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cristyffb69ed2010-12-25 00:06:48 +000011 <title>ImageMagick: MagickCore, C API for ImageMagick: Morphological Erosions, Dilations, Openings, and Closings</title>
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192
cristy6f77f692011-02-15 15:31:39 +0000193<h1>Module morphology
194 Methods</h1>
195<p class="navigation-index">[<a href="#** This macro IsNaN">** This macro IsNaN</a> &bull; <a href="#AcquireKernelInfo">AcquireKernelInfo</a> &bull; <a href="#AcquireKernelBuiltIn">AcquireKernelBuiltIn</a> &bull; <a href="#CloneKernelInfo">CloneKernelInfo</a> &bull; <a href="#DestroyKernelInfo">DestroyKernelInfo</a> &bull; <a href="#MorphologyApply">MorphologyApply</a> &bull; <a href="#MorphologyImageChannel">MorphologyImageChannel</a> &bull; <a href="#ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a> &bull; <a href="#ScaleKernelInfo">ScaleKernelInfo</a> &bull; <a href="#ShowKernelInfo">ShowKernelInfo</a> &bull; <a href="#UnityAddKernelInfo">UnityAddKernelInfo</a> &bull; <a href="#ZeroKernelNans">ZeroKernelNans</a>]</p>
cristyc4960862010-05-23 22:24:24 +0000196
cristy6f77f692011-02-15 15:31:39 +0000197<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
198_8c.html" id="**_This macro IsNaN">** This macro IsNaN</a></h2>
199<div class="doc-section">
200
201<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
202 </div>
203<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
204_8c.html" id="AcquireKernelInfo">AcquireKernelInfo</a></h2>
205<div class="doc-section">
206
207<p>AcquireKernelInfo() takes the given string (generally supplied by the user) and converts it into a Morphology/Convolution Kernel. This allows users to specify a kernel from a number of pre-defined kernels, or to fully specify their own kernel for a specific Convolution or Morphology Operation.</p>
208
209<p>The kernel so generated can be any rectangular array of floating point values (doubles) with the 'control point' or 'pixel being affected' anywhere within that array of values.</p>
210
211<p>Previously IM was restricted to a square of odd size using the exact center as origin, this is no longer the case, and any rectangular kernel with any value being declared the origin. This in turn allows the use of highly asymmetrical kernels.</p>
212
213<p>The floating point values in the kernel can also include a special value known as 'nan' or 'not a number' to indicate that this value is not part of the kernel array. This allows you to shaped the kernel within its rectangular area. That is 'nan' values provide a 'mask' for the kernel shape. However at least one non-nan value must be provided for correct working of a kernel.</p>
214
215<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p>
216
217<p>Input kernel defintion strings can consist of any of three types.</p>
218
219<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p>
220
221<p>"WxH[+X+Y][@><]:num, num, num ..." a kernel of size W by H, with W*H floating point numbers following. the 'center' can be optionally be defined at +X+Y (such that +0+0 is top left corner). If not defined the pixel in the center, for odd sizes, or to the immediate top or left of center for even sizes is automatically selected.</p>
222
223<p>"num, num, num, num, ..." list of floating point numbers defining an 'old style' odd sized square kernel. At least 9 values should be provided for a 3x3 square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc. Values can be space or comma separated. This is not recommended.</p>
224
225<p>You can define a 'list of kernels' which can be used by some morphology operators A list is defined as a semi-colon seperated list kernels.</p>
226
227<p>" kernel ; kernel ; kernel ; "</p>
228
229<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p>
230
231<p>The special flags will expand a single kernel, into a list of rotated kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree cyclic rotations, while a '>' will generate a list of 90-degree rotations. The '<' also exands using 90-degree rotates, but giving a 180-degree reflected kernel before the +/- 90-degree rotations, which can be important for Thinning operations.</p>
232
233<p>Note that 'name' kernels will start with an alphabetic character while the new kernel specification has a ':' character in its specification string. If neither is the case, it is assumed an old style of a simple list of numbers generating a odd-sized square kernel has been given.</p>
234
235<p>The format of the AcquireKernal method is:</p>
236
237<pre class="code">
238 KernelInfo *AcquireKernelInfo(const char *kernel_string)
239</pre>
240
241<p>A description of each parameter follows:</p>
242
243<h5>kernel_string</h5>
244<p>the Morphology/Convolution kernel wanted.</p>
245
246 </div>
247<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
248_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2>
249<div class="doc-section">
250
251<p>AcquireKernelBuiltIn() returned one of the 'named' built-in types of kernels used for special purposes such as gaussian blurring, skeleton pruning, and edge distance determination.</p>
252
253<p>They take a KernelType, and a set of geometry style arguments, which were typically decoded from a user supplied string, or from a more complex Morphology Method that was requested.</p>
254
255<p>The format of the AcquireKernalBuiltIn method is:</p>
256
257<pre class="code">
258 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
259 const GeometryInfo args)
260</pre>
261
262<p>A description of each parameter follows:</p>
263
264<h5>type</h5>
265<p>the pre-defined type of kernel wanted</p>
266
267<h5>args</h5>
268<p>arguments defining or modifying the kernel</p>
269
270<p>Convolution Kernels</p>
271
272<p>Unity the No-Op kernel, also requivelent to Gaussian of sigma zero. Basically a 3x3 kernel of a 1 surrounded by zeros.</p>
273
274<p>Gaussian:{radius},{sigma} Generate a two-dimentional gaussian kernel, as used by -gaussian. The sigma for the curve is required. The resulting kernel is normalized,</p>
275
276<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
277
278<p>NOTE: that the 'radius' is optional, but if provided can limit (clip) the final size of the resulting kernel to a square 2*radius+1 in size. The radius should be at least 2 times that of the sigma value, or sever clipping and aliasing may result. If not given or set to 0 the radius will be determined so as to produce the best minimal error result, which is usally much larger than is normally needed.</p>
279
280<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p>
281
282<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p>
283
284<p>DoG:{radius},{sigma1},{sigma2} "Difference of Gaussians" Kernel. As "Gaussian" but with a gaussian produced by 'sigma2' subtracted from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1. The result is a zero-summing kernel.</p>
285
286<p>Blur:{radius},{sigma}[,{angle}] Generates a 1 dimensional or linear gaussian blur, at the angle given (current restricted to orthogonal angles). If a 'radius' is given the kernel is clipped to a width of 2*radius+1. Kernel can be rotated by a 90 degree angle.</p>
287
288<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
289
290<p>Note that two convolutions with two "Blur" kernels perpendicular to each other, is equivelent to a far larger "Gaussian" kernel with the same sigma value, However it is much faster to apply. This is how the "-blur" operator actually works.</p>
291
292<p>Comet:{width},{sigma},{angle} Blur in one direction only, much like how a bright object leaves a comet like trail. The Kernel is actually half a gaussian curve, Adding two such blurs in opposite directions produces a Blur Kernel. Angle can be rotated in multiples of 90 degrees.</p>
293
294<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p>
295
296<p># Still to be implemented... # # Filter2D # Filter1D # Set kernel values using a resize filter, and given scale (sigma) # Cylindrical or Linear. Is this posible with an image? #</p>
297
298<p>Named Constant Convolution Kernels</p>
299
300<p>All these are unscaled, zero-summing kernels by default. As such for non-HDRI version of ImageMagick some form of normalization, user scaling, and biasing the results is recommended, to prevent the resulting image being 'clipped'.</p>
301
302<p>The 3x3 kernels (most of these) can be circularly rotated in multiples of 45 degrees to generate the 8 angled varients of each of the kernels.</p>
303
304<p>Laplacian:{type} Discrete Lapacian Kernels, (without normalization) Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood) Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood) Type 2 : 3x3 with center:4 edge:1 corner:-2 Type 3 : 3x3 with center:4 edge:-2 corner:1 Type 5 : 5x5 laplacian Type 7 : 7x7 laplacian Type 15 : 5x5 LoG (sigma approx 1.4) Type 19 : 9x9 LoG (sigma approx 1.4)</p>
305
306<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p>
307
308<p>Sobel:{type},{angle} Type 0: default un-nomalized version shown above.</p>
309
310<p>Type 1: As default but pre-normalized | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p>
311
312<p>Type 2: Diagonal version with same normalization as 1 | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p>
313
314<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p>
315
316<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p>
317
318<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p>
319
320<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p>
321
322<p>FreiChen:{angle} Frei-Chen Edge Detector is based on a kernel that is similar to the Sobel Kernel, but is designed to be isotropic. That is it takes into account the distance of the diagonal in the kernel.</p>
323
324<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p>
325
326<p>FreiChen:{type},{angle}</p>
327
328<p>Frei-Chen Pre-weighted kernels...</p>
329
330<p>Type 0: default un-nomalized version shown above.</p>
331
332<p>Type 1: Orthogonal Kernel (same as type 11 below) | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p>
333
334<p>Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 |</p>
335
336<p>However this kernel is als at the heart of the FreiChen Edge Detection Process which uses a set of 9 specially weighted kernel. These 9 kernels not be normalized, but directly applied to the image. The results is then added together, to produce the intensity of an edge in a specific direction. The square root of the pixel value can then be taken as the cosine of the edge, and at least 2 such runs at 90 degrees from each other, both the direction and the strength of the edge can be determined.</p>
337
338<p>Type 10: All 9 of the following pre-weighted kernels...</p>
339
340<p>Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p>
341
342<p>Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 |</p>
343
344<p>Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) |</p>
345
346<p>Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 |</p>
347
348<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p>
349
350<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p>
351
352<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p>
353
354<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p>
355
356<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p>
357
358<p>The first 4 are for edge detection, the next 4 are for line detection and the last is to add a average component to the results.</p>
359
360<p>Using a special type of '-1' will return all 9 pre-weighted kernels as a multi-kernel list, so that you can use them directly (without normalization) with the special "-set option:morphology:compose Plus" setting to apply the full FreiChen Edge Detection Technique.</p>
361
362<p>If 'type' is large it will be taken to be an actual rotation angle for the default FreiChen (type 0) kernel. As such FreiChen:45 will look like a Sobel:45 but with 'sqrt(2)' instead of '2' values.</p>
363
364<p>WARNING: The above was layed out as per http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf But rotated 90 degrees so direction is from left rather than the top. I have yet to find any secondary confirmation of the above. The only other source found was actual source code at http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf Neigher paper defineds the kernels in a way that looks locical or correct when taken as a whole.</p>
365
366<p>Boolean Kernels</p>
367
368<p>Diamond:[{radius}[,{scale}]] Generate a diamond shaped kernel with given radius to the points. Kernel size will again be radius*2+1 square and defaults to radius 1, generating a 3x3 kernel that is slightly larger than a square.</p>
369
370<p>Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1).</p>
371
372<p>Note that using a larger radius for the "Square" or the "Diamond" is also equivelent to iterating the basic morphological method that many times. However iterating with the smaller radius is actually faster than using a larger kernel radius.</p>
373
374<p>Rectangle:{geometry} Simply generate a rectangle of 1's with the size given. You can also specify the location of the 'control point', otherwise the closest pixel to the center of the rectangle is selected.</p>
375
376<p>Properly centered and odd sized rectangles work the best.</p>
377
378<p>Disk:[{radius}[,{scale}]] Generate a binary disk of the radius given, radius may be a float. Kernel size will be ceil(radius)*2+1 square. NOTE: Here are some disk shapes of specific interest "Disk:1" => "diamond" or "cross:1" "Disk:1.5" => "square" "Disk:2" => "diamond:2" "Disk:2.5" => a general disk shape of radius 2 "Disk:2.9" => "square:2" "Disk:3.5" => default - octagonal/disk shape of radius 3 "Disk:4.2" => roughly octagonal shape of radius 4 "Disk:4.3" => a general disk shape of radius 4 After this all the kernel shape becomes more and more circular.</p>
379
380<p>Because a "disk" is more circular when using a larger radius, using a larger radius is preferred over iterating the morphological operation.</p>
381
382<p>Symbol Dilation Kernels</p>
383
384<p>These kernel is not a good general morphological kernel, but is used more for highlighting and marking any single pixels in an image using, a "Dilate" method as appropriate.</p>
385
386<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p>
387
388<p>Plus:[{radius}[,{scale}]] Cross:[{radius}[,{scale}]] Generate a kernel in the shape of a 'plus' or a 'cross' with a each arm the length of the given radius (default 2).</p>
389
390<p>NOTE: "plus:1" is equivelent to a "Diamond" kernel.</p>
391
392<p>Ring:{radius1},{radius2}[,{scale}] A ring of the values given that falls between the two radii. Defaults to a ring of approximataly 3 radius in a 7x7 kernel. This is the 'edge' pixels of the default "Disk" kernel, More specifically, "Ring" -> "Ring:2.5,3.5,1.0"</p>
393
394<p>Hit and Miss Kernels</p>
395
396<p>Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal thicken kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Experimental Variation to try to present left-right symmetry Type 4: Experimental Variation to preserve left-right symmetry</p>
397
398<p>Distance Measuring Kernels</p>
399
400<p>Different types of distance measuring methods, which are used with the a 'Distance' morphology method for generating a gradient based on distance from an edge of a binary shape, though there is a technique for handling a anti-aliased shape.</p>
401
402<p>See the 'Distance' Morphological Method, for information of how it is applied.</p>
403
404<p>Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev Distance) is a value of one to any neighbour, orthogonal or diagonal. One why of thinking of it is the number of squares a 'King' or 'Queen' in chess needs to traverse reach any other position on a chess board. It results in a 'square' like distance function, but one where diagonals are closer than expected.</p>
405
406<p>Manhattan:[{radius}][x{scale}[!]] Manhattan Distance (also known as Rectilinear Distance, or the Taxi Cab metric), is the distance needed when you can only travel in orthogonal (horizontal or vertical) only. It is the distance a 'Rook' in chess would travel. It results in a diamond like distances, where diagonals are further than expected.</p>
407
408<p>Euclidean:[{radius}][x{scale}[!]] Euclidean Distance is the 'direct' or 'as the crow flys distance. However by default the kernel size only has a radius of 1, which limits the distance to 'Knight' like moves, with only orthogonal and diagonal measurements being correct. As such for the default kernel you will get octagonal like distance function, which is reasonally accurate.</p>
409
410<p>However if you use a larger radius such as "Euclidean:4" you will get a much smoother distance gradient from the edge of the shape. Of course a larger kernel is slower to use, and generally not needed.</p>
411
412<p>To allow the use of fractional distances that you get with diagonals the actual distance is scaled by a fixed value which the user can provide. This is not actually nessary for either ""Chebyshev" or "Manhattan" distance kernels, but is done for all three distance kernels. If no scale is provided it is set to a value of 100, allowing for a maximum distance measurement of 655 pixels using a Q16 version of IM, from any edge. However for small images this can result in quite a dark gradient.</p>
413
414 </div>
415<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
416_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2>
417<div class="doc-section">
418
419<p>CloneKernelInfo() creates a new clone of the given Kernel List so that its can be modified without effecting the original. The cloned kernel should be destroyed using DestoryKernelInfo() when no longer needed.</p>
420
421<p>The format of the CloneKernelInfo method is:</p>
422
423<pre class="code">
424 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
425</pre>
426
427<p>A description of each parameter follows:</p>
428
429<h5>kernel</h5>
430<p>the Morphology/Convolution kernel to be cloned</p>
431
432 </div>
433<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
434_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2>
435<div class="doc-section">
436
437<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p>
438
439<p>The format of the DestroyKernelInfo method is:</p>
440
441<pre class="code">
442 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
443</pre>
444
445<p>A description of each parameter follows:</p>
446
447<h5>kernel</h5>
448<p>the Morphology/Convolution kernel to be destroyed</p>
449
450 </div>
451<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
452_8c.html" id="MorphologyApply">MorphologyApply</a></h2>
453<div class="doc-section">
454
455<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p>
456
457<p>It is basically equivelent to as MorphologyImageChannel() (see below) but without any user controls. This allows internel programs to use this function, to actually perform a specific task without posible interference by any API user supplied settings.</p>
458
459<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p>
460
461<p>More specifically kernels are not normalized/scaled/blended by the 'convolve:scale' Image Artifact (setting), nor is the convolve bias (-bias setting or image->bias) loooked at, but must be supplied from the function arguments.</p>
462
463<p>The format of the MorphologyApply method is:</p>
464
465<pre class="code">
466 Image *MorphologyApply(const Image *image,MorphologyMethod method,
467 const ssize_t iterations,const KernelInfo *kernel,
468 const CompositeMethod compose, const double bias,
469 ExceptionInfo *exception)
470</pre>
471
472<p>A description of each parameter follows:</p>
473
474<h5>image</h5>
475<p>the source image</p>
476
477<h5>method</h5>
478<p>the morphology method to be applied.</p>
479
480<h5>iterations</h5>
481<p>apply the operation this many times (or no change). A value of -1 means loop until no change found. How this is applied may depend on the morphology method. Typically this is a value of 1.</p>
482
483<h5>channel</h5>
484<p>the channel type.</p>
485
486<h5>kernel</h5>
487<p>An array of double representing the morphology kernel.</p>
488
489<h5>compose</h5>
490<p>How to handle or merge multi-kernel results. If 'UndefinedCompositeOp' use default for the Morphology method. If 'NoCompositeOp' force image to be re-iterated by each kernel. Otherwise merge the results using the compose method given.</p>
491
492<h5>bias</h5>
493<p>Convolution Output Bias.</p>
494
495<h5>exception</h5>
496<p>return any errors or warnings in this structure.</p>
497
498 </div>
499<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
500_8c.html" id="MorphologyImageChannel">MorphologyImageChannel</a></h2>
501<div class="doc-section">
502
503<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p>
504
505<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p>
506
507<p>User defined settings include... * Output Bias for Convolution and correlation ("-bias") * Kernel Scale/normalize settings ("-set 'option:convolve:scale'") This can also includes the addition of a scaled unity kernel. * Show Kernel being applied ("-set option:showkernel 1")</p>
508
509<p>The format of the MorphologyImage method is:</p>
510
511<pre class="code">
512 Image *MorphologyImage(const Image *image,MorphologyMethod method,
513 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
514</pre>
515
516<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p>
517
518<p>A description of each parameter follows:</p>
519
520<h5>image</h5>
521<p>the image.</p>
522
523<h5>method</h5>
524<p>the morphology method to be applied.</p>
525
526<h5>iterations</h5>
527<p>apply the operation this many times (or no change). A value of -1 means loop until no change found. How this is applied may depend on the morphology method. Typically this is a value of 1.</p>
528
529<h5>channel</h5>
530<p>the channel type.</p>
531
532<h5>kernel</h5>
533<p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p>
534
535<h5>exception</h5>
536<p>return any errors or warnings in this structure.</p>
537
538 </div>
539<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
540_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2>
541<div class="doc-section">
542
543<p>ScaleGeometryKernelInfo() takes a geometry argument string, typically provided as a "-set option:convolve:scale {geometry}" user setting, and modifies the kernel according to the parsed arguments of that setting.</p>
544
545<p>The first argument (and any normalization flags) are passed to ScaleKernelInfo() to scale/normalize the kernel. The second argument is then passed to UnityAddKernelInfo() to add a scled unity kernel into the scaled/normalized kernel.</p>
546
547<p>The format of the ScaleGeometryKernelInfo method is:</p>
548
549<pre class="code">
550 void ScaleGeometryKernelInfo(KernelInfo *kernel,
551 const double scaling_factor,const MagickStatusType normalize_flags)
552</pre>
553
554<p>A description of each parameter follows:</p>
555
556<h5>kernel</h5>
557<p>the Morphology/Convolution kernel to modify</p>
558
559<p>o geometry:</p>
560
561<pre class="text">
562 "-set option:convolve:scale {geometry}" setting.
563</pre>
564
565 </div>
566<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
567_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2>
568<div class="doc-section">
569
570<p>ScaleKernelInfo() scales the given kernel list by the given amount, with or without normalization of the sum of the kernel values (as per given flags).</p>
571
572<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p>
573
574<p>If either of the two 'normalize_flags' are given the kernel will first be normalized and then further scaled by the scaling factor value given.</p>
575
576<p>Kernel normalization ('normalize_flags' given) is designed to ensure that any use of the kernel scaling factor with 'Convolve' or 'Correlate' morphology methods will fall into -1.0 to +1.0 range. Note that for non-HDRI versions of IM this may cause images to have any negative results clipped, unless some 'bias' is used.</p>
577
578<p>More specifically. Kernels which only contain positive values (such as a 'Gaussian' kernel) will be scaled so that those values sum to +1.0, ensuring a 0.0 to +1.0 output range for non-HDRI images.</p>
579
580<p>For Kernels that contain some negative values, (such as 'Sharpen' kernels) the kernel will be scaled by the absolute of the sum of kernel values, so that it will generally fall within the +/- 1.0 range.</p>
581
582<p>For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel will be scaled by just the sum of the postive values, so that its output range will again fall into the +/- 1.0 range.</p>
583
584<p>For special kernels designed for locating shapes using 'Correlate', (often only containing +1 and -1 values, representing foreground/brackground matching) a special normalization method is provided to scale the positive values seperatally to those of the negative values, so the kernel will be forced to become a zero-sum kernel better suited to such searches.</p>
585
586<p>WARNING: Correct normalization of the kernel assumes that the '*_range' attributes within the kernel structure have been correctly set during the kernels creation.</p>
587
588<p>NOTE: The values used for 'normalize_flags' have been selected specifically to match the use of geometry options, so that '!' means NormalizeValue, '^' means CorrelateNormalizeValue. All other GeometryFlags values are ignored.</p>
589
590<p>The format of the ScaleKernelInfo method is:</p>
591
592<pre class="code">
593 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
594 const MagickStatusType normalize_flags )
595</pre>
596
597<p>A description of each parameter follows:</p>
598
599<h5>kernel</h5>
600<p>the Morphology/Convolution kernel</p>
601
602<p>o scaling_factor:</p>
603
604<pre class="text">
605 zero. If the kernel is normalized regardless of any flags.
606</pre>
607
608<p>o normalize_flags:</p>
609
610<pre class="text">
611 specifically: NormalizeValue, CorrelateNormalizeValue,
612 and/or PercentValue
613</pre>
614
615 </div>
616<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
617_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2>
618<div class="doc-section">
619
620<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p>
621
622<p>The format of the ShowKernel method is:</p>
623
624<pre class="code">
625 void ShowKernelInfo(KernelInfo *kernel)
626</pre>
627
628<p>A description of each parameter follows:</p>
629
630<h5>kernel</h5>
631<p>the Morphology/Convolution kernel</p>
632
633 </div>
634<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
635_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2>
636<div class="doc-section">
637
638<p>UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel to the given pre-scaled and normalized Kernel. This in effect adds that amount of the original image into the resulting convolution kernel. This value is usually provided by the user as a percentage value in the 'convolve:scale' setting.</p>
639
640<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p>
641
642<p>The format of the UnityAdditionKernelInfo method is:</p>
643
644<pre class="code">
645 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
646</pre>
647
648<p>A description of each parameter follows:</p>
649
650<h5>kernel</h5>
651<p>the Morphology/Convolution kernel</p>
652
653<p>o scale:</p>
654
655<pre class="text">
656 the given kernel.
657</pre>
658
659 </div>
660<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
661_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2>
662<div class="doc-section">
663
664<p>ZeroKernelNans() replaces any special 'nan' value that may be present in the kernel with a zero value. This is typically done when the kernel will be used in special hardware (GPU) convolution processors, to simply matters.</p>
665
666<p>The format of the ZeroKernelNans method is:</p>
667
668<pre class="code">
669 void ZeroKernelNans (KernelInfo *kernel)
670</pre>
671
672<p>A description of each parameter follows:</p>
673
674<h5>kernel</h5>
675<p>the Morphology/Convolution kernel</p>
676
677 </div>
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