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12 <title>ImageMagick: MagickCore, C API for ImageMagick: Morphological Erosions, Dilations, Openings, and Closings</title>
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162
163<div class="main">
164
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]165<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]166
167<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]168_8c.html" target="source" name="** This macro IsNaN">** This macro IsNaN</a></h2>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]169<div class="doc-section">
170
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]171<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]172 </div>
173<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
174_8c.html" target="source" name="AcquireKernelInfo">AcquireKernelInfo</a></h2>
175<div class="doc-section">
176
177<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>
178
179<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>
180
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]181<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]182
183<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>
184
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]185<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]186
187<p>Input kernel defintion strings can consist of any of three types.</p></ol>
188
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]189<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]190
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]191<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]192
193<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>
194
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]195<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>
196
197<p>" kernel ; kernel ; kernel ; "</p></ol>
198
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]199<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]200
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]201<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>
202
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]203<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]204
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]205<p>The format of the AcquireKernal method is:</p>
206
207<pre class="code">
208 KernelInfo *AcquireKernelInfo(const char *kernel_string)
209</pre>
210
211<p>A description of each parameter follows:</p></ol>
212
213<h5>kernel_string</h5>
214<ol><p>the Morphology/Convolution kernel wanted.</p></ol>
215
216 </div>
217<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
218_8c.html" target="source" name="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2>
219<div class="doc-section">
220
221<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>
222
223<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>
224
225<p>The format of the AcquireKernalBuiltIn method is:</p>
226
227<pre class="code">
228 KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type,
229 const GeometryInfo args)
230</pre>
231
232<p>A description of each parameter follows:</p></ol>
233
234<h5>type</h5>
235<ol><p>the pre-defined type of kernel wanted</p></ol>
236
237<h5>args</h5>
238<ol><p>arguments defining or modifying the kernel</p></ol>
239
240<p>Convolution Kernels</p></ol>
241
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]242<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>
243
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]244<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>
245
246<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]247
248<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>
249
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]250<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]251
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]252<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]253
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]254<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]255
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]256<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>
257
258<p>If 'sigma' is zero, you get a single pixel on a field of zeros.</p></ol>
259
260<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>
261
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]262<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]263
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]264<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]265
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]266<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]267
268<p>Named Constant Convolution Kernels</p></ol>
269
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]270<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]271
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]272<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>
273
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]274<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]275
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]276<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]277
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]278<p>Sobel:{type},{angle} Type 0: default un-nomalized version shown above.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]279
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]280<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]281
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]282<p>Type 2: Diagonal version with same normalization as 1 | 1, 0, -1 | | 2, 0, -2 | / 4 | 1, 0, -1 |</p></ol>
283
284<p>Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 |</p></ol>
285
286<p>Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 |</p></ol>
287
288<p>Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 |</p></ol>
289
290<p>Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 |</p></ol>
291
292<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>
293
294<p>| 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 |</p></ol>
295
296<p>FreiChen:{type},{angle}</p></ol>
297
298<p>Frei-Chen Pre-weighted kernels...</p></ol>
299
300<p>Type 0: default un-nomalized version shown above.</p></ol>
301
302<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>
303
304<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]305
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]306<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]307
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]308<p>Type 10: All 9 of the following pre-weighted kernels...</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]309
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]310<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]311
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]312<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]313
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]314<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]315
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]316<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]317
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]318<p>Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]319
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]320<p>Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]321
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]322<p>Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]323
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]324<p>Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 |</p></ol>
325
326<p>Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 |</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]327
328<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>
329
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]330<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>
331
332<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>
333
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]334<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]335
336<p>Boolean Kernels</p></ol>
337
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]338<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]339
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]340<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]341
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]342<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]343
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]344<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>
345
346<p>Properly centered and odd sized rectangles work the best.</p></ol>
347
348<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]349
350<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>
351
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]352<p>Symbol Dilation Kernels</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]353
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]354<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]355
356<p>For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol.</p></ol>
357
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]358<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>
359
360<p>NOTE: "plus:1" is equivelent to a "Diamond" kernel.</p></ol>
361
362<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>
363
cristy6f6c2962010-05-14 01:39:00 +0000[diff] [blame]364<p>Hit and Miss Kernels</p></ol>
365
cristy4cb39ab2010-06-07 13:59:16 +0000[diff] [blame]366<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]367
368<p>Distance Measuring Kernels</p></ol>
369
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]370<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>
371
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]372<p>See the 'Distance' Morphological Method, for information of how it is applied.</p></ol>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]373
cristyf1310b52010-05-15 16:42:04 +0000[diff] [blame]374<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]375
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]376<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]377
378<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]379
380<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>
381
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]382<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]383
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]384 </div>
385<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
386_8c.html" target="source" name="CloneKernelInfo">CloneKernelInfo</a></h2>
387<div class="doc-section">
388
cristya7cb4312010-06-26 00:47:03 +0000[diff] [blame]389<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]390
cristy0291e2d2010-04-04 17:50:18 +0000[diff] [blame]391<p>The format of the CloneKernelInfo method is:</p>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]392
393<pre class="code">
394 KernelInfo *CloneKernelInfo(const KernelInfo *kernel)
395</pre>
396
397<p>A description of each parameter follows:</p></ol>
398
399<h5>kernel</h5>
400<ol><p>the Morphology/Convolution kernel to be cloned</p></ol>
401
402 </div>
403<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
404_8c.html" target="source" name="DestroyKernelInfo">DestroyKernelInfo</a></h2>
405<div class="doc-section">
406
407<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p></ol>
408
409<p>The format of the DestroyKernelInfo method is:</p>
410
411<pre class="code">
412 KernelInfo *DestroyKernelInfo(KernelInfo *kernel)
413</pre>
414
415<p>A description of each parameter follows:</p></ol>
416
417<h5>kernel</h5>
418<ol><p>the Morphology/Convolution kernel to be destroyed</p></ol>
419
420 </div>
421<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]422_8c.html" target="source" name="MorphologyApply">MorphologyApply</a></h2>
423<div class="doc-section">
424
425<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels.</p></ol>
426
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]427<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]428
cristyfe0019b2010-06-07 02:23:32 +0000[diff] [blame]429<p>It is MorphologyImageChannel() task to extract any such user controls, and pass them to this function for processing.</p></ol>
430
431<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]432
433<p>The format of the MorphologyApply method is:</p>
434
435<pre class="code">
436 Image *MorphologyApply(const Image *image,MorphologyMethod method,
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]437 const ssize_t iterations,const KernelInfo *kernel,
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]438 const CompositeMethod compose, const double bias,
439 ExceptionInfo *exception)
440</pre>
441
442<p>A description of each parameter follows:</p></ol>
443
444<h5>image</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]445<ol><p>the source image</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]446
447<h5>method</h5>
448<ol><p>the morphology method to be applied.</p></ol>
449
450<h5>iterations</h5>
451<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>
452
453<h5>channel</h5>
454<ol><p>the channel type.</p></ol>
455
456<h5>kernel</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]457<ol><p>An array of double representing the morphology kernel.</p></ol>
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]458
459<h5>compose</h5>
cristybac7a162010-06-15 19:57:29 +0000[diff] [blame]460<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]461
462<h5>bias</h5>
463<ol><p>Convolution Output Bias.</p></ol>
464
465<h5>exception</h5>
466<ol><p>return any errors or warnings in this structure.</p></ol>
467
468 </div>
469<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]470_8c.html" target="source" name="MorphologyImageChannel">MorphologyImageChannel</a></h2>
471<div class="doc-section">
472
473<p>MorphologyImageChannel() applies a user supplied kernel to the image according to the given mophology method.</p></ol>
474
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]475<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p></ol>
476
477<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]478
479<p>The format of the MorphologyImage method is:</p>
480
481<pre class="code">
482 Image *MorphologyImage(const Image *image,MorphologyMethod method,
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]483 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception)
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]484</pre>
485
cristybb503372010-05-27 20:51:26 +0000[diff] [blame]486<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]487
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]488<p>A description of each parameter follows:</p></ol>
489
490<h5>image</h5>
491<ol><p>the image.</p></ol>
492
493<h5>method</h5>
494<ol><p>the morphology method to be applied.</p></ol>
495
496<h5>iterations</h5>
497<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>
498
499<h5>channel</h5>
500<ol><p>the channel type.</p></ol>
501
502<h5>kernel</h5>
503<ol><p>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method.</p></ol>
504
505<h5>exception</h5>
506<ol><p>return any errors or warnings in this structure.</p></ol>
507
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]508 </div>
509<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
510_8c.html" target="source" name="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2>
511<div class="doc-section">
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]512
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]513<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>
514
515<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>
516
517<p>The format of the ScaleKernelInfo method is:</p>
518
519<pre class="code">
520 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
521 const MagickStatusType normalize_flags )
522</pre>
523
524<p>A description of each parameter follows:</p></ol>
525
526<h5>kernel</h5>
527<ol><p>the Morphology/Convolution kernel to modify</p></ol>
528
529<p>o geometry:</p>
530
531<pre class="text">
532 "-set option:convolve:scale {geometry}" setting.
533</pre>
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]534
535 </div>
536<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
537_8c.html" target="source" name="ScaleKernelInfo">ScaleKernelInfo</a></h2>
538<div class="doc-section">
539
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]540<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]541
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]542<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]543
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]544<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]545
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]546<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]547
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]548<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]549
550<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>
551
552<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>
553
554<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>
555
cristyf0d1f5d2010-05-13 16:29:38 +0000[diff] [blame]556<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]557
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]558<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]559
560<p>The format of the ScaleKernelInfo method is:</p>
561
562<pre class="code">
563 void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor,
564 const MagickStatusType normalize_flags )
565</pre>
566
567<p>A description of each parameter follows:</p></ol>
568
569<h5>kernel</h5>
570<ol><p>the Morphology/Convolution kernel</p></ol>
571
572<p>o scaling_factor:</p>
573
574<pre class="text">
575 zero. If the kernel is normalized regardless of any flags.
576</pre>
577
578<p>o normalize_flags:</p>
579
580<pre class="text">
581 specifically: NormalizeValue, CorrelateNormalizeValue,
582 and/or PercentValue
583</pre>
584
cristyc4960862010-05-23 22:24:24 +0000[diff] [blame]585 </div>
586<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
587_8c.html" target="source" name="ShowKernelInfo">ShowKernelInfo</a></h2>
588<div class="doc-section">
589
590<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'showkernel' option request.</p></ol>
591
592<p>The format of the ShowKernel method is:</p>
593
594<pre class="code">
595 void ShowKernelInfo(KernelInfo *kernel)
596</pre>
597
598<p>A description of each parameter follows:</p></ol>
599
600<h5>kernel</h5>
601<ol><p>the Morphology/Convolution kernel</p></ol>
602
603 </div>
604<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
605_8c.html" target="source" name="UnityAddKernelInfo">UnityAddKernelInfo</a></h2>
606<div class="doc-section">
607
608<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>
609
cristyc3186d52010-06-04 13:55:23 +0000[diff] [blame]610<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]611
612<p>The format of the UnityAdditionKernelInfo method is:</p>
613
614<pre class="code">
615 void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale )
616</pre>
617
618<p>A description of each parameter follows:</p></ol>
619
620<h5>kernel</h5>
621<ol><p>the Morphology/Convolution kernel</p></ol>
622
623<p>o scale:</p>
624
625<pre class="text">
626 the given kernel.
627</pre>
628
629 </div>
630<h2><a href="http://www.imagemagick.org/api/MagickCore/morphology
631_8c.html" target="source" name="ZeroKernelNans">ZeroKernelNans</a></h2>
632<div class="doc-section">
633
634<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>
635
636<p>The format of the ZeroKernelNans method is:</p>
637
638<pre class="code">
639 void ZeroKernelNans (KernelInfo *kernel)
640</pre>
641
642<p>A description of each parameter follows:</p></ol>
643
644<h5>kernel</h5>
645<ol><p>the Morphology/Convolution kernel</p></ol>
646
cristyd2baf7d2010-03-06 04:26:44 +0000[diff] [blame]647 </div>
cristy3eaa0ef2010-03-06 20:35:26 +0000[diff] [blame]648
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