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cristyffb69ed2010-12-25 00:06:48 +0000[diff] [blame]11 <title>ImageMagick: MagickCore, C API for ImageMagick: Morphological Erosions, Dilations, Openings, and Closings</title>
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217
cristy20398432011-08-11 01:31:50 +0000[diff] [blame]218<h1>Module morphology Methods</h1>
219<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]220
cristy20398432011-08-11 01:31:50 +0000[diff] [blame]221<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="**_This macro IsNaN">** This macro IsNaN</a></h2>
222<div class="doc-section">
223
224<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
225 </div>
226<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="AcquireKernelInfo">AcquireKernelInfo</a></h2>
227<div class="doc-section">
228
229<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>
230
231<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>
232
233<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>
234
235<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>
236
237<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p>
238
239<p>Input kernel defintion strings can consist of any of three types.</p>
240
241<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p>
242
243<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>
244
245<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>
246
247<p>You can define a 'list of kernels' which can be used by some morphology operators A list is defined as a semi-colon separated list kernels.</p>
248
249<p>" kernel ; kernel ; kernel ; "</p>
250
251<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p>
252
253<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>
254
255<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>
256
257<p>The format of the AcquireKernal method is:</p>
258
259<pre class="code">
260 KernelInfo *AcquireKernelInfo(const char *kernel_string)
261</pre>
262
263<p>A description of each parameter follows:</p>
264
265<h5>kernel_string</h5>
266<p>the Morphology/Convolution kernel wanted.</p>
267
268 </div>
269<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2>
270<div class="doc-section">
271
272<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>
273
274<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>
275
276<p>The format of the AcquireKernalBuiltIn method is:</p>
277
278<pre class="code">
279 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
280 const GeometryInfo args)
281</pre>
282
283<p>A description of each parameter follows:</p>
284
285<h5>type</h5>
286<p>the pre-defined type of kernel wanted</p>
287
288<h5>args</h5>
289<p>arguments defining or modifying the kernel</p>
290
291<p>Convolution Kernels</p>
292
293<p>Unity The a No-Op or Scaling single element kernel.</p>
294
295<p>Gaussian:{radius},{sigma} Generate a two-dimensional gaussian kernel, as used by -gaussian. The sigma for the curve is required. The resulting kernel is normalized,</p>
296
297<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
298
299<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>
300
301<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p>
302
303<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p>
304
305<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>
306
307<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>
308
309<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
310
311<p>Note that two convolutions with two "Blur" kernels perpendicular to each other, is equivalent 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>
312
313<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>
314
315<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p>
316
317<p># Still to be implemented... # # Filter2D # Filter1D # Set kernel values using a resize filter, and given scale (sigma) # Cylindrical or Linear. Is this possible with an image? #</p>
318
319<p>Named Constant Convolution Kernels</p>
320
321<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>
322
323<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>
324
325<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>
326
327<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p>
328
329<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p>
330
331<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p>
332
333<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p>
334
335<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p>
336
337<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>
338
339<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p>
340
341<p>FreiChen:{type},{angle}</p>
342
343<p>Frei-Chen Pre-weighted kernels...</p>
344
345<p>Type 0: default un-nomalized version shown above.</p>
346
347<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>
348
349<p>Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 |</p>
350
351<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>
352
353<p>Type 10: All 9 of the following pre-weighted kernels...</p>
354
355<p>Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p>
356
357<p>Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 |</p>
358
359<p>Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) |</p>
360
361<p>Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 |</p>
362
363<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p>
364
365<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p>
366
367<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p>
368
369<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p>
370
371<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p>
372
373<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>
374
375<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>
376
377<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>
378
379<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>
380
381<p>Boolean Kernels</p>
382
383<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>
384
385<p>Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1).</p>
386
387<p>Octagon:[{radius}[,{scale}]] Generate octagonal shaped kernel of given radius and constant scale. Default radius is 3 producing a 7x7 kernel. A radius of 1 will result in "Diamond" kernel.</p>
388
389<p>Disk:[{radius}[,{scale}]] Generate a binary disk, thresholded at the radius given, the radius may be a float-point value. Final Kernel size is floor(radius)*2+1 square. A radius of 5.3 is the default.</p>
390
391<p>NOTE: That a low radii Disk kernels produce the same results as many of the previously defined kernels, but differ greatly at larger radii. Here is a table of equivalences... "Disk:1" => "Diamond", "Octagon:1", or "Cross:1" "Disk:1.5" => "Square" "Disk:2" => "Diamond:2" "Disk:2.5" => "Octagon" "Disk:2.9" => "Square:2" "Disk:3.5" => "Octagon:3" "Disk:4.5" => "Octagon:4" "Disk:5.4" => "Octagon:5" "Disk:6.4" => "Octagon:6" All other Disk shapes are unique to this kernel, but because a "Disk" is more circular when using a larger radius, using a larger radius is preferred over iterating the morphological operation.</p>
392
393<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>
394
395<p>Properly centered and odd sized rectangles work the best.</p>
396
397<p>Symbol Dilation Kernels</p>
398
399<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>
400
401<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p>
402
403<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>
404
405<p>NOTE: "plus:1" is equivalent to a "Diamond" kernel.</p>
406
407<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>
408
409<p>Hit and Miss Kernels</p>
410
411<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 Diagonals:type A special kernel to thin the 'outside' of diagonals 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 Thickening 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: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper.</p>
412
413<p>Distance Measuring Kernels</p>
414
415<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>
416
417<p>See the 'Distance' Morphological Method, for information of how it is applied.</p>
418
419<p>Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev or Chessboard 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 given a value that is closer than expected.</p>
420
421<p>Manhattan:[{radius}][x{scale}[!]] Manhattan Distance (also known as Rectilinear, City Block, or the Taxi Cab distance metric), it is the distance needed when you can only travel in horizontal or vertical directions only. It is the distance a 'Rook' in chess would have to travel, and results in a diamond like distances, where diagonals are further than expected.</p>
422
423<p>Octagonal:[{radius}][x{scale}[!]] An interleving of Manhatten and Chebyshev metrics producing an increasing octagonally shaped distance. Distances matches those of the "Octagon" shaped kernel of the same radius. The minimum radius and default is 2, producing a 5x5 kernel.</p>
424
425<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.</p>
426
427<p>However using a larger radius such as "Euclidean:4" you will get a much smoother distance gradient from the edge of the shape. Especially if the image is pre-processed to include any anti-aliasing pixels. Of course a larger kernel is slower to use, and not always needed.</p>
428
429<p>The first three Distance Measuring Kernels will only generate distances of exact multiples of {scale} in binary images. As such you can use a scale of 1 without loosing any information. However you also need some scaling when handling non-binary anti-aliased shapes.</p>
430
431<p>The "Euclidean" Distance Kernel however does generate a non-integer fractional results, and as such scaling is vital even for binary shapes.</p>
432
433 </div>
434<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2>
435<div class="doc-section">
436
437<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>
438
439<p>The format of the CloneKernelInfo method is:</p>
440
441<pre class="code">
442 KernelInfo *CloneKernelInfo(const 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 cloned</p>
449
450 </div>
451<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2>
452<div class="doc-section">
453
454<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p>
455
456<p>The format of the DestroyKernelInfo method is:</p>
457
458<pre class="code">
459 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
460</pre>
461
462<p>A description of each parameter follows:</p>
463
464<h5>kernel</h5>
465<p>the Morphology/Convolution kernel to be destroyed</p>
466
467 </div>
468<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyApply">MorphologyApply</a></h2>
469<div class="doc-section">
470
471<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p>
472
473<p>It is basically equivalent 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 possible interference by any API user supplied settings.</p>
474
475<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p>
476
477<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>
478
479<p>The format of the MorphologyApply method is:</p>
480
481<pre class="code">
482 Image *MorphologyApply(const Image *image,MorphologyMethod method,
483 const ChannelType channel, const ssize_t iterations,
484 const KernelInfo *kernel, const CompositeMethod compose,
485 const double bias, ExceptionInfo *exception)
486</pre>
487
488<p>A description of each parameter follows:</p>
489
490<h5>image</h5>
491<p>the source image</p>
492
493<h5>method</h5>
494<p>the morphology method to be applied.</p>
495
496<h5>channel</h5>
497<p>the channels to which the operations are applied The channel 'sync' flag determines if 'alpha weighting' is applied for convolution style operations.</p>
498
499<h5>iterations</h5>
500<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>
501
502<h5>channel</h5>
503<p>the channel type.</p>
504
505<h5>kernel</h5>
506<p>An array of double representing the morphology kernel.</p>
507
508<h5>compose</h5>
509<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>
510
511<h5>bias</h5>
512<p>Convolution Output Bias.</p>
513
514<h5>exception</h5>
515<p>return any errors or warnings in this structure.</p>
516
517 </div>
518<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyImageChannel">MorphologyImageChannel</a></h2>
519<div class="doc-section">
520
521<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p>
522
523<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p>
524
525<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>
526
527<p>The format of the MorphologyImage method is:</p>
528
529<pre class="code">
530 Image *MorphologyImage(const Image *image,MorphologyMethod method,
531 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
532</pre>
533
534<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p>
535
536<p>A description of each parameter follows:</p>
537
538<h5>image</h5>
539<p>the image.</p>
540
541<h5>method</h5>
542<p>the morphology method to be applied.</p>
543
544<h5>iterations</h5>
545<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>
546
547<h5>channel</h5>
548<p>the channel type.</p>
549
550<h5>kernel</h5>
551<p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p>
552
553<h5>exception</h5>
554<p>return any errors or warnings in this structure.</p>
555
556 </div>
557<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2>
558<div class="doc-section">
559
560<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>
561
562<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>
563
564<p>The format of the ScaleGeometryKernelInfo method is:</p>
565
566<pre class="code">
567 void ScaleGeometryKernelInfo(KernelInfo *kernel,
568 const double scaling_factor,const MagickStatusType normalize_flags)
569</pre>
570
571<p>A description of each parameter follows:</p>
572
573<h5>kernel</h5>
574<p>the Morphology/Convolution kernel to modify</p>
575
576<p>o geometry:</p>
577
578<pre class="text">
579 "-set option:convolve:scale {geometry}" setting.
580</pre>
581
582 </div>
583<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2>
584<div class="doc-section">
585
586<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>
587
588<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p>
589
590<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>
591
592<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>
593
594<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>
595
596<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>
597
598<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>
599
600<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 separately to those of the negative values, so the kernel will be forced to become a zero-sum kernel better suited to such searches.</p>
601
602<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>
603
604<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>
605
606<p>The format of the ScaleKernelInfo method is:</p>
607
608<pre class="code">
609 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
610 const MagickStatusType normalize_flags )
611</pre>
612
613<p>A description of each parameter follows:</p>
614
615<h5>kernel</h5>
616<p>the Morphology/Convolution kernel</p>
617
618<p>o scaling_factor:</p>
619
620<pre class="text">
621 zero. If the kernel is normalized regardless of any flags.
622</pre>
623
624<p>o normalize_flags:</p>
625
626<pre class="text">
627 specifically: NormalizeValue, CorrelateNormalizeValue,
628 and/or PercentValue
629</pre>
630
631 </div>
632<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2>
633<div class="doc-section">
634
635<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p>
636
637<p>The format of the ShowKernel method is:</p>
638
639<pre class="code">
640 void ShowKernelInfo(const KernelInfo *kernel)
641</pre>
642
643<p>A description of each parameter follows:</p>
644
645<h5>kernel</h5>
646<p>the Morphology/Convolution kernel</p>
647
648 </div>
649<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2>
650<div class="doc-section">
651
652<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>
653
654<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p>
655
656<p>The format of the UnityAdditionKernelInfo method is:</p>
657
658<pre class="code">
659 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
660</pre>
661
662<p>A description of each parameter follows:</p>
663
664<h5>kernel</h5>
665<p>the Morphology/Convolution kernel</p>
666
667<p>o scale:</p>
668
669<pre class="text">
670 the given kernel.
671</pre>
672
673 </div>
674<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2>
675<div class="doc-section">
676
677<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>
678
679<p>The format of the ZeroKernelNans method is:</p>
680
681<pre class="code">
682 void ZeroKernelNans (KernelInfo *kernel)
683</pre>
684
685<p>A description of each parameter follows:</p>
686
687<h5>kernel</h5>
688<p>the Morphology/Convolution kernel</p>
689
690 </div>
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