclideanKernel:
4259 /* These may be rotatable at non-90 angles in the future */
4260 /* but simply rotating them in multiples of 90 degrees is useless */
4267 /* These only allows a +/-90 degree rotation (by transpose) */
4268 /* A 180 degree rotation is useless */
4270 if ( 135.0 < angle && angle <= 225.0 )
4272 if ( 225.0 < angle && angle <= 315.0 )
4279 /* Attempt rotations by 45 degrees -- 3x3 kernels only */
4280 if ( 22.5 < fmod(angle,90.0) && fmod(angle,90.0) <= 67.5 )
4282 if ( kernel->width == 3 && kernel->height == 3 )
4283 { /* Rotate a 3x3 square by 45 degree angle */
4284 double t = kernel->values[0];
4285 kernel->values[0] = kernel->values[3];
4286 kernel->values[3] = kernel->values[6];
4287 kernel->values[6] = kernel->values[7];
4288 kernel->values[7] = kernel->values[8];
4289 kernel->values[8] = kernel->values[5];
4290 kernel->values[5] = kernel->values[2];
4291 kernel->values[2] = kernel->values[1];
4292 kernel->values[1] = t;
4293 /* rotate non-centered origin */
4294 if ( kernel->x != 1 || kernel->y != 1 ) {
4296 x = (ssize_t) kernel->x-1;
4297 y = (ssize_t) kernel->y-1;
4298 if ( x == y ) x = 0;
4299 else if ( x == 0 ) x = -y;
4300 else if ( x == -y ) y = 0;
4301 else if ( y == 0 ) y = x;
4302 kernel->x = (ssize_t) x+1;
4303 kernel->y = (ssize_t) y+1;
4305 angle = fmod(angle+315.0, 360.0); /* angle reduced 45 degrees */
4306 kernel->angle = fmod(kernel->angle+45.0, 360.0);
4309 perror("Unable to rotate non-3x3 kernel by 45 degrees");
4311 if ( 45.0 < fmod(angle, 180.0) && fmod(angle,180.0) <= 135.0 )
4313 if ( kernel->width == 1 || kernel->height == 1 )
4314 { /* Do a transpose of a 1 dimensional kernel,
4315 ** which results in a fast 90 degree rotation of some type.
4319 t = (ssize_t) kernel->width;
4320 kernel->width = kernel->height;
4321 kernel->height = (size_t) t;
4323 kernel->x = kernel->y;
4325 if ( kernel->width == 1 ) {
4326 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4327 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4329 angle = fmod(angle+90.0, 360.0); /* angle increased 90 degrees */
4330 kernel->angle = fmod(kernel->angle+270.0, 360.0);
4333 else if ( kernel->width == kernel->height )
4334 { /* Rotate a square array of values by 90 degrees */
4338 register MagickRealType
4342 for( i=0, x=(ssize_t) kernel->width-1; i<=x; i++, x--)
4343 for( j=0, y=(ssize_t) kernel->height-1; j<y; j++, y--)
4344 { t = k[i+j*kernel->width];
4345 k[i+j*kernel->width] = k[j+x*kernel->width];
4346 k[j+x*kernel->width] = k[x+y*kernel->width];
4347 k[x+y*kernel->width] = k[y+i*kernel->width];
4348 k[y+i*kernel->width] = t;
4351 /* rotate the origin - relative to center of array */
4352 { register ssize_t x,y;
4353 x = (ssize_t) (kernel->x*2-kernel->width+1);
4354 y = (ssize_t) (kernel->y*2-kernel->height+1);
4355 kernel->x = (ssize_t) ( -y +(ssize_t) kernel->width-1)/2;
4356 kernel->y = (ssize_t) ( +x +(ssize_t) kernel->height-1)/2;
4358 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4359 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4362 perror("Unable to rotate a non-square, non-linear kernel 90 degrees");
4364 if ( 135.0 < angle && angle <= 225.0 )
4366 /* For a 180 degree rotation - also know as a reflection
4367 * This is actually a very very common operation!
4368 * Basically all that is needed is a reversal of the kernel data!
4369 * And a reflection of the origon
4374 register MagickRealType
4382 j=(ssize_t) (kernel->width*kernel->height-1);
4383 for (i=0; i < j; i++, j--)
4384 t=k[i], k[i]=k[j], k[j]=t;
4386 kernel->x = (ssize_t) kernel->width - kernel->x - 1;
4387 kernel->y = (ssize_t) kernel->height - kernel->y - 1;
4388 angle = fmod(angle-180.0, 360.0); /* angle+180 degrees */
4389 kernel->angle = fmod(kernel->angle+180.0, 360.0);
4391 /* At this point angle should at least between -45 (315) and +45 degrees
4392 * In the future some form of non-orthogonal angled rotates could be
4393 * performed here, posibily with a linear kernel restriction.
4400 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4404 % S c a l e G e o m e t r y K e r n e l I n f o %
4408 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4410 % ScaleGeometryKernelInfo() takes a geometry argument string, typically
4411 % provided as a "-set option:convolve:scale {geometry}" user setting,
4412 % and modifies the kernel according to the parsed arguments of that setting.
4414 % The first argument (and any normalization flags) are passed to
4415 % ScaleKernelInfo() to scale/normalize the kernel. The second argument
4416 % is then passed to UnityAddKernelInfo() to add a scled unity kernel
4417 % into the scaled/normalized kernel.
4419 % The format of the ScaleGeometryKernelInfo method is:
4421 % void ScaleGeometryKernelInfo(KernelInfo *kernel,
4422 % const double scaling_factor,const MagickStatusType normalize_flags)
4424 % A description of each parameter follows:
4426 % o kernel: the Morphology/Convolution kernel to modify
4429 % The geometry string to parse, typically from the user provided
4430 % "-set option:convolve:scale {geometry}" setting.
4433 MagickExport void ScaleGeometryKernelInfo (KernelInfo *kernel,
4434 const char *geometry)
4442 SetGeometryInfo(&args);
4443 flags = ParseGeometry(geometry, &args);
4446 /* For Debugging Geometry Input */
4447 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
4448 flags, args.rho, args.sigma, args.xi, args.psi );
4451 if ( (flags & PercentValue) != 0 ) /* Handle Percentage flag*/
4452 args.rho *= 0.01, args.sigma *= 0.01;
4454 if ( (flags & RhoValue) == 0 ) /* Set Defaults for missing args */
4456 if ( (flags & SigmaValue) == 0 )
4459 /* Scale/Normalize the input kernel */
4460 ScaleKernelInfo(kernel, args.rho, (GeometryFlags) flags);
4462 /* Add Unity Kernel, for blending with original */
4463 if ( (flags & SigmaValue) != 0 )
4464 UnityAddKernelInfo(kernel, args.sigma);
4469 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4473 % S c a l e K e r n e l I n f o %
4477 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4479 % ScaleKernelInfo() scales the given kernel list by the given amount, with or
4480 % without normalization of the sum of the kernel values (as per given flags).
4482 % By default (no flags given) the values within the kernel is scaled
4483 % directly using given scaling factor without change.
4485 % If either of the two 'normalize_flags' are given the kernel will first be
4486 % normalized and then further scaled by the scaling factor value given.
4488 % Kernel normalization ('normalize_flags' given) is designed to ensure that
4489 % any use of the kernel scaling factor with 'Convolve' or 'Correlate'
4490 % morphology methods will fall into -1.0 to +1.0 range. Note that for
4491 % non-HDRI versions of IM this may cause images to have any negative results
4492 % clipped, unless some 'bias' is used.
4494 % More specifically. Kernels which only contain positive values (such as a
4495 % 'Gaussian' kernel) will be scaled so that those values sum to +1.0,
4496 % ensuring a 0.0 to +1.0 output range for non-HDRI images.
4498 % For Kernels that contain some negative values, (such as 'Sharpen' kernels)
4499 % the kernel will be scaled by the absolute of the sum of kernel values, so
4500 % that it will generally fall within the +/- 1.0 range.
4502 % For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel
4503 % will be scaled by just the sum of the postive values, so that its output
4504 % range will again fall into the +/- 1.0 range.
4506 % For special kernels designed for locating shapes using 'Correlate', (often
4507 % only containing +1 and -1 values, representing foreground/brackground
4508 % matching) a special normalization method is provided to scale the positive
4509 % values separately to those of the negative values, so the kernel will be
4510 % forced to become a zero-sum kernel better suited to such searches.
4512 % WARNING: Correct normalization of the kernel assumes that the '*_range'
4513 % attributes within the kernel structure have been correctly set during the
4516 % NOTE: The values used for 'normalize_flags' have been selected specifically
4517 % to match the use of geometry options, so that '!' means NormalizeValue, '^'
4518 % means CorrelateNormalizeValue. All other GeometryFlags values are ignored.
4520 % The format of the ScaleKernelInfo method is:
4522 % void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
4523 % const MagickStatusType normalize_flags )
4525 % A description of each parameter follows:
4527 % o kernel: the Morphology/Convolution kernel
4530 % multiply all values (after normalization) by this factor if not
4531 % zero. If the kernel is normalized regardless of any flags.
4533 % o normalize_flags:
4534 % GeometryFlags defining normalization method to use.
4535 % specifically: NormalizeValue, CorrelateNormalizeValue,
4536 % and/or PercentValue
4539 MagickExport void ScaleKernelInfo(KernelInfo *kernel,
4540 const double scaling_factor,const GeometryFlags normalize_flags)
4549 /* do the other kernels in a multi-kernel list first */
4550 if ( kernel->next != (KernelInfo *) NULL)
4551 ScaleKernelInfo(kernel->next, scaling_factor, normalize_flags);
4553 /* Normalization of Kernel */
4555 if ( (normalize_flags&NormalizeValue) != 0 ) {
4556 if ( fabs(kernel->positive_range + kernel->negative_range) >= MagickEpsilon )
4557 /* non-zero-summing kernel (generally positive) */
4558 pos_scale = fabs(kernel->positive_range + kernel->negative_range);
4560 /* zero-summing kernel */
4561 pos_scale = kernel->positive_range;
4563 /* Force kernel into a normalized zero-summing kernel */
4564 if ( (normalize_flags&CorrelateNormalizeValue) != 0 ) {
4565 pos_scale = ( fabs(kernel->positive_range) >= MagickEpsilon )
4566 ? kernel->positive_range : 1.0;
4567 neg_scale = ( fabs(kernel->negative_range) >= MagickEpsilon )
4568 ? -kernel->negative_range : 1.0;
4571 neg_scale = pos_scale;
4573 /* finialize scaling_factor for positive and negative components */
4574 pos_scale = scaling_factor/pos_scale;
4575 neg_scale = scaling_factor/neg_scale;
4577 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
4578 if (!IsNaN(kernel->values[i]))
4579 kernel->values[i] *= (kernel->values[i] >= 0) ? pos_scale : neg_scale;
4581 /* convolution output range */
4582 kernel->positive_range *= pos_scale;
4583 kernel->negative_range *= neg_scale;
4584 /* maximum and minimum values in kernel */
4585 kernel->maximum *= (kernel->maximum >= 0.0) ? pos_scale : neg_scale;
4586 kernel->minimum *= (kernel->minimum >= 0.0) ? pos_scale : neg_scale;
4588 /* swap kernel settings if user's scaling factor is negative */
4589 if ( scaling_factor < MagickEpsilon ) {
4591 t = kernel->positive_range;
4592 kernel->positive_range = kernel->negative_range;
4593 kernel->negative_range = t;
4594 t = kernel->maximum;
4595 kernel->maximum = kernel->minimum;
4596 kernel->minimum = 1;
4603 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4607 % S h o w K e r n e l I n f o %
4611 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4613 % ShowKernelInfo() outputs the details of the given kernel defination to
4614 % standard error, generally due to a users 'morphology:showKernel' option
4617 % The format of the ShowKernel method is:
4619 % void ShowKernelInfo(const KernelInfo *kernel)
4621 % A description of each parameter follows:
4623 % o kernel: the Morphology/Convolution kernel
4626 MagickPrivate void ShowKernelInfo(const KernelInfo *kernel)
4634 for (c=0, k=kernel; k != (KernelInfo *) NULL; c++, k=k->next ) {
4636 (void) FormatLocaleFile(stderr, "Kernel");
4637 if ( kernel->next != (KernelInfo *) NULL )
4638 (void) FormatLocaleFile(stderr, " #%lu", (unsigned long) c );
4639 (void) FormatLocaleFile(stderr, " \"%s",
4640 CommandOptionToMnemonic(MagickKernelOptions, k->type) );
4641 if ( fabs(k->angle) >= MagickEpsilon )
4642 (void) FormatLocaleFile(stderr, "@%lg", k->angle);
4643 (void) FormatLocaleFile(stderr, "\" of size %lux%lu%+ld%+ld",(unsigned long)
4644 k->width,(unsigned long) k->height,(long) k->x,(long) k->y);
4645 (void) FormatLocaleFile(stderr,
4646 " with values from %.*lg to %.*lg\n",
4647 GetMagickPrecision(), k->minimum,
4648 GetMagickPrecision(), k->maximum);
4649 (void) FormatLocaleFile(stderr, "Forming a output range from %.*lg to %.*lg",
4650 GetMagickPrecision(), k->negative_range,
4651 GetMagickPrecision(), k->positive_range);
4652 if ( fabs(k->positive_range+k->negative_range) < MagickEpsilon )
4653 (void) FormatLocaleFile(stderr, " (Zero-Summing)\n");
4654 else if ( fabs(k->positive_range+k->negative_range-1.0) < MagickEpsilon )
4655 (void) FormatLocaleFile(stderr, " (Normalized)\n");
4657 (void) FormatLocaleFile(stderr, " (Sum %.*lg)\n",
4658 GetMagickPrecision(), k->positive_range+k->negative_range);
4659 for (i=v=0; v < k->height; v++) {
4660 (void) FormatLocaleFile(stderr, "%2lu:", (unsigned long) v );
4661 for (u=0; u < k->width; u++, i++)
4662 if (IsNaN(k->values[i]))
4663 (void) FormatLocaleFile(stderr," %*s", GetMagickPrecision()+3, "nan");
4665 (void) FormatLocaleFile(stderr," %*.*lg", GetMagickPrecision()+3,
4666 GetMagickPrecision(), (double) k->values[i]);
4667 (void) FormatLocaleFile(stderr,"\n");
4673 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4677 % U n i t y A d d K e r n a l I n f o %
4681 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4683 % UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel
4684 % to the given pre-scaled and normalized Kernel. This in effect adds that
4685 % amount of the original image into the resulting convolution kernel. This
4686 % value is usually provided by the user as a percentage value in the
4687 % 'convolve:scale' setting.
4689 % The resulting effect is to convert the defined kernels into blended
4690 % soft-blurs, unsharp kernels or into sharpening kernels.
4692 % The format of the UnityAdditionKernelInfo method is:
4694 % void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
4696 % A description of each parameter follows:
4698 % o kernel: the Morphology/Convolution kernel
4701 % scaling factor for the unity kernel to be added to
4705 MagickExport void UnityAddKernelInfo(KernelInfo *kernel,
4708 /* do the other kernels in a multi-kernel list first */
4709 if ( kernel->next != (KernelInfo *) NULL)
4710 UnityAddKernelInfo(kernel->next, scale);
4712 /* Add the scaled unity kernel to the existing kernel */
4713 kernel->values[kernel->x+kernel->y*kernel->width] += scale;
4714 CalcKernelMetaData(kernel); /* recalculate the meta-data */
4720 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4724 % Z e r o K e r n e l N a n s %
4728 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4730 % ZeroKernelNans() replaces any special 'nan' value that may be present in
4731 % the kernel with a zero value. This is typically done when the kernel will
4732 % be used in special hardware (GPU) convolution processors, to simply
4735 % The format of the ZeroKernelNans method is:
4737 % void ZeroKernelNans (KernelInfo *kernel)
4739 % A description of each parameter follows:
4741 % o kernel: the Morphology/Convolution kernel
4744 MagickPrivate void ZeroKernelNans(KernelInfo *kernel)
4749 /* do the other kernels in a multi-kernel list first */
4750 if (kernel->next != (KernelInfo *) NULL)
4751 ZeroKernelNans(kernel->next);
4753 for (i=0; i < (kernel->width*kernel->height); i++)
4754 if (IsNaN(kernel->values[i]))
4755 kernel->values[i]=0.0;