diff options
Diffstat (limited to 'extern/draco/draco/src/draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_tex_coords_portable_predictor.h')
| -rw-r--r-- | extern/draco/draco/src/draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_tex_coords_portable_predictor.h | 256 |
1 files changed, 256 insertions, 0 deletions
diff --git a/extern/draco/draco/src/draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_tex_coords_portable_predictor.h b/extern/draco/draco/src/draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_tex_coords_portable_predictor.h new file mode 100644 index 00000000000..5d8a5060133 --- /dev/null +++ b/extern/draco/draco/src/draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_tex_coords_portable_predictor.h @@ -0,0 +1,256 @@ +// Copyright 2017 The Draco Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +#ifndef DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_TEX_COORDS_PORTABLE_PREDICTOR_H_ +#define DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_TEX_COORDS_PORTABLE_PREDICTOR_H_ + +#include <math.h> + +#include "draco/attributes/point_attribute.h" +#include "draco/core/math_utils.h" +#include "draco/core/vector_d.h" +#include "draco/mesh/corner_table.h" + +namespace draco { + +// Predictor functionality used for portable UV prediction by both encoder and +// decoder. +template <typename DataTypeT, class MeshDataT> +class MeshPredictionSchemeTexCoordsPortablePredictor { + public: + static constexpr int kNumComponents = 2; + + explicit MeshPredictionSchemeTexCoordsPortablePredictor(const MeshDataT &md) + : pos_attribute_(nullptr), + entry_to_point_id_map_(nullptr), + mesh_data_(md) {} + void SetPositionAttribute(const PointAttribute &position_attribute) { + pos_attribute_ = &position_attribute; + } + void SetEntryToPointIdMap(const PointIndex *map) { + entry_to_point_id_map_ = map; + } + bool IsInitialized() const { return pos_attribute_ != nullptr; } + + VectorD<int64_t, 3> GetPositionForEntryId(int entry_id) const { + const PointIndex point_id = entry_to_point_id_map_[entry_id]; + VectorD<int64_t, 3> pos; + pos_attribute_->ConvertValue(pos_attribute_->mapped_index(point_id), + &pos[0]); + return pos; + } + + VectorD<int64_t, 2> GetTexCoordForEntryId(int entry_id, + const DataTypeT *data) const { + const int data_offset = entry_id * kNumComponents; + return VectorD<int64_t, 2>(data[data_offset], data[data_offset + 1]); + } + + // Computes predicted UV coordinates on a given corner. The coordinates are + // stored in |predicted_value_| member. + template <bool is_encoder_t> + bool ComputePredictedValue(CornerIndex corner_id, const DataTypeT *data, + int data_id); + + const DataTypeT *predicted_value() const { return predicted_value_; } + bool orientation(int i) const { return orientations_[i]; } + void set_orientation(int i, bool v) { orientations_[i] = v; } + size_t num_orientations() const { return orientations_.size(); } + void ResizeOrientations(int num_orientations) { + orientations_.resize(num_orientations); + } + + private: + const PointAttribute *pos_attribute_; + const PointIndex *entry_to_point_id_map_; + DataTypeT predicted_value_[kNumComponents]; + // Encoded / decoded array of UV flips. + // TODO(ostava): We should remove this and replace this with in-place encoding + // and decoding to avoid unnecessary copy. + std::vector<bool> orientations_; + MeshDataT mesh_data_; +}; + +template <typename DataTypeT, class MeshDataT> +template <bool is_encoder_t> +bool MeshPredictionSchemeTexCoordsPortablePredictor< + DataTypeT, MeshDataT>::ComputePredictedValue(CornerIndex corner_id, + const DataTypeT *data, + int data_id) { + // Compute the predicted UV coordinate from the positions on all corners + // of the processed triangle. For the best prediction, the UV coordinates + // on the next/previous corners need to be already encoded/decoded. + const CornerIndex next_corner_id = mesh_data_.corner_table()->Next(corner_id); + const CornerIndex prev_corner_id = + mesh_data_.corner_table()->Previous(corner_id); + // Get the encoded data ids from the next and previous corners. + // The data id is the encoding order of the UV coordinates. + int next_data_id, prev_data_id; + + int next_vert_id, prev_vert_id; + next_vert_id = mesh_data_.corner_table()->Vertex(next_corner_id).value(); + prev_vert_id = mesh_data_.corner_table()->Vertex(prev_corner_id).value(); + + next_data_id = mesh_data_.vertex_to_data_map()->at(next_vert_id); + prev_data_id = mesh_data_.vertex_to_data_map()->at(prev_vert_id); + + if (prev_data_id < data_id && next_data_id < data_id) { + // Both other corners have available UV coordinates for prediction. + const VectorD<int64_t, 2> n_uv = GetTexCoordForEntryId(next_data_id, data); + const VectorD<int64_t, 2> p_uv = GetTexCoordForEntryId(prev_data_id, data); + if (p_uv == n_uv) { + // We cannot do a reliable prediction on degenerated UV triangles. + predicted_value_[0] = p_uv[0]; + predicted_value_[1] = p_uv[1]; + return true; + } + + // Get positions at all corners. + const VectorD<int64_t, 3> tip_pos = GetPositionForEntryId(data_id); + const VectorD<int64_t, 3> next_pos = GetPositionForEntryId(next_data_id); + const VectorD<int64_t, 3> prev_pos = GetPositionForEntryId(prev_data_id); + // We use the positions of the above triangle to predict the texture + // coordinate on the tip corner C. + // To convert the triangle into the UV coordinate system we first compute + // position X on the vector |prev_pos - next_pos| that is the projection of + // point C onto vector |prev_pos - next_pos|: + // + // C + // /. \ + // / . \ + // / . \ + // N---X----------P + // + // Where next_pos is point (N), prev_pos is point (P) and tip_pos is the + // position of predicted coordinate (C). + // + const VectorD<int64_t, 3> pn = prev_pos - next_pos; + const uint64_t pn_norm2_squared = pn.SquaredNorm(); + if (pn_norm2_squared != 0) { + // Compute the projection of C onto PN by computing dot product of CN with + // PN and normalizing it by length of PN. This gives us a factor |s| where + // |s = PN.Dot(CN) / PN.SquaredNorm2()|. This factor can be used to + // compute X in UV space |X_UV| as |X_UV = N_UV + s * PN_UV|. + const VectorD<int64_t, 3> cn = tip_pos - next_pos; + const int64_t cn_dot_pn = pn.Dot(cn); + + const VectorD<int64_t, 2> pn_uv = p_uv - n_uv; + // Because we perform all computations with integers, we don't explicitly + // compute the normalized factor |s|, but rather we perform all operations + // over UV vectors in a non-normalized coordinate system scaled with a + // scaling factor |pn_norm2_squared|: + // + // x_uv = X_UV * PN.Norm2Squared() + // + const VectorD<int64_t, 2> x_uv = + n_uv * pn_norm2_squared + (cn_dot_pn * pn_uv); + + // Compute squared length of vector CX in position coordinate system: + const VectorD<int64_t, 3> x_pos = + next_pos + (cn_dot_pn * pn) / pn_norm2_squared; + const uint64_t cx_norm2_squared = (tip_pos - x_pos).SquaredNorm(); + + // Compute vector CX_UV in the uv space by rotating vector PN_UV by 90 + // degrees and scaling it with factor CX.Norm2() / PN.Norm2(): + // + // CX_UV = (CX.Norm2() / PN.Norm2()) * Rot(PN_UV) + // + // To preserve precision, we perform all operations in scaled space as + // explained above, so we want the final vector to be: + // + // cx_uv = CX_UV * PN.Norm2Squared() + // + // We can then rewrite the formula as: + // + // cx_uv = CX.Norm2() * PN.Norm2() * Rot(PN_UV) + // + VectorD<int64_t, 2> cx_uv(pn_uv[1], -pn_uv[0]); // Rotated PN_UV. + // Compute CX.Norm2() * PN.Norm2() + const uint64_t norm_squared = + IntSqrt(cx_norm2_squared * pn_norm2_squared); + // Final cx_uv in the scaled coordinate space. + cx_uv = cx_uv * norm_squared; + + // Predicted uv coordinate is then computed by either adding or + // subtracting CX_UV to/from X_UV. + VectorD<int64_t, 2> predicted_uv; + if (is_encoder_t) { + // When encoding, compute both possible vectors and determine which one + // results in a better prediction. + // Both vectors need to be transformed back from the scaled space to + // the real UV coordinate space. + const VectorD<int64_t, 2> predicted_uv_0((x_uv + cx_uv) / + pn_norm2_squared); + const VectorD<int64_t, 2> predicted_uv_1((x_uv - cx_uv) / + pn_norm2_squared); + const VectorD<int64_t, 2> c_uv = GetTexCoordForEntryId(data_id, data); + if ((c_uv - predicted_uv_0).SquaredNorm() < + (c_uv - predicted_uv_1).SquaredNorm()) { + predicted_uv = predicted_uv_0; + orientations_.push_back(true); + } else { + predicted_uv = predicted_uv_1; + orientations_.push_back(false); + } + } else { + // When decoding the data, we already know which orientation to use. + if (orientations_.empty()) { + return false; + } + const bool orientation = orientations_.back(); + orientations_.pop_back(); + if (orientation) { + predicted_uv = (x_uv + cx_uv) / pn_norm2_squared; + } else { + predicted_uv = (x_uv - cx_uv) / pn_norm2_squared; + } + } + predicted_value_[0] = static_cast<int>(predicted_uv[0]); + predicted_value_[1] = static_cast<int>(predicted_uv[1]); + return true; + } + } + // Else we don't have available textures on both corners or the position data + // is invalid. For such cases we can't use positions for predicting the uv + // value and we resort to delta coding. + int data_offset = 0; + if (prev_data_id < data_id) { + // Use the value on the previous corner as the prediction. + data_offset = prev_data_id * kNumComponents; + } + if (next_data_id < data_id) { + // Use the value on the next corner as the prediction. + data_offset = next_data_id * kNumComponents; + } else { + // None of the other corners have a valid value. Use the last encoded value + // as the prediction if possible. + if (data_id > 0) { + data_offset = (data_id - 1) * kNumComponents; + } else { + // We are encoding the first value. Predict 0. + for (int i = 0; i < kNumComponents; ++i) { + predicted_value_[i] = 0; + } + return true; + } + } + for (int i = 0; i < kNumComponents; ++i) { + predicted_value_[i] = data[data_offset + i]; + } + return true; +} + +} // namespace draco + +#endif // DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_TEX_COORDS_PORTABLE_PREDICTOR_H_ |