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197<div class="main">
198
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]199<h1>Module morphology Methods</h1>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]200<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]201
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]202<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]203<div class="doc-section">
204
205<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
206 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]207<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]208<div class="doc-section">
209
210<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>
211
212<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>
213
214<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>
215
216<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>
217
218<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p>
219
220<p>Input kernel defintion strings can consist of any of three types.</p>
221
222<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p>
223
224<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>
225
226<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>
227
cristybe3c5be2011-03-05 17:35:07 +0000[diff] [blame]228<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]229
230<p>" kernel ; kernel ; kernel ; "</p>
231
232<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p>
233
234<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>
235
236<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>
237
238<p>The format of the AcquireKernal method is:</p>
239
240<pre class="code">
241 KernelInfo *AcquireKernelInfo(const char *kernel_string)
242</pre>
243
244<p>A description of each parameter follows:</p>
245
246<h5>kernel_string</h5>
247<p>the Morphology/Convolution kernel wanted.</p>
248
249 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]250<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]251<div class="doc-section">
252
253<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>
254
255<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>
256
257<p>The format of the AcquireKernalBuiltIn method is:</p>
258
259<pre class="code">
260 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
261 const GeometryInfo args)
262</pre>
263
264<p>A description of each parameter follows:</p>
265
266<h5>type</h5>
267<p>the pre-defined type of kernel wanted</p>
268
269<h5>args</h5>
270<p>arguments defining or modifying the kernel</p>
271
272<p>Convolution Kernels</p>
273
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]274<p>Unity The a No-Op or Scaling single element kernel.</p>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]275
276<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>
277
278<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
279
280<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>
281
282<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p>
283
284<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p>
285
286<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>
287
288<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>
289
290<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
291
292<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>
293
294<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>
295
296<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p>
297
298<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>
299
300<p>Named Constant Convolution Kernels</p>
301
302<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>
303
304<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>
305
306<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>
307
308<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p>
309
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]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
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]368<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>
369
370<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>
371
372<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]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
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]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
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]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 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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]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
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]400<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]401
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]402<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]403
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]404<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]405
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]406<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]407
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]408<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>
409
410<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>
411
412<p>The "Euclidean" Distance Kernel however does generate a non-integer fractional results, and as such scaling is vital even for binary shapes.</p>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]413
414 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]415<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]416<div class="doc-section">
417
418<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>
419
420<p>The format of the CloneKernelInfo method is:</p>
421
422<pre class="code">
423 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
424</pre>
425
426<p>A description of each parameter follows:</p>
427
428<h5>kernel</h5>
429<p>the Morphology/Convolution kernel to be cloned</p>
430
431 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]432<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]433<div class="doc-section">
434
435<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p>
436
437<p>The format of the DestroyKernelInfo method is:</p>
438
439<pre class="code">
440 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
441</pre>
442
443<p>A description of each parameter follows:</p>
444
445<h5>kernel</h5>
446<p>the Morphology/Convolution kernel to be destroyed</p>
447
448 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]449<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]450<div class="doc-section">
451
452<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p>
453
454<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>
455
456<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p>
457
458<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>
459
460<p>The format of the MorphologyApply method is:</p>
461
462<pre class="code">
463 Image *MorphologyApply(const Image *image,MorphologyMethod method,
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]464 const ChannelType channel, const ssize_t iterations,
465 const KernelInfo *kernel, const CompositeMethod compose,
466 const double bias, ExceptionInfo *exception)
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]467</pre>
468
469<p>A description of each parameter follows:</p>
470
471<h5>image</h5>
472<p>the source image</p>
473
474<h5>method</h5>
475<p>the morphology method to be applied.</p>
476
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]477<h5>channel</h5>
478<p>the channels to which the operations are applied The channel 'sync' flag determines if 'alpha weighting' is applied for convolution style operations.</p>
479
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]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>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]499<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]500<div class="doc-section">
501
502<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p>
503
504<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p>
505
506<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>
507
508<p>The format of the MorphologyImage method is:</p>
509
510<pre class="code">
511 Image *MorphologyImage(const Image *image,MorphologyMethod method,
512 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
513</pre>
514
515<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p>
516
517<p>A description of each parameter follows:</p>
518
519<h5>image</h5>
520<p>the image.</p>
521
522<h5>method</h5>
523<p>the morphology method to be applied.</p>
524
525<h5>iterations</h5>
526<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>
527
528<h5>channel</h5>
529<p>the channel type.</p>
530
531<h5>kernel</h5>
532<p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p>
533
534<h5>exception</h5>
535<p>return any errors or warnings in this structure.</p>
536
537 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]538<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]539<div class="doc-section">
540
541<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>
542
543<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>
544
545<p>The format of the ScaleGeometryKernelInfo method is:</p>
546
547<pre class="code">
548 void ScaleGeometryKernelInfo(KernelInfo *kernel,
549 const double scaling_factor,const MagickStatusType normalize_flags)
550</pre>
551
552<p>A description of each parameter follows:</p>
553
554<h5>kernel</h5>
555<p>the Morphology/Convolution kernel to modify</p>
556
557<p>o geometry:</p>
558
559<pre class="text">
560 "-set option:convolve:scale {geometry}" setting.
561</pre>
562
563 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]564<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]565<div class="doc-section">
566
567<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>
568
569<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p>
570
571<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>
572
573<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>
574
575<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>
576
577<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>
578
579<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>
580
cristybe3c5be2011-03-05 17:35:07 +0000[diff] [blame]581<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]582
583<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>
584
585<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>
586
587<p>The format of the ScaleKernelInfo method is:</p>
588
589<pre class="code">
590 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
591 const MagickStatusType normalize_flags )
592</pre>
593
594<p>A description of each parameter follows:</p>
595
596<h5>kernel</h5>
597<p>the Morphology/Convolution kernel</p>
598
599<p>o scaling_factor:</p>
600
601<pre class="text">
602 zero. If the kernel is normalized regardless of any flags.
603</pre>
604
605<p>o normalize_flags:</p>
606
607<pre class="text">
608 specifically: NormalizeValue, CorrelateNormalizeValue,
609 and/or PercentValue
610</pre>
611
612 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]613<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]614<div class="doc-section">
615
616<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p>
617
618<p>The format of the ShowKernel method is:</p>
619
620<pre class="code">
621 void ShowKernelInfo(KernelInfo *kernel)
622</pre>
623
624<p>A description of each parameter follows:</p>
625
626<h5>kernel</h5>
627<p>the Morphology/Convolution kernel</p>
628
629 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]630<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]631<div class="doc-section">
632
633<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>
634
635<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p>
636
637<p>The format of the UnityAdditionKernelInfo method is:</p>
638
639<pre class="code">
640 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
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<p>o scale:</p>
649
650<pre class="text">
651 the given kernel.
652</pre>
653
654 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]655<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]656<div class="doc-section">
657
658<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>
659
660<p>The format of the ZeroKernelNans method is:</p>
661
662<pre class="code">
663 void ZeroKernelNans (KernelInfo *kernel)
664</pre>
665
666<p>A description of each parameter follows:</p>
667
668<h5>kernel</h5>
669<p>the Morphology/Convolution kernel</p>
670
671 </div>
cristy3eaa0ef2010-03-06 20:35:26 +0000[diff] [blame]672
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