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input_data.cpp
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input_data.cpp
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#include <filesystem>
#include "input_data.hpp"
#include "cv_utils.hpp"
namespace fs = std::filesystem;
using namespace torch::indexing;
namespace ns{ InputData inputDataFromNerfStudio(const std::string &projectRoot); }
namespace cm{ InputData inputDataFromColmap(const std::string &projectRoot); }
InputData inputDataFromX(const std::string &projectRoot){
fs::path root(projectRoot);
if (fs::exists(root / "transforms.json")){
return ns::inputDataFromNerfStudio(projectRoot);
}else if (fs::exists(root / "sparse") || fs::exists(root / "cameras.bin")){
return cm::inputDataFromColmap(projectRoot);
}else{
throw std::runtime_error("Invalid project folder (must be either a colmap or nerfstudio project folder)");
}
}
torch::Tensor Camera::getIntrinsicsMatrix(){
return torch::tensor({{fx, 0.0f, cx},
{0.0f, fy, cy},
{0.0f, 0.0f, 1.0f}}, torch::kFloat32);
}
void Camera::loadImage(float downscaleFactor){
// Populates image and K, then updates the camera parameters
// Caution: this function has destructive behaviors
// and should be called only once
if (image.numel()) std::runtime_error("loadImage already called");
std::cout << "Loading " << filePath << std::endl;
float scaleFactor = 1.0f / downscaleFactor;
cv::Mat cImg = imreadRGB(filePath);
float rescaleF = 1.0f;
// If camera intrinsics don't match the image dimensions
if (cImg.rows != height || cImg.cols != width){
rescaleF = static_cast<float>(cImg.rows) / static_cast<float>(height);
}
fx *= scaleFactor * rescaleF;
fy *= scaleFactor * rescaleF;
cx *= scaleFactor * rescaleF;
cy *= scaleFactor * rescaleF;
if (downscaleFactor > 1.0f){
float f = 1.0f / downscaleFactor;
cv::resize(cImg, cImg, cv::Size(), f, f, cv::INTER_AREA);
}
K = getIntrinsicsMatrix();
cv::Rect roi;
if (hasDistortionParameters()){
// Undistort
std::vector<float> distCoeffs = undistortionParameters();
cv::Mat cK = floatNxNtensorToMat(K);
cv::Mat newK = cv::getOptimalNewCameraMatrix(cK, distCoeffs, cv::Size(cImg.cols, cImg.rows), 0, cv::Size(), &roi);
cv::Mat undistorted = cv::Mat::zeros(cImg.rows, cImg.cols, cImg.type());
cv::undistort(cImg, undistorted, cK, distCoeffs, newK);
image = imageToTensor(undistorted);
K = floatNxNMatToTensor(newK);
}else{
roi = cv::Rect(0, 0, cImg.cols, cImg.rows);
image = imageToTensor(cImg);
}
// Crop to ROI
image = image.index({Slice(roi.y, roi.y + roi.height), Slice(roi.x, roi.x + roi.width), Slice()});
// Update parameters
height = image.size(0);
width = image.size(1);
fx = K[0][0].item<float>();
fy = K[1][1].item<float>();
cx = K[0][2].item<float>();
cy = K[1][2].item<float>();
}
torch::Tensor Camera::getImage(int downscaleFactor){
if (downscaleFactor <= 1) return image;
else{
// torch::jit::script::Module container = torch::jit::load("gt.pt");
// return container.attr("val").toTensor();
if (imagePyramids.find(downscaleFactor) != imagePyramids.end()){
return imagePyramids[downscaleFactor];
}
// Rescale, store and return
cv::Mat cImg = tensorToImage(image);
cv::resize(cImg, cImg, cv::Size(cImg.cols / downscaleFactor, cImg.rows / downscaleFactor), 0.0, 0.0, cv::INTER_AREA);
torch::Tensor t = imageToTensor(cImg);
imagePyramids[downscaleFactor] = t;
return t;
}
}
bool Camera::hasDistortionParameters(){
return k1 != 0.0f || k2 != 0.0f || k3 != 0.0f || p1 != 0.0f || p2 != 0.0f;
}
std::vector<float> Camera::undistortionParameters(){
std::vector<float> p = { k1, k2, p1, p2, k3, 0.0f, 0.0f, 0.0f };
return p;
}
std::tuple<std::vector<Camera>, Camera *> InputData::getCameras(bool validate, const std::string &valImage){
if (!validate) return std::make_tuple(cameras, nullptr);
else{
size_t valIdx = -1;
std::srand(42);
if (valImage == "random"){
valIdx = std::rand() % cameras.size();
}else{
for (size_t i = 0; i < cameras.size(); i++){
if (fs::path(cameras[i].filePath).filename().string() == valImage){
valIdx = i;
break;
}
}
if (valIdx == -1) throw std::runtime_error(valImage + " not in the list of cameras");
}
std::vector<Camera> cams;
Camera *valCam = nullptr;
for (size_t i = 0; i < cameras.size(); i++){
if (i != valIdx) cams.push_back(cameras[i]);
else valCam = &cameras[i];
}
return std::make_tuple(cams, valCam);
}
}