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192
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]193<h1>Module morphology Methods</h1>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]194<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 +0000[diff] [blame]195
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]196<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="**_This macro IsNaN">** This macro IsNaN</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]197<div class="doc-section">
198
199<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
200 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]201<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="AcquireKernelInfo">AcquireKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]202<div class="doc-section">
203
204<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>
205
206<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>
207
208<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>
209
210<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>
211
212<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p>
213
214<p>Input kernel defintion strings can consist of any of three types.</p>
215
216<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p>
217
218<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>
219
220<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>
221
222<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>
223
224<p>" kernel ; kernel ; kernel ; "</p>
225
226<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p>
227
228<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>
229
230<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>
231
232<p>The format of the AcquireKernal method is:</p>
233
234<pre class="code">
235 KernelInfo *AcquireKernelInfo(const char *kernel_string)
236</pre>
237
238<p>A description of each parameter follows:</p>
239
240<h5>kernel_string</h5>
241<p>the Morphology/Convolution kernel wanted.</p>
242
243 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]244<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]245<div class="doc-section">
246
247<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>
248
249<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>
250
251<p>The format of the AcquireKernalBuiltIn method is:</p>
252
253<pre class="code">
254 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
255 const GeometryInfo args)
256</pre>
257
258<p>A description of each parameter follows:</p>
259
260<h5>type</h5>
261<p>the pre-defined type of kernel wanted</p>
262
263<h5>args</h5>
264<p>arguments defining or modifying the kernel</p>
265
266<p>Convolution Kernels</p>
267
268<p>Unity the No-Op kernel, also requivelent to Gaussian of sigma zero. Basically a 3x3 kernel of a 1 surrounded by zeros.</p>
269
270<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>
271
272<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
273
274<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>
275
276<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p>
277
278<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p>
279
280<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>
281
282<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>
283
284<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
285
286<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>
287
288<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>
289
290<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p>
291
292<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>
293
294<p>Named Constant Convolution Kernels</p>
295
296<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>
297
298<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>
299
300<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>
301
302<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p>
303
304<p>Sobel:{type},{angle} Type 0: default un-nomalized version shown above.</p>
305
306<p>Type 1: As default but pre-normalized | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p>
307
308<p>Type 2: Diagonal version with same normalization as 1 | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p>
309
310<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p>
311
312<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p>
313
314<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p>
315
316<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p>
317
318<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>
319
320<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p>
321
322<p>FreiChen:{type},{angle}</p>
323
324<p>Frei-Chen Pre-weighted kernels...</p>
325
326<p>Type 0: default un-nomalized version shown above.</p>
327
328<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>
329
330<p>Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 |</p>
331
332<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>
333
334<p>Type 10: All 9 of the following pre-weighted kernels...</p>
335
336<p>Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p>
337
338<p>Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 |</p>
339
340<p>Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) |</p>
341
342<p>Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 |</p>
343
344<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p>
345
346<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p>
347
348<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p>
349
350<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p>
351
352<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p>
353
354<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>
355
356<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>
357
358<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>
359
360<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>
361
362<p>Boolean Kernels</p>
363
364<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>
365
366<p>Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1).</p>
367
368<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>
369
370<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>
371
372<p>Properly centered and odd sized rectangles work the best.</p>
373
374<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>
375
376<p>Because a "disk" is more circular when using a larger radius, using a larger radius is preferred over iterating the morphological operation.</p>
377
378<p>Symbol Dilation Kernels</p>
379
380<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>
381
382<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p>
383
384<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>
385
386<p>NOTE: "plus:1" is equivelent to a "Diamond" kernel.</p>
387
388<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>
389
390<p>Hit and Miss Kernels</p>
391
392<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>
393
394<p>Distance Measuring Kernels</p>
395
396<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>
397
398<p>See the 'Distance' Morphological Method, for information of how it is applied.</p>
399
400<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>
401
402<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>
403
404<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>
405
406<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>
407
408<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>
409
410 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]411<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]412<div class="doc-section">
413
414<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>
415
416<p>The format of the CloneKernelInfo method is:</p>
417
418<pre class="code">
419 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
420</pre>
421
422<p>A description of each parameter follows:</p>
423
424<h5>kernel</h5>
425<p>the Morphology/Convolution kernel to be cloned</p>
426
427 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]428<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]429<div class="doc-section">
430
431<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p>
432
433<p>The format of the DestroyKernelInfo method is:</p>
434
435<pre class="code">
436 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
437</pre>
438
439<p>A description of each parameter follows:</p>
440
441<h5>kernel</h5>
442<p>the Morphology/Convolution kernel to be destroyed</p>
443
444 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]445<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyApply">MorphologyApply</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]446<div class="doc-section">
447
448<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p>
449
450<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>
451
452<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p>
453
454<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>
455
456<p>The format of the MorphologyApply method is:</p>
457
458<pre class="code">
459 Image *MorphologyApply(const Image *image,MorphologyMethod method,
460 const ssize_t iterations,const KernelInfo *kernel,
461 const CompositeMethod compose, const double bias,
462 ExceptionInfo *exception)
463</pre>
464
465<p>A description of each parameter follows:</p>
466
467<h5>image</h5>
468<p>the source image</p>
469
470<h5>method</h5>
471<p>the morphology method to be applied.</p>
472
473<h5>iterations</h5>
474<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>
475
476<h5>channel</h5>
477<p>the channel type.</p>
478
479<h5>kernel</h5>
480<p>An array of double representing the morphology kernel.</p>
481
482<h5>compose</h5>
483<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>
484
485<h5>bias</h5>
486<p>Convolution Output Bias.</p>
487
488<h5>exception</h5>
489<p>return any errors or warnings in this structure.</p>
490
491 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]492<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyImageChannel">MorphologyImageChannel</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]493<div class="doc-section">
494
495<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p>
496
497<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p>
498
499<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>
500
501<p>The format of the MorphologyImage method is:</p>
502
503<pre class="code">
504 Image *MorphologyImage(const Image *image,MorphologyMethod method,
505 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
506</pre>
507
508<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p>
509
510<p>A description of each parameter follows:</p>
511
512<h5>image</h5>
513<p>the image.</p>
514
515<h5>method</h5>
516<p>the morphology method to be applied.</p>
517
518<h5>iterations</h5>
519<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>
520
521<h5>channel</h5>
522<p>the channel type.</p>
523
524<h5>kernel</h5>
525<p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p>
526
527<h5>exception</h5>
528<p>return any errors or warnings in this structure.</p>
529
530 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]531<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]532<div class="doc-section">
533
534<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>
535
536<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>
537
538<p>The format of the ScaleGeometryKernelInfo method is:</p>
539
540<pre class="code">
541 void ScaleGeometryKernelInfo(KernelInfo *kernel,
542 const double scaling_factor,const MagickStatusType normalize_flags)
543</pre>
544
545<p>A description of each parameter follows:</p>
546
547<h5>kernel</h5>
548<p>the Morphology/Convolution kernel to modify</p>
549
550<p>o geometry:</p>
551
552<pre class="text">
553 "-set option:convolve:scale {geometry}" setting.
554</pre>
555
556 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]557<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]558<div class="doc-section">
559
560<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>
561
562<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p>
563
564<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>
565
566<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>
567
568<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>
569
570<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>
571
572<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>
573
574<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>
575
576<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>
577
578<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>
579
580<p>The format of the ScaleKernelInfo method is:</p>
581
582<pre class="code">
583 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
584 const MagickStatusType normalize_flags )
585</pre>
586
587<p>A description of each parameter follows:</p>
588
589<h5>kernel</h5>
590<p>the Morphology/Convolution kernel</p>
591
592<p>o scaling_factor:</p>
593
594<pre class="text">
595 zero. If the kernel is normalized regardless of any flags.
596</pre>
597
598<p>o normalize_flags:</p>
599
600<pre class="text">
601 specifically: NormalizeValue, CorrelateNormalizeValue,
602 and/or PercentValue
603</pre>
604
605 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]606<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]607<div class="doc-section">
608
609<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p>
610
611<p>The format of the ShowKernel method is:</p>
612
613<pre class="code">
614 void ShowKernelInfo(KernelInfo *kernel)
615</pre>
616
617<p>A description of each parameter follows:</p>
618
619<h5>kernel</h5>
620<p>the Morphology/Convolution kernel</p>
621
622 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]623<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]624<div class="doc-section">
625
626<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>
627
628<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p>
629
630<p>The format of the UnityAdditionKernelInfo method is:</p>
631
632<pre class="code">
633 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
634</pre>
635
636<p>A description of each parameter follows:</p>
637
638<h5>kernel</h5>
639<p>the Morphology/Convolution kernel</p>
640
641<p>o scale:</p>
642
643<pre class="text">
644 the given kernel.
645</pre>
646
647 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame^]648<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]649<div class="doc-section">
650
651<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>
652
653<p>The format of the ZeroKernelNans method is:</p>
654
655<pre class="code">
656 void ZeroKernelNans (KernelInfo *kernel)
657</pre>
658
659<p>A description of each parameter follows:</p>
660
661<h5>kernel</h5>
662<p>the Morphology/Convolution kernel</p>
663
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