37 #ifndef VIGRA_STDCONVOLUTION_HXX
38 #define VIGRA_STDCONVOLUTION_HXX
41 #include "stdimage.hxx"
42 #include "bordertreatment.hxx"
43 #include "separableconvolution.hxx"
45 #include "sized_int.hxx"
46 #include "multi_iterator.hxx"
47 #include "multi_shape.hxx"
51 template <
class ARITHTYPE>
59 template <
class SrcIterator,
class SrcAccessor,
60 class DestIterator,
class DestAccessor,
61 class KernelIterator,
class KernelAccessor>
62 void convolveImage(SrcIterator src_ul, SrcIterator src_lr, SrcAccessor src_acc,
63 DestIterator dest_ul, DestAccessor dest_acc,
64 KernelIterator ki, KernelAccessor ak,
65 Diff2D kul, Diff2D klr, BorderTreatmentMode border)
67 vigra_precondition((border == BORDER_TREATMENT_CLIP ||
68 border == BORDER_TREATMENT_AVOID ||
69 border == BORDER_TREATMENT_REFLECT ||
70 border == BORDER_TREATMENT_REPEAT ||
71 border == BORDER_TREATMENT_WRAP ||
72 border == BORDER_TREATMENT_ZEROPAD),
74 " Border treatment must be one of follow treatments:\n"
75 " - BORDER_TREATMENT_CLIP\n"
76 " - BORDER_TREATMENT_AVOID\n"
77 " - BORDER_TREATMENT_REFLECT\n"
78 " - BORDER_TREATMENT_REPEAT\n"
79 " - BORDER_TREATMENT_WRAP\n"
80 " - BORDER_TREATMENT_ZEROPAD\n");
82 vigra_precondition(kul.x <= 0 && kul.y <= 0,
83 "convolveImage(): coordinates of "
84 "kernel's upper left must be <= 0.");
85 vigra_precondition(klr.x >= 0 && klr.y >= 0,
86 "convolveImage(): coordinates of "
87 "kernel's lower right must be >= 0.");
91 PromoteTraits<
typename SrcAccessor::value_type,
92 typename KernelAccessor::value_type>::Promote SumType;
94 NumericTraits<typename KernelAccessor::value_type>::RealPromote KernelSumType;
95 typedef typename DestAccessor::value_type DestType;
98 int w = src_lr.x - src_ul.x;
99 int h = src_lr.y - src_ul.y;
102 int kernel_width = klr.x - kul.x + 1;
103 int kernel_height = klr.y - kul.y + 1;
105 vigra_precondition(w >= std::max(klr.x, -kul.x) + 1 && h >= std::max(klr.y, -kul.y) + 1,
106 "convolveImage(): kernel larger than image.");
108 KernelSumType
norm = KernelSumType();
109 if(border == BORDER_TREATMENT_CLIP)
112 KernelIterator yk = ki + klr;
115 for(
int y = 0; y < kernel_height; ++y, --yk.y)
117 KernelIterator xk = yk;
118 for(
int x = 0; x < kernel_width; ++x, --xk.x)
123 vigra_precondition(
norm != NumericTraits<KernelSumType>::zero(),
124 "convolveImage(): Cannot use BORDER_TREATMENT_CLIP with a DC-free kernel");
127 DestIterator yd = dest_ul;
128 SrcIterator ys = src_ul;
131 for(
int y=0; y<h; ++y, ++ys.y, ++yd.y)
137 for(
int x=0; x < w; ++x, ++xs.x, ++xd.x)
140 SumType
sum = NumericTraits<SumType>::zero();
141 KernelIterator ykernel = ki + klr;
143 if(x >= klr.x && y >= klr.y && x < w + kul.x && y < h + kul.y)
146 SrcIterator yys = xs - klr;
147 SrcIterator yyend = xs - kul;
149 for(; yys.y <= yyend.y; ++yys.y, --ykernel.y)
151 typename SrcIterator::row_iterator xxs = yys.rowIterator();
152 typename SrcIterator::row_iterator xxe = xxs + kernel_width;
153 typename KernelIterator::row_iterator xkernel= ykernel.rowIterator();
155 for(; xxs < xxe; ++xxs, --xkernel)
157 sum += ak(xkernel) * src_acc(xxs);
161 else if(border == BORDER_TREATMENT_REPEAT)
164 for(
int yk = klr.y; yk >= kul.y; --yk, --ykernel.y)
166 diff.y = std::min(std::max(y - yk, 0), h-1);
167 typename KernelIterator::row_iterator xkernel = ykernel.rowIterator();
169 for(
int xk = klr.x; xk >= kul.x; --xk, --xkernel)
171 diff.x = std::min(std::max(x - xk, 0), w-1);
172 sum += ak(xkernel) * src_acc(src_ul, diff);
176 else if(border == BORDER_TREATMENT_REFLECT)
179 for(
int yk = klr.y; yk >= kul.y; --yk , --ykernel.y)
181 diff.y =
abs(y - yk);
183 diff.y = 2*h - 2 - diff.y;
184 typename KernelIterator::row_iterator xkernel = ykernel.rowIterator();
186 for(
int xk = klr.x; xk >= kul.x; --xk, --xkernel)
188 diff.x =
abs(x - xk);
190 diff.x = 2*w - 2 - diff.x;
191 sum += ak(xkernel) * src_acc(src_ul, diff);
195 else if(border == BORDER_TREATMENT_WRAP)
198 for(
int yk = klr.y; yk >= kul.y; --yk, --ykernel.y)
200 diff.y = (y - yk + h) % h;
201 typename KernelIterator::row_iterator xkernel = ykernel.rowIterator();
203 for(
int xk = klr.x; xk >= kul.x; --xk, --xkernel)
205 diff.x = (x - xk + w) % w;
206 sum += ak(xkernel) * src_acc(src_ul, diff);
210 else if(border == BORDER_TREATMENT_CLIP)
212 KernelSumType ksum = NumericTraits<KernelSumType>::zero();
214 for(
int yk = klr.y; yk >= kul.y; --yk, --ykernel.y)
217 if(diff.y < 0 || diff.y >= h)
219 typename KernelIterator::row_iterator xkernel = ykernel.rowIterator();
221 for(
int xk = klr.x; xk >= kul.x; --xk, --xkernel)
224 if(diff.x < 0 || diff.x >= w)
227 sum += ak(xkernel) * src_acc(src_ul, diff);
233 else if(border == BORDER_TREATMENT_ZEROPAD)
236 for(
int yk = klr.y; yk >= kul.y; --yk, --ykernel.y)
239 if(diff.y < 0 || diff.y >= h)
241 typename KernelIterator::row_iterator xkernel = ykernel.rowIterator();
243 for(
int xk = klr.x; xk >= kul.x; --xk, --xkernel)
246 if(diff.x < 0 || diff.x >= w)
248 sum += ak(xkernel) * src_acc(src_ul, diff);
252 else if(border == BORDER_TREATMENT_AVOID)
258 dest_acc.set(detail::RequiresExplicitCast<DestType>::cast(
sum), xd);
263 template <
class SrcIterator,
class SrcAccessor,
264 class DestIterator,
class DestAccessor,
265 class KernelIterator,
class KernelAccessor>
267 convolveImage(triple<SrcIterator, SrcIterator, SrcAccessor> src,
268 pair<DestIterator, DestAccessor> dest,
269 tuple5<KernelIterator, KernelAccessor, Diff2D, Diff2D,
270 BorderTreatmentMode> kernel)
273 dest.first, dest.second,
274 kernel.first, kernel.second, kernel.third,
275 kernel.fourth, kernel.fifth);
278 template <
class T1,
class S1,
283 MultiArrayView<2, T2, S2> dest,
284 Kernel2D<T3>
const & kernel)
286 vigra_precondition(src.shape() == dest.shape(),
287 "convolveImage(): shape mismatch between input and output.");
456 template <
class SrcIterator,
class SrcAccessor,
457 class DestIterator,
class DestAccessor,
458 class MaskIterator,
class MaskAccessor,
459 class KernelIterator,
class KernelAccessor>
462 MaskIterator
mul, MaskAccessor am,
463 DestIterator dest_ul, DestAccessor dest_acc,
464 KernelIterator ki, KernelAccessor ak,
467 vigra_precondition((border == BORDER_TREATMENT_CLIP ||
468 border == BORDER_TREATMENT_AVOID),
469 "normalizedConvolveImage(): "
470 "Border treatment must be BORDER_TREATMENT_CLIP or BORDER_TREATMENT_AVOID.");
472 vigra_precondition(kul.
x <= 0 && kul.
y <= 0,
473 "normalizedConvolveImage(): left borders must be <= 0.");
474 vigra_precondition(klr.
x >= 0 && klr.
y >= 0,
475 "normalizedConvolveImage(): right borders must be >= 0.");
479 NumericTraits<typename SrcAccessor::value_type>::RealPromote SumType;
481 NumericTraits<typename KernelAccessor::value_type>::RealPromote KSumType;
483 NumericTraits<typename DestAccessor::value_type> DestTraits;
486 int w = src_lr.x - src_ul.x;
487 int h = src_lr.y - src_ul.y;
488 int kernel_width = klr.
x - kul.
x + 1;
489 int kernel_height = klr.
y - kul.
y + 1;
492 int ystart = (border == BORDER_TREATMENT_AVOID) ? klr.
y : 0;
493 int yend = (border == BORDER_TREATMENT_AVOID) ? h+kul.
y : h;
494 int xstart = (border == BORDER_TREATMENT_AVOID) ? klr.
x : 0;
495 int xend = (border == BORDER_TREATMENT_AVOID) ? w+kul.
x : w;
498 DestIterator yd = dest_ul +
Diff2D(xstart, ystart);
499 SrcIterator ys = src_ul +
Diff2D(xstart, ystart);
500 MaskIterator ym =
mul +
Diff2D(xstart, ystart);
502 KSumType
norm = ak(ki);
504 KernelIterator yk = ki + klr;
505 for(yy=0; yy<kernel_height; ++yy, --yk.y)
507 KernelIterator xk = yk;
509 for(xx=0; xx<kernel_width; ++xx, --xk.x)
517 for(y=ystart; y < yend; ++y, ++ys.y, ++yd.y, ++ym.y)
524 for(x=xstart; x < xend; ++x, ++xs.x, ++xd.x, ++xm.x)
529 y0 = (y<klr.
y) ? -y : -klr.
y;
530 y1 = (h-y-1<-kul.
y) ? h-y-1 : -kul.
y;
531 x0 = (x<klr.
x) ? -x : -klr.
x;
532 x1 = (w-x-1<-kul.
x) ? w-x-1 : -kul.
x;
536 SumType
sum = NumericTraits<SumType>::zero();
537 KSumType ksum = NumericTraits<KSumType>::zero();
539 SrcIterator yys = xs +
Diff2D(x0, y0);
540 MaskIterator yym = xm +
Diff2D(x0, y0);
541 KernelIterator yk = ki -
Diff2D(x0, y0);
543 int kernel_width, kernel_height;
544 kernel_width = x1 - x0 + 1;
545 kernel_height = y1 - y0 + 1;
546 for(yy=0; yy<kernel_height; ++yy, ++yys.y, --yk.y, ++yym.y)
548 typename SrcIterator::row_iterator xxs = yys.rowIterator();
549 typename SrcIterator::row_iterator xxend = xxs + kernel_width;
550 typename MaskIterator::row_iterator xxm = yym.rowIterator();
551 typename KernelIterator::row_iterator xk = yk.rowIterator();
553 for(xx=0; xxs < xxend; ++xxs, --xk, ++xxm)
555 if(!am(xxm))
continue;
559 sum = detail::RequiresExplicitCast<SumType>::cast(ak(xk) * src_acc(xxs));
565 sum = detail::RequiresExplicitCast<SumType>::cast(
sum + ak(xk) * src_acc(xxs));
571 if(ksum != NumericTraits<KSumType>::zero())
573 dest_acc.set(DestTraits::fromRealPromote(
574 detail::RequiresExplicitCast<SumType>::cast((
norm / ksum) *
sum)), xd);
581 template <
class SrcIterator,
class SrcAccessor,
582 class DestIterator,
class DestAccessor,
583 class MaskIterator,
class MaskAccessor,
584 class KernelIterator,
class KernelAccessor>
587 pair<MaskIterator, MaskAccessor> mask,
588 pair<DestIterator, DestAccessor> dest,
589 tuple5<KernelIterator, KernelAccessor, Diff2D, Diff2D,
590 BorderTreatmentMode> kernel)
593 mask.first, mask.second,
594 dest.first, dest.second,
595 kernel.first, kernel.second, kernel.third,
596 kernel.fourth, kernel.fifth);
599 template <
class T1,
class S1,
605 MultiArrayView<2, TM, SM>
const & mask,
606 MultiArrayView<2, T2, S2> dest,
607 Kernel2D<T3>
const & kernel)
609 vigra_precondition(src.shape() == mask.shape() && src.shape() == dest.shape(),
610 "normalizedConvolveImage(): shape mismatch between input and output.");
673 template <
class SrcIterator,
class SrcAccessor,
674 class DestIterator,
class DestAccessor,
675 class MaskIterator,
class MaskAccessor,
676 class KernelIterator,
class KernelAccessor>
679 MaskIterator
mul, MaskAccessor am,
680 DestIterator dest_ul, DestAccessor dest_acc,
681 KernelIterator ki, KernelAccessor ak,
687 ki, ak, kul, klr, border);
690 template <
class SrcIterator,
class SrcAccessor,
691 class DestIterator,
class DestAccessor,
692 class MaskIterator,
class MaskAccessor,
693 class KernelIterator,
class KernelAccessor>
696 triple<SrcIterator, SrcIterator, SrcAccessor> src,
697 pair<MaskIterator, MaskAccessor> mask,
698 pair<DestIterator, DestAccessor> dest,
699 tuple5<KernelIterator, KernelAccessor, Diff2D, Diff2D,
700 BorderTreatmentMode> kernel)
703 mask.first, mask.second,
704 dest.first, dest.second,
705 kernel.first, kernel.second, kernel.third,
706 kernel.fourth, kernel.fifth);
759 template <
class ARITHTYPE =
double>
789 : iter_(i), base_(i),
790 count_(count), sum_(count),
797 vigra_precondition(count_ == 1 || count_ == sum_,
798 "Kernel2D::initExplicitly(): "
799 "Too few init values.");
804 if(count_ == sum_) norm_ = *iter_;
807 vigra_precondition(count_ > 0,
808 "Kernel2D::initExplicitly(): "
809 "Too many init values.");
824 static value_type one() {
return NumericTraits<value_type>::one(); }
831 : kernel_(1, 1, one()),
835 border_treatment_(BORDER_TREATMENT_REFLECT)
841 : kernel_(k.kernel_),
845 border_treatment_(k.border_treatment_)
858 border_treatment_ = k.border_treatment_;
884 int size = (right_.
x - left_.
x + 1) *
885 (right_.
y - left_.
y + 1);
887 norm_ = (double)size*v;
889 return InitProxy(kernel_.
begin(), size, norm_);
915 int w = right_.
x - left_.
x + 1;
916 int h = right_.
y - left_.
y + 1;
924 KIter kiy = ky.
center() + left_.
y;
927 for(
int y=left_.
y; y<=right_.
y; ++y, ++kiy, ++iy.y)
929 KIter kix = kx.
center() + left_.
x;
931 for(
int x=left_.
x; x<=right_.
x; ++x, ++kix, ++ix.x)
933 *ix = ka(kix) * ka(kiy);
961 template <
class KernelIterator>
963 KernelIterator kycenter,
int yleft,
int yright)
965 vigra_precondition(xleft <= 0 && yleft <= 0,
966 "Kernel2D::initSeparable(): left borders must be <= 0.");
967 vigra_precondition(xright >= 0 && yright >= 0,
968 "Kernel2D::initSeparable(): right borders must be >= 0.");
971 right_ =
Point2D(xright, yright);
973 int w = right_.
x - left_.
x + 1;
974 int h = right_.
y - left_.
y + 1;
977 KernelIterator kiy = kycenter + left_.
y;
980 for(
int y=left_.
y; y<=right_.
y; ++y, ++kiy, ++iy.y)
982 KernelIterator kix = kxcenter + left_.
x;
984 for(
int x=left_.
x; x<=right_.
x; ++x, ++kix, ++ix.x)
1023 return initGaussian(std_dev, NumericTraits<value_type>::one());
1048 vigra_precondition(radius > 0,
1049 "Kernel2D::initDisk(): radius must be > 0.");
1051 left_ =
Point2D(-radius, -radius);
1052 right_ =
Point2D(radius, radius);
1053 int w = right_.
x - left_.
x + 1;
1054 int h = right_.
y - left_.
y + 1;
1056 norm_ = NumericTraits<value_type>::one();
1058 kernel_ = NumericTraits<value_type>::zero();
1062 double r2 = (double)radius*radius;
1065 for(i=0; i<= radius; ++i)
1067 double r = (double) i - 0.5;
1068 int w = (int)(VIGRA_CSTD::sqrt(r2 - r*r) + 0.5);
1069 for(
int j=-w; j<=w; ++j)
1071 k(j, i) = NumericTraits<value_type>::one();
1072 k(j, -i) = NumericTraits<value_type>::one();
1073 count += (i != 0) ? 2.0 : 1.0;
1077 count = 1.0 / count;
1079 for(
int y=-radius; y<=radius; ++y)
1081 for(
int x=-radius; x<=radius; ++x)
1083 k(x,y) = count * k(x,y);
1128 vigra_precondition(upperleft[0] <= 0 && upperleft[1] <= 0,
1129 "Kernel2D::initExplicitly(): left borders must be <= 0.");
1130 vigra_precondition(lowerright[0] >= 0 && lowerright[1] >= 0,
1131 "Kernel2D::initExplicitly(): right borders must be >= 0.");
1133 left_ =
Point2D(upperleft[0], upperleft[1]);
1134 right_ =
Point2D(lowerright[0], lowerright[1]);
1136 int w = right_.
x - left_.
x + 1;
1137 int h = right_.
y - left_.
y + 1;
1159 vigra_precondition(image.
width() % 2 != 0 && image.
height() % 2 != 0,
1160 "Kernel2D::initExplicitly(): kernel sizes must be odd.");
1166 for (
auto iter = image.
begin(); iter != image.
end(); ++iter)
1186 int width()
const {
return right_.
x - left_.
x + 1; }
1203 {
return kernel_[
Diff2D(x,y) - left_]; }
1208 {
return kernel_[
Diff2D(x,y) - left_]; }
1213 {
return kernel_[d - left_]; }
1218 {
return kernel_[d - left_]; }
1251 typename NumericTraits<value_type>::RealPromote
sum = *i;
1254 for(; i!= iend; ++i)
1260 i = kernel_.
begin();
1261 for(; i != iend; ++i)
1279 {
return border_treatment_; }
1287 vigra_precondition((new_mode == BORDER_TREATMENT_CLIP ||
1288 new_mode == BORDER_TREATMENT_AVOID ||
1289 new_mode == BORDER_TREATMENT_REFLECT ||
1290 new_mode == BORDER_TREATMENT_REPEAT ||
1291 new_mode == BORDER_TREATMENT_WRAP),
1292 "convolveImage():\n"
1293 " Border treatment must be one of follow treatments:\n"
1294 " - BORDER_TREATMENT_CLIP\n"
1295 " - BORDER_TREATMENT_AVOID\n"
1296 " - BORDER_TREATMENT_REFLECT\n"
1297 " - BORDER_TREATMENT_REPEAT\n"
1298 " - BORDER_TREATMENT_WRAP\n");
1300 border_treatment_ = new_mode;
1308 BorderTreatmentMode border_treatment_;
1319 template <
class KernelIterator,
class KernelAccessor>
1321 tuple5<KernelIterator, KernelAccessor, Diff2D, Diff2D, BorderTreatmentMode>
1322 kernel2d(KernelIterator ik, KernelAccessor ak, Diff2D kul, Diff2D klr,
1323 BorderTreatmentMode border)
1327 tuple5<KernelIterator, KernelAccessor, Diff2D, Diff2D, BorderTreatmentMode> (
1328 ik, ak, kul, klr, border);
1333 tuple5<typename Kernel2D<T>::ConstIterator,
1335 Diff2D, Diff2D, BorderTreatmentMode>
1336 kernel2d(Kernel2D<T>
const & k)
1340 tuple5<typename Kernel2D<T>::ConstIterator,
1342 Diff2D, Diff2D, BorderTreatmentMode>(
1345 k.upperLeft(), k.lowerRight(),
1346 k.borderTreatment());
1351 tuple5<typename Kernel2D<T>::ConstIterator,
1353 Diff2D, Diff2D, BorderTreatmentMode>
1354 kernel2d(Kernel2D<T>
const & k, BorderTreatmentMode border)
1358 tuple5<typename Kernel2D<T>::ConstIterator,
1360 Diff2D, Diff2D, BorderTreatmentMode>(
1363 k.upperLeft(), k.lowerRight(),
Fundamental class template for images.
Definition: basicimage.hxx:476
std::ptrdiff_t height() const
Definition: basicimage.hxx:847
void resize(std::ptrdiff_t width, std::ptrdiff_t height)
Definition: basicimage.hxx:778
std::ptrdiff_t width() const
Definition: basicimage.hxx:840
iterator end()
Definition: basicimage.hxx:974
iterator begin()
Definition: basicimage.hxx:965
traverser upperLeft()
Definition: basicimage.hxx:925
Two dimensional difference vector.
Definition: diff2d.hxx:186
int y
Definition: diff2d.hxx:392
int x
Definition: diff2d.hxx:385
Generic 1 dimensional convolution kernel.
Definition: separableconvolution.hxx:1367
int right() const
Definition: separableconvolution.hxx:2153
value_type norm() const
Definition: separableconvolution.hxx:2171
void initGaussian(double std_dev, value_type norm, double windowRatio=0.0)
Definition: separableconvolution.hxx:2249
InternalVector::const_iterator const_iterator
Definition: separableconvolution.hxx:1393
void initAveraging(int radius, value_type norm)
Definition: separableconvolution.hxx:2480
int left() const
Definition: separableconvolution.hxx:2149
iterator center()
Definition: separableconvolution.hxx:2119
Generic 2 dimensional convolution kernel.
Definition: stdconvolution.hxx:761
BasicImage< value_type >::ConstAccessor ConstAccessor
Definition: stdconvolution.hxx:781
void initGaussian(double std_dev, value_type norm)
Definition: stdconvolution.hxx:1012
Kernel2D & operator=(Kernel2D const &k)
Definition: stdconvolution.hxx:850
Kernel2D & initExplicitly(BasicImage< value_type > const &image)
Definition: stdconvolution.hxx:1157
value_type norm() const
Definition: stdconvolution.hxx:1222
value_type operator[](Diff2D const &d) const
Definition: stdconvolution.hxx:1217
InitProxy operator=(value_type const &v)
Definition: stdconvolution.hxx:882
void initSeparable(KernelIterator kxcenter, int xleft, int xright, KernelIterator kycenter, int yleft, int yright)
Definition: stdconvolution.hxx:962
BorderTreatmentMode borderTreatment() const
Definition: stdconvolution.hxx:1278
Point2D upperLeft() const
Definition: stdconvolution.hxx:1178
ConstIterator center() const
Definition: stdconvolution.hxx:1198
void setBorderTreatment(BorderTreatmentMode new_mode)
Definition: stdconvolution.hxx:1285
ConstAccessor accessor() const
Definition: stdconvolution.hxx:1230
void initSeparable(Kernel1D< value_type > const &kx, Kernel1D< value_type > const &ky)
Definition: stdconvolution.hxx:910
Point2D lowerRight() const
Definition: stdconvolution.hxx:1182
~Kernel2D()
Definition: stdconvolution.hxx:894
value_type & operator()(int x, int y)
Definition: stdconvolution.hxx:1202
BasicImage< value_type >::traverser Iterator
Definition: stdconvolution.hxx:769
Kernel2D(Kernel2D const &k)
Definition: stdconvolution.hxx:840
void initAveraging(int radius)
Definition: stdconvolution.hxx:1003
Iterator center()
Definition: stdconvolution.hxx:1194
BasicImage< value_type >::const_traverser ConstIterator
Definition: stdconvolution.hxx:773
void initDisk(int radius)
Definition: stdconvolution.hxx:1046
value_type operator()(int x, int y) const
Definition: stdconvolution.hxx:1207
ARITHTYPE value_type
Definition: stdconvolution.hxx:765
void normalize(value_type norm)
Definition: stdconvolution.hxx:1247
Kernel2D & initExplicitly(Shape2 const &upperleft, Shape2 const &lowerright)
Definition: stdconvolution.hxx:1126
void initGaussian(double std_dev)
Definition: stdconvolution.hxx:1021
Accessor accessor()
Definition: stdconvolution.hxx:1226
value_type & operator[](Diff2D const &d)
Definition: stdconvolution.hxx:1212
void normalize()
Definition: stdconvolution.hxx:1271
int height() const
Definition: stdconvolution.hxx:1190
int width() const
Definition: stdconvolution.hxx:1186
BasicImage< value_type >::Accessor Accessor
Definition: stdconvolution.hxx:777
Kernel2D()
Definition: stdconvolution.hxx:830
Two dimensional point or position.
Definition: diff2d.hxx:593
Encapsulate access to the values an iterator points to.
Definition: accessor.hxx:134
NumericTraits< V >::Promote sum(TinyVectorBase< V, SIZE, D1, D2 > const &l)
sum of the vector's elements
Definition: tinyvector.hxx:2073
FFTWComplex< R >::NormType norm(const FFTWComplex< R > &a)
norm (= magnitude)
Definition: fftw3.hxx:1037
void normalizedConvolveImage(...)
Performs a 2-dimensional normalized convolution, i.e. convolution with a mask image.
FFTWComplex< R >::NormType abs(const FFTWComplex< R > &a)
absolute value (= magnitude)
Definition: fftw3.hxx:1002
void mul(FixedPoint< IntBits1, FracBits1 > l, FixedPoint< IntBits2, FracBits2 > r, FixedPoint< IntBits3, FracBits3 > &result)
multiplication with enforced result type.
Definition: fixedpoint.hxx:605
void convolveImage(...)
Convolve an image with the given kernel(s).
doxygen_overloaded_function(template<... > void separableConvolveBlockwise) template< unsigned int N
Separated convolution on ChunkedArrays.
void convolveImageWithMask(...)
Deprecated name of 2-dimensional normalized convolution, i.e. convolution with a mask image.