17 #if !defined(__mlKernelTools_H)
18 #define __mlKernelTools_H
72 template<
class T,
typename DATATYPE>
102 { (pOb->*pLF)(inSubImg, outSubImg, numVox);}
121 template <
typename DATATYPE>
126 return (v >= minVal) && (v <= maxVal);
128 return (v < maxVal) || (v > minVal);
133 template <
typename DATATYPE>
139 template <
typename DATATYPE>
150 template <
typename IN_DATATYPE,
typename OUT_DATATYPE>
152 OUT_DATATYPE *outCursor,
156 if (inCursor ==
nullptr || outCursor ==
nullptr) {
161 inCursor += srcVoxelOffset;
162 for (
size_t i=0; i<numVox; ++i){
164 *outCursor =
static_cast<OUT_DATATYPE
>(*inCursor);
184 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
186 OUT_DATATYPE *outCursor,
188 const K_DATATYPE *valTab,
199 for (
size_t i=0; i < numVox; i++){
202 IntermediateType retVal = ml_cast_from_scalar<IntermediateType>(0);
203 for (
size_t c=0; c < indexTabSize; c++){
204 retVal +=
static_cast<IntermediateType
>(inCursor[indexTab[c]]) *
205 ml_scalar_factor_cast<IntermediateType>(valTab[c]);
209 *outCursor = ml_cast_from_intermediate_type_without_clamping<OUT_DATATYPE>(retVal);
233 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
235 OUT_DATATYPE *outCursor,
237 const K_DATATYPE *valTab,
245 const bool normal = minVal <= maxVal;
250 for (
size_t i=0; i < numVox; i++){
251 IN_DATATYPE srcVox = *(inCursor+srcVoxelOffset);
256 *outCursor =
static_cast<OUT_DATATYPE
>(srcVox);
264 IntermediateType retVal = ml_cast_from_scalar<IntermediateType>(0);
265 for (
size_t c=0; c < indexTabSize; c++){
266 retVal +=
static_cast<IntermediateType
>(inCursor[indexTab[c]]) *
267 ml_scalar_factor_cast<IntermediateType>(valTab[c]);
271 *outCursor = ml_cast_from_intermediate_type_without_clamping<OUT_DATATYPE>(retVal);
303 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
305 OUT_DATATYPE *outCursor,
307 const K_DATATYPE *valTab,
315 const bool normal = minVal <= maxVal;
320 for (
size_t i=0; i < numVox; i++){
327 IntermediateType retVal = ml_cast_from_scalar<IntermediateType>(0);
328 for (
size_t c=0; c < indexTabSize; c++){
329 IN_DATATYPE val = inCursor[indexTab[c]];
335 retVal +=
static_cast<IntermediateType
>(val) *
336 ml_scalar_factor_cast<IntermediateType>(valTab[c]);
337 posValSum += valTab[c];
340 negValSum += valTab[c];
348 *outCursor =
static_cast<OUT_DATATYPE
>(inCursor[srcVoxelOffset]);
350 *outCursor = ml_cast_from_intermediate_type_without_clamping<OUT_DATATYPE>(retVal *
351 ml_scalar_factor_cast<IntermediateType>((posValSum + negValSum) / posValSum));
385 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
387 OUT_DATATYPE *outCursor,
389 const K_DATATYPE *valTab,
399 const bool normalImgInt = imgIntMinVal <= imgIntMaxVal;
400 const bool normalKernInt = kernIntMinVal <= kernIntMaxVal;
405 for (
size_t i=0; i < numVox; i++){
408 const IN_DATATYPE *voxPtr = inCursor;
409 IN_DATATYPE srcVox = *(voxPtr+srcVoxelOffset);
414 *outCursor =
static_cast<OUT_DATATYPE
>(srcVox);
424 IntermediateType retVal = ml_cast_from_scalar<IntermediateType>(0);
425 for (
size_t c=0; c < indexTabSize; c++){
426 IN_DATATYPE val = inCursor[indexTab[c]];
432 retVal +=
static_cast<IntermediateType
>(val) *
433 ml_scalar_factor_cast<IntermediateType>(valTab[c]);
434 posValSum += valTab[c];
437 negValSum += valTab[c];
445 *outCursor =
static_cast<OUT_DATATYPE
>(inCursor[srcVoxelOffset]);
447 *outCursor = ml_cast_from_intermediate_type_without_clamping<OUT_DATATYPE>(retVal *
448 ml_scalar_factor_cast<IntermediateType>((posValSum + negValSum) / posValSum));
471 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
473 OUT_DATATYPE *outCursor,
475 const K_DATATYPE *valTab,
486 if (useImgInt && !useKernInt) {
497 }
else if (!useImgInt && useKernInt) {
508 }
else if (useImgInt && useKernInt) {
709 NUM_BORDER_HANDLINGS};
714 static const char*
const BorderHandlingNames[];
753 MLint numIterations=1);
774 MLint numIterations);
797 bool isSeparable=
false,
798 MLint numIterations=1);
824 bool isSeparable=
false,
825 MLint numIterations=1);
854 template <
typename IN_DATATYPE,
typename OUT_DATATYPE>
856 OUT_DATATYPE *outCursor,
864 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
866 OUT_DATATYPE *outCursor,
868 const K_DATATYPE *valTab,
876 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
878 OUT_DATATYPE *outCursor,
880 const K_DATATYPE *valTab,
891 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
893 OUT_DATATYPE *outCursor,
895 const K_DATATYPE *valTab,
906 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
908 OUT_DATATYPE *outCursor,
910 const K_DATATYPE *valTab,
920 imgIntMinVal, imgIntMaxVal, kernIntMinVal, kernIntMaxVal);
924 template <
typename IN_DATATYPE,
typename OUT_DATATYPE,
typename K_DATATYPE>
926 OUT_DATATYPE *outCursor,
928 const K_DATATYPE *valTab,
940 imgIntMinVal, imgIntMaxVal, kernIntMinVal, kernIntMaxVal, useImgInt, useKernInt);
944 template <
typename DATATYPE>
956 template <
typename DATATYPE>
994 template <
typename DATATYPE>
1025 template <
typename DATATYPE>
1036 KernelTools::fillBorders(inImgExt, negKernelExt, posKernelExt, borderHandling, fillValue, outSubImg, inSubImg);
1039 SubImageBox area(KernelTools::calcAreaToBeCalculated(inImgExt, negKernelExt, posKernelExt, borderHandling));
1049 ImageVector pOf = KernelTools::calcOutInCoordShift(borderHandling, negKernelExt);
1055 MLint rowLength = ov2.
x - ov1.
x + 1;
1056 for (p.
u=ov1.
u; p.
u<=ov2.
u; p.
u++){
1057 for (p.
t=ov1.
t; p.
t<=ov2.
t; p.
t++){
1058 for (p.
c=ov1.
c; p.
c<=ov2.
c; p.
c++){
1059 for (p.
z=ov1.
z; p.
z<=ov2.
z; p.
z++){
1060 rowStart .
set(0, 0, p.
z, p.
c, p.
t, p.
u);
1061 for (p.
y=ov1.
y; p.
y<=ov2.
y; p.
y++){
1066 rowStart .
x = ov1.
x;
1075 lineFilter(&inSubImg, &outSubImg,
static_cast<size_t>(rowLength));
The LineApplicator class provides pure virtual functions used by the old applyFiltering functions fro...
virtual ImageVector getNegativeExtent() const
Returns the negative extent of a kernel used for filtering.
virtual ImageVector getPositiveExtent() const
Returns the positive extent of a kernel used for filtering.
The class PagedImage, representing a fragmented image that manages properties and data of an image lo...
This class manages/represents a rectangular 6D image region that is organized linearly in memory.
const SubImageBox & getBox() const
Returns the box describing the origin/extent of the subimage.
void set(const ComponentType v=0)
Sets all components to v or - if v is not specified - to 0.
VectorType v1
Corner v1 of the subimage region (included in region).
static TSubImageBox< intT > intersect(const TSubImageBox< intT > &box1, const TSubImageBox< intT > &box2)
Returns the overlapping region of subimage regions box1 and box2.
VectorType v2
Corner v2 of the subimage region (also included in region!).
A class that offers a TSubImage with a TSubImageCursor.
void setCursorImagePosition(const ImageVector &position)
Sets the cursor to the given 6D position relative to the origin of the complete image region.
ComponentType c
Color component of the vector.
ComponentType t
Time component of the vector.
ComponentType u
Unit/Modality/User component of the vector.
ComponentType z
Z component of the vector.
ComponentType x
X component of the vector.
ComponentType y
Y component of the vector.
bool MLValueIs0WOM(MLint8 a)
Returns true if value is 0; otherwise, it returns false.
#define MLKERNELEXPORT
Includes files used in many parts of the dll, defined dll-specific macros and controls any system dep...
MLint MLsoffset
Signed ML offset type that is a 32-bit signed integer on 32-bit platforms and a 64-bit integer on 64-...
MLint64 MLint
A signed ML integer type with at least 64 bits used for index calculations on very large images even ...
static void MLKernelToolsCorrelateLineWithImageAndKernelInterval(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, const K_DATATYPE *valTab, const MLsoffset *indexTab, size_t indexTabSize, MLsoffset srcVoxelOffset, MLdouble imgIntMinVal, MLdouble imgIntMaxVal, MLdouble kernIntMinVal, MLdouble kernIntMaxVal)
Correlate all voxels of the a row pointed to by inCursor with the current kernel and write the result...
static bool isScalarValueInRange(const DATATYPE &v, bool normal, double minVal, double maxVal)
static void MLKernelToolsCorrelateLineEvtWithIntervals(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, const K_DATATYPE *valTab, const MLsoffset *indexTab, size_t indexTabSize, MLsoffset srcVoxelOffset, MLdouble imgIntMinVal, MLdouble imgIntMaxVal, MLdouble kernIntMinVal, MLdouble kernIntMaxVal, bool useImgInt, bool useKernInt)
Correlate all voxels of the a row pointed to by inCursor with the current kernel and write the result...
static void MLKernelToolsCopyLine(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, MLsoffset srcVoxelOffset)
Copy the line from input subimage which corresponds to the line to be modified in the output image.
static void MLKernelToolsApplyFiltering(const ImageVector &inImgExt, const fctLineFilter< DATATYPE > &lineFilter, const ImageVector &negKernelExt, const ImageVector &posKernelExt, KernelTools::BorderHandling borderHandling, MLdouble fillValue, TSubImageWithCursor< DATATYPE > &outSubImg, TSubImageWithCursor< DATATYPE > &inSubImg)
Compute the page outSubImg by filtering inSubImg with the passed function taking care of correct inte...
static void MLKernelToolsCorrelateLineWithKernelInterval(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, const K_DATATYPE *valTab, const MLsoffset *indexTab, size_t indexTabSize, MLsoffset srcVoxelOffset, MLdouble minVal, MLdouble maxVal)
Correlate all voxels of the a row pointed to by inCursor with the current kernel and write the result...
static void MLKernelToolsCorrelateLineWithImageInterval(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, const K_DATATYPE *valTab, const MLsoffset *indexTab, size_t indexTabSize, MLsoffset srcVoxelOffset, MLdouble minVal, MLdouble maxVal)
Correlate all voxels of the a row pointed to by inCursor with the current kernel and write the result...
static bool _isScalarValueInRange(const DATATYPE &, bool, double, double, OverloadSelector::OnFalse)
static void MLKernelToolsCorrelateLine(const IN_DATATYPE *inCursor, OUT_DATATYPE *outCursor, size_t numVox, const K_DATATYPE *valTab, const MLsoffset *indexTab, size_t indexTabSize)
Correlate all voxels of the a row pointed to by inCursor with the current kernel and write the result...
Helper types to switch between implementations of functions by overloading.
The basic implementation of a filter to filter an image row with kernel operations.
virtual ~fctLineFilter()
Virtual destructor to avoid warnings. Currently it does nothing.
Wrapper for the use of object bound row filters.
T * pOb
Pointer to store the pointer to the class type object passed to the explicit useObjectLineFilter as p...
~useObjectLineFilter() override
Virtual destructor to avoid warnings. Currently it does nothing.
useObjectLineFilter(T *po, void(T::*pf)(TSubImageWithCursor< DATATYPE > *, TSubImageWithCursor< DATATYPE > *, size_t))
Constructor.