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87 </script> 88 89 </div> 90 91 <main class="container"> 92 <div class="magick-template"> 93<div class="magick-header"> 94<p class="text-center"><a href="morphology.html#AcquireKernelInfo">AcquireKernelInfo</a> • <a href="morphology.html#AcquireKernelBuiltIn">AcquireKernelBuiltIn</a> • <a href="morphology.html#CloneKernelInfo">CloneKernelInfo</a> • <a href="morphology.html#DestroyKernelInfo">DestroyKernelInfo</a> • <a href="morphology.html#MorphologyApply">MorphologyApply</a> • <a href="morphology.html#This is almost identical to the MorphologyPrimative">This is almost identical to the MorphologyPrimative</a> • <a href="morphology.html#MorphologyImage">MorphologyImage</a> • <a href="morphology.html#ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a> • <a href="morphology.html#ScaleKernelInfo">ScaleKernelInfo</a> • <a href="morphology.html#ShowKernelInfo">ShowKernelInfo</a> • <a href="morphology.html#UnityAddKernelInfo">UnityAddKernelInfo</a> • <a href="morphology.html#ZeroKernelNans">ZeroKernelNans</a></p> 95 96<h2><a href="../../api/MagickCore/morphology_8c.html" id="AcquireKernelInfo">AcquireKernelInfo</a></h2> 97 98<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> 99 100<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> 101 102<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> 103 104<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> 105 106<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p> 107 108<p>Input kernel defintion strings can consist of any of three types.</p> 109 110<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p> 111 112<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> 113 114<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> 115 116<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> 117 118<p>" kernel ; kernel ; kernel ; "</p> 119 120<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p> 121 122<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> 123 124<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> 125 126<p>The format of the AcquireKernal method is:</p> 127 128<pre class="text"> 129KernelInfo *AcquireKernelInfo(const char *kernel_string) 130</pre> 131 132<p>A description of each parameter follows:</p> 133 134<dd> 135</dd> 136 137<dd> </dd> 138<dl class="dl-horizontal"> 139<dt>kernel_string</dt> 140<dd>the Morphology/Convolution kernel wanted. </dd> 141 142<dd> </dd> 143</dl> 144<h2><a href="../../api/MagickCore/morphology_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2> 145 146<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> 147 148<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> 149 150<p>The format of the AcquireKernalBuiltIn method is:</p> 151 152<pre class="text"> 153KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type, 154 const GeometryInfo args) 155</pre> 156 157<p>A description of each parameter follows:</p> 158 159<dd> 160</dd> 161 162<dd> </dd> 163<dl class="dl-horizontal"> 164<dt>type</dt> 165<dd>the pre-defined type of kernel wanted </dd> 166 167<dd> </dd> 168<dt>args</dt> 169<dd>arguments defining or modifying the kernel </dd> 170 171<dd> Convolution Kernels </dd> 172 173<dd> Unity The a No-Op or Scaling single element kernel. </dd> 174 175<dd> Gaussian:{radius},{sigma} Generate a two-dimensional gaussian kernel, as used by -gaussian. The sigma for the curve is required. The resulting kernel is normalized, </dd> 176 177<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 178 179<dd> 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. </dd> 180 181<dd> LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel. </dd> 182 183<dd> An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia). </dd> 184 185<dd> 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. </dd> 186 187<dd> 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. </dd> 188 189<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 190 191<dd> 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. </dd> 192 193<dd> 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. </dd> 194 195<dd> Note that the first argument is the width of the kernel and not the radius of the kernel. </dd> 196 197<dd> Binomial:[{radius}] Generate a discrete kernel using a 2 dimentional Pascel's Triangle of values. Used for special forma of image filters. </dd> 198 199<dd> # Still to be implemented... # # Filter2D # Filter1D # Set kernel values using a resize filter, and given scale (sigma) # Cylindrical or Linear. Is this possible with an image? # </dd> 200 201<dd> Named Constant Convolution Kernels </dd> 202 203<dd> 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'. </dd> 204 205<dd> 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. </dd> 206 207<dd> 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) </dd> 208 209<dd> Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 | </dd> 210 211<dd> Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 | </dd> 212 213<dd> Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 | </dd> 214 215<dd> Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 | </dd> 216 217<dd> Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 | </dd> 218 219<dd> 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. </dd> 220 221<dd> | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 | </dd> 222 223<dd> FreiChen:{type},{angle} </dd> 224 225<dd> Frei-Chen Pre-weighted kernels... </dd> 226 227<dd> Type 0: default un-nomalized version shown above. </dd> 228 229<dd> Type 1: Orthogonal Kernel (same as type 11 below) | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 | </dd> 230 231<dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd> 232 233<dd> 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. </dd> 234 235<dd> Type 10: All 9 of the following pre-weighted kernels... </dd> 236 237<dd> Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 | </dd> 238 239<dd> Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 | </dd> 240 241<dd> Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) | </dd> 242 243<dd> Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 | </dd> 244 245<dd> Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 | </dd> 246 247<dd> Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 | </dd> 248 249<dd> Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 | </dd> 250 251<dd> Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 | </dd> 252 253<dd> Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 | </dd> 254 255<dd> 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. </dd> 256 257<dd> 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. </dd> 258 259<dd> 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. </dd> 260 261<dd> 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. </dd> 262 263<dd> Boolean Kernels </dd> 264 265<dd> 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. </dd> 266 267<dd> Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1). </dd> 268 269<dd> 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. </dd> 270 271<dd> 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. </dd> 272 273<dd> 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. </dd> 274 275<dd> 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. </dd> 276 277<dd> Properly centered and odd sized rectangles work the best. </dd> 278 279<dd> Symbol Dilation Kernels </dd> 280 281<dd> 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. </dd> 282 283<dd> For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol. </dd> 284 285<dd> 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). </dd> 286 287<dd> NOTE: "plus:1" is equivalent to a "Diamond" kernel. </dd> 288 289<dd> 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" </dd> 290 291<dd> Hit and Miss Kernels </dd> 292 293<dd> 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. </dd> 294 295<dd> Distance Measuring Kernels </dd> 296 297<dd> 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. </dd> 298 299<dd> See the 'Distance' Morphological Method, for information of how it is applied. </dd> 300 301<dd> 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. </dd> 302 303<dd> 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. </dd> 304 305<dd> 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. </dd> 306 307<dd> 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. </dd> 308 309<dd> 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. </dd> 310 311<dd> 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. </dd> 312 313<dd> The "Euclidean" Distance Kernel however does generate a non-integer fractional results, and as such scaling is vital even for binary shapes. </dd> 314 315<dd> </dd> 316</dl> 317<h2><a href="../../api/MagickCore/morphology_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2> 318 319<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> 320 321<p>The format of the CloneKernelInfo method is:</p> 322 323<pre class="text"> 324KernelInfo *CloneKernelInfo(const KernelInfo *kernel) 325</pre> 326 327<p>A description of each parameter follows:</p> 328 329<dd> 330</dd> 331 332<dd> </dd> 333<dl class="dl-horizontal"> 334<dt>kernel</dt> 335<dd>the Morphology/Convolution kernel to be cloned </dd> 336 337<dd> </dd> 338</dl> 339<h2><a href="../../api/MagickCore/morphology_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2> 340 341<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p> 342 343<p>The format of the DestroyKernelInfo method is:</p> 344 345<pre class="text"> 346KernelInfo *DestroyKernelInfo(KernelInfo *kernel) 347</pre> 348 349<p>A description of each parameter follows:</p> 350 351<dd> 352</dd> 353 354<dd> </dd> 355<dl class="dl-horizontal"> 356<dt>kernel</dt> 357<dd>the Morphology/Convolution kernel to be destroyed </dd> 358 359<dd> </dd> 360</dl> 361<h2><a href="../../api/MagickCore/morphology_8c.html" id="MorphologyApply">MorphologyApply</a></h2> 362 363<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels. This is the method that should be called by other 'operators' that internally use morphology operations as part of their processing.</p> 364 365<p>It is basically equivalent to as MorphologyImage() (see below) but without any user controls. This allows internel programs to use this method to perform a specific task without possible interference by any API user supplied settings.</p> 366 367<p>It is MorphologyImage() task to extract any such user controls, and pass them to this function for processing.</p> 368 369<p>More specifically all given kernels should already be scaled, normalised, and blended appropriatally before being parred to this routine. The appropriate bias, and compose (typically 'UndefinedComposeOp') given.</p> 370 371<p>The format of the MorphologyApply method is:</p> 372 373<pre class="text"> 374Image *MorphologyApply(const Image *image,MorphologyMethod method, 375 const ssize_t iterations,const KernelInfo *kernel, 376 const CompositeMethod compose,const double bias, 377 ExceptionInfo *exception) 378</pre> 379 380<p>A description of each parameter follows:</p> 381 382<dd> 383</dd> 384 385<dd> </dd> 386<dl class="dl-horizontal"> 387<dt>image</dt> 388<dd>the source image </dd> 389 390<dd> </dd> 391<dt>method</dt> 392<dd>the morphology method to be applied. </dd> 393 394<dd> </dd> 395<dt>iterations</dt> 396<dd>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. </dd> 397 398<dd> </dd> 399<dt>channel</dt> 400<dd>the channel type. </dd> 401 402<dd> </dd> 403<dt>kernel</dt> 404<dd>An array of double representing the morphology kernel. </dd> 405 406<dd> </dd> 407<dt>compose</dt> 408<dd>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. </dd> 409 410<dd> </dd> 411<dt>bias</dt> 412<dd>Convolution Output Bias. </dd> 413 414<dd> </dd> 415<dt>exception</dt> 416<dd>return any errors or warnings in this structure. </dd> 417 418<dd> </dd> 419</dl> 420<h2><a href="../../api/MagickCore/morphology_8c.html" id="This_is almost identical to the MorphologyPrimative">This is almost identical to the MorphologyPrimative</a></h2> 421 422<p>This is almost identical to the MorphologyPrimative() function above, but applies the primitive directly to the actual image using two passes, once in each direction, with the results of the previous (and current) row being re-used.</p> 423 424<p>That is after each row is 'Sync'ed' into the image, the next row makes use of those values as part of the calculation of the next row. It repeats, but going in the oppisite (bottom-up) direction.</p> 425 426<p>Because of this 're-use of results' this function can not make use of multi- threaded, parellel processing. </p> 427<h2><a href="../../api/MagickCore/morphology_8c.html" id="MorphologyImage">MorphologyImage</a></h2> 428 429<p>MorphologyImage() applies a user supplied kernel to the image according to the given mophology method.</p> 430 431<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p> 432 433<p>User defined settings include... * Output Bias for Convolution and correlation ("-define convolve:bias=??") * Kernel Scale/normalize settings ("-define convolve:scale=??") This can also includes the addition of a scaled unity kernel. * Show Kernel being applied ("-define morphology:showKernel=1")</p> 434 435<p>Other operators that do not want user supplied options interfering, especially "convolve:bias" and "morphology:showKernel" should use MorphologyApply() directly.</p> 436 437<p>The format of the MorphologyImage method is:</p> 438 439<pre class="text"> 440Image *MorphologyImage(const Image *image,MorphologyMethod method, 441 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception) 442</pre> 443 444<p>A description of each parameter follows:</p> 445 446<dd> 447</dd> 448 449<dd> </dd> 450<dl class="dl-horizontal"> 451<dt>image</dt> 452<dd>the image. </dd> 453 454<dd> </dd> 455<dt>method</dt> 456<dd>the morphology method to be applied. </dd> 457 458<dd> </dd> 459<dt>iterations</dt> 460<dd>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. </dd> 461 462<dd> </dd> 463<dt>kernel</dt> 464<dd>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method. </dd> 465 466<dd> </dd> 467<dt>exception</dt> 468<dd>return any errors or warnings in this structure. </dd> 469 470<dd> </dd> 471</dl> 472<h2><a href="../../api/MagickCore/morphology_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2> 473 474<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> 475 476<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> 477 478<p>The format of the ScaleGeometryKernelInfo method is:</p> 479 480<pre class="text"> 481void ScaleGeometryKernelInfo(KernelInfo *kernel, 482 const double scaling_factor,const MagickStatusType normalize_flags) 483</pre> 484 485<p>A description of each parameter follows:</p> 486 487<dd> 488</dd> 489 490<dd> </dd> 491<dl class="dl-horizontal"> 492<dt>kernel</dt> 493<dd>the Morphology/Convolution kernel to modify </dd> 494 495<dd> o geometry: </dd> 496 497<pre class="text"> 498 "-set option:convolve:scale {geometry}" setting. 499</pre> 500 501<p></dd> 502</dl> 503<h2><a href="../../api/MagickCore/morphology_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2> 504 505<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> 506 507<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p> 508 509<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> 510 511<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> 512 513<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> 514 515<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> 516 517<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> 518 519<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> 520 521<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> 522 523<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> 524 525<p>The format of the ScaleKernelInfo method is:</p> 526 527<pre class="text"> 528void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor, 529 const MagickStatusType normalize_flags ) 530</pre> 531 532<p>A description of each parameter follows:</p> 533 534<dd> 535</dd> 536 537<dd> </dd> 538<dl class="dl-horizontal"> 539<dt>kernel</dt> 540<dd>the Morphology/Convolution kernel </dd> 541 542<dd> o scaling_factor: </dd> 543 544<pre class="text"> 545 zero. If the kernel is normalized regardless of any flags. 546</pre> 547 548<p>o normalize_flags: </dd> 549 550<pre class="text"> 551 specifically: NormalizeValue, CorrelateNormalizeValue, 552 and/or PercentValue 553</pre> 554 555<p></dd> 556</dl> 557<h2><a href="../../api/MagickCore/morphology_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2> 558 559<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'morphology:showKernel' option request.</p> 560 561<p>The format of the ShowKernel method is:</p> 562 563<pre class="text"> 564void ShowKernelInfo(const KernelInfo *kernel) 565</pre> 566 567<p>A description of each parameter follows:</p> 568 569<dd> 570</dd> 571 572<dd> </dd> 573<dl class="dl-horizontal"> 574<dt>kernel</dt> 575<dd>the Morphology/Convolution kernel </dd> 576 577<dd> </dd> 578</dl> 579<h2><a href="../../api/MagickCore/morphology_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2> 580 581<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> 582 583<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p> 584 585<p>The format of the UnityAdditionKernelInfo method is:</p> 586 587<pre class="text"> 588void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale ) 589</pre> 590 591<p>A description of each parameter follows:</p> 592 593<dd> 594</dd> 595 596<dd> </dd> 597<dl class="dl-horizontal"> 598<dt>kernel</dt> 599<dd>the Morphology/Convolution kernel </dd> 600 601<dd> o scale: </dd> 602 603<pre class="text"> 604 the given kernel. 605</pre> 606 607<p></dd> 608</dl> 609<h2><a href="../../api/MagickCore/morphology_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2> 610 611<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> 612 613<p>The format of the ZeroKernelNans method is:</p> 614 615<pre class="text"> 616void ZeroKernelNans (KernelInfo *kernel) 617</pre> 618 619<p>A description of each parameter follows:</p> 620 621<dd> 622</dd> 623 624<dd> </dd> 625<dl class="dl-horizontal"> 626<dt>kernel</dt> 627<dd>the Morphology/Convolution kernel </dd> 628 629<dd> </dd> 630</dl> 631</div> 632 </div> 633 </main><!-- /.container --> 634 <footer class="magick-footer"> 635 <div class="container-fluid"> 636 <a href="../../www/security-policy.html">Security</a> • 637 <a href="../../www/news.html">News</a> 638 639 <a href="morphology.html#"><img class="d-inline" id="wand" alt="And Now a Touch of Magick" width="16" height="16" src="../../../images/wand.ico"/></a> 640 641 <a href="../../www/links.html">Related</a> • 642 <a href="../../www/sitemap.html">Sitemap</a> 643 <br/> 644 <a href="../../www/support.html">Sponsor</a> • 645 <a href="../../www/cite.html">Cite</a> • 646 <a href="http://pgp.mit.edu/pks/lookup?op=get&search=0x89AB63D48277377A">Public Key</a> • 647 <a href="../../www/contact.html">Contact Us</a> 648 <br/> 649 <a href="https://github.com/imagemagick/imagemagick" target="_blank" rel="noopener" aria-label="GitHub"><svg xmlns="http://www.w3.org/2000/svg" class="navbar-nav-svg" viewBox="0 0 512 499.36" width="2%" height="2%" role="img" focusable="false"><title>GitHub</title><path fill="currentColor" fill-rule="evenodd" d="M256 0C114.64 0 0 114.61 0 256c0 113.09 73.34 209 175.08 242.9 12.8 2.35 17.47-5.56 17.47-12.34 0-6.08-.22-22.18-.35-43.54-71.2 15.49-86.2-34.34-86.2-34.34-11.64-29.57-28.42-37.45-28.42-37.45-23.27-15.84 1.73-15.55 1.73-15.55 25.69 1.81 39.21 26.38 39.21 26.38 22.84 39.12 59.92 27.82 74.5 21.27 2.33-16.54 8.94-27.82 16.25-34.22-56.84-6.43-116.6-28.43-116.6-126.49 0-27.95 10-50.8 26.35-68.69-2.63-6.48-11.42-32.5 2.51-67.75 0 0 21.49-6.88 70.4 26.24a242.65 242.65 0 0 1 128.18 0c48.87-33.13 70.33-26.24 70.33-26.24 14 35.25 5.18 61.27 2.55 67.75 16.41 17.9 26.31 40.75 26.31 68.69 0 98.35-59.85 120-116.88 126.32 9.19 7.9 17.38 23.53 17.38 47.41 0 34.22-.31 61.83-.31 70.23 0 6.85 4.61 14.81 17.6 12.31C438.72 464.97 512 369.08 512 256.02 512 114.62 397.37 0 256 0z"/></svg></a> • 650 <a href="https://twitter.com/imagemagick" target="_blank" rel="noopener" aria-label="Twitter"><svg xmlns="http://www.w3.org/2000/svg" class="navbar-nav-svg" viewBox="0 0 512 416.32" width="2%" height="2%" role="img" focusable="false"><title>Twitter</title><path fill="currentColor" d="M160.83 416.32c193.2 0 298.92-160.22 298.92-298.92 0-4.51 0-9-.2-13.52A214 214 0 0 0 512 49.38a212.93 212.93 0 0 1-60.44 16.6 105.7 105.7 0 0 0 46.3-58.19 209 209 0 0 1-66.79 25.37 105.09 105.09 0 0 0-181.73 71.91 116.12 116.12 0 0 0 2.66 24c-87.28-4.3-164.73-46.3-216.56-109.82A105.48 105.48 0 0 0 68 159.6a106.27 106.27 0 0 1-47.53-13.11v1.43a105.28 105.28 0 0 0 84.21 103.06 105.67 105.67 0 0 1-47.33 1.84 105.06 105.06 0 0 0 98.14 72.94A210.72 210.72 0 0 1 25 370.84a202.17 202.17 0 0 1-25-1.43 298.85 298.85 0 0 0 160.83 46.92"/></svg></a> 651 <br/> 652 <small>© 1999-2021 ImageMagick Studio LLC</small> 653 </div> 654 </footer> 655 656 <!-- Javascript assets --> 657 <script src="../../assets/magick.js" ></script> 658 </body> 659</html> 660
