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// Copyright 2016 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_PARALLELOGRAM_ENCODER_H_
#define DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_PARALLELOGRAM_ENCODER_H_
#include "draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_encoder.h"
#include "draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_parallelogram_shared.h"
namespace draco {
// Parallelogram prediction predicts an attribute value V from three vertices
// on the opposite face to the predicted vertex. The values on the three
// vertices are used to construct a parallelogram V' = O - A - B, where O is the
// value on the opposite vertex, and A, B are values on the shared vertices:
// V
// / \
// / \
// / \
// A-------B
// \ /
// \ /
// \ /
// O
//
template <typename DataTypeT, class TransformT, class MeshDataT>
class MeshPredictionSchemeParallelogramEncoder
: public MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT> {
public:
using CorrType =
typename PredictionSchemeEncoder<DataTypeT, TransformT>::CorrType;
using CornerTable = typename MeshDataT::CornerTable;
explicit MeshPredictionSchemeParallelogramEncoder(
const PointAttribute *attribute)
: MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT>(
attribute) {}
MeshPredictionSchemeParallelogramEncoder(const PointAttribute *attribute,
const TransformT &transform,
const MeshDataT &mesh_data)
: MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT>(
attribute, transform, mesh_data) {}
bool ComputeCorrectionValues(
const DataTypeT *in_data, CorrType *out_corr, int size,
int num_components, const PointIndex *entry_to_point_id_map) override;
PredictionSchemeMethod GetPredictionMethod() const override {
return MESH_PREDICTION_PARALLELOGRAM;
}
bool IsInitialized() const override {
return this->mesh_data().IsInitialized();
}
};
template <typename DataTypeT, class TransformT, class MeshDataT>
bool MeshPredictionSchemeParallelogramEncoder<DataTypeT, TransformT,
MeshDataT>::
ComputeCorrectionValues(const DataTypeT *in_data, CorrType *out_corr,
int size, int num_components,
const PointIndex * /* entry_to_point_id_map */) {
this->transform().Init(in_data, size, num_components);
// For storage of prediction values (already initialized to zero).
std::unique_ptr<DataTypeT[]> pred_vals(new DataTypeT[num_components]());
// We start processing from the end because this prediction uses data from
// previous entries that could be overwritten when an entry is processed.
const CornerTable *const table = this->mesh_data().corner_table();
const std::vector<int32_t> *const vertex_to_data_map =
this->mesh_data().vertex_to_data_map();
for (int p =
static_cast<int>(this->mesh_data().data_to_corner_map()->size() - 1);
p > 0; --p) {
const CornerIndex corner_id = this->mesh_data().data_to_corner_map()->at(p);
const int dst_offset = p * num_components;
if (!ComputeParallelogramPrediction(p, corner_id, table,
*vertex_to_data_map, in_data,
num_components, pred_vals.get())) {
// Parallelogram could not be computed, Possible because some of the
// vertices are not valid (not encoded yet).
// We use the last encoded point as a reference (delta coding).
const int src_offset = (p - 1) * num_components;
this->transform().ComputeCorrection(
in_data + dst_offset, in_data + src_offset, out_corr + dst_offset);
} else {
// Apply the parallelogram prediction.
this->transform().ComputeCorrection(in_data + dst_offset, pred_vals.get(),
out_corr + dst_offset);
}
}
// First element is always fixed because it cannot be predicted.
for (int i = 0; i < num_components; ++i) {
pred_vals[i] = static_cast<DataTypeT>(0);
}
this->transform().ComputeCorrection(in_data, pred_vals.get(), out_corr);
return true;
}
} // namespace draco
#endif // DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_PARALLELOGRAM_ENCODER_H_
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