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| 1 | // Copyright Contributors to the OpenVDB Project | ||
| 2 | // SPDX-License-Identifier: MPL-2.0 | ||
| 3 | // | ||
| 4 | /// @file Statistics.h | ||
| 5 | /// | ||
| 6 | /// @brief Functions to efficiently compute histograms, extrema | ||
| 7 | /// (min/max) and statistics (mean, variance, etc.) of grid values | ||
| 8 | |||
| 9 | #ifndef OPENVDB_TOOLS_STATISTICS_HAS_BEEN_INCLUDED | ||
| 10 | #define OPENVDB_TOOLS_STATISTICS_HAS_BEEN_INCLUDED | ||
| 11 | |||
| 12 | #include <openvdb/Types.h> | ||
| 13 | #include <openvdb/Exceptions.h> | ||
| 14 | #include <openvdb/math/Stats.h> | ||
| 15 | #include "ValueTransformer.h" | ||
| 16 | |||
| 17 | |||
| 18 | namespace openvdb { | ||
| 19 | OPENVDB_USE_VERSION_NAMESPACE | ||
| 20 | namespace OPENVDB_VERSION_NAME { | ||
| 21 | namespace tools { | ||
| 22 | |||
| 23 | /// @brief Iterate over a scalar grid and compute a histogram of the values | ||
| 24 | /// of the voxels that are visited, or iterate over a vector-valued grid | ||
| 25 | /// and compute a histogram of the magnitudes of the vectors. | ||
| 26 | /// @param iter an iterator over the values of a grid or its tree | ||
| 27 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 28 | /// @param minVal the smallest value that can be added to the histogram | ||
| 29 | /// @param maxVal the largest value that can be added to the histogram | ||
| 30 | /// @param numBins the number of histogram bins | ||
| 31 | /// @param threaded if true, iterate over the grid in parallel | ||
| 32 | template<typename IterT> | ||
| 33 | inline math::Histogram | ||
| 34 | histogram(const IterT& iter, double minVal, double maxVal, | ||
| 35 | size_t numBins = 10, bool threaded = true); | ||
| 36 | |||
| 37 | /// @brief Iterate over a scalar grid and compute extrema (min/max) of the | ||
| 38 | /// values of the voxels that are visited, or iterate over a vector-valued grid | ||
| 39 | /// and compute extrema of the magnitudes of the vectors. | ||
| 40 | /// @param iter an iterator over the values of a grid or its tree | ||
| 41 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 42 | /// @param threaded if true, iterate over the grid in parallel | ||
| 43 | template<typename IterT> | ||
| 44 | inline math::Extrema | ||
| 45 | extrema(const IterT& iter, bool threaded = true); | ||
| 46 | |||
| 47 | /// @brief Iterate over a scalar grid and compute statistics (mean, variance, etc.) | ||
| 48 | /// of the values of the voxels that are visited, or iterate over a vector-valued grid | ||
| 49 | /// and compute statistics of the magnitudes of the vectors. | ||
| 50 | /// @param iter an iterator over the values of a grid or its tree | ||
| 51 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 52 | /// @param threaded if true, iterate over the grid in parallel | ||
| 53 | template<typename IterT> | ||
| 54 | inline math::Stats | ||
| 55 | statistics(const IterT& iter, bool threaded = true); | ||
| 56 | |||
| 57 | /// @brief Iterate over a grid and compute extrema (min/max) of | ||
| 58 | /// the values produced by applying the given functor at each voxel that is visited. | ||
| 59 | /// @param iter an iterator over the values of a grid or its tree | ||
| 60 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 61 | /// @param op a functor of the form <tt>void op(const IterT&, math::Stats&)</tt>, | ||
| 62 | /// where @c IterT is the type of @a iter, that inserts zero or more | ||
| 63 | /// floating-point values into the provided @c math::Stats object | ||
| 64 | /// @param threaded if true, iterate over the grid in parallel | ||
| 65 | /// @note When @a threaded is true, each thread gets its own copy of the functor. | ||
| 66 | /// | ||
| 67 | /// @par Example: | ||
| 68 | /// Compute statistics of just the active and positive-valued voxels of a scalar, | ||
| 69 | /// floating-point grid. | ||
| 70 | /// @code | ||
| 71 | /// struct Local { | ||
| 72 | /// static inline | ||
| 73 | /// void addIfPositive(const FloatGrid::ValueOnCIter& iter, math::Extrema& ex) | ||
| 74 | /// { | ||
| 75 | /// const float f = *iter; | ||
| 76 | /// if (f > 0.0) { | ||
| 77 | /// if (iter.isVoxelValue()) ex.add(f); | ||
| 78 | /// else ex.add(f, iter.getVoxelCount()); | ||
| 79 | /// } | ||
| 80 | /// } | ||
| 81 | /// }; | ||
| 82 | /// FloatGrid grid = ...; | ||
| 83 | /// math::Extrema stats = | ||
| 84 | /// tools::extrema(grid.cbeginValueOn(), Local::addIfPositive, /*threaded=*/true); | ||
| 85 | /// @endcode | ||
| 86 | template<typename IterT, typename ValueOp> | ||
| 87 | inline math::Extrema | ||
| 88 | extrema(const IterT& iter, const ValueOp& op, bool threaded); | ||
| 89 | |||
| 90 | /// @brief Iterate over a grid and compute statistics (mean, variance, etc.) of | ||
| 91 | /// the values produced by applying the given functor at each voxel that is visited. | ||
| 92 | /// @param iter an iterator over the values of a grid or its tree | ||
| 93 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 94 | /// @param op a functor of the form <tt>void op(const IterT&, math::Stats&)</tt>, | ||
| 95 | /// where @c IterT is the type of @a iter, that inserts zero or more | ||
| 96 | /// floating-point values into the provided @c math::Stats object | ||
| 97 | /// @param threaded if true, iterate over the grid in parallel | ||
| 98 | /// @note When @a threaded is true, each thread gets its own copy of the functor. | ||
| 99 | /// | ||
| 100 | /// @par Example: | ||
| 101 | /// Compute statistics of just the active and positive-valued voxels of a scalar, | ||
| 102 | /// floating-point grid. | ||
| 103 | /// @code | ||
| 104 | /// struct Local { | ||
| 105 | /// static inline | ||
| 106 | /// void addIfPositive(const FloatGrid::ValueOnCIter& iter, math::Stats& stats) | ||
| 107 | /// { | ||
| 108 | /// const float f = *iter; | ||
| 109 | /// if (f > 0.0) { | ||
| 110 | /// if (iter.isVoxelValue()) stats.add(f); | ||
| 111 | /// else stats.add(f, iter.getVoxelCount()); | ||
| 112 | /// } | ||
| 113 | /// } | ||
| 114 | /// }; | ||
| 115 | /// FloatGrid grid = ...; | ||
| 116 | /// math::Stats stats = | ||
| 117 | /// tools::statistics(grid.cbeginValueOn(), Local::addIfPositive, /*threaded=*/true); | ||
| 118 | /// @endcode | ||
| 119 | template<typename IterT, typename ValueOp> | ||
| 120 | inline math::Stats | ||
| 121 | statistics(const IterT& iter, const ValueOp& op, bool threaded); | ||
| 122 | |||
| 123 | |||
| 124 | /// @brief Iterate over a grid and compute statistics (mean, variance, etc.) | ||
| 125 | /// of the values produced by applying a given operator (see math/Operators.h) | ||
| 126 | /// at each voxel that is visited. | ||
| 127 | /// @param iter an iterator over the values of a grid or its tree | ||
| 128 | /// (@c Grid::ValueOnCIter, @c Tree::ValueOffIter, etc.) | ||
| 129 | /// @param op an operator object with a method of the form | ||
| 130 | /// <tt>double result(Accessor&, const Coord&)</tt> | ||
| 131 | /// @param threaded if true, iterate over the grid in parallel | ||
| 132 | /// @note World-space operators, whose @c result() methods are of the form | ||
| 133 | /// <tt>double result(const Map&, Accessor&, const Coord&)</tt>, must be wrapped | ||
| 134 | /// in a math::MapAdapter. | ||
| 135 | /// @note Vector-valued operators like math::Gradient must be wrapped in an adapter | ||
| 136 | /// such as math::OpMagnitude. | ||
| 137 | /// | ||
| 138 | /// @par Example: | ||
| 139 | /// Compute statistics of the magnitude of the gradient at the active voxels of | ||
| 140 | /// a scalar, floating-point grid. (Note the use of the math::MapAdapter and | ||
| 141 | /// math::OpMagnitude adapters.) | ||
| 142 | /// @code | ||
| 143 | /// FloatGrid grid = ...; | ||
| 144 | /// | ||
| 145 | /// // Assume that we know that the grid has a uniform scale map. | ||
| 146 | /// using MapType = math::UniformScaleMap; | ||
| 147 | /// // Specify a world-space gradient operator that uses first-order differencing. | ||
| 148 | /// using GradientOp = math::Gradient<MapType, math::FD_1ST>; | ||
| 149 | /// // Wrap the operator with an adapter that computes the magnitude of the gradient. | ||
| 150 | /// using MagnitudeOp = math::OpMagnitude<GradientOp, MapType>; | ||
| 151 | /// // Wrap the operator with an adapter that associates a map with it. | ||
| 152 | /// using CompoundOp = math::MapAdapter<MapType, GradientOp, double>; | ||
| 153 | /// | ||
| 154 | /// if (MapType::Ptr map = grid.constTransform().constMap<MapType>()) { | ||
| 155 | /// math::Stats stats = tools::opStatistics(grid.cbeginValueOn(), CompoundOp(*map)); | ||
| 156 | /// } | ||
| 157 | /// @endcode | ||
| 158 | /// | ||
| 159 | /// @par Example: | ||
| 160 | /// Compute statistics of the divergence at the active voxels of a vector-valued grid. | ||
| 161 | /// @code | ||
| 162 | /// Vec3SGrid grid = ...; | ||
| 163 | /// | ||
| 164 | /// // Assume that we know that the grid has a uniform scale map. | ||
| 165 | /// using MapType = math::UniformScaleMap; | ||
| 166 | /// // Specify a world-space divergence operator that uses first-order differencing. | ||
| 167 | /// using DivergenceOp = math::Divergence<MapType, math::FD_1ST>; | ||
| 168 | /// // Wrap the operator with an adapter that associates a map with it. | ||
| 169 | /// using CompoundOp = math::MapAdapter<MapType, DivergenceOp, double>; | ||
| 170 | /// | ||
| 171 | /// if (MapType::Ptr map = grid.constTransform().constMap<MapType>()) { | ||
| 172 | /// math::Stats stats = tools::opStatistics(grid.cbeginValueOn(), CompoundOp(*map)); | ||
| 173 | /// } | ||
| 174 | /// @endcode | ||
| 175 | /// | ||
| 176 | /// @par Example: | ||
| 177 | /// As above, but computing the divergence in index space. | ||
| 178 | /// @code | ||
| 179 | /// Vec3SGrid grid = ...; | ||
| 180 | /// | ||
| 181 | /// // Specify an index-space divergence operator that uses first-order differencing. | ||
| 182 | /// using DivergenceOp = math::ISDivergence<math::FD_1ST>; | ||
| 183 | /// | ||
| 184 | /// math::Stats stats = tools::opStatistics(grid.cbeginValueOn(), DivergenceOp()); | ||
| 185 | /// @endcode | ||
| 186 | template<typename OperatorT, typename IterT> | ||
| 187 | inline math::Stats | ||
| 188 | opStatistics(const IterT& iter, const OperatorT& op = OperatorT(), bool threaded = true); | ||
| 189 | |||
| 190 | /// @brief Same as opStatistics except it returns a math::Extrema vs a math::Stats | ||
| 191 | template<typename OperatorT, typename IterT> | ||
| 192 | inline math::Extrema | ||
| 193 | opExtrema(const IterT& iter, const OperatorT& op = OperatorT(), bool threaded = true); | ||
| 194 | |||
| 195 | //////////////////////////////////////// | ||
| 196 | |||
| 197 | /// @cond OPENVDB_DOCS_INTERNAL | ||
| 198 | |||
| 199 | namespace stats_internal { | ||
| 200 | |||
| 201 | /// @todo This traits class is needed because tree::TreeValueIteratorBase uses | ||
| 202 | /// the name ValueT for the type of the value to which the iterator points, | ||
| 203 | /// whereas node-level iterators use the name ValueType. | ||
| 204 | template<typename IterT, typename AuxT = void> | ||
| 205 | struct IterTraits { | ||
| 206 | using ValueType = typename IterT::ValueType; | ||
| 207 | }; | ||
| 208 | |||
| 209 | template<typename TreeT, typename ValueIterT> | ||
| 210 | struct IterTraits<tree::TreeValueIteratorBase<TreeT, ValueIterT> > { | ||
| 211 | using ValueType = typename tree::TreeValueIteratorBase<TreeT, ValueIterT>::ValueT; | ||
| 212 | }; | ||
| 213 | |||
| 214 | |||
| 215 | // Helper class to compute a scalar value from either a scalar or a vector value | ||
| 216 | // (the latter by computing the vector's magnitude) | ||
| 217 | template<typename T, bool IsVector> struct GetValImpl; | ||
| 218 | |||
| 219 | template<typename T> | ||
| 220 | struct GetValImpl<T, /*IsVector=*/false> { | ||
| 221 |
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1905449 | static inline double get(const T& val) { return double(val); } |
| 222 | }; | ||
| 223 | |||
| 224 | template<typename T> | ||
| 225 | struct GetValImpl<T, /*IsVector=*/true> { | ||
| 226 |
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1378896 | static inline double get(const T& val) { return val.length(); } |
| 227 | }; | ||
| 228 | |||
| 229 | |||
| 230 | // Helper class to compute a scalar value from a tree or node iterator | ||
| 231 | // that points to a value in either a scalar or a vector grid, and to | ||
| 232 | // add that value to a math::Stats object. | ||
| 233 | template<typename IterT, typename StatsT> | ||
| 234 | struct GetVal | ||
| 235 | { | ||
| 236 | using ValueT = typename IterTraits<IterT>::ValueType; | ||
| 237 | using ImplT = GetValImpl<ValueT, VecTraits<ValueT>::IsVec>; | ||
| 238 | |||
| 239 |
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9401232 | inline void operator()(const IterT& iter, StatsT& stats) const { |
| 240 | 6312336 | if (iter.isVoxelValue()) stats.add(ImplT::get(*iter)); | |
| 241 | 331102 | else stats.add(ImplT::get(*iter), iter.getVoxelCount()); | |
| 242 | 9401232 | } | |
| 243 | }; | ||
| 244 | |||
| 245 | // Helper class to accumulate scalar voxel values or vector voxel magnitudes | ||
| 246 | // into a math::Stats object | ||
| 247 | template<typename IterT, typename ValueOp, typename StatsT> | ||
| 248 |
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34 | struct StatsOp |
| 249 | { | ||
| 250 |
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15 | StatsOp(const ValueOp& op): getValue(op) {} |
| 251 | |||
| 252 | // Accumulate voxel and tile values into this functor's Stats object. | ||
| 253 | 6079511 | inline void operator()(const IterT& iter) { getValue(iter, stats); } | |
| 254 | |||
| 255 | // Accumulate another functor's Stats object into this functor's. | ||
| 256 |
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59 | inline void join(StatsOp& other) { stats.add(other.stats); } |
| 257 | |||
| 258 | StatsT stats; | ||
| 259 | ValueOp getValue; | ||
| 260 | }; | ||
| 261 | |||
| 262 | |||
| 263 | // Helper class to accumulate scalar voxel values or vector voxel magnitudes | ||
| 264 | // into a math::Histogram object | ||
| 265 | template<typename IterT, typename ValueOp> | ||
| 266 |
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42 | struct HistOp |
| 267 | { | ||
| 268 | 5 | HistOp(const ValueOp& op, double vmin, double vmax, size_t bins): | |
| 269 | 5 | hist(vmin, vmax, bins), getValue(op) | |
| 270 | {} | ||
| 271 | |||
| 272 | // Accumulate voxel and tile values into this functor's Histogram object. | ||
| 273 | 1378897 | inline void operator()(const IterT& iter) { getValue(iter, hist); } | |
| 274 | |||
| 275 | // Accumulate another functor's Histogram object into this functor's. | ||
| 276 | 16 | inline void join(HistOp& other) { hist.add(other.hist); } | |
| 277 | |||
| 278 | math::Histogram hist; | ||
| 279 | ValueOp getValue; | ||
| 280 | }; | ||
| 281 | |||
| 282 | |||
| 283 | // Helper class to apply an operator such as math::Gradient or math::Laplacian | ||
| 284 | // to voxels and accumulate the scalar results or the magnitudes of vector results | ||
| 285 | // into a math::Stats object | ||
| 286 | template<typename IterT, typename OpT, typename StatsT> | ||
| 287 |
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105 | struct MathOp |
| 288 | { | ||
| 289 | using TreeT = typename IterT::TreeT; | ||
| 290 | using ValueT = typename TreeT::ValueType; | ||
| 291 | using ConstAccessor = typename tree::ValueAccessor<const TreeT>; | ||
| 292 | |||
| 293 | // Each thread gets its own accessor and its own copy of the operator. | ||
| 294 | ConstAccessor mAcc; | ||
| 295 | OpT mOp; | ||
| 296 | StatsT mStats; | ||
| 297 | |||
| 298 | template<typename TreeT> | ||
| 299 | 32 | static inline TreeT* THROW_IF_NULL(TreeT* ptr) { | |
| 300 |
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32 | if (ptr == nullptr) OPENVDB_THROW(ValueError, "iterator references a null tree"); |
| 301 | 32 | return ptr; | |
| 302 | } | ||
| 303 | |||
| 304 | 32 | MathOp(const IterT& iter, const OpT& op): | |
| 305 | 32 | mAcc(*THROW_IF_NULL(iter.getTree())), mOp(op) | |
| 306 | 32 | {} | |
| 307 | |||
| 308 | // Accumulate voxel and tile values into this functor's Stats object. | ||
| 309 |
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11031168 | void operator()(const IterT& it) |
| 310 | { | ||
| 311 | if (it.isVoxelValue()) { | ||
| 312 | // Add the magnitude of the gradient at a single voxel. | ||
| 313 | 12251880 | mStats.add(mOp.result(mAcc, it.getCoord())); | |
| 314 | } else { | ||
| 315 | // Iterate over the voxels enclosed by a tile and add the results | ||
| 316 | // of applying the operator at each voxel. | ||
| 317 | /// @todo This could be specialized to be done more efficiently for some operators. | ||
| 318 | /// For example, all voxels in the interior of a tile (i.e., not on the borders) | ||
| 319 | /// have gradient zero, so there's no need to apply the operator to every voxel. | ||
| 320 | CoordBBox bbox = it.getBoundingBox(); | ||
| 321 | Coord xyz; | ||
| 322 | int &x = xyz.x(), &y = xyz.y(), &z = xyz.z(); | ||
| 323 |
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1265472 | for (x = bbox.min().x(); x <= bbox.max().x(); ++x) { |
| 324 |
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10123776 | for (y = bbox.min().y(); y <= bbox.max().y(); ++y) { |
| 325 |
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80990208 | for (z = bbox.min().z(); z <= bbox.max().z(); ++z) { |
| 326 | 80990208 | mStats.add(mOp.result(mAcc, it.getCoord())); | |
| 327 | } | ||
| 328 | } | ||
| 329 | } | ||
| 330 | } | ||
| 331 | 11031168 | } | |
| 332 | |||
| 333 | // Accumulate another functor's Stats object into this functor's. | ||
| 334 |
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48 | inline void join(MathOp& other) { mStats.add(other.mStats); } |
| 335 | }; // struct MathOp | ||
| 336 | |||
| 337 | } // namespace stats_internal | ||
| 338 | |||
| 339 | /// @endcond | ||
| 340 | |||
| 341 | template<typename IterT> | ||
| 342 | inline math::Histogram | ||
| 343 | 10 | histogram(const IterT& iter, double vmin, double vmax, size_t numBins, bool threaded) | |
| 344 | { | ||
| 345 | using ValueOp = stats_internal::GetVal<IterT, math::Histogram>; | ||
| 346 | ValueOp valOp; | ||
| 347 | stats_internal::HistOp<IterT, ValueOp> op(valOp, vmin, vmax, numBins); | ||
| 348 |
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10 | tools::accumulate(iter, op, threaded); |
| 349 | 10 | return op.hist; | |
| 350 | } | ||
| 351 | |||
| 352 | template<typename IterT> | ||
| 353 | inline math::Extrema | ||
| 354 | extrema(const IterT& iter, bool threaded) | ||
| 355 | { | ||
| 356 | stats_internal::GetVal<IterT, math::Extrema> valOp; | ||
| 357 | return extrema(iter, valOp, threaded); | ||
| 358 | } | ||
| 359 | |||
| 360 | template<typename IterT> | ||
| 361 | inline math::Stats | ||
| 362 | statistics(const IterT& iter, bool threaded) | ||
| 363 | { | ||
| 364 | stats_internal::GetVal<IterT, math::Stats> valOp; | ||
| 365 |
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6 | return statistics(iter, valOp, threaded); |
| 366 | } | ||
| 367 | |||
| 368 | template<typename IterT, typename ValueOp> | ||
| 369 | inline math::Extrema | ||
| 370 | 2 | extrema(const IterT& iter, const ValueOp& valOp, bool threaded) | |
| 371 | { | ||
| 372 | stats_internal::StatsOp<IterT, const ValueOp, math::Extrema> op(valOp); | ||
| 373 |
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17 | tools::accumulate(iter, op, threaded); |
| 374 |
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19 | return op.stats; |
| 375 | } | ||
| 376 | |||
| 377 | template<typename IterT, typename ValueOp> | ||
| 378 | inline math::Stats | ||
| 379 | 20 | statistics(const IterT& iter, const ValueOp& valOp, bool threaded) | |
| 380 | { | ||
| 381 | stats_internal::StatsOp<IterT, const ValueOp, math::Stats> op(valOp); | ||
| 382 |
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20 | tools::accumulate(iter, op, threaded); |
| 383 |
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24 | return op.stats; |
| 384 | } | ||
| 385 | |||
| 386 | |||
| 387 | template<typename OperatorT, typename IterT> | ||
| 388 | inline math::Extrema | ||
| 389 | 2 | opExtrema(const IterT& iter, const OperatorT& op, bool threaded) | |
| 390 | { | ||
| 391 | 2 | stats_internal::MathOp<IterT, OperatorT, math::Extrema> func(iter, op); | |
| 392 |
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2 | tools::accumulate(iter, func, threaded); |
| 393 |
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4 | return func.mStats; |
| 394 | } | ||
| 395 | |||
| 396 | template<typename OperatorT, typename IterT> | ||
| 397 | inline math::Stats | ||
| 398 | 14 | opStatistics(const IterT& iter, const OperatorT& op, bool threaded) | |
| 399 | { | ||
| 400 | 14 | stats_internal::MathOp<IterT, OperatorT, math::Stats> func(iter, op); | |
| 401 | 14 | tools::accumulate(iter, func, threaded); | |
| 402 | 28 | return func.mStats; | |
| 403 | } | ||
| 404 | |||
| 405 | } // namespace tools | ||
| 406 | } // namespace OPENVDB_VERSION_NAME | ||
| 407 | } // namespace openvdb | ||
| 408 | |||
| 409 | #endif // OPENVDB_TOOLS_STATISTICS_HAS_BEEN_INCLUDED | ||
| 410 |