OpenSplat/input_data.cpp at main · YHZN-REPOS/OpenSplat · GitHub

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#include <filesystem>

#include <mutex>

#include <atomic>

#ifdef USE_CUDA

#include <cuda_runtime_api.h>

#elif defined(USE_HIP)

#include <hip/hip_runtime_api.h>

#endif

#ifdef __APPLE__

#include <sys/sysctl.h>

#endif

#include <nlohmann/json.hpp>

#include "input_data.hpp"

#include "cv_utils.hpp"

#include "undistort.hpp"

namespace fs = std::filesystem;

using namespace torch::indexing;

using json = nlohmann::json;

namespace ns{ InputData inputDataFromNerfStudio(const std::string &projectRoot); }

namespace cm{ InputData inputDataFromColmap(const std::string &projectRoot, const std::string& imageSourcePath); }

namespace osfm { InputData inputDataFromOpenSfM(const std::string &projectRoot, const std::string& imageSourcePath = ""); }

namespace omvg { InputData inputDataFromOpenMVG(const std::string &projectRoot); }

// Helper function to check if a directory exists and is not empty

bool isNonEmptyDirectory(const fs::path& path) {

if (!fs::exists(path) || !fs::is_directory(path)) return false;

return !fs::is_empty(path);

}

InputData inputDataFromX(const std::string &projectRoot, const std::string& colmapImageSourcePath, const std::string& opensfmImageSourcePath){

fs::path root(projectRoot);

if (fs::exists(root / "transforms.json")){

return ns::inputDataFromNerfStudio(projectRoot);

}else if (isNonEmptyDirectory(root / "sparse") || fs::exists(root / "cameras.bin")){

// Only use COLMAP if sparse directory is non-empty or cameras.bin exists

return cm::inputDataFromColmap(projectRoot, colmapImageSourcePath);

}else if (fs::exists(root / "reconstruction.json")){

return osfm::inputDataFromOpenSfM(projectRoot, opensfmImageSourcePath);

}else if (fs::exists(root / "opensfm" / "reconstruction.json")){

return osfm::inputDataFromOpenSfM((root / "opensfm").string(), opensfmImageSourcePath);

}else if (fs::exists(root / "sfm_data.json")){

return omvg::inputDataFromOpenMVG((root).string());

}

else{

throw std::runtime_error("Invalid project folder (must be either a colmap or nerfstudio or openmvg 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");

{

static std::mutex logMutex;

std::lock_guard<std::mutex> lock(logMutex);

std::cout << "Loading " << fs::path(filePath).filename().string() << std::endl;

}

cv::Mat cImg = imreadRGB(filePath);

cv::Mat cMask;

if (!maskPath.empty()){

cMask = cv::imread(maskPath, cv::IMREAD_GRAYSCALE);

if (cMask.empty()) throw std::runtime_error("Cannot read mask " + maskPath);

}

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 *= rescaleF;

fy *= rescaleF;

cx *= rescaleF;

cy *= rescaleF;

if (downscaleFactor > 1.0f){

float scaleFactor = 1.0f / downscaleFactor;

cv::resize(cImg, cImg, cv::Size(), scaleFactor, scaleFactor, cv::INTER_AREA);

fx *= scaleFactor;

fy *= scaleFactor;

cx *= scaleFactor;

cy *= scaleFactor;

}

if (!cMask.empty()){

cv::threshold(cMask, cMask, 127, 255, cv::THRESH_BINARY);

if (cMask.rows != cImg.rows || cMask.cols != cImg.cols){

cv::resize(cMask, cMask, cv::Size(cImg.cols, cImg.rows), 0.0, 0.0, cv::INTER_LINEAR);

}

if (hasDistortionParameters()){

UndistortParams p = computeUndistortParams(fx, fy, cx, cy, cImg.cols, cImg.rows,

k1, k2, k3, k4, k5, k6, p1, p2);

cv::Mat mapx, mapy;

buildUndistortMaps(p, mapx, mapy);

cv::Mat undistorted;

cv::remap(cImg, undistorted, mapx, mapy, cv::INTER_LINEAR, cv::BORDER_CONSTANT);

image = imageToTensor(undistorted);

if (!cMask.empty()){

cv::Mat remapped;

cv::remap(cMask, remapped, mapx, mapy, cv::INTER_LINEAR, cv::BORDER_CONSTANT);

cMask = remapped;

}

fx = p.dstFx;

fy = p.dstFy;

cx = p.dstCx;

cy = p.dstCy;

}else{

image = imageToTensor(cImg);

}

height = image.size(0);

width = image.size(1);

K = getIntrinsicsMatrix();

if (!cMask.empty()){

torch::Tensor m = torch::from_blob(cMask.data, {cMask.rows, cMask.cols}, torch::kU8)

.to(torch::kFloat32).div(255.0f).clone();

mask = (m >= 0.5f).to(torch::kFloat32);

}

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 || k4 != 0.0f || k5 != 0.0f || k6 != 0.0f || p1 != 0.0f || p2 != 0.0f;

}

torch::Tensor Camera::getMask(int downscaleFactor){

if (!hasMask()) return mask;

if (downscaleFactor <= 1) return mask;

if (maskPyramids.find(downscaleFactor) != maskPyramids.end()){

return maskPyramids[downscaleFactor];

}

torch::Tensor m = mask.unsqueeze(0).unsqueeze(0);

m = torch::nn::functional::interpolate(m,

torch::nn::functional::InterpolateFuncOptions()

.size(std::vector<int64_t>{ mask.size(0) / downscaleFactor, mask.size(1) / downscaleFactor })

.mode(torch::kBilinear).align_corners(false));

m = (m.squeeze(0).squeeze(0) >= 0.5f).to(torch::kFloat32);

maskPyramids[downscaleFactor] = m;

return m;

}

bool Camera::gpuCacheEnabled = true;

// Half the free VRAM at first use (CUDA/HIP), a quarter of system RAM on

// Apple unified memory, 1GB otherwise

static long long gpuCacheBudget(){

#ifdef USE_CUDA

size_t freeB = 0, totalB = 0;

if (cudaMemGetInfo(&freeB, &totalB) == cudaSuccess){

return static_cast<long long>(freeB / 2);

}

#elif defined(USE_HIP)

size_t freeB = 0, totalB = 0;

if (hipMemGetInfo(&freeB, &totalB) == hipSuccess){

return static_cast<long long>(freeB / 2);

}

#endif

#ifdef __APPLE__

int64_t ram = 0;

size_t size = sizeof(ram);

if (sysctlbyname("hw.memsize", &ram, &size, nullptr, 0) == 0){

return ram / 4;

}

#endif

return 1LL << 30;

}

// Cache device-side tensors per camera to avoid re-uploading every iteration

static torch::Tensor gpuCached(std::unordered_map<int, torch::Tensor> &cache, int key,

const torch::Tensor &src, const torch::Device &device){

if (device == torch::kCPU || !Camera::gpuCacheEnabled) return src.to(device);

auto it = cache.find(key);

if (it != cache.end()) return it->second;

static std::atomic<long long> gpuCacheBytes{0};

static const long long budget = gpuCacheBudget();

long long bytes = src.numel() * src.element_size();

if (gpuCacheBytes.load() + bytes > budget) return src.to(device);

gpuCacheBytes += bytes;

torch::Tensor t = src.to(device);

cache[key] = t;

return t;

}

torch::Tensor Camera::getImageGpu(int downscaleFactor, const torch::Device &device){

return gpuCached(gpuImageCache, downscaleFactor, getImage(downscaleFactor), device);

}

torch::Tensor Camera::getMaskGpu(int downscaleFactor, const torch::Device &device){

torch::Tensor m = getMask(downscaleFactor);

if (!m.defined() || m.numel() == 0) return m;

return gpuCached(gpuMaskCache, downscaleFactor, m, device);

}

torch::Tensor Camera::getEdgeMapGpu(int downscaleFactor, const torch::Device &device){

return gpuCached(gpuEdgeCache, downscaleFactor, getEdgeMap(downscaleFactor).contiguous(), device);

}

torch::Tensor Camera::getEdgeMap(int downscaleFactor){

if (edgePyramids.find(downscaleFactor) != edgePyramids.end()){

return edgePyramids[downscaleFactor];

}

cv::Mat cImg = tensorToImage(getImage(downscaleFactor));

cv::Mat gray, edges;

cv::cvtColor(cImg, gray, cv::COLOR_RGB2GRAY);

cv::Canny(gray, edges, 50, 150);

torch::Tensor e = torch::from_blob(edges.data, {edges.rows, edges.cols}, torch::kU8)

.to(torch::kFloat32).div(255.0f).clone();

edgePyramids[downscaleFactor] = e;

return e;

}

std::string findMaskPath(const std::string &imagePath, const std::string &projectRoot){

static const char *folders[] = { "masks", "mask", "segmentation", "dynamic_masks" };

static const char *extensions[] = { ".png", ".jpg", ".jpeg", ".mask.png" };

fs::path img(imagePath);

std::string stem = img.stem().string();

std::string name = img.filename().string();

for (const char *folder : folders){

fs::path dir = fs::path(projectRoot) / folder;

if (!fs::exists(dir) || !fs::is_directory(dir)) continue;

for (const char *ext : extensions){

fs::path cand = dir / (stem + ext);

if (fs::exists(cand)) return cand.string();

cand = dir / (name + ext);

if (fs::exists(cand)) return cand.string();

}

return "";

}

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);

}

void InputData::saveCameras(const std::string &filename, bool keepCrs){

json j = json::array();

for (size_t i = 0; i < cameras.size(); i++){

Camera &cam = cameras[i];

json camera = json::object();

camera["id"] = i;

camera["img_name"] = fs::path(cam.filePath).filename().string();

camera["width"] = cam.width;

camera["height"] = cam.height;

camera["fx"] = cam.fx;

camera["fy"] = cam.fy;

torch::Tensor R = cam.camToWorld.index({Slice(None, 3), Slice(None, 3)});

torch::Tensor T = cam.camToWorld.index({Slice(None, 3), Slice(3,4)}).squeeze();

// Flip z and y

R = torch::matmul(R, torch::diag(torch::tensor({1.0f, -1.0f, -1.0f})));

if (keepCrs) T = (T / scale) + translation;

std::vector<float> position(3);

std::vector<std::vector<float>> rotation(3, std::vector<float>(3));

for (int i = 0; i < 3; i++) {

position[i] = T[i].item<float>();

for (int j = 0; j < 3; j++) {

rotation[i][j] = R[i][j].item<float>();

}

camera["position"] = position;

camera["rotation"] = rotation;

j.push_back(camera);

}

std::ofstream of(filename);

of << j;

of.close();

std::cout << "Wrote " << filename << std::endl;

}

OpenSplat/input_data.cpp at main · YHZN-REPOS/OpenSplat · GitHub

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