From e4949e122f1c8d7699d1d06d678f62325089644b Mon Sep 17 00:00:00 2001 From: wanghy Date: Fri, 25 Oct 2024 16:02:26 +0100 Subject: [PATCH] Add support for NeRFstudio --- README.md | 21 +++++++++++-- assets/spanner-gs.gif | Bin 0 -> 3603294 bytes demo.py | 63 +++++++++++++++++++++++++++++++++++++++ spann3r/datasets/demo.py | 2 +- 4 files changed, 83 insertions(+), 3 deletions(-) create mode 100644 assets/spanner-gs.gif diff --git a/README.md b/README.md index 357661d..3a11181 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,8 @@ ## Update +[2024-10-25] Add support for [Nerfstudio](assets/spanner-gs.gif) + [2024-10-18] Add camera param estimation [2024-09-30] [@hugoycj](https://github.com/hugoycj) adds a gradio demo @@ -73,11 +75,26 @@ ``` For visualization `--vis`, it will give you a window to adjust the rendering view. Once you find the view to render, please click `space key` and close the window. The code will then do the rendering of the incremental reconstruction. + +3. Nerfstudio: + + ``` + # Run demo use --save_ori to save scaled intrinsics for original images + python demo.py --demo_path ./examples/s00567 --kf_every 10 --vis --vis_cam --save_ori + + # Run splatfacto + ns-train splatfacto --data ./output/demo/s00567 --pipeline.model.camera-optimizer.mode SO3xR3 + + # Render your results + ns-render interpolate --load-config [path-to-your-config]/config.yml + ``` + + Note that here you can use `--save_ori` to save the scaled intrinsics into `transform.json` to train NeRF/3D Gaussians with original images.' -## Gradio interface 🤗 +## Gradio interface -We also provide a Gradio interface for a better experience, just run by: +We also provide a Gradio interface for a better experience, just run by: ```bash # For Linux and Windows users (and macOS with Intel??) diff --git a/assets/spanner-gs.gif b/assets/spanner-gs.gif new file mode 100644 index 0000000000000000000000000000000000000000..a636798d512383165f297dff40efb5ffa61bc9f4 GIT binary patch literal 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N;{zT54GiGG{{c+{WETJc literal 0 HcmV?d00001 diff --git a/demo.py b/demo.py index 65d8eef..c94c93b 100644 --- a/demo.py +++ b/demo.py @@ -1,5 +1,6 @@ import os import cv2 +import json import time import torch import argparse @@ -13,6 +14,7 @@ from dust3r.utils.geometry import inv from dust3r.inference import inference from dust3r.image_pairs import make_pairs +from dust3r.utils.image import imread_cv2 from dust3r.post_process import estimate_focal_knowing_depth from spann3r.datasets import * @@ -33,9 +35,41 @@ def get_args_parser(): parser.add_argument('--kf_every', type=int, default=10, help='map every kf_every frames') parser.add_argument('--vis', action='store_true', help='visualize') parser.add_argument('--vis_cam', action='store_true', help='visualize camera pose') + parser.add_argument('--save_ori', action='store_true', help='save original parameters for NeRF') return parser +def get_transform_json(H, W, focal, poses_all, ply_file_path, ori_path=None): + transform_dict = { + 'w': W, + 'h': H, + 'fl_x': focal.item(), + 'fl_y': focal.item(), + 'cx': W/2, + 'cy': H/2, + 'k1': 0, + 'k2': 0, + 'p1': 0, + 'p2': 0, + 'camera_model': 'OPENCV', + } + frames = [] + + for i, pose in enumerate(poses_all): + # CV2 GL format + pose[:3, 1] *= -1 + pose[:3, 2] *= -1 + frame = { + 'file_path': f"imgs/img_{i:04d}.png" if ori_path is None else ori_path[i], + 'transform_matrix': pose.tolist() + } + frames.append(frame) + + transform_dict['frames'] = frames + transform_dict['ply_file_path'] = ply_file_path + + return transform_dict + @torch.no_grad() def main(args): @@ -188,6 +222,35 @@ def main(args): render_frames(pts_all, images_all, camera_parameters, save_demo_path, mask=conf_sig_all>args.conf_thresh) vis_pred_and_imgs(pts_all, save_demo_path, images_all=images_all, conf_all=conf_sig_all) + + # Save transform.json + if args.save_ori: + scale_factor = ordered_batch[0]['camera_intrinsics'][0, 0, 0] + assert scale_factor < 1.0, "Scale factor should be less than 1.0" + focal_ori = focal / scale_factor + + image = imread_cv2(ordered_batch[0]['label'][0]) + + H_ori, W_ori = image.shape[:2] + + paths_all = [osp.normpath(osp.join(osp.abspath(os.getcwd()), view['label'][0])) + for view in ordered_batch] + + transform_dict = get_transform_json(H_ori, W_ori, focal_ori, poses_all, + f"{demo_name}_conf{args.conf_thresh}.ply", + ori_path=paths_all) + + + + + else: + transform_dict = get_transform_json(H, W, focal, poses_all, f"{demo_name}_conf{args.conf_thresh}.ply") + + + # Save to json + with open(os.path.join(save_demo_path, 'transforms.json'), 'w') as f: + json.dump(transform_dict, f, indent=4) + diff --git a/spann3r/datasets/demo.py b/spann3r/datasets/demo.py index f63ce3c..2c414a2 100644 --- a/spann3r/datasets/demo.py +++ b/spann3r/datasets/demo.py @@ -37,7 +37,7 @@ def _get_views(self, idx, resolution, rng): img_idxs = self.sample_frame_idx(img_idxs, rng, full_video=self.full_video) # pseudo intrinsics - fx, fy = 525, 525 + fx, fy = 1.0, 1.0 views = [] imgs_idxs = deque(img_idxs)

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