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174
175<div class="main">
176
cristy307ba182010-12-25 01:00:34 +0000[diff] [blame]177<h1>Module morphology
178 Methods</h1>
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]179<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>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]180
181<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]182_8c.html" target="source" name="** This macro IsNaN">** This macro IsNaN</a></h2>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]183<div class="doc-section">
184
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]185<p>** This macro IsNaN() is thus is only true if the value given is NaN. </p>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]186 </div>
187<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
188_8c.html" target="source" name="AcquireKernelInfo">AcquireKernelInfo</a></h2>
189<div class="doc-section">
190
191<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></ol>
192
193<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></ol>
194
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]195<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]196
197<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></ol>
198
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]199<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]200
201<p>Input kernel defintion strings can consist of any of three types.</p></ol>
202
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]203<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]204
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]205<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]206
207<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></ol>
208
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]209<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></ol>
210
211<p>" kernel ; kernel ; kernel ; "</p></ol>
212
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]213<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]214
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]215<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></ol>
216
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]217<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></ol>
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]218
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]219<p>The format of the AcquireKernal method is:</p>
220
221<pre class="code">
222 KernelInfo *AcquireKernelInfo(const char *kernel_string)
223</pre>
224
225<p>A description of each parameter follows:</p></ol>
226
227<h5>kernel_string</h5>
228<ol><p>the Morphology/Convolution kernel wanted.</p></ol>
229
230 </div>
231<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
232_8c.html" target="source" name="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2>
233<div class="doc-section">
234
235<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></ol>
236
237<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></ol>
238
239<p>The format of the AcquireKernalBuiltIn method is:</p>
240
241<pre class="code">
242 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
243 const GeometryInfo args)
244</pre>
245
246<p>A description of each parameter follows:</p></ol>
247
248<h5>type</h5>
249<ol><p>the pre-defined type of kernel wanted</p></ol>
250
251<h5>args</h5>
252<ol><p>arguments defining or modifying the kernel</p></ol>
253
254<p>Convolution Kernels</p></ol>
255
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]256<p>Unity the No-Op kernel, also requivelent to Gaussian of sigma zero. Basically a 3x3 kernel of a 1 surrounded by zeros.</p></ol>
257
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]258<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></ol>
259
260<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]261
262<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></ol>
263
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]264<p>LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]265
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]266<p>An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia).</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]267
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]268<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></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]269
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]270<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></ol>
271
272<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p></ol>
273
274<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></ol>
275
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]276<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]277
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]278<p>Note that the first argument is the width of the kernel and not the radius of the kernel.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]279
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]280<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></ol>
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]281
282<p>Named Constant Convolution Kernels</p></ol>
283
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]284<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></ol>
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]285
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]286<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></ol>
287
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]288<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></ol>
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]289
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]290<p>Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]291
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]292<p>Sobel:{type},{angle} Type 0: default un-nomalized version shown above.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]293
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]294<p>Type 1: As default but pre-normalized | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p></ol>
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]295
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]296<p>Type 2: Diagonal version with same normalization as 1 | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p></ol>
297
298<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p></ol>
299
300<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p></ol>
301
302<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p></ol>
303
304<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p></ol>
305
306<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></ol>
307
308<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p></ol>
309
310<p>FreiChen:{type},{angle}</p></ol>
311
312<p>Frei-Chen Pre-weighted kernels...</p></ol>
313
314<p>Type 0: default un-nomalized version shown above.</p></ol>
315
316<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></ol>
317
318<p>Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 |</p></ol>
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]319
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]320<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></ol>
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]321
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]322<p>Type 10: All 9 of the following pre-weighted kernels...</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]323
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]324<p>Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]325
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]326<p>Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]327
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]328<p>Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]329
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]330<p>Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]331
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]332<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]333
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]334<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]335
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]336<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]337
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]338<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p></ol>
339
340<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]341
342<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></ol>
343
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]344<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></ol>
345
346<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></ol>
347
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]348<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]349
350<p>Boolean Kernels</p></ol>
351
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]352<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]353
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]354<p>Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1).</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]355
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]356<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]357
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]358<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></ol>
359
360<p>Properly centered and odd sized rectangles work the best.</p></ol>
361
362<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]363
364<p>Because a "disk" is more circular when using a larger radius, using a larger radius is preferred over iterating the morphological operation.</p></ol>
365
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]366<p>Symbol Dilation Kernels</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]367
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]368<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></ol>
cristy6f6c2962010-05-14 01:39:00 +0000[diff] [blame]369
370<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p></ol>
371
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]372<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></ol>
373
374<p>NOTE: "plus:1" is equivelent to a "Diamond" kernel.</p></ol>
375
376<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></ol>
377
cristy6f6c2962010-05-14 01:39:00 +0000[diff] [blame]378<p>Hit and Miss Kernels</p></ol>
379
cristy4cb39ab2010-06-07 13:59:16 +0000[diff] [blame]380<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]381
382<p>Distance Measuring Kernels</p></ol>
383
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]384<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></ol>
385
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]386<p>See the 'Distance' Morphological Method, for information of how it is applied.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]387
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]388<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]389
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]390<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></ol>
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]391
392<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]393
394<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></ol>
395
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]396<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]397
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]398 </div>
399<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
400_8c.html" target="source" name="CloneKernelInfo">CloneKernelInfo</a></h2>
401<div class="doc-section">
402
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]403<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]404
cristy0291e2d2010-04-04 17:50:18 +0000[diff] [blame]405<p>The format of the CloneKernelInfo method is:</p>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]406
407<pre class="code">
408 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
409</pre>
410
411<p>A description of each parameter follows:</p></ol>
412
413<h5>kernel</h5>
414<ol><p>the Morphology/Convolution kernel to be cloned</p></ol>
415
416 </div>
417<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
418_8c.html" target="source" name="DestroyKernelInfo">DestroyKernelInfo</a></h2>
419<div class="doc-section">
420
421<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p></ol>
422
423<p>The format of the DestroyKernelInfo method is:</p>
424
425<pre class="code">
426 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
427</pre>
428
429<p>A description of each parameter follows:</p></ol>
430
431<h5>kernel</h5>
432<ol><p>the Morphology/Convolution kernel to be destroyed</p></ol>
433
434 </div>
435<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]436_8c.html" target="source" name="MorphologyApply">MorphologyApply</a></h2>
437<div class="doc-section">
438
439<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p></ol>
440
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]441<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></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]442
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]443<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p></ol>
444
445<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></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]446
447<p>The format of the MorphologyApply method is:</p>
448
449<pre class="code">
450 Image *MorphologyApply(const Image *image,MorphologyMethod method,
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]451 const ssize_t iterations,const KernelInfo *kernel,
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]452 const CompositeMethod compose, const double bias,
453 ExceptionInfo *exception)
454</pre>
455
456<p>A description of each parameter follows:</p></ol>
457
458<h5>image</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]459<ol><p>the source image</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]460
461<h5>method</h5>
462<ol><p>the morphology method to be applied.</p></ol>
463
464<h5>iterations</h5>
465<ol><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></ol>
466
467<h5>channel</h5>
468<ol><p>the channel type.</p></ol>
469
470<h5>kernel</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]471<ol><p>An array of double representing the morphology kernel.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]472
473<h5>compose</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]474<ol><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></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]475
476<h5>bias</h5>
477<ol><p>Convolution Output Bias.</p></ol>
478
479<h5>exception</h5>
480<ol><p>return any errors or warnings in this structure.</p></ol>
481
482 </div>
483<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]484_8c.html" target="source" name="MorphologyImageChannel">MorphologyImageChannel</a></h2>
485<div class="doc-section">
486
487<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p></ol>
488
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]489<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p></ol>
490
491<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]492
493<p>The format of the MorphologyImage method is:</p>
494
495<pre class="code">
496 Image *MorphologyImage(const Image *image,MorphologyMethod method,
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]497 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]498</pre>
499
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]500<p>Image *MorphologyImageChannel(const Image *image, const ChannelType channel,MorphologyMethod method,const ssize_t iterations, KernelInfo *kernel,ExceptionInfo *exception)</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]501
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]502<p>A description of each parameter follows:</p></ol>
503
504<h5>image</h5>
505<ol><p>the image.</p></ol>
506
507<h5>method</h5>
508<ol><p>the morphology method to be applied.</p></ol>
509
510<h5>iterations</h5>
511<ol><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></ol>
512
513<h5>channel</h5>
514<ol><p>the channel type.</p></ol>
515
516<h5>kernel</h5>
517<ol><p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p></ol>
518
519<h5>exception</h5>
520<ol><p>return any errors or warnings in this structure.</p></ol>
521
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]522 </div>
523<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
524_8c.html" target="source" name="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2>
525<div class="doc-section">
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]526
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]527<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></ol>
528
529<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></ol>
530
531<p>The format of the ScaleKernelInfo method is:</p>
532
533<pre class="code">
534 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
535 const MagickStatusType normalize_flags )
536</pre>
537
538<p>A description of each parameter follows:</p></ol>
539
540<h5>kernel</h5>
541<ol><p>the Morphology/Convolution kernel to modify</p></ol>
542
543<p>o geometry:</p>
544
545<pre class="text">
546 "-set option:convolve:scale {geometry}" setting.
547</pre>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]548
549 </div>
550<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
551_8c.html" target="source" name="ScaleKernelInfo">ScaleKernelInfo</a></h2>
552<div class="doc-section">
553
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]554<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]555
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]556<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]557
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]558<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]559
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]560<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]561
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]562<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]563
564<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></ol>
565
566<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></ol>
567
568<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></ol>
569
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]570<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]571
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]572<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></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]573
574<p>The format of the ScaleKernelInfo method is:</p>
575
576<pre class="code">
577 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
578 const MagickStatusType normalize_flags )
579</pre>
580
581<p>A description of each parameter follows:</p></ol>
582
583<h5>kernel</h5>
584<ol><p>the Morphology/Convolution kernel</p></ol>
585
586<p>o scaling_factor:</p>
587
588<pre class="text">
589 zero. If the kernel is normalized regardless of any flags.
590</pre>
591
592<p>o normalize_flags:</p>
593
594<pre class="text">
595 specifically: NormalizeValue, CorrelateNormalizeValue,
596 and/or PercentValue
597</pre>
598
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]599 </div>
600<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
601_8c.html" target="source" name="ShowKernelInfo">ShowKernelInfo</a></h2>
602<div class="doc-section">
603
604<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p></ol>
605
606<p>The format of the ShowKernel method is:</p>
607
608<pre class="code">
609 void ShowKernelInfo(KernelInfo *kernel)
610</pre>
611
612<p>A description of each parameter follows:</p></ol>
613
614<h5>kernel</h5>
615<ol><p>the Morphology/Convolution kernel</p></ol>
616
617 </div>
618<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
619_8c.html" target="source" name="UnityAddKernelInfo">UnityAddKernelInfo</a></h2>
620<div class="doc-section">
621
622<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></ol>
623
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]624<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]625
626<p>The format of the UnityAdditionKernelInfo method is:</p>
627
628<pre class="code">
629 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
630</pre>
631
632<p>A description of each parameter follows:</p></ol>
633
634<h5>kernel</h5>
635<ol><p>the Morphology/Convolution kernel</p></ol>
636
637<p>o scale:</p>
638
639<pre class="text">
640 the given kernel.
641</pre>
642
643 </div>
644<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
645_8c.html" target="source" name="ZeroKernelNans">ZeroKernelNans</a></h2>
646<div class="doc-section">
647
648<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></ol>
649
650<p>The format of the ZeroKernelNans method is:</p>
651
652<pre class="code">
653 void ZeroKernelNans (KernelInfo *kernel)
654</pre>
655
656<p>A description of each parameter follows:</p></ol>
657
658<h5>kernel</h5>
659<ol><p>the Morphology/Convolution kernel</p></ol>
660
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]661 </div>
cristy3eaa0ef2010-03-06 20:35:26 +0000[diff] [blame]662
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