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Moved internal camera implementation.

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Daniel
2025-11-24 19:00:53 +01:00
parent 9a4e35f229
commit 1ab099098e
4 changed files with 435 additions and 432 deletions
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rpt/triangulator.cpp
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@ -83,409 +83,6 @@
// =================================================================================================
// =================================================================================================
CameraInternal::CameraInternal(const Camera &cam)
{
this->cam = cam;
this->invR = transpose3x3(cam.R);
// Camera center:
// C = -(Rᵀ * t) = -(Rᵀ * (R * (T * -1))) = -(Rᵀ * (R * -T)) = -(Rᵀ * -R * T) = -(-T) = T
this->center = {cam.T[0][0], cam.T[1][0], cam.T[2][0]};
// Undistort camera matrix
// As with the undistortion, the own implementation avoids some overhead compared to OpenCV
if (cam.type == "fisheye")
{
newK = calc_optimal_camera_matrix_fisheye(1.0, {cam.width, cam.height});
}
else
{
newK = calc_optimal_camera_matrix_pinhole(1.0, {cam.width, cam.height});
}
this->invK = invert3x3(newK);
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::transpose3x3(
const std::array<std::array<float, 3>, 3> &M)
{
return {{{M[0][0], M[1][0], M[2][0]},
{M[0][1], M[1][1], M[2][1]},
{M[0][2], M[1][2], M[2][2]}}};
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::invert3x3(
const std::array<std::array<float, 3>, 3> &M)
{
// Compute the inverse using the adjugate method
// See: https://scicomp.stackexchange.com/a/29206
std::array<std::array<float, 3>, 3> adj = {
{{
M[1][1] * M[2][2] - M[1][2] * M[2][1],
M[0][2] * M[2][1] - M[0][1] * M[2][2],
M[0][1] * M[1][2] - M[0][2] * M[1][1],
},
{
M[1][2] * M[2][0] - M[1][0] * M[2][2],
M[0][0] * M[2][2] - M[0][2] * M[2][0],
M[0][2] * M[1][0] - M[0][0] * M[1][2],
},
{
M[1][0] * M[2][1] - M[1][1] * M[2][0],
M[0][1] * M[2][0] - M[0][0] * M[2][1],
M[0][0] * M[1][1] - M[0][1] * M[1][0],
}}};
float det = M[0][0] * adj[0][0] + M[0][1] * adj[1][0] + M[0][2] * adj[2][0];
if (std::fabs(det) < 1e-6f)
{
return {{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}};
}
float idet = 1.0f / det;
std::array<std::array<float, 3>, 3> inv = {
{{
adj[0][0] * idet,
adj[0][1] * idet,
adj[0][2] * idet,
},
{
adj[1][0] * idet,
adj[1][1] * idet,
adj[1][2] * idet,
},
{
adj[2][0] * idet,
adj[2][1] * idet,
adj[2][2] * idet,
}}};
return inv;
}
// =================================================================================================
void CameraInternal::undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k)
{
// Following: cv::cvUndistortPointsInternal
// Uses only the distortion coefficients: [k1, k2, p1, p2, k3]
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/undistort.dispatch.cpp#L432
float x0 = p[0];
float y0 = p[1];
float x = x0;
float y = y0;
// Iteratively refine the estimate for the undistorted point.
int max_iterations = 5;
for (int iter = 0; iter < max_iterations; ++iter)
{
float r2 = x * x + y * y;
double icdist = 1.0 / (1 + ((k[4] * r2 + k[1]) * r2 + k[0]) * r2);
if (icdist < 0)
{
x = x0;
y = y0;
break;
}
float deltaX = 2 * k[2] * x * y + k[3] * (r2 + 2 * x * x);
float deltaY = k[2] * (r2 + 2 * y * y) + 2 * k[3] * x * y;
x = (x0 - deltaX) * icdist;
y = (y0 - deltaY) * icdist;
}
p[0] = x;
p[1] = y;
}
// =================================================================================================
void CameraInternal::undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k)
{
// Following: cv::fisheye::undistortPoints
// Uses only the distortion coefficients: [k1, k2, k3, k4]
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L429
float theta_d = std::sqrt(p[0] * p[0] + p[1] * p[1]);
float pi_half = std::numbers::pi * 0.5;
theta_d = std::min(std::max(-pi_half, theta_d), pi_half);
if (theta_d < 1e-6)
{
return;
}
float scale = 0.0;
float theta = theta_d;
int max_iterations = 5;
for (int iter = 0; iter < max_iterations; ++iter)
{
float theta2 = theta * theta;
float theta4 = theta2 * theta2;
float theta6 = theta4 * theta2;
float theta8 = theta4 * theta4;
float k0_theta2 = k[0] * theta2;
float k1_theta4 = k[1] * theta4;
float k2_theta6 = k[2] * theta6;
float k3_theta8 = k[3] * theta8;
float theta_fix = (theta * (1 + k0_theta2 + k1_theta4 + k2_theta6 + k3_theta8) - theta_d) /
(1 + 3 * k0_theta2 + 5 * k1_theta4 + 7 * k2_theta6 + 9 * k3_theta8);
theta = theta - theta_fix;
if (std::fabs(theta_fix) < 1e-6)
{
break;
}
}
scale = std::tan(theta) / theta_d;
p[0] *= scale;
p[1] *= scale;
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_fisheye(
float balance, std::pair<int, int> new_size)
{
// Following: cv::fisheye::estimateNewCameraMatrixForUndistortRectify
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L630
float fov_scale = 1.0;
float w = static_cast<float>(cam.width);
float h = static_cast<float>(cam.height);
balance = std::min(std::max(balance, 0.0f), 1.0f);
// Define four key points at the middle of each edge
std::vector<std ::array<float, 2>> pts = {
{w * 0.5f, 0.0},
{w, h * 0.5f},
{w * 0.5f, h},
{0.0, h * 0.5f}};
// Extract intrinsic parameters
float fx = cam.K[0][0];
float fy = cam.K[1][1];
float cx = cam.K[0][2];
float cy = cam.K[1][2];
// Undistort the edge points
for (auto &pt : pts)
{
std::array<float, 3> p_normed = {(pt[0] - cx) / fx, (pt[1] - cy) / fy, 1.0};
undistort_point_fisheye(p_normed, cam.DC);
pt[0] = p_normed[0];
pt[1] = p_normed[1];
}
// Compute center mass of the undistorted edge points
float sum_x = 0.0, sum_y = 0.0;
for (const auto &pt : pts)
{
sum_x += pt[0];
sum_y += pt[1];
}
float cn_x = sum_x / pts.size();
float cn_y = sum_y / pts.size();
// Convert to identity ratio
float aspect_ratio = fx / fy;
cn_y *= aspect_ratio;
for (auto &pt : pts)
pt[1] *= aspect_ratio;
// Find the bounding box of the undistorted points
float minx = std::numeric_limits<float>::max();
float miny = std::numeric_limits<float>::max();
float maxx = -std::numeric_limits<float>::max();
float maxy = -std::numeric_limits<float>::max();
for (const auto &pt : pts)
{
minx = std::min(minx, pt[0]);
maxx = std::max(maxx, pt[0]);
miny = std::min(miny, pt[1]);
maxy = std::max(maxy, pt[1]);
}
// Calculate candidate focal lengths
float f1 = (w * 0.5) / (cn_x - minx);
float f2 = (w * 0.5) / (maxx - cn_x);
float f3 = (h * 0.5 * aspect_ratio) / (cn_y - miny);
float f4 = (h * 0.5 * aspect_ratio) / (maxy - cn_y);
float fmin = std::min({f1, f2, f3, f4});
float fmax = std::max({f1, f2, f3, f4});
// Blend the candidate focal lengths
float f_val = balance * fmin + (1.0f - balance) * fmax;
if (fov_scale > 0.0f)
f_val /= fov_scale;
// Compute new intrinsic parameters
float new_fx = f_val;
float new_fy = f_val;
float new_cx = -cn_x * f_val + (w * 0.5);
float new_cy = -cn_y * f_val + (h * aspect_ratio * 0.5);
// Restore aspect ratio
new_fy /= aspect_ratio;
new_cy /= aspect_ratio;
// Optionally scale parameters to new new image size
if (new_size.first > 0 && new_size.second > 0)
{
float rx = static_cast<float>(new_size.first) / w;
float ry = static_cast<float>(new_size.second) / h;
new_fx *= rx;
new_fy *= ry;
new_cx *= rx;
new_cy *= ry;
}
std::array<std::array<float, 3>, 3> newK = {
{{new_fx, 0.0, new_cx},
{0.0, new_fy, new_cy},
{0.0, 0.0, 1.0}}};
return newK;
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_pinhole(
float alpha, std::pair<int, int> new_size)
{
// Following: cv::getOptimalNewCameraMatrix
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/calibration_base.cpp#L1565
bool center_principal_point = false;
bool use_pix_roi = false;
float w = static_cast<float>(cam.width);
float h = static_cast<float>(cam.height);
alpha = std::min(std::max(alpha, 0.0f), 1.0f);
if (center_principal_point || use_pix_roi)
{
// Not implemented
exit(1);
}
// Define key points
// Calculate only the contour points of the image, and use less points,
// the edges and centers should be enough if the camera has no strange distortions
const size_t N = 3;
std::vector<std ::array<float, 2>> pts;
pts.reserve(4 * (N - 1));
for (size_t y = 0; y < N; ++y)
{
for (size_t x = 0; x < N; ++x)
{
if (x != 0 && x != N - 1 && y != 0 && y != N - 1)
{
continue;
}
pts.push_back({x * (w - 1) / (N - 1), y * (h - 1) / (N - 1)});
}
}
// Extract intrinsic parameters
float fx = cam.K[0][0];
float fy = cam.K[1][1];
float cx = cam.K[0][2];
float cy = cam.K[1][2];
// Undistort the key points
for (auto &pt : pts)
{
std::array<float, 3> p_normed = {(pt[0] - cx) / fx, (pt[1] - cy) / fy, 1.0};
undistort_point_pinhole(p_normed, cam.DC);
pt[0] = p_normed[0];
pt[1] = p_normed[1];
}
// Get inscribed and circumscribed rectangles in normalized coordinates
float iX0 = -std::numeric_limits<float>::max();
float iX1 = std::numeric_limits<float>::max();
float iY0 = -std::numeric_limits<float>::max();
float iY1 = std::numeric_limits<float>::max();
float oX0 = std::numeric_limits<float>::max();
float oX1 = -std::numeric_limits<float>::max();
float oY0 = std::numeric_limits<float>::max();
float oY1 = -std::numeric_limits<float>::max();
size_t k = 0;
for (size_t y = 0; y < N; ++y)
{
for (size_t x = 0; x < N; ++x)
{
if (x != 0 && x != N - 1 && y != 0 && y != N - 1)
{
continue;
}
auto &pt = pts[k];
k += 1;
if (x == 0)
{
oX0 = std::min(oX0, pt[0]);
iX0 = std::max(iX0, pt[0]);
}
if (x == N - 1)
{
oX1 = std::max(oX1, pt[0]);
iX1 = std::min(iX1, pt[0]);
}
if (y == 0)
{
oY0 = std::min(oY0, pt[1]);
iY0 = std::max(iY0, pt[1]);
}
if (y == N - 1)
{
oY1 = std::max(oY1, pt[1]);
iY1 = std::min(iY1, pt[1]);
}
}
}
float inner_width = iX1 - iX0;
float inner_height = iY1 - iY0;
float outer_width = oX1 - oX0;
float outer_height = oY1 - oY0;
// Projection mapping inner rectangle to viewport
float fx0 = (new_size.first - 1) / inner_width;
float fy0 = (new_size.second - 1) / inner_height;
float cx0 = -fx0 * iX0;
float cy0 = -fy0 * iY0;
// Projection mapping outer rectangle to viewport
float fx1 = (new_size.first - 1) / outer_width;
float fy1 = (new_size.second - 1) / outer_height;
float cx1 = -fx1 * oX0;
float cy1 = -fy1 * oY0;
// Interpolate between the two optimal projections
float new_fx = fx0 * (1 - alpha) + fx1 * alpha;
float new_fy = fy0 * (1 - alpha) + fy1 * alpha;
float new_cx = cx0 * (1 - alpha) + cx1 * alpha;
float new_cy = cy0 * (1 - alpha) + cy1 * alpha;
std::array<std::array<float, 3>, 3> newK = {
{{new_fx, 0.0, new_cx},
{0.0, new_fy, new_cy},
{0.0, 0.0, 1.0}}};
return newK;
}
// =================================================================================================
// =================================================================================================
TriangulatorInternal::TriangulatorInternal(float min_match_score, size_t min_group_size)
{
this->min_match_score = min_match_score;