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207
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]208<h1>Module morphology Methods</h1>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]209<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]210
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]211<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]212<div class="doc-section">
213
214<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
215 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]216<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]217<div class="doc-section">
218
219<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>
220
221<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>
222
223<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>
224
225<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>
226
227<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p>
228
229<p>Input kernel defintion strings can consist of any of three types.</p>
230
231<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p>
232
233<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>
234
235<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>
236
cristybe3c5be2011-03-05 17:35:07 +0000[diff] [blame]237<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]238
239<p>" kernel ; kernel ; kernel ; "</p>
240
241<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p>
242
243<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>
244
245<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>
246
247<p>The format of the AcquireKernal method is:</p>
248
249<pre class="code">
250 KernelInfo *AcquireKernelInfo(const char *kernel_string)
251</pre>
252
253<p>A description of each parameter follows:</p>
254
255<h5>kernel_string</h5>
256<p>the Morphology/Convolution kernel wanted.</p>
257
258 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]259<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]260<div class="doc-section">
261
262<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>
263
264<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>
265
266<p>The format of the AcquireKernalBuiltIn method is:</p>
267
268<pre class="code">
269 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
270 const GeometryInfo args)
271</pre>
272
273<p>A description of each parameter follows:</p>
274
275<h5>type</h5>
276<p>the pre-defined type of kernel wanted</p>
277
278<h5>args</h5>
279<p>arguments defining or modifying the kernel</p>
280
281<p>Convolution Kernels</p>
282
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]283<p>Unity The a No-Op or Scaling single element kernel.</p>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]284
285<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>
286
287<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
288
289<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>
290
291<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p>
292
293<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p>
294
295<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>
296
297<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>
298
299<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p>
300
cristybaacd172011-04-30 23:11:03 +0000[diff] [blame]301<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>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]302
303<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>
304
305<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p>
306
307<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>
308
309<p>Named Constant Convolution Kernels</p>
310
311<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>
312
313<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>
314
315<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>
316
317<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p>
318
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]319<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p>
320
321<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p>
322
323<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p>
324
325<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p>
326
327<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>
328
329<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p>
330
331<p>FreiChen:{type},{angle}</p>
332
333<p>Frei-Chen Pre-weighted kernels...</p>
334
335<p>Type 0: default un-nomalized version shown above.</p>
336
337<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>
338
339<p>Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 |</p>
340
341<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>
342
343<p>Type 10: All 9 of the following pre-weighted kernels...</p>
344
345<p>Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p>
346
347<p>Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 |</p>
348
349<p>Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) |</p>
350
351<p>Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 |</p>
352
353<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p>
354
355<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p>
356
357<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p>
358
359<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p>
360
361<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p>
362
363<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>
364
365<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>
366
367<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>
368
369<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>
370
371<p>Boolean Kernels</p>
372
373<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>
374
375<p>Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1).</p>
376
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]377<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>
378
379<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>
380
381<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]382
383<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>
384
385<p>Properly centered and odd sized rectangles work the best.</p>
386
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]387<p>Symbol Dilation Kernels</p>
388
389<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>
390
391<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p>
392
393<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>
394
cristybaacd172011-04-30 23:11:03 +0000[diff] [blame]395<p>NOTE: "plus:1" is equivalent to a "Diamond" kernel.</p>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]396
397<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>
398
399<p>Hit and Miss Kernels</p>
400
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]401<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]402
403<p>Distance Measuring Kernels</p>
404
405<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>
406
407<p>See the 'Distance' Morphological Method, for information of how it is applied.</p>
408
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]409<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]410
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]411<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]412
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]413<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]414
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]415<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]416
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]417<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>
418
419<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>
420
421<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]422
423 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]424<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]425<div class="doc-section">
426
427<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>
428
429<p>The format of the CloneKernelInfo method is:</p>
430
431<pre class="code">
432 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
433</pre>
434
435<p>A description of each parameter follows:</p>
436
437<h5>kernel</h5>
438<p>the Morphology/Convolution kernel to be cloned</p>
439
440 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]441<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]442<div class="doc-section">
443
444<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p>
445
446<p>The format of the DestroyKernelInfo method is:</p>
447
448<pre class="code">
449 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
450</pre>
451
452<p>A description of each parameter follows:</p>
453
454<h5>kernel</h5>
455<p>the Morphology/Convolution kernel to be destroyed</p>
456
457 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]458<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]459<div class="doc-section">
460
461<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p>
462
cristybaacd172011-04-30 23:11:03 +0000[diff] [blame]463<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 posible interference by any API user supplied settings.</p>
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]464
465<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p>
466
467<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>
468
469<p>The format of the MorphologyApply method is:</p>
470
471<pre class="code">
472 Image *MorphologyApply(const Image *image,MorphologyMethod method,
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]473 const ChannelType channel, const ssize_t iterations,
474 const KernelInfo *kernel, const CompositeMethod compose,
475 const double bias, ExceptionInfo *exception)
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]476</pre>
477
478<p>A description of each parameter follows:</p>
479
480<h5>image</h5>
481<p>the source image</p>
482
483<h5>method</h5>
484<p>the morphology method to be applied.</p>
485
cristye9a32c32011-04-07 01:11:05 +0000[diff] [blame]486<h5>channel</h5>
487<p>the channels to which the operations are applied The channel 'sync' flag determines if 'alpha weighting' is applied for convolution style operations.</p>
488
cristy6f77f692011-02-15 15:31:39 +0000[diff] [blame]489<h5>iterations</h5>
490<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>
491
492<h5>channel</h5>
493<p>the channel type.</p>
494
495<h5>kernel</h5>
496<p>An array of double representing the morphology kernel.</p>
497
498<h5>compose</h5>
499<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>
500
501<h5>bias</h5>
502<p>Convolution Output Bias.</p>
503
504<h5>exception</h5>
505<p>return any errors or warnings in this structure.</p>
506
507 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]508<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]509<div class="doc-section">
510
511<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p>
512
513<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p>
514
515<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>
516
517<p>The format of the MorphologyImage method is:</p>
518
519<pre class="code">
520 Image *MorphologyImage(const Image *image,MorphologyMethod method,
521 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
522</pre>
523
524<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p>
525
526<p>A description of each parameter follows:</p>
527
528<h5>image</h5>
529<p>the image.</p>
530
531<h5>method</h5>
532<p>the morphology method to be applied.</p>
533
534<h5>iterations</h5>
535<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>
536
537<h5>channel</h5>
538<p>the channel type.</p>
539
540<h5>kernel</h5>
541<p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p>
542
543<h5>exception</h5>
544<p>return any errors or warnings in this structure.</p>
545
546 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]547<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]548<div class="doc-section">
549
550<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>
551
552<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>
553
554<p>The format of the ScaleGeometryKernelInfo method is:</p>
555
556<pre class="code">
557 void ScaleGeometryKernelInfo(KernelInfo *kernel,
558 const double scaling_factor,const MagickStatusType normalize_flags)
559</pre>
560
561<p>A description of each parameter follows:</p>
562
563<h5>kernel</h5>
564<p>the Morphology/Convolution kernel to modify</p>
565
566<p>o geometry:</p>
567
568<pre class="text">
569 "-set option:convolve:scale {geometry}" setting.
570</pre>
571
572 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]573<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]574<div class="doc-section">
575
576<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>
577
578<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p>
579
580<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>
581
582<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>
583
584<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>
585
586<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>
587
588<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>
589
cristybe3c5be2011-03-05 17:35:07 +0000[diff] [blame]590<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]591
592<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>
593
594<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>
595
596<p>The format of the ScaleKernelInfo method is:</p>
597
598<pre class="code">
599 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
600 const MagickStatusType normalize_flags )
601</pre>
602
603<p>A description of each parameter follows:</p>
604
605<h5>kernel</h5>
606<p>the Morphology/Convolution kernel</p>
607
608<p>o scaling_factor:</p>
609
610<pre class="text">
611 zero. If the kernel is normalized regardless of any flags.
612</pre>
613
614<p>o normalize_flags:</p>
615
616<pre class="text">
617 specifically: NormalizeValue, CorrelateNormalizeValue,
618 and/or PercentValue
619</pre>
620
621 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]622<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]623<div class="doc-section">
624
625<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p>
626
627<p>The format of the ShowKernel method is:</p>
628
629<pre class="code">
630 void ShowKernelInfo(KernelInfo *kernel)
631</pre>
632
633<p>A description of each parameter follows:</p>
634
635<h5>kernel</h5>
636<p>the Morphology/Convolution kernel</p>
637
638 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]639<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]640<div class="doc-section">
641
642<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>
643
644<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p>
645
646<p>The format of the UnityAdditionKernelInfo method is:</p>
647
648<pre class="code">
649 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
650</pre>
651
652<p>A description of each parameter follows:</p>
653
654<h5>kernel</h5>
655<p>the Morphology/Convolution kernel</p>
656
657<p>o scale:</p>
658
659<pre class="text">
660 the given kernel.
661</pre>
662
663 </div>
cristy350dea42011-02-15 17:31:04 +0000[diff] [blame]664<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]665<div class="doc-section">
666
667<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>
668
669<p>The format of the ZeroKernelNans method is:</p>
670
671<pre class="code">
672 void ZeroKernelNans (KernelInfo *kernel)
673</pre>
674
675<p>A description of each parameter follows:</p>
676
677<h5>kernel</h5>
678<p>the Morphology/Convolution kernel</p>
679
680 </div>
cristy3eaa0ef2010-03-06 20:35:26 +0000[diff] [blame]681
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