forked from bartvdbraak/blender
6fb6a08bf8
Even tho it's currently only used by Libmv we might use it for something else in the future. Plus, it's actually where it logically belongs to.
123 lines
4.9 KiB
C++
123 lines
4.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2015 Google Inc. All rights reserved.
|
|
// http://ceres-solver.org/
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without
|
|
// modification, are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistributions of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
// * Redistributions in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
// * Neither the name of Google Inc. nor the names of its contributors may be
|
|
// used to endorse or promote products derived from this software without
|
|
// specific prior written permission.
|
|
//
|
|
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
|
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
|
|
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
|
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
|
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
|
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
|
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
|
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
// POSSIBILITY OF SUCH DAMAGE.
|
|
//
|
|
// Author: sameeragarwal@google.com (Sameer Agarwal)
|
|
|
|
#include "ceres/compressed_col_sparse_matrix_utils.h"
|
|
|
|
#include <vector>
|
|
#include <algorithm>
|
|
#include "ceres/internal/port.h"
|
|
#include "glog/logging.h"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
using std::vector;
|
|
|
|
void CompressedColumnScalarMatrixToBlockMatrix(
|
|
const int* scalar_rows,
|
|
const int* scalar_cols,
|
|
const vector<int>& row_blocks,
|
|
const vector<int>& col_blocks,
|
|
vector<int>* block_rows,
|
|
vector<int>* block_cols) {
|
|
CHECK_NOTNULL(block_rows)->clear();
|
|
CHECK_NOTNULL(block_cols)->clear();
|
|
const int num_row_blocks = row_blocks.size();
|
|
const int num_col_blocks = col_blocks.size();
|
|
|
|
vector<int> row_block_starts(num_row_blocks);
|
|
for (int i = 0, cursor = 0; i < num_row_blocks; ++i) {
|
|
row_block_starts[i] = cursor;
|
|
cursor += row_blocks[i];
|
|
}
|
|
|
|
// This loop extracts the block sparsity of the scalar sparse matrix
|
|
// It does so by iterating over the columns, but only considering
|
|
// the columns corresponding to the first element of each column
|
|
// block. Within each column, the inner loop iterates over the rows,
|
|
// and detects the presence of a row block by checking for the
|
|
// presence of a non-zero entry corresponding to its first element.
|
|
block_cols->push_back(0);
|
|
int c = 0;
|
|
for (int col_block = 0; col_block < num_col_blocks; ++col_block) {
|
|
int column_size = 0;
|
|
for (int idx = scalar_cols[c]; idx < scalar_cols[c + 1]; ++idx) {
|
|
vector<int>::const_iterator it =
|
|
std::lower_bound(row_block_starts.begin(),
|
|
row_block_starts.end(),
|
|
scalar_rows[idx]);
|
|
// Since we are using lower_bound, it will return the row id
|
|
// where the row block starts. For everything but the first row
|
|
// of the block, where these values will be the same, we can
|
|
// skip, as we only need the first row to detect the presence of
|
|
// the block.
|
|
//
|
|
// For rows all but the first row in the last row block,
|
|
// lower_bound will return row_block_starts.end(), but those can
|
|
// be skipped like the rows in other row blocks too.
|
|
if (it == row_block_starts.end() || *it != scalar_rows[idx]) {
|
|
continue;
|
|
}
|
|
|
|
block_rows->push_back(it - row_block_starts.begin());
|
|
++column_size;
|
|
}
|
|
block_cols->push_back(block_cols->back() + column_size);
|
|
c += col_blocks[col_block];
|
|
}
|
|
}
|
|
|
|
void BlockOrderingToScalarOrdering(const vector<int>& blocks,
|
|
const vector<int>& block_ordering,
|
|
vector<int>* scalar_ordering) {
|
|
CHECK_EQ(blocks.size(), block_ordering.size());
|
|
const int num_blocks = blocks.size();
|
|
|
|
// block_starts = [0, block1, block1 + block2 ..]
|
|
vector<int> block_starts(num_blocks);
|
|
for (int i = 0, cursor = 0; i < num_blocks ; ++i) {
|
|
block_starts[i] = cursor;
|
|
cursor += blocks[i];
|
|
}
|
|
|
|
scalar_ordering->resize(block_starts.back() + blocks.back());
|
|
int cursor = 0;
|
|
for (int i = 0; i < num_blocks; ++i) {
|
|
const int block_id = block_ordering[i];
|
|
const int block_size = blocks[block_id];
|
|
int block_position = block_starts[block_id];
|
|
for (int j = 0; j < block_size; ++j) {
|
|
(*scalar_ordering)[cursor++] = block_position++;
|
|
}
|
|
}
|
|
}
|
|
} // namespace internal
|
|
} // namespace ceres
|