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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> 89 90<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> 91 92<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> 93 94<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> 95 96<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p> 97 98<p>Input kernel defintion strings can consist of any of three types.</p> 99 100<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p> 101 102<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> 103 104<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> 105 106<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> 107 108<p>" kernel ; kernel ; kernel ; "</p> 109 110<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p> 111 112<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> 113 114<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> 115 116<p>The format of the AcquireKernal method is:</p> 117 118<pre class="text"> 119KernelInfo *AcquireKernelInfo(const char *kernel_string) 120</pre> 121 122<p>A description of each parameter follows:</p> 123 124<dd> 125</dd> 126 127<dd> </dd> 128<dl class="dl-horizontal"> 129<dt>kernel_string</dt> 130<dd>the Morphology/Convolution kernel wanted. </dd> 131 132<dd> </dd> 133</dl> 134<h2><a href="../../api/MagickCore/morphology_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2> 135 136<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> 137 138<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> 139 140<p>The format of the AcquireKernalBuiltIn method is:</p> 141 142<pre class="text"> 143KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type, 144 const GeometryInfo args) 145</pre> 146 147<p>A description of each parameter follows:</p> 148 149<dd> 150</dd> 151 152<dd> </dd> 153<dl class="dl-horizontal"> 154<dt>type</dt> 155<dd>the pre-defined type of kernel wanted </dd> 156 157<dd> </dd> 158<dt>args</dt> 159<dd>arguments defining or modifying the kernel </dd> 160 161<dd> Convolution Kernels </dd> 162 163<dd> Unity The a No-Op or Scaling single element kernel. </dd> 164 165<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> 166 167<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 168 169<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> 170 171<dd> LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel. </dd> 172 173<dd> An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia). </dd> 174 175<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> 176 177<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> 178 179<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 180 181<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> 182 183<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> 184 185<dd> Note that the first argument is the width of the kernel and not the radius of the kernel. </dd> 186 187<dd> Binomial:[{radius}] Generate a discrete kernel using a 2 dimentional Pascel's Triangle of values. Used for special forma of image filters. </dd> 188 189<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> 190 191<dd> Named Constant Convolution Kernels </dd> 192 193<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> 194 195<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> 196 197<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> 198 199<dd> Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 | </dd> 200 201<dd> Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 | </dd> 202 203<dd> Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 | </dd> 204 205<dd> Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 | </dd> 206 207<dd> Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 | </dd> 208 209<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> 210 211<dd> | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 | </dd> 212 213<dd> FreiChen:{type},{angle} </dd> 214 215<dd> Frei-Chen Pre-weighted kernels... </dd> 216 217<dd> Type 0: default un-nomalized version shown above. </dd> 218 219<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> 220 221<dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd> 222 223<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> 224 225<dd> Type 10: All 9 of the following pre-weighted kernels... </dd> 226 227<dd> Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 | </dd> 228 229<dd> Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 | </dd> 230 231<dd> Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) | </dd> 232 233<dd> Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 | </dd> 234 235<dd> Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 | </dd> 236 237<dd> Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 | </dd> 238 239<dd> Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 | </dd> 240 241<dd> Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 | </dd> 242 243<dd> Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 | </dd> 244 245<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> 246 247<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> 248 249<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> 250 251<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> 252 253<dd> Boolean Kernels </dd> 254 255<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> 256 257<dd> Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1). </dd> 258 259<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> 260 261<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> 262 263<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> 264 265<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> 266 267<dd> Properly centered and odd sized rectangles work the best. </dd> 268 269<dd> Symbol Dilation Kernels </dd> 270 271<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> 272 273<dd> For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol. </dd> 274 275<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> 276 277<dd> NOTE: "plus:1" is equivalent to a "Diamond" kernel. </dd> 278 279<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> 280 281<dd> Hit and Miss Kernels </dd> 282 283<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> 284 285<dd> Distance Measuring Kernels </dd> 286 287<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> 288 289<dd> See the 'Distance' Morphological Method, for information of how it is applied. </dd> 290 291<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> 292 293<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> 294 295<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> 296 297<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> 298 299<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> 300 301<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> 302 303<dd> The "Euclidean" Distance Kernel however does generate a non-integer fractional results, and as such scaling is vital even for binary shapes. </dd> 304 305<dd> </dd> 306</dl> 307<h2><a href="../../api/MagickCore/morphology_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2> 308 309<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> 310 311<p>The format of the CloneKernelInfo method is:</p> 312 313<pre class="text"> 314KernelInfo *CloneKernelInfo(const KernelInfo *kernel) 315</pre> 316 317<p>A description of each parameter follows:</p> 318 319<dd> 320</dd> 321 322<dd> </dd> 323<dl class="dl-horizontal"> 324<dt>kernel</dt> 325<dd>the Morphology/Convolution kernel to be cloned </dd> 326 327<dd> </dd> 328</dl> 329<h2><a href="../../api/MagickCore/morphology_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2> 330 331<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p> 332 333<p>The format of the DestroyKernelInfo method is:</p> 334 335<pre class="text"> 336KernelInfo *DestroyKernelInfo(KernelInfo *kernel) 337</pre> 338 339<p>A description of each parameter follows:</p> 340 341<dd> 342</dd> 343 344<dd> </dd> 345<dl class="dl-horizontal"> 346<dt>kernel</dt> 347<dd>the Morphology/Convolution kernel to be destroyed </dd> 348 349<dd> </dd> 350</dl> 351<h2><a href="../../api/MagickCore/morphology_8c.html" id="MorphologyApply">MorphologyApply</a></h2> 352 353<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> 354 355<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> 356 357<p>It is MorphologyImage() task to extract any such user controls, and pass them to this function for processing.</p> 358 359<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> 360 361<p>The format of the MorphologyApply method is:</p> 362 363<pre class="text"> 364Image *MorphologyApply(const Image *image,MorphologyMethod method, 365 const ssize_t iterations,const KernelInfo *kernel, 366 const CompositeMethod compose,const double bias, 367 ExceptionInfo *exception) 368</pre> 369 370<p>A description of each parameter follows:</p> 371 372<dd> 373</dd> 374 375<dd> </dd> 376<dl class="dl-horizontal"> 377<dt>image</dt> 378<dd>the source image </dd> 379 380<dd> </dd> 381<dt>method</dt> 382<dd>the morphology method to be applied. </dd> 383 384<dd> </dd> 385<dt>iterations</dt> 386<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> 387 388<dd> </dd> 389<dt>channel</dt> 390<dd>the channel type. </dd> 391 392<dd> </dd> 393<dt>kernel</dt> 394<dd>An array of double representing the morphology kernel. </dd> 395 396<dd> </dd> 397<dt>compose</dt> 398<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> 399 400<dd> </dd> 401<dt>bias</dt> 402<dd>Convolution Output Bias. </dd> 403 404<dd> </dd> 405<dt>exception</dt> 406<dd>return any errors or warnings in this structure. </dd> 407 408<dd> </dd> 409</dl> 410<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> 411 412<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> 413 414<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> 415 416<p>Because of this 're-use of results' this function can not make use of multi- threaded, parellel processing. </p> 417<h2><a href="../../api/MagickCore/morphology_8c.html" id="MorphologyImage">MorphologyImage</a></h2> 418 419<p>MorphologyImage() applies a user supplied kernel to the image according to the given mophology method.</p> 420 421<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p> 422 423<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> 424 425<p>Other operators that do not want user supplied options interfering, especially "convolve:bias" and "morphology:showKernel" should use MorphologyApply() directly.</p> 426 427<p>The format of the MorphologyImage method is:</p> 428 429<pre class="text"> 430Image *MorphologyImage(const Image *image,MorphologyMethod method, 431 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception) 432</pre> 433 434<p>A description of each parameter follows:</p> 435 436<dd> 437</dd> 438 439<dd> </dd> 440<dl class="dl-horizontal"> 441<dt>image</dt> 442<dd>the image. </dd> 443 444<dd> </dd> 445<dt>method</dt> 446<dd>the morphology method to be applied. </dd> 447 448<dd> </dd> 449<dt>iterations</dt> 450<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> 451 452<dd> </dd> 453<dt>kernel</dt> 454<dd>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method. </dd> 455 456<dd> </dd> 457<dt>exception</dt> 458<dd>return any errors or warnings in this structure. </dd> 459 460<dd> </dd> 461</dl> 462<h2><a href="../../api/MagickCore/morphology_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2> 463 464<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> 465 466<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> 467 468<p>The format of the ScaleGeometryKernelInfo method is:</p> 469 470<pre class="text"> 471void ScaleGeometryKernelInfo(KernelInfo *kernel, 472 const double scaling_factor,const MagickStatusType normalize_flags) 473</pre> 474 475<p>A description of each parameter follows:</p> 476 477<dd> 478</dd> 479 480<dd> </dd> 481<dl class="dl-horizontal"> 482<dt>kernel</dt> 483<dd>the Morphology/Convolution kernel to modify </dd> 484 485<dd> o geometry: </dd> 486 487<pre class="text"> 488 "-set option:convolve:scale {geometry}" setting. 489</pre> 490 491<p></dd> 492</dl> 493<h2><a href="../../api/MagickCore/morphology_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2> 494 495<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> 496 497<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p> 498 499<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> 500 501<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> 502 503<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> 504 505<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> 506 507<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> 508 509<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> 510 511<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> 512 513<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> 514 515<p>The format of the ScaleKernelInfo method is:</p> 516 517<pre class="text"> 518void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor, 519 const MagickStatusType normalize_flags ) 520</pre> 521 522<p>A description of each parameter follows:</p> 523 524<dd> 525</dd> 526 527<dd> </dd> 528<dl class="dl-horizontal"> 529<dt>kernel</dt> 530<dd>the Morphology/Convolution kernel </dd> 531 532<dd> o scaling_factor: </dd> 533 534<pre class="text"> 535 zero. If the kernel is normalized regardless of any flags. 536</pre> 537 538<p>o normalize_flags: </dd> 539 540<pre class="text"> 541 specifically: NormalizeValue, CorrelateNormalizeValue, 542 and/or PercentValue 543</pre> 544 545<p></dd> 546</dl> 547<h2><a href="../../api/MagickCore/morphology_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2> 548 549<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'morphology:showKernel' option request.</p> 550 551<p>The format of the ShowKernel method is:</p> 552 553<pre class="text"> 554void ShowKernelInfo(const KernelInfo *kernel) 555</pre> 556 557<p>A description of each parameter follows:</p> 558 559<dd> 560</dd> 561 562<dd> </dd> 563<dl class="dl-horizontal"> 564<dt>kernel</dt> 565<dd>the Morphology/Convolution kernel </dd> 566 567<dd> </dd> 568</dl> 569<h2><a href="../../api/MagickCore/morphology_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2> 570 571<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> 572 573<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p> 574 575<p>The format of the UnityAdditionKernelInfo method is:</p> 576 577<pre class="text"> 578void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale ) 579</pre> 580 581<p>A description of each parameter follows:</p> 582 583<dd> 584</dd> 585 586<dd> </dd> 587<dl class="dl-horizontal"> 588<dt>kernel</dt> 589<dd>the Morphology/Convolution kernel </dd> 590 591<dd> o scale: </dd> 592 593<pre class="text"> 594 the given kernel. 595</pre> 596 597<p></dd> 598</dl> 599<h2><a href="../../api/MagickCore/morphology_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2> 600 601<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> 602 603<p>The format of the ZeroKernelNans method is:</p> 604 605<pre class="text"> 606void ZeroKernelNans (KernelInfo *kernel) 607</pre> 608 609<p>A description of each parameter follows:</p> 610 611<dd> 612</dd> 613 614<dd> </dd> 615<dl class="dl-horizontal"> 616<dt>kernel</dt> 617<dd>the Morphology/Convolution kernel </dd> 618 619<dd> </dd> 620</dl> 621</div> 622 </div> 623 </main><!-- /.container --> 624 <footer class="magick-footer"> 625 <p><a href="../../www/security-policy.html">Security</a> • 626 <a href="../../www/architecture.html">Architecture</a> • 627 <a href="../../www/links.html">Related</a> • 628 <a href="../../www/sitemap.html">Sitemap</a> 629 630 <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> 631 632 <a href="http://pgp.mit.edu/pks/lookup?op=get&search=0x89AB63D48277377A">Public Key</a> • 633 <a href="../../www/support.html">Donate</a> • 634 <a href="../../www/contact.html">Contact Us</a> 635 <br/> 636 <small>© 1999-2019 ImageMagick Studio LLC</small></p> 637 </footer> 638 639 <!-- Javascript assets --> 640 <script src="../assets/magick.js" crossorigin="anonymous"></script> 641 <script>window.jQuery || document.write('<script src="https://localhost/ajax/libs/jquery/3.3.1/jquery.min.js"><\/script>')</script> 642</body> 643</html> 644<!-- Magick Cache 2nd January 2019 23:14 -->
