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 1999-2013 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 % http://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 % Morpology is the the application of various kernels, of any size and even
37 % shape, to a image in various ways (typically binary, but not always).
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/color-private.h"
56 #include "MagickCore/enhance.h"
57 #include "MagickCore/exception.h"
58 #include "MagickCore/exception-private.h"
59 #include "MagickCore/gem.h"
60 #include "MagickCore/gem-private.h"
61 #include "MagickCore/hashmap.h"
62 #include "MagickCore/image.h"
63 #include "MagickCore/image-private.h"
64 #include "MagickCore/list.h"
65 #include "MagickCore/magick.h"
66 #include "MagickCore/memory_.h"
67 #include "MagickCore/memory-private.h"
68 #include "MagickCore/monitor-private.h"
69 #include "MagickCore/morphology.h"
70 #include "MagickCore/morphology-private.h"
71 #include "MagickCore/option.h"
72 #include "MagickCore/pixel-accessor.h"
73 #include "MagickCore/pixel-private.h"
74 #include "MagickCore/prepress.h"
75 #include "MagickCore/quantize.h"
76 #include "MagickCore/resource_.h"
77 #include "MagickCore/registry.h"
78 #include "MagickCore/semaphore.h"
79 #include "MagickCore/splay-tree.h"
80 #include "MagickCore/statistic.h"
81 #include "MagickCore/string_.h"
82 #include "MagickCore/string-private.h"
83 #include "MagickCore/thread-private.h"
84 #include "MagickCore/token.h"
85 #include "MagickCore/utility.h"
86 #include "MagickCore/utility-private.h"
89 Other global definitions used by module.
91 static inline double MagickMin(const double x,const double y)
93 return( x < y ? x : y);
95 static inline double MagickMax(const double x,const double y)
97 return( x > y ? x : y);
99 #define Minimize(assign,value) assign=MagickMin(assign,value)
100 #define Maximize(assign,value) assign=MagickMax(assign,value)
102 /* Integer Factorial Function - for a Binomial kernel */
104 static inline size_t fact(size_t n)
107 for(f=1, l=2; l <= n; f=f*l, l++);
110 #elif 1 /* glibc floating point alternatives */
111 #define fact(n) ((size_t)tgamma((double)n+1))
113 #define fact(n) ((size_t)lgamma((double)n+1))
117 /* Currently these are only internal to this module */
119 CalcKernelMetaData(KernelInfo *),
120 ExpandMirrorKernelInfo(KernelInfo *),
121 ExpandRotateKernelInfo(KernelInfo *, const double),
122 RotateKernelInfo(KernelInfo *, double);
125 /* Quick function to find last kernel in a kernel list */
126 static inline KernelInfo *LastKernelInfo(KernelInfo *kernel)
128 while (kernel->next != (KernelInfo *) NULL)
129 kernel = kernel->next;
134 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
138 % A c q u i r e K e r n e l I n f o %
142 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
144 % AcquireKernelInfo() takes the given string (generally supplied by the
145 % user) and converts it into a Morphology/Convolution Kernel. This allows
146 % users to specify a kernel from a number of pre-defined kernels, or to fully
147 % specify their own kernel for a specific Convolution or Morphology
150 % The kernel so generated can be any rectangular array of floating point
151 % values (doubles) with the 'control point' or 'pixel being affected'
152 % anywhere within that array of values.
154 % Previously IM was restricted to a square of odd size using the exact
155 % center as origin, this is no longer the case, and any rectangular kernel
156 % with any value being declared the origin. This in turn allows the use of
157 % highly asymmetrical kernels.
159 % The floating point values in the kernel can also include a special value
160 % known as 'nan' or 'not a number' to indicate that this value is not part
161 % of the kernel array. This allows you to shaped the kernel within its
162 % rectangular area. That is 'nan' values provide a 'mask' for the kernel
163 % shape. However at least one non-nan value must be provided for correct
164 % working of a kernel.
166 % The returned kernel should be freed using the DestroyKernelInfo() when you
167 % are finished with it. Do not free this memory yourself.
169 % Input kernel defintion strings can consist of any of three types.
172 % Select from one of the built in kernels, using the name and
173 % geometry arguments supplied. See AcquireKernelBuiltIn()
175 % "WxH[+X+Y][@><]:num, num, num ..."
176 % a kernel of size W by H, with W*H floating point numbers following.
177 % the 'center' can be optionally be defined at +X+Y (such that +0+0
178 % is top left corner). If not defined the pixel in the center, for
179 % odd sizes, or to the immediate top or left of center for even sizes
180 % is automatically selected.
182 % "num, num, num, num, ..."
183 % list of floating point numbers defining an 'old style' odd sized
184 % square kernel. At least 9 values should be provided for a 3x3
185 % square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc.
186 % Values can be space or comma separated. This is not recommended.
188 % You can define a 'list of kernels' which can be used by some morphology
189 % operators A list is defined as a semi-colon separated list kernels.
191 % " kernel ; kernel ; kernel ; "
193 % Any extra ';' characters, at start, end or between kernel defintions are
196 % The special flags will expand a single kernel, into a list of rotated
197 % kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree
198 % cyclic rotations, while a '>' will generate a list of 90-degree rotations.
199 % The '<' also exands using 90-degree rotates, but giving a 180-degree
200 % reflected kernel before the +/- 90-degree rotations, which can be important
201 % for Thinning operations.
203 % Note that 'name' kernels will start with an alphabetic character while the
204 % new kernel specification has a ':' character in its specification string.
205 % If neither is the case, it is assumed an old style of a simple list of
206 % numbers generating a odd-sized square kernel has been given.
208 % The format of the AcquireKernal method is:
210 % KernelInfo *AcquireKernelInfo(const char *kernel_string)
212 % A description of each parameter follows:
214 % o kernel_string: the Morphology/Convolution kernel wanted.
218 /* This was separated so that it could be used as a separate
219 ** array input handling function, such as for -color-matrix
221 static KernelInfo *ParseKernelArray(const char *kernel_string)
227 token[MaxTextExtent];
237 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
245 kernel=(KernelInfo *) AcquireQuantumMemory(1,sizeof(*kernel));
246 if (kernel == (KernelInfo *)NULL)
248 (void) ResetMagickMemory(kernel,0,sizeof(*kernel));
249 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
250 kernel->negative_range = kernel->positive_range = 0.0;
251 kernel->type = UserDefinedKernel;
252 kernel->next = (KernelInfo *) NULL;
253 kernel->signature = MagickSignature;
254 if (kernel_string == (const char *) NULL)
257 /* find end of this specific kernel definition string */
258 end = strchr(kernel_string, ';');
259 if ( end == (char *) NULL )
260 end = strchr(kernel_string, '\0');
262 /* clear flags - for Expanding kernel lists thorugh rotations */
265 /* Has a ':' in argument - New user kernel specification
266 FUTURE: this split on ':' could be done by StringToken()
268 p = strchr(kernel_string, ':');
269 if ( p != (char *) NULL && p < end)
271 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
272 memcpy(token, kernel_string, (size_t) (p-kernel_string));
273 token[p-kernel_string] = '\0';
274 SetGeometryInfo(&args);
275 flags = ParseGeometry(token, &args);
277 /* Size handling and checks of geometry settings */
278 if ( (flags & WidthValue) == 0 ) /* if no width then */
279 args.rho = args.sigma; /* then width = height */
280 if ( args.rho < 1.0 ) /* if width too small */
281 args.rho = 1.0; /* then width = 1 */
282 if ( args.sigma < 1.0 ) /* if height too small */
283 args.sigma = args.rho; /* then height = width */
284 kernel->width = (size_t)args.rho;
285 kernel->height = (size_t)args.sigma;
287 /* Offset Handling and Checks */
288 if ( args.xi < 0.0 || args.psi < 0.0 )
289 return(DestroyKernelInfo(kernel));
290 kernel->x = ((flags & XValue)!=0) ? (ssize_t)args.xi
291 : (ssize_t) (kernel->width-1)/2;
292 kernel->y = ((flags & YValue)!=0) ? (ssize_t)args.psi
293 : (ssize_t) (kernel->height-1)/2;
294 if ( kernel->x >= (ssize_t) kernel->width ||
295 kernel->y >= (ssize_t) kernel->height )
296 return(DestroyKernelInfo(kernel));
298 p++; /* advance beyond the ':' */
301 { /* ELSE - Old old specification, forming odd-square kernel */
302 /* count up number of values given */
303 p=(const char *) kernel_string;
304 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
305 p++; /* ignore "'" chars for convolve filter usage - Cristy */
306 for (i=0; p < end; i++)
308 GetMagickToken(p,&p,token);
310 GetMagickToken(p,&p,token);
312 /* set the size of the kernel - old sized square */
313 kernel->width = kernel->height= (size_t) sqrt((double) i+1.0);
314 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
315 p=(const char *) kernel_string;
316 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
317 p++; /* ignore "'" chars for convolve filter usage - Cristy */
320 /* Read in the kernel values from rest of input string argument */
321 kernel->values=(MagickRealType *) MagickAssumeAligned(AcquireAlignedMemory(
322 kernel->width,kernel->height*sizeof(*kernel->values)));
323 if (kernel->values == (MagickRealType *) NULL)
324 return(DestroyKernelInfo(kernel));
325 kernel->minimum = +MagickHuge;
326 kernel->maximum = -MagickHuge;
327 kernel->negative_range = kernel->positive_range = 0.0;
328 for (i=0; (i < (ssize_t) (kernel->width*kernel->height)) && (p < end); i++)
330 GetMagickToken(p,&p,token);
332 GetMagickToken(p,&p,token);
333 if ( LocaleCompare("nan",token) == 0
334 || LocaleCompare("-",token) == 0 ) {
335 kernel->values[i] = nan; /* this value is not part of neighbourhood */
338 kernel->values[i] = StringToDouble(token,(char **) NULL);
339 ( kernel->values[i] < 0)
340 ? ( kernel->negative_range += kernel->values[i] )
341 : ( kernel->positive_range += kernel->values[i] );
342 Minimize(kernel->minimum, kernel->values[i]);
343 Maximize(kernel->maximum, kernel->values[i]);
347 /* sanity check -- no more values in kernel definition */
348 GetMagickToken(p,&p,token);
349 if ( *token != '\0' && *token != ';' && *token != '\'' )
350 return(DestroyKernelInfo(kernel));
353 /* this was the old method of handling a incomplete kernel */
354 if ( i < (ssize_t) (kernel->width*kernel->height) ) {
355 Minimize(kernel->minimum, kernel->values[i]);
356 Maximize(kernel->maximum, kernel->values[i]);
357 for ( ; i < (ssize_t) (kernel->width*kernel->height); i++)
358 kernel->values[i]=0.0;
361 /* Number of values for kernel was not enough - Report Error */
362 if ( i < (ssize_t) (kernel->width*kernel->height) )
363 return(DestroyKernelInfo(kernel));
366 /* check that we recieved at least one real (non-nan) value! */
367 if ( kernel->minimum == MagickHuge )
368 return(DestroyKernelInfo(kernel));
370 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel size */
371 ExpandRotateKernelInfo(kernel, 45.0); /* cyclic rotate 3x3 kernels */
372 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
373 ExpandRotateKernelInfo(kernel, 90.0); /* 90 degree rotate of kernel */
374 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
375 ExpandMirrorKernelInfo(kernel); /* 90 degree mirror rotate */
380 static KernelInfo *ParseKernelName(const char *kernel_string)
383 token[MaxTextExtent];
401 /* Parse special 'named' kernel */
402 GetMagickToken(kernel_string,&p,token);
403 type=ParseCommandOption(MagickKernelOptions,MagickFalse,token);
404 if ( type < 0 || type == UserDefinedKernel )
405 return((KernelInfo *)NULL); /* not a valid named kernel */
407 while (((isspace((int) ((unsigned char) *p)) != 0) ||
408 (*p == ',') || (*p == ':' )) && (*p != '\0') && (*p != ';'))
411 end = strchr(p, ';'); /* end of this kernel defintion */
412 if ( end == (char *) NULL )
413 end = strchr(p, '\0');
415 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
416 memcpy(token, p, (size_t) (end-p));
418 SetGeometryInfo(&args);
419 flags = ParseGeometry(token, &args);
422 /* For Debugging Geometry Input */
423 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
424 flags, args.rho, args.sigma, args.xi, args.psi );
427 /* special handling of missing values in input string */
429 /* Shape Kernel Defaults */
431 if ( (flags & WidthValue) == 0 )
432 args.rho = 1.0; /* Default scale = 1.0, zero is valid */
440 if ( (flags & HeightValue) == 0 )
441 args.sigma = 1.0; /* Default scale = 1.0, zero is valid */
444 if ( (flags & XValue) == 0 )
445 args.xi = 1.0; /* Default scale = 1.0, zero is valid */
447 case RectangleKernel: /* Rectangle - set size defaults */
448 if ( (flags & WidthValue) == 0 ) /* if no width then */
449 args.rho = args.sigma; /* then width = height */
450 if ( args.rho < 1.0 ) /* if width too small */
451 args.rho = 3; /* then width = 3 */
452 if ( args.sigma < 1.0 ) /* if height too small */
453 args.sigma = args.rho; /* then height = width */
454 if ( (flags & XValue) == 0 ) /* center offset if not defined */
455 args.xi = (double)(((ssize_t)args.rho-1)/2);
456 if ( (flags & YValue) == 0 )
457 args.psi = (double)(((ssize_t)args.sigma-1)/2);
459 /* Distance Kernel Defaults */
460 case ChebyshevKernel:
461 case ManhattanKernel:
462 case OctagonalKernel:
463 case EuclideanKernel:
464 if ( (flags & HeightValue) == 0 ) /* no distance scale */
465 args.sigma = 100.0; /* default distance scaling */
466 else if ( (flags & AspectValue ) != 0 ) /* '!' flag */
467 args.sigma = QuantumRange/(args.sigma+1); /* maximum pixel distance */
468 else if ( (flags & PercentValue ) != 0 ) /* '%' flag */
469 args.sigma *= QuantumRange/100.0; /* percentage of color range */
475 kernel = AcquireKernelBuiltIn((KernelInfoType)type, &args);
476 if ( kernel == (KernelInfo *) NULL )
479 /* global expand to rotated kernel list - only for single kernels */
480 if ( kernel->next == (KernelInfo *) NULL ) {
481 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel args */
482 ExpandRotateKernelInfo(kernel, 45.0);
483 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
484 ExpandRotateKernelInfo(kernel, 90.0);
485 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
486 ExpandMirrorKernelInfo(kernel);
492 MagickExport KernelInfo *AcquireKernelInfo(const char *kernel_string)
500 token[MaxTextExtent];
508 if (kernel_string == (const char *) NULL)
509 return(ParseKernelArray(kernel_string));
514 while ( GetMagickToken(p,NULL,token), *token != '\0' ) {
516 /* ignore extra or multiple ';' kernel separators */
517 if ( *token != ';' ) {
519 /* tokens starting with alpha is a Named kernel */
520 if (isalpha((int) *token) != 0)
521 new_kernel = ParseKernelName(p);
522 else /* otherwise a user defined kernel array */
523 new_kernel = ParseKernelArray(p);
525 /* Error handling -- this is not proper error handling! */
526 if ( new_kernel == (KernelInfo *) NULL ) {
527 (void) FormatLocaleFile(stderr,"Failed to parse kernel number #%.20g\n",
528 (double) kernel_number);
529 if ( kernel != (KernelInfo *) NULL )
530 kernel=DestroyKernelInfo(kernel);
531 return((KernelInfo *) NULL);
534 /* initialise or append the kernel list */
535 if ( kernel == (KernelInfo *) NULL )
538 LastKernelInfo(kernel)->next = new_kernel;
541 /* look for the next kernel in list */
543 if ( p == (char *) NULL )
553 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
557 % A c q u i r e K e r n e l B u i l t I n %
561 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
563 % AcquireKernelBuiltIn() returned one of the 'named' built-in types of
564 % kernels used for special purposes such as gaussian blurring, skeleton
565 % pruning, and edge distance determination.
567 % They take a KernelType, and a set of geometry style arguments, which were
568 % typically decoded from a user supplied string, or from a more complex
569 % Morphology Method that was requested.
571 % The format of the AcquireKernalBuiltIn method is:
573 % KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
574 % const GeometryInfo args)
576 % A description of each parameter follows:
578 % o type: the pre-defined type of kernel wanted
580 % o args: arguments defining or modifying the kernel
582 % Convolution Kernels
585 % The a No-Op or Scaling single element kernel.
587 % Gaussian:{radius},{sigma}
588 % Generate a two-dimensional gaussian kernel, as used by -gaussian.
589 % The sigma for the curve is required. The resulting kernel is
592 % If 'sigma' is zero, you get a single pixel on a field of zeros.
594 % NOTE: that the 'radius' is optional, but if provided can limit (clip)
595 % the final size of the resulting kernel to a square 2*radius+1 in size.
596 % The radius should be at least 2 times that of the sigma value, or
597 % sever clipping and aliasing may result. If not given or set to 0 the
598 % radius will be determined so as to produce the best minimal error
599 % result, which is usally much larger than is normally needed.
601 % LoG:{radius},{sigma}
602 % "Laplacian of a Gaussian" or "Mexician Hat" Kernel.
603 % The supposed ideal edge detection, zero-summing kernel.
605 % An alturnative to this kernel is to use a "DoG" with a sigma ratio of
606 % approx 1.6 (according to wikipedia).
608 % DoG:{radius},{sigma1},{sigma2}
609 % "Difference of Gaussians" Kernel.
610 % As "Gaussian" but with a gaussian produced by 'sigma2' subtracted
611 % from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1.
612 % The result is a zero-summing kernel.
614 % Blur:{radius},{sigma}[,{angle}]
615 % Generates a 1 dimensional or linear gaussian blur, at the angle given
616 % (current restricted to orthogonal angles). If a 'radius' is given the
617 % kernel is clipped to a width of 2*radius+1. Kernel can be rotated
618 % by a 90 degree angle.
620 % If 'sigma' is zero, you get a single pixel on a field of zeros.
622 % Note that two convolutions with two "Blur" kernels perpendicular to
623 % each other, is equivalent to a far larger "Gaussian" kernel with the
624 % same sigma value, However it is much faster to apply. This is how the
625 % "-blur" operator actually works.
627 % Comet:{width},{sigma},{angle}
628 % Blur in one direction only, much like how a bright object leaves
629 % a comet like trail. The Kernel is actually half a gaussian curve,
630 % Adding two such blurs in opposite directions produces a Blur Kernel.
631 % Angle can be rotated in multiples of 90 degrees.
633 % Note that the first argument is the width of the kernel and not the
634 % radius of the kernel.
636 % Binomial:[{radius}]
637 % Generate a discrete kernel using a 2 dimentional Pascel's Triangle
638 % of values. Used for special forma of image filters.
640 % # Still to be implemented...
644 % # Set kernel values using a resize filter, and given scale (sigma)
645 % # Cylindrical or Linear. Is this possible with an image?
648 % Named Constant Convolution Kernels
650 % All these are unscaled, zero-summing kernels by default. As such for
651 % non-HDRI version of ImageMagick some form of normalization, user scaling,
652 % and biasing the results is recommended, to prevent the resulting image
655 % The 3x3 kernels (most of these) can be circularly rotated in multiples of
656 % 45 degrees to generate the 8 angled varients of each of the kernels.
659 % Discrete Lapacian Kernels, (without normalization)
660 % Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood)
661 % Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood)
662 % Type 2 : 3x3 with center:4 edge:1 corner:-2
663 % Type 3 : 3x3 with center:4 edge:-2 corner:1
664 % Type 5 : 5x5 laplacian
665 % Type 7 : 7x7 laplacian
666 % Type 15 : 5x5 LoG (sigma approx 1.4)
667 % Type 19 : 9x9 LoG (sigma approx 1.4)
670 % Sobel 'Edge' convolution kernel (3x3)
676 % Roberts convolution kernel (3x3)
682 % Prewitt Edge convolution kernel (3x3)
688 % Prewitt's "Compass" convolution kernel (3x3)
694 % Kirsch's "Compass" convolution kernel (3x3)
700 % Frei-Chen Edge Detector is based on a kernel that is similar to
701 % the Sobel Kernel, but is designed to be isotropic. That is it takes
702 % into account the distance of the diagonal in the kernel.
705 % | sqrt(2), 0, -sqrt(2) |
708 % FreiChen:{type},{angle}
710 % Frei-Chen Pre-weighted kernels...
712 % Type 0: default un-nomalized version shown above.
714 % Type 1: Orthogonal Kernel (same as type 11 below)
716 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
719 % Type 2: Diagonal form of Kernel...
721 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
724 % However this kernel is als at the heart of the FreiChen Edge Detection
725 % Process which uses a set of 9 specially weighted kernel. These 9
726 % kernels not be normalized, but directly applied to the image. The
727 % results is then added together, to produce the intensity of an edge in
728 % a specific direction. The square root of the pixel value can then be
729 % taken as the cosine of the edge, and at least 2 such runs at 90 degrees
730 % from each other, both the direction and the strength of the edge can be
733 % Type 10: All 9 of the following pre-weighted kernels...
735 % Type 11: | 1, 0, -1 |
736 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
739 % Type 12: | 1, sqrt(2), 1 |
740 % | 0, 0, 0 | / 2*sqrt(2)
743 % Type 13: | sqrt(2), -1, 0 |
744 % | -1, 0, 1 | / 2*sqrt(2)
747 % Type 14: | 0, 1, -sqrt(2) |
748 % | -1, 0, 1 | / 2*sqrt(2)
751 % Type 15: | 0, -1, 0 |
755 % Type 16: | 1, 0, -1 |
759 % Type 17: | 1, -2, 1 |
763 % Type 18: | -2, 1, -2 |
767 % Type 19: | 1, 1, 1 |
771 % The first 4 are for edge detection, the next 4 are for line detection
772 % and the last is to add a average component to the results.
774 % Using a special type of '-1' will return all 9 pre-weighted kernels
775 % as a multi-kernel list, so that you can use them directly (without
776 % normalization) with the special "-set option:morphology:compose Plus"
777 % setting to apply the full FreiChen Edge Detection Technique.
779 % If 'type' is large it will be taken to be an actual rotation angle for
780 % the default FreiChen (type 0) kernel. As such FreiChen:45 will look
781 % like a Sobel:45 but with 'sqrt(2)' instead of '2' values.
783 % WARNING: The above was layed out as per
784 % http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf
785 % But rotated 90 degrees so direction is from left rather than the top.
786 % I have yet to find any secondary confirmation of the above. The only
787 % other source found was actual source code at
788 % http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf
789 % Neigher paper defineds the kernels in a way that looks locical or
790 % correct when taken as a whole.
794 % Diamond:[{radius}[,{scale}]]
795 % Generate a diamond shaped kernel with given radius to the points.
796 % Kernel size will again be radius*2+1 square and defaults to radius 1,
797 % generating a 3x3 kernel that is slightly larger than a square.
799 % Square:[{radius}[,{scale}]]
800 % Generate a square shaped kernel of size radius*2+1, and defaulting
801 % to a 3x3 (radius 1).
803 % Octagon:[{radius}[,{scale}]]
804 % Generate octagonal shaped kernel of given radius and constant scale.
805 % Default radius is 3 producing a 7x7 kernel. A radius of 1 will result
806 % in "Diamond" kernel.
808 % Disk:[{radius}[,{scale}]]
809 % Generate a binary disk, thresholded at the radius given, the radius
810 % may be a float-point value. Final Kernel size is floor(radius)*2+1
811 % square. A radius of 5.3 is the default.
813 % NOTE: That a low radii Disk kernels produce the same results as
814 % many of the previously defined kernels, but differ greatly at larger
815 % radii. Here is a table of equivalences...
816 % "Disk:1" => "Diamond", "Octagon:1", or "Cross:1"
817 % "Disk:1.5" => "Square"
818 % "Disk:2" => "Diamond:2"
819 % "Disk:2.5" => "Octagon"
820 % "Disk:2.9" => "Square:2"
821 % "Disk:3.5" => "Octagon:3"
822 % "Disk:4.5" => "Octagon:4"
823 % "Disk:5.4" => "Octagon:5"
824 % "Disk:6.4" => "Octagon:6"
825 % All other Disk shapes are unique to this kernel, but because a "Disk"
826 % is more circular when using a larger radius, using a larger radius is
827 % preferred over iterating the morphological operation.
829 % Rectangle:{geometry}
830 % Simply generate a rectangle of 1's with the size given. You can also
831 % specify the location of the 'control point', otherwise the closest
832 % pixel to the center of the rectangle is selected.
834 % Properly centered and odd sized rectangles work the best.
836 % Symbol Dilation Kernels
838 % These kernel is not a good general morphological kernel, but is used
839 % more for highlighting and marking any single pixels in an image using,
840 % a "Dilate" method as appropriate.
842 % For the same reasons iterating these kernels does not produce the
843 % same result as using a larger radius for the symbol.
845 % Plus:[{radius}[,{scale}]]
846 % Cross:[{radius}[,{scale}]]
847 % Generate a kernel in the shape of a 'plus' or a 'cross' with
848 % a each arm the length of the given radius (default 2).
850 % NOTE: "plus:1" is equivalent to a "Diamond" kernel.
852 % Ring:{radius1},{radius2}[,{scale}]
853 % A ring of the values given that falls between the two radii.
854 % Defaults to a ring of approximataly 3 radius in a 7x7 kernel.
855 % This is the 'edge' pixels of the default "Disk" kernel,
856 % More specifically, "Ring" -> "Ring:2.5,3.5,1.0"
858 % Hit and Miss Kernels
860 % Peak:radius1,radius2
861 % Find any peak larger than the pixels the fall between the two radii.
862 % The default ring of pixels is as per "Ring".
864 % Find flat orthogonal edges of a binary shape
866 % Find 90 degree corners of a binary shape
868 % A special kernel to thin the 'outside' of diagonals
870 % Find end points of lines (for pruning a skeletion)
871 % Two types of lines ends (default to both) can be searched for
872 % Type 0: All line ends
873 % Type 1: single kernel for 4-conneected line ends
874 % Type 2: single kernel for simple line ends
876 % Find three line junctions (within a skeletion)
877 % Type 0: all line junctions
878 % Type 1: Y Junction kernel
879 % Type 2: Diagonal T Junction kernel
880 % Type 3: Orthogonal T Junction kernel
881 % Type 4: Diagonal X Junction kernel
882 % Type 5: Orthogonal + Junction kernel
884 % Find single pixel ridges or thin lines
885 % Type 1: Fine single pixel thick lines and ridges
886 % Type 2: Find two pixel thick lines and ridges
888 % Octagonal Thickening Kernel, to generate convex hulls of 45 degrees
890 % Traditional skeleton generating kernels.
891 % Type 1: Tradional Skeleton kernel (4 connected skeleton)
892 % Type 2: HIPR2 Skeleton kernel (8 connected skeleton)
893 % Type 3: Thinning skeleton based on a ressearch paper by
894 % Dan S. Bloomberg (Default Type)
896 % A huge variety of Thinning Kernels designed to preserve conectivity.
897 % many other kernel sets use these kernels as source definitions.
898 % Type numbers are 41-49, 81-89, 481, and 482 which are based on
899 % the super and sub notations used in the source research paper.
901 % Distance Measuring Kernels
903 % Different types of distance measuring methods, which are used with the
904 % a 'Distance' morphology method for generating a gradient based on
905 % distance from an edge of a binary shape, though there is a technique
906 % for handling a anti-aliased shape.
908 % See the 'Distance' Morphological Method, for information of how it is
911 % Chebyshev:[{radius}][x{scale}[%!]]
912 % Chebyshev Distance (also known as Tchebychev or Chessboard distance)
913 % is a value of one to any neighbour, orthogonal or diagonal. One why
914 % of thinking of it is the number of squares a 'King' or 'Queen' in
915 % chess needs to traverse reach any other position on a chess board.
916 % It results in a 'square' like distance function, but one where
917 % diagonals are given a value that is closer than expected.
919 % Manhattan:[{radius}][x{scale}[%!]]
920 % Manhattan Distance (also known as Rectilinear, City Block, or the Taxi
921 % Cab distance metric), it is the distance needed when you can only
922 % travel in horizontal or vertical directions only. It is the
923 % distance a 'Rook' in chess would have to travel, and results in a
924 % diamond like distances, where diagonals are further than expected.
926 % Octagonal:[{radius}][x{scale}[%!]]
927 % An interleving of Manhatten and Chebyshev metrics producing an
928 % increasing octagonally shaped distance. Distances matches those of
929 % the "Octagon" shaped kernel of the same radius. The minimum radius
930 % and default is 2, producing a 5x5 kernel.
932 % Euclidean:[{radius}][x{scale}[%!]]
933 % Euclidean distance is the 'direct' or 'as the crow flys' distance.
934 % However by default the kernel size only has a radius of 1, which
935 % limits the distance to 'Knight' like moves, with only orthogonal and
936 % diagonal measurements being correct. As such for the default kernel
937 % you will get octagonal like distance function.
939 % However using a larger radius such as "Euclidean:4" you will get a
940 % much smoother distance gradient from the edge of the shape. Especially
941 % if the image is pre-processed to include any anti-aliasing pixels.
942 % Of course a larger kernel is slower to use, and not always needed.
944 % The first three Distance Measuring Kernels will only generate distances
945 % of exact multiples of {scale} in binary images. As such you can use a
946 % scale of 1 without loosing any information. However you also need some
947 % scaling when handling non-binary anti-aliased shapes.
949 % The "Euclidean" Distance Kernel however does generate a non-integer
950 % fractional results, and as such scaling is vital even for binary shapes.
954 MagickExport KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
955 const GeometryInfo *args)
968 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
970 /* Generate a new empty kernel if needed */
971 kernel=(KernelInfo *) NULL;
973 case UndefinedKernel: /* These should not call this function */
974 case UserDefinedKernel:
975 assert("Should not call this function" != (char *)NULL);
977 case LaplacianKernel: /* Named Descrete Convolution Kernels */
978 case SobelKernel: /* these are defined using other kernels */
984 case EdgesKernel: /* Hit and Miss kernels */
986 case DiagonalsKernel:
988 case LineJunctionsKernel:
990 case ConvexHullKernel:
993 break; /* A pre-generated kernel is not needed */
995 /* set to 1 to do a compile-time check that we haven't missed anything */
1002 case BinomialKernel:
1005 case RectangleKernel:
1012 case ChebyshevKernel:
1013 case ManhattanKernel:
1014 case OctangonalKernel:
1015 case EuclideanKernel:
1019 /* Generate the base Kernel Structure */
1020 kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
1021 if (kernel == (KernelInfo *) NULL)
1023 (void) ResetMagickMemory(kernel,0,sizeof(*kernel));
1024 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
1025 kernel->negative_range = kernel->positive_range = 0.0;
1026 kernel->type = type;
1027 kernel->next = (KernelInfo *) NULL;
1028 kernel->signature = MagickSignature;
1038 kernel->height = kernel->width = (size_t) 1;
1039 kernel->x = kernel->y = (ssize_t) 0;
1040 kernel->values=(MagickRealType *) MagickAssumeAligned(
1041 AcquireAlignedMemory(1,sizeof(*kernel->values)));
1042 if (kernel->values == (MagickRealType *) NULL)
1043 return(DestroyKernelInfo(kernel));
1044 kernel->maximum = kernel->values[0] = args->rho;
1048 case GaussianKernel:
1052 sigma = fabs(args->sigma),
1053 sigma2 = fabs(args->xi),
1056 if ( args->rho >= 1.0 )
1057 kernel->width = (size_t)args->rho*2+1;
1058 else if ( (type != DoGKernel) || (sigma >= sigma2) )
1059 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma);
1061 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma2);
1062 kernel->height = kernel->width;
1063 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1064 kernel->values=(MagickRealType *) MagickAssumeAligned(
1065 AcquireAlignedMemory(kernel->width,kernel->height*
1066 sizeof(*kernel->values)));
1067 if (kernel->values == (MagickRealType *) NULL)
1068 return(DestroyKernelInfo(kernel));
1070 /* WARNING: The following generates a 'sampled gaussian' kernel.
1071 * What we really want is a 'discrete gaussian' kernel.
1073 * How to do this is I don't know, but appears to be basied on the
1074 * Error Function 'erf()' (intergral of a gaussian)
1077 if ( type == GaussianKernel || type == DoGKernel )
1078 { /* Calculate a Gaussian, OR positive half of a DoG */
1079 if ( sigma > MagickEpsilon )
1080 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1081 B = (double) (1.0/(Magick2PI*sigma*sigma));
1082 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1083 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1084 kernel->values[i] = exp(-((double)(u*u+v*v))*A)*B;
1086 else /* limiting case - a unity (normalized Dirac) kernel */
1087 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1088 kernel->width*kernel->height*sizeof(*kernel->values));
1089 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1093 if ( type == DoGKernel )
1094 { /* Subtract a Negative Gaussian for "Difference of Gaussian" */
1095 if ( sigma2 > MagickEpsilon )
1096 { sigma = sigma2; /* simplify loop expressions */
1097 A = 1.0/(2.0*sigma*sigma);
1098 B = (double) (1.0/(Magick2PI*sigma*sigma));
1099 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1100 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1101 kernel->values[i] -= exp(-((double)(u*u+v*v))*A)*B;
1103 else /* limiting case - a unity (normalized Dirac) kernel */
1104 kernel->values[kernel->x+kernel->y*kernel->width] -= 1.0;
1107 if ( type == LoGKernel )
1108 { /* Calculate a Laplacian of a Gaussian - Or Mexician Hat */
1109 if ( sigma > MagickEpsilon )
1110 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1111 B = (double) (1.0/(MagickPI*sigma*sigma*sigma*sigma));
1112 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1113 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1114 { R = ((double)(u*u+v*v))*A;
1115 kernel->values[i] = (1-R)*exp(-R)*B;
1118 else /* special case - generate a unity kernel */
1119 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1120 kernel->width*kernel->height*sizeof(*kernel->values));
1121 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1125 /* Note the above kernels may have been 'clipped' by a user defined
1126 ** radius, producing a smaller (darker) kernel. Also for very small
1127 ** sigma's (> 0.1) the central value becomes larger than one, and thus
1128 ** producing a very bright kernel.
1130 ** Normalization will still be needed.
1133 /* Normalize the 2D Gaussian Kernel
1135 ** NB: a CorrelateNormalize performs a normal Normalize if
1136 ** there are no negative values.
1138 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1139 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1145 sigma = fabs(args->sigma),
1148 if ( args->rho >= 1.0 )
1149 kernel->width = (size_t)args->rho*2+1;
1151 kernel->width = GetOptimalKernelWidth1D(args->rho,sigma);
1153 kernel->x = (ssize_t) (kernel->width-1)/2;
1155 kernel->negative_range = kernel->positive_range = 0.0;
1156 kernel->values=(MagickRealType *) MagickAssumeAligned(
1157 AcquireAlignedMemory(kernel->width,kernel->height*
1158 sizeof(*kernel->values)));
1159 if (kernel->values == (MagickRealType *) NULL)
1160 return(DestroyKernelInfo(kernel));
1163 #define KernelRank 3
1164 /* Formula derived from GetBlurKernel() in "effect.c" (plus bug fix).
1165 ** It generates a gaussian 3 times the width, and compresses it into
1166 ** the expected range. This produces a closer normalization of the
1167 ** resulting kernel, especially for very low sigma values.
1168 ** As such while wierd it is prefered.
1170 ** I am told this method originally came from Photoshop.
1172 ** A properly normalized curve is generated (apart from edge clipping)
1173 ** even though we later normalize the result (for edge clipping)
1174 ** to allow the correct generation of a "Difference of Blurs".
1178 v = (ssize_t) (kernel->width*KernelRank-1)/2; /* start/end points to fit range */
1179 (void) ResetMagickMemory(kernel->values,0, (size_t)
1180 kernel->width*kernel->height*sizeof(*kernel->values));
1181 /* Calculate a Positive 1D Gaussian */
1182 if ( sigma > MagickEpsilon )
1183 { sigma *= KernelRank; /* simplify loop expressions */
1184 alpha = 1.0/(2.0*sigma*sigma);
1185 beta= (double) (1.0/(MagickSQ2PI*sigma ));
1186 for ( u=-v; u <= v; u++) {
1187 kernel->values[(u+v)/KernelRank] +=
1188 exp(-((double)(u*u))*alpha)*beta;
1191 else /* special case - generate a unity kernel */
1192 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1194 /* Direct calculation without curve averaging
1195 This is equivelent to a KernelRank of 1 */
1197 /* Calculate a Positive Gaussian */
1198 if ( sigma > MagickEpsilon )
1199 { alpha = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1200 beta = 1.0/(MagickSQ2PI*sigma);
1201 for ( i=0, u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1202 kernel->values[i] = exp(-((double)(u*u))*alpha)*beta;
1204 else /* special case - generate a unity kernel */
1205 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1206 kernel->width*kernel->height*sizeof(*kernel->values));
1207 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1210 /* Note the above kernel may have been 'clipped' by a user defined
1211 ** radius, producing a smaller (darker) kernel. Also for very small
1212 ** sigma's (> 0.1) the central value becomes larger than one, as a
1213 ** result of not generating a actual 'discrete' kernel, and thus
1214 ** producing a very bright 'impulse'.
1216 ** Becuase of these two factors Normalization is required!
1219 /* Normalize the 1D Gaussian Kernel
1221 ** NB: a CorrelateNormalize performs a normal Normalize if
1222 ** there are no negative values.
1224 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1225 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1227 /* rotate the 1D kernel by given angle */
1228 RotateKernelInfo(kernel, args->xi );
1233 sigma = fabs(args->sigma),
1236 if ( args->rho < 1.0 )
1237 kernel->width = (GetOptimalKernelWidth1D(args->rho,sigma)-1)/2+1;
1239 kernel->width = (size_t)args->rho;
1240 kernel->x = kernel->y = 0;
1242 kernel->negative_range = kernel->positive_range = 0.0;
1243 kernel->values=(MagickRealType *) MagickAssumeAligned(
1244 AcquireAlignedMemory(kernel->width,kernel->height*
1245 sizeof(*kernel->values)));
1246 if (kernel->values == (MagickRealType *) NULL)
1247 return(DestroyKernelInfo(kernel));
1249 /* A comet blur is half a 1D gaussian curve, so that the object is
1250 ** blurred in one direction only. This may not be quite the right
1251 ** curve to use so may change in the future. The function must be
1252 ** normalised after generation, which also resolves any clipping.
1254 ** As we are normalizing and not subtracting gaussians,
1255 ** there is no need for a divisor in the gaussian formula
1257 ** It is less comples
1259 if ( sigma > MagickEpsilon )
1262 #define KernelRank 3
1263 v = (ssize_t) kernel->width*KernelRank; /* start/end points */
1264 (void) ResetMagickMemory(kernel->values,0, (size_t)
1265 kernel->width*sizeof(*kernel->values));
1266 sigma *= KernelRank; /* simplify the loop expression */
1267 A = 1.0/(2.0*sigma*sigma);
1268 /* B = 1.0/(MagickSQ2PI*sigma); */
1269 for ( u=0; u < v; u++) {
1270 kernel->values[u/KernelRank] +=
1271 exp(-((double)(u*u))*A);
1272 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1274 for (i=0; i < (ssize_t) kernel->width; i++)
1275 kernel->positive_range += kernel->values[i];
1277 A = 1.0/(2.0*sigma*sigma); /* simplify the loop expression */
1278 /* B = 1.0/(MagickSQ2PI*sigma); */
1279 for ( i=0; i < (ssize_t) kernel->width; i++)
1280 kernel->positive_range +=
1281 kernel->values[i] = exp(-((double)(i*i))*A);
1282 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1285 else /* special case - generate a unity kernel */
1286 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1287 kernel->width*kernel->height*sizeof(*kernel->values));
1288 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1289 kernel->positive_range = 1.0;
1292 kernel->minimum = 0.0;
1293 kernel->maximum = kernel->values[0];
1294 kernel->negative_range = 0.0;
1296 ScaleKernelInfo(kernel, 1.0, NormalizeValue); /* Normalize */
1297 RotateKernelInfo(kernel, args->xi); /* Rotate by angle */
1300 case BinomialKernel:
1305 if (args->rho < 1.0)
1306 kernel->width = kernel->height = 3; /* default radius = 1 */
1308 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1309 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1311 order_f = fact(kernel->width-1);
1313 kernel->values=(MagickRealType *) MagickAssumeAligned(
1314 AcquireAlignedMemory(kernel->width,kernel->height*
1315 sizeof(*kernel->values)));
1316 if (kernel->values == (MagickRealType *) NULL)
1317 return(DestroyKernelInfo(kernel));
1319 /* set all kernel values within diamond area to scale given */
1320 for ( i=0, v=0; v < (ssize_t)kernel->height; v++)
1322 alpha = order_f / ( fact((size_t) v) * fact(kernel->height-v-1) );
1323 for ( u=0; u < (ssize_t)kernel->width; u++, i++)
1324 kernel->positive_range += kernel->values[i] = (double)
1325 (alpha * order_f / ( fact((size_t) u) * fact(kernel->height-u-1) ));
1327 kernel->minimum = 1.0;
1328 kernel->maximum = kernel->values[kernel->x+kernel->y*kernel->width];
1329 kernel->negative_range = 0.0;
1334 Convolution Kernels - Well Known Named Constant Kernels
1336 case LaplacianKernel:
1337 { switch ( (int) args->rho ) {
1339 default: /* laplacian square filter -- default */
1340 kernel=ParseKernelArray("3: -1,-1,-1 -1,8,-1 -1,-1,-1");
1342 case 1: /* laplacian diamond filter */
1343 kernel=ParseKernelArray("3: 0,-1,0 -1,4,-1 0,-1,0");
1346 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1349 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 1,-2,1");
1351 case 5: /* a 5x5 laplacian */
1352 kernel=ParseKernelArray(
1353 "5: -4,-1,0,-1,-4 -1,2,3,2,-1 0,3,4,3,0 -1,2,3,2,-1 -4,-1,0,-1,-4");
1355 case 7: /* a 7x7 laplacian */
1356 kernel=ParseKernelArray(
1357 "7:-10,-5,-2,-1,-2,-5,-10 -5,0,3,4,3,0,-5 -2,3,6,7,6,3,-2 -1,4,7,8,7,4,-1 -2,3,6,7,6,3,-2 -5,0,3,4,3,0,-5 -10,-5,-2,-1,-2,-5,-10" );
1359 case 15: /* a 5x5 LoG (sigma approx 1.4) */
1360 kernel=ParseKernelArray(
1361 "5: 0,0,-1,0,0 0,-1,-2,-1,0 -1,-2,16,-2,-1 0,-1,-2,-1,0 0,0,-1,0,0");
1363 case 19: /* a 9x9 LoG (sigma approx 1.4) */
1364 /* http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue1/IJIP-15.pdf */
1365 kernel=ParseKernelArray(
1366 "9: 0,-1,-1,-2,-2,-2,-1,-1,0 -1,-2,-4,-5,-5,-5,-4,-2,-1 -1,-4,-5,-3,-0,-3,-5,-4,-1 -2,-5,-3,12,24,12,-3,-5,-2 -2,-5,-0,24,40,24,-0,-5,-2 -2,-5,-3,12,24,12,-3,-5,-2 -1,-4,-5,-3,-0,-3,-5,-4,-1 -1,-2,-4,-5,-5,-5,-4,-2,-1 0,-1,-1,-2,-2,-2,-1,-1,0");
1369 if (kernel == (KernelInfo *) NULL)
1371 kernel->type = type;
1375 { /* Simple Sobel Kernel */
1376 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1377 if (kernel == (KernelInfo *) NULL)
1379 kernel->type = type;
1380 RotateKernelInfo(kernel, args->rho);
1385 kernel=ParseKernelArray("3: 0,0,0 1,-1,0 0,0,0");
1386 if (kernel == (KernelInfo *) NULL)
1388 kernel->type = type;
1389 RotateKernelInfo(kernel, args->rho);
1394 kernel=ParseKernelArray("3: 1,0,-1 1,0,-1 1,0,-1");
1395 if (kernel == (KernelInfo *) NULL)
1397 kernel->type = type;
1398 RotateKernelInfo(kernel, args->rho);
1403 kernel=ParseKernelArray("3: 1,1,-1 1,-2,-1 1,1,-1");
1404 if (kernel == (KernelInfo *) NULL)
1406 kernel->type = type;
1407 RotateKernelInfo(kernel, args->rho);
1412 kernel=ParseKernelArray("3: 5,-3,-3 5,0,-3 5,-3,-3");
1413 if (kernel == (KernelInfo *) NULL)
1415 kernel->type = type;
1416 RotateKernelInfo(kernel, args->rho);
1419 case FreiChenKernel:
1420 /* Direction is set to be left to right positive */
1421 /* http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf -- RIGHT? */
1422 /* http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf -- WRONG? */
1423 { switch ( (int) args->rho ) {
1426 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1427 if (kernel == (KernelInfo *) NULL)
1429 kernel->type = type;
1430 kernel->values[3] = +(MagickRealType) MagickSQ2;
1431 kernel->values[5] = -(MagickRealType) MagickSQ2;
1432 CalcKernelMetaData(kernel); /* recalculate meta-data */
1435 kernel=ParseKernelArray("3: 1,2,0 2,0,-2 0,-2,-1");
1436 if (kernel == (KernelInfo *) NULL)
1438 kernel->type = type;
1439 kernel->values[1] = kernel->values[3]= +(MagickRealType) MagickSQ2;
1440 kernel->values[5] = kernel->values[7]= -(MagickRealType) MagickSQ2;
1441 CalcKernelMetaData(kernel); /* recalculate meta-data */
1442 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1445 kernel=AcquireKernelInfo("FreiChen:11;FreiChen:12;FreiChen:13;FreiChen:14;FreiChen:15;FreiChen:16;FreiChen:17;FreiChen:18;FreiChen:19");
1446 if (kernel == (KernelInfo *) NULL)
1451 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1452 if (kernel == (KernelInfo *) NULL)
1454 kernel->type = type;
1455 kernel->values[3] = +(MagickRealType) MagickSQ2;
1456 kernel->values[5] = -(MagickRealType) MagickSQ2;
1457 CalcKernelMetaData(kernel); /* recalculate meta-data */
1458 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1461 kernel=ParseKernelArray("3: 1,2,1 0,0,0 1,2,1");
1462 if (kernel == (KernelInfo *) NULL)
1464 kernel->type = type;
1465 kernel->values[1] = +(MagickRealType) MagickSQ2;
1466 kernel->values[7] = +(MagickRealType) MagickSQ2;
1467 CalcKernelMetaData(kernel);
1468 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1471 kernel=ParseKernelArray("3: 2,-1,0 -1,0,1 0,1,-2");
1472 if (kernel == (KernelInfo *) NULL)
1474 kernel->type = type;
1475 kernel->values[0] = +(MagickRealType) MagickSQ2;
1476 kernel->values[8] = -(MagickRealType) MagickSQ2;
1477 CalcKernelMetaData(kernel);
1478 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1481 kernel=ParseKernelArray("3: 0,1,-2 -1,0,1 2,-1,0");
1482 if (kernel == (KernelInfo *) NULL)
1484 kernel->type = type;
1485 kernel->values[2] = -(MagickRealType) MagickSQ2;
1486 kernel->values[6] = +(MagickRealType) MagickSQ2;
1487 CalcKernelMetaData(kernel);
1488 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1491 kernel=ParseKernelArray("3: 0,-1,0 1,0,1 0,-1,0");
1492 if (kernel == (KernelInfo *) NULL)
1494 kernel->type = type;
1495 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1498 kernel=ParseKernelArray("3: 1,0,-1 0,0,0 -1,0,1");
1499 if (kernel == (KernelInfo *) NULL)
1501 kernel->type = type;
1502 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1505 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 -1,-2,1");
1506 if (kernel == (KernelInfo *) NULL)
1508 kernel->type = type;
1509 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1512 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1513 if (kernel == (KernelInfo *) NULL)
1515 kernel->type = type;
1516 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1519 kernel=ParseKernelArray("3: 1,1,1 1,1,1 1,1,1");
1520 if (kernel == (KernelInfo *) NULL)
1522 kernel->type = type;
1523 ScaleKernelInfo(kernel, 1.0/3.0, NoValue);
1526 if ( fabs(args->sigma) >= MagickEpsilon )
1527 /* Rotate by correctly supplied 'angle' */
1528 RotateKernelInfo(kernel, args->sigma);
1529 else if ( args->rho > 30.0 || args->rho < -30.0 )
1530 /* Rotate by out of bounds 'type' */
1531 RotateKernelInfo(kernel, args->rho);
1536 Boolean or Shaped Kernels
1540 if (args->rho < 1.0)
1541 kernel->width = kernel->height = 3; /* default radius = 1 */
1543 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1544 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1546 kernel->values=(MagickRealType *) MagickAssumeAligned(
1547 AcquireAlignedMemory(kernel->width,kernel->height*
1548 sizeof(*kernel->values)));
1549 if (kernel->values == (MagickRealType *) NULL)
1550 return(DestroyKernelInfo(kernel));
1552 /* set all kernel values within diamond area to scale given */
1553 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1554 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1555 if ( (labs((long) u)+labs((long) v)) <= (long) kernel->x)
1556 kernel->positive_range += kernel->values[i] = args->sigma;
1558 kernel->values[i] = nan;
1559 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1563 case RectangleKernel:
1566 if ( type == SquareKernel )
1568 if (args->rho < 1.0)
1569 kernel->width = kernel->height = 3; /* default radius = 1 */
1571 kernel->width = kernel->height = (size_t) (2*args->rho+1);
1572 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1573 scale = args->sigma;
1576 /* NOTE: user defaults set in "AcquireKernelInfo()" */
1577 if ( args->rho < 1.0 || args->sigma < 1.0 )
1578 return(DestroyKernelInfo(kernel)); /* invalid args given */
1579 kernel->width = (size_t)args->rho;
1580 kernel->height = (size_t)args->sigma;
1581 if ( args->xi < 0.0 || args->xi > (double)kernel->width ||
1582 args->psi < 0.0 || args->psi > (double)kernel->height )
1583 return(DestroyKernelInfo(kernel)); /* invalid args given */
1584 kernel->x = (ssize_t) args->xi;
1585 kernel->y = (ssize_t) args->psi;
1588 kernel->values=(MagickRealType *) MagickAssumeAligned(
1589 AcquireAlignedMemory(kernel->width,kernel->height*
1590 sizeof(*kernel->values)));
1591 if (kernel->values == (MagickRealType *) NULL)
1592 return(DestroyKernelInfo(kernel));
1594 /* set all kernel values to scale given */
1595 u=(ssize_t) (kernel->width*kernel->height);
1596 for ( i=0; i < u; i++)
1597 kernel->values[i] = scale;
1598 kernel->minimum = kernel->maximum = scale; /* a flat shape */
1599 kernel->positive_range = scale*u;
1604 if (args->rho < 1.0)
1605 kernel->width = kernel->height = 5; /* default radius = 2 */
1607 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1608 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1610 kernel->values=(MagickRealType *) MagickAssumeAligned(
1611 AcquireAlignedMemory(kernel->width,kernel->height*
1612 sizeof(*kernel->values)));
1613 if (kernel->values == (MagickRealType *) NULL)
1614 return(DestroyKernelInfo(kernel));
1616 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1617 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1618 if ( (labs((long) u)+labs((long) v)) <=
1619 ((long)kernel->x + (long)(kernel->x/2)) )
1620 kernel->positive_range += kernel->values[i] = args->sigma;
1622 kernel->values[i] = nan;
1623 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1629 limit = (ssize_t)(args->rho*args->rho);
1631 if (args->rho < 0.4) /* default radius approx 4.3 */
1632 kernel->width = kernel->height = 9L, limit = 18L;
1634 kernel->width = kernel->height = (size_t)fabs(args->rho)*2+1;
1635 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1637 kernel->values=(MagickRealType *) MagickAssumeAligned(
1638 AcquireAlignedMemory(kernel->width,kernel->height*
1639 sizeof(*kernel->values)));
1640 if (kernel->values == (MagickRealType *) NULL)
1641 return(DestroyKernelInfo(kernel));
1643 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1644 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1645 if ((u*u+v*v) <= limit)
1646 kernel->positive_range += kernel->values[i] = args->sigma;
1648 kernel->values[i] = nan;
1649 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1654 if (args->rho < 1.0)
1655 kernel->width = kernel->height = 5; /* default radius 2 */
1657 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1658 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1660 kernel->values=(MagickRealType *) MagickAssumeAligned(
1661 AcquireAlignedMemory(kernel->width,kernel->height*
1662 sizeof(*kernel->values)));
1663 if (kernel->values == (MagickRealType *) NULL)
1664 return(DestroyKernelInfo(kernel));
1666 /* set all kernel values along axises to given scale */
1667 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1668 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1669 kernel->values[i] = (u == 0 || v == 0) ? args->sigma : nan;
1670 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1671 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1676 if (args->rho < 1.0)
1677 kernel->width = kernel->height = 5; /* default radius 2 */
1679 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1680 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1682 kernel->values=(MagickRealType *) MagickAssumeAligned(
1683 AcquireAlignedMemory(kernel->width,kernel->height*
1684 sizeof(*kernel->values)));
1685 if (kernel->values == (MagickRealType *) NULL)
1686 return(DestroyKernelInfo(kernel));
1688 /* set all kernel values along axises to given scale */
1689 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1690 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1691 kernel->values[i] = (u == v || u == -v) ? args->sigma : nan;
1692 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1693 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1707 if (args->rho < args->sigma)
1709 kernel->width = ((size_t)args->sigma)*2+1;
1710 limit1 = (ssize_t)(args->rho*args->rho);
1711 limit2 = (ssize_t)(args->sigma*args->sigma);
1715 kernel->width = ((size_t)args->rho)*2+1;
1716 limit1 = (ssize_t)(args->sigma*args->sigma);
1717 limit2 = (ssize_t)(args->rho*args->rho);
1720 kernel->width = 7L, limit1 = 7L, limit2 = 11L;
1722 kernel->height = kernel->width;
1723 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1724 kernel->values=(MagickRealType *) MagickAssumeAligned(
1725 AcquireAlignedMemory(kernel->width,kernel->height*
1726 sizeof(*kernel->values)));
1727 if (kernel->values == (MagickRealType *) NULL)
1728 return(DestroyKernelInfo(kernel));
1730 /* set a ring of points of 'scale' ( 0.0 for PeaksKernel ) */
1731 scale = (ssize_t) (( type == PeaksKernel) ? 0.0 : args->xi);
1732 for ( i=0, v= -kernel->y; v <= (ssize_t)kernel->y; v++)
1733 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1734 { ssize_t radius=u*u+v*v;
1735 if (limit1 < radius && radius <= limit2)
1736 kernel->positive_range += kernel->values[i] = (double) scale;
1738 kernel->values[i] = nan;
1740 kernel->minimum = kernel->maximum = (double) scale;
1741 if ( type == PeaksKernel ) {
1742 /* set the central point in the middle */
1743 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1744 kernel->positive_range = 1.0;
1745 kernel->maximum = 1.0;
1751 kernel=AcquireKernelInfo("ThinSE:482");
1752 if (kernel == (KernelInfo *) NULL)
1754 kernel->type = type;
1755 ExpandMirrorKernelInfo(kernel); /* mirror expansion of kernels */
1760 kernel=AcquireKernelInfo("ThinSE:87");
1761 if (kernel == (KernelInfo *) NULL)
1763 kernel->type = type;
1764 ExpandRotateKernelInfo(kernel, 90.0); /* Expand 90 degree rotations */
1767 case DiagonalsKernel:
1769 switch ( (int) args->rho ) {
1774 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1775 if (kernel == (KernelInfo *) NULL)
1777 kernel->type = type;
1778 new_kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1779 if (new_kernel == (KernelInfo *) NULL)
1780 return(DestroyKernelInfo(kernel));
1781 new_kernel->type = type;
1782 LastKernelInfo(kernel)->next = new_kernel;
1783 ExpandMirrorKernelInfo(kernel);
1787 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1790 kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1793 if (kernel == (KernelInfo *) NULL)
1795 kernel->type = type;
1796 RotateKernelInfo(kernel, args->sigma);
1799 case LineEndsKernel:
1800 { /* Kernels for finding the end of thin lines */
1801 switch ( (int) args->rho ) {
1804 /* set of kernels to find all end of lines */
1805 return(AcquireKernelInfo("LineEnds:1>;LineEnds:2>"));
1807 /* kernel for 4-connected line ends - no rotation */
1808 kernel=ParseKernelArray("3: 0,0,- 0,1,1 0,0,-");
1811 /* kernel to add for 8-connected lines - no rotation */
1812 kernel=ParseKernelArray("3: 0,0,0 0,1,0 0,0,1");
1815 /* kernel to add for orthogonal line ends - does not find corners */
1816 kernel=ParseKernelArray("3: 0,0,0 0,1,1 0,0,0");
1819 /* traditional line end - fails on last T end */
1820 kernel=ParseKernelArray("3: 0,0,0 0,1,- 0,0,-");
1823 if (kernel == (KernelInfo *) NULL)
1825 kernel->type = type;
1826 RotateKernelInfo(kernel, args->sigma);
1829 case LineJunctionsKernel:
1830 { /* kernels for finding the junctions of multiple lines */
1831 switch ( (int) args->rho ) {
1834 /* set of kernels to find all line junctions */
1835 return(AcquireKernelInfo("LineJunctions:1@;LineJunctions:2>"));
1838 kernel=ParseKernelArray("3: 1,-,1 -,1,- -,1,-");
1841 /* Diagonal T Junctions */
1842 kernel=ParseKernelArray("3: 1,-,- -,1,- 1,-,1");
1845 /* Orthogonal T Junctions */
1846 kernel=ParseKernelArray("3: -,-,- 1,1,1 -,1,-");
1849 /* Diagonal X Junctions */
1850 kernel=ParseKernelArray("3: 1,-,1 -,1,- 1,-,1");
1853 /* Orthogonal X Junctions - minimal diamond kernel */
1854 kernel=ParseKernelArray("3: -,1,- 1,1,1 -,1,-");
1857 if (kernel == (KernelInfo *) NULL)
1859 kernel->type = type;
1860 RotateKernelInfo(kernel, args->sigma);
1864 { /* Ridges - Ridge finding kernels */
1867 switch ( (int) args->rho ) {
1870 kernel=ParseKernelArray("3x1:0,1,0");
1871 if (kernel == (KernelInfo *) NULL)
1873 kernel->type = type;
1874 ExpandRotateKernelInfo(kernel, 90.0); /* 2 rotated kernels (symmetrical) */
1877 kernel=ParseKernelArray("4x1:0,1,1,0");
1878 if (kernel == (KernelInfo *) NULL)
1880 kernel->type = type;
1881 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotated kernels */
1883 /* Kernels to find a stepped 'thick' line, 4 rotates + mirrors */
1884 /* Unfortunatally we can not yet rotate a non-square kernel */
1885 /* But then we can't flip a non-symetrical kernel either */
1886 new_kernel=ParseKernelArray("4x3+1+1:0,1,1,- -,1,1,- -,1,1,0");
1887 if (new_kernel == (KernelInfo *) NULL)
1888 return(DestroyKernelInfo(kernel));
1889 new_kernel->type = type;
1890 LastKernelInfo(kernel)->next = new_kernel;
1891 new_kernel=ParseKernelArray("4x3+2+1:0,1,1,- -,1,1,- -,1,1,0");
1892 if (new_kernel == (KernelInfo *) NULL)
1893 return(DestroyKernelInfo(kernel));
1894 new_kernel->type = type;
1895 LastKernelInfo(kernel)->next = new_kernel;
1896 new_kernel=ParseKernelArray("4x3+1+1:-,1,1,0 -,1,1,- 0,1,1,-");
1897 if (new_kernel == (KernelInfo *) NULL)
1898 return(DestroyKernelInfo(kernel));
1899 new_kernel->type = type;
1900 LastKernelInfo(kernel)->next = new_kernel;
1901 new_kernel=ParseKernelArray("4x3+2+1:-,1,1,0 -,1,1,- 0,1,1,-");
1902 if (new_kernel == (KernelInfo *) NULL)
1903 return(DestroyKernelInfo(kernel));
1904 new_kernel->type = type;
1905 LastKernelInfo(kernel)->next = new_kernel;
1906 new_kernel=ParseKernelArray("3x4+1+1:0,-,- 1,1,1 1,1,1 -,-,0");
1907 if (new_kernel == (KernelInfo *) NULL)
1908 return(DestroyKernelInfo(kernel));
1909 new_kernel->type = type;
1910 LastKernelInfo(kernel)->next = new_kernel;
1911 new_kernel=ParseKernelArray("3x4+1+2:0,-,- 1,1,1 1,1,1 -,-,0");
1912 if (new_kernel == (KernelInfo *) NULL)
1913 return(DestroyKernelInfo(kernel));
1914 new_kernel->type = type;
1915 LastKernelInfo(kernel)->next = new_kernel;
1916 new_kernel=ParseKernelArray("3x4+1+1:-,-,0 1,1,1 1,1,1 0,-,-");
1917 if (new_kernel == (KernelInfo *) NULL)
1918 return(DestroyKernelInfo(kernel));
1919 new_kernel->type = type;
1920 LastKernelInfo(kernel)->next = new_kernel;
1921 new_kernel=ParseKernelArray("3x4+1+2:-,-,0 1,1,1 1,1,1 0,-,-");
1922 if (new_kernel == (KernelInfo *) NULL)
1923 return(DestroyKernelInfo(kernel));
1924 new_kernel->type = type;
1925 LastKernelInfo(kernel)->next = new_kernel;
1930 case ConvexHullKernel:
1934 /* first set of 8 kernels */
1935 kernel=ParseKernelArray("3: 1,1,- 1,0,- 1,-,0");
1936 if (kernel == (KernelInfo *) NULL)
1938 kernel->type = type;
1939 ExpandRotateKernelInfo(kernel, 90.0);
1940 /* append the mirror versions too - no flip function yet */
1941 new_kernel=ParseKernelArray("3: 1,1,1 1,0,- -,-,0");
1942 if (new_kernel == (KernelInfo *) NULL)
1943 return(DestroyKernelInfo(kernel));
1944 new_kernel->type = type;
1945 ExpandRotateKernelInfo(new_kernel, 90.0);
1946 LastKernelInfo(kernel)->next = new_kernel;
1949 case SkeletonKernel:
1951 switch ( (int) args->rho ) {
1954 /* Traditional Skeleton...
1955 ** A cyclically rotated single kernel
1957 kernel=AcquireKernelInfo("ThinSE:482");
1958 if (kernel == (KernelInfo *) NULL)
1960 kernel->type = type;
1961 ExpandRotateKernelInfo(kernel, 45.0); /* 8 rotations */
1964 /* HIPR Variation of the cyclic skeleton
1965 ** Corners of the traditional method made more forgiving,
1966 ** but the retain the same cyclic order.
1968 kernel=AcquireKernelInfo("ThinSE:482; ThinSE:87x90;");
1969 if (kernel == (KernelInfo *) NULL)
1971 if (kernel->next == (KernelInfo *) NULL)
1972 return(DestroyKernelInfo(kernel));
1973 kernel->type = type;
1974 kernel->next->type = type;
1975 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotations of the 2 kernels */
1978 /* Dan Bloomberg Skeleton, from his paper on 3x3 thinning SE's
1979 ** "Connectivity-Preserving Morphological Image Thransformations"
1980 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1981 ** http://www.leptonica.com/papers/conn.pdf
1983 kernel=AcquireKernelInfo(
1984 "ThinSE:41; ThinSE:42; ThinSE:43");
1985 if (kernel == (KernelInfo *) NULL)
1987 kernel->type = type;
1988 kernel->next->type = type;
1989 kernel->next->next->type = type;
1990 ExpandMirrorKernelInfo(kernel); /* 12 kernels total */
1996 { /* Special kernels for general thinning, while preserving connections
1997 ** "Connectivity-Preserving Morphological Image Thransformations"
1998 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1999 ** http://www.leptonica.com/papers/conn.pdf
2001 ** http://tpgit.github.com/Leptonica/ccthin_8c_source.html
2003 ** Note kernels do not specify the origin pixel, allowing them
2004 ** to be used for both thickening and thinning operations.
2006 switch ( (int) args->rho ) {
2007 /* SE for 4-connected thinning */
2008 case 41: /* SE_4_1 */
2009 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,-,1");
2011 case 42: /* SE_4_2 */
2012 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,0,-");
2014 case 43: /* SE_4_3 */
2015 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,-,1");
2017 case 44: /* SE_4_4 */
2018 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,-");
2020 case 45: /* SE_4_5 */
2021 kernel=ParseKernelArray("3: -,0,1 0,-,1 -,0,-");
2023 case 46: /* SE_4_6 */
2024 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,1");
2026 case 47: /* SE_4_7 */
2027 kernel=ParseKernelArray("3: -,1,1 0,-,1 -,0,-");
2029 case 48: /* SE_4_8 */
2030 kernel=ParseKernelArray("3: -,-,1 0,-,1 0,-,1");
2032 case 49: /* SE_4_9 */
2033 kernel=ParseKernelArray("3: 0,-,1 0,-,1 -,-,1");
2035 /* SE for 8-connected thinning - negatives of the above */
2036 case 81: /* SE_8_0 */
2037 kernel=ParseKernelArray("3: -,1,- 0,-,1 -,1,-");
2039 case 82: /* SE_8_2 */
2040 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,-,-");
2042 case 83: /* SE_8_3 */
2043 kernel=ParseKernelArray("3: 0,-,- 0,-,1 -,1,-");
2045 case 84: /* SE_8_4 */
2046 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,-");
2048 case 85: /* SE_8_5 */
2049 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,-");
2051 case 86: /* SE_8_6 */
2052 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,1");
2054 case 87: /* SE_8_7 */
2055 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,0,-");
2057 case 88: /* SE_8_8 */
2058 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,1,-");
2060 case 89: /* SE_8_9 */
2061 kernel=ParseKernelArray("3: 0,1,- 0,-,1 -,1,-");
2063 /* Special combined SE kernels */
2064 case 423: /* SE_4_2 , SE_4_3 Combined Kernel */
2065 kernel=ParseKernelArray("3: -,-,1 0,-,- -,0,-");
2067 case 823: /* SE_8_2 , SE_8_3 Combined Kernel */
2068 kernel=ParseKernelArray("3: -,1,- -,-,1 0,-,-");
2070 case 481: /* SE_48_1 - General Connected Corner Kernel */
2071 kernel=ParseKernelArray("3: -,1,1 0,-,1 0,0,-");
2074 case 482: /* SE_48_2 - General Edge Kernel */
2075 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,1");
2078 if (kernel == (KernelInfo *) NULL)
2080 kernel->type = type;
2081 RotateKernelInfo(kernel, args->sigma);
2085 Distance Measuring Kernels
2087 case ChebyshevKernel:
2089 if (args->rho < 1.0)
2090 kernel->width = kernel->height = 3; /* default radius = 1 */
2092 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2093 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2095 kernel->values=(MagickRealType *) MagickAssumeAligned(
2096 AcquireAlignedMemory(kernel->width,kernel->height*
2097 sizeof(*kernel->values)));
2098 if (kernel->values == (MagickRealType *) NULL)
2099 return(DestroyKernelInfo(kernel));
2101 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2102 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2103 kernel->positive_range += ( kernel->values[i] =
2104 args->sigma*MagickMax(fabs((double)u),fabs((double)v)) );
2105 kernel->maximum = kernel->values[0];
2108 case ManhattanKernel:
2110 if (args->rho < 1.0)
2111 kernel->width = kernel->height = 3; /* default radius = 1 */
2113 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2114 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2116 kernel->values=(MagickRealType *) MagickAssumeAligned(
2117 AcquireAlignedMemory(kernel->width,kernel->height*
2118 sizeof(*kernel->values)));
2119 if (kernel->values == (MagickRealType *) NULL)
2120 return(DestroyKernelInfo(kernel));
2122 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2123 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2124 kernel->positive_range += ( kernel->values[i] =
2125 args->sigma*(labs((long) u)+labs((long) v)) );
2126 kernel->maximum = kernel->values[0];
2129 case OctagonalKernel:
2131 if (args->rho < 2.0)
2132 kernel->width = kernel->height = 5; /* default/minimum radius = 2 */
2134 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2135 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2137 kernel->values=(MagickRealType *) MagickAssumeAligned(
2138 AcquireAlignedMemory(kernel->width,kernel->height*
2139 sizeof(*kernel->values)));
2140 if (kernel->values == (MagickRealType *) NULL)
2141 return(DestroyKernelInfo(kernel));
2143 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2144 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2147 r1 = MagickMax(fabs((double)u),fabs((double)v)),
2148 r2 = floor((double)(labs((long)u)+labs((long)v)+1)/1.5);
2149 kernel->positive_range += kernel->values[i] =
2150 args->sigma*MagickMax(r1,r2);
2152 kernel->maximum = kernel->values[0];
2155 case EuclideanKernel:
2157 if (args->rho < 1.0)
2158 kernel->width = kernel->height = 3; /* default radius = 1 */
2160 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2161 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2163 kernel->values=(MagickRealType *) MagickAssumeAligned(
2164 AcquireAlignedMemory(kernel->width,kernel->height*
2165 sizeof(*kernel->values)));
2166 if (kernel->values == (MagickRealType *) NULL)
2167 return(DestroyKernelInfo(kernel));
2169 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2170 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2171 kernel->positive_range += ( kernel->values[i] =
2172 args->sigma*sqrt((double)(u*u+v*v)) );
2173 kernel->maximum = kernel->values[0];
2178 /* No-Op Kernel - Basically just a single pixel on its own */
2179 kernel=ParseKernelArray("1:1");
2180 if (kernel == (KernelInfo *) NULL)
2182 kernel->type = UndefinedKernel;
2191 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2195 % C l o n e K e r n e l I n f o %
2199 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2201 % CloneKernelInfo() creates a new clone of the given Kernel List so that its
2202 % can be modified without effecting the original. The cloned kernel should
2203 % be destroyed using DestoryKernelInfo() when no longer needed.
2205 % The format of the CloneKernelInfo method is:
2207 % KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2209 % A description of each parameter follows:
2211 % o kernel: the Morphology/Convolution kernel to be cloned
2214 MagickExport KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2222 assert(kernel != (KernelInfo *) NULL);
2223 new_kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
2224 if (new_kernel == (KernelInfo *) NULL)
2226 *new_kernel=(*kernel); /* copy values in structure */
2228 /* replace the values with a copy of the values */
2229 new_kernel->values=(MagickRealType *) MagickAssumeAligned(
2230 AcquireAlignedMemory(kernel->width,kernel->height*sizeof(*kernel->values)));
2231 if (new_kernel->values == (MagickRealType *) NULL)
2232 return(DestroyKernelInfo(new_kernel));
2233 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
2234 new_kernel->values[i]=kernel->values[i];
2236 /* Also clone the next kernel in the kernel list */
2237 if ( kernel->next != (KernelInfo *) NULL ) {
2238 new_kernel->next = CloneKernelInfo(kernel->next);
2239 if ( new_kernel->next == (KernelInfo *) NULL )
2240 return(DestroyKernelInfo(new_kernel));
2247 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2251 % D e s t r o y K e r n e l I n f o %
2255 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2257 % DestroyKernelInfo() frees the memory used by a Convolution/Morphology
2260 % The format of the DestroyKernelInfo method is:
2262 % KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2264 % A description of each parameter follows:
2266 % o kernel: the Morphology/Convolution kernel to be destroyed
2269 MagickExport KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2271 assert(kernel != (KernelInfo *) NULL);
2272 if ( kernel->next != (KernelInfo *) NULL )
2273 kernel->next=DestroyKernelInfo(kernel->next);
2274 kernel->values=(MagickRealType *) RelinquishAlignedMemory(kernel->values);
2275 kernel=(KernelInfo *) RelinquishMagickMemory(kernel);
2280 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2284 + E x p a n d M i r r o r K e r n e l I n f o %
2288 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2290 % ExpandMirrorKernelInfo() takes a single kernel, and expands it into a
2291 % sequence of 90-degree rotated kernels but providing a reflected 180
2292 % rotatation, before the -/+ 90-degree rotations.
2294 % This special rotation order produces a better, more symetrical thinning of
2297 % The format of the ExpandMirrorKernelInfo method is:
2299 % void ExpandMirrorKernelInfo(KernelInfo *kernel)
2301 % A description of each parameter follows:
2303 % o kernel: the Morphology/Convolution kernel
2305 % This function is only internel to this module, as it is not finalized,
2306 % especially with regard to non-orthogonal angles, and rotation of larger
2311 static void FlopKernelInfo(KernelInfo *kernel)
2312 { /* Do a Flop by reversing each row. */
2320 for ( y=0, k=kernel->values; y < kernel->height; y++, k+=kernel->width)
2321 for ( x=0, r=kernel->width-1; x<kernel->width/2; x++, r--)
2322 t=k[x], k[x]=k[r], k[r]=t;
2324 kernel->x = kernel->width - kernel->x - 1;
2325 angle = fmod(angle+180.0, 360.0);
2329 static void ExpandMirrorKernelInfo(KernelInfo *kernel)
2337 clone = CloneKernelInfo(last);
2338 RotateKernelInfo(clone, 180); /* flip */
2339 LastKernelInfo(last)->next = clone;
2342 clone = CloneKernelInfo(last);
2343 RotateKernelInfo(clone, 90); /* transpose */
2344 LastKernelInfo(last)->next = clone;
2347 clone = CloneKernelInfo(last);
2348 RotateKernelInfo(clone, 180); /* flop */
2349 LastKernelInfo(last)->next = clone;
2355 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2359 + E x p a n d R o t a t e K e r n e l I n f o %
2363 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2365 % ExpandRotateKernelInfo() takes a kernel list, and expands it by rotating
2366 % incrementally by the angle given, until the kernel repeats.
2368 % WARNING: 45 degree rotations only works for 3x3 kernels.
2369 % While 90 degree roatations only works for linear and square kernels
2371 % The format of the ExpandRotateKernelInfo method is:
2373 % void ExpandRotateKernelInfo(KernelInfo *kernel, double angle)
2375 % A description of each parameter follows:
2377 % o kernel: the Morphology/Convolution kernel
2379 % o angle: angle to rotate in degrees
2381 % This function is only internel to this module, as it is not finalized,
2382 % especially with regard to non-orthogonal angles, and rotation of larger
2386 /* Internal Routine - Return true if two kernels are the same */
2387 static MagickBooleanType SameKernelInfo(const KernelInfo *kernel1,
2388 const KernelInfo *kernel2)
2393 /* check size and origin location */
2394 if ( kernel1->width != kernel2->width
2395 || kernel1->height != kernel2->height
2396 || kernel1->x != kernel2->x
2397 || kernel1->y != kernel2->y )
2400 /* check actual kernel values */
2401 for (i=0; i < (kernel1->width*kernel1->height); i++) {
2402 /* Test for Nan equivalence */
2403 if ( IsNaN(kernel1->values[i]) && !IsNaN(kernel2->values[i]) )
2405 if ( IsNaN(kernel2->values[i]) && !IsNaN(kernel1->values[i]) )
2407 /* Test actual values are equivalent */
2408 if ( fabs(kernel1->values[i] - kernel2->values[i]) >= MagickEpsilon )
2415 static void ExpandRotateKernelInfo(KernelInfo *kernel, const double angle)
2423 clone = CloneKernelInfo(last);
2424 RotateKernelInfo(clone, angle);
2425 if ( SameKernelInfo(kernel, clone) == MagickTrue )
2427 LastKernelInfo(last)->next = clone;
2430 clone = DestroyKernelInfo(clone); /* kernel has repeated - junk the clone */
2435 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2439 + C a l c M e t a K e r n a l I n f o %
2443 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2445 % CalcKernelMetaData() recalculate the KernelInfo meta-data of this kernel only,
2446 % using the kernel values. This should only ne used if it is not possible to
2447 % calculate that meta-data in some easier way.
2449 % It is important that the meta-data is correct before ScaleKernelInfo() is
2450 % used to perform kernel normalization.
2452 % The format of the CalcKernelMetaData method is:
2454 % void CalcKernelMetaData(KernelInfo *kernel, const double scale )
2456 % A description of each parameter follows:
2458 % o kernel: the Morphology/Convolution kernel to modify
2460 % WARNING: Minimum and Maximum values are assumed to include zero, even if
2461 % zero is not part of the kernel (as in Gaussian Derived kernels). This
2462 % however is not true for flat-shaped morphological kernels.
2464 % WARNING: Only the specific kernel pointed to is modified, not a list of
2467 % This is an internal function and not expected to be useful outside this
2468 % module. This could change however.
2470 static void CalcKernelMetaData(KernelInfo *kernel)
2475 kernel->minimum = kernel->maximum = 0.0;
2476 kernel->negative_range = kernel->positive_range = 0.0;
2477 for (i=0; i < (kernel->width*kernel->height); i++)
2479 if ( fabs(kernel->values[i]) < MagickEpsilon )
2480 kernel->values[i] = 0.0;
2481 ( kernel->values[i] < 0)
2482 ? ( kernel->negative_range += kernel->values[i] )
2483 : ( kernel->positive_range += kernel->values[i] );
2484 Minimize(kernel->minimum, kernel->values[i]);
2485 Maximize(kernel->maximum, kernel->values[i]);
2492 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2496 % M o r p h o l o g y A p p l y %
2500 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2502 % MorphologyApply() applies a morphological method, multiple times using
2503 % a list of multiple kernels. This is the method that should be called by
2504 % other 'operators' that internally use morphology operations as part of
2507 % It is basically equivalent to as MorphologyImage() (see below) but
2508 % without any user controls. This allows internel programs to use this
2509 % function, to actually perform a specific task without possible interference
2510 % by any API user supplied settings.
2512 % It is MorphologyImage() task to extract any such user controls, and
2513 % pass them to this function for processing.
2515 % More specifically all given kernels should already be scaled, normalised,
2516 % and blended appropriatally before being parred to this routine. The
2517 % appropriate bias, and compose (typically 'UndefinedComposeOp') given.
2519 % The format of the MorphologyApply method is:
2521 % Image *MorphologyApply(const Image *image,MorphologyMethod method,
2522 % const ssize_t iterations,const KernelInfo *kernel,
2523 % const CompositeMethod compose,const double bias,
2524 % ExceptionInfo *exception)
2526 % A description of each parameter follows:
2528 % o image: the source image
2530 % o method: the morphology method to be applied.
2532 % o iterations: apply the operation this many times (or no change).
2533 % A value of -1 means loop until no change found.
2534 % How this is applied may depend on the morphology method.
2535 % Typically this is a value of 1.
2537 % o channel: the channel type.
2539 % o kernel: An array of double representing the morphology kernel.
2541 % o compose: How to handle or merge multi-kernel results.
2542 % If 'UndefinedCompositeOp' use default for the Morphology method.
2543 % If 'NoCompositeOp' force image to be re-iterated by each kernel.
2544 % Otherwise merge the results using the compose method given.
2546 % o bias: Convolution Output Bias.
2548 % o exception: return any errors or warnings in this structure.
2552 /* Apply a Morphology Primative to an image using the given kernel.
2553 ** Two pre-created images must be provided, and no image is created.
2554 ** It returns the number of pixels that changed between the images
2555 ** for result convergence determination.
2557 static ssize_t MorphologyPrimitive(const Image *image,Image *morphology_image,
2558 const MorphologyMethod method,const KernelInfo *kernel,const double bias,
2559 ExceptionInfo *exception)
2561 #define MorphologyTag "Morphology/Image"
2580 assert(image != (Image *) NULL);
2581 assert(image->signature == MagickSignature);
2582 assert(morphology_image != (Image *) NULL);
2583 assert(morphology_image->signature == MagickSignature);
2584 assert(kernel != (KernelInfo *) NULL);
2585 assert(kernel->signature == MagickSignature);
2586 assert(exception != (ExceptionInfo *) NULL);
2587 assert(exception->signature == MagickSignature);
2593 image_view=AcquireVirtualCacheView(image,exception);
2594 morphology_view=AcquireAuthenticCacheView(morphology_image,exception);
2595 virt_width=image->columns+kernel->width-1;
2597 /* Some methods (including convolve) needs use a reflected kernel.
2598 * Adjust 'origin' offsets to loop though kernel as a reflection.
2603 case ConvolveMorphology:
2604 case DilateMorphology:
2605 case DilateIntensityMorphology:
2606 case IterativeDistanceMorphology:
2607 /* kernel needs to used with reflection about origin */
2608 offx = (ssize_t) kernel->width-offx-1;
2609 offy = (ssize_t) kernel->height-offy-1;
2611 case ErodeMorphology:
2612 case ErodeIntensityMorphology:
2613 case HitAndMissMorphology:
2614 case ThinningMorphology:
2615 case ThickenMorphology:
2616 /* kernel is used as is, without reflection */
2619 assert("Not a Primitive Morphology Method" != (char *) NULL);
2623 if ( method == ConvolveMorphology && kernel->width == 1 )
2624 { /* Special handling (for speed) of vertical (blur) kernels.
2625 ** This performs its handling in columns rather than in rows.
2626 ** This is only done for convolve as it is the only method that
2627 ** generates very large 1-D vertical kernels (such as a 'BlurKernel')
2629 ** Timing tests (on single CPU laptop)
2630 ** Using a vertical 1-d Blue with normal row-by-row (below)
2631 ** time convert logo: -morphology Convolve Blur:0x10+90 null:
2633 ** Using this column method
2634 ** time convert logo: -morphology Convolve Blur:0x10+90 null:
2637 ** Anthony Thyssen, 14 June 2010
2642 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2643 #pragma omp parallel for schedule(static,4) shared(progress,status) \
2644 magick_threads(image,morphology_image,image->columns,1)
2646 for (x=0; x < (ssize_t) image->columns; x++)
2648 register const Quantum
2660 if (status == MagickFalse)
2662 p=GetCacheViewVirtualPixels(image_view,x,-offy,1,image->rows+
2663 kernel->height-1,exception);
2664 q=GetCacheViewAuthenticPixels(morphology_view,x,0,1,
2665 morphology_image->rows,exception);
2666 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2671 center=(ssize_t) GetPixelChannels(image)*offy;
2672 for (y=0; y < (ssize_t) image->rows; y++)
2677 for (i=0; i < (ssize_t) GetPixelChannels(image); i++)
2691 register const MagickRealType
2694 register const Quantum
2703 channel=GetPixelChannelChannel(image,i);
2704 traits=GetPixelChannelTraits(image,channel);
2705 morphology_traits=GetPixelChannelTraits(morphology_image,channel);
2706 if ((traits == UndefinedPixelTrait) ||
2707 (morphology_traits == UndefinedPixelTrait))
2709 if (((morphology_traits & CopyPixelTrait) != 0) ||
2710 (GetPixelMask(image,p) != 0))
2712 SetPixelChannel(morphology_image,channel,p[center+i],q);
2715 k=(&kernel->values[kernel->height-1]);
2719 if ((morphology_traits & BlendPixelTrait) == 0)
2724 for (v=0; v < (ssize_t) kernel->height; v++)
2726 for (u=0; u < (ssize_t) kernel->width; u++)
2728 if (IsNaN(*k) != MagickFalse)
2730 pixel+=(*k)*pixels[i];
2733 pixels+=GetPixelChannels(image);
2736 gamma=PerceptibleReciprocal(gamma);
2738 if (fabs(pixel-p[center+i]) > MagickEpsilon)
2740 SetPixelChannel(morphology_image,channel,ClampToQuantum(pixel),q);
2746 for (v=0; v < (ssize_t) kernel->width; v++)
2748 for (u=0; u < (ssize_t) kernel->width; u++)
2750 if (IsNaN(*k) != MagickFalse)
2752 alpha=(double) (QuantumScale*GetPixelAlpha(image,pixels));
2753 pixel+=(*k)*alpha*pixels[i];
2756 pixels+=GetPixelChannels(image);
2759 gamma=PerceptibleReciprocal(gamma);
2761 if (fabs(pixel-p[center+i]) > MagickEpsilon)
2763 SetPixelChannel(morphology_image,channel,ClampToQuantum(pixel),q);
2765 p+=GetPixelChannels(image);
2766 q+=GetPixelChannels(morphology_image);
2768 if ( SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
2770 if (image->progress_monitor != (MagickProgressMonitor) NULL)
2775 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2776 #pragma omp critical (MagickCore_MorphologyImage)
2778 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows);
2779 if (proceed == MagickFalse)
2783 morphology_image->type=image->type;
2784 morphology_view=DestroyCacheView(morphology_view);
2785 image_view=DestroyCacheView(image_view);
2786 return(status ? (ssize_t) changed : 0);
2790 ** Normal handling of horizontal or rectangular kernels (row by row)
2792 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2793 #pragma omp parallel for schedule(static,4) shared(progress,status) \
2794 magick_threads(image,morphology_image,image->rows,1)
2796 for (y=0; y < (ssize_t) image->rows; y++)
2798 register const Quantum
2810 if (status == MagickFalse)
2812 p=GetCacheViewVirtualPixels(image_view,-offx,y-offy,virt_width,
2813 kernel->height,exception);
2814 q=GetCacheViewAuthenticPixels(morphology_view,0,y,morphology_image->columns,
2816 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2821 /* offset to origin in 'p'. while 'q' points to it directly */
2822 r = GetPixelChannels(image)*virt_width*offy + GetPixelChannels(image)*offx;
2824 for (x=0; x < (ssize_t) image->columns; x++)
2831 register const MagickRealType
2834 register const Quantum
2843 /* Copy input image to the output image for unused channels
2844 * This removes need for 'cloning' a new image every iteration
2846 SetPixelRed(morphology_image,GetPixelRed(image,p+r),q);
2847 SetPixelGreen(morphology_image,GetPixelGreen(image,p+r),q);
2848 SetPixelBlue(morphology_image,GetPixelBlue(image,p+r),q);
2849 if (image->colorspace == CMYKColorspace)
2850 SetPixelBlack(morphology_image,GetPixelBlack(image,p+r),q);
2857 min.black = (double) QuantumRange;
2862 max.black = (double) 0;
2863 /* default result is the original pixel value */
2864 result.red = (double) GetPixelRed(image,p+r);
2865 result.green = (double) GetPixelGreen(image,p+r);
2866 result.blue = (double) GetPixelBlue(image,p+r);
2868 if (image->colorspace == CMYKColorspace)
2869 result.black = (double) GetPixelBlack(image,p+r);
2870 result.alpha=(double) GetPixelAlpha(image,p+r);
2873 case ConvolveMorphology:
2874 /* Set the bias of the weighted average output */
2879 result.black = bias;
2881 case DilateIntensityMorphology:
2882 case ErodeIntensityMorphology:
2883 /* use a boolean flag indicating when first match found */
2884 result.red = 0.0; /* result is not used otherwise */
2891 case ConvolveMorphology:
2892 /* Weighted Average of pixels using reflected kernel
2894 ** NOTE for correct working of this operation for asymetrical
2895 ** kernels, the kernel needs to be applied in its reflected form.
2896 ** That is its values needs to be reversed.
2898 ** Correlation is actually the same as this but without reflecting
2899 ** the kernel, and thus 'lower-level' that Convolution. However
2900 ** as Convolution is the more common method used, and it does not
2901 ** really cost us much in terms of processing to use a reflected
2902 ** kernel, so it is Convolution that is implemented.
2904 ** Correlation will have its kernel reflected before calling
2905 ** this function to do a Convolve.
2907 ** For more details of Correlation vs Convolution see
2908 ** http://www.cs.umd.edu/~djacobs/CMSC426/Convolution.pdf
2910 k = &kernel->values[ kernel->width*kernel->height-1 ];
2912 if ( (image->channel_mask != DefaultChannels) ||
2913 (image->alpha_trait != BlendPixelTrait) )
2914 { /* No 'Sync' involved.
2915 ** Convolution is simple greyscale channel operation
2917 for (v=0; v < (ssize_t) kernel->height; v++) {
2918 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
2919 if ( IsNaN(*k) ) continue;
2921 GetPixelRed(image,k_pixels+u*GetPixelChannels(image));
2922 result.green += (*k)*
2923 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image));
2924 result.blue += (*k)*
2925 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image));
2926 if (image->colorspace == CMYKColorspace)
2927 result.black += (*k)*
2928 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image));
2929 result.alpha += (*k)*
2930 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image));
2932 k_pixels += virt_width*GetPixelChannels(image);
2934 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
2935 SetPixelRed(morphology_image,ClampToQuantum(result.red),
2937 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
2938 SetPixelGreen(morphology_image,ClampToQuantum(result.green),
2940 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
2941 SetPixelBlue(morphology_image,ClampToQuantum(result.blue),
2943 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
2944 (image->colorspace == CMYKColorspace))
2945 SetPixelBlack(morphology_image,ClampToQuantum(result.black),
2947 if (((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0) &&
2948 (image->alpha_trait == BlendPixelTrait))
2949 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),
2953 { /* Channel 'Sync' Flag, and Alpha Channel enabled.
2954 ** Weight the color channels with Alpha Channel so that
2955 ** transparent pixels are not part of the results.
2958 gamma; /* divisor, sum of color alpha weighting */
2961 alpha; /* alpha weighting for colors : alpha */
2964 count; /* alpha valus collected, number kernel values */
2968 for (v=0; v < (ssize_t) kernel->height; v++) {
2969 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
2970 if ( IsNaN(*k) ) continue;
2971 alpha=QuantumScale*GetPixelAlpha(image,
2972 k_pixels+u*GetPixelChannels(image));
2973 gamma += alpha; /* normalize alpha weights only */
2974 count++; /* number of alpha values collected */
2975 alpha=alpha*(*k); /* include kernel weighting now */
2976 result.red += alpha*
2977 GetPixelRed(image,k_pixels+u*GetPixelChannels(image));
2978 result.green += alpha*
2979 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image));
2980 result.blue += alpha*
2981 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image));
2982 if (image->colorspace == CMYKColorspace)
2983 result.black += alpha*
2984 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image));
2985 result.alpha += (*k)*
2986 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image));
2988 k_pixels += virt_width*GetPixelChannels(image);
2990 /* Sync'ed channels, all channels are modified */
2991 gamma=(double)count/(fabs((double) gamma) < MagickEpsilon ? MagickEpsilon : gamma);
2992 SetPixelRed(morphology_image,
2993 ClampToQuantum(gamma*result.red),q);
2994 SetPixelGreen(morphology_image,
2995 ClampToQuantum(gamma*result.green),q);
2996 SetPixelBlue(morphology_image,
2997 ClampToQuantum(gamma*result.blue),q);
2998 if (image->colorspace == CMYKColorspace)
2999 SetPixelBlack(morphology_image,
3000 ClampToQuantum(gamma*result.black),q);
3001 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
3005 case ErodeMorphology:
3006 /* Minimum Value within kernel neighbourhood
3008 ** NOTE that the kernel is not reflected for this operation!
3010 ** NOTE: in normal Greyscale Morphology, the kernel value should
3011 ** be added to the real value, this is currently not done, due to
3012 ** the nature of the boolean kernels being used.
3016 for (v=0; v < (ssize_t) kernel->height; v++) {
3017 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
3018 if ( IsNaN(*k) || (*k) < 0.5 ) continue;
3019 Minimize(min.red, (double)
3020 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3021 Minimize(min.green, (double)
3022 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3023 Minimize(min.blue, (double)
3024 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3025 Minimize(min.alpha, (double)
3026 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3027 if (image->colorspace == CMYKColorspace)
3028 Minimize(min.black, (double)
3029 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3031 k_pixels += virt_width*GetPixelChannels(image);
3035 case DilateMorphology:
3036 /* Maximum Value within kernel neighbourhood
3038 ** NOTE for correct working of this operation for asymetrical
3039 ** kernels, the kernel needs to be applied in its reflected form.
3040 ** That is its values needs to be reversed.
3042 ** NOTE: in normal Greyscale Morphology, the kernel value should
3043 ** be added to the real value, this is currently not done, due to
3044 ** the nature of the boolean kernels being used.
3047 k = &kernel->values[ kernel->width*kernel->height-1 ];
3049 for (v=0; v < (ssize_t) kernel->height; v++) {
3050 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3051 if ( IsNaN(*k) || (*k) < 0.5 ) continue;
3052 Maximize(max.red, (double)
3053 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3054 Maximize(max.green, (double)
3055 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3056 Maximize(max.blue, (double)
3057 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3058 Maximize(max.alpha, (double)
3059 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3060 if (image->colorspace == CMYKColorspace)
3061 Maximize(max.black, (double)
3062 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3064 k_pixels += virt_width*GetPixelChannels(image);
3068 case HitAndMissMorphology:
3069 case ThinningMorphology:
3070 case ThickenMorphology:
3071 /* Minimum of Foreground Pixel minus Maxumum of Background Pixels
3073 ** NOTE that the kernel is not reflected for this operation,
3074 ** and consists of both foreground and background pixel
3075 ** neighbourhoods, 0.0 for background, and 1.0 for foreground
3076 ** with either Nan or 0.5 values for don't care.
3078 ** Note that this will never produce a meaningless negative
3079 ** result. Such results can cause Thinning/Thicken to not work
3080 ** correctly when used against a greyscale image.
3084 for (v=0; v < (ssize_t) kernel->height; v++) {
3085 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
3086 if ( IsNaN(*k) ) continue;
3088 { /* minimim of foreground pixels */
3089 Minimize(min.red, (double)
3090 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3091 Minimize(min.green, (double)
3092 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3093 Minimize(min.blue, (double)
3094 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3095 Minimize(min.alpha,(double)
3096 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3097 if ( image->colorspace == CMYKColorspace)
3098 Minimize(min.black,(double)
3099 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3101 else if ( (*k) < 0.3 )
3102 { /* maximum of background pixels */
3103 Maximize(max.red, (double)
3104 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3105 Maximize(max.green, (double)
3106 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3107 Maximize(max.blue, (double)
3108 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3109 Maximize(max.alpha,(double)
3110 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3111 if (image->colorspace == CMYKColorspace)
3112 Maximize(max.black, (double)
3113 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3116 k_pixels += virt_width*GetPixelChannels(image);
3118 /* Pattern Match if difference is positive */
3119 min.red -= max.red; Maximize( min.red, 0.0 );
3120 min.green -= max.green; Maximize( min.green, 0.0 );
3121 min.blue -= max.blue; Maximize( min.blue, 0.0 );
3122 min.black -= max.black; Maximize( min.black, 0.0 );
3123 min.alpha -= max.alpha; Maximize( min.alpha, 0.0 );
3126 case ErodeIntensityMorphology:
3127 /* Select Pixel with Minimum Intensity within kernel neighbourhood
3129 ** WARNING: the intensity test fails for CMYK and does not
3130 ** take into account the moderating effect of the alpha channel
3131 ** on the intensity.
3133 ** NOTE that the kernel is not reflected for this operation!
3137 for (v=0; v < (ssize_t) kernel->height; v++) {
3138 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
3139 if ( IsNaN(*k) || (*k) < 0.5 ) continue;
3140 if ( result.red == 0.0 ||
3141 GetPixelIntensity(image,k_pixels+u*GetPixelChannels(image)) < GetPixelIntensity(morphology_image,q) ) {
3142 /* copy the whole pixel - no channel selection */
3143 SetPixelRed(morphology_image,GetPixelRed(image,
3144 k_pixels+u*GetPixelChannels(image)),q);
3145 SetPixelGreen(morphology_image,GetPixelGreen(image,
3146 k_pixels+u*GetPixelChannels(image)),q);
3147 SetPixelBlue(morphology_image,GetPixelBlue(image,
3148 k_pixels+u*GetPixelChannels(image)),q);
3149 SetPixelAlpha(morphology_image,GetPixelAlpha(image,
3150 k_pixels+u*GetPixelChannels(image)),q);
3151 if ( result.red > 0.0 ) changed++;
3155 k_pixels += virt_width*GetPixelChannels(image);
3159 case DilateIntensityMorphology:
3160 /* Select Pixel with Maximum Intensity within kernel neighbourhood
3162 ** WARNING: the intensity test fails for CMYK and does not
3163 ** take into account the moderating effect of the alpha channel
3164 ** on the intensity (yet).
3166 ** NOTE for correct working of this operation for asymetrical
3167 ** kernels, the kernel needs to be applied in its reflected form.
3168 ** That is its values needs to be reversed.
3170 k = &kernel->values[ kernel->width*kernel->height-1 ];
3172 for (v=0; v < (ssize_t) kernel->height; v++) {
3173 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3174 if ( IsNaN(*k) || (*k) < 0.5 ) continue; /* boolean kernel */
3175 if ( result.red == 0.0 ||
3176 GetPixelIntensity(image,k_pixels+u*GetPixelChannels(image)) > GetPixelIntensity(morphology_image,q) ) {
3177 /* copy the whole pixel - no channel selection */
3178 SetPixelRed(morphology_image,GetPixelRed(image,
3179 k_pixels+u*GetPixelChannels(image)),q);
3180 SetPixelGreen(morphology_image,GetPixelGreen(image,
3181 k_pixels+u*GetPixelChannels(image)),q);
3182 SetPixelBlue(morphology_image,GetPixelBlue(image,
3183 k_pixels+u*GetPixelChannels(image)),q);
3184 SetPixelAlpha(morphology_image,GetPixelAlpha(image,
3185 k_pixels+u*GetPixelChannels(image)),q);
3186 if ( result.red > 0.0 ) changed++;
3190 k_pixels += virt_width*GetPixelChannels(image);
3194 case IterativeDistanceMorphology:
3195 /* Work out an iterative distance from black edge of a white image
3196 ** shape. Essentually white values are decreased to the smallest
3197 ** 'distance from edge' it can find.
3199 ** It works by adding kernel values to the neighbourhood, and and
3200 ** select the minimum value found. The kernel is rotated before
3201 ** use, so kernel distances match resulting distances, when a user
3202 ** provided asymmetric kernel is applied.
3205 ** This code is almost identical to True GrayScale Morphology But
3208 ** GreyDilate Kernel values added, maximum value found Kernel is
3209 ** rotated before use.
3211 ** GrayErode: Kernel values subtracted and minimum value found No
3212 ** kernel rotation used.
3214 ** Note the the Iterative Distance method is essentially a
3215 ** GrayErode, but with negative kernel values, and kernel
3216 ** rotation applied.
3218 k = &kernel->values[ kernel->width*kernel->height-1 ];
3220 for (v=0; v < (ssize_t) kernel->height; v++) {
3221 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3222 if ( IsNaN(*k) ) continue;
3223 Minimize(result.red, (*k)+(double)
3224 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3225 Minimize(result.green, (*k)+(double)
3226 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3227 Minimize(result.blue, (*k)+(double)
3228 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3229 Minimize(result.alpha, (*k)+(double)
3230 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3231 if ( image->colorspace == CMYKColorspace)
3232 Maximize(result.black, (*k)+(double)
3233 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3235 k_pixels += virt_width*GetPixelChannels(image);
3239 case UndefinedMorphology:
3241 break; /* Do nothing */
3243 /* Final mathematics of results (combine with original image?)
3245 ** NOTE: Difference Morphology operators Edge* and *Hat could also
3246 ** be done here but works better with iteration as a image difference
3247 ** in the controling function (below). Thicken and Thinning however
3248 ** should be done here so thay can be iterated correctly.
3251 case HitAndMissMorphology:
3252 case ErodeMorphology:
3253 result = min; /* minimum of neighbourhood */
3255 case DilateMorphology:
3256 result = max; /* maximum of neighbourhood */
3258 case ThinningMorphology:
3259 /* subtract pattern match from original */
3260 result.red -= min.red;
3261 result.green -= min.green;
3262 result.blue -= min.blue;
3263 result.black -= min.black;
3264 result.alpha -= min.alpha;
3266 case ThickenMorphology:
3267 /* Add the pattern matchs to the original */
3268 result.red += min.red;
3269 result.green += min.green;
3270 result.blue += min.blue;
3271 result.black += min.black;
3272 result.alpha += min.alpha;
3275 /* result directly calculated or assigned */
3278 /* Assign the resulting pixel values - Clamping Result */
3280 case UndefinedMorphology:
3281 case ConvolveMorphology:
3282 case DilateIntensityMorphology:
3283 case ErodeIntensityMorphology:
3284 break; /* full pixel was directly assigned - not a channel method */
3286 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3287 SetPixelRed(morphology_image,ClampToQuantum(result.red),q);
3288 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3289 SetPixelGreen(morphology_image,ClampToQuantum(result.green),q);
3290 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3291 SetPixelBlue(morphology_image,ClampToQuantum(result.blue),q);
3292 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3293 (image->colorspace == CMYKColorspace))
3294 SetPixelBlack(morphology_image,ClampToQuantum(result.black),q);
3295 if (((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0) &&
3296 (image->alpha_trait == BlendPixelTrait))
3297 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
3300 /* Count up changed pixels */
3301 if ((GetPixelRed(image,p+r) != GetPixelRed(morphology_image,q)) ||
3302 (GetPixelGreen(image,p+r) != GetPixelGreen(morphology_image,q)) ||
3303 (GetPixelBlue(image,p+r) != GetPixelBlue(morphology_image,q)) ||
3304 (GetPixelAlpha(image,p+r) != GetPixelAlpha(morphology_image,q)) ||
3305 ((image->colorspace == CMYKColorspace) &&
3306 (GetPixelBlack(image,p+r) != GetPixelBlack(morphology_image,q))))
3307 changed++; /* The pixel was changed in some way! */
3308 p+=GetPixelChannels(image);
3309 q+=GetPixelChannels(morphology_image);
3311 if ( SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
3313 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3318 #if defined(MAGICKCORE_OPENMP_SUPPORT)
3319 #pragma omp critical (MagickCore_MorphologyImage)
3321 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows);
3322 if (proceed == MagickFalse)
3326 morphology_view=DestroyCacheView(morphology_view);
3327 image_view=DestroyCacheView(image_view);
3328 return(status ? (ssize_t)changed : -1);
3331 /* This is almost identical to the MorphologyPrimative() function above,
3332 ** but will apply the primitive directly to the actual image using two
3333 ** passes, once in each direction, with the results of the previous (and
3334 ** current) row being re-used.
3336 ** That is after each row is 'Sync'ed' into the image, the next row will
3337 ** make use of those values as part of the calculation of the next row.
3338 ** It then repeats, but going in the oppisite (bottom-up) direction.
3340 ** Because of this 're-use of results' this function can not make use
3341 ** of multi-threaded, parellel processing.
3343 static ssize_t MorphologyPrimitiveDirect(Image *image,
3344 const MorphologyMethod method,const KernelInfo *kernel,
3345 ExceptionInfo *exception)
3368 assert(image != (Image *) NULL);
3369 assert(image->signature == MagickSignature);
3370 assert(kernel != (KernelInfo *) NULL);
3371 assert(kernel->signature == MagickSignature);
3372 assert(exception != (ExceptionInfo *) NULL);
3373 assert(exception->signature == MagickSignature);
3375 /* Some methods (including convolve) needs use a reflected kernel.
3376 * Adjust 'origin' offsets to loop though kernel as a reflection.
3381 case DistanceMorphology:
3382 case VoronoiMorphology:
3383 /* kernel needs to used with reflection about origin */
3384 offx = (ssize_t) kernel->width-offx-1;
3385 offy = (ssize_t) kernel->height-offy-1;
3388 case ?????Morphology:
3389 /* kernel is used as is, without reflection */
3393 assert("Not a PrimativeDirect Morphology Method" != (char *) NULL);
3397 /* DO NOT THREAD THIS CODE! */
3398 /* two views into same image (virtual, and actual) */
3399 virt_view=AcquireVirtualCacheView(image,exception);
3400 auth_view=AcquireAuthenticCacheView(image,exception);
3401 virt_width=image->columns+kernel->width-1;
3403 for (y=0; y < (ssize_t) image->rows; y++)
3405 register const Quantum
3417 /* NOTE read virtual pixels, and authentic pixels, from the same image!
3418 ** we read using virtual to get virtual pixel handling, but write back
3419 ** into the same image.
3421 ** Only top half of kernel is processed as we do a single pass downward
3422 ** through the image iterating the distance function as we go.
3424 if (status == MagickFalse)
3426 p=GetCacheViewVirtualPixels(virt_view,-offx,y-offy,virt_width,(size_t)
3428 q=GetCacheViewAuthenticPixels(auth_view, 0, y, image->columns, 1,
3430 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3432 if (status == MagickFalse)
3435 /* offset to origin in 'p'. while 'q' points to it directly */
3436 r = (ssize_t) GetPixelChannels(image)*virt_width*offy + GetPixelChannels(image)*offx;
3438 for (x=0; x < (ssize_t) image->columns; x++)
3443 register const MagickRealType
3446 register const Quantum
3455 /* Starting Defaults */
3456 GetPixelInfo(image,&result);
3457 GetPixelInfoPixel(image,q,&result);
3458 if ( method != VoronoiMorphology )
3459 result.alpha = QuantumRange - result.alpha;
3462 case DistanceMorphology:
3463 /* Add kernel Value and select the minimum value found. */
3464 k = &kernel->values[ kernel->width*kernel->height-1 ];
3466 for (v=0; v <= (ssize_t) offy; v++) {
3467 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3468 if ( IsNaN(*k) ) continue;
3469 Minimize(result.red, (*k)+
3470 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3471 Minimize(result.green, (*k)+
3472 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3473 Minimize(result.blue, (*k)+
3474 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3475 if (image->colorspace == CMYKColorspace)
3476 Minimize(result.black,(*k)+
3477 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3478 Minimize(result.alpha, (*k)+
3479 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3481 k_pixels += virt_width*GetPixelChannels(image);
3483 /* repeat with the just processed pixels of this row */
3484 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3485 k_pixels = q-offx*GetPixelChannels(image);
3486 for (u=0; u < (ssize_t) offx; u++, k--) {
3487 if ( x+u-offx < 0 ) continue; /* off the edge! */
3488 if ( IsNaN(*k) ) continue;
3489 Minimize(result.red, (*k)+
3490 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3491 Minimize(result.green, (*k)+
3492 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3493 Minimize(result.blue, (*k)+
3494 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3495 if (image->colorspace == CMYKColorspace)
3496 Minimize(result.black,(*k)+
3497 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3498 Minimize(result.alpha,(*k)+
3499 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3502 case VoronoiMorphology:
3503 /* Apply Distance to 'Matte' channel, while coping the color
3504 ** values of the closest pixel.
3506 ** This is experimental, and realy the 'alpha' component should
3507 ** be completely separate 'masking' channel so that alpha can
3508 ** also be used as part of the results.
3510 k = &kernel->values[ kernel->width*kernel->height-1 ];
3512 for (v=0; v <= (ssize_t) offy; v++) {
3513 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3514 if ( IsNaN(*k) ) continue;
3515 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3517 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3522 k_pixels += virt_width*GetPixelChannels(image);
3524 /* repeat with the just processed pixels of this row */
3525 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3526 k_pixels = q-offx*GetPixelChannels(image);
3527 for (u=0; u < (ssize_t) offx; u++, k--) {
3528 if ( x+u-offx < 0 ) continue; /* off the edge! */
3529 if ( IsNaN(*k) ) continue;
3530 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3532 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3539 /* result directly calculated or assigned */
3542 /* Assign the resulting pixel values - Clamping Result */
3544 case VoronoiMorphology:
3545 SetPixelInfoPixel(image,&result,q);
3548 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3549 SetPixelRed(image,ClampToQuantum(result.red),q);
3550 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3551 SetPixelGreen(image,ClampToQuantum(result.green),q);
3552 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3553 SetPixelBlue(image,ClampToQuantum(result.blue),q);
3554 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3555 (image->colorspace == CMYKColorspace))
3556 SetPixelBlack(image,ClampToQuantum(result.black),q);
3557 if ((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0 &&
3558 (image->alpha_trait == BlendPixelTrait))
3559 SetPixelAlpha(image,ClampToQuantum(result.alpha),q);
3562 /* Count up changed pixels */
3563 if ((GetPixelRed(image,p+r) != GetPixelRed(image,q)) ||
3564 (GetPixelGreen(image,p+r) != GetPixelGreen(image,q)) ||
3565 (GetPixelBlue(image,p+r) != GetPixelBlue(image,q)) ||
3566 (GetPixelAlpha(image,p+r) != GetPixelAlpha(image,q)) ||
3567 ((image->colorspace == CMYKColorspace) &&
3568 (GetPixelBlack(image,p+r) != GetPixelBlack(image,q))))
3569 changed++; /* The pixel was changed in some way! */
3571 p+=GetPixelChannels(image); /* increment pixel buffers */
3572 q+=GetPixelChannels(image);
3575 if ( SyncCacheViewAuthenticPixels(auth_view,exception) == MagickFalse)
3577 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3578 if ( SetImageProgress(image,MorphologyTag,progress++,image->rows)
3584 /* Do the reversed pass through the image */
3585 for (y=(ssize_t)image->rows-1; y >= 0; y--)
3587 register const Quantum
3599 if (status == MagickFalse)
3601 /* NOTE read virtual pixels, and authentic pixels, from the same image!
3602 ** we read using virtual to get virtual pixel handling, but write back
3603 ** into the same image.
3605 ** Only the bottom half of the kernel will be processes as we
3608 p=GetCacheViewVirtualPixels(virt_view,-offx,y,virt_width,(size_t)
3609 kernel->y+1,exception);
3610 q=GetCacheViewAuthenticPixels(auth_view, 0, y, image->columns, 1,
3612 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3614 if (status == MagickFalse)
3617 /* adjust positions to end of row */
3618 p += (image->columns-1)*GetPixelChannels(image);
3619 q += (image->columns-1)*GetPixelChannels(image);
3621 /* offset to origin in 'p'. while 'q' points to it directly */
3622 r = GetPixelChannels(image)*offx;
3624 for (x=(ssize_t)image->columns-1; x >= 0; x--)
3629 register const MagickRealType
3632 register const Quantum
3641 /* Default - previously modified pixel */
3642 GetPixelInfo(image,&result);
3643 GetPixelInfoPixel(image,q,&result);
3644 if ( method != VoronoiMorphology )
3645 result.alpha = QuantumRange - result.alpha;
3648 case DistanceMorphology:
3649 /* Add kernel Value and select the minimum value found. */
3650 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3652 for (v=offy; v < (ssize_t) kernel->height; v++) {
3653 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3654 if ( IsNaN(*k) ) continue;
3655 Minimize(result.red, (*k)+
3656 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3657 Minimize(result.green, (*k)+
3658 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3659 Minimize(result.blue, (*k)+
3660 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3661 if ( image->colorspace == CMYKColorspace)
3662 Minimize(result.black,(*k)+
3663 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3664 Minimize(result.alpha, (*k)+
3665 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3667 k_pixels += virt_width*GetPixelChannels(image);
3669 /* repeat with the just processed pixels of this row */
3670 k = &kernel->values[ kernel->width*(kernel->y)+kernel->x-1 ];
3671 k_pixels = q-offx*GetPixelChannels(image);
3672 for (u=offx+1; u < (ssize_t) kernel->width; u++, k--) {
3673 if ( (x+u-offx) >= (ssize_t)image->columns ) continue;
3674 if ( IsNaN(*k) ) continue;
3675 Minimize(result.red, (*k)+
3676 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3677 Minimize(result.green, (*k)+
3678 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3679 Minimize(result.blue, (*k)+
3680 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3681 if ( image->colorspace == CMYKColorspace)
3682 Minimize(result.black, (*k)+
3683 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3684 Minimize(result.alpha, (*k)+
3685 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3688 case VoronoiMorphology:
3689 /* Apply Distance to 'Matte' channel, coping the closest color.
3691 ** This is experimental, and realy the 'alpha' component should
3692 ** be completely separate 'masking' channel.
3694 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3696 for (v=offy; v < (ssize_t) kernel->height; v++) {
3697 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3698 if ( IsNaN(*k) ) continue;
3699 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3701 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3706 k_pixels += virt_width*GetPixelChannels(image);
3708 /* repeat with the just processed pixels of this row */
3709 k = &kernel->values[ kernel->width*(kernel->y)+kernel->x-1 ];
3710 k_pixels = q-offx*GetPixelChannels(image);
3711 for (u=offx+1; u < (ssize_t) kernel->width; u++, k--) {
3712 if ( (x+u-offx) >= (ssize_t)image->columns ) continue;
3713 if ( IsNaN(*k) ) continue;
3714 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3716 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3723 /* result directly calculated or assigned */
3726 /* Assign the resulting pixel values - Clamping Result */
3728 case VoronoiMorphology:
3729 SetPixelInfoPixel(image,&result,q);
3732 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3733 SetPixelRed(image,ClampToQuantum(result.red),q);
3734 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3735 SetPixelGreen(image,ClampToQuantum(result.green),q);
3736 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3737 SetPixelBlue(image,ClampToQuantum(result.blue),q);
3738 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3739 (image->colorspace == CMYKColorspace))
3740 SetPixelBlack(image,ClampToQuantum(result.black),q);
3741 if ((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0 &&
3742 (image->alpha_trait == BlendPixelTrait))
3743 SetPixelAlpha(image,ClampToQuantum(result.alpha),q);
3746 /* Count up changed pixels */
3747 if ( (GetPixelRed(image,p+r) != GetPixelRed(image,q))
3748 || (GetPixelGreen(image,p+r) != GetPixelGreen(image,q))
3749 || (GetPixelBlue(image,p+r) != GetPixelBlue(image,q))
3750 || (GetPixelAlpha(image,p+r) != GetPixelAlpha(image,q))
3751 || ((image->colorspace == CMYKColorspace) &&
3752 (GetPixelBlack(image,p+r) != GetPixelBlack(image,q))))
3753 changed++; /* The pixel was changed in some way! */
3755 p-=GetPixelChannels(image); /* go backward through pixel buffers */
3756 q-=GetPixelChannels(image);
3758 if ( SyncCacheViewAuthenticPixels(auth_view,exception) == MagickFalse)
3760 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3761 if ( SetImageProgress(image,MorphologyTag,progress++,image->rows)
3767 auth_view=DestroyCacheView(auth_view);
3768 virt_view=DestroyCacheView(virt_view);
3769 return(status ? (ssize_t) changed : -1);
3772 /* Apply a Morphology by calling one of the above low level primitive
3773 ** application functions. This function handles any iteration loops,
3774 ** composition or re-iteration of results, and compound morphology methods
3775 ** that is based on multiple low-level (staged) morphology methods.
3777 ** Basically this provides the complex glue between the requested morphology
3778 ** method and raw low-level implementation (above).
3780 MagickPrivate Image *MorphologyApply(const Image *image,
3781 const MorphologyMethod method, const ssize_t iterations,
3782 const KernelInfo *kernel, const CompositeOperator compose,const double bias,
3783 ExceptionInfo *exception)
3789 *curr_image, /* Image we are working with or iterating */
3790 *work_image, /* secondary image for primitive iteration */
3791 *save_image, /* saved image - for 'edge' method only */
3792 *rslt_image; /* resultant image - after multi-kernel handling */
3795 *reflected_kernel, /* A reflected copy of the kernel (if needed) */
3796 *norm_kernel, /* the current normal un-reflected kernel */
3797 *rflt_kernel, /* the current reflected kernel (if needed) */
3798 *this_kernel; /* the kernel being applied */
3801 primitive; /* the current morphology primitive being applied */
3804 rslt_compose; /* multi-kernel compose method for results to use */
3807 special, /* do we use a direct modify function? */
3808 verbose; /* verbose output of results */
3811 method_loop, /* Loop 1: number of compound method iterations (norm 1) */
3812 method_limit, /* maximum number of compound method iterations */
3813 kernel_number, /* Loop 2: the kernel number being applied */
3814 stage_loop, /* Loop 3: primitive loop for compound morphology */
3815 stage_limit, /* how many primitives are in this compound */
3816 kernel_loop, /* Loop 4: iterate the kernel over image */
3817 kernel_limit, /* number of times to iterate kernel */
3818 count, /* total count of primitive steps applied */
3819 kernel_changed, /* total count of changed using iterated kernel */
3820 method_changed; /* total count of changed over method iteration */
3823 changed; /* number pixels changed by last primitive operation */
3828 assert(image != (Image *) NULL);
3829 assert(image->signature == MagickSignature);
3830 assert(kernel != (KernelInfo *) NULL);
3831 assert(kernel->signature == MagickSignature);
3832 assert(exception != (ExceptionInfo *) NULL);
3833 assert(exception->signature == MagickSignature);
3835 count = 0; /* number of low-level morphology primitives performed */
3836 if ( iterations == 0 )
3837 return((Image *)NULL); /* null operation - nothing to do! */
3839 kernel_limit = (size_t) iterations;
3840 if ( iterations < 0 ) /* negative interations = infinite (well alomst) */
3841 kernel_limit = image->columns>image->rows ? image->columns : image->rows;
3843 verbose = IsStringTrue(GetImageArtifact(image,"verbose"));
3845 /* initialise for cleanup */
3846 curr_image = (Image *) image;
3847 curr_compose = image->compose;
3848 (void) curr_compose;
3849 work_image = save_image = rslt_image = (Image *) NULL;
3850 reflected_kernel = (KernelInfo *) NULL;
3852 /* Initialize specific methods
3853 * + which loop should use the given iteratations
3854 * + how many primitives make up the compound morphology
3855 * + multi-kernel compose method to use (by default)
3857 method_limit = 1; /* just do method once, unless otherwise set */
3858 stage_limit = 1; /* assume method is not a compound */
3859 special = MagickFalse; /* assume it is NOT a direct modify primitive */
3860 rslt_compose = compose; /* and we are composing multi-kernels as given */
3862 case SmoothMorphology: /* 4 primitive compound morphology */
3865 case OpenMorphology: /* 2 primitive compound morphology */
3866 case OpenIntensityMorphology:
3867 case TopHatMorphology:
3868 case CloseMorphology:
3869 case CloseIntensityMorphology:
3870 case BottomHatMorphology:
3871 case EdgeMorphology:
3874 case HitAndMissMorphology:
3875 rslt_compose = LightenCompositeOp; /* Union of multi-kernel results */
3877 case ThinningMorphology:
3878 case ThickenMorphology:
3879 method_limit = kernel_limit; /* iterate the whole method */
3880 kernel_limit = 1; /* do not do kernel iteration */
3882 case DistanceMorphology:
3883 case VoronoiMorphology:
3884 special = MagickTrue; /* use special direct primative */
3890 /* Apply special methods with special requirments
3891 ** For example, single run only, or post-processing requirements
3893 if ( special == MagickTrue )
3895 rslt_image=CloneImage(image,0,0,MagickTrue,exception);
3896 if (rslt_image == (Image *) NULL)
3898 if (SetImageStorageClass(rslt_image,DirectClass,exception) == MagickFalse)
3901 changed = MorphologyPrimitiveDirect(rslt_image, method,
3904 if ( IfMagickTrue(verbose) )
3905 (void) (void) FormatLocaleFile(stderr,
3906 "%s:%.20g.%.20g #%.20g => Changed %.20g\n",
3907 CommandOptionToMnemonic(MagickMorphologyOptions, method),
3908 1.0,0.0,1.0, (double) changed);
3913 if ( method == VoronoiMorphology ) {
3914 /* Preserve the alpha channel of input image - but turned off */
3915 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3917 (void) CompositeImage(rslt_image,image,CopyAlphaCompositeOp,
3918 MagickTrue,0,0,exception);
3919 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3925 /* Handle user (caller) specified multi-kernel composition method */
3926 if ( compose != UndefinedCompositeOp )
3927 rslt_compose = compose; /* override default composition for method */
3928 if ( rslt_compose == UndefinedCompositeOp )
3929 rslt_compose = NoCompositeOp; /* still not defined! Then re-iterate */
3931 /* Some methods require a reflected kernel to use with primitives.
3932 * Create the reflected kernel for those methods. */
3934 case CorrelateMorphology:
3935 case CloseMorphology:
3936 case CloseIntensityMorphology:
3937 case BottomHatMorphology:
3938 case SmoothMorphology:
3939 reflected_kernel = CloneKernelInfo(kernel);
3940 if (reflected_kernel == (KernelInfo *) NULL)
3942 RotateKernelInfo(reflected_kernel,180);
3948 /* Loops around more primitive morpholgy methods
3949 ** erose, dilate, open, close, smooth, edge, etc...
3951 /* Loop 1: iterate the compound method */
3954 while ( method_loop < method_limit && method_changed > 0 ) {
3958 /* Loop 2: iterate over each kernel in a multi-kernel list */
3959 norm_kernel = (KernelInfo *) kernel;
3960 this_kernel = (KernelInfo *) kernel;
3961 rflt_kernel = reflected_kernel;
3964 while ( norm_kernel != NULL ) {
3966 /* Loop 3: Compound Morphology Staging - Select Primative to apply */
3967 stage_loop = 0; /* the compound morphology stage number */
3968 while ( stage_loop < stage_limit ) {
3969 stage_loop++; /* The stage of the compound morphology */
3971 /* Select primitive morphology for this stage of compound method */
3972 this_kernel = norm_kernel; /* default use unreflected kernel */
3973 primitive = method; /* Assume method is a primitive */
3975 case ErodeMorphology: /* just erode */
3976 case EdgeInMorphology: /* erode and image difference */
3977 primitive = ErodeMorphology;
3979 case DilateMorphology: /* just dilate */
3980 case EdgeOutMorphology: /* dilate and image difference */
3981 primitive = DilateMorphology;
3983 case OpenMorphology: /* erode then dialate */
3984 case TopHatMorphology: /* open and image difference */
3985 primitive = ErodeMorphology;
3986 if ( stage_loop == 2 )
3987 primitive = DilateMorphology;
3989 case OpenIntensityMorphology:
3990 primitive = ErodeIntensityMorphology;
3991 if ( stage_loop == 2 )
3992 primitive = DilateIntensityMorphology;
3994 case CloseMorphology: /* dilate, then erode */
3995 case BottomHatMorphology: /* close and image difference */
3996 this_kernel = rflt_kernel; /* use the reflected kernel */
3997 primitive = DilateMorphology;
3998 if ( stage_loop == 2 )
3999 primitive = ErodeMorphology;
4001 case CloseIntensityMorphology:
4002 this_kernel = rflt_kernel; /* use the reflected kernel */
4003 primitive = DilateIntensityMorphology;
4004 if ( stage_loop == 2 )
4005 primitive = ErodeIntensityMorphology;
4007 case SmoothMorphology: /* open, close */
4008 switch ( stage_loop ) {
4009 case 1: /* start an open method, which starts with Erode */
4010 primitive = ErodeMorphology;
4012 case 2: /* now Dilate the Erode */
4013 primitive = DilateMorphology;
4015 case 3: /* Reflect kernel a close */
4016 this_kernel = rflt_kernel; /* use the reflected kernel */
4017 primitive = DilateMorphology;
4019 case 4: /* Finish the Close */
4020 this_kernel = rflt_kernel; /* use the reflected kernel */
4021 primitive = ErodeMorphology;
4025 case EdgeMorphology: /* dilate and erode difference */
4026 primitive = DilateMorphology;
4027 if ( stage_loop == 2 ) {
4028 save_image = curr_image; /* save the image difference */
4029 curr_image = (Image *) image;
4030 primitive = ErodeMorphology;
4033 case CorrelateMorphology:
4034 /* A Correlation is a Convolution with a reflected kernel.
4035 ** However a Convolution is a weighted sum using a reflected
4036 ** kernel. It may seem stange to convert a Correlation into a
4037 ** Convolution as the Correlation is the simplier method, but
4038 ** Convolution is much more commonly used, and it makes sense to
4039 ** implement it directly so as to avoid the need to duplicate the
4040 ** kernel when it is not required (which is typically the
4043 this_kernel = rflt_kernel; /* use the reflected kernel */
4044 primitive = ConvolveMorphology;
4049 assert( this_kernel != (KernelInfo *) NULL );
4051 /* Extra information for debugging compound operations */
4052 if ( IfMagickTrue(verbose) ) {
4053 if ( stage_limit > 1 )
4054 (void) FormatLocaleString(v_info,MaxTextExtent,"%s:%.20g.%.20g -> ",
4055 CommandOptionToMnemonic(MagickMorphologyOptions,method),(double)
4056 method_loop,(double) stage_loop);
4057 else if ( primitive != method )
4058 (void) FormatLocaleString(v_info, MaxTextExtent, "%s:%.20g -> ",
4059 CommandOptionToMnemonic(MagickMorphologyOptions, method),(double)
4065 /* Loop 4: Iterate the kernel with primitive */
4069 while ( kernel_loop < kernel_limit && changed > 0 ) {
4070 kernel_loop++; /* the iteration of this kernel */
4072 /* Create a clone as the destination image, if not yet defined */
4073 if ( work_image == (Image *) NULL )
4075 work_image=CloneImage(image,0,0,MagickTrue,exception);
4076 if (work_image == (Image *) NULL)
4078 if (SetImageStorageClass(work_image,DirectClass,exception) == MagickFalse)
4080 /* work_image->type=image->type; ??? */
4083 /* APPLY THE MORPHOLOGICAL PRIMITIVE (curr -> work) */
4085 changed = MorphologyPrimitive(curr_image, work_image, primitive,
4086 this_kernel, bias, exception);
4088 if ( IfMagickTrue(verbose) ) {
4089 if ( kernel_loop > 1 )
4090 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line from previous */
4091 (void) (void) FormatLocaleFile(stderr,
4092 "%s%s%s:%.20g.%.20g #%.20g => Changed %.20g",
4093 v_info,CommandOptionToMnemonic(MagickMorphologyOptions,
4094 primitive),(this_kernel == rflt_kernel ) ? "*" : "",
4095 (double) (method_loop+kernel_loop-1),(double) kernel_number,
4096 (double) count,(double) changed);
4100 kernel_changed += changed;
4101 method_changed += changed;
4103 /* prepare next loop */
4104 { Image *tmp = work_image; /* swap images for iteration */
4105 work_image = curr_image;
4108 if ( work_image == image )
4109 work_image = (Image *) NULL; /* replace input 'image' */
4111 } /* End Loop 4: Iterate the kernel with primitive */
4113 if ( IfMagickTrue(verbose) && kernel_changed != (size_t)changed )
4114 (void) FormatLocaleFile(stderr, " Total %.20g",(double) kernel_changed);
4115 if ( IfMagickTrue(verbose) && stage_loop < stage_limit )
4116 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line before looping */
4119 (void) FormatLocaleFile(stderr, "--E-- image=0x%lx\n", (unsigned long)image);
4120 (void) FormatLocaleFile(stderr, " curr =0x%lx\n", (unsigned long)curr_image);
4121 (void) FormatLocaleFile(stderr, " work =0x%lx\n", (unsigned long)work_image);
4122 (void) FormatLocaleFile(stderr, " save =0x%lx\n", (unsigned long)save_image);
4123 (void) FormatLocaleFile(stderr, " union=0x%lx\n", (unsigned long)rslt_image);
4126 } /* End Loop 3: Primative (staging) Loop for Coumpound Methods */
4128 /* Final Post-processing for some Compound Methods
4130 ** The removal of any 'Sync' channel flag in the Image Compositon
4131 ** below ensures the methematical compose method is applied in a
4132 ** purely mathematical way, and only to the selected channels.
4133 ** Turn off SVG composition 'alpha blending'.
4136 case EdgeOutMorphology:
4137 case EdgeInMorphology:
4138 case TopHatMorphology:
4139 case BottomHatMorphology:
4140 if ( IfMagickTrue(verbose) )
4141 (void) FormatLocaleFile(stderr,
4142 "\n%s: Difference with original image",CommandOptionToMnemonic(
4143 MagickMorphologyOptions, method) );
4144 (void) CompositeImage(curr_image,image,DifferenceCompositeOp,
4145 MagickTrue,0,0,exception);
4147 case EdgeMorphology:
4148 if ( IfMagickTrue(verbose) )
4149 (void) FormatLocaleFile(stderr,
4150 "\n%s: Difference of Dilate and Erode",CommandOptionToMnemonic(
4151 MagickMorphologyOptions, method) );
4152 (void) CompositeImage(curr_image,save_image,DifferenceCompositeOp,
4153 MagickTrue,0,0,exception);
4154 save_image = DestroyImage(save_image); /* finished with save image */
4160 /* multi-kernel handling: re-iterate, or compose results */
4161 if ( kernel->next == (KernelInfo *) NULL )
4162 rslt_image = curr_image; /* just return the resulting image */
4163 else if ( rslt_compose == NoCompositeOp )
4164 { if ( IfMagickTrue(verbose) ) {
4165 if ( this_kernel->next != (KernelInfo *) NULL )
4166 (void) FormatLocaleFile(stderr, " (re-iterate)");
4168 (void) FormatLocaleFile(stderr, " (done)");
4170 rslt_image = curr_image; /* return result, and re-iterate */
4172 else if ( rslt_image == (Image *) NULL)
4173 { if ( IfMagickTrue(verbose) )
4174 (void) FormatLocaleFile(stderr, " (save for compose)");
4175 rslt_image = curr_image;
4176 curr_image = (Image *) image; /* continue with original image */
4179 { /* Add the new 'current' result to the composition
4181 ** The removal of any 'Sync' channel flag in the Image Compositon
4182 ** below ensures the methematical compose method is applied in a
4183 ** purely mathematical way, and only to the selected channels.
4184 ** IE: Turn off SVG composition 'alpha blending'.
4186 if ( IfMagickTrue(verbose) )
4187 (void) FormatLocaleFile(stderr, " (compose \"%s\")",
4188 CommandOptionToMnemonic(MagickComposeOptions, rslt_compose) );
4189 (void) CompositeImage(rslt_image,curr_image,rslt_compose,MagickTrue,
4191 curr_image = DestroyImage(curr_image);
4192 curr_image = (Image *) image; /* continue with original image */
4194 if ( IfMagickTrue(verbose) )
4195 (void) FormatLocaleFile(stderr, "\n");
4197 /* loop to the next kernel in a multi-kernel list */
4198 norm_kernel = norm_kernel->next;
4199 if ( rflt_kernel != (KernelInfo *) NULL )
4200 rflt_kernel = rflt_kernel->next;
4202 } /* End Loop 2: Loop over each kernel */
4204 } /* End Loop 1: compound method interation */
4208 /* Yes goto's are bad, but it makes cleanup lot more efficient */
4210 if ( curr_image == rslt_image )
4211 curr_image = (Image *) NULL;
4212 if ( rslt_image != (Image *) NULL )
4213 rslt_image = DestroyImage(rslt_image);
4215 if ( curr_image == rslt_image || curr_image == image )
4216 curr_image = (Image *) NULL;
4217 if ( curr_image != (Image *) NULL )
4218 curr_image = DestroyImage(curr_image);
4219 if ( work_image != (Image *) NULL )
4220 work_image = DestroyImage(work_image);
4221 if ( save_image != (Image *) NULL )
4222 save_image = DestroyImage(save_image);
4223 if ( reflected_kernel != (KernelInfo *) NULL )
4224 reflected_kernel = DestroyKernelInfo(reflected_kernel);
4230 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4234 % M o r p h o l o g y I m a g e %
4238 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4240 % MorphologyImage() applies a user supplied kernel to the image
4241 % according to the given mophology method.
4243 % This function applies any and all user defined settings before calling
4244 % the above internal function MorphologyApply().
4246 % User defined settings include...
4247 % * Output Bias for Convolution and correlation ("-define convolve:bias=??")
4248 % * Kernel Scale/normalize settings ("-define convolve:scale=??")
4249 % This can also includes the addition of a scaled unity kernel.
4250 % * Show Kernel being applied ("-define showkernel=1")
4252 % Other operators that do not want user supplied options interfering,
4253 % especially "convolve:bias" and "showkernel" should use MorphologyApply()
4256 % The format of the MorphologyImage method is:
4258 % Image *MorphologyImage(const Image *image,MorphologyMethod method,
4259 % const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
4261 % A description of each parameter follows:
4263 % o image: the image.
4265 % o method: the morphology method to be applied.
4267 % o iterations: apply the operation this many times (or no change).
4268 % A value of -1 means loop until no change found.
4269 % How this is applied may depend on the morphology method.
4270 % Typically this is a value of 1.
4272 % o kernel: An array of double representing the morphology kernel.
4273 % Warning: kernel may be normalized for the Convolve method.
4275 % o exception: return any errors or warnings in this structure.
4278 MagickExport Image *MorphologyImage(const Image *image,
4279 const MorphologyMethod method,const ssize_t iterations,
4280 const KernelInfo *kernel,ExceptionInfo *exception)
4294 curr_kernel = (KernelInfo *) kernel;
4296 compose = UndefinedCompositeOp; /* use default for method */
4298 /* Apply Convolve/Correlate Normalization and Scaling Factors.
4299 * This is done BEFORE the ShowKernelInfo() function is called so that
4300 * users can see the results of the 'option:convolve:scale' option.
4302 if ( method == ConvolveMorphology || method == CorrelateMorphology ) {
4306 /* Get the bias value as it will be needed */
4307 artifact = GetImageArtifact(image,"convolve:bias");
4308 if ( artifact != (const char *) NULL) {
4309 if (IfMagickFalse(IsGeometry(artifact)))
4310 (void) ThrowMagickException(exception,GetMagickModule(),
4311 OptionWarning,"InvalidSetting","'%s' '%s'",
4312 "convolve:bias",artifact);
4314 bias=StringToDoubleInterval(artifact,(double) QuantumRange+1.0);
4317 /* Scale kernel according to user wishes */
4318 artifact = GetImageArtifact(image,"convolve:scale");
4319 if ( artifact != (const char *)NULL ) {
4320 if (IfMagickFalse(IsGeometry(artifact)))
4321 (void) ThrowMagickException(exception,GetMagickModule(),
4322 OptionWarning,"InvalidSetting","'%s' '%s'",
4323 "convolve:scale",artifact);
4325 if ( curr_kernel == kernel )
4326 curr_kernel = CloneKernelInfo(kernel);
4327 if (curr_kernel == (KernelInfo *) NULL)
4328 return((Image *) NULL);
4329 ScaleGeometryKernelInfo(curr_kernel, artifact);
4334 /* display the (normalized) kernel via stderr */
4335 if ( IfStringTrue(GetImageArtifact(image,"showkernel"))
4336 || IfStringTrue(GetImageArtifact(image,"convolve:showkernel"))
4337 || IfStringTrue(GetImageArtifact(image,"morphology:showkernel")) )
4338 ShowKernelInfo(curr_kernel);
4340 /* Override the default handling of multi-kernel morphology results
4341 * If 'Undefined' use the default method
4342 * If 'None' (default for 'Convolve') re-iterate previous result
4343 * Otherwise merge resulting images using compose method given.
4344 * Default for 'HitAndMiss' is 'Lighten'.
4351 artifact = GetImageArtifact(image,"morphology:compose");
4352 if ( artifact != (const char *) NULL) {
4353 parse=ParseCommandOption(MagickComposeOptions,
4354 MagickFalse,artifact);
4356 (void) ThrowMagickException(exception,GetMagickModule(),
4357 OptionWarning,"UnrecognizedComposeOperator","'%s' '%s'",
4358 "morphology:compose",artifact);
4360 compose=(CompositeOperator)parse;
4363 /* Apply the Morphology */
4364 morphology_image = MorphologyApply(image,method,iterations,
4365 curr_kernel,compose,bias,exception);
4367 /* Cleanup and Exit */
4368 if ( curr_kernel != kernel )
4369 curr_kernel=DestroyKernelInfo(curr_kernel);
4370 return(morphology_image);
4374 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4378 + R o t a t e K e r n e l I n f o %
4382 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4384 % RotateKernelInfo() rotates the kernel by the angle given.
4386 % Currently it is restricted to 90 degree angles, of either 1D kernels
4387 % or square kernels. And 'circular' rotations of 45 degrees for 3x3 kernels.
4388 % It will ignore usless rotations for specific 'named' built-in kernels.
4390 % The format of the RotateKernelInfo method is:
4392 % void RotateKernelInfo(KernelInfo *kernel, double angle)
4394 % A description of each parameter follows:
4396 % o kernel: the Morphology/Convolution kernel
4398 % o angle: angle to rotate in degrees
4400 % This function is currently internal to this module only, but can be exported
4401 % to other modules if needed.
4403 static void RotateKernelInfo(KernelInfo *kernel, double angle)
4405 /* angle the lower kernels first */
4406 if ( kernel->next != (KernelInfo *) NULL)
4407 RotateKernelInfo(kernel->next, angle);
4409 /* WARNING: Currently assumes the kernel (rightly) is horizontally symetrical
4411 ** TODO: expand beyond simple 90 degree rotates, flips and flops
4414 /* Modulus the angle */
4415 angle = fmod(angle, 360.0);
4419 if ( 337.5 < angle || angle <= 22.5 )
4420 return; /* Near zero angle - no change! - At least not at this time */
4422 /* Handle special cases */
4423 switch (kernel->type) {
4424 /* These built-in kernels are cylindrical kernels, rotating is useless */
4425 case GaussianKernel:
4430 case LaplacianKernel:
4431 case ChebyshevKernel:
4432 case ManhattanKernel:
4433 case EuclideanKernel:
4436 /* These may be rotatable at non-90 angles in the future */
4437 /* but simply rotating them in multiples of 90 degrees is useless */
4444 /* These only allows a +/-90 degree rotation (by transpose) */
4445 /* A 180 degree rotation is useless */
4447 if ( 135.0 < angle && angle <= 225.0 )
4449 if ( 225.0 < angle && angle <= 315.0 )
4456 /* Attempt rotations by 45 degrees -- 3x3 kernels only */
4457 if ( 22.5 < fmod(angle,90.0) && fmod(angle,90.0) <= 67.5 )
4459 if ( kernel->width == 3 && kernel->height == 3 )
4460 { /* Rotate a 3x3 square by 45 degree angle */
4461 double t = kernel->values[0];
4462 kernel->values[0] = kernel->values[3];
4463 kernel->values[3] = kernel->values[6];
4464 kernel->values[6] = kernel->values[7];
4465 kernel->values[7] = kernel->values[8];
4466 kernel->values[8] = kernel->values[5];
4467 kernel->values[5] = kernel->values[2];
4468 kernel->values[2] = kernel->values[1];
4469 kernel->values[1] = t;
4470 /* rotate non-centered origin */
4471 if ( kernel->x != 1 || kernel->y != 1 ) {
4473 x = (ssize_t) kernel->x-1;
4474 y = (ssize_t) kernel->y-1;
4475 if ( x == y ) x = 0;
4476 else if ( x == 0 ) x = -y;
4477 else if ( x == -y ) y = 0;
4478 else if ( y == 0 ) y = x;
4479 kernel->x = (ssize_t) x+1;
4480 kernel->y = (ssize_t) y+1;
4482 angle = fmod(angle+315.0, 360.0); /* angle reduced 45 degrees */
4483 kernel->angle = fmod(kernel->angle+45.0, 360.0);
4486 perror("Unable to rotate non-3x3 kernel by 45 degrees");
4488 if ( 45.0 < fmod(angle, 180.0) && fmod(angle,180.0) <= 135.0 )
4490 if ( kernel->width == 1 || kernel->height == 1 )
4491 { /* Do a transpose of a 1 dimensional kernel,
4492 ** which results in a fast 90 degree rotation of some type.
4496 t = (ssize_t) kernel->width;
4497 kernel->width = kernel->height;
4498 kernel->height = (size_t) t;
4500 kernel->x = kernel->y;
4502 if ( kernel->width == 1 ) {
4503 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4504 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4506 angle = fmod(angle+90.0, 360.0); /* angle increased 90 degrees */
4507 kernel->angle = fmod(kernel->angle+270.0, 360.0);
4510 else if ( kernel->width == kernel->height )
4511 { /* Rotate a square array of values by 90 degrees */
4515 register MagickRealType
4519 for( i=0, x=(ssize_t) kernel->width-1; i<=x; i++, x--)
4520 for( j=0, y=(ssize_t) kernel->height-1; j<y; j++, y--)
4521 { t = k[i+j*kernel->width];
4522 k[i+j*kernel->width] = k[j+x*kernel->width];
4523 k[j+x*kernel->width] = k[x+y*kernel->width];
4524 k[x+y*kernel->width] = k[y+i*kernel->width];
4525 k[y+i*kernel->width] = t;
4528 /* rotate the origin - relative to center of array */
4529 { register ssize_t x,y;
4530 x = (ssize_t) (kernel->x*2-kernel->width+1);
4531 y = (ssize_t) (kernel->y*2-kernel->height+1);
4532 kernel->x = (ssize_t) ( -y +(ssize_t) kernel->width-1)/2;
4533 kernel->y = (ssize_t) ( +x +(ssize_t) kernel->height-1)/2;
4535 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4536 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4539 perror("Unable to rotate a non-square, non-linear kernel 90 degrees");
4541 if ( 135.0 < angle && angle <= 225.0 )
4543 /* For a 180 degree rotation - also know as a reflection
4544 * This is actually a very very common operation!
4545 * Basically all that is needed is a reversal of the kernel data!
4546 * And a reflection of the origon
4551 register MagickRealType
4559 j=(ssize_t) (kernel->width*kernel->height-1);
4560 for (i=0; i < j; i++, j--)
4561 t=k[i], k[i]=k[j], k[j]=t;
4563 kernel->x = (ssize_t) kernel->width - kernel->x - 1;
4564 kernel->y = (ssize_t) kernel->height - kernel->y - 1;
4565 angle = fmod(angle-180.0, 360.0); /* angle+180 degrees */
4566 kernel->angle = fmod(kernel->angle+180.0, 360.0);
4568 /* At this point angle should at least between -45 (315) and +45 degrees
4569 * In the future some form of non-orthogonal angled rotates could be
4570 * performed here, posibily with a linear kernel restriction.
4577 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4581 % S c a l e G e o m e t r y K e r n e l I n f o %
4585 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4587 % ScaleGeometryKernelInfo() takes a geometry argument string, typically
4588 % provided as a "-set option:convolve:scale {geometry}" user setting,
4589 % and modifies the kernel according to the parsed arguments of that setting.
4591 % The first argument (and any normalization flags) are passed to
4592 % ScaleKernelInfo() to scale/normalize the kernel. The second argument
4593 % is then passed to UnityAddKernelInfo() to add a scled unity kernel
4594 % into the scaled/normalized kernel.
4596 % The format of the ScaleGeometryKernelInfo method is:
4598 % void ScaleGeometryKernelInfo(KernelInfo *kernel,
4599 % const double scaling_factor,const MagickStatusType normalize_flags)
4601 % A description of each parameter follows:
4603 % o kernel: the Morphology/Convolution kernel to modify
4606 % The geometry string to parse, typically from the user provided
4607 % "-set option:convolve:scale {geometry}" setting.
4610 MagickExport void ScaleGeometryKernelInfo (KernelInfo *kernel,
4611 const char *geometry)
4619 SetGeometryInfo(&args);
4620 flags = ParseGeometry(geometry, &args);
4623 /* For Debugging Geometry Input */
4624 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
4625 flags, args.rho, args.sigma, args.xi, args.psi );
4628 if ( (flags & PercentValue) != 0 ) /* Handle Percentage flag*/
4629 args.rho *= 0.01, args.sigma *= 0.01;
4631 if ( (flags & RhoValue) == 0 ) /* Set Defaults for missing args */
4633 if ( (flags & SigmaValue) == 0 )
4636 /* Scale/Normalize the input kernel */
4637 ScaleKernelInfo(kernel, args.rho, (GeometryFlags) flags);
4639 /* Add Unity Kernel, for blending with original */
4640 if ( (flags & SigmaValue) != 0 )
4641 UnityAddKernelInfo(kernel, args.sigma);
4646 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4650 % S c a l e K e r n e l I n f o %
4654 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4656 % ScaleKernelInfo() scales the given kernel list by the given amount, with or
4657 % without normalization of the sum of the kernel values (as per given flags).
4659 % By default (no flags given) the values within the kernel is scaled
4660 % directly using given scaling factor without change.
4662 % If either of the two 'normalize_flags' are given the kernel will first be
4663 % normalized and then further scaled by the scaling factor value given.
4665 % Kernel normalization ('normalize_flags' given) is designed to ensure that
4666 % any use of the kernel scaling factor with 'Convolve' or 'Correlate'
4667 % morphology methods will fall into -1.0 to +1.0 range. Note that for
4668 % non-HDRI versions of IM this may cause images to have any negative results
4669 % clipped, unless some 'bias' is used.
4671 % More specifically. Kernels which only contain positive values (such as a
4672 % 'Gaussian' kernel) will be scaled so that those values sum to +1.0,
4673 % ensuring a 0.0 to +1.0 output range for non-HDRI images.
4675 % For Kernels that contain some negative values, (such as 'Sharpen' kernels)
4676 % the kernel will be scaled by the absolute of the sum of kernel values, so
4677 % that it will generally fall within the +/- 1.0 range.
4679 % For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel
4680 % will be scaled by just the sum of the postive values, so that its output
4681 % range will again fall into the +/- 1.0 range.
4683 % For special kernels designed for locating shapes using 'Correlate', (often
4684 % only containing +1 and -1 values, representing foreground/brackground
4685 % matching) a special normalization method is provided to scale the positive
4686 % values separately to those of the negative values, so the kernel will be
4687 % forced to become a zero-sum kernel better suited to such searches.
4689 % WARNING: Correct normalization of the kernel assumes that the '*_range'
4690 % attributes within the kernel structure have been correctly set during the
4693 % NOTE: The values used for 'normalize_flags' have been selected specifically
4694 % to match the use of geometry options, so that '!' means NormalizeValue, '^'
4695 % means CorrelateNormalizeValue. All other GeometryFlags values are ignored.
4697 % The format of the ScaleKernelInfo method is:
4699 % void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
4700 % const MagickStatusType normalize_flags )
4702 % A description of each parameter follows:
4704 % o kernel: the Morphology/Convolution kernel
4707 % multiply all values (after normalization) by this factor if not
4708 % zero. If the kernel is normalized regardless of any flags.
4710 % o normalize_flags:
4711 % GeometryFlags defining normalization method to use.
4712 % specifically: NormalizeValue, CorrelateNormalizeValue,
4713 % and/or PercentValue
4716 MagickExport void ScaleKernelInfo(KernelInfo *kernel,
4717 const double scaling_factor,const GeometryFlags normalize_flags)
4726 /* do the other kernels in a multi-kernel list first */
4727 if ( kernel->next != (KernelInfo *) NULL)
4728 ScaleKernelInfo(kernel->next, scaling_factor, normalize_flags);
4730 /* Normalization of Kernel */
4732 if ( (normalize_flags&NormalizeValue) != 0 ) {
4733 if ( fabs(kernel->positive_range + kernel->negative_range) >= MagickEpsilon )
4734 /* non-zero-summing kernel (generally positive) */
4735 pos_scale = fabs(kernel->positive_range + kernel->negative_range);
4737 /* zero-summing kernel */
4738 pos_scale = kernel->positive_range;
4740 /* Force kernel into a normalized zero-summing kernel */
4741 if ( (normalize_flags&CorrelateNormalizeValue) != 0 ) {
4742 pos_scale = ( fabs(kernel->positive_range) >= MagickEpsilon )
4743 ? kernel->positive_range : 1.0;
4744 neg_scale = ( fabs(kernel->negative_range) >= MagickEpsilon )
4745 ? -kernel->negative_range : 1.0;
4748 neg_scale = pos_scale;
4750 /* finialize scaling_factor for positive and negative components */
4751 pos_scale = scaling_factor/pos_scale;
4752 neg_scale = scaling_factor/neg_scale;
4754 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
4755 if ( ! IsNaN(kernel->values[i]) )
4756 kernel->values[i] *= (kernel->values[i] >= 0) ? pos_scale : neg_scale;
4758 /* convolution output range */
4759 kernel->positive_range *= pos_scale;
4760 kernel->negative_range *= neg_scale;
4761 /* maximum and minimum values in kernel */
4762 kernel->maximum *= (kernel->maximum >= 0.0) ? pos_scale : neg_scale;
4763 kernel->minimum *= (kernel->minimum >= 0.0) ? pos_scale : neg_scale;
4765 /* swap kernel settings if user's scaling factor is negative */
4766 if ( scaling_factor < MagickEpsilon ) {
4768 t = kernel->positive_range;
4769 kernel->positive_range = kernel->negative_range;
4770 kernel->negative_range = t;
4771 t = kernel->maximum;
4772 kernel->maximum = kernel->minimum;
4773 kernel->minimum = 1;
4780 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4784 % S h o w K e r n e l I n f o %
4788 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4790 % ShowKernelInfo() outputs the details of the given kernel defination to
4791 % standard error, generally due to a users 'showkernel' option request.
4793 % The format of the ShowKernel method is:
4795 % void ShowKernelInfo(const KernelInfo *kernel)
4797 % A description of each parameter follows:
4799 % o kernel: the Morphology/Convolution kernel
4802 MagickPrivate void ShowKernelInfo(const KernelInfo *kernel)
4810 for (c=0, k=kernel; k != (KernelInfo *) NULL; c++, k=k->next ) {
4812 (void) FormatLocaleFile(stderr, "Kernel");
4813 if ( kernel->next != (KernelInfo *) NULL )
4814 (void) FormatLocaleFile(stderr, " #%lu", (unsigned long) c );
4815 (void) FormatLocaleFile(stderr, " \"%s",
4816 CommandOptionToMnemonic(MagickKernelOptions, k->type) );
4817 if ( fabs(k->angle) >= MagickEpsilon )
4818 (void) FormatLocaleFile(stderr, "@%lg", k->angle);
4819 (void) FormatLocaleFile(stderr, "\" of size %lux%lu%+ld%+ld",(unsigned long)
4820 k->width,(unsigned long) k->height,(long) k->x,(long) k->y);
4821 (void) FormatLocaleFile(stderr,
4822 " with values from %.*lg to %.*lg\n",
4823 GetMagickPrecision(), k->minimum,
4824 GetMagickPrecision(), k->maximum);
4825 (void) FormatLocaleFile(stderr, "Forming a output range from %.*lg to %.*lg",
4826 GetMagickPrecision(), k->negative_range,
4827 GetMagickPrecision(), k->positive_range);
4828 if ( fabs(k->positive_range+k->negative_range) < MagickEpsilon )
4829 (void) FormatLocaleFile(stderr, " (Zero-Summing)\n");
4830 else if ( fabs(k->positive_range+k->negative_range-1.0) < MagickEpsilon )
4831 (void) FormatLocaleFile(stderr, " (Normalized)\n");
4833 (void) FormatLocaleFile(stderr, " (Sum %.*lg)\n",
4834 GetMagickPrecision(), k->positive_range+k->negative_range);
4835 for (i=v=0; v < k->height; v++) {
4836 (void) FormatLocaleFile(stderr, "%2lu:", (unsigned long) v );
4837 for (u=0; u < k->width; u++, i++)
4838 if ( IsNaN(k->values[i]) )
4839 (void) FormatLocaleFile(stderr," %*s", GetMagickPrecision()+3, "nan");
4841 (void) FormatLocaleFile(stderr," %*.*lg", GetMagickPrecision()+3,
4842 GetMagickPrecision(), (double) k->values[i]);
4843 (void) FormatLocaleFile(stderr,"\n");
4849 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4853 % U n i t y A d d K e r n a l I n f o %
4857 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4859 % UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel
4860 % to the given pre-scaled and normalized Kernel. This in effect adds that
4861 % amount of the original image into the resulting convolution kernel. This
4862 % value is usually provided by the user as a percentage value in the
4863 % 'convolve:scale' setting.
4865 % The resulting effect is to convert the defined kernels into blended
4866 % soft-blurs, unsharp kernels or into sharpening kernels.
4868 % The format of the UnityAdditionKernelInfo method is:
4870 % void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
4872 % A description of each parameter follows:
4874 % o kernel: the Morphology/Convolution kernel
4877 % scaling factor for the unity kernel to be added to
4881 MagickExport void UnityAddKernelInfo(KernelInfo *kernel,
4884 /* do the other kernels in a multi-kernel list first */
4885 if ( kernel->next != (KernelInfo *) NULL)
4886 UnityAddKernelInfo(kernel->next, scale);
4888 /* Add the scaled unity kernel to the existing kernel */
4889 kernel->values[kernel->x+kernel->y*kernel->width] += scale;
4890 CalcKernelMetaData(kernel); /* recalculate the meta-data */
4896 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4900 % Z e r o K e r n e l N a n s %
4904 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4906 % ZeroKernelNans() replaces any special 'nan' value that may be present in
4907 % the kernel with a zero value. This is typically done when the kernel will
4908 % be used in special hardware (GPU) convolution processors, to simply
4911 % The format of the ZeroKernelNans method is:
4913 % void ZeroKernelNans (KernelInfo *kernel)
4915 % A description of each parameter follows:
4917 % o kernel: the Morphology/Convolution kernel
4920 MagickPrivate void ZeroKernelNans(KernelInfo *kernel)
4925 /* do the other kernels in a multi-kernel list first */
4926 if ( kernel->next != (KernelInfo *) NULL)
4927 ZeroKernelNans(kernel->next);
4929 for (i=0; i < (kernel->width*kernel->height); i++)
4930 if ( IsNaN(kernel->values[i]) )
4931 kernel->values[i] = 0.0;