0dd9a4a576
Is based on Google style which was used in the Libmv project before, but is now consistently applied for the sources of the library itself and to C-API. With some time C-API will likely be removed, and it makes it easier to make it follow Libmv style, hence the diversion from Blender's style. There are quite some exceptions (clang-format off) in the code around Eigen matrix initialization. It is rather annoying, and there could be some neat way to make initialization readable without such exception. Could be some places where loss of readability in matrix initialization got lost as the change is quite big. If this has happened it is easier to address readability once actually working on the code. This change allowed to spot some missing header guards, so that's nice. Doing it in bundled version, as the upstream library needs to have some of the recent development ported over from bundle to upstream. There should be no functional changes.
423 lines
17 KiB
C++
423 lines
17 KiB
C++
// Copyright (c) 2014 libmv authors.
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//
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to
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// deal in the Software without restriction, including without limitation the
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// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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// sell copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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//
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// The above copyright notice and this permission notice shall be included in
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// all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
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// IN THE SOFTWARE.
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#include "libmv/simple_pipeline/distortion_models.h"
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#include "libmv/numeric/levenberg_marquardt.h"
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namespace libmv {
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namespace {
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struct InvertPolynomialIntrinsicsCostFunction {
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public:
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typedef Vec2 FMatrixType;
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typedef Vec2 XMatrixType;
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InvertPolynomialIntrinsicsCostFunction(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double k3,
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const double p1,
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const double p2,
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const double image_x,
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const double image_y)
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: focal_length_x_(focal_length_x),
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focal_length_y_(focal_length_y),
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principal_point_x_(principal_point_x),
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principal_point_y_(principal_point_y),
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k1_(k1),
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k2_(k2),
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k3_(k3),
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p1_(p1),
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p2_(p2),
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x_(image_x),
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y_(image_y) {}
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Vec2 operator()(const Vec2& u) const {
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double xx, yy;
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ApplyPolynomialDistortionModel(focal_length_x_,
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focal_length_y_,
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principal_point_x_,
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principal_point_y_,
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k1_,
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k2_,
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k3_,
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p1_,
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p2_,
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u(0),
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u(1),
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&xx,
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&yy);
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Vec2 fx;
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fx << (xx - x_), (yy - y_);
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return fx;
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}
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double focal_length_x_;
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double focal_length_y_;
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double principal_point_x_;
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double principal_point_y_;
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double k1_, k2_, k3_;
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double p1_, p2_;
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double x_, y_;
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};
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struct InvertDivisionIntrinsicsCostFunction {
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public:
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typedef Vec2 FMatrixType;
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typedef Vec2 XMatrixType;
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InvertDivisionIntrinsicsCostFunction(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double image_x,
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const double image_y)
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: focal_length_x_(focal_length_x),
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focal_length_y_(focal_length_y),
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principal_point_x_(principal_point_x),
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principal_point_y_(principal_point_y),
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k1_(k1),
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k2_(k2),
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x_(image_x),
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y_(image_y) {}
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Vec2 operator()(const Vec2& u) const {
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double xx, yy;
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ApplyDivisionDistortionModel(focal_length_x_,
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focal_length_y_,
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principal_point_x_,
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principal_point_y_,
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k1_,
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k2_,
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u(0),
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u(1),
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&xx,
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&yy);
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Vec2 fx;
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fx << (xx - x_), (yy - y_);
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return fx;
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}
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double focal_length_x_;
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double focal_length_y_;
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double principal_point_x_;
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double principal_point_y_;
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double k1_, k2_;
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double x_, y_;
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};
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struct InvertBrownIntrinsicsCostFunction {
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public:
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typedef Vec2 FMatrixType;
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typedef Vec2 XMatrixType;
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InvertBrownIntrinsicsCostFunction(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double k3,
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const double k4,
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const double p1,
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const double p2,
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const double image_x,
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const double image_y)
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: focal_length_x_(focal_length_x),
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focal_length_y_(focal_length_y),
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principal_point_x_(principal_point_x),
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principal_point_y_(principal_point_y),
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k1_(k1),
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k2_(k2),
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k3_(k3),
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k4_(k4),
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p1_(p1),
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p2_(p2),
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x_(image_x),
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y_(image_y) {}
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Vec2 operator()(const Vec2& u) const {
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double xx, yy;
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ApplyBrownDistortionModel(focal_length_x_,
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focal_length_y_,
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principal_point_x_,
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principal_point_y_,
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k1_,
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k2_,
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k3_,
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k4_,
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p1_,
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p2_,
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u(0),
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u(1),
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&xx,
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&yy);
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Vec2 fx;
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fx << (xx - x_), (yy - y_);
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return fx;
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}
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double focal_length_x_;
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double focal_length_y_;
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double principal_point_x_;
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double principal_point_y_;
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double k1_, k2_, k3_, k4_;
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double p1_, p2_;
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double x_, y_;
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};
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} // namespace
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void InvertPolynomialDistortionModel(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double k3,
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const double p1,
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const double p2,
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const double image_x,
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const double image_y,
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double* normalized_x,
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double* normalized_y) {
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// Compute the initial guess. For a camera with no distortion, this will also
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// be the final answer; the LM iteration will terminate immediately.
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Vec2 normalized;
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normalized(0) = (image_x - principal_point_x) / focal_length_x;
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normalized(1) = (image_y - principal_point_y) / focal_length_y;
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typedef LevenbergMarquardt<InvertPolynomialIntrinsicsCostFunction> Solver;
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InvertPolynomialIntrinsicsCostFunction intrinsics_cost(focal_length_x,
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focal_length_y,
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principal_point_x,
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principal_point_y,
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k1,
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k2,
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k3,
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p1,
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p2,
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image_x,
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image_y);
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Solver::SolverParameters params;
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Solver solver(intrinsics_cost);
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/*Solver::Results results =*/solver.minimize(params, &normalized);
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// TODO(keir): Better error handling.
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*normalized_x = normalized(0);
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*normalized_y = normalized(1);
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}
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void InvertDivisionDistortionModel(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double image_x,
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const double image_y,
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double* normalized_x,
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double* normalized_y) {
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// Compute the initial guess. For a camera with no distortion, this will also
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// be the final answer; the LM iteration will terminate immediately.
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Vec2 normalized;
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normalized(0) = (image_x - principal_point_x) / focal_length_x;
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normalized(1) = (image_y - principal_point_y) / focal_length_y;
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// TODO(sergey): Use Ceres minimizer instead.
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typedef LevenbergMarquardt<InvertDivisionIntrinsicsCostFunction> Solver;
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InvertDivisionIntrinsicsCostFunction intrinsics_cost(focal_length_x,
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focal_length_y,
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principal_point_x,
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principal_point_y,
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k1,
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k2,
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image_x,
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image_y);
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Solver::SolverParameters params;
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Solver solver(intrinsics_cost);
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/*Solver::Results results =*/solver.minimize(params, &normalized);
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// TODO(keir): Better error handling.
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*normalized_x = normalized(0);
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*normalized_y = normalized(1);
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}
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void InvertBrownDistortionModel(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const double k1,
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const double k2,
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const double k3,
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const double k4,
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const double p1,
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const double p2,
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const double image_x,
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const double image_y,
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double* normalized_x,
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double* normalized_y) {
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// Compute the initial guess. For a camera with no distortion, this will also
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// be the final answer; the LM iteration will terminate immediately.
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Vec2 normalized;
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normalized(0) = (image_x - principal_point_x) / focal_length_x;
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normalized(1) = (image_y - principal_point_y) / focal_length_y;
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typedef LevenbergMarquardt<InvertBrownIntrinsicsCostFunction> Solver;
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InvertBrownIntrinsicsCostFunction intrinsics_cost(focal_length_x,
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focal_length_y,
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principal_point_x,
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principal_point_y,
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k1,
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k2,
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k3,
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k4,
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p1,
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p2,
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image_x,
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image_y);
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Solver::SolverParameters params;
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Solver solver(intrinsics_cost);
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/*Solver::Results results =*/solver.minimize(params, &normalized);
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// TODO(keir): Better error handling.
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*normalized_x = normalized(0);
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*normalized_y = normalized(1);
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}
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struct ApplyNukeIntrinsicsCostFunction {
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public:
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typedef Vec2 FMatrixType;
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typedef Vec2 XMatrixType;
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ApplyNukeIntrinsicsCostFunction(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const int image_width,
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const int image_height,
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const double k1,
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const double k2,
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const double expected_normalized_x,
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const double expected_normalized_y)
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: focal_length_x_(focal_length_x),
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focal_length_y_(focal_length_y),
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principal_point_x_(principal_point_x),
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principal_point_y_(principal_point_y),
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image_width_(image_width),
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image_height_(image_height),
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k1_(k1),
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k2_(k2),
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expected_normalized_x_(expected_normalized_x),
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expected_normalized_y_(expected_normalized_y) {}
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Vec2 operator()(const Vec2& image_coordinate) const {
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double actual_normalized_x, actual_normalized_y;
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InvertNukeDistortionModel(focal_length_x_,
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focal_length_y_,
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principal_point_x_,
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principal_point_y_,
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image_width_,
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image_height_,
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k1_,
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k2_,
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image_coordinate(0),
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image_coordinate(1),
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&actual_normalized_x,
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&actual_normalized_y);
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Vec2 fx;
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fx << (actual_normalized_x - expected_normalized_x_),
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(actual_normalized_y - expected_normalized_y_);
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return fx;
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}
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double focal_length_x_;
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double focal_length_y_;
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double principal_point_x_;
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double principal_point_y_;
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int image_width_;
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int image_height_;
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double k1_, k2_;
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double expected_normalized_x_, expected_normalized_y_;
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};
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void ApplyNukeDistortionModel(const double focal_length_x,
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const double focal_length_y,
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const double principal_point_x,
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const double principal_point_y,
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const int image_width,
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const int image_height,
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const double k1,
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const double k2,
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const double normalized_x,
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const double normalized_y,
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double* image_x,
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double* image_y) {
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// Compute the initial guess. For a camera with no distortion, this will also
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// be the final answer; the LM iteration will terminate immediately.
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Vec2 image;
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image(0) = normalized_x * focal_length_x + principal_point_x;
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image(1) = normalized_y * focal_length_y + principal_point_y;
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// TODO(sergey): Use Ceres minimizer instead.
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typedef LevenbergMarquardt<ApplyNukeIntrinsicsCostFunction> Solver;
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ApplyNukeIntrinsicsCostFunction intrinsics_cost(focal_length_x,
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focal_length_y,
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principal_point_x,
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principal_point_y,
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image_width,
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image_height,
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k1,
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k2,
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normalized_x,
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normalized_y);
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Solver::SolverParameters params;
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Solver solver(intrinsics_cost);
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/*Solver::Results results =*/solver.minimize(params, &image);
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// TODO(keir): Better error handling.
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*image_x = image(0);
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*image_y = image(1);
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}
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} // namespace libmv
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