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nerfstudio.cpp
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nerfstudio.cpp
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#include <filesystem>
#include <cstdlib>
#include <nlohmann/json.hpp>
#include "nerfstudio.hpp"
#include "point_io.hpp"
#include "cv_utils.hpp"
#include "tensor_math.hpp"
namespace fs = std::filesystem;
using json = nlohmann::json;
using namespace torch::indexing;
namespace ns{
void to_json(json &j, const Frame &f){
j = json{ {"file_path", f.filePath },
{"w", f.width },
{"h", f.height },
{"fl_x", f.fx },
{"fl_y", f.fy },
{"cx", f.cx },
{"cy", f.cy },
{"k1", f.k1 },
{"k2", f.k2 },
{"p1", f.p1 },
{"p2", f.p2 },
{"k3", f.k3 },
{"transform_matrix", f.transformMatrix },
};
}
void from_json(const json& j, Frame &f){
j.at("file_path").get_to(f.filePath);
j.at("transform_matrix").get_to(f.transformMatrix);
if (j.contains("w")) j.at("w").get_to(f.width);
if (j.contains("h")) j.at("h").get_to(f.height);
if (j.contains("fl_x")) j.at("fl_x").get_to(f.fx);
if (j.contains("fl_y")) j.at("fl_y").get_to(f.fy);
if (j.contains("cx")) j.at("cx").get_to(f.cx);
if (j.contains("cy")) j.at("cy").get_to(f.cy);
if (j.contains("k1")) j.at("k1").get_to(f.k1);
if (j.contains("k2")) j.at("k2").get_to(f.k2);
if (j.contains("p1")) j.at("p1").get_to(f.p1);
if (j.contains("p2")) j.at("p2").get_to(f.p2);
if (j.contains("k3")) j.at("k3").get_to(f.k3);
}
void to_json(json &j, const Transforms &t){
j = json{ {"camera_model", t.cameraModel },
{"frames", t.frames },
{"ply_file_path", t.plyFilePath },
};
}
void from_json(const json& j, Transforms &t){
j.at("camera_model").get_to(t.cameraModel);
j.at("frames").get_to(t.frames);
if (j.contains("ply_file_path")) j.at("ply_file_path").get_to(t.plyFilePath);
// Globals
int width = 0;
int height = 0;
float fx = 0;
float fy = 0;
float cx = 0;
float cy = 0;
float k1 = 0;
float k2 = 0;
float k3 = 0;
float p1 = 0;
float p2 = 0;
if (j.contains("w")) j.at("w").get_to(width);
if (j.contains("h")) j.at("h").get_to(height);
if (j.contains("fl_x")) j.at("fl_x").get_to(fx);
if (j.contains("fl_y")) j.at("fl_y").get_to(fy);
if (j.contains("cx")) j.at("cx").get_to(cx);
if (j.contains("cy")) j.at("cy").get_to(cy);
if (j.contains("k1")) j.at("k1").get_to(k1);
if (j.contains("k2")) j.at("k2").get_to(k2);
if (j.contains("p1")) j.at("p1").get_to(p1);
if (j.contains("p2")) j.at("p2").get_to(p2);
if (j.contains("k3")) j.at("k3").get_to(k3);
// Assign per-frame intrinsics if missing
for (Frame &f : t.frames){
if (!f.width && width) f.width = width;
if (!f.height && height) f.height = height;
if (!f.fx && fx) f.fx = fx;
if (!f.fy && fy) f.fy = fy;
if (!f.cx && cx) f.cx = cx;
if (!f.cy && cy) f.cy = cy;
if (!f.k1 && k1) f.k1 = k1;
if (!f.k2 && k2) f.k2 = k2;
if (!f.p1 && p1) f.p1 = p1;
if (!f.p2 && p2) f.p2 = p2;
if (!f.k3 && k3) f.k3 = k3;
}
std::sort(t.frames.begin(), t.frames.end(),
[](Frame const &a, Frame const &b) {
return a.filePath < b.filePath;
});
}
Transforms readTransforms(const std::string &filename){
std::ifstream f(filename);
json data = json::parse(f);
f.close();
return data.template get<Transforms>();
}
torch::Tensor posesFromTransforms(const Transforms &t){
torch::Tensor poses = torch::zeros({static_cast<long int>(t.frames.size()), 4, 4}, torch::kFloat32);
for (size_t c = 0; c < t.frames.size(); c++){
for (size_t i = 0; i < 4; i++){
for (size_t j = 0; j < 4; j++){
poses[c][i][j] = t.frames[c].transformMatrix[i][j];
}
}
}
return poses;
}
InputData inputDataFromNerfStudio(const std::string &projectRoot){
InputData ret;
fs::path nsRoot(projectRoot);
fs::path transformsPath = nsRoot / "transforms.json";
if (!fs::exists(transformsPath)) throw std::runtime_error(transformsPath.string() + " does not exist");
Transforms t = readTransforms(transformsPath.string());
if (t.plyFilePath.empty()) throw std::runtime_error("ply_file_path is empty");
PointSet *pSet = readPointSet((nsRoot / t.plyFilePath).string());
torch::Tensor unorientedPoses = posesFromTransforms(t);
auto r = autoScaleAndCenterPoses(unorientedPoses);
torch::Tensor poses = std::get<0>(r);
ret.translation = std::get<1>(r);
ret.scale = std::get<2>(r);
// aabbScale = [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
for (size_t i = 0; i < t.frames.size(); i++){
Frame f = t.frames[i];
ret.cameras.emplace_back(Camera(f.width, f.height,
static_cast<float>(f.fx), static_cast<float>(f.fy),
static_cast<float>(f.cx), static_cast<float>(f.cy),
static_cast<float>(f.k1), static_cast<float>(f.k2), static_cast<float>(f.k3),
static_cast<float>(f.p1), static_cast<float>(f.p2),
poses[i], (nsRoot / f.filePath).string()));
}
torch::Tensor points = pSet->pointsTensor().clone();
ret.points.xyz = (points - ret.translation) * ret.scale;
ret.points.rgb = pSet->colorsTensor().clone();
RELEASE_POINTSET(pSet);
return ret;
}
}