diff --git a/include/neural-graphics-primitives/testbed.h b/include/neural-graphics-primitives/testbed.h
index 4514162b062e06a4d568d2ef88b7ceafd3315226..284f54e45ead18ab08077cf8b5625002ee01c5e9 100644
--- a/include/neural-graphics-primitives/testbed.h
+++ b/include/neural-graphics-primitives/testbed.h
@@ -69,6 +69,7 @@ public:
Testbed(ETestbedMode mode, const std::string& data_path, const nlohmann::json& network_config) : Testbed(mode, data_path) { reload_network_from_json(network_config); }
bool clear_tmp_dir();
+ void update_imgui_paths();
void load_training_data(const std::string& data_path);
void reload_training_data();
void clear_training_data();
@@ -273,7 +274,7 @@ public:
);
void train_volume(size_t target_batch_size, bool get_loss_scalar, cudaStream_t stream);
void training_prep_volume(uint32_t batch_size, cudaStream_t stream) {}
- void load_volume();
+ void load_volume(const filesystem::path& data_path);
void render_sdf(
const distance_fun_t& distance_function,
@@ -302,9 +303,9 @@ public:
static ELossType string_to_loss_type(const std::string& str);
void reset_network(bool clear_density_grid = true);
void create_empty_nerf_dataset(size_t n_images, int aabb_scale = 1, bool is_hdr = false);
- void load_nerf();
+ void load_nerf(const filesystem::path& data_path);
void load_nerf_post();
- void load_mesh();
+ void load_mesh(const filesystem::path& data_path);
void set_exposure(float exposure) { m_exposure = exposure; }
void set_max_level(float maxlevel);
void set_min_level(float minlevel);
@@ -402,10 +403,10 @@ public:
void draw_gui();
bool frame();
bool want_repl();
- void load_image();
- void load_exr_image();
- void load_stbi_image();
- void load_binary_image();
+ void load_image(const filesystem::path& data_path);
+ void load_exr_image(const filesystem::path& data_path);
+ void load_stbi_image(const filesystem::path& data_path);
+ void load_binary_image(const filesystem::path& data_path);
uint32_t n_dimensions_to_visualize() const;
float fov() const ;
void set_fov(float val) ;
@@ -823,7 +824,17 @@ public:
bool m_single_view = true; // Whether a single neuron is visualized, or all in a tiled grid
float m_picture_in_picture_res = 0.f; // if non zero, requests a small second picture :)
- bool m_imgui_enabled = true; // tab to toggle
+ struct ImGuiVars {
+ static const uint32_t MAX_PATH_LEN = 1024;
+
+ bool enabled = true; // tab to toggle
+ char cam_path_path[MAX_PATH_LEN] = "cam.json";
+ char extrinsics_path[MAX_PATH_LEN] = "extrinsics.json";
+ char mesh_path[MAX_PATH_LEN] = "base.obj";
+ char snapshot_path[MAX_PATH_LEN] = "base.msgpack";
+ char video_path[MAX_PATH_LEN] = "video.mp4";
+ } m_imgui;
+
bool m_visualize_unit_cube = false;
bool m_snap_to_pixel_centers = false;
bool m_edit_render_aabb = false;
diff --git a/src/camera_path.cu b/src/camera_path.cu
index ac90362508abf1c57992cfbebe8fd907e93cf1b0..9c23a71633d71378f1346ca344ea9a11318f994a 100644
--- a/src/camera_path.cu
+++ b/src/camera_path.cu
@@ -143,11 +143,11 @@ void CameraPath::load(const std::string& filepath_string, const Eigen::Matrix<fl
}
#ifdef NGP_GUI
-int CameraPath::imgui(char path_filename_buf[128], float frame_milliseconds, Matrix<float, 3, 4>& camera, float slice_plane_z, float scale, float fov, float aperture_size, float bounding_radius, const Eigen::Matrix<float, 3, 4>& first_xform, int glow_mode, float glow_y_cutoff) {
+int CameraPath::imgui(char path_filename_buf[1024], float frame_milliseconds, Matrix<float, 3, 4>& camera, float slice_plane_z, float scale, float fov, float aperture_size, float bounding_radius, const Eigen::Matrix<float, 3, 4>& first_xform, int glow_mode, float glow_y_cutoff) {
int n = std::max(0, int(keyframes.size()) - 1);
int read = 0; // 1=smooth, 2=hard
- ImGui::InputText("##PathFile", path_filename_buf, 128);
+ ImGui::InputText("##PathFile", path_filename_buf, sizeof(path_filename_buf));
ImGui::SameLine();
static std::string camera_path_load_error_string = "";
diff --git a/src/python_api.cu b/src/python_api.cu
index f0e086ac553521447fb36bce22deadfc06e5439f..91d32c497013b2088afa12c0471cfbec663cbc67 100644
--- a/src/python_api.cu
+++ b/src/python_api.cu
@@ -473,7 +473,10 @@ PYBIND11_MODULE(pyngp, m) {
.def_readonly("sdf", &Testbed::m_sdf)
.def_readonly("image", &Testbed::m_image)
.def_readwrite("camera_smoothing", &Testbed::m_camera_smoothing)
- .def_readwrite("display_gui", &Testbed::m_imgui_enabled)
+ .def_property("display_gui",
+ [](py::object& obj) { return obj.cast<Testbed&>().m_imgui.enabled; },
+ [](const py::object& obj, bool value) { obj.cast<Testbed&>().m_imgui.enabled = value; }
+ )
.def_readwrite("visualize_unit_cube", &Testbed::m_visualize_unit_cube)
.def_readwrite("snap_to_pixel_centers", &Testbed::m_snap_to_pixel_centers)
.def_readwrite("parallax_shift", &Testbed::m_parallax_shift)
diff --git a/src/testbed.cu b/src/testbed.cu
index ab674113ae56b85086f29c869376775e7bd493e6..20e5c6538852bd3ec0cb5b2265c97a2ffcd227b7 100644
--- a/src/testbed.cu
+++ b/src/testbed.cu
@@ -450,18 +450,6 @@ void Testbed::set_train(bool mtrain) {
m_train = mtrain;
}
-std::string get_filename_in_data_path_with_suffix(fs::path data_path, fs::path network_config_path, const char* suffix) {
- // use the network config name along with the data path to build a filename with the requested suffix & extension
- std::string default_name = network_config_path.basename();
- if (default_name == "") default_name = "base";
- if (data_path.empty())
- return default_name + std::string(suffix);
- if (data_path.is_directory())
- return (data_path / (default_name + std::string{suffix})).str();
- else
- return data_path.stem().str() + "_" + default_name + std::string(suffix);
-}
-
void Testbed::compute_and_save_marching_cubes_mesh(const char* filename, Vector3i res3d , BoundingBox aabb, float thresh, bool unwrap_it) {
Matrix3f render_aabb_to_local = Matrix3f::Identity();
if (aabb.is_empty()) {
@@ -613,13 +601,8 @@ void Testbed::imgui() {
m_picture_in_picture_res = 0;
if (ImGui::Begin("Camera path", 0, ImGuiWindowFlags_NoScrollbar)) {
if (ImGui::CollapsingHeader("Path manipulation", ImGuiTreeNodeFlags_DefaultOpen)) {
- static char path_filename_buf[128] = "";
- if (path_filename_buf[0] == '\0') {
- snprintf(path_filename_buf, sizeof(path_filename_buf), "%s", get_filename_in_data_path_with_suffix(m_data_path, m_network_config_path, "_cam.json").c_str());
- }
-
if (int read = m_camera_path.imgui(
- path_filename_buf,
+ m_imgui.cam_path_path,
m_render_ms.val(),
m_camera,
m_slice_plane_z,
@@ -712,9 +695,8 @@ void Testbed::imgui() {
if (m_camera_path.rendering) { ImGui::BeginDisabled(); }
- static char video_filename_buf[1024] = "video.mp4";
- ImGui::InputText("File##Video file path", video_filename_buf, sizeof(video_filename_buf));
- m_camera_path.render_settings.filename = video_filename_buf;
+ ImGui::InputText("File##Video file path", m_imgui.video_path, sizeof(m_imgui.video_path));
+ m_camera_path.render_settings.filename = m_imgui.video_path;
ImGui::InputInt2("Resolution", &m_camera_path.render_settings.resolution.x());
ImGui::InputFloat("Duration (seconds)", &m_camera_path.render_settings.duration_seconds);
@@ -759,14 +741,14 @@ void Testbed::imgui() {
if (m_nerf.training.dataset.n_extra_learnable_dims) {
ImGui::Checkbox("Train latent codes", &m_nerf.training.optimize_extra_dims);
}
- static char opt_extr_filename_buf[1024] = "./trajectory.json";
static bool export_extrinsics_in_quat_format = true;
if (imgui_colored_button("Export extrinsics", 0.4f)) {
- m_nerf.training.export_camera_extrinsics(opt_extr_filename_buf, export_extrinsics_in_quat_format);
+ m_nerf.training.export_camera_extrinsics(m_imgui.extrinsics_path, export_extrinsics_in_quat_format);
}
+
ImGui::SameLine();
ImGui::PushItemWidth(400.f);
- ImGui::InputText("File##Extrinsics file path", opt_extr_filename_buf, sizeof(opt_extr_filename_buf));
+ ImGui::InputText("File##Extrinsics file path", m_imgui.extrinsics_path, sizeof(m_imgui.extrinsics_path));
ImGui::PopItemWidth();
ImGui::SameLine();
ImGui::Checkbox("Quaternion format", &export_extrinsics_in_quat_format);
@@ -1200,7 +1182,7 @@ void Testbed::imgui() {
if (m_testbed_mode == ETestbedMode::Sdf) {
size_t n = strlen(buf);
- snprintf(buf+n, sizeof(buf)-n,
+ snprintf(buf + n, sizeof(buf) - n,
"testbed.sdf.shadow_sharpness = %0.3f\n"
"testbed.sdf.analytic_normals = %s\n"
"testbed.sdf.use_triangle_octree = %s\n\n"
@@ -1233,20 +1215,15 @@ void Testbed::imgui() {
}
if (ImGui::CollapsingHeader("Snapshot")) {
- static char snapshot_filename_buf[128] = "";
- if (snapshot_filename_buf[0] == '\0') {
- snprintf(snapshot_filename_buf, sizeof(snapshot_filename_buf), "%s", get_filename_in_data_path_with_suffix(m_data_path, m_network_config_path, ".msgpack").c_str());
- }
-
ImGui::Text("Snapshot");
ImGui::SameLine();
if (ImGui::Button("Save")) {
- save_snapshot(snapshot_filename_buf, m_include_optimizer_state_in_snapshot);
+ save_snapshot(m_imgui.snapshot_path, m_include_optimizer_state_in_snapshot);
}
ImGui::SameLine();
if (ImGui::Button("Load")) {
try {
- load_snapshot(snapshot_filename_buf);
+ load_snapshot(m_imgui.snapshot_path);
} catch (std::exception& e) {
imgui_error_string = fmt::format("Failed to load snapshot: {}", e.what());
ImGui::OpenPopup("Error");
@@ -1259,7 +1236,7 @@ void Testbed::imgui() {
ImGui::SameLine();
ImGui::Checkbox("w/ optimizer state", &m_include_optimizer_state_in_snapshot);
- ImGui::InputText("File##Snapshot file path", snapshot_filename_buf, sizeof(snapshot_filename_buf));
+ ImGui::InputText("File##Snapshot file path", m_imgui.snapshot_path, sizeof(m_imgui.snapshot_path));
}
if (m_testbed_mode == ETestbedMode::Nerf || m_testbed_mode == ETestbedMode::Sdf) {
@@ -1332,22 +1309,18 @@ void Testbed::imgui() {
ImGui::Checkbox("Swap Y&Z", &flip_y_and_z_axes);
ImGui::SliderFloat("PNG Density Range", &density_range, 0.001f, 8.f);
- static char obj_filename_buf[128] = "";
ImGui::SliderInt("Res:", &m_mesh.res, 16, 2048, "%d", ImGuiSliderFlags_Logarithmic);
ImGui::SameLine();
ImGui::Text("%dx%dx%d", res3d.x(), res3d.y(), res3d.z());
- if (obj_filename_buf[0] == '\0') {
- snprintf(obj_filename_buf, sizeof(obj_filename_buf), "%s", get_filename_in_data_path_with_suffix(m_data_path, m_network_config_path, ".obj").c_str());
- }
float thresh_range = (m_testbed_mode == ETestbedMode::Sdf) ? 0.5f : 10.f;
ImGui::SliderFloat("MC density threshold",&m_mesh.thresh, -thresh_range, thresh_range);
ImGui::Combo("Mesh render mode", (int*)&m_mesh_render_mode, "Off\0Vertex Colors\0Vertex Normals\0Face IDs\0");
ImGui::Checkbox("Unwrap mesh", &m_mesh.unwrap);
if (uint32_t tricount = m_mesh.indices.size()/3) {
- ImGui::InputText("##OBJFile", obj_filename_buf, sizeof(obj_filename_buf));
+ ImGui::InputText("##OBJFile", m_imgui.mesh_path, sizeof(m_imgui.mesh_path));
if (ImGui::Button("Save it!")) {
- save_mesh(m_mesh.verts, m_mesh.vert_normals, m_mesh.vert_colors, m_mesh.indices, obj_filename_buf, m_mesh.unwrap, m_nerf.training.dataset.scale, m_nerf.training.dataset.offset);
+ save_mesh(m_mesh.verts, m_mesh.vert_normals, m_mesh.vert_colors, m_mesh.indices, m_imgui.mesh_path, m_mesh.unwrap, m_nerf.training.dataset.scale, m_nerf.training.dataset.offset);
}
ImGui::SameLine();
ImGui::Text("Mesh has %d triangles\n", tricount);
@@ -1406,8 +1379,9 @@ void Testbed::imgui() {
if (m_histo_level < m_num_levels) {
LevelStats& s = m_level_stats[m_histo_level];
static bool excludezero = false;
- if (excludezero)
+ if (excludezero) {
m_histo[128] = 0.f;
+ }
ImGui::PlotHistogram("Values histogram", m_histo, 257, 0, "", FLT_MAX, FLT_MAX, ImVec2(0, 120.f));
ImGui::SliderFloat("Histogram horizontal scale", &m_histo_scale, 0.01f, 2.f);
ImGui::Checkbox("Exclude 'zero' from histogram", &excludezero);
@@ -1782,7 +1756,7 @@ bool Testbed::begin_frame_and_handle_user_input() {
ImGuizmo::BeginFrame();
if (ImGui::IsKeyPressed(GLFW_KEY_TAB) || ImGui::IsKeyPressed(GLFW_KEY_GRAVE_ACCENT)) {
- m_imgui_enabled = !m_imgui_enabled;
+ m_imgui.enabled = !m_imgui.enabled;
}
ImVec2 m = ImGui::GetMousePos();
@@ -1813,7 +1787,7 @@ bool Testbed::begin_frame_and_handle_user_input() {
mouse_wheel({m.x / (float)m_window_res.y(), m.y / (float)m_window_res.y()}, mw);
mouse_drag({relm.x, relm.y}, mb);
- if (m_imgui_enabled) {
+ if (m_imgui.enabled) {
imgui();
}
@@ -3585,7 +3559,7 @@ void Testbed::load_snapshot(const std::string& filepath_string) {
// and render using just that.
if (m_data_path.empty() && snapshot["nerf"].contains("dataset")) {
m_nerf.training.dataset = snapshot["nerf"]["dataset"];
- load_nerf();
+ load_nerf(m_data_path);
} else {
if (snapshot["nerf"].contains("aabb_scale")) {
m_nerf.training.dataset.aabb_scale = snapshot["nerf"]["aabb_scale"];