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 1999-2012 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/monitor-private.h"
68 #include "MagickCore/morphology.h"
69 #include "MagickCore/morphology-private.h"
70 #include "MagickCore/option.h"
71 #include "MagickCore/pixel-accessor.h"
72 #include "MagickCore/prepress.h"
73 #include "MagickCore/quantize.h"
74 #include "MagickCore/registry.h"
75 #include "MagickCore/semaphore.h"
76 #include "MagickCore/splay-tree.h"
77 #include "MagickCore/statistic.h"
78 #include "MagickCore/string_.h"
79 #include "MagickCore/string-private.h"
80 #include "MagickCore/token.h"
81 #include "MagickCore/utility.h"
82 #include "MagickCore/utility-private.h"
86 ** The following test is for special floating point numbers of value NaN (not
87 ** a number), that may be used within a Kernel Definition. NaN's are defined
88 ** as part of the IEEE standard for floating point number representation.
90 ** These are used as a Kernel value to mean that this kernel position is not
91 ** part of the kernel neighbourhood for convolution or morphology processing,
92 ** and thus should be ignored. This allows the use of 'shaped' kernels.
94 ** The special properity that two NaN's are never equal, even if they are from
95 ** the same variable allow you to test if a value is special NaN value.
97 ** This macro IsNaN() is thus is only true if the value given is NaN.
99 #define IsNan(a) ((a)!=(a))
102 Other global definitions used by module.
104 static inline double MagickMin(const double x,const double y)
106 return( x < y ? x : y);
108 static inline double MagickMax(const double x,const double y)
110 return( x > y ? x : y);
112 #define Minimize(assign,value) assign=MagickMin(assign,value)
113 #define Maximize(assign,value) assign=MagickMax(assign,value)
115 /* Currently these are only internal to this module */
117 CalcKernelMetaData(KernelInfo *),
118 ExpandMirrorKernelInfo(KernelInfo *),
119 ExpandRotateKernelInfo(KernelInfo *, const double),
120 RotateKernelInfo(KernelInfo *, double);
123 /* Quick function to find last kernel in a kernel list */
124 static inline KernelInfo *LastKernelInfo(KernelInfo *kernel)
126 while (kernel->next != (KernelInfo *) NULL)
127 kernel = kernel->next;
132 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
136 % A c q u i r e K e r n e l I n f o %
140 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
142 % AcquireKernelInfo() takes the given string (generally supplied by the
143 % user) and converts it into a Morphology/Convolution Kernel. This allows
144 % users to specify a kernel from a number of pre-defined kernels, or to fully
145 % specify their own kernel for a specific Convolution or Morphology
148 % The kernel so generated can be any rectangular array of floating point
149 % values (doubles) with the 'control point' or 'pixel being affected'
150 % anywhere within that array of values.
152 % Previously IM was restricted to a square of odd size using the exact
153 % center as origin, this is no longer the case, and any rectangular kernel
154 % with any value being declared the origin. This in turn allows the use of
155 % highly asymmetrical kernels.
157 % The floating point values in the kernel can also include a special value
158 % known as 'nan' or 'not a number' to indicate that this value is not part
159 % of the kernel array. This allows you to shaped the kernel within its
160 % rectangular area. That is 'nan' values provide a 'mask' for the kernel
161 % shape. However at least one non-nan value must be provided for correct
162 % working of a kernel.
164 % The returned kernel should be freed using the DestroyKernelInfo() when you
165 % are finished with it. Do not free this memory yourself.
167 % Input kernel defintion strings can consist of any of three types.
170 % Select from one of the built in kernels, using the name and
171 % geometry arguments supplied. See AcquireKernelBuiltIn()
173 % "WxH[+X+Y][@><]:num, num, num ..."
174 % a kernel of size W by H, with W*H floating point numbers following.
175 % the 'center' can be optionally be defined at +X+Y (such that +0+0
176 % is top left corner). If not defined the pixel in the center, for
177 % odd sizes, or to the immediate top or left of center for even sizes
178 % is automatically selected.
180 % "num, num, num, num, ..."
181 % list of floating point numbers defining an 'old style' odd sized
182 % square kernel. At least 9 values should be provided for a 3x3
183 % square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc.
184 % Values can be space or comma separated. This is not recommended.
186 % You can define a 'list of kernels' which can be used by some morphology
187 % operators A list is defined as a semi-colon separated list kernels.
189 % " kernel ; kernel ; kernel ; "
191 % Any extra ';' characters, at start, end or between kernel defintions are
194 % The special flags will expand a single kernel, into a list of rotated
195 % kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree
196 % cyclic rotations, while a '>' will generate a list of 90-degree rotations.
197 % The '<' also exands using 90-degree rotates, but giving a 180-degree
198 % reflected kernel before the +/- 90-degree rotations, which can be important
199 % for Thinning operations.
201 % Note that 'name' kernels will start with an alphabetic character while the
202 % new kernel specification has a ':' character in its specification string.
203 % If neither is the case, it is assumed an old style of a simple list of
204 % numbers generating a odd-sized square kernel has been given.
206 % The format of the AcquireKernal method is:
208 % KernelInfo *AcquireKernelInfo(const char *kernel_string)
210 % A description of each parameter follows:
212 % o kernel_string: the Morphology/Convolution kernel wanted.
216 /* This was separated so that it could be used as a separate
217 ** array input handling function, such as for -color-matrix
219 static KernelInfo *ParseKernelArray(const char *kernel_string)
225 token[MaxTextExtent];
235 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
243 kernel=(KernelInfo *) AcquireQuantumMemory(1,sizeof(*kernel));
244 if (kernel == (KernelInfo *)NULL)
246 (void) ResetMagickMemory(kernel,0,sizeof(*kernel));
247 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
248 kernel->negative_range = kernel->positive_range = 0.0;
249 kernel->type = UserDefinedKernel;
250 kernel->next = (KernelInfo *) NULL;
251 kernel->signature = MagickSignature;
252 if (kernel_string == (const char *) NULL)
255 /* find end of this specific kernel definition string */
256 end = strchr(kernel_string, ';');
257 if ( end == (char *) NULL )
258 end = strchr(kernel_string, '\0');
260 /* clear flags - for Expanding kernel lists thorugh rotations */
263 /* Has a ':' in argument - New user kernel specification */
264 p = strchr(kernel_string, ':');
265 if ( p != (char *) NULL && p < end)
267 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
268 memcpy(token, kernel_string, (size_t) (p-kernel_string));
269 token[p-kernel_string] = '\0';
270 SetGeometryInfo(&args);
271 flags = ParseGeometry(token, &args);
273 /* Size handling and checks of geometry settings */
274 if ( (flags & WidthValue) == 0 ) /* if no width then */
275 args.rho = args.sigma; /* then width = height */
276 if ( args.rho < 1.0 ) /* if width too small */
277 args.rho = 1.0; /* then width = 1 */
278 if ( args.sigma < 1.0 ) /* if height too small */
279 args.sigma = args.rho; /* then height = width */
280 kernel->width = (size_t)args.rho;
281 kernel->height = (size_t)args.sigma;
283 /* Offset Handling and Checks */
284 if ( args.xi < 0.0 || args.psi < 0.0 )
285 return(DestroyKernelInfo(kernel));
286 kernel->x = ((flags & XValue)!=0) ? (ssize_t)args.xi
287 : (ssize_t) (kernel->width-1)/2;
288 kernel->y = ((flags & YValue)!=0) ? (ssize_t)args.psi
289 : (ssize_t) (kernel->height-1)/2;
290 if ( kernel->x >= (ssize_t) kernel->width ||
291 kernel->y >= (ssize_t) kernel->height )
292 return(DestroyKernelInfo(kernel));
294 p++; /* advance beyond the ':' */
297 { /* ELSE - Old old specification, forming odd-square kernel */
298 /* count up number of values given */
299 p=(const char *) kernel_string;
300 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
301 p++; /* ignore "'" chars for convolve filter usage - Cristy */
302 for (i=0; p < end; i++)
304 GetMagickToken(p,&p,token);
306 GetMagickToken(p,&p,token);
308 /* set the size of the kernel - old sized square */
309 kernel->width = kernel->height= (size_t) sqrt((double) i+1.0);
310 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
311 p=(const char *) kernel_string;
312 while ((isspace((int) ((unsigned char) *p)) != 0) || (*p == '\''))
313 p++; /* ignore "'" chars for convolve filter usage - Cristy */
316 /* Read in the kernel values from rest of input string argument */
317 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
318 kernel->height*sizeof(*kernel->values));
319 if (kernel->values == (MagickRealType *) NULL)
320 return(DestroyKernelInfo(kernel));
321 kernel->minimum = +MagickHuge;
322 kernel->maximum = -MagickHuge;
323 kernel->negative_range = kernel->positive_range = 0.0;
324 for (i=0; (i < (ssize_t) (kernel->width*kernel->height)) && (p < end); i++)
326 GetMagickToken(p,&p,token);
328 GetMagickToken(p,&p,token);
329 if ( LocaleCompare("nan",token) == 0
330 || LocaleCompare("-",token) == 0 ) {
331 kernel->values[i] = nan; /* this value is not part of neighbourhood */
334 kernel->values[i] = StringToDouble(token,(char **) NULL);
335 ( kernel->values[i] < 0)
336 ? ( kernel->negative_range += kernel->values[i] )
337 : ( kernel->positive_range += kernel->values[i] );
338 Minimize(kernel->minimum, kernel->values[i]);
339 Maximize(kernel->maximum, kernel->values[i]);
343 /* sanity check -- no more values in kernel definition */
344 GetMagickToken(p,&p,token);
345 if ( *token != '\0' && *token != ';' && *token != '\'' )
346 return(DestroyKernelInfo(kernel));
349 /* this was the old method of handling a incomplete kernel */
350 if ( i < (ssize_t) (kernel->width*kernel->height) ) {
351 Minimize(kernel->minimum, kernel->values[i]);
352 Maximize(kernel->maximum, kernel->values[i]);
353 for ( ; i < (ssize_t) (kernel->width*kernel->height); i++)
354 kernel->values[i]=0.0;
357 /* Number of values for kernel was not enough - Report Error */
358 if ( i < (ssize_t) (kernel->width*kernel->height) )
359 return(DestroyKernelInfo(kernel));
362 /* check that we recieved at least one real (non-nan) value! */
363 if ( kernel->minimum == MagickHuge )
364 return(DestroyKernelInfo(kernel));
366 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel size */
367 ExpandRotateKernelInfo(kernel, 45.0); /* cyclic rotate 3x3 kernels */
368 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
369 ExpandRotateKernelInfo(kernel, 90.0); /* 90 degree rotate of kernel */
370 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
371 ExpandMirrorKernelInfo(kernel); /* 90 degree mirror rotate */
376 static KernelInfo *ParseKernelName(const char *kernel_string)
379 token[MaxTextExtent];
397 /* Parse special 'named' kernel */
398 GetMagickToken(kernel_string,&p,token);
399 type=ParseCommandOption(MagickKernelOptions,MagickFalse,token);
400 if ( type < 0 || type == UserDefinedKernel )
401 return((KernelInfo *)NULL); /* not a valid named kernel */
403 while (((isspace((int) ((unsigned char) *p)) != 0) ||
404 (*p == ',') || (*p == ':' )) && (*p != '\0') && (*p != ';'))
407 end = strchr(p, ';'); /* end of this kernel defintion */
408 if ( end == (char *) NULL )
409 end = strchr(p, '\0');
411 /* ParseGeometry() needs the geometry separated! -- Arrgghh */
412 memcpy(token, p, (size_t) (end-p));
414 SetGeometryInfo(&args);
415 flags = ParseGeometry(token, &args);
418 /* For Debugging Geometry Input */
419 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
420 flags, args.rho, args.sigma, args.xi, args.psi );
423 /* special handling of missing values in input string */
425 /* Shape Kernel Defaults */
427 if ( (flags & WidthValue) == 0 )
428 args.rho = 1.0; /* Default scale = 1.0, zero is valid */
436 if ( (flags & HeightValue) == 0 )
437 args.sigma = 1.0; /* Default scale = 1.0, zero is valid */
440 if ( (flags & XValue) == 0 )
441 args.xi = 1.0; /* Default scale = 1.0, zero is valid */
443 case RectangleKernel: /* Rectangle - set size defaults */
444 if ( (flags & WidthValue) == 0 ) /* if no width then */
445 args.rho = args.sigma; /* then width = height */
446 if ( args.rho < 1.0 ) /* if width too small */
447 args.rho = 3; /* then width = 3 */
448 if ( args.sigma < 1.0 ) /* if height too small */
449 args.sigma = args.rho; /* then height = width */
450 if ( (flags & XValue) == 0 ) /* center offset if not defined */
451 args.xi = (double)(((ssize_t)args.rho-1)/2);
452 if ( (flags & YValue) == 0 )
453 args.psi = (double)(((ssize_t)args.sigma-1)/2);
455 /* Distance Kernel Defaults */
456 case ChebyshevKernel:
457 case ManhattanKernel:
458 case OctagonalKernel:
459 case EuclideanKernel:
460 if ( (flags & HeightValue) == 0 ) /* no distance scale */
461 args.sigma = 100.0; /* default distance scaling */
462 else if ( (flags & AspectValue ) != 0 ) /* '!' flag */
463 args.sigma = QuantumRange/(args.sigma+1); /* maximum pixel distance */
464 else if ( (flags & PercentValue ) != 0 ) /* '%' flag */
465 args.sigma *= QuantumRange/100.0; /* percentage of color range */
471 kernel = AcquireKernelBuiltIn((KernelInfoType)type, &args);
472 if ( kernel == (KernelInfo *) NULL )
475 /* global expand to rotated kernel list - only for single kernels */
476 if ( kernel->next == (KernelInfo *) NULL ) {
477 if ( (flags & AreaValue) != 0 ) /* '@' symbol in kernel args */
478 ExpandRotateKernelInfo(kernel, 45.0);
479 else if ( (flags & GreaterValue) != 0 ) /* '>' symbol in kernel args */
480 ExpandRotateKernelInfo(kernel, 90.0);
481 else if ( (flags & LessValue) != 0 ) /* '<' symbol in kernel args */
482 ExpandMirrorKernelInfo(kernel);
488 MagickExport KernelInfo *AcquireKernelInfo(const char *kernel_string)
496 token[MaxTextExtent];
504 if (kernel_string == (const char *) NULL)
505 return(ParseKernelArray(kernel_string));
510 while ( GetMagickToken(p,NULL,token), *token != '\0' ) {
512 /* ignore extra or multiple ';' kernel separators */
513 if ( *token != ';' ) {
515 /* tokens starting with alpha is a Named kernel */
516 if (isalpha((int) *token) != 0)
517 new_kernel = ParseKernelName(p);
518 else /* otherwise a user defined kernel array */
519 new_kernel = ParseKernelArray(p);
521 /* Error handling -- this is not proper error handling! */
522 if ( new_kernel == (KernelInfo *) NULL ) {
523 (void) FormatLocaleFile(stderr, "Failed to parse kernel number #%.20g\n",
524 (double) kernel_number);
525 if ( kernel != (KernelInfo *) NULL )
526 kernel=DestroyKernelInfo(kernel);
527 return((KernelInfo *) NULL);
530 /* initialise or append the kernel list */
531 if ( kernel == (KernelInfo *) NULL )
534 LastKernelInfo(kernel)->next = new_kernel;
537 /* look for the next kernel in list */
539 if ( p == (char *) NULL )
549 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
553 % A c q u i r e K e r n e l B u i l t I n %
557 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
559 % AcquireKernelBuiltIn() returned one of the 'named' built-in types of
560 % kernels used for special purposes such as gaussian blurring, skeleton
561 % pruning, and edge distance determination.
563 % They take a KernelType, and a set of geometry style arguments, which were
564 % typically decoded from a user supplied string, or from a more complex
565 % Morphology Method that was requested.
567 % The format of the AcquireKernalBuiltIn method is:
569 % KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
570 % const GeometryInfo args)
572 % A description of each parameter follows:
574 % o type: the pre-defined type of kernel wanted
576 % o args: arguments defining or modifying the kernel
578 % Convolution Kernels
581 % The a No-Op or Scaling single element kernel.
583 % Gaussian:{radius},{sigma}
584 % Generate a two-dimensional gaussian kernel, as used by -gaussian.
585 % The sigma for the curve is required. The resulting kernel is
588 % If 'sigma' is zero, you get a single pixel on a field of zeros.
590 % NOTE: that the 'radius' is optional, but if provided can limit (clip)
591 % the final size of the resulting kernel to a square 2*radius+1 in size.
592 % The radius should be at least 2 times that of the sigma value, or
593 % sever clipping and aliasing may result. If not given or set to 0 the
594 % radius will be determined so as to produce the best minimal error
595 % result, which is usally much larger than is normally needed.
597 % LoG:{radius},{sigma}
598 % "Laplacian of a Gaussian" or "Mexician Hat" Kernel.
599 % The supposed ideal edge detection, zero-summing kernel.
601 % An alturnative to this kernel is to use a "DoG" with a sigma ratio of
602 % approx 1.6 (according to wikipedia).
604 % DoG:{radius},{sigma1},{sigma2}
605 % "Difference of Gaussians" Kernel.
606 % As "Gaussian" but with a gaussian produced by 'sigma2' subtracted
607 % from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1.
608 % The result is a zero-summing kernel.
610 % Blur:{radius},{sigma}[,{angle}]
611 % Generates a 1 dimensional or linear gaussian blur, at the angle given
612 % (current restricted to orthogonal angles). If a 'radius' is given the
613 % kernel is clipped to a width of 2*radius+1. Kernel can be rotated
614 % by a 90 degree angle.
616 % If 'sigma' is zero, you get a single pixel on a field of zeros.
618 % Note that two convolutions with two "Blur" kernels perpendicular to
619 % each other, is equivalent to a far larger "Gaussian" kernel with the
620 % same sigma value, However it is much faster to apply. This is how the
621 % "-blur" operator actually works.
623 % Comet:{width},{sigma},{angle}
624 % Blur in one direction only, much like how a bright object leaves
625 % a comet like trail. The Kernel is actually half a gaussian curve,
626 % Adding two such blurs in opposite directions produces a Blur Kernel.
627 % Angle can be rotated in multiples of 90 degrees.
629 % Note that the first argument is the width of the kernel and not the
630 % radius of the kernel.
632 % # Still to be implemented...
636 % # Set kernel values using a resize filter, and given scale (sigma)
637 % # Cylindrical or Linear. Is this possible with an image?
640 % Named Constant Convolution Kernels
642 % All these are unscaled, zero-summing kernels by default. As such for
643 % non-HDRI version of ImageMagick some form of normalization, user scaling,
644 % and biasing the results is recommended, to prevent the resulting image
647 % The 3x3 kernels (most of these) can be circularly rotated in multiples of
648 % 45 degrees to generate the 8 angled varients of each of the kernels.
651 % Discrete Lapacian Kernels, (without normalization)
652 % Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood)
653 % Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood)
654 % Type 2 : 3x3 with center:4 edge:1 corner:-2
655 % Type 3 : 3x3 with center:4 edge:-2 corner:1
656 % Type 5 : 5x5 laplacian
657 % Type 7 : 7x7 laplacian
658 % Type 15 : 5x5 LoG (sigma approx 1.4)
659 % Type 19 : 9x9 LoG (sigma approx 1.4)
662 % Sobel 'Edge' convolution kernel (3x3)
668 % Roberts convolution kernel (3x3)
674 % Prewitt Edge convolution kernel (3x3)
680 % Prewitt's "Compass" convolution kernel (3x3)
686 % Kirsch's "Compass" convolution kernel (3x3)
692 % Frei-Chen Edge Detector is based on a kernel that is similar to
693 % the Sobel Kernel, but is designed to be isotropic. That is it takes
694 % into account the distance of the diagonal in the kernel.
697 % | sqrt(2), 0, -sqrt(2) |
700 % FreiChen:{type},{angle}
702 % Frei-Chen Pre-weighted kernels...
704 % Type 0: default un-nomalized version shown above.
706 % Type 1: Orthogonal Kernel (same as type 11 below)
708 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
711 % Type 2: Diagonal form of Kernel...
713 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
716 % However this kernel is als at the heart of the FreiChen Edge Detection
717 % Process which uses a set of 9 specially weighted kernel. These 9
718 % kernels not be normalized, but directly applied to the image. The
719 % results is then added together, to produce the intensity of an edge in
720 % a specific direction. The square root of the pixel value can then be
721 % taken as the cosine of the edge, and at least 2 such runs at 90 degrees
722 % from each other, both the direction and the strength of the edge can be
725 % Type 10: All 9 of the following pre-weighted kernels...
727 % Type 11: | 1, 0, -1 |
728 % | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2)
731 % Type 12: | 1, sqrt(2), 1 |
732 % | 0, 0, 0 | / 2*sqrt(2)
735 % Type 13: | sqrt(2), -1, 0 |
736 % | -1, 0, 1 | / 2*sqrt(2)
739 % Type 14: | 0, 1, -sqrt(2) |
740 % | -1, 0, 1 | / 2*sqrt(2)
743 % Type 15: | 0, -1, 0 |
747 % Type 16: | 1, 0, -1 |
751 % Type 17: | 1, -2, 1 |
755 % Type 18: | -2, 1, -2 |
759 % Type 19: | 1, 1, 1 |
763 % The first 4 are for edge detection, the next 4 are for line detection
764 % and the last is to add a average component to the results.
766 % Using a special type of '-1' will return all 9 pre-weighted kernels
767 % as a multi-kernel list, so that you can use them directly (without
768 % normalization) with the special "-set option:morphology:compose Plus"
769 % setting to apply the full FreiChen Edge Detection Technique.
771 % If 'type' is large it will be taken to be an actual rotation angle for
772 % the default FreiChen (type 0) kernel. As such FreiChen:45 will look
773 % like a Sobel:45 but with 'sqrt(2)' instead of '2' values.
775 % WARNING: The above was layed out as per
776 % http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf
777 % But rotated 90 degrees so direction is from left rather than the top.
778 % I have yet to find any secondary confirmation of the above. The only
779 % other source found was actual source code at
780 % http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf
781 % Neigher paper defineds the kernels in a way that looks locical or
782 % correct when taken as a whole.
786 % Diamond:[{radius}[,{scale}]]
787 % Generate a diamond shaped kernel with given radius to the points.
788 % Kernel size will again be radius*2+1 square and defaults to radius 1,
789 % generating a 3x3 kernel that is slightly larger than a square.
791 % Square:[{radius}[,{scale}]]
792 % Generate a square shaped kernel of size radius*2+1, and defaulting
793 % to a 3x3 (radius 1).
795 % Octagon:[{radius}[,{scale}]]
796 % Generate octagonal shaped kernel of given radius and constant scale.
797 % Default radius is 3 producing a 7x7 kernel. A radius of 1 will result
798 % in "Diamond" kernel.
800 % Disk:[{radius}[,{scale}]]
801 % Generate a binary disk, thresholded at the radius given, the radius
802 % may be a float-point value. Final Kernel size is floor(radius)*2+1
803 % square. A radius of 5.3 is the default.
805 % NOTE: That a low radii Disk kernels produce the same results as
806 % many of the previously defined kernels, but differ greatly at larger
807 % radii. Here is a table of equivalences...
808 % "Disk:1" => "Diamond", "Octagon:1", or "Cross:1"
809 % "Disk:1.5" => "Square"
810 % "Disk:2" => "Diamond:2"
811 % "Disk:2.5" => "Octagon"
812 % "Disk:2.9" => "Square:2"
813 % "Disk:3.5" => "Octagon:3"
814 % "Disk:4.5" => "Octagon:4"
815 % "Disk:5.4" => "Octagon:5"
816 % "Disk:6.4" => "Octagon:6"
817 % All other Disk shapes are unique to this kernel, but because a "Disk"
818 % is more circular when using a larger radius, using a larger radius is
819 % preferred over iterating the morphological operation.
821 % Rectangle:{geometry}
822 % Simply generate a rectangle of 1's with the size given. You can also
823 % specify the location of the 'control point', otherwise the closest
824 % pixel to the center of the rectangle is selected.
826 % Properly centered and odd sized rectangles work the best.
828 % Symbol Dilation Kernels
830 % These kernel is not a good general morphological kernel, but is used
831 % more for highlighting and marking any single pixels in an image using,
832 % a "Dilate" method as appropriate.
834 % For the same reasons iterating these kernels does not produce the
835 % same result as using a larger radius for the symbol.
837 % Plus:[{radius}[,{scale}]]
838 % Cross:[{radius}[,{scale}]]
839 % Generate a kernel in the shape of a 'plus' or a 'cross' with
840 % a each arm the length of the given radius (default 2).
842 % NOTE: "plus:1" is equivalent to a "Diamond" kernel.
844 % Ring:{radius1},{radius2}[,{scale}]
845 % A ring of the values given that falls between the two radii.
846 % Defaults to a ring of approximataly 3 radius in a 7x7 kernel.
847 % This is the 'edge' pixels of the default "Disk" kernel,
848 % More specifically, "Ring" -> "Ring:2.5,3.5,1.0"
850 % Hit and Miss Kernels
852 % Peak:radius1,radius2
853 % Find any peak larger than the pixels the fall between the two radii.
854 % The default ring of pixels is as per "Ring".
856 % Find flat orthogonal edges of a binary shape
858 % Find 90 degree corners of a binary shape
860 % A special kernel to thin the 'outside' of diagonals
862 % Find end points of lines (for pruning a skeletion)
863 % Two types of lines ends (default to both) can be searched for
864 % Type 0: All line ends
865 % Type 1: single kernel for 4-conneected line ends
866 % Type 2: single kernel for simple line ends
868 % Find three line junctions (within a skeletion)
869 % Type 0: all line junctions
870 % Type 1: Y Junction kernel
871 % Type 2: Diagonal T Junction kernel
872 % Type 3: Orthogonal T Junction kernel
873 % Type 4: Diagonal X Junction kernel
874 % Type 5: Orthogonal + Junction kernel
876 % Find single pixel ridges or thin lines
877 % Type 1: Fine single pixel thick lines and ridges
878 % Type 2: Find two pixel thick lines and ridges
880 % Octagonal Thickening Kernel, to generate convex hulls of 45 degrees
882 % Traditional skeleton generating kernels.
883 % Type 1: Tradional Skeleton kernel (4 connected skeleton)
884 % Type 2: HIPR2 Skeleton kernel (8 connected skeleton)
885 % Type 3: Thinning skeleton based on a ressearch paper by
886 % Dan S. Bloomberg (Default Type)
888 % A huge variety of Thinning Kernels designed to preserve conectivity.
889 % many other kernel sets use these kernels as source definitions.
890 % Type numbers are 41-49, 81-89, 481, and 482 which are based on
891 % the super and sub notations used in the source research paper.
893 % Distance Measuring Kernels
895 % Different types of distance measuring methods, which are used with the
896 % a 'Distance' morphology method for generating a gradient based on
897 % distance from an edge of a binary shape, though there is a technique
898 % for handling a anti-aliased shape.
900 % See the 'Distance' Morphological Method, for information of how it is
903 % Chebyshev:[{radius}][x{scale}[%!]]
904 % Chebyshev Distance (also known as Tchebychev or Chessboard distance)
905 % is a value of one to any neighbour, orthogonal or diagonal. One why
906 % of thinking of it is the number of squares a 'King' or 'Queen' in
907 % chess needs to traverse reach any other position on a chess board.
908 % It results in a 'square' like distance function, but one where
909 % diagonals are given a value that is closer than expected.
911 % Manhattan:[{radius}][x{scale}[%!]]
912 % Manhattan Distance (also known as Rectilinear, City Block, or the Taxi
913 % Cab distance metric), it is the distance needed when you can only
914 % travel in horizontal or vertical directions only. It is the
915 % distance a 'Rook' in chess would have to travel, and results in a
916 % diamond like distances, where diagonals are further than expected.
918 % Octagonal:[{radius}][x{scale}[%!]]
919 % An interleving of Manhatten and Chebyshev metrics producing an
920 % increasing octagonally shaped distance. Distances matches those of
921 % the "Octagon" shaped kernel of the same radius. The minimum radius
922 % and default is 2, producing a 5x5 kernel.
924 % Euclidean:[{radius}][x{scale}[%!]]
925 % Euclidean distance is the 'direct' or 'as the crow flys' distance.
926 % However by default the kernel size only has a radius of 1, which
927 % limits the distance to 'Knight' like moves, with only orthogonal and
928 % diagonal measurements being correct. As such for the default kernel
929 % you will get octagonal like distance function.
931 % However using a larger radius such as "Euclidean:4" you will get a
932 % much smoother distance gradient from the edge of the shape. Especially
933 % if the image is pre-processed to include any anti-aliasing pixels.
934 % Of course a larger kernel is slower to use, and not always needed.
936 % The first three Distance Measuring Kernels will only generate distances
937 % of exact multiples of {scale} in binary images. As such you can use a
938 % scale of 1 without loosing any information. However you also need some
939 % scaling when handling non-binary anti-aliased shapes.
941 % The "Euclidean" Distance Kernel however does generate a non-integer
942 % fractional results, and as such scaling is vital even for binary shapes.
946 MagickExport KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
947 const GeometryInfo *args)
960 nan = sqrt((double)-1.0); /* Special Value : Not A Number */
962 /* Generate a new empty kernel if needed */
963 kernel=(KernelInfo *) NULL;
965 case UndefinedKernel: /* These should not call this function */
966 case UserDefinedKernel:
967 assert("Should not call this function" != (char *)NULL);
969 case LaplacianKernel: /* Named Descrete Convolution Kernels */
970 case SobelKernel: /* these are defined using other kernels */
976 case EdgesKernel: /* Hit and Miss kernels */
978 case DiagonalsKernel:
980 case LineJunctionsKernel:
982 case ConvexHullKernel:
985 break; /* A pre-generated kernel is not needed */
987 /* set to 1 to do a compile-time check that we haven't missed anything */
996 case RectangleKernel:
1003 case ChebyshevKernel:
1004 case ManhattanKernel:
1005 case OctangonalKernel:
1006 case EuclideanKernel:
1010 /* Generate the base Kernel Structure */
1011 kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
1012 if (kernel == (KernelInfo *) NULL)
1014 (void) ResetMagickMemory(kernel,0,sizeof(*kernel));
1015 kernel->minimum = kernel->maximum = kernel->angle = 0.0;
1016 kernel->negative_range = kernel->positive_range = 0.0;
1017 kernel->type = type;
1018 kernel->next = (KernelInfo *) NULL;
1019 kernel->signature = MagickSignature;
1029 kernel->height = kernel->width = (size_t) 1;
1030 kernel->x = kernel->y = (ssize_t) 0;
1031 kernel->values=(MagickRealType *) AcquireAlignedMemory(1,
1032 sizeof(*kernel->values));
1033 if (kernel->values == (MagickRealType *) NULL)
1034 return(DestroyKernelInfo(kernel));
1035 kernel->maximum = kernel->values[0] = args->rho;
1039 case GaussianKernel:
1043 sigma = fabs(args->sigma),
1044 sigma2 = fabs(args->xi),
1047 if ( args->rho >= 1.0 )
1048 kernel->width = (size_t)args->rho*2+1;
1049 else if ( (type != DoGKernel) || (sigma >= sigma2) )
1050 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma);
1052 kernel->width = GetOptimalKernelWidth2D(args->rho,sigma2);
1053 kernel->height = kernel->width;
1054 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1055 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1056 kernel->height*sizeof(*kernel->values));
1057 if (kernel->values == (MagickRealType *) NULL)
1058 return(DestroyKernelInfo(kernel));
1060 /* WARNING: The following generates a 'sampled gaussian' kernel.
1061 * What we really want is a 'discrete gaussian' kernel.
1063 * How to do this is I don't know, but appears to be basied on the
1064 * Error Function 'erf()' (intergral of a gaussian)
1067 if ( type == GaussianKernel || type == DoGKernel )
1068 { /* Calculate a Gaussian, OR positive half of a DoG */
1069 if ( sigma > MagickEpsilon )
1070 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1071 B = (double) (1.0/(Magick2PI*sigma*sigma));
1072 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1073 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1074 kernel->values[i] = exp(-((double)(u*u+v*v))*A)*B;
1076 else /* limiting case - a unity (normalized Dirac) kernel */
1077 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1078 kernel->width*kernel->height*sizeof(double));
1079 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1083 if ( type == DoGKernel )
1084 { /* Subtract a Negative Gaussian for "Difference of Gaussian" */
1085 if ( sigma2 > MagickEpsilon )
1086 { sigma = sigma2; /* simplify loop expressions */
1087 A = 1.0/(2.0*sigma*sigma);
1088 B = (double) (1.0/(Magick2PI*sigma*sigma));
1089 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1090 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1091 kernel->values[i] -= exp(-((double)(u*u+v*v))*A)*B;
1093 else /* limiting case - a unity (normalized Dirac) kernel */
1094 kernel->values[kernel->x+kernel->y*kernel->width] -= 1.0;
1097 if ( type == LoGKernel )
1098 { /* Calculate a Laplacian of a Gaussian - Or Mexician Hat */
1099 if ( sigma > MagickEpsilon )
1100 { A = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1101 B = (double) (1.0/(MagickPI*sigma*sigma*sigma*sigma));
1102 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1103 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1104 { R = ((double)(u*u+v*v))*A;
1105 kernel->values[i] = (1-R)*exp(-R)*B;
1108 else /* special case - generate a unity kernel */
1109 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1110 kernel->width*kernel->height*sizeof(double));
1111 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1115 /* Note the above kernels may have been 'clipped' by a user defined
1116 ** radius, producing a smaller (darker) kernel. Also for very small
1117 ** sigma's (> 0.1) the central value becomes larger than one, and thus
1118 ** producing a very bright kernel.
1120 ** Normalization will still be needed.
1123 /* Normalize the 2D Gaussian Kernel
1125 ** NB: a CorrelateNormalize performs a normal Normalize if
1126 ** there are no negative values.
1128 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1129 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1135 sigma = fabs(args->sigma),
1138 if ( args->rho >= 1.0 )
1139 kernel->width = (size_t)args->rho*2+1;
1141 kernel->width = GetOptimalKernelWidth1D(args->rho,sigma);
1143 kernel->x = (ssize_t) (kernel->width-1)/2;
1145 kernel->negative_range = kernel->positive_range = 0.0;
1146 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1147 kernel->height*sizeof(*kernel->values));
1148 if (kernel->values == (MagickRealType *) NULL)
1149 return(DestroyKernelInfo(kernel));
1152 #define KernelRank 3
1153 /* Formula derived from GetBlurKernel() in "effect.c" (plus bug fix).
1154 ** It generates a gaussian 3 times the width, and compresses it into
1155 ** the expected range. This produces a closer normalization of the
1156 ** resulting kernel, especially for very low sigma values.
1157 ** As such while wierd it is prefered.
1159 ** I am told this method originally came from Photoshop.
1161 ** A properly normalized curve is generated (apart from edge clipping)
1162 ** even though we later normalize the result (for edge clipping)
1163 ** to allow the correct generation of a "Difference of Blurs".
1167 v = (ssize_t) (kernel->width*KernelRank-1)/2; /* start/end points to fit range */
1168 (void) ResetMagickMemory(kernel->values,0, (size_t)
1169 kernel->width*kernel->height*sizeof(double));
1170 /* Calculate a Positive 1D Gaussian */
1171 if ( sigma > MagickEpsilon )
1172 { sigma *= KernelRank; /* simplify loop expressions */
1173 alpha = 1.0/(2.0*sigma*sigma);
1174 beta= (double) (1.0/(MagickSQ2PI*sigma ));
1175 for ( u=-v; u <= v; u++) {
1176 kernel->values[(u+v)/KernelRank] +=
1177 exp(-((double)(u*u))*alpha)*beta;
1180 else /* special case - generate a unity kernel */
1181 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1183 /* Direct calculation without curve averaging */
1185 /* Calculate a Positive Gaussian */
1186 if ( sigma > MagickEpsilon )
1187 { alpha = 1.0/(2.0*sigma*sigma); /* simplify loop expressions */
1188 beta = 1.0/(MagickSQ2PI*sigma);
1189 for ( i=0, u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1190 kernel->values[i] = exp(-((double)(u*u))*alpha)*beta;
1192 else /* special case - generate a unity kernel */
1193 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1194 kernel->width*kernel->height*sizeof(double));
1195 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1198 /* Note the above kernel may have been 'clipped' by a user defined
1199 ** radius, producing a smaller (darker) kernel. Also for very small
1200 ** sigma's (> 0.1) the central value becomes larger than one, and thus
1201 ** producing a very bright kernel.
1203 ** Normalization will still be needed.
1206 /* Normalize the 1D Gaussian Kernel
1208 ** NB: a CorrelateNormalize performs a normal Normalize if
1209 ** there are no negative values.
1211 CalcKernelMetaData(kernel); /* the other kernel meta-data */
1212 ScaleKernelInfo(kernel, 1.0, CorrelateNormalizeValue);
1214 /* rotate the 1D kernel by given angle */
1215 RotateKernelInfo(kernel, args->xi );
1220 sigma = fabs(args->sigma),
1223 if ( args->rho < 1.0 )
1224 kernel->width = (GetOptimalKernelWidth1D(args->rho,sigma)-1)/2+1;
1226 kernel->width = (size_t)args->rho;
1227 kernel->x = kernel->y = 0;
1229 kernel->negative_range = kernel->positive_range = 0.0;
1230 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1231 kernel->height*sizeof(*kernel->values));
1232 if (kernel->values == (MagickRealType *) NULL)
1233 return(DestroyKernelInfo(kernel));
1235 /* A comet blur is half a 1D gaussian curve, so that the object is
1236 ** blurred in one direction only. This may not be quite the right
1237 ** curve to use so may change in the future. The function must be
1238 ** normalised after generation, which also resolves any clipping.
1240 ** As we are normalizing and not subtracting gaussians,
1241 ** there is no need for a divisor in the gaussian formula
1243 ** It is less comples
1245 if ( sigma > MagickEpsilon )
1248 #define KernelRank 3
1249 v = (ssize_t) kernel->width*KernelRank; /* start/end points */
1250 (void) ResetMagickMemory(kernel->values,0, (size_t)
1251 kernel->width*sizeof(double));
1252 sigma *= KernelRank; /* simplify the loop expression */
1253 A = 1.0/(2.0*sigma*sigma);
1254 /* B = 1.0/(MagickSQ2PI*sigma); */
1255 for ( u=0; u < v; u++) {
1256 kernel->values[u/KernelRank] +=
1257 exp(-((double)(u*u))*A);
1258 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1260 for (i=0; i < (ssize_t) kernel->width; i++)
1261 kernel->positive_range += kernel->values[i];
1263 A = 1.0/(2.0*sigma*sigma); /* simplify the loop expression */
1264 /* B = 1.0/(MagickSQ2PI*sigma); */
1265 for ( i=0; i < (ssize_t) kernel->width; i++)
1266 kernel->positive_range +=
1268 exp(-((double)(i*i))*A);
1269 /* exp(-((double)(i*i))/2.0*sigma*sigma)/(MagickSQ2PI*sigma); */
1272 else /* special case - generate a unity kernel */
1273 { (void) ResetMagickMemory(kernel->values,0, (size_t)
1274 kernel->width*kernel->height*sizeof(double));
1275 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1276 kernel->positive_range = 1.0;
1279 kernel->minimum = 0.0;
1280 kernel->maximum = kernel->values[0];
1281 kernel->negative_range = 0.0;
1283 ScaleKernelInfo(kernel, 1.0, NormalizeValue); /* Normalize */
1284 RotateKernelInfo(kernel, args->xi); /* Rotate by angle */
1289 Convolution Kernels - Well Known Named Constant Kernels
1291 case LaplacianKernel:
1292 { switch ( (int) args->rho ) {
1294 default: /* laplacian square filter -- default */
1295 kernel=ParseKernelArray("3: -1,-1,-1 -1,8,-1 -1,-1,-1");
1297 case 1: /* laplacian diamond filter */
1298 kernel=ParseKernelArray("3: 0,-1,0 -1,4,-1 0,-1,0");
1301 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1304 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 1,-2,1");
1306 case 5: /* a 5x5 laplacian */
1307 kernel=ParseKernelArray(
1308 "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");
1310 case 7: /* a 7x7 laplacian */
1311 kernel=ParseKernelArray(
1312 "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" );
1314 case 15: /* a 5x5 LoG (sigma approx 1.4) */
1315 kernel=ParseKernelArray(
1316 "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");
1318 case 19: /* a 9x9 LoG (sigma approx 1.4) */
1319 /* http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue1/IJIP-15.pdf */
1320 kernel=ParseKernelArray(
1321 "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");
1324 if (kernel == (KernelInfo *) NULL)
1326 kernel->type = type;
1330 { /* Simple Sobel Kernel */
1331 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1332 if (kernel == (KernelInfo *) NULL)
1334 kernel->type = type;
1335 RotateKernelInfo(kernel, args->rho);
1340 kernel=ParseKernelArray("3: 0,0,0 1,-1,0 0,0,0");
1341 if (kernel == (KernelInfo *) NULL)
1343 kernel->type = type;
1344 RotateKernelInfo(kernel, args->rho);
1349 kernel=ParseKernelArray("3: 1,0,-1 1,0,-1 1,0,-1");
1350 if (kernel == (KernelInfo *) NULL)
1352 kernel->type = type;
1353 RotateKernelInfo(kernel, args->rho);
1358 kernel=ParseKernelArray("3: 1,1,-1 1,-2,-1 1,1,-1");
1359 if (kernel == (KernelInfo *) NULL)
1361 kernel->type = type;
1362 RotateKernelInfo(kernel, args->rho);
1367 kernel=ParseKernelArray("3: 5,-3,-3 5,0,-3 5,-3,-3");
1368 if (kernel == (KernelInfo *) NULL)
1370 kernel->type = type;
1371 RotateKernelInfo(kernel, args->rho);
1374 case FreiChenKernel:
1375 /* Direction is set to be left to right positive */
1376 /* http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf -- RIGHT? */
1377 /* http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf -- WRONG? */
1378 { switch ( (int) args->rho ) {
1381 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1382 if (kernel == (KernelInfo *) NULL)
1384 kernel->type = type;
1385 kernel->values[3]+=(MagickRealType) MagickSQ2;
1386 kernel->values[5]-=(MagickRealType) MagickSQ2;
1387 CalcKernelMetaData(kernel); /* recalculate meta-data */
1390 kernel=ParseKernelArray("3: 1,2,0 2,0,-2 0,-2,-1");
1391 if (kernel == (KernelInfo *) NULL)
1393 kernel->type = type;
1394 kernel->values[1] = kernel->values[3]+=(MagickRealType) MagickSQ2;
1395 kernel->values[5] = kernel->values[7]-=(MagickRealType) MagickSQ2;
1396 CalcKernelMetaData(kernel); /* recalculate meta-data */
1397 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1400 kernel=AcquireKernelInfo("FreiChen:11;FreiChen:12;FreiChen:13;FreiChen:14;FreiChen:15;FreiChen:16;FreiChen:17;FreiChen:18;FreiChen:19");
1401 if (kernel == (KernelInfo *) NULL)
1406 kernel=ParseKernelArray("3: 1,0,-1 2,0,-2 1,0,-1");
1407 if (kernel == (KernelInfo *) NULL)
1409 kernel->type = type;
1410 kernel->values[3]+=(MagickRealType) MagickSQ2;
1411 kernel->values[5]-=(MagickRealType) MagickSQ2;
1412 CalcKernelMetaData(kernel); /* recalculate meta-data */
1413 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1416 kernel=ParseKernelArray("3: 1,2,1 0,0,0 1,2,1");
1417 if (kernel == (KernelInfo *) NULL)
1419 kernel->type = type;
1420 kernel->values[1]+=(MagickRealType) MagickSQ2;
1421 kernel->values[7]+=(MagickRealType) MagickSQ2;
1422 CalcKernelMetaData(kernel);
1423 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1426 kernel=ParseKernelArray("3: 2,-1,0 -1,0,1 0,1,-2");
1427 if (kernel == (KernelInfo *) NULL)
1429 kernel->type = type;
1430 kernel->values[0]+=(MagickRealType) MagickSQ2;
1431 kernel->values[8]-=(MagickRealType) MagickSQ2;
1432 CalcKernelMetaData(kernel);
1433 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1436 kernel=ParseKernelArray("3: 0,1,-2 -1,0,1 2,-1,0");
1437 if (kernel == (KernelInfo *) NULL)
1439 kernel->type = type;
1440 kernel->values[2]-=(MagickRealType) MagickSQ2;
1441 kernel->values[6]+=(MagickRealType) MagickSQ2;
1442 CalcKernelMetaData(kernel);
1443 ScaleKernelInfo(kernel, (double) (1.0/2.0*MagickSQ2), NoValue);
1446 kernel=ParseKernelArray("3: 0,-1,0 1,0,1 0,-1,0");
1447 if (kernel == (KernelInfo *) NULL)
1449 kernel->type = type;
1450 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1453 kernel=ParseKernelArray("3: 1,0,-1 0,0,0 -1,0,1");
1454 if (kernel == (KernelInfo *) NULL)
1456 kernel->type = type;
1457 ScaleKernelInfo(kernel, 1.0/2.0, NoValue);
1460 kernel=ParseKernelArray("3: 1,-2,1 -2,4,-2 -1,-2,1");
1461 if (kernel == (KernelInfo *) NULL)
1463 kernel->type = type;
1464 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1467 kernel=ParseKernelArray("3: -2,1,-2 1,4,1 -2,1,-2");
1468 if (kernel == (KernelInfo *) NULL)
1470 kernel->type = type;
1471 ScaleKernelInfo(kernel, 1.0/6.0, NoValue);
1474 kernel=ParseKernelArray("3: 1,1,1 1,1,1 1,1,1");
1475 if (kernel == (KernelInfo *) NULL)
1477 kernel->type = type;
1478 ScaleKernelInfo(kernel, 1.0/3.0, NoValue);
1481 if ( fabs(args->sigma) > MagickEpsilon )
1482 /* Rotate by correctly supplied 'angle' */
1483 RotateKernelInfo(kernel, args->sigma);
1484 else if ( args->rho > 30.0 || args->rho < -30.0 )
1485 /* Rotate by out of bounds 'type' */
1486 RotateKernelInfo(kernel, args->rho);
1491 Boolean or Shaped Kernels
1495 if (args->rho < 1.0)
1496 kernel->width = kernel->height = 3; /* default radius = 1 */
1498 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1499 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1501 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1502 kernel->height*sizeof(*kernel->values));
1503 if (kernel->values == (MagickRealType *) NULL)
1504 return(DestroyKernelInfo(kernel));
1506 /* set all kernel values within diamond area to scale given */
1507 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1508 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1509 if ( (labs((long) u)+labs((long) v)) <= (long) kernel->x)
1510 kernel->positive_range += kernel->values[i] = args->sigma;
1512 kernel->values[i] = nan;
1513 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1517 case RectangleKernel:
1520 if ( type == SquareKernel )
1522 if (args->rho < 1.0)
1523 kernel->width = kernel->height = 3; /* default radius = 1 */
1525 kernel->width = kernel->height = (size_t) (2*args->rho+1);
1526 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1527 scale = args->sigma;
1530 /* NOTE: user defaults set in "AcquireKernelInfo()" */
1531 if ( args->rho < 1.0 || args->sigma < 1.0 )
1532 return(DestroyKernelInfo(kernel)); /* invalid args given */
1533 kernel->width = (size_t)args->rho;
1534 kernel->height = (size_t)args->sigma;
1535 if ( args->xi < 0.0 || args->xi > (double)kernel->width ||
1536 args->psi < 0.0 || args->psi > (double)kernel->height )
1537 return(DestroyKernelInfo(kernel)); /* invalid args given */
1538 kernel->x = (ssize_t) args->xi;
1539 kernel->y = (ssize_t) args->psi;
1542 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1543 kernel->height*sizeof(*kernel->values));
1544 if (kernel->values == (MagickRealType *) NULL)
1545 return(DestroyKernelInfo(kernel));
1547 /* set all kernel values to scale given */
1548 u=(ssize_t) (kernel->width*kernel->height);
1549 for ( i=0; i < u; i++)
1550 kernel->values[i] = scale;
1551 kernel->minimum = kernel->maximum = scale; /* a flat shape */
1552 kernel->positive_range = scale*u;
1557 if (args->rho < 1.0)
1558 kernel->width = kernel->height = 5; /* default radius = 2 */
1560 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1561 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1563 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1564 kernel->height*sizeof(*kernel->values));
1565 if (kernel->values == (MagickRealType *) NULL)
1566 return(DestroyKernelInfo(kernel));
1568 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1569 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1570 if ( (labs((long) u)+labs((long) v)) <=
1571 ((long)kernel->x + (long)(kernel->x/2)) )
1572 kernel->positive_range += kernel->values[i] = args->sigma;
1574 kernel->values[i] = nan;
1575 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1581 limit = (ssize_t)(args->rho*args->rho);
1583 if (args->rho < 0.4) /* default radius approx 4.3 */
1584 kernel->width = kernel->height = 9L, limit = 18L;
1586 kernel->width = kernel->height = (size_t)fabs(args->rho)*2+1;
1587 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1589 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1590 kernel->height*sizeof(*kernel->values));
1591 if (kernel->values == (MagickRealType *) NULL)
1592 return(DestroyKernelInfo(kernel));
1594 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1595 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1596 if ((u*u+v*v) <= limit)
1597 kernel->positive_range += kernel->values[i] = args->sigma;
1599 kernel->values[i] = nan;
1600 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1605 if (args->rho < 1.0)
1606 kernel->width = kernel->height = 5; /* default radius 2 */
1608 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1609 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1611 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1612 kernel->height*sizeof(*kernel->values));
1613 if (kernel->values == (MagickRealType *) NULL)
1614 return(DestroyKernelInfo(kernel));
1616 /* set all kernel values along axises to given scale */
1617 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1618 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1619 kernel->values[i] = (u == 0 || v == 0) ? args->sigma : nan;
1620 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1621 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1626 if (args->rho < 1.0)
1627 kernel->width = kernel->height = 5; /* default radius 2 */
1629 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
1630 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1632 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1633 kernel->height*sizeof(*kernel->values));
1634 if (kernel->values == (MagickRealType *) NULL)
1635 return(DestroyKernelInfo(kernel));
1637 /* set all kernel values along axises to given scale */
1638 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
1639 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1640 kernel->values[i] = (u == v || u == -v) ? args->sigma : nan;
1641 kernel->minimum = kernel->maximum = args->sigma; /* a flat shape */
1642 kernel->positive_range = args->sigma*(kernel->width*2.0 - 1.0);
1656 if (args->rho < args->sigma)
1658 kernel->width = ((size_t)args->sigma)*2+1;
1659 limit1 = (ssize_t)(args->rho*args->rho);
1660 limit2 = (ssize_t)(args->sigma*args->sigma);
1664 kernel->width = ((size_t)args->rho)*2+1;
1665 limit1 = (ssize_t)(args->sigma*args->sigma);
1666 limit2 = (ssize_t)(args->rho*args->rho);
1669 kernel->width = 7L, limit1 = 7L, limit2 = 11L;
1671 kernel->height = kernel->width;
1672 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
1673 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
1674 kernel->height*sizeof(*kernel->values));
1675 if (kernel->values == (MagickRealType *) NULL)
1676 return(DestroyKernelInfo(kernel));
1678 /* set a ring of points of 'scale' ( 0.0 for PeaksKernel ) */
1679 scale = (ssize_t) (( type == PeaksKernel) ? 0.0 : args->xi);
1680 for ( i=0, v= -kernel->y; v <= (ssize_t)kernel->y; v++)
1681 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
1682 { ssize_t radius=u*u+v*v;
1683 if (limit1 < radius && radius <= limit2)
1684 kernel->positive_range += kernel->values[i] = (double) scale;
1686 kernel->values[i] = nan;
1688 kernel->minimum = kernel->maximum = (double) scale;
1689 if ( type == PeaksKernel ) {
1690 /* set the central point in the middle */
1691 kernel->values[kernel->x+kernel->y*kernel->width] = 1.0;
1692 kernel->positive_range = 1.0;
1693 kernel->maximum = 1.0;
1699 kernel=AcquireKernelInfo("ThinSE:482");
1700 if (kernel == (KernelInfo *) NULL)
1702 kernel->type = type;
1703 ExpandMirrorKernelInfo(kernel); /* mirror expansion of kernels */
1708 kernel=AcquireKernelInfo("ThinSE:87");
1709 if (kernel == (KernelInfo *) NULL)
1711 kernel->type = type;
1712 ExpandRotateKernelInfo(kernel, 90.0); /* Expand 90 degree rotations */
1715 case DiagonalsKernel:
1717 switch ( (int) args->rho ) {
1722 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1723 if (kernel == (KernelInfo *) NULL)
1725 kernel->type = type;
1726 new_kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1727 if (new_kernel == (KernelInfo *) NULL)
1728 return(DestroyKernelInfo(kernel));
1729 new_kernel->type = type;
1730 LastKernelInfo(kernel)->next = new_kernel;
1731 ExpandMirrorKernelInfo(kernel);
1735 kernel=ParseKernelArray("3: 0,0,0 0,-,1 1,1,-");
1738 kernel=ParseKernelArray("3: 0,0,1 0,-,1 0,1,-");
1741 if (kernel == (KernelInfo *) NULL)
1743 kernel->type = type;
1744 RotateKernelInfo(kernel, args->sigma);
1747 case LineEndsKernel:
1748 { /* Kernels for finding the end of thin lines */
1749 switch ( (int) args->rho ) {
1752 /* set of kernels to find all end of lines */
1753 return(AcquireKernelInfo("LineEnds:1>;LineEnds:2>"));
1755 /* kernel for 4-connected line ends - no rotation */
1756 kernel=ParseKernelArray("3: 0,0,- 0,1,1 0,0,-");
1759 /* kernel to add for 8-connected lines - no rotation */
1760 kernel=ParseKernelArray("3: 0,0,0 0,1,0 0,0,1");
1763 /* kernel to add for orthogonal line ends - does not find corners */
1764 kernel=ParseKernelArray("3: 0,0,0 0,1,1 0,0,0");
1767 /* traditional line end - fails on last T end */
1768 kernel=ParseKernelArray("3: 0,0,0 0,1,- 0,0,-");
1771 if (kernel == (KernelInfo *) NULL)
1773 kernel->type = type;
1774 RotateKernelInfo(kernel, args->sigma);
1777 case LineJunctionsKernel:
1778 { /* kernels for finding the junctions of multiple lines */
1779 switch ( (int) args->rho ) {
1782 /* set of kernels to find all line junctions */
1783 return(AcquireKernelInfo("LineJunctions:1@;LineJunctions:2>"));
1786 kernel=ParseKernelArray("3: 1,-,1 -,1,- -,1,-");
1789 /* Diagonal T Junctions */
1790 kernel=ParseKernelArray("3: 1,-,- -,1,- 1,-,1");
1793 /* Orthogonal T Junctions */
1794 kernel=ParseKernelArray("3: -,-,- 1,1,1 -,1,-");
1797 /* Diagonal X Junctions */
1798 kernel=ParseKernelArray("3: 1,-,1 -,1,- 1,-,1");
1801 /* Orthogonal X Junctions - minimal diamond kernel */
1802 kernel=ParseKernelArray("3: -,1,- 1,1,1 -,1,-");
1805 if (kernel == (KernelInfo *) NULL)
1807 kernel->type = type;
1808 RotateKernelInfo(kernel, args->sigma);
1812 { /* Ridges - Ridge finding kernels */
1815 switch ( (int) args->rho ) {
1818 kernel=ParseKernelArray("3x1:0,1,0");
1819 if (kernel == (KernelInfo *) NULL)
1821 kernel->type = type;
1822 ExpandRotateKernelInfo(kernel, 90.0); /* 2 rotated kernels (symmetrical) */
1825 kernel=ParseKernelArray("4x1:0,1,1,0");
1826 if (kernel == (KernelInfo *) NULL)
1828 kernel->type = type;
1829 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotated kernels */
1831 /* Kernels to find a stepped 'thick' line, 4 rotates + mirrors */
1832 /* Unfortunatally we can not yet rotate a non-square kernel */
1833 /* But then we can't flip a non-symetrical kernel either */
1834 new_kernel=ParseKernelArray("4x3+1+1:0,1,1,- -,1,1,- -,1,1,0");
1835 if (new_kernel == (KernelInfo *) NULL)
1836 return(DestroyKernelInfo(kernel));
1837 new_kernel->type = type;
1838 LastKernelInfo(kernel)->next = new_kernel;
1839 new_kernel=ParseKernelArray("4x3+2+1:0,1,1,- -,1,1,- -,1,1,0");
1840 if (new_kernel == (KernelInfo *) NULL)
1841 return(DestroyKernelInfo(kernel));
1842 new_kernel->type = type;
1843 LastKernelInfo(kernel)->next = new_kernel;
1844 new_kernel=ParseKernelArray("4x3+1+1:-,1,1,0 -,1,1,- 0,1,1,-");
1845 if (new_kernel == (KernelInfo *) NULL)
1846 return(DestroyKernelInfo(kernel));
1847 new_kernel->type = type;
1848 LastKernelInfo(kernel)->next = new_kernel;
1849 new_kernel=ParseKernelArray("4x3+2+1:-,1,1,0 -,1,1,- 0,1,1,-");
1850 if (new_kernel == (KernelInfo *) NULL)
1851 return(DestroyKernelInfo(kernel));
1852 new_kernel->type = type;
1853 LastKernelInfo(kernel)->next = new_kernel;
1854 new_kernel=ParseKernelArray("3x4+1+1:0,-,- 1,1,1 1,1,1 -,-,0");
1855 if (new_kernel == (KernelInfo *) NULL)
1856 return(DestroyKernelInfo(kernel));
1857 new_kernel->type = type;
1858 LastKernelInfo(kernel)->next = new_kernel;
1859 new_kernel=ParseKernelArray("3x4+1+2:0,-,- 1,1,1 1,1,1 -,-,0");
1860 if (new_kernel == (KernelInfo *) NULL)
1861 return(DestroyKernelInfo(kernel));
1862 new_kernel->type = type;
1863 LastKernelInfo(kernel)->next = new_kernel;
1864 new_kernel=ParseKernelArray("3x4+1+1:-,-,0 1,1,1 1,1,1 0,-,-");
1865 if (new_kernel == (KernelInfo *) NULL)
1866 return(DestroyKernelInfo(kernel));
1867 new_kernel->type = type;
1868 LastKernelInfo(kernel)->next = new_kernel;
1869 new_kernel=ParseKernelArray("3x4+1+2:-,-,0 1,1,1 1,1,1 0,-,-");
1870 if (new_kernel == (KernelInfo *) NULL)
1871 return(DestroyKernelInfo(kernel));
1872 new_kernel->type = type;
1873 LastKernelInfo(kernel)->next = new_kernel;
1878 case ConvexHullKernel:
1882 /* first set of 8 kernels */
1883 kernel=ParseKernelArray("3: 1,1,- 1,0,- 1,-,0");
1884 if (kernel == (KernelInfo *) NULL)
1886 kernel->type = type;
1887 ExpandRotateKernelInfo(kernel, 90.0);
1888 /* append the mirror versions too - no flip function yet */
1889 new_kernel=ParseKernelArray("3: 1,1,1 1,0,- -,-,0");
1890 if (new_kernel == (KernelInfo *) NULL)
1891 return(DestroyKernelInfo(kernel));
1892 new_kernel->type = type;
1893 ExpandRotateKernelInfo(new_kernel, 90.0);
1894 LastKernelInfo(kernel)->next = new_kernel;
1897 case SkeletonKernel:
1899 switch ( (int) args->rho ) {
1902 /* Traditional Skeleton...
1903 ** A cyclically rotated single kernel
1905 kernel=AcquireKernelInfo("ThinSE:482");
1906 if (kernel == (KernelInfo *) NULL)
1908 kernel->type = type;
1909 ExpandRotateKernelInfo(kernel, 45.0); /* 8 rotations */
1912 /* HIPR Variation of the cyclic skeleton
1913 ** Corners of the traditional method made more forgiving,
1914 ** but the retain the same cyclic order.
1916 kernel=AcquireKernelInfo("ThinSE:482; ThinSE:87x90;");
1917 if (kernel == (KernelInfo *) NULL)
1919 if (kernel->next == (KernelInfo *) NULL)
1920 return(DestroyKernelInfo(kernel));
1921 kernel->type = type;
1922 kernel->next->type = type;
1923 ExpandRotateKernelInfo(kernel, 90.0); /* 4 rotations of the 2 kernels */
1926 /* Dan Bloomberg Skeleton, from his paper on 3x3 thinning SE's
1927 ** "Connectivity-Preserving Morphological Image Thransformations"
1928 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1929 ** http://www.leptonica.com/papers/conn.pdf
1931 kernel=AcquireKernelInfo(
1932 "ThinSE:41; ThinSE:42; ThinSE:43");
1933 if (kernel == (KernelInfo *) NULL)
1935 kernel->type = type;
1936 kernel->next->type = type;
1937 kernel->next->next->type = type;
1938 ExpandMirrorKernelInfo(kernel); /* 12 kernels total */
1944 { /* Special kernels for general thinning, while preserving connections
1945 ** "Connectivity-Preserving Morphological Image Thransformations"
1946 ** by Dan S. Bloomberg, available on Leptonica, Selected Papers,
1947 ** http://www.leptonica.com/papers/conn.pdf
1949 ** http://tpgit.github.com/Leptonica/ccthin_8c_source.html
1951 ** Note kernels do not specify the origin pixel, allowing them
1952 ** to be used for both thickening and thinning operations.
1954 switch ( (int) args->rho ) {
1955 /* SE for 4-connected thinning */
1956 case 41: /* SE_4_1 */
1957 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,-,1");
1959 case 42: /* SE_4_2 */
1960 kernel=ParseKernelArray("3: -,-,1 0,-,1 -,0,-");
1962 case 43: /* SE_4_3 */
1963 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,-,1");
1965 case 44: /* SE_4_4 */
1966 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,-");
1968 case 45: /* SE_4_5 */
1969 kernel=ParseKernelArray("3: -,0,1 0,-,1 -,0,-");
1971 case 46: /* SE_4_6 */
1972 kernel=ParseKernelArray("3: -,0,- 0,-,1 -,0,1");
1974 case 47: /* SE_4_7 */
1975 kernel=ParseKernelArray("3: -,1,1 0,-,1 -,0,-");
1977 case 48: /* SE_4_8 */
1978 kernel=ParseKernelArray("3: -,-,1 0,-,1 0,-,1");
1980 case 49: /* SE_4_9 */
1981 kernel=ParseKernelArray("3: 0,-,1 0,-,1 -,-,1");
1983 /* SE for 8-connected thinning - negatives of the above */
1984 case 81: /* SE_8_0 */
1985 kernel=ParseKernelArray("3: -,1,- 0,-,1 -,1,-");
1987 case 82: /* SE_8_2 */
1988 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,-,-");
1990 case 83: /* SE_8_3 */
1991 kernel=ParseKernelArray("3: 0,-,- 0,-,1 -,1,-");
1993 case 84: /* SE_8_4 */
1994 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,-");
1996 case 85: /* SE_8_5 */
1997 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,-");
1999 case 86: /* SE_8_6 */
2000 kernel=ParseKernelArray("3: 0,-,- 0,-,1 0,-,1");
2002 case 87: /* SE_8_7 */
2003 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,0,-");
2005 case 88: /* SE_8_8 */
2006 kernel=ParseKernelArray("3: -,1,- 0,-,1 0,1,-");
2008 case 89: /* SE_8_9 */
2009 kernel=ParseKernelArray("3: 0,1,- 0,-,1 -,1,-");
2011 /* Special combined SE kernels */
2012 case 423: /* SE_4_2 , SE_4_3 Combined Kernel */
2013 kernel=ParseKernelArray("3: -,-,1 0,-,- -,0,-");
2015 case 823: /* SE_8_2 , SE_8_3 Combined Kernel */
2016 kernel=ParseKernelArray("3: -,1,- -,-,1 0,-,-");
2018 case 481: /* SE_48_1 - General Connected Corner Kernel */
2019 kernel=ParseKernelArray("3: -,1,1 0,-,1 0,0,-");
2022 case 482: /* SE_48_2 - General Edge Kernel */
2023 kernel=ParseKernelArray("3: 0,-,1 0,-,1 0,-,1");
2026 if (kernel == (KernelInfo *) NULL)
2028 kernel->type = type;
2029 RotateKernelInfo(kernel, args->sigma);
2033 Distance Measuring Kernels
2035 case ChebyshevKernel:
2037 if (args->rho < 1.0)
2038 kernel->width = kernel->height = 3; /* default radius = 1 */
2040 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2041 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2043 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
2044 kernel->height*sizeof(*kernel->values));
2045 if (kernel->values == (MagickRealType *) NULL)
2046 return(DestroyKernelInfo(kernel));
2048 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2049 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2050 kernel->positive_range += ( kernel->values[i] =
2051 args->sigma*MagickMax(fabs((double)u),fabs((double)v)) );
2052 kernel->maximum = kernel->values[0];
2055 case ManhattanKernel:
2057 if (args->rho < 1.0)
2058 kernel->width = kernel->height = 3; /* default radius = 1 */
2060 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2061 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2063 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
2064 kernel->height*sizeof(*kernel->values));
2065 if (kernel->values == (MagickRealType *) NULL)
2066 return(DestroyKernelInfo(kernel));
2068 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2069 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2070 kernel->positive_range += ( kernel->values[i] =
2071 args->sigma*(labs((long) u)+labs((long) v)) );
2072 kernel->maximum = kernel->values[0];
2075 case OctagonalKernel:
2077 if (args->rho < 2.0)
2078 kernel->width = kernel->height = 5; /* default/minimum radius = 2 */
2080 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2081 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2083 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
2084 kernel->height*sizeof(*kernel->values));
2085 if (kernel->values == (MagickRealType *) NULL)
2086 return(DestroyKernelInfo(kernel));
2088 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2089 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2092 r1 = MagickMax(fabs((double)u),fabs((double)v)),
2093 r2 = floor((double)(labs((long)u)+labs((long)v)+1)/1.5);
2094 kernel->positive_range += kernel->values[i] =
2095 args->sigma*MagickMax(r1,r2);
2097 kernel->maximum = kernel->values[0];
2100 case EuclideanKernel:
2102 if (args->rho < 1.0)
2103 kernel->width = kernel->height = 3; /* default radius = 1 */
2105 kernel->width = kernel->height = ((size_t)args->rho)*2+1;
2106 kernel->x = kernel->y = (ssize_t) (kernel->width-1)/2;
2108 kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
2109 kernel->height*sizeof(*kernel->values));
2110 if (kernel->values == (MagickRealType *) NULL)
2111 return(DestroyKernelInfo(kernel));
2113 for ( i=0, v=-kernel->y; v <= (ssize_t)kernel->y; v++)
2114 for ( u=-kernel->x; u <= (ssize_t)kernel->x; u++, i++)
2115 kernel->positive_range += ( kernel->values[i] =
2116 args->sigma*sqrt((double)(u*u+v*v)) );
2117 kernel->maximum = kernel->values[0];
2122 /* No-Op Kernel - Basically just a single pixel on its own */
2123 kernel=ParseKernelArray("1:1");
2124 if (kernel == (KernelInfo *) NULL)
2126 kernel->type = UndefinedKernel;
2135 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2139 % C l o n e K e r n e l I n f o %
2143 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2145 % CloneKernelInfo() creates a new clone of the given Kernel List so that its
2146 % can be modified without effecting the original. The cloned kernel should
2147 % be destroyed using DestoryKernelInfo() when no longer needed.
2149 % The format of the CloneKernelInfo method is:
2151 % KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2153 % A description of each parameter follows:
2155 % o kernel: the Morphology/Convolution kernel to be cloned
2158 MagickExport KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
2166 assert(kernel != (KernelInfo *) NULL);
2167 new_kernel=(KernelInfo *) AcquireMagickMemory(sizeof(*kernel));
2168 if (new_kernel == (KernelInfo *) NULL)
2170 *new_kernel=(*kernel); /* copy values in structure */
2172 /* replace the values with a copy of the values */
2173 new_kernel->values=(MagickRealType *) AcquireAlignedMemory(kernel->width,
2174 kernel->height*sizeof(*kernel->values));
2175 if (new_kernel->values == (MagickRealType *) NULL)
2176 return(DestroyKernelInfo(new_kernel));
2177 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
2178 new_kernel->values[i]=kernel->values[i];
2180 /* Also clone the next kernel in the kernel list */
2181 if ( kernel->next != (KernelInfo *) NULL ) {
2182 new_kernel->next = CloneKernelInfo(kernel->next);
2183 if ( new_kernel->next == (KernelInfo *) NULL )
2184 return(DestroyKernelInfo(new_kernel));
2191 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2195 % D e s t r o y K e r n e l I n f o %
2199 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2201 % DestroyKernelInfo() frees the memory used by a Convolution/Morphology
2204 % The format of the DestroyKernelInfo method is:
2206 % KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2208 % A description of each parameter follows:
2210 % o kernel: the Morphology/Convolution kernel to be destroyed
2213 MagickExport KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
2215 assert(kernel != (KernelInfo *) NULL);
2216 if ( kernel->next != (KernelInfo *) NULL )
2217 kernel->next=DestroyKernelInfo(kernel->next);
2218 kernel->values=(MagickRealType *) RelinquishAlignedMemory(kernel->values);
2219 kernel=(KernelInfo *) RelinquishMagickMemory(kernel);
2224 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2228 + E x p a n d M i r r o r K e r n e l I n f o %
2232 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2234 % ExpandMirrorKernelInfo() takes a single kernel, and expands it into a
2235 % sequence of 90-degree rotated kernels but providing a reflected 180
2236 % rotatation, before the -/+ 90-degree rotations.
2238 % This special rotation order produces a better, more symetrical thinning of
2241 % The format of the ExpandMirrorKernelInfo method is:
2243 % void ExpandMirrorKernelInfo(KernelInfo *kernel)
2245 % A description of each parameter follows:
2247 % o kernel: the Morphology/Convolution kernel
2249 % This function is only internel to this module, as it is not finalized,
2250 % especially with regard to non-orthogonal angles, and rotation of larger
2255 static void FlopKernelInfo(KernelInfo *kernel)
2256 { /* Do a Flop by reversing each row. */
2264 for ( y=0, k=kernel->values; y < kernel->height; y++, k+=kernel->width)
2265 for ( x=0, r=kernel->width-1; x<kernel->width/2; x++, r--)
2266 t=k[x], k[x]=k[r], k[r]=t;
2268 kernel->x = kernel->width - kernel->x - 1;
2269 angle = fmod(angle+180.0, 360.0);
2273 static void ExpandMirrorKernelInfo(KernelInfo *kernel)
2281 clone = CloneKernelInfo(last);
2282 RotateKernelInfo(clone, 180); /* flip */
2283 LastKernelInfo(last)->next = clone;
2286 clone = CloneKernelInfo(last);
2287 RotateKernelInfo(clone, 90); /* transpose */
2288 LastKernelInfo(last)->next = clone;
2291 clone = CloneKernelInfo(last);
2292 RotateKernelInfo(clone, 180); /* flop */
2293 LastKernelInfo(last)->next = clone;
2299 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2303 + E x p a n d R o t a t e K e r n e l I n f o %
2307 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2309 % ExpandRotateKernelInfo() takes a kernel list, and expands it by rotating
2310 % incrementally by the angle given, until the kernel repeats.
2312 % WARNING: 45 degree rotations only works for 3x3 kernels.
2313 % While 90 degree roatations only works for linear and square kernels
2315 % The format of the ExpandRotateKernelInfo method is:
2317 % void ExpandRotateKernelInfo(KernelInfo *kernel, double angle)
2319 % A description of each parameter follows:
2321 % o kernel: the Morphology/Convolution kernel
2323 % o angle: angle to rotate in degrees
2325 % This function is only internel to this module, as it is not finalized,
2326 % especially with regard to non-orthogonal angles, and rotation of larger
2330 /* Internal Routine - Return true if two kernels are the same */
2331 static MagickBooleanType SameKernelInfo(const KernelInfo *kernel1,
2332 const KernelInfo *kernel2)
2337 /* check size and origin location */
2338 if ( kernel1->width != kernel2->width
2339 || kernel1->height != kernel2->height
2340 || kernel1->x != kernel2->x
2341 || kernel1->y != kernel2->y )
2344 /* check actual kernel values */
2345 for (i=0; i < (kernel1->width*kernel1->height); i++) {
2346 /* Test for Nan equivalence */
2347 if ( IsNan(kernel1->values[i]) && !IsNan(kernel2->values[i]) )
2349 if ( IsNan(kernel2->values[i]) && !IsNan(kernel1->values[i]) )
2351 /* Test actual values are equivalent */
2352 if ( fabs(kernel1->values[i] - kernel2->values[i]) > MagickEpsilon )
2359 static void ExpandRotateKernelInfo(KernelInfo *kernel, const double angle)
2367 clone = CloneKernelInfo(last);
2368 RotateKernelInfo(clone, angle);
2369 if ( SameKernelInfo(kernel, clone) == MagickTrue )
2371 LastKernelInfo(last)->next = clone;
2374 clone = DestroyKernelInfo(clone); /* kernel has repeated - junk the clone */
2379 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2383 + C a l c M e t a K e r n a l I n f o %
2387 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2389 % CalcKernelMetaData() recalculate the KernelInfo meta-data of this kernel only,
2390 % using the kernel values. This should only ne used if it is not possible to
2391 % calculate that meta-data in some easier way.
2393 % It is important that the meta-data is correct before ScaleKernelInfo() is
2394 % used to perform kernel normalization.
2396 % The format of the CalcKernelMetaData method is:
2398 % void CalcKernelMetaData(KernelInfo *kernel, const double scale )
2400 % A description of each parameter follows:
2402 % o kernel: the Morphology/Convolution kernel to modify
2404 % WARNING: Minimum and Maximum values are assumed to include zero, even if
2405 % zero is not part of the kernel (as in Gaussian Derived kernels). This
2406 % however is not true for flat-shaped morphological kernels.
2408 % WARNING: Only the specific kernel pointed to is modified, not a list of
2411 % This is an internal function and not expected to be useful outside this
2412 % module. This could change however.
2414 static void CalcKernelMetaData(KernelInfo *kernel)
2419 kernel->minimum = kernel->maximum = 0.0;
2420 kernel->negative_range = kernel->positive_range = 0.0;
2421 for (i=0; i < (kernel->width*kernel->height); i++)
2423 if ( fabs(kernel->values[i]) < MagickEpsilon )
2424 kernel->values[i] = 0.0;
2425 ( kernel->values[i] < 0)
2426 ? ( kernel->negative_range += kernel->values[i] )
2427 : ( kernel->positive_range += kernel->values[i] );
2428 Minimize(kernel->minimum, kernel->values[i]);
2429 Maximize(kernel->maximum, kernel->values[i]);
2436 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2440 % M o r p h o l o g y A p p l y %
2444 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2446 % MorphologyApply() applies a morphological method, multiple times using
2447 % a list of multiple kernels.
2449 % It is basically equivalent to as MorphologyImage() (see below) but
2450 % without any user controls. This allows internel programs to use this
2451 % function, to actually perform a specific task without possible interference
2452 % by any API user supplied settings.
2454 % It is MorphologyImage() task to extract any such user controls, and
2455 % pass them to this function for processing.
2457 % More specifically kernels are not normalized/scaled/blended by the
2458 % 'convolve:scale' Image Artifact (setting), nor is the convolve bias
2459 % ('convolve:bias' artifact) looked at, but must be supplied from the
2460 % function arguments.
2462 % The format of the MorphologyApply method is:
2464 % Image *MorphologyApply(const Image *image,MorphologyMethod method,
2465 % const ssize_t iterations,const KernelInfo *kernel,
2466 % const CompositeMethod compose,const double bias,
2467 % ExceptionInfo *exception)
2469 % A description of each parameter follows:
2471 % o image: the source image
2473 % o method: the morphology method to be applied.
2475 % o iterations: apply the operation this many times (or no change).
2476 % A value of -1 means loop until no change found.
2477 % How this is applied may depend on the morphology method.
2478 % Typically this is a value of 1.
2480 % o channel: the channel type.
2482 % o kernel: An array of double representing the morphology kernel.
2484 % o compose: How to handle or merge multi-kernel results.
2485 % If 'UndefinedCompositeOp' use default for the Morphology method.
2486 % If 'NoCompositeOp' force image to be re-iterated by each kernel.
2487 % Otherwise merge the results using the compose method given.
2489 % o bias: Convolution Output Bias.
2491 % o exception: return any errors or warnings in this structure.
2495 /* Apply a Morphology Primative to an image using the given kernel.
2496 ** Two pre-created images must be provided, and no image is created.
2497 ** It returns the number of pixels that changed between the images
2498 ** for result convergence determination.
2500 static ssize_t MorphologyPrimitive(const Image *image,Image *morphology_image,
2501 const MorphologyMethod method,const KernelInfo *kernel,const double bias,
2502 ExceptionInfo *exception)
2504 #define MorphologyTag "Morphology/Image"
2523 assert(image != (Image *) NULL);
2524 assert(image->signature == MagickSignature);
2525 assert(morphology_image != (Image *) NULL);
2526 assert(morphology_image->signature == MagickSignature);
2527 assert(kernel != (KernelInfo *) NULL);
2528 assert(kernel->signature == MagickSignature);
2529 assert(exception != (ExceptionInfo *) NULL);
2530 assert(exception->signature == MagickSignature);
2536 image_view=AcquireCacheView(image);
2537 morphology_view=AcquireCacheView(morphology_image);
2538 virt_width=image->columns+kernel->width-1;
2540 /* Some methods (including convolve) needs use a reflected kernel.
2541 * Adjust 'origin' offsets to loop though kernel as a reflection.
2546 case ConvolveMorphology:
2547 case DilateMorphology:
2548 case DilateIntensityMorphology:
2549 case IterativeDistanceMorphology:
2550 /* kernel needs to used with reflection about origin */
2551 offx = (ssize_t) kernel->width-offx-1;
2552 offy = (ssize_t) kernel->height-offy-1;
2554 case ErodeMorphology:
2555 case ErodeIntensityMorphology:
2556 case HitAndMissMorphology:
2557 case ThinningMorphology:
2558 case ThickenMorphology:
2559 /* kernel is used as is, without reflection */
2562 assert("Not a Primitive Morphology Method" != (char *) NULL);
2566 if ( method == ConvolveMorphology && kernel->width == 1 )
2567 { /* Special handling (for speed) of vertical (blur) kernels.
2568 ** This performs its handling in columns rather than in rows.
2569 ** This is only done for convolve as it is the only method that
2570 ** generates very large 1-D vertical kernels (such as a 'BlurKernel')
2572 ** Timing tests (on single CPU laptop)
2573 ** Using a vertical 1-d Blue with normal row-by-row (below)
2574 ** time convert logo: -morphology Convolve Blur:0x10+90 null:
2576 ** Using this column method
2577 ** time convert logo: -morphology Convolve Blur:0x10+90 null:
2580 ** Anthony Thyssen, 14 June 2010
2585 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2586 #pragma omp parallel for schedule(static,4) shared(progress,status)
2588 for (x=0; x < (ssize_t) image->columns; x++)
2590 register const Quantum
2602 if (status == MagickFalse)
2604 p=GetCacheViewVirtualPixels(image_view,x,-offy,1,image->rows+
2605 kernel->height-1,exception);
2606 q=GetCacheViewAuthenticPixels(morphology_view,x,0,1,
2607 morphology_image->rows,exception);
2608 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2613 /* offset to origin in 'p'. while 'q' points to it directly */
2616 for (y=0; y < (ssize_t) image->rows; y++)
2624 register const double
2627 register const Quantum
2630 /* Copy input image to the output image for unused channels
2631 * This removes need for 'cloning' a new image every iteration
2633 SetPixelRed(morphology_image,GetPixelRed(image,p+r*
2634 GetPixelChannels(image)),q);
2635 SetPixelGreen(morphology_image,GetPixelGreen(image,p+r*
2636 GetPixelChannels(image)),q);
2637 SetPixelBlue(morphology_image,GetPixelBlue(image,p+r*
2638 GetPixelChannels(image)),q);
2639 if (image->colorspace == CMYKColorspace)
2640 SetPixelBlack(morphology_image,GetPixelBlack(image,p+r*
2641 GetPixelChannels(image)),q);
2643 /* Set the bias of the weighted average output */
2648 result.black = bias;
2651 /* Weighted Average of pixels using reflected kernel
2653 ** NOTE for correct working of this operation for asymetrical
2654 ** kernels, the kernel needs to be applied in its reflected form.
2655 ** That is its values needs to be reversed.
2657 k = &kernel->values[ kernel->height-1 ];
2659 if ( (image->channel_mask != DefaultChannels) ||
2660 (image->matte == MagickFalse) )
2661 { /* No 'Sync' involved.
2662 ** Convolution is just a simple greyscale channel operation
2664 for (v=0; v < (ssize_t) kernel->height; v++) {
2665 if ( IsNan(*k) ) continue;
2666 result.red += (*k)*GetPixelRed(image,k_pixels);
2667 result.green += (*k)*GetPixelGreen(image,k_pixels);
2668 result.blue += (*k)*GetPixelBlue(image,k_pixels);
2669 if (image->colorspace == CMYKColorspace)
2670 result.black+=(*k)*GetPixelBlack(image,k_pixels);
2671 result.alpha += (*k)*GetPixelAlpha(image,k_pixels);
2673 k_pixels+=GetPixelChannels(image);
2675 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
2676 SetPixelRed(morphology_image,ClampToQuantum(result.red),q);
2677 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
2678 SetPixelGreen(morphology_image,ClampToQuantum(result.green),q);
2679 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
2680 SetPixelBlue(morphology_image,ClampToQuantum(result.blue),q);
2681 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
2682 (image->colorspace == CMYKColorspace))
2683 SetPixelBlack(morphology_image,ClampToQuantum(result.black),q);
2684 if (((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0) &&
2685 (image->matte == MagickTrue))
2686 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
2689 { /* Channel 'Sync' Flag, and Alpha Channel enabled.
2690 ** Weight the color channels with Alpha Channel so that
2691 ** transparent pixels are not part of the results.
2694 alpha, /* alpha weighting for colors : alpha */
2695 gamma; /* divisor, sum of color alpha weighting */
2697 count; /* alpha valus collected, number kernel values */
2701 for (v=0; v < (ssize_t) kernel->height; v++) {
2702 if ( IsNan(*k) ) continue;
2703 alpha=QuantumScale*GetPixelAlpha(image,k_pixels);
2704 gamma += alpha; /* normalize alpha weights only */
2705 count++; /* number of alpha values collected */
2706 alpha*=(*k); /* include kernel weighting now */
2707 result.red += alpha*GetPixelRed(image,k_pixels);
2708 result.green += alpha*GetPixelGreen(image,k_pixels);
2709 result.blue += alpha*GetPixelBlue(image,k_pixels);
2710 if (image->colorspace == CMYKColorspace)
2711 result.black += alpha*GetPixelBlack(image,k_pixels);
2712 result.alpha += (*k)*GetPixelAlpha(image,k_pixels);
2714 k_pixels+=GetPixelChannels(image);
2716 /* Sync'ed channels, all channels are modified */
2717 gamma=(double)count/(fabs((double) gamma) <= MagickEpsilon ? 1.0 : gamma);
2718 SetPixelRed(morphology_image,ClampToQuantum(gamma*result.red),q);
2719 SetPixelGreen(morphology_image,ClampToQuantum(gamma*result.green),q);
2720 SetPixelBlue(morphology_image,ClampToQuantum(gamma*result.blue),q);
2721 if (image->colorspace == CMYKColorspace)
2722 SetPixelBlack(morphology_image,ClampToQuantum(gamma*result.black),q);
2723 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
2726 /* Count up changed pixels */
2727 if ((GetPixelRed(image,p+r*GetPixelChannels(image)) != GetPixelRed(morphology_image,q))
2728 || (GetPixelGreen(image,p+r*GetPixelChannels(image)) != GetPixelGreen(morphology_image,q))
2729 || (GetPixelBlue(image,p+r*GetPixelChannels(image)) != GetPixelBlue(morphology_image,q))
2730 || (GetPixelAlpha(image,p+r*GetPixelChannels(image)) != GetPixelAlpha(morphology_image,q))
2731 || ((image->colorspace == CMYKColorspace) &&
2732 (GetPixelBlack(image,p+r*GetPixelChannels(image)) != GetPixelBlack(morphology_image,q))))
2733 changed++; /* The pixel was changed in some way! */
2734 p+=GetPixelChannels(image);
2735 q+=GetPixelChannels(morphology_image);
2737 if ( SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
2739 if (image->progress_monitor != (MagickProgressMonitor) NULL)
2744 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2745 #pragma omp critical (MagickCore_MorphologyImage)
2747 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows);
2748 if (proceed == MagickFalse)
2752 morphology_image->type=image->type;
2753 morphology_view=DestroyCacheView(morphology_view);
2754 image_view=DestroyCacheView(image_view);
2755 return(status ? (ssize_t) changed : 0);
2759 ** Normal handling of horizontal or rectangular kernels (row by row)
2761 #if defined(MAGICKCORE_OPENMP_SUPPORT)
2762 #pragma omp parallel for schedule(static,4) shared(progress,status)
2764 for (y=0; y < (ssize_t) image->rows; y++)
2766 register const Quantum
2778 if (status == MagickFalse)
2780 p=GetCacheViewVirtualPixels(image_view, -offx, y-offy, virt_width,
2781 kernel->height, exception);
2782 q=GetCacheViewAuthenticPixels(morphology_view,0,y,
2783 morphology_image->columns,1,exception);
2784 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
2789 /* offset to origin in 'p'. while 'q' points to it directly */
2790 r = virt_width*offy + offx;
2792 for (x=0; x < (ssize_t) image->columns; x++)
2800 register const double
2803 register const Quantum
2811 /* Copy input image to the output image for unused channels
2812 * This removes need for 'cloning' a new image every iteration
2814 SetPixelRed(morphology_image,GetPixelRed(image,p+r*
2815 GetPixelChannels(image)),q);
2816 SetPixelGreen(morphology_image,GetPixelGreen(image,p+r*
2817 GetPixelChannels(image)),q);
2818 SetPixelBlue(morphology_image,GetPixelBlue(image,p+r*
2819 GetPixelChannels(image)),q);
2820 if (image->colorspace == CMYKColorspace)
2821 SetPixelBlack(morphology_image,GetPixelBlack(image,p+r*
2822 GetPixelChannels(image)),q);
2829 min.black = (MagickRealType) QuantumRange;
2834 max.black = (MagickRealType) 0;
2835 /* default result is the original pixel value */
2836 result.red = (MagickRealType) GetPixelRed(image,p+r*GetPixelChannels(image));
2837 result.green = (MagickRealType) GetPixelGreen(image,p+r*GetPixelChannels(image));
2838 result.blue = (MagickRealType) GetPixelBlue(image,p+r*GetPixelChannels(image));
2840 if (image->colorspace == CMYKColorspace)
2841 result.black = (MagickRealType) GetPixelBlack(image,p+r*GetPixelChannels(image));
2842 result.alpha=(MagickRealType) GetPixelAlpha(image,p+r*GetPixelChannels(image));
2845 case ConvolveMorphology:
2846 /* Set the bias of the weighted average output */
2851 result.black = bias;
2853 case DilateIntensityMorphology:
2854 case ErodeIntensityMorphology:
2855 /* use a boolean flag indicating when first match found */
2856 result.red = 0.0; /* result is not used otherwise */
2863 case ConvolveMorphology:
2864 /* Weighted Average of pixels using reflected kernel
2866 ** NOTE for correct working of this operation for asymetrical
2867 ** kernels, the kernel needs to be applied in its reflected form.
2868 ** That is its values needs to be reversed.
2870 ** Correlation is actually the same as this but without reflecting
2871 ** the kernel, and thus 'lower-level' that Convolution. However
2872 ** as Convolution is the more common method used, and it does not
2873 ** really cost us much in terms of processing to use a reflected
2874 ** kernel, so it is Convolution that is implemented.
2876 ** Correlation will have its kernel reflected before calling
2877 ** this function to do a Convolve.
2879 ** For more details of Correlation vs Convolution see
2880 ** http://www.cs.umd.edu/~djacobs/CMSC426/Convolution.pdf
2882 k = &kernel->values[ kernel->width*kernel->height-1 ];
2884 if ( (image->channel_mask != DefaultChannels) ||
2885 (image->matte == MagickFalse) )
2886 { /* No 'Sync' involved.
2887 ** Convolution is simple greyscale channel operation
2889 for (v=0; v < (ssize_t) kernel->height; v++) {
2890 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
2891 if ( IsNan(*k) ) continue;
2893 GetPixelRed(image,k_pixels+u*GetPixelChannels(image));
2894 result.green += (*k)*
2895 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image));
2896 result.blue += (*k)*
2897 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image));
2898 if (image->colorspace == CMYKColorspace)
2899 result.black += (*k)*
2900 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image));
2901 result.alpha += (*k)*
2902 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image));
2904 k_pixels += virt_width*GetPixelChannels(image);
2906 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
2907 SetPixelRed(morphology_image,ClampToQuantum(result.red),
2909 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
2910 SetPixelGreen(morphology_image,ClampToQuantum(result.green),
2912 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
2913 SetPixelBlue(morphology_image,ClampToQuantum(result.blue),
2915 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
2916 (image->colorspace == CMYKColorspace))
2917 SetPixelBlack(morphology_image,ClampToQuantum(result.black),
2919 if (((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0) &&
2920 (image->matte == MagickTrue))
2921 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),
2925 { /* Channel 'Sync' Flag, and Alpha Channel enabled.
2926 ** Weight the color channels with Alpha Channel so that
2927 ** transparent pixels are not part of the results.
2930 alpha, /* alpha weighting for colors : alpha */
2931 gamma; /* divisor, sum of color alpha weighting */
2933 count; /* alpha valus collected, number kernel values */
2937 for (v=0; v < (ssize_t) kernel->height; v++) {
2938 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
2939 if ( IsNan(*k) ) continue;
2940 alpha=QuantumScale*GetPixelAlpha(image,
2941 k_pixels+u*GetPixelChannels(image));
2942 gamma += alpha; /* normalize alpha weights only */
2943 count++; /* number of alpha values collected */
2944 alpha=alpha*(*k); /* include kernel weighting now */
2945 result.red += alpha*
2946 GetPixelRed(image,k_pixels+u*GetPixelChannels(image));
2947 result.green += alpha*
2948 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image));
2949 result.blue += alpha*
2950 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image));
2951 if (image->colorspace == CMYKColorspace)
2952 result.black += alpha*
2953 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image));
2954 result.alpha += (*k)*
2955 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image));
2957 k_pixels += virt_width*GetPixelChannels(image);
2959 /* Sync'ed channels, all channels are modified */
2960 gamma=(double)count/(fabs((double) gamma) <= MagickEpsilon ? 1.0 : gamma);
2961 SetPixelRed(morphology_image,
2962 ClampToQuantum(gamma*result.red),q);
2963 SetPixelGreen(morphology_image,
2964 ClampToQuantum(gamma*result.green),q);
2965 SetPixelBlue(morphology_image,
2966 ClampToQuantum(gamma*result.blue),q);
2967 if (image->colorspace == CMYKColorspace)
2968 SetPixelBlack(morphology_image,
2969 ClampToQuantum(gamma*result.black),q);
2970 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
2974 case ErodeMorphology:
2975 /* Minimum Value within kernel neighbourhood
2977 ** NOTE that the kernel is not reflected for this operation!
2979 ** NOTE: in normal Greyscale Morphology, the kernel value should
2980 ** be added to the real value, this is currently not done, due to
2981 ** the nature of the boolean kernels being used.
2985 for (v=0; v < (ssize_t) kernel->height; v++) {
2986 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
2987 if ( IsNan(*k) || (*k) < 0.5 ) continue;
2988 Minimize(min.red, (double)
2989 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
2990 Minimize(min.green, (double)
2991 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
2992 Minimize(min.blue, (double)
2993 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
2994 Minimize(min.alpha, (double)
2995 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
2996 if (image->colorspace == CMYKColorspace)
2997 Minimize(min.black, (double)
2998 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3000 k_pixels += virt_width*GetPixelChannels(image);
3004 case DilateMorphology:
3005 /* Maximum Value within kernel neighbourhood
3007 ** NOTE for correct working of this operation for asymetrical
3008 ** kernels, the kernel needs to be applied in its reflected form.
3009 ** That is its values needs to be reversed.
3011 ** NOTE: in normal Greyscale Morphology, the kernel value should
3012 ** be added to the real value, this is currently not done, due to
3013 ** the nature of the boolean kernels being used.
3016 k = &kernel->values[ kernel->width*kernel->height-1 ];
3018 for (v=0; v < (ssize_t) kernel->height; v++) {
3019 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3020 if ( IsNan(*k) || (*k) < 0.5 ) continue;
3021 Maximize(max.red, (double)
3022 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3023 Maximize(max.green, (double)
3024 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3025 Maximize(max.blue, (double)
3026 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3027 Maximize(max.alpha, (double)
3028 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3029 if (image->colorspace == CMYKColorspace)
3030 Maximize(max.black, (double)
3031 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3033 k_pixels += virt_width*GetPixelChannels(image);
3037 case HitAndMissMorphology:
3038 case ThinningMorphology:
3039 case ThickenMorphology:
3040 /* Minimum of Foreground Pixel minus Maxumum of Background Pixels
3042 ** NOTE that the kernel is not reflected for this operation,
3043 ** and consists of both foreground and background pixel
3044 ** neighbourhoods, 0.0 for background, and 1.0 for foreground
3045 ** with either Nan or 0.5 values for don't care.
3047 ** Note that this will never produce a meaningless negative
3048 ** result. Such results can cause Thinning/Thicken to not work
3049 ** correctly when used against a greyscale image.
3053 for (v=0; v < (ssize_t) kernel->height; v++) {
3054 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
3055 if ( IsNan(*k) ) continue;
3057 { /* minimim of foreground pixels */
3058 Minimize(min.red, (double)
3059 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3060 Minimize(min.green, (double)
3061 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3062 Minimize(min.blue, (double)
3063 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3064 Minimize(min.alpha,(double)
3065 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3066 if ( image->colorspace == CMYKColorspace)
3067 Minimize(min.black,(double)
3068 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3070 else if ( (*k) < 0.3 )
3071 { /* maximum of background pixels */
3072 Maximize(max.red, (double)
3073 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3074 Maximize(max.green, (double)
3075 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3076 Maximize(max.blue, (double)
3077 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3078 Maximize(max.alpha,(double)
3079 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3080 if (image->colorspace == CMYKColorspace)
3081 Maximize(max.black, (double)
3082 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3085 k_pixels += virt_width*GetPixelChannels(image);
3087 /* Pattern Match if difference is positive */
3088 min.red -= max.red; Maximize( min.red, 0.0 );
3089 min.green -= max.green; Maximize( min.green, 0.0 );
3090 min.blue -= max.blue; Maximize( min.blue, 0.0 );
3091 min.black -= max.black; Maximize( min.black, 0.0 );
3092 min.alpha -= max.alpha; Maximize( min.alpha, 0.0 );
3095 case ErodeIntensityMorphology:
3096 /* Select Pixel with Minimum Intensity within kernel neighbourhood
3098 ** WARNING: the intensity test fails for CMYK and does not
3099 ** take into account the moderating effect of the alpha channel
3100 ** on the intensity.
3102 ** NOTE that the kernel is not reflected for this operation!
3106 for (v=0; v < (ssize_t) kernel->height; v++) {
3107 for (u=0; u < (ssize_t) kernel->width; u++, k++) {
3108 if ( IsNan(*k) || (*k) < 0.5 ) continue;
3109 if ( result.red == 0.0 ||
3110 GetPixelIntensity(image,k_pixels+u*GetPixelChannels(image)) < GetPixelIntensity(morphology_image,q) ) {
3111 /* copy the whole pixel - no channel selection */
3112 SetPixelRed(morphology_image,GetPixelRed(image,
3113 k_pixels+u*GetPixelChannels(image)),q);
3114 SetPixelGreen(morphology_image,GetPixelGreen(image,
3115 k_pixels+u*GetPixelChannels(image)),q);
3116 SetPixelBlue(morphology_image,GetPixelBlue(image,
3117 k_pixels+u*GetPixelChannels(image)),q);
3118 SetPixelAlpha(morphology_image,GetPixelAlpha(image,
3119 k_pixels+u*GetPixelChannels(image)),q);
3120 if ( result.red > 0.0 ) changed++;
3124 k_pixels += virt_width*GetPixelChannels(image);
3128 case DilateIntensityMorphology:
3129 /* Select Pixel with Maximum Intensity within kernel neighbourhood
3131 ** WARNING: the intensity test fails for CMYK and does not
3132 ** take into account the moderating effect of the alpha channel
3133 ** on the intensity (yet).
3135 ** NOTE for correct working of this operation for asymetrical
3136 ** kernels, the kernel needs to be applied in its reflected form.
3137 ** That is its values needs to be reversed.
3139 k = &kernel->values[ kernel->width*kernel->height-1 ];
3141 for (v=0; v < (ssize_t) kernel->height; v++) {
3142 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3143 if ( IsNan(*k) || (*k) < 0.5 ) continue; /* boolean kernel */
3144 if ( result.red == 0.0 ||
3145 GetPixelIntensity(image,k_pixels+u*GetPixelChannels(image)) > GetPixelIntensity(morphology_image,q) ) {
3146 /* copy the whole pixel - no channel selection */
3147 SetPixelRed(morphology_image,GetPixelRed(image,
3148 k_pixels+u*GetPixelChannels(image)),q);
3149 SetPixelGreen(morphology_image,GetPixelGreen(image,
3150 k_pixels+u*GetPixelChannels(image)),q);
3151 SetPixelBlue(morphology_image,GetPixelBlue(image,
3152 k_pixels+u*GetPixelChannels(image)),q);
3153 SetPixelAlpha(morphology_image,GetPixelAlpha(image,
3154 k_pixels+u*GetPixelChannels(image)),q);
3155 if ( result.red > 0.0 ) changed++;
3159 k_pixels += virt_width*GetPixelChannels(image);
3163 case IterativeDistanceMorphology:
3164 /* Work out an iterative distance from black edge of a white image
3165 ** shape. Essentually white values are decreased to the smallest
3166 ** 'distance from edge' it can find.
3168 ** It works by adding kernel values to the neighbourhood, and and
3169 ** select the minimum value found. The kernel is rotated before
3170 ** use, so kernel distances match resulting distances, when a user
3171 ** provided asymmetric kernel is applied.
3174 ** This code is almost identical to True GrayScale Morphology But
3177 ** GreyDilate Kernel values added, maximum value found Kernel is
3178 ** rotated before use.
3180 ** GrayErode: Kernel values subtracted and minimum value found No
3181 ** kernel rotation used.
3183 ** Note the the Iterative Distance method is essentially a
3184 ** GrayErode, but with negative kernel values, and kernel
3185 ** rotation applied.
3187 k = &kernel->values[ kernel->width*kernel->height-1 ];
3189 for (v=0; v < (ssize_t) kernel->height; v++) {
3190 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3191 if ( IsNan(*k) ) continue;
3192 Minimize(result.red, (*k)+(double)
3193 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3194 Minimize(result.green, (*k)+(double)
3195 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3196 Minimize(result.blue, (*k)+(double)
3197 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3198 Minimize(result.alpha, (*k)+(double)
3199 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3200 if ( image->colorspace == CMYKColorspace)
3201 Maximize(result.black, (*k)+(double)
3202 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3204 k_pixels += virt_width*GetPixelChannels(image);
3208 case UndefinedMorphology:
3210 break; /* Do nothing */
3212 /* Final mathematics of results (combine with original image?)
3214 ** NOTE: Difference Morphology operators Edge* and *Hat could also
3215 ** be done here but works better with iteration as a image difference
3216 ** in the controling function (below). Thicken and Thinning however
3217 ** should be done here so thay can be iterated correctly.
3220 case HitAndMissMorphology:
3221 case ErodeMorphology:
3222 result = min; /* minimum of neighbourhood */
3224 case DilateMorphology:
3225 result = max; /* maximum of neighbourhood */
3227 case ThinningMorphology:
3228 /* subtract pattern match from original */
3229 result.red -= min.red;
3230 result.green -= min.green;
3231 result.blue -= min.blue;
3232 result.black -= min.black;
3233 result.alpha -= min.alpha;
3235 case ThickenMorphology:
3236 /* Add the pattern matchs to the original */
3237 result.red += min.red;
3238 result.green += min.green;
3239 result.blue += min.blue;
3240 result.black += min.black;
3241 result.alpha += min.alpha;
3244 /* result directly calculated or assigned */
3247 /* Assign the resulting pixel values - Clamping Result */
3249 case UndefinedMorphology:
3250 case ConvolveMorphology:
3251 case DilateIntensityMorphology:
3252 case ErodeIntensityMorphology:
3253 break; /* full pixel was directly assigned - not a channel method */
3255 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3256 SetPixelRed(morphology_image,ClampToQuantum(result.red),q);
3257 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3258 SetPixelGreen(morphology_image,ClampToQuantum(result.green),q);
3259 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3260 SetPixelBlue(morphology_image,ClampToQuantum(result.blue),q);
3261 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3262 (image->colorspace == CMYKColorspace))
3263 SetPixelBlack(morphology_image,ClampToQuantum(result.black),q);
3264 if (((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0) &&
3265 (image->matte == MagickTrue))
3266 SetPixelAlpha(morphology_image,ClampToQuantum(result.alpha),q);
3269 /* Count up changed pixels */
3270 if ((GetPixelRed(image,p+r*GetPixelChannels(image)) != GetPixelRed(morphology_image,q)) ||
3271 (GetPixelGreen(image,p+r*GetPixelChannels(image)) != GetPixelGreen(morphology_image,q)) ||
3272 (GetPixelBlue(image,p+r*GetPixelChannels(image)) != GetPixelBlue(morphology_image,q)) ||
3273 (GetPixelAlpha(image,p+r*GetPixelChannels(image)) != GetPixelAlpha(morphology_image,q)) ||
3274 ((image->colorspace == CMYKColorspace) &&
3275 (GetPixelBlack(image,p+r*GetPixelChannels(image)) != GetPixelBlack(morphology_image,q))))
3276 changed++; /* The pixel was changed in some way! */
3277 p+=GetPixelChannels(image);
3278 q+=GetPixelChannels(morphology_image);
3280 if ( SyncCacheViewAuthenticPixels(morphology_view,exception) == MagickFalse)
3282 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3287 #if defined(MAGICKCORE_OPENMP_SUPPORT)
3288 #pragma omp critical (MagickCore_MorphologyImage)
3290 proceed=SetImageProgress(image,MorphologyTag,progress++,image->rows);
3291 if (proceed == MagickFalse)
3295 morphology_view=DestroyCacheView(morphology_view);
3296 image_view=DestroyCacheView(image_view);
3297 return(status ? (ssize_t)changed : -1);
3300 /* This is almost identical to the MorphologyPrimative() function above,
3301 ** but will apply the primitive directly to the actual image using two
3302 ** passes, once in each direction, with the results of the previous (and
3303 ** current) row being re-used.
3305 ** That is after each row is 'Sync'ed' into the image, the next row will
3306 ** make use of those values as part of the calculation of the next row.
3307 ** It then repeats, but going in the oppisite (bottom-up) direction.
3309 ** Because of this 're-use of results' this function can not make use
3310 ** of multi-threaded, parellel processing.
3312 static ssize_t MorphologyPrimitiveDirect(Image *image,
3313 const MorphologyMethod method,const KernelInfo *kernel,
3314 ExceptionInfo *exception)
3337 assert(image != (Image *) NULL);
3338 assert(image->signature == MagickSignature);
3339 assert(kernel != (KernelInfo *) NULL);
3340 assert(kernel->signature == MagickSignature);
3341 assert(exception != (ExceptionInfo *) NULL);
3342 assert(exception->signature == MagickSignature);
3344 /* Some methods (including convolve) needs use a reflected kernel.
3345 * Adjust 'origin' offsets to loop though kernel as a reflection.
3350 case DistanceMorphology:
3351 case VoronoiMorphology:
3352 /* kernel needs to used with reflection about origin */
3353 offx = (ssize_t) kernel->width-offx-1;
3354 offy = (ssize_t) kernel->height-offy-1;
3357 case ?????Morphology:
3358 /* kernel is used as is, without reflection */
3362 assert("Not a PrimativeDirect Morphology Method" != (char *) NULL);
3366 /* DO NOT THREAD THIS CODE! */
3367 /* two views into same image (virtual, and actual) */
3368 virt_view=AcquireCacheView(image);
3369 auth_view=AcquireCacheView(image);
3370 virt_width=image->columns+kernel->width-1;
3372 for (y=0; y < (ssize_t) image->rows; y++)
3374 register const Quantum
3386 /* NOTE read virtual pixels, and authentic pixels, from the same image!
3387 ** we read using virtual to get virtual pixel handling, but write back
3388 ** into the same image.
3390 ** Only top half of kernel is processed as we do a single pass downward
3391 ** through the image iterating the distance function as we go.
3393 if (status == MagickFalse)
3395 p=GetCacheViewVirtualPixels(virt_view,-offx,y-offy,virt_width,(size_t)
3397 q=GetCacheViewAuthenticPixels(auth_view, 0, y, image->columns, 1,
3399 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3401 if (status == MagickFalse)
3404 /* offset to origin in 'p'. while 'q' points to it directly */
3405 r = (ssize_t) virt_width*offy + offx;
3407 for (x=0; x < (ssize_t) image->columns; x++)
3415 register const double
3418 register const Quantum
3424 /* Starting Defaults */
3425 GetPixelInfo(image,&result);
3426 GetPixelInfoPixel(image,q,&result);
3427 if ( method != VoronoiMorphology )
3428 result.alpha = QuantumRange - result.alpha;
3431 case DistanceMorphology:
3432 /* Add kernel Value and select the minimum value found. */
3433 k = &kernel->values[ kernel->width*kernel->height-1 ];
3435 for (v=0; v <= (ssize_t) offy; v++) {
3436 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3437 if ( IsNan(*k) ) continue;
3438 Minimize(result.red, (*k)+
3439 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3440 Minimize(result.green, (*k)+
3441 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3442 Minimize(result.blue, (*k)+
3443 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3444 if (image->colorspace == CMYKColorspace)
3445 Minimize(result.black,(*k)+
3446 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3447 Minimize(result.alpha, (*k)+
3448 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3450 k_pixels += virt_width*GetPixelChannels(image);
3452 /* repeat with the just processed pixels of this row */
3453 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3454 k_pixels = q-offx*GetPixelChannels(image);
3455 for (u=0; u < (ssize_t) offx; u++, k--) {
3456 if ( x+u-offx < 0 ) continue; /* off the edge! */
3457 if ( IsNan(*k) ) continue;
3458 Minimize(result.red, (*k)+
3459 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3460 Minimize(result.green, (*k)+
3461 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3462 Minimize(result.blue, (*k)+
3463 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3464 if (image->colorspace == CMYKColorspace)
3465 Minimize(result.black,(*k)+
3466 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3467 Minimize(result.alpha,(*k)+
3468 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3471 case VoronoiMorphology:
3472 /* Apply Distance to 'Matte' channel, while coping the color
3473 ** values of the closest pixel.
3475 ** This is experimental, and realy the 'alpha' component should
3476 ** be completely separate 'masking' channel so that alpha can
3477 ** also be used as part of the results.
3479 k = &kernel->values[ kernel->width*kernel->height-1 ];
3481 for (v=0; v <= (ssize_t) offy; v++) {
3482 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3483 if ( IsNan(*k) ) continue;
3484 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3486 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3491 k_pixels += virt_width*GetPixelChannels(image);
3493 /* repeat with the just processed pixels of this row */
3494 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3495 k_pixels = q-offx*GetPixelChannels(image);
3496 for (u=0; u < (ssize_t) offx; u++, k--) {
3497 if ( x+u-offx < 0 ) continue; /* off the edge! */
3498 if ( IsNan(*k) ) continue;
3499 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3501 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3508 /* result directly calculated or assigned */
3511 /* Assign the resulting pixel values - Clamping Result */
3513 case VoronoiMorphology:
3514 SetPixelInfoPixel(image,&result,q);
3517 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3518 SetPixelRed(image,ClampToQuantum(result.red),q);
3519 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3520 SetPixelGreen(image,ClampToQuantum(result.green),q);
3521 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3522 SetPixelBlue(image,ClampToQuantum(result.blue),q);
3523 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3524 (image->colorspace == CMYKColorspace))
3525 SetPixelBlack(image,ClampToQuantum(result.black),q);
3526 if ((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0 &&
3527 (image->matte == MagickTrue))
3528 SetPixelAlpha(image,ClampToQuantum(result.alpha),q);
3531 /* Count up changed pixels */
3532 if ((GetPixelRed(image,p+r*GetPixelChannels(image)) != GetPixelRed(image,q)) ||
3533 (GetPixelGreen(image,p+r*GetPixelChannels(image)) != GetPixelGreen(image,q)) ||
3534 (GetPixelBlue(image,p+r*GetPixelChannels(image)) != GetPixelBlue(image,q)) ||
3535 (GetPixelAlpha(image,p+r*GetPixelChannels(image)) != GetPixelAlpha(image,q)) ||
3536 ((image->colorspace == CMYKColorspace) &&
3537 (GetPixelBlack(image,p+r*GetPixelChannels(image)) != GetPixelBlack(image,q))))
3538 changed++; /* The pixel was changed in some way! */
3540 p+=GetPixelChannels(image); /* increment pixel buffers */
3541 q+=GetPixelChannels(image);
3544 if ( SyncCacheViewAuthenticPixels(auth_view,exception) == MagickFalse)
3546 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3547 if ( SetImageProgress(image,MorphologyTag,progress++,image->rows)
3553 /* Do the reversed pass through the image */
3554 for (y=(ssize_t)image->rows-1; y >= 0; y--)
3556 register const Quantum
3568 if (status == MagickFalse)
3570 /* NOTE read virtual pixels, and authentic pixels, from the same image!
3571 ** we read using virtual to get virtual pixel handling, but write back
3572 ** into the same image.
3574 ** Only the bottom half of the kernel will be processes as we
3577 p=GetCacheViewVirtualPixels(virt_view,-offx,y,virt_width,(size_t)
3578 kernel->y+1,exception);
3579 q=GetCacheViewAuthenticPixels(auth_view, 0, y, image->columns, 1,
3581 if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL))
3583 if (status == MagickFalse)
3586 /* adjust positions to end of row */
3587 p += (image->columns-1)*GetPixelChannels(image);
3588 q += (image->columns-1)*GetPixelChannels(image);
3590 /* offset to origin in 'p'. while 'q' points to it directly */
3593 for (x=(ssize_t)image->columns-1; x >= 0; x--)
3601 register const double
3604 register const Quantum
3610 /* Default - previously modified pixel */
3611 GetPixelInfo(image,&result);
3612 GetPixelInfoPixel(image,q,&result);
3613 if ( method != VoronoiMorphology )
3614 result.alpha = QuantumRange - result.alpha;
3617 case DistanceMorphology:
3618 /* Add kernel Value and select the minimum value found. */
3619 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3621 for (v=offy; v < (ssize_t) kernel->height; v++) {
3622 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3623 if ( IsNan(*k) ) continue;
3624 Minimize(result.red, (*k)+
3625 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3626 Minimize(result.green, (*k)+
3627 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3628 Minimize(result.blue, (*k)+
3629 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3630 if ( image->colorspace == CMYKColorspace)
3631 Minimize(result.black,(*k)+
3632 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3633 Minimize(result.alpha, (*k)+
3634 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3636 k_pixels += virt_width*GetPixelChannels(image);
3638 /* repeat with the just processed pixels of this row */
3639 k = &kernel->values[ kernel->width*(kernel->y)+kernel->x-1 ];
3640 k_pixels = q-offx*GetPixelChannels(image);
3641 for (u=offx+1; u < (ssize_t) kernel->width; u++, k--) {
3642 if ( (x+u-offx) >= (ssize_t)image->columns ) continue;
3643 if ( IsNan(*k) ) continue;
3644 Minimize(result.red, (*k)+
3645 GetPixelRed(image,k_pixels+u*GetPixelChannels(image)));
3646 Minimize(result.green, (*k)+
3647 GetPixelGreen(image,k_pixels+u*GetPixelChannels(image)));
3648 Minimize(result.blue, (*k)+
3649 GetPixelBlue(image,k_pixels+u*GetPixelChannels(image)));
3650 if ( image->colorspace == CMYKColorspace)
3651 Minimize(result.black, (*k)+
3652 GetPixelBlack(image,k_pixels+u*GetPixelChannels(image)));
3653 Minimize(result.alpha, (*k)+
3654 GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)));
3657 case VoronoiMorphology:
3658 /* Apply Distance to 'Matte' channel, coping the closest color.
3660 ** This is experimental, and realy the 'alpha' component should
3661 ** be completely separate 'masking' channel.
3663 k = &kernel->values[ kernel->width*(kernel->y+1)-1 ];
3665 for (v=offy; v < (ssize_t) kernel->height; v++) {
3666 for (u=0; u < (ssize_t) kernel->width; u++, k--) {
3667 if ( IsNan(*k) ) continue;
3668 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3670 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3675 k_pixels += virt_width*GetPixelChannels(image);
3677 /* repeat with the just processed pixels of this row */
3678 k = &kernel->values[ kernel->width*(kernel->y)+kernel->x-1 ];
3679 k_pixels = q-offx*GetPixelChannels(image);
3680 for (u=offx+1; u < (ssize_t) kernel->width; u++, k--) {
3681 if ( (x+u-offx) >= (ssize_t)image->columns ) continue;
3682 if ( IsNan(*k) ) continue;
3683 if( result.alpha > (*k)+GetPixelAlpha(image,k_pixels+u*GetPixelChannels(image)) )
3685 GetPixelInfoPixel(image,k_pixels+u*GetPixelChannels(image),
3692 /* result directly calculated or assigned */
3695 /* Assign the resulting pixel values - Clamping Result */
3697 case VoronoiMorphology:
3698 SetPixelInfoPixel(image,&result,q);
3701 if ((GetPixelRedTraits(image) & UpdatePixelTrait) != 0)
3702 SetPixelRed(image,ClampToQuantum(result.red),q);
3703 if ((GetPixelGreenTraits(image) & UpdatePixelTrait) != 0)
3704 SetPixelGreen(image,ClampToQuantum(result.green),q);
3705 if ((GetPixelBlueTraits(image) & UpdatePixelTrait) != 0)
3706 SetPixelBlue(image,ClampToQuantum(result.blue),q);
3707 if (((GetPixelBlackTraits(image) & UpdatePixelTrait) != 0) &&
3708 (image->colorspace == CMYKColorspace))
3709 SetPixelBlack(image,ClampToQuantum(result.black),q);
3710 if ((GetPixelAlphaTraits(image) & UpdatePixelTrait) != 0 &&
3711 (image->matte == MagickTrue))
3712 SetPixelAlpha(image,ClampToQuantum(result.alpha),q);
3715 /* Count up changed pixels */
3716 if ( (GetPixelRed(image,p+r*GetPixelChannels(image)) != GetPixelRed(image,q))
3717 || (GetPixelGreen(image,p+r*GetPixelChannels(image)) != GetPixelGreen(image,q))
3718 || (GetPixelBlue(image,p+r*GetPixelChannels(image)) != GetPixelBlue(image,q))
3719 || (GetPixelAlpha(image,p+r*GetPixelChannels(image)) != GetPixelAlpha(image,q))
3720 || ((image->colorspace == CMYKColorspace) &&
3721 (GetPixelBlack(image,p+r*GetPixelChannels(image)) != GetPixelBlack(image,q))))
3722 changed++; /* The pixel was changed in some way! */
3724 p-=GetPixelChannels(image); /* go backward through pixel buffers */
3725 q-=GetPixelChannels(image);
3727 if ( SyncCacheViewAuthenticPixels(auth_view,exception) == MagickFalse)
3729 if (image->progress_monitor != (MagickProgressMonitor) NULL)
3730 if ( SetImageProgress(image,MorphologyTag,progress++,image->rows)
3736 auth_view=DestroyCacheView(auth_view);
3737 virt_view=DestroyCacheView(virt_view);
3738 return(status ? (ssize_t) changed : -1);
3741 /* Apply a Morphology by calling one of the above low level primitive
3742 ** application functions. This function handles any iteration loops,
3743 ** composition or re-iteration of results, and compound morphology methods
3744 ** that is based on multiple low-level (staged) morphology methods.
3746 ** Basically this provides the complex grue between the requested morphology
3747 ** method and raw low-level implementation (above).
3749 MagickPrivate Image *MorphologyApply(const Image *image,
3750 const MorphologyMethod method, const ssize_t iterations,
3751 const KernelInfo *kernel, const CompositeOperator compose,const double bias,
3752 ExceptionInfo *exception)
3758 *curr_image, /* Image we are working with or iterating */
3759 *work_image, /* secondary image for primitive iteration */
3760 *save_image, /* saved image - for 'edge' method only */
3761 *rslt_image; /* resultant image - after multi-kernel handling */
3764 *reflected_kernel, /* A reflected copy of the kernel (if needed) */
3765 *norm_kernel, /* the current normal un-reflected kernel */
3766 *rflt_kernel, /* the current reflected kernel (if needed) */
3767 *this_kernel; /* the kernel being applied */
3770 primitive; /* the current morphology primitive being applied */
3773 rslt_compose; /* multi-kernel compose method for results to use */
3776 special, /* do we use a direct modify function? */
3777 verbose; /* verbose output of results */
3780 method_loop, /* Loop 1: number of compound method iterations (norm 1) */
3781 method_limit, /* maximum number of compound method iterations */
3782 kernel_number, /* Loop 2: the kernel number being applied */
3783 stage_loop, /* Loop 3: primitive loop for compound morphology */
3784 stage_limit, /* how many primitives are in this compound */
3785 kernel_loop, /* Loop 4: iterate the kernel over image */
3786 kernel_limit, /* number of times to iterate kernel */
3787 count, /* total count of primitive steps applied */
3788 kernel_changed, /* total count of changed using iterated kernel */
3789 method_changed; /* total count of changed over method iteration */
3792 changed; /* number pixels changed by last primitive operation */
3797 assert(image != (Image *) NULL);
3798 assert(image->signature == MagickSignature);
3799 assert(kernel != (KernelInfo *) NULL);
3800 assert(kernel->signature == MagickSignature);
3801 assert(exception != (ExceptionInfo *) NULL);
3802 assert(exception->signature == MagickSignature);
3804 count = 0; /* number of low-level morphology primitives performed */
3805 if ( iterations == 0 )
3806 return((Image *)NULL); /* null operation - nothing to do! */
3808 kernel_limit = (size_t) iterations;
3809 if ( iterations < 0 ) /* negative interations = infinite (well alomst) */
3810 kernel_limit = image->columns>image->rows ? image->columns : image->rows;
3812 verbose = IsStringTrue(GetImageArtifact(image,"verbose"));
3814 /* initialise for cleanup */
3815 curr_image = (Image *) image;
3816 curr_compose = image->compose;
3817 (void) curr_compose;
3818 work_image = save_image = rslt_image = (Image *) NULL;
3819 reflected_kernel = (KernelInfo *) NULL;
3821 /* Initialize specific methods
3822 * + which loop should use the given iteratations
3823 * + how many primitives make up the compound morphology
3824 * + multi-kernel compose method to use (by default)
3826 method_limit = 1; /* just do method once, unless otherwise set */
3827 stage_limit = 1; /* assume method is not a compound */
3828 special = MagickFalse; /* assume it is NOT a direct modify primitive */
3829 rslt_compose = compose; /* and we are composing multi-kernels as given */
3831 case SmoothMorphology: /* 4 primitive compound morphology */
3834 case OpenMorphology: /* 2 primitive compound morphology */
3835 case OpenIntensityMorphology:
3836 case TopHatMorphology:
3837 case CloseMorphology:
3838 case CloseIntensityMorphology:
3839 case BottomHatMorphology:
3840 case EdgeMorphology:
3843 case HitAndMissMorphology:
3844 rslt_compose = LightenCompositeOp; /* Union of multi-kernel results */
3846 case ThinningMorphology:
3847 case ThickenMorphology:
3848 method_limit = kernel_limit; /* iterate the whole method */
3849 kernel_limit = 1; /* do not do kernel iteration */
3851 case DistanceMorphology:
3852 case VoronoiMorphology:
3853 special = MagickTrue; /* use special direct primative */
3859 /* Apply special methods with special requirments
3860 ** For example, single run only, or post-processing requirements
3862 if ( special == MagickTrue )
3864 rslt_image=CloneImage(image,0,0,MagickTrue,exception);
3865 if (rslt_image == (Image *) NULL)
3867 if (SetImageStorageClass(rslt_image,DirectClass,exception) == MagickFalse)
3870 changed = MorphologyPrimitiveDirect(rslt_image, method,
3873 if ( IfMagickTrue(verbose) )
3874 (void) (void) FormatLocaleFile(stderr,
3875 "%s:%.20g.%.20g #%.20g => Changed %.20g\n",
3876 CommandOptionToMnemonic(MagickMorphologyOptions, method),
3877 1.0,0.0,1.0, (double) changed);
3882 if ( method == VoronoiMorphology ) {
3883 /* Preserve the alpha channel of input image - but turned off */
3884 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3886 (void) CompositeImage(rslt_image,image,CopyAlphaCompositeOp,
3887 MagickTrue,0,0,exception);
3888 (void) SetImageAlphaChannel(rslt_image, DeactivateAlphaChannel,
3894 /* Handle user (caller) specified multi-kernel composition method */
3895 if ( compose != UndefinedCompositeOp )
3896 rslt_compose = compose; /* override default composition for method */
3897 if ( rslt_compose == UndefinedCompositeOp )
3898 rslt_compose = NoCompositeOp; /* still not defined! Then re-iterate */
3900 /* Some methods require a reflected kernel to use with primitives.
3901 * Create the reflected kernel for those methods. */
3903 case CorrelateMorphology:
3904 case CloseMorphology:
3905 case CloseIntensityMorphology:
3906 case BottomHatMorphology:
3907 case SmoothMorphology:
3908 reflected_kernel = CloneKernelInfo(kernel);
3909 if (reflected_kernel == (KernelInfo *) NULL)
3911 RotateKernelInfo(reflected_kernel,180);
3917 /* Loops around more primitive morpholgy methods
3918 ** erose, dilate, open, close, smooth, edge, etc...
3920 /* Loop 1: iterate the compound method */
3923 while ( method_loop < method_limit && method_changed > 0 ) {
3927 /* Loop 2: iterate over each kernel in a multi-kernel list */
3928 norm_kernel = (KernelInfo *) kernel;
3929 this_kernel = (KernelInfo *) kernel;
3930 rflt_kernel = reflected_kernel;
3933 while ( norm_kernel != NULL ) {
3935 /* Loop 3: Compound Morphology Staging - Select Primative to apply */
3936 stage_loop = 0; /* the compound morphology stage number */
3937 while ( stage_loop < stage_limit ) {
3938 stage_loop++; /* The stage of the compound morphology */
3940 /* Select primitive morphology for this stage of compound method */
3941 this_kernel = norm_kernel; /* default use unreflected kernel */
3942 primitive = method; /* Assume method is a primitive */
3944 case ErodeMorphology: /* just erode */
3945 case EdgeInMorphology: /* erode and image difference */
3946 primitive = ErodeMorphology;
3948 case DilateMorphology: /* just dilate */
3949 case EdgeOutMorphology: /* dilate and image difference */
3950 primitive = DilateMorphology;
3952 case OpenMorphology: /* erode then dialate */
3953 case TopHatMorphology: /* open and image difference */
3954 primitive = ErodeMorphology;
3955 if ( stage_loop == 2 )
3956 primitive = DilateMorphology;
3958 case OpenIntensityMorphology:
3959 primitive = ErodeIntensityMorphology;
3960 if ( stage_loop == 2 )
3961 primitive = DilateIntensityMorphology;
3963 case CloseMorphology: /* dilate, then erode */
3964 case BottomHatMorphology: /* close and image difference */
3965 this_kernel = rflt_kernel; /* use the reflected kernel */
3966 primitive = DilateMorphology;
3967 if ( stage_loop == 2 )
3968 primitive = ErodeMorphology;
3970 case CloseIntensityMorphology:
3971 this_kernel = rflt_kernel; /* use the reflected kernel */
3972 primitive = DilateIntensityMorphology;
3973 if ( stage_loop == 2 )
3974 primitive = ErodeIntensityMorphology;
3976 case SmoothMorphology: /* open, close */
3977 switch ( stage_loop ) {
3978 case 1: /* start an open method, which starts with Erode */
3979 primitive = ErodeMorphology;
3981 case 2: /* now Dilate the Erode */
3982 primitive = DilateMorphology;
3984 case 3: /* Reflect kernel a close */
3985 this_kernel = rflt_kernel; /* use the reflected kernel */
3986 primitive = DilateMorphology;
3988 case 4: /* Finish the Close */
3989 this_kernel = rflt_kernel; /* use the reflected kernel */
3990 primitive = ErodeMorphology;
3994 case EdgeMorphology: /* dilate and erode difference */
3995 primitive = DilateMorphology;
3996 if ( stage_loop == 2 ) {
3997 save_image = curr_image; /* save the image difference */
3998 curr_image = (Image *) image;
3999 primitive = ErodeMorphology;
4002 case CorrelateMorphology:
4003 /* A Correlation is a Convolution with a reflected kernel.
4004 ** However a Convolution is a weighted sum using a reflected
4005 ** kernel. It may seem stange to convert a Correlation into a
4006 ** Convolution as the Correlation is the simplier method, but
4007 ** Convolution is much more commonly used, and it makes sense to
4008 ** implement it directly so as to avoid the need to duplicate the
4009 ** kernel when it is not required (which is typically the
4012 this_kernel = rflt_kernel; /* use the reflected kernel */
4013 primitive = ConvolveMorphology;
4018 assert( this_kernel != (KernelInfo *) NULL );
4020 /* Extra information for debugging compound operations */
4021 if ( IfMagickTrue(verbose) ) {
4022 if ( stage_limit > 1 )
4023 (void) FormatLocaleString(v_info,MaxTextExtent,"%s:%.20g.%.20g -> ",
4024 CommandOptionToMnemonic(MagickMorphologyOptions,method),(double)
4025 method_loop,(double) stage_loop);
4026 else if ( primitive != method )
4027 (void) FormatLocaleString(v_info, MaxTextExtent, "%s:%.20g -> ",
4028 CommandOptionToMnemonic(MagickMorphologyOptions, method),(double)
4034 /* Loop 4: Iterate the kernel with primitive */
4038 while ( kernel_loop < kernel_limit && changed > 0 ) {
4039 kernel_loop++; /* the iteration of this kernel */
4041 /* Create a clone as the destination image, if not yet defined */
4042 if ( work_image == (Image *) NULL )
4044 work_image=CloneImage(image,0,0,MagickTrue,exception);
4045 if (work_image == (Image *) NULL)
4047 if (SetImageStorageClass(work_image,DirectClass,exception) == MagickFalse)
4049 /* work_image->type=image->type; ??? */
4052 /* APPLY THE MORPHOLOGICAL PRIMITIVE (curr -> work) */
4054 changed = MorphologyPrimitive(curr_image, work_image, primitive,
4055 this_kernel, bias, exception);
4057 if ( IfMagickTrue(verbose) ) {
4058 if ( kernel_loop > 1 )
4059 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line from previous */
4060 (void) (void) FormatLocaleFile(stderr,
4061 "%s%s%s:%.20g.%.20g #%.20g => Changed %.20g",
4062 v_info,CommandOptionToMnemonic(MagickMorphologyOptions,
4063 primitive),(this_kernel == rflt_kernel ) ? "*" : "",
4064 (double) (method_loop+kernel_loop-1),(double) kernel_number,
4065 (double) count,(double) changed);
4069 kernel_changed += changed;
4070 method_changed += changed;
4072 /* prepare next loop */
4073 { Image *tmp = work_image; /* swap images for iteration */
4074 work_image = curr_image;
4077 if ( work_image == image )
4078 work_image = (Image *) NULL; /* replace input 'image' */
4080 } /* End Loop 4: Iterate the kernel with primitive */
4082 if ( IfMagickTrue(verbose) && kernel_changed != (size_t)changed )
4083 (void) FormatLocaleFile(stderr, " Total %.20g",(double) kernel_changed);
4084 if ( IfMagickTrue(verbose) && stage_loop < stage_limit )
4085 (void) FormatLocaleFile(stderr, "\n"); /* add end-of-line before looping */
4088 (void) FormatLocaleFile(stderr, "--E-- image=0x%lx\n", (unsigned long)image);
4089 (void) FormatLocaleFile(stderr, " curr =0x%lx\n", (unsigned long)curr_image);
4090 (void) FormatLocaleFile(stderr, " work =0x%lx\n", (unsigned long)work_image);
4091 (void) FormatLocaleFile(stderr, " save =0x%lx\n", (unsigned long)save_image);
4092 (void) FormatLocaleFile(stderr, " union=0x%lx\n", (unsigned long)rslt_image);
4095 } /* End Loop 3: Primative (staging) Loop for Coumpound Methods */
4097 /* Final Post-processing for some Compound Methods
4099 ** The removal of any 'Sync' channel flag in the Image Compositon
4100 ** below ensures the methematical compose method is applied in a
4101 ** purely mathematical way, and only to the selected channels.
4102 ** Turn off SVG composition 'alpha blending'.
4105 case EdgeOutMorphology:
4106 case EdgeInMorphology:
4107 case TopHatMorphology:
4108 case BottomHatMorphology:
4109 if ( IfMagickTrue(verbose) )
4110 (void) FormatLocaleFile(stderr,
4111 "\n%s: Difference with original image",CommandOptionToMnemonic(
4112 MagickMorphologyOptions, method) );
4113 (void) CompositeImage(curr_image,image,DifferenceCompositeOp,
4114 MagickTrue,0,0,exception);
4116 case EdgeMorphology:
4117 if ( IfMagickTrue(verbose) )
4118 (void) FormatLocaleFile(stderr,
4119 "\n%s: Difference of Dilate and Erode",CommandOptionToMnemonic(
4120 MagickMorphologyOptions, method) );
4121 (void) CompositeImage(curr_image,save_image,DifferenceCompositeOp,
4122 MagickTrue,0,0,exception);
4123 save_image = DestroyImage(save_image); /* finished with save image */
4129 /* multi-kernel handling: re-iterate, or compose results */
4130 if ( kernel->next == (KernelInfo *) NULL )
4131 rslt_image = curr_image; /* just return the resulting image */
4132 else if ( rslt_compose == NoCompositeOp )
4133 { if ( IfMagickTrue(verbose) ) {
4134 if ( this_kernel->next != (KernelInfo *) NULL )
4135 (void) FormatLocaleFile(stderr, " (re-iterate)");
4137 (void) FormatLocaleFile(stderr, " (done)");
4139 rslt_image = curr_image; /* return result, and re-iterate */
4141 else if ( rslt_image == (Image *) NULL)
4142 { if ( IfMagickTrue(verbose) )
4143 (void) FormatLocaleFile(stderr, " (save for compose)");
4144 rslt_image = curr_image;
4145 curr_image = (Image *) image; /* continue with original image */
4148 { /* Add the new 'current' result to the composition
4150 ** The removal of any 'Sync' channel flag in the Image Compositon
4151 ** below ensures the methematical compose method is applied in a
4152 ** purely mathematical way, and only to the selected channels.
4153 ** IE: Turn off SVG composition 'alpha blending'.
4155 if ( IfMagickTrue(verbose) )
4156 (void) FormatLocaleFile(stderr, " (compose \"%s\")",
4157 CommandOptionToMnemonic(MagickComposeOptions, rslt_compose) );
4158 (void) CompositeImage(rslt_image,curr_image,rslt_compose,MagickTrue,
4160 curr_image = DestroyImage(curr_image);
4161 curr_image = (Image *) image; /* continue with original image */
4163 if ( IfMagickTrue(verbose) )
4164 (void) FormatLocaleFile(stderr, "\n");
4166 /* loop to the next kernel in a multi-kernel list */
4167 norm_kernel = norm_kernel->next;
4168 if ( rflt_kernel != (KernelInfo *) NULL )
4169 rflt_kernel = rflt_kernel->next;
4171 } /* End Loop 2: Loop over each kernel */
4173 } /* End Loop 1: compound method interation */
4177 /* Yes goto's are bad, but it makes cleanup lot more efficient */
4179 if ( curr_image == rslt_image )
4180 curr_image = (Image *) NULL;
4181 if ( rslt_image != (Image *) NULL )
4182 rslt_image = DestroyImage(rslt_image);
4184 if ( curr_image == rslt_image || curr_image == image )
4185 curr_image = (Image *) NULL;
4186 if ( curr_image != (Image *) NULL )
4187 curr_image = DestroyImage(curr_image);
4188 if ( work_image != (Image *) NULL )
4189 work_image = DestroyImage(work_image);
4190 if ( save_image != (Image *) NULL )
4191 save_image = DestroyImage(save_image);
4192 if ( reflected_kernel != (KernelInfo *) NULL )
4193 reflected_kernel = DestroyKernelInfo(reflected_kernel);
4199 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4203 % M o r p h o l o g y I m a g e %
4207 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4209 % MorphologyImage() applies a user supplied kernel to the image
4210 % according to the given mophology method.
4212 % This function applies any and all user defined settings before calling
4213 % the above internal function MorphologyApply().
4215 % User defined settings include...
4216 % * Output Bias for Convolution and correlation ('-define convolve:bias=??")
4217 % * Kernel Scale/normalize settings ("-define convolve:scale=??")
4218 % This can also includes the addition of a scaled unity kernel.
4219 % * Show Kernel being applied ("-define showkernel=1")
4221 % The format of the MorphologyImage method is:
4223 % Image *MorphologyImage(const Image *image,MorphologyMethod method,
4224 % const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
4226 % Image *MorphologyImage(const Image *image, const ChannelType
4227 % channel,MorphologyMethod method,const ssize_t iterations,
4228 % KernelInfo *kernel,ExceptionInfo *exception)
4230 % A description of each parameter follows:
4232 % o image: the image.
4234 % o method: the morphology method to be applied.
4236 % o iterations: apply the operation this many times (or no change).
4237 % A value of -1 means loop until no change found.
4238 % How this is applied may depend on the morphology method.
4239 % Typically this is a value of 1.
4241 % o kernel: An array of double representing the morphology kernel.
4242 % Warning: kernel may be normalized for the Convolve method.
4244 % o exception: return any errors or warnings in this structure.
4247 MagickExport Image *MorphologyImage(const Image *image,
4248 const MorphologyMethod method,const ssize_t iterations,
4249 const KernelInfo *kernel,ExceptionInfo *exception)
4263 /* Apply Convolve/Correlate Normalization and Scaling Factors.
4264 * This is done BEFORE the ShowKernelInfo() function is called so that
4265 * users can see the results of the 'option:convolve:scale' option.
4267 curr_kernel = (KernelInfo *) kernel;
4268 bias=0.0; /* curr_kernel->bias; should we get from kernel */
4269 if ( method == ConvolveMorphology || method == CorrelateMorphology )
4274 artifact = GetImageArtifact(image,"convolve:scale");
4275 if ( artifact != (const char *)NULL ) {
4276 if ( curr_kernel == kernel )
4277 curr_kernel = CloneKernelInfo(kernel);
4278 if (curr_kernel == (KernelInfo *) NULL) {
4279 curr_kernel=DestroyKernelInfo(curr_kernel);
4280 return((Image *) NULL);
4282 ScaleGeometryKernelInfo(curr_kernel, artifact);
4285 artifact = GetImageArtifact(image,"convolve:bias");
4286 compose = UndefinedCompositeOp; /* use default for method */
4287 if ( artifact != (const char *) NULL)
4288 bias=StringToDouble(artifact, (char **) NULL);
4291 /* display the (normalized) kernel via stderr */
4292 if ( IfMagickTrue(IsStringTrue(GetImageArtifact(image,"showkernel")))
4293 || IfMagickTrue(IsStringTrue(GetImageArtifact(image,"convolve:showkernel")))
4294 || IfMagickTrue(IsStringTrue(GetImageArtifact(image,"morphology:showkernel"))) )
4295 ShowKernelInfo(curr_kernel);
4297 /* Override the default handling of multi-kernel morphology results
4298 * If 'Undefined' use the default method
4299 * If 'None' (default for 'Convolve') re-iterate previous result
4300 * Otherwise merge resulting images using compose method given.
4301 * Default for 'HitAndMiss' is 'Lighten'.
4305 compose = UndefinedCompositeOp; /* use default for method */
4306 artifact = GetImageArtifact(image,"morphology:compose");
4307 if ( artifact != (const char *) NULL)
4308 compose=(CompositeOperator) ParseCommandOption(MagickComposeOptions,
4309 MagickFalse,artifact);
4311 /* Apply the Morphology */
4312 morphology_image = MorphologyApply(image,method,iterations,
4313 curr_kernel,compose,bias,exception);
4315 /* Cleanup and Exit */
4316 if ( curr_kernel != kernel )
4317 curr_kernel=DestroyKernelInfo(curr_kernel);
4318 return(morphology_image);
4322 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4326 + R o t a t e K e r n e l I n f o %
4330 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4332 % RotateKernelInfo() rotates the kernel by the angle given.
4334 % Currently it is restricted to 90 degree angles, of either 1D kernels
4335 % or square kernels. And 'circular' rotations of 45 degrees for 3x3 kernels.
4336 % It will ignore usless rotations for specific 'named' built-in kernels.
4338 % The format of the RotateKernelInfo method is:
4340 % void RotateKernelInfo(KernelInfo *kernel, double angle)
4342 % A description of each parameter follows:
4344 % o kernel: the Morphology/Convolution kernel
4346 % o angle: angle to rotate in degrees
4348 % This function is currently internal to this module only, but can be exported
4349 % to other modules if needed.
4351 static void RotateKernelInfo(KernelInfo *kernel, double angle)
4353 /* angle the lower kernels first */
4354 if ( kernel->next != (KernelInfo *) NULL)
4355 RotateKernelInfo(kernel->next, angle);
4357 /* WARNING: Currently assumes the kernel (rightly) is horizontally symetrical
4359 ** TODO: expand beyond simple 90 degree rotates, flips and flops
4362 /* Modulus the angle */
4363 angle = fmod(angle, 360.0);
4367 if ( 337.5 < angle || angle <= 22.5 )
4368 return; /* Near zero angle - no change! - At least not at this time */
4370 /* Handle special cases */
4371 switch (kernel->type) {
4372 /* These built-in kernels are cylindrical kernels, rotating is useless */
4373 case GaussianKernel:
4378 case LaplacianKernel:
4379 case ChebyshevKernel:
4380 case ManhattanKernel:
4381 case EuclideanKernel:
4384 /* These may be rotatable at non-90 angles in the future */
4385 /* but simply rotating them in multiples of 90 degrees is useless */
4392 /* These only allows a +/-90 degree rotation (by transpose) */
4393 /* A 180 degree rotation is useless */
4395 if ( 135.0 < angle && angle <= 225.0 )
4397 if ( 225.0 < angle && angle <= 315.0 )
4404 /* Attempt rotations by 45 degrees -- 3x3 kernels only */
4405 if ( 22.5 < fmod(angle,90.0) && fmod(angle,90.0) <= 67.5 )
4407 if ( kernel->width == 3 && kernel->height == 3 )
4408 { /* Rotate a 3x3 square by 45 degree angle */
4409 MagickRealType t = kernel->values[0];
4410 kernel->values[0] = kernel->values[3];
4411 kernel->values[3] = kernel->values[6];
4412 kernel->values[6] = kernel->values[7];
4413 kernel->values[7] = kernel->values[8];
4414 kernel->values[8] = kernel->values[5];
4415 kernel->values[5] = kernel->values[2];
4416 kernel->values[2] = kernel->values[1];
4417 kernel->values[1] = t;
4418 /* rotate non-centered origin */
4419 if ( kernel->x != 1 || kernel->y != 1 ) {
4421 x = (ssize_t) kernel->x-1;
4422 y = (ssize_t) kernel->y-1;
4423 if ( x == y ) x = 0;
4424 else if ( x == 0 ) x = -y;
4425 else if ( x == -y ) y = 0;
4426 else if ( y == 0 ) y = x;
4427 kernel->x = (ssize_t) x+1;
4428 kernel->y = (ssize_t) y+1;
4430 angle = fmod(angle+315.0, 360.0); /* angle reduced 45 degrees */
4431 kernel->angle = fmod(kernel->angle+45.0, 360.0);
4434 perror("Unable to rotate non-3x3 kernel by 45 degrees");
4436 if ( 45.0 < fmod(angle, 180.0) && fmod(angle,180.0) <= 135.0 )
4438 if ( kernel->width == 1 || kernel->height == 1 )
4439 { /* Do a transpose of a 1 dimensional kernel,
4440 ** which results in a fast 90 degree rotation of some type.
4444 t = (ssize_t) kernel->width;
4445 kernel->width = kernel->height;
4446 kernel->height = (size_t) t;
4448 kernel->x = kernel->y;
4450 if ( kernel->width == 1 ) {
4451 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4452 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4454 angle = fmod(angle+90.0, 360.0); /* angle increased 90 degrees */
4455 kernel->angle = fmod(kernel->angle+270.0, 360.0);
4458 else if ( kernel->width == kernel->height )
4459 { /* Rotate a square array of values by 90 degrees */
4465 for( i=0, x=kernel->width-1; i<=x; i++, x--)
4466 for( j=0, y=kernel->height-1; j<y; j++, y--)
4467 { t = k[i+j*kernel->width];
4468 k[i+j*kernel->width] = k[j+x*kernel->width];
4469 k[j+x*kernel->width] = k[x+y*kernel->width];
4470 k[x+y*kernel->width] = k[y+i*kernel->width];
4471 k[y+i*kernel->width] = t;
4474 /* rotate the origin - relative to center of array */
4475 { register ssize_t x,y;
4476 x = (ssize_t) (kernel->x*2-kernel->width+1);
4477 y = (ssize_t) (kernel->y*2-kernel->height+1);
4478 kernel->x = (ssize_t) ( -y +(ssize_t) kernel->width-1)/2;
4479 kernel->y = (ssize_t) ( +x +(ssize_t) kernel->height-1)/2;
4481 angle = fmod(angle+270.0, 360.0); /* angle reduced 90 degrees */
4482 kernel->angle = fmod(kernel->angle+90.0, 360.0);
4485 perror("Unable to rotate a non-square, non-linear kernel 90 degrees");
4487 if ( 135.0 < angle && angle <= 225.0 )
4489 /* For a 180 degree rotation - also know as a reflection
4490 * This is actually a very very common operation!
4491 * Basically all that is needed is a reversal of the kernel data!
4492 * And a reflection of the origon
4505 j=(ssize_t) (kernel->width*kernel->height-1);
4506 for (i=0; i < j; i++, j--)
4507 t=k[i], k[i]=k[j], k[j]=t;
4509 kernel->x = (ssize_t) kernel->width - kernel->x - 1;
4510 kernel->y = (ssize_t) kernel->height - kernel->y - 1;
4511 angle = fmod(angle-180.0, 360.0); /* angle+180 degrees */
4512 kernel->angle = fmod(kernel->angle+180.0, 360.0);
4514 /* At this point angle should at least between -45 (315) and +45 degrees
4515 * In the future some form of non-orthogonal angled rotates could be
4516 * performed here, posibily with a linear kernel restriction.
4523 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4527 % S c a l e G e o m e t r y K e r n e l I n f o %
4531 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4533 % ScaleGeometryKernelInfo() takes a geometry argument string, typically
4534 % provided as a "-set option:convolve:scale {geometry}" user setting,
4535 % and modifies the kernel according to the parsed arguments of that setting.
4537 % The first argument (and any normalization flags) are passed to
4538 % ScaleKernelInfo() to scale/normalize the kernel. The second argument
4539 % is then passed to UnityAddKernelInfo() to add a scled unity kernel
4540 % into the scaled/normalized kernel.
4542 % The format of the ScaleGeometryKernelInfo method is:
4544 % void ScaleGeometryKernelInfo(KernelInfo *kernel,
4545 % const double scaling_factor,const MagickStatusType normalize_flags)
4547 % A description of each parameter follows:
4549 % o kernel: the Morphology/Convolution kernel to modify
4552 % The geometry string to parse, typically from the user provided
4553 % "-set option:convolve:scale {geometry}" setting.
4556 MagickExport void ScaleGeometryKernelInfo (KernelInfo *kernel,
4557 const char *geometry)
4564 SetGeometryInfo(&args);
4565 flags = (GeometryFlags) ParseGeometry(geometry, &args);
4568 /* For Debugging Geometry Input */
4569 (void) FormatLocaleFile(stderr, "Geometry = 0x%04X : %lg x %lg %+lg %+lg\n",
4570 flags, args.rho, args.sigma, args.xi, args.psi );
4573 if ( (flags & PercentValue) != 0 ) /* Handle Percentage flag*/
4574 args.rho *= 0.01, args.sigma *= 0.01;
4576 if ( (flags & RhoValue) == 0 ) /* Set Defaults for missing args */
4578 if ( (flags & SigmaValue) == 0 )
4581 /* Scale/Normalize the input kernel */
4582 ScaleKernelInfo(kernel, args.rho, flags);
4584 /* Add Unity Kernel, for blending with original */
4585 if ( (flags & SigmaValue) != 0 )
4586 UnityAddKernelInfo(kernel, args.sigma);
4591 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4595 % S c a l e K e r n e l I n f o %
4599 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4601 % ScaleKernelInfo() scales the given kernel list by the given amount, with or
4602 % without normalization of the sum of the kernel values (as per given flags).
4604 % By default (no flags given) the values within the kernel is scaled
4605 % directly using given scaling factor without change.
4607 % If either of the two 'normalize_flags' are given the kernel will first be
4608 % normalized and then further scaled by the scaling factor value given.
4610 % Kernel normalization ('normalize_flags' given) is designed to ensure that
4611 % any use of the kernel scaling factor with 'Convolve' or 'Correlate'
4612 % morphology methods will fall into -1.0 to +1.0 range. Note that for
4613 % non-HDRI versions of IM this may cause images to have any negative results
4614 % clipped, unless some 'bias' is used.
4616 % More specifically. Kernels which only contain positive values (such as a
4617 % 'Gaussian' kernel) will be scaled so that those values sum to +1.0,
4618 % ensuring a 0.0 to +1.0 output range for non-HDRI images.
4620 % For Kernels that contain some negative values, (such as 'Sharpen' kernels)
4621 % the kernel will be scaled by the absolute of the sum of kernel values, so
4622 % that it will generally fall within the +/- 1.0 range.
4624 % For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel
4625 % will be scaled by just the sum of the postive values, so that its output
4626 % range will again fall into the +/- 1.0 range.
4628 % For special kernels designed for locating shapes using 'Correlate', (often
4629 % only containing +1 and -1 values, representing foreground/brackground
4630 % matching) a special normalization method is provided to scale the positive
4631 % values separately to those of the negative values, so the kernel will be
4632 % forced to become a zero-sum kernel better suited to such searches.
4634 % WARNING: Correct normalization of the kernel assumes that the '*_range'
4635 % attributes within the kernel structure have been correctly set during the
4638 % NOTE: The values used for 'normalize_flags' have been selected specifically
4639 % to match the use of geometry options, so that '!' means NormalizeValue, '^'
4640 % means CorrelateNormalizeValue. All other GeometryFlags values are ignored.
4642 % The format of the ScaleKernelInfo method is:
4644 % void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
4645 % const MagickStatusType normalize_flags )
4647 % A description of each parameter follows:
4649 % o kernel: the Morphology/Convolution kernel
4652 % multiply all values (after normalization) by this factor if not
4653 % zero. If the kernel is normalized regardless of any flags.
4655 % o normalize_flags:
4656 % GeometryFlags defining normalization method to use.
4657 % specifically: NormalizeValue, CorrelateNormalizeValue,
4658 % and/or PercentValue
4661 MagickExport void ScaleKernelInfo(KernelInfo *kernel,
4662 const double scaling_factor,const GeometryFlags normalize_flags)
4671 /* do the other kernels in a multi-kernel list first */
4672 if ( kernel->next != (KernelInfo *) NULL)
4673 ScaleKernelInfo(kernel->next, scaling_factor, normalize_flags);
4675 /* Normalization of Kernel */
4677 if ( (normalize_flags&NormalizeValue) != 0 ) {
4678 if ( fabs(kernel->positive_range + kernel->negative_range) > MagickEpsilon )
4679 /* non-zero-summing kernel (generally positive) */
4680 pos_scale = fabs(kernel->positive_range + kernel->negative_range);
4682 /* zero-summing kernel */
4683 pos_scale = kernel->positive_range;
4685 /* Force kernel into a normalized zero-summing kernel */
4686 if ( (normalize_flags&CorrelateNormalizeValue) != 0 ) {
4687 pos_scale = ( fabs(kernel->positive_range) > MagickEpsilon )
4688 ? kernel->positive_range : 1.0;
4689 neg_scale = ( fabs(kernel->negative_range) > MagickEpsilon )
4690 ? -kernel->negative_range : 1.0;
4693 neg_scale = pos_scale;
4695 /* finialize scaling_factor for positive and negative components */
4696 pos_scale = scaling_factor/pos_scale;
4697 neg_scale = scaling_factor/neg_scale;
4699 for (i=0; i < (ssize_t) (kernel->width*kernel->height); i++)
4700 if ( ! IsNan(kernel->values[i]) )
4701 kernel->values[i] *= (kernel->values[i] >= 0) ? pos_scale : neg_scale;
4703 /* convolution output range */
4704 kernel->positive_range *= pos_scale;
4705 kernel->negative_range *= neg_scale;
4706 /* maximum and minimum values in kernel */
4707 kernel->maximum *= (kernel->maximum >= 0.0) ? pos_scale : neg_scale;
4708 kernel->minimum *= (kernel->minimum >= 0.0) ? pos_scale : neg_scale;
4710 /* swap kernel settings if user's scaling factor is negative */
4711 if ( scaling_factor < MagickEpsilon ) {
4713 t = kernel->positive_range;
4714 kernel->positive_range = kernel->negative_range;
4715 kernel->negative_range = t;
4716 t = kernel->maximum;
4717 kernel->maximum = kernel->minimum;
4718 kernel->minimum = 1;
4725 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4729 % S h o w K e r n e l I n f o %
4733 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4735 % ShowKernelInfo() outputs the details of the given kernel defination to
4736 % standard error, generally due to a users 'showkernel' option request.
4738 % The format of the ShowKernel method is:
4740 % void ShowKernelInfo(const KernelInfo *kernel)
4742 % A description of each parameter follows:
4744 % o kernel: the Morphology/Convolution kernel
4747 MagickPrivate void ShowKernelInfo(const KernelInfo *kernel)
4755 for (c=0, k=kernel; k != (KernelInfo *) NULL; c++, k=k->next ) {
4757 (void) FormatLocaleFile(stderr, "Kernel");
4758 if ( kernel->next != (KernelInfo *) NULL )
4759 (void) FormatLocaleFile(stderr, " #%lu", (unsigned long) c );
4760 (void) FormatLocaleFile(stderr, " \"%s",
4761 CommandOptionToMnemonic(MagickKernelOptions, k->type) );
4762 if ( fabs(k->angle) > MagickEpsilon )
4763 (void) FormatLocaleFile(stderr, "@%lg", k->angle);
4764 (void) FormatLocaleFile(stderr, "\" of size %lux%lu%+ld%+ld",(unsigned long)
4765 k->width,(unsigned long) k->height,(long) k->x,(long) k->y);
4766 (void) FormatLocaleFile(stderr,
4767 " with values from %.*lg to %.*lg\n",
4768 GetMagickPrecision(), k->minimum,
4769 GetMagickPrecision(), k->maximum);
4770 (void) FormatLocaleFile(stderr, "Forming a output range from %.*lg to %.*lg",
4771 GetMagickPrecision(), k->negative_range,
4772 GetMagickPrecision(), k->positive_range);
4773 if ( fabs(k->positive_range+k->negative_range) < MagickEpsilon )
4774 (void) FormatLocaleFile(stderr, " (Zero-Summing)\n");
4775 else if ( fabs(k->positive_range+k->negative_range-1.0) < MagickEpsilon )
4776 (void) FormatLocaleFile(stderr, " (Normalized)\n");
4778 (void) FormatLocaleFile(stderr, " (Sum %.*lg)\n",
4779 GetMagickPrecision(), k->positive_range+k->negative_range);
4780 for (i=v=0; v < k->height; v++) {
4781 (void) FormatLocaleFile(stderr, "%2lu:", (unsigned long) v );
4782 for (u=0; u < k->width; u++, i++)
4783 if ( IsNan(k->values[i]) )
4784 (void) FormatLocaleFile(stderr," %*s", GetMagickPrecision()+3, "nan");
4786 (void) FormatLocaleFile(stderr," %*.*lg", GetMagickPrecision()+3,
4787 GetMagickPrecision(), k->values[i]);
4788 (void) FormatLocaleFile(stderr,"\n");
4794 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4798 % U n i t y A d d K e r n a l I n f o %
4802 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4804 % UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel
4805 % to the given pre-scaled and normalized Kernel. This in effect adds that
4806 % amount of the original image into the resulting convolution kernel. This
4807 % value is usually provided by the user as a percentage value in the
4808 % 'convolve:scale' setting.
4810 % The resulting effect is to convert the defined kernels into blended
4811 % soft-blurs, unsharp kernels or into sharpening kernels.
4813 % The format of the UnityAdditionKernelInfo method is:
4815 % void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
4817 % A description of each parameter follows:
4819 % o kernel: the Morphology/Convolution kernel
4822 % scaling factor for the unity kernel to be added to
4826 MagickExport void UnityAddKernelInfo(KernelInfo *kernel,
4829 /* do the other kernels in a multi-kernel list first */
4830 if ( kernel->next != (KernelInfo *) NULL)
4831 UnityAddKernelInfo(kernel->next, scale);
4833 /* Add the scaled unity kernel to the existing kernel */
4834 kernel->values[kernel->x+kernel->y*kernel->width] += scale;
4835 CalcKernelMetaData(kernel); /* recalculate the meta-data */
4841 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4845 % Z e r o K e r n e l N a n s %
4849 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4851 % ZeroKernelNans() replaces any special 'nan' value that may be present in
4852 % the kernel with a zero value. This is typically done when the kernel will
4853 % be used in special hardware (GPU) convolution processors, to simply
4856 % The format of the ZeroKernelNans method is:
4858 % void ZeroKernelNans (KernelInfo *kernel)
4860 % A description of each parameter follows:
4862 % o kernel: the Morphology/Convolution kernel
4865 MagickPrivate void ZeroKernelNans(KernelInfo *kernel)
4870 /* do the other kernels in a multi-kernel list first */
4871 if ( kernel->next != (KernelInfo *) NULL)
4872 ZeroKernelNans(kernel->next);
4874 for (i=0; i < (kernel->width*kernel->height); i++)
4875 if ( IsNan(kernel->values[i]) )
4876 kernel->values[i] = 0.0;