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Repository: eldar/flash3d
Branch: main
Commit: a71c9b92b07a
Files: 129
Total size: 10.7 MB
Directory structure:
gitextract_gv2hn7lw/
├── .gitignore
├── AUTHORS
├── README.md
├── configs/
│ ├── config.yaml
│ ├── dataset/
│ │ ├── kitti.yaml
│ │ ├── nyuv2.yaml
│ │ └── re10k.yaml
│ ├── experiment/
│ │ ├── layered_kitti.yaml
│ │ ├── layered_nyuv2.yaml
│ │ └── layered_re10k.yaml
│ ├── hydra/
│ │ ├── cluster.yaml
│ │ └── defaults.yaml
│ ├── loss/
│ │ ├── reconstruction.yaml
│ │ └── regularization.yaml
│ └── model/
│ ├── backbone/
│ │ └── resnet.yaml
│ ├── depth/
│ │ └── unidepth.yaml
│ └── gaussian.yaml
├── datasets/
│ ├── colmap_misc.py
│ ├── colmap_utils.py
│ ├── data.py
│ ├── download_realestate10k.py
│ ├── download_realestate10k_colmap.sh
│ ├── kitti.py
│ ├── kitti_raw/
│ │ └── orb-slam_poses/
│ │ ├── 2011_09_26/
│ │ │ ├── 2011_09_26_drive_0001_sync.txt
│ │ │ ├── 2011_09_26_drive_0002_sync.txt
│ │ │ ├── 2011_09_26_drive_0005_sync.txt
│ │ │ ├── 2011_09_26_drive_0009_sync.txt
│ │ │ ├── 2011_09_26_drive_0011_sync.txt
│ │ │ ├── 2011_09_26_drive_0013_sync.txt
│ │ │ ├── 2011_09_26_drive_0014_sync.txt
│ │ │ ├── 2011_09_26_drive_0015_sync.txt
│ │ │ ├── 2011_09_26_drive_0017_sync.txt
│ │ │ ├── 2011_09_26_drive_0018_sync.txt
│ │ │ ├── 2011_09_26_drive_0019_sync.txt
│ │ │ ├── 2011_09_26_drive_0020_sync.txt
│ │ │ ├── 2011_09_26_drive_0022_sync.txt
│ │ │ ├── 2011_09_26_drive_0023_sync.txt
│ │ │ ├── 2011_09_26_drive_0027_sync.txt
│ │ │ ├── 2011_09_26_drive_0028_sync.txt
│ │ │ ├── 2011_09_26_drive_0029_sync.txt
│ │ │ ├── 2011_09_26_drive_0032_sync.txt
│ │ │ ├── 2011_09_26_drive_0035_sync.txt
│ │ │ ├── 2011_09_26_drive_0036_sync.txt
│ │ │ ├── 2011_09_26_drive_0039_sync.txt
│ │ │ ├── 2011_09_26_drive_0046_sync.txt
│ │ │ ├── 2011_09_26_drive_0048_sync.txt
│ │ │ ├── 2011_09_26_drive_0051_sync.txt
│ │ │ ├── 2011_09_26_drive_0052_sync.txt
│ │ │ ├── 2011_09_26_drive_0056_sync.txt
│ │ │ ├── 2011_09_26_drive_0057_sync.txt
│ │ │ ├── 2011_09_26_drive_0059_sync.txt
│ │ │ ├── 2011_09_26_drive_0060_sync.txt
│ │ │ ├── 2011_09_26_drive_0061_sync.txt
│ │ │ ├── 2011_09_26_drive_0064_sync.txt
│ │ │ ├── 2011_09_26_drive_0070_sync.txt
│ │ │ ├── 2011_09_26_drive_0079_sync.txt
│ │ │ ├── 2011_09_26_drive_0084_sync.txt
│ │ │ ├── 2011_09_26_drive_0086_sync.txt
│ │ │ ├── 2011_09_26_drive_0087_sync.txt
│ │ │ ├── 2011_09_26_drive_0091_sync.txt
│ │ │ ├── 2011_09_26_drive_0093_sync.txt
│ │ │ ├── 2011_09_26_drive_0095_sync.txt
│ │ │ ├── 2011_09_26_drive_0096_sync.txt
│ │ │ ├── 2011_09_26_drive_0101_sync.txt
│ │ │ ├── 2011_09_26_drive_0104_sync.txt
│ │ │ ├── 2011_09_26_drive_0106_sync.txt
│ │ │ ├── 2011_09_26_drive_0113_sync.txt
│ │ │ └── 2011_09_26_drive_0117_sync.txt
│ │ ├── 2011_09_28/
│ │ │ ├── 2011_09_28_drive_0001_sync.txt
│ │ │ └── 2011_09_28_drive_0002_sync.txt
│ │ ├── 2011_09_29/
│ │ │ ├── 2011_09_29_drive_0004_sync.txt
│ │ │ ├── 2011_09_29_drive_0026_sync.txt
│ │ │ └── 2011_09_29_drive_0071_sync.txt
│ │ ├── 2011_09_30/
│ │ │ ├── 2011_09_30_drive_0016_sync.txt
│ │ │ ├── 2011_09_30_drive_0018_sync.txt
│ │ │ ├── 2011_09_30_drive_0020_sync.txt
│ │ │ ├── 2011_09_30_drive_0027_sync.txt
│ │ │ ├── 2011_09_30_drive_0028_sync.txt
│ │ │ ├── 2011_09_30_drive_0033_sync.txt
│ │ │ └── 2011_09_30_drive_0034_sync.txt
│ │ └── 2011_10_03/
│ │ ├── 2011_10_03_drive_0027_sync.txt
│ │ ├── 2011_10_03_drive_0034_sync.txt
│ │ ├── 2011_10_03_drive_0042_sync.txt
│ │ └── 2011_10_03_drive_0047_sync.txt
│ ├── nyu/
│ │ ├── camera.py
│ │ ├── compute_colmap.py
│ │ └── dataset.py
│ ├── preprocess_realestate10k.py
│ ├── re10k.py
│ ├── tardataset.py
│ └── util.py
├── evaluate.py
├── evaluate.sh
├── evaluation/
│ └── evaluator.py
├── misc/
│ ├── depth.py
│ ├── download_pretrained_models.py
│ ├── localstorage.py
│ ├── logger.py
│ ├── util.py
│ └── visualise_3d.py
├── models/
│ ├── decoder/
│ │ ├── gauss_util.py
│ │ ├── gaussian_decoder.py
│ │ └── resnet_decoder.py
│ ├── encoder/
│ │ ├── layers.py
│ │ ├── resnet_encoder.py
│ │ └── unidepth_encoder.py
│ └── model.py
├── pyproject.toml
├── requirements-torch.txt
├── requirements.txt
├── splits/
│ ├── eldar/
│ │ ├── test_files.txt
│ │ ├── train_files.txt
│ │ └── val_files.txt
│ ├── nyuv2/
│ │ └── val_files.txt
│ ├── re10k_latentsplat/
│ │ ├── test_closer_as_src.txt
│ │ ├── test_first_as_src.txt
│ │ └── test_second_as_src.txt
│ ├── re10k_mine_filtered/
│ │ ├── test_files.txt
│ │ └── val_files.txt
│ ├── re10k_pixelsplat/
│ │ ├── preprocess_2_frames_split.py
│ │ ├── test_closer_as_src.txt
│ │ ├── test_first_as_src.txt
│ │ └── test_second_as_src.txt
│ └── tulsiani2/
│ ├── test_files.txt
│ ├── train_files.txt
│ └── val_files.txt
├── train.py
├── train.sh
└── trainer.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
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*.egg-info/
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*.egg
MANIFEST
# PyInstaller
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# Translations
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# Django stuff:
*.log
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.scrapy
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# For a library or package, you might want to ignore these files since the code is
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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exp
experiments_out
data
================================================
FILE: AUTHORS
================================================
Eldar Insafutdinov
Stan Szymanowicz
Chuanxia Zheng
================================================
FILE: README.md
================================================
[](https://arxiv.org/abs/2406.04343)
[](https://www.robots.ox.ac.uk/~vgg/research/flash3d/)
[](https://huggingface.co/spaces/szymanowiczs/flash3d)
# Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image
<p align="center">
<img src="assets/teaser_video.gif" alt="animated" />
</p>
> [Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image](https://www.robots.ox.ac.uk/~vgg/research/flash3d/)
> Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, João F. Henriques, Christian Rupprecht, Andrea Vedaldi
> 3DV, 2025.
> *[arXiv 2406.04343](https://arxiv.org/pdf/2406.04343.pdf)*
# News
- [x] `19.07.2024`: Training code and data release
# Setup
## Create a python environment
Flash3D has been trained and tested with the followings software versions:
- Python 3.10
- Pytorch 2.2.2
- CUDA 11.8
- GCC 11.2 (or more recent)
Begin by installing CUDA 11.8 and adding the path containing the `nvcc` compiler to the `PATH` environmental variable.
Then the python environment can be created either via conda:
```sh
conda create -y python=3.10 -n flash3d
conda activate flash3d
```
or using Python's venv module (assuming you already have access to Python 3.10 on your system):
```sh
python3.10 -m venv .venv
. .venv/bin/activate
```
Finally, install the required packages as follows:
```sh
pip install -r requirements-torch.txt --extra-index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
## Download training data
### RealEstate10K dataset
For downloading the RealEstate10K dataset we base our instructions on the [Behind The Scenes](https://github.com/Brummi/BehindTheScenes/tree/main?tab=readme-ov-file#-datasets) scripts.
First you need to download the video sequence metadata including camera poses from https://google.github.io/realestate10k/download.html and unpack it into `data/` such that the folder layout is as follows:
```
data/RealEstate10K/train
data/RealEstate10K/test
```
Finally download the training and test sets of the dataset with the following commands:
```sh
python datasets/download_realestate10k.py -d data/RealEstate10K -o data/RealEstate10K -m train
python datasets/download_realestate10k.py -d data/RealEstate10K -o data/RealEstate10K -m test
```
This step will take several days to complete. Finally, download additional data for the RealEstate10K dataset.
In particular, we provide pre-processed COLMAP cache containing sparse point clouds which are used to estimate the scaling factor for depth predictions.
The last two commands filter the training and testing set from any missing video sequences.
```sh
sh datasets/dowload_realestate10k_colmap.sh
python -m datasets.preprocess_realestate10k -d data/RealEstate10K -s train
python -m datasets.preprocess_realestate10k -d data/RealEstate10K -s test
```
## Download and evaluate the pretrained model
We provide model weights that could be downloaded and evaluated on RealEstate10K test set:
```sh
python -m misc.download_pretrained_models -o exp/re10k_v2
sh evaluate.sh exp/re10k_v2
```
## Training
In order to train the model on RealEstate10K dataset execute this command:
```sh
python train.py \
+experiment=layered_re10k \
model.depth.version=v1 \
train.logging=false
```
For multiple GPU, we can run with this command:
```sh
sh train.sh
```
You can modify the cluster information in ```configs/hydra/cluster```.
## BibTeX
```
@article{szymanowicz2024flash3d,
author = {Szymanowicz, Stanislaw and Insafutdinov, Eldar and Zheng, Chuanxia and Campbell, Dylan and Henriques, Joao and Rupprecht, Christian and Vedaldi, Andrea},
title = {Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image},
journal = {arxiv},
year = {2024},
}
```
================================================
FILE: configs/config.yaml
================================================
defaults:
- _self_
- hydra: defaults
- model: gaussian
- dataset: re10k
- loss: [reconstruction]
config:
exp_name: "debug"
file: "config.yaml"
data_loader:
batch_size: 16
num_workers: 16
train:
logging: true
mixed_precision: #32-true, 16-mixed
num_gpus: 1
load_weights_folder:
ema:
use: true
update_every: 10
update_after_step: 100
beta: 0.9999
optimiser:
learning_rate: 1e-4
num_epochs: 20
scheduler_lambda_step_size: 60000
run:
resume_ckpt: null
dirpath: null
debug: false
random_seed: 42
git_hash: null
log_frequency: 250
save_frequency: 5000
val_frequency: 5000
num_keep_ckpts: 5
================================================
FILE: configs/dataset/kitti.yaml
================================================
name: kitti
data_path: /scratch/shared/nfs1/eldar/data/kitti_raw
pose_path: /users/cxzheng/code/facilitate4d/datasets/kitti_raw/orb-slam_poses
depth_path: null
split_path: /users/cxzheng/code/facilitate4d/splits
split: tulsiani2 # eigen_zhou", "eigen_full", "odom", "benchmark", "eldar
png: false
height: 128
width: 384
znear: 0.01
zfar: 100.0
stereo: true
color_aug: false
keep_aspect_ratio: false
precise_intrinsics: true
flip_left_right: false
pad_border_aug: 0
scale_pose_by_depth: false
================================================
FILE: configs/dataset/nyuv2.yaml
================================================
name: nyuv2
split: original
data_path: /scratch/shared/beegfs/eldar/data/nyuv2_raw
colmap_path: /work/eldar/data/datasets/nyuv2_colmap
split_path: splits/nyuv2/val_files.txt
height: 256
width: 384
znear: 0.01
zfar: 100.0
resize: true
max_fov: 100.0
color_aug: false
skip_bad_shape: false
crop_principal_point: false
subset: -1
pad_border_aug: 32
scale_pose_by_depth: true
================================================
FILE: configs/dataset/re10k.yaml
================================================
name: re10k
split: original
data_path: data/RealEstate10K
depth_path:
unpack_pcl_tar: false
preload_depths: false
ransac_on_the_fly: false
test_split_path: splits/re10k_mine_filtered/val_files.txt
height: 256
width: 384
znear: 0.01
zfar: 100.0
max_fov: 100.0
from_tar: false
copy_to_local: true
color_aug: false
skip_bad_shape: true
dilation: random
max_dilation: 15
pad_border_aug: 32
subset: -1 # use subset frames for small set overfitting
frame_sampling_method: random
scale_pose_by_depth: true
test_split: mine
================================================
FILE: configs/experiment/layered_kitti.yaml
================================================
# @package _global_
config:
exp_name: debug
defaults:
- override /dataset: kitti
- override /model: gaussian
- override /loss: [regularization, reconstruction]
optimiser:
scheduler_lambda_step_size: 500000
train:
scale_pose_by_depth: true
use_gt_poses: true
model:
name: unidepth
gauss_novel_frames: [1, 2]
renderer_w_pose: true
scale_with_depth: false
opacity_scale: 1.0
depth_scale: 0.1
xyz_scale: 0.2
max_depth: 20
depth_cond: true
================================================
FILE: configs/experiment/layered_nyuv2.yaml
================================================
# @package _global_
config:
exp_name: debug
defaults:
- override /dataset: nyuv2
- override /model: gaussian
- override /loss: [regularization, reconstruction]
optimiser:
scheduler_lambda_step_size: 500000
train:
scale_pose_by_depth: true
use_gt_poses: true
model:
name: unidepth
gauss_novel_frames: [-1, 1, 2]
renderer_w_pose: true
scale_with_depth: false
opacity_scale: 1.0
depth_scale: 0.1
xyz_scale: 0.2
max_depth: 20
depth_cond: true
================================================
FILE: configs/experiment/layered_re10k.yaml
================================================
# @package _global_
config:
exp_name: debug
defaults:
- override /dataset: re10k
- override /model: gaussian
- override /loss: [regularization, reconstruction]
optimiser:
scheduler_lambda_step_size: 500000
train:
scale_pose_by_depth: true
use_gt_poses: true
model:
name: unidepth
gauss_novel_frames: [-1, 1, 2]
renderer_w_pose: true
scale_with_depth: false
opacity_scale: 1.0
depth_scale: 0.1
xyz_scale: 0.2
max_depth: 20
depth_cond: true
================================================
FILE: configs/hydra/cluster.yaml
================================================
---
run:
dir: exp
sweep:
dir: exp
subdir: ${hydra.job.override_dirname}
job:
chdir: True
launcher:
submitit_folder: ${hydra.sweep.dir}/.submitit/%j
timeout_min: 1
cpus_per_task: 16
gpus_per_node: 1
partition: gpu
constraint: a6000
tasks_per_node: 1
mem_gb: 128
nodes: 1
name: ${hydra.job.override_dirname}
_target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
comment: null
exclude: gnodek1,gnodeg1,gnodeg3,gnodee4,gnodek2
max_num_timeout: 0
additional_parameters: {time: "3-00:00:00"}
array_parallelism: 32
setup: null
================================================
FILE: configs/hydra/defaults.yaml
================================================
---
run:
dir: exp/${now:%Y-%m-%d}/${now:%H-%M-%S}
sweep:
dir: exp/${now:%Y-%m-%d}/${now:%H-%M-%S}
subdir: ${hydra.job.override_dirname}
job:
chdir: True
================================================
FILE: configs/loss/reconstruction.yaml
================================================
mse:
weight: 1.0
type: l1
ssim:
weight: 0.85
lpips:
weight: 0.01
apply_after_step: 50000
================================================
FILE: configs/loss/regularization.yaml
================================================
gauss_scale:
weight: 0.001
thresh: 2.0
gauss_offset:
weight: 0.01
thresh: 1.0
================================================
FILE: configs/model/backbone/resnet.yaml
================================================
name: resnet
num_layers: 50 # 18, 34, 50, 101, 152
num_ch_dec: [32,32,64,128,256]
resnet_bn_order: pre_bn # monodepth, pre_bn
weights_init: pretrained
upsample_mode: nearest
depth_cond: true
================================================
FILE: configs/model/depth/unidepth.yaml
================================================
version: v1
backbone: vitl14
================================================
FILE: configs/model/gaussian.yaml
================================================
defaults:
- depth: unidepth
- backbone: resnet
name: unidepth
frame_ids: [0, -1, 1]
scales: [0]
gauss_novel_frames: [1, 2]
min_depth: 0.1
max_depth: 100
# gaussian parameters
gaussians_per_pixel: 2
gaussian_rendering: true
randomise_bg_colour: true
max_sh_degree: 1
scaled_offset: false
one_gauss_decoder: false
predict_offset: true
bg_colour: [0.5, 0.5, 0.5]
shift_rays_half_pixel: forward
depth_type: depth_inc
depth_scale: 1.0
xyz_scale: 1e-02
opacity_scale: 1e-3
scale_scale: 1e-1
sh_scale: 1.0
scale_lambda: 0.01
depth_bias: -0.1
xyz_bias: 0.0
opacity_bias: 0.0
scale_bias: 0.02
================================================
FILE: datasets/colmap_misc.py
================================================
import numpy as np
import torch
from datasets.colmap_utils import \
read_images_binary, \
read_points3d_binary, \
read_model, \
qvec2rotmat
def is_computed(sparse_dir):
try:
cameras, images, points3D = read_model(sparse_dir, ".bin")
is_good = True
except:
is_good = False
return is_good
def read_colmap_pose(image):
R = qvec2rotmat(image.qvec).astype(np.float32)
t = image.tvec.astype(np.float32)
T_w2c = np.vstack([
np.hstack((R, np.expand_dims(t, axis=1))),
np.array([0, 0, 0, 1])
])
return T_w2c.astype(np.float32)
def read_camera_params(camera):
W = camera.width
H = camera.height
intr = camera.params
K = np.eye(3, dtype=np.float32)
K[0, 0] = intr[0]
K[1, 1] = intr[1]
K[0, 2] = intr[2]
K[1, 2] = intr[3]
return H, W, K
def load_sparse_pcl_colmap(dir_recon):
ext = "bin"
images = read_images_binary(dir_recon / f"images.{ext}")
points3D = read_points3d_binary(dir_recon / f"points3D.{ext}")
# convert 3D coordinates to an easier to process format
xyz_ids = np.array(list(points3D.keys()))
xyz = np.zeros((np.max(xyz_ids)+1, 3), dtype=np.float32)
for id in xyz_ids:
xyz[id, :] = points3D[id].xyz
image_ids = sorted(list(images.keys()))
xys = [images[image_id].xys for image_id in image_ids]
p3D_ids = [images[image_id].point3D_ids for image_id in image_ids]
return {
"images": images,
"xys": xys,
"p3D_ids": p3D_ids,
"xyz": xyz
}
def get_sparse_depth(T_w2c, img_size, crop_margin, sparse_pcl, frame_idx):
"""
img_size: (W, H) - original size of the image before resizing as used by COLMAP
"""
# image_id-1 == frame_idx
xys_all = sparse_pcl["xys"]
p3D_ids_all = sparse_pcl["p3D_ids"]
xyz = sparse_pcl["xyz"]
xys = xys_all[frame_idx]
p3D_ids = p3D_ids_all[frame_idx]
visible_points = p3D_ids != -1
xys = xys[visible_points, :]
p3D_ids = p3D_ids[visible_points]
xyz_image = xyz[p3D_ids, :]
xyz_image_h = np.hstack((xyz_image, np.ones_like(xyz_image[:, :1])))
# ===== compute point projections onto image with network data ====
# index to -1 because image_ids are 1-indexed
# K = _process_projs(pose_data["intrinsics"][image_id-1], H, W)
# load the extrinsic matrixself.num_scales
# P = K @ T_w2c
xyz_pix = np.einsum("ji,ni->nj", T_w2c, xyz_image_h)[:, :3]
depth = xyz_pix[:, 2:]
xys_scaled = ((xys - crop_margin) / img_size - 0.5) * 2
xyd = np.concatenate([xys_scaled, depth], axis=1)
return torch.from_numpy(xyd).to(torch.float32)
================================================
FILE: datasets/colmap_utils.py
================================================
# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
# its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
import os
import sys
import collections
import numpy as np
import struct
import argparse
CameraModel = collections.namedtuple(
"CameraModel", ["model_id", "model_name", "num_params"])
Camera = collections.namedtuple(
"Camera", ["id", "model", "width", "height", "params"])
BaseImage = collections.namedtuple(
"Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
Point3D = collections.namedtuple(
"Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
class Image(BaseImage):
def qvec2rotmat(self):
return qvec2rotmat(self.qvec)
CAMERA_MODELS = {
CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
CameraModel(model_id=3, model_name="RADIAL", num_params=5),
CameraModel(model_id=4, model_name="OPENCV", num_params=8),
CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
CameraModel(model_id=7, model_name="FOV", num_params=5),
CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
}
CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model)
for camera_model in CAMERA_MODELS])
CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
for camera_model in CAMERA_MODELS])
def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
"""Read and unpack the next bytes from a binary file.
:param fid:
:param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
:param endian_character: Any of {@, =, <, >, !}
:return: Tuple of read and unpacked values.
"""
data = fid.read(num_bytes)
return struct.unpack(endian_character + format_char_sequence, data)
def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
"""pack and write to a binary file.
:param fid:
:param data: data to send, if multiple elements are sent at the same time,
they should be encapsuled either in a list or a tuple
:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
should be the same length as the data list or tuple
:param endian_character: Any of {@, =, <, >, !}
"""
if isinstance(data, (list, tuple)):
bytes = struct.pack(endian_character + format_char_sequence, *data)
else:
bytes = struct.pack(endian_character + format_char_sequence, data)
fid.write(bytes)
def read_cameras_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
cameras = {}
with open(path, "r") as fid:
while True:
line = fid.readline()
if not line:
break
line = line.strip()
if len(line) > 0 and line[0] != "#":
elems = line.split()
camera_id = int(elems[0])
model = elems[1]
width = int(elems[2])
height = int(elems[3])
params = np.array(tuple(map(float, elems[4:])))
cameras[camera_id] = Camera(id=camera_id, model=model,
width=width, height=height,
params=params)
return cameras
def read_cameras_binary(path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasBinary(const std::string& path)
void Reconstruction::ReadCamerasBinary(const std::string& path)
"""
cameras = {}
with open(path_to_model_file, "rb") as fid:
num_cameras = read_next_bytes(fid, 8, "Q")[0]
for _ in range(num_cameras):
camera_properties = read_next_bytes(
fid, num_bytes=24, format_char_sequence="iiQQ")
camera_id = camera_properties[0]
model_id = camera_properties[1]
model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
width = camera_properties[2]
height = camera_properties[3]
num_params = CAMERA_MODEL_IDS[model_id].num_params
params = read_next_bytes(fid, num_bytes=8*num_params,
format_char_sequence="d"*num_params)
cameras[camera_id] = Camera(id=camera_id,
model=model_name,
width=width,
height=height,
params=np.array(params))
assert len(cameras) == num_cameras
return cameras
def write_cameras_text(cameras, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
HEADER = "# Camera list with one line of data per camera:\n"
"# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n"
"# Number of cameras: {}\n".format(len(cameras))
with open(path, "w") as fid:
fid.write(HEADER)
for _, cam in cameras.items():
to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
line = " ".join([str(elem) for elem in to_write])
fid.write(line + "\n")
def write_cameras_binary(cameras, path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasBinary(const std::string& path)
void Reconstruction::ReadCamerasBinary(const std::string& path)
"""
with open(path_to_model_file, "wb") as fid:
write_next_bytes(fid, len(cameras), "Q")
for _, cam in cameras.items():
model_id = CAMERA_MODEL_NAMES[cam.model].model_id
camera_properties = [cam.id,
model_id,
cam.width,
cam.height]
write_next_bytes(fid, camera_properties, "iiQQ")
for p in cam.params:
write_next_bytes(fid, float(p), "d")
return cameras
def read_images_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesText(const std::string& path)
void Reconstruction::WriteImagesText(const std::string& path)
"""
images = {}
with open(path, "r") as fid:
while True:
line = fid.readline()
if not line:
break
line = line.strip()
if len(line) > 0 and line[0] != "#":
elems = line.split()
image_id = int(elems[0])
qvec = np.array(tuple(map(float, elems[1:5])))
tvec = np.array(tuple(map(float, elems[5:8])))
camera_id = int(elems[8])
image_name = elems[9]
elems = fid.readline().split()
xys = np.column_stack([tuple(map(float, elems[0::3])),
tuple(map(float, elems[1::3]))])
point3D_ids = np.array(tuple(map(int, elems[2::3])))
images[image_id] = Image(
id=image_id, qvec=qvec, tvec=tvec,
camera_id=camera_id, name=image_name,
xys=xys, point3D_ids=point3D_ids)
return images
def read_images_binary(path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesBinary(const std::string& path)
void Reconstruction::WriteImagesBinary(const std::string& path)
"""
images = {}
with open(path_to_model_file, "rb") as fid:
num_reg_images = read_next_bytes(fid, 8, "Q")[0]
for _ in range(num_reg_images):
binary_image_properties = read_next_bytes(
fid, num_bytes=64, format_char_sequence="idddddddi")
image_id = binary_image_properties[0]
qvec = np.array(binary_image_properties[1:5])
tvec = np.array(binary_image_properties[5:8])
camera_id = binary_image_properties[8]
image_name = ""
current_char = read_next_bytes(fid, 1, "c")[0]
while current_char != b"\x00": # look for the ASCII 0 entry
image_name += current_char.decode("utf-8")
current_char = read_next_bytes(fid, 1, "c")[0]
num_points2D = read_next_bytes(fid, num_bytes=8,
format_char_sequence="Q")[0]
x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
format_char_sequence="ddq"*num_points2D)
xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),
tuple(map(float, x_y_id_s[1::3]))])
point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
images[image_id] = Image(
id=image_id, qvec=qvec, tvec=tvec,
camera_id=camera_id, name=image_name,
xys=xys, point3D_ids=point3D_ids)
return images
def write_images_text(images, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesText(const std::string& path)
void Reconstruction::WriteImagesText(const std::string& path)
"""
if len(images) == 0:
mean_observations = 0
else:
mean_observations = sum((len(img.point3D_ids) for _, img in images.items()))/len(images)
HEADER = "# Image list with two lines of data per image:\n"
"# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n"
"# POINTS2D[] as (X, Y, POINT3D_ID)\n"
"# Number of images: {}, mean observations per image: {}\n".format(len(images), mean_observations)
with open(path, "w") as fid:
fid.write(HEADER)
for _, img in images.items():
image_header = [img.id, *img.qvec, *img.tvec, img.camera_id, img.name]
first_line = " ".join(map(str, image_header))
fid.write(first_line + "\n")
points_strings = []
for xy, point3D_id in zip(img.xys, img.point3D_ids):
points_strings.append(" ".join(map(str, [*xy, point3D_id])))
fid.write(" ".join(points_strings) + "\n")
def write_images_binary(images, path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesBinary(const std::string& path)
void Reconstruction::WriteImagesBinary(const std::string& path)
"""
with open(path_to_model_file, "wb") as fid:
write_next_bytes(fid, len(images), "Q")
for _, img in images.items():
write_next_bytes(fid, img.id, "i")
write_next_bytes(fid, img.qvec.tolist(), "dddd")
write_next_bytes(fid, img.tvec.tolist(), "ddd")
write_next_bytes(fid, img.camera_id, "i")
for char in img.name:
write_next_bytes(fid, char.encode("utf-8"), "c")
write_next_bytes(fid, b"\x00", "c")
write_next_bytes(fid, len(img.point3D_ids), "Q")
for xy, p3d_id in zip(img.xys, img.point3D_ids):
write_next_bytes(fid, [*xy, p3d_id], "ddq")
def read_points3D_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DText(const std::string& path)
void Reconstruction::WritePoints3DText(const std::string& path)
"""
points3D = {}
with open(path, "r") as fid:
while True:
line = fid.readline()
if not line:
break
line = line.strip()
if len(line) > 0 and line[0] != "#":
elems = line.split()
point3D_id = int(elems[0])
xyz = np.array(tuple(map(float, elems[1:4])))
rgb = np.array(tuple(map(int, elems[4:7])))
error = float(elems[7])
image_ids = np.array(tuple(map(int, elems[8::2])))
point2D_idxs = np.array(tuple(map(int, elems[9::2])))
points3D[point3D_id] = Point3D(id=point3D_id, xyz=xyz, rgb=rgb,
error=error, image_ids=image_ids,
point2D_idxs=point2D_idxs)
return points3D
def read_points3d_binary(path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DBinary(const std::string& path)
void Reconstruction::WritePoints3DBinary(const std::string& path)
"""
points3D = {}
with open(path_to_model_file, "rb") as fid:
num_points = read_next_bytes(fid, 8, "Q")[0]
for _ in range(num_points):
binary_point_line_properties = read_next_bytes(
fid, num_bytes=43, format_char_sequence="QdddBBBd")
point3D_id = binary_point_line_properties[0]
xyz = np.array(binary_point_line_properties[1:4])
rgb = np.array(binary_point_line_properties[4:7])
error = np.array(binary_point_line_properties[7])
track_length = read_next_bytes(
fid, num_bytes=8, format_char_sequence="Q")[0]
track_elems = read_next_bytes(
fid, num_bytes=8*track_length,
format_char_sequence="ii"*track_length)
image_ids = np.array(tuple(map(int, track_elems[0::2])))
point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
points3D[point3D_id] = Point3D(
id=point3D_id, xyz=xyz, rgb=rgb,
error=error, image_ids=image_ids,
point2D_idxs=point2D_idxs)
return points3D
def write_points3D_text(points3D, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DText(const std::string& path)
void Reconstruction::WritePoints3DText(const std::string& path)
"""
if len(points3D) == 0:
mean_track_length = 0
else:
mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items()))/len(points3D)
HEADER = "# 3D point list with one line of data per point:\n"
"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
"# Number of points: {}, mean track length: {}\n".format(len(points3D), mean_track_length)
with open(path, "w") as fid:
fid.write(HEADER)
for _, pt in points3D.items():
point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
fid.write(" ".join(map(str, point_header)) + " ")
track_strings = []
for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
track_strings.append(" ".join(map(str, [image_id, point2D])))
fid.write(" ".join(track_strings) + "\n")
def write_points3d_binary(points3D, path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DBinary(const std::string& path)
void Reconstruction::WritePoints3DBinary(const std::string& path)
"""
with open(path_to_model_file, "wb") as fid:
write_next_bytes(fid, len(points3D), "Q")
for _, pt in points3D.items():
write_next_bytes(fid, pt.id, "Q")
write_next_bytes(fid, pt.xyz.tolist(), "ddd")
write_next_bytes(fid, pt.rgb.tolist(), "BBB")
write_next_bytes(fid, pt.error, "d")
track_length = pt.image_ids.shape[0]
write_next_bytes(fid, track_length, "Q")
for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
write_next_bytes(fid, [image_id, point2D_id], "ii")
def detect_model_format(path, ext):
if os.path.isfile(os.path.join(path, "cameras" + ext)) and \
os.path.isfile(os.path.join(path, "images" + ext)) and \
os.path.isfile(os.path.join(path, "points3D" + ext)):
return True
return False
def read_model(path, ext=""):
# try to detect the extension automatically
if ext == "":
if detect_model_format(path, ".bin"):
ext = ".bin"
elif detect_model_format(path, ".txt"):
ext = ".txt"
else:
print("Provide model format: '.bin' or '.txt'")
return
if ext == ".txt":
cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
images = read_images_text(os.path.join(path, "images" + ext))
points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
else:
cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
images = read_images_binary(os.path.join(path, "images" + ext))
points3D = read_points3d_binary(os.path.join(path, "points3D") + ext)
return cameras, images, points3D
def write_model(cameras, images, points3D, path, ext=".bin"):
if ext == ".txt":
write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
write_images_text(images, os.path.join(path, "images" + ext))
write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
else:
write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
write_images_binary(images, os.path.join(path, "images" + ext))
write_points3d_binary(points3D, os.path.join(path, "points3D") + ext)
return cameras, images, points3D
def qvec2rotmat(qvec):
return np.array([
[1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
[2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
[2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
def rotmat2qvec(R):
Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
K = np.array([
[Rxx - Ryy - Rzz, 0, 0, 0],
[Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
[Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
[Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
eigvals, eigvecs = np.linalg.eigh(K)
qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
if qvec[0] < 0:
qvec *= -1
return qvec
def main():
parser = argparse.ArgumentParser(description="Read and write COLMAP binary and text models")
parser.add_argument("--input_model", help="path to input model folder")
parser.add_argument("--input_format", choices=[".bin", ".txt"],
help="input model format", default="")
parser.add_argument("--output_model",
help="path to output model folder")
parser.add_argument("--output_format", choices=[".bin", ".txt"],
help="outut model format", default=".txt")
args = parser.parse_args()
cameras, images, points3D = read_model(path=args.input_model, ext=args.input_format)
print("num_cameras:", len(cameras))
print("num_images:", len(images))
print("num_points3D:", len(points3D))
if args.output_model is not None:
write_model(cameras, images, points3D, path=args.output_model, ext=args.output_format)
if __name__ == "__main__":
main()
================================================
FILE: datasets/data.py
================================================
from PIL import Image
import numpy as np
import torch
def process_projs(proj):
# pose in dataset is normalised by resolution
# need to unnormalise it for metric projection
K = np.eye(3, dtype=np.float32)
K[0, 0] = proj[0]
K[1, 1] = proj[1]
K[0, 2] = proj[2]
K[1, 2] = proj[3]
return K
def pose_to_4x4(w2c):
if w2c.shape[0] == 3:
w2c = np.concatenate((w2c.astype(np.float32),
np.array([[0, 0, 0, 1]], dtype=np.float32)), axis=0)
return w2c
def data_to_c2w(w2c):
w2c = pose_to_4x4(w2c)
c2w = np.linalg.inv(w2c)
return c2w
def pil_loader(path):
# open path as file to avoid ResourceWarning
# (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def get_sparse_depth(pose_data, orig_size, sparse_pcl, frame_idx):
# image_id-1 == frame_idx
xys_all = sparse_pcl["xys"]
p3D_ids_all = sparse_pcl["p3D_ids"]
xyz = sparse_pcl["xyz"]
xys = xys_all[frame_idx]
p3D_ids = p3D_ids_all[frame_idx]
W, H = orig_size
visible_points = p3D_ids != -1
xys = xys[visible_points, :]
p3D_ids = p3D_ids[visible_points]
xyz_image = xyz[p3D_ids, :]
xyz_image_h = np.hstack((xyz_image, np.ones_like(xyz_image[:, :1])))
# ===== compute point projections onto image with network data ====
# index to -1 because image_ids are 1-indexed
# K = _process_projs(pose_data["intrinsics"][image_id-1], H, W)
# load the extrinsic matrixself.num_scales
T_w2c = pose_to_4x4(pose_data["poses"][frame_idx])
# P = K @ T_w2c
xyz_pix = np.einsum("ji,ni->nj", T_w2c, xyz_image_h)[:, :3]
depth = xyz_pix[:, 2:]
img_dim = np.array([[W, H]])
xys_scaled = (xys / img_dim - 0.5) * 2
xyd = np.concatenate([xys_scaled, depth], axis=1)
return torch.from_numpy(xyd).to(torch.float32)
================================================
FILE: datasets/download_realestate10k.py
================================================
"""
Author: Felix Wimbauer
Source: https://github.com/Brummi/BehindTheScenes/blob/main/datasets/realestate10k/download_realestate10k.py
"""
import argparse
import os
import subprocess
from multiprocessing import Pool
from pathlib import Path
from time import sleep
from pytubefix import YouTube
import tqdm
from subprocess import call
class Data:
def __init__(self, url, seqname, list_timestamps):
self.url = url
self.list_seqnames = []
self.list_list_timestamps = []
self.list_seqnames.append(seqname)
self.list_list_timestamps.append(list_timestamps)
def add(self, seqname, list_timestamps):
self.list_seqnames.append(seqname)
self.list_list_timestamps.append(list_timestamps)
def __len__(self):
return len(self.list_seqnames)
def process(data, seq_id, videoname, output_root):
seqname = data.list_seqnames[seq_id]
out_path = output_root / seqname
if not out_path.exists():
out_path.mkdir(exist_ok=True, parents=True)
else:
print("[INFO] Something Wrong, stop process")
return True
list_str_timestamps = []
for timestamp in data.list_list_timestamps[seq_id]:
timestamp = int(timestamp / 1000)
str_hour = str(int(timestamp / 3600000)).zfill(2)
str_min = str(int(int(timestamp % 3600000) / 60000)).zfill(2)
str_sec = str(int(int(int(timestamp % 3600000) % 60000) / 1000)).zfill(2)
str_mill = str(int(int(int(timestamp % 3600000) % 60000) % 1000)).zfill(3)
_str_timestamp = str_hour + ":" + str_min + ":" + str_sec + "." + str_mill
list_str_timestamps.append(_str_timestamp)
# extract frames from a video
for idx, str_timestamp in enumerate(list_str_timestamps):
call(("ffmpeg", "-ss", str_timestamp, "-i", str(videoname), "-vframes", "1", "-f", "image2", str(out_path / f'{data.list_list_timestamps[seq_id][idx]}.jpg')), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
return False
def wrap_process(list_args):
return process(*list_args)
class DataDownloader:
def __init__(self, data_path: Path, out_path: Path, tmp_path: Path, mode='test'):
print("[INFO] Loading data list ... ", end='')
self.data_path = data_path
self.out_path = out_path
self.tmp_path = tmp_path
self.mode = mode
self.list_seqnames = sorted(self.data_path.glob('*.txt'))
self.is_done = out_path.exists()
out_path.mkdir(exist_ok=True, parents=True)
self.list_data = {}
for txt_file in tqdm.tqdm(self.list_seqnames):
seq_name = txt_file.stem
# extract info from txt
with open(txt_file, "r") as seq_file:
lines = seq_file.readlines()
youtube_url = ""
list_timestamps = []
for idx, line in enumerate(lines):
if idx == 0:
youtube_url = line.strip()
else:
timestamp = int(line.split(' ')[0])
list_timestamps.append(timestamp)
if youtube_url in self.list_data:
self.list_data[youtube_url].add(seq_name, list_timestamps)
else:
self.list_data[youtube_url] = Data(youtube_url, seq_name, list_timestamps)
print(" Done! ")
print("[INFO] {} movies are used in {} mode".format(len(self.list_data), self.mode))
def run(self):
print("[INFO] Start downloading {} movies".format(len(self.list_data)))
for global_count, data in enumerate(self.list_data.values()):
print("[INFO] Downloading {} ".format(data.url))
current_file = self.tmp_path / f"current_{self.mode}"
call(("rm", "-r", str(current_file)))
try:
# sometimes this fails because of known issues of pytube and unknown factors
yt = YouTube(data.url)
stream = yt.streams.filter(res='360p').first()
stream.download(str(current_file))
except:
with open(os.path.join(str(self.data_path.parent), 'failed_videos_' + self.mode + '.txt'), 'a') as f:
for seqname in data.list_seqnames:
f.writelines(seqname + '\n')
continue
sleep(1)
current_file = next(current_file.iterdir())
if len(data) == 1: # len(data) is len(data.list_seqnames)
process(data, 0, current_file, self.out_path)
else:
with Pool(processes=4) as pool:
pool.map(wrap_process, [(data, seq_id, current_file, self.out_path) for seq_id in range(len(data))])
print(f"[INFO] Extracted {sum(map(len, data.list_list_timestamps))}")
# remove videos
call(("rm", str(current_file)))
# os.system(command)
if self.is_done:
return False
return True
def show(self):
print("########################################")
global_count = 0
for data in self.list_data.values():
# print(" URL : {}".format(data.url))
for idx in range(len(data)):
# print(" SEQ_{} : {}".format(idx, data.list_seqnames[idx]))
# print(" LEN_{} : {}".format(idx, len(data.list_list_timestamps[idx])))
global_count = global_count + 1
# print("----------------------------------------")
print("TOTAL : {} sequnces".format(global_count))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mode", type=str)
parser.add_argument("-d", "--data_path", type=str)
parser.add_argument("-o", "--out_path", type=str)
tmpdir = os.environ.get('TMPDIR')
parser.add_argument("-t", "--tmp_path", default=tmpdir, type=str)
args = parser.parse_args()
mode = args.mode
data_path = Path(args.data_path)
out_path = Path(args.out_path)
tmp_path = Path(args.tmp_path)
if mode not in ["test", "train"]:
raise ValueError(f"Invalid split mode: {mode}")
data_path = data_path / mode
out_path = out_path / mode
downloader = DataDownloader(
data_path=data_path,
out_path=out_path,
tmp_path=tmp_path,
mode=mode)
downloader.show()
is_ok = downloader.run()
if is_ok:
print("Done!")
else:
print("Failed")
if __name__ == "__main__":
main()
================================================
FILE: datasets/download_realestate10k_colmap.sh
================================================
#!/bin/sh
DATA_PATH=data/RealEstate10K
file_list=(
test.pickle.gz
train.pickle.gz
pcl.test.tar
pcl.train.tar
valid_seq_ids.train.pickle.gz
SHA512SUMS
)
ROOT_URL=https://thor.robots.ox.ac.uk/flash3d
cd $DATA_PATH
for item in "${file_list[@]}"; do
curl -O $ROOT_URL/$item
done
sha512sum -c SHA512SUMS
================================================
FILE: datasets/kitti.py
================================================
import os
import random
import torch
import numpy as np
import torch.utils.data as data
import torchvision.transforms as T
from PIL import Image
from typing import Optional
from pathlib import Path
from datasets.data import pil_loader
# This could also be retrieved from
BASE_SIZES = {
"2011_09_26": (375, 1242),
"2011_09_28": (370, 1224),
"2011_09_29": (374, 1238),
"2011_09_30": (370, 1226),
"2011_10_03": (376, 1241),
}
def readlines(filename):
"""Read all the lines in a text file and return as a list
"""
with open(filename, 'r') as f:
lines = f.read().splitlines()
return lines
class KITTIDataset(data.Dataset):
def __init__(self,
cfg,
split: Optional[str]=None,
):
super().__init__()
self.cfg = cfg
self.data_path = Path(self.cfg.dataset.data_path)
self.split = split
fpath = os.path.join(cfg.dataset.split_path, cfg.dataset.split, f"{split}_files.txt")
self.filenames = readlines(fpath)
self.image_size = (self.cfg.dataset.height, self.cfg.dataset.width)
if self.cfg.dataset.pad_border_aug != 0:
self.pad_border_fn = T.Pad((self.cfg.dataset.pad_border_aug,
self.cfg.dataset.pad_border_aug))
self.num_scales = len(cfg.model.scales)
self.interp = Image.LANCZOS
self.loader = pil_loader
self.to_tensor = T.ToTensor()
if cfg.model.gaussian_rendering:
frame_idxs = [0] + cfg.model.gauss_novel_frames
if cfg.dataset.stereo:
if split == "train":
stereo_frames = []
for frame_id in frame_idxs:
stereo_frames += [f"s{frame_id}"]
frame_idxs += stereo_frames
else:
frame_idxs = [0, "s0"]
else:
# SfMLearner frames, eg. [0, -1, 1]
frame_idxs = cfg.model.frame_ids.copy()
self.frame_idxs = frame_idxs
self.is_train = split == "train"
self.img_ext = '.png' if cfg.dataset.png else '.jpg'
# We need to specify augmentations differently in newer versions of torchvision.
# We first try the newer tuple version; if this fails we fall back to scalars
try:
self.brightness = (0.8, 1.2)
self.contrast = (0.8, 1.2)
self.saturation = (0.8, 1.2)
self.hue = (-0.1, 0.1)
except TypeError:
self.brightness = 0.2
self.contrast = 0.2
self.saturation = 0.2
self.hue = 0.1
# multiple resolution support
self.resize = {}
for i in range(self.num_scales):
s = 2 ** i
new_size = (self.image_size[0] // s, self.image_size[1] // s)
self.resize[i] = T.Resize(new_size, interpolation=self.interp)
self.resize_depth = T.Resize(self.image_size, interpolation=T.InterpolationMode.NEAREST)
# NOTE: Make sure your intrinsics matrix is *normalized* by the original image size.
# To normalize you need to scale the first row by 1 / image_width and the second row
# by 1 / image_height. Monodepth2 assumes a principal point to be exactly centered.
# If your principal point is far from the center you might need to disable the horizontal
# flip augmentation.
self.K = np.array([[0.58, 0, 0.5, 0],
[0, 1.92, 0.5, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=np.float32)
self.full_res_shape = (1242, 375)
self.side_map = {"2": 2, "3": 3, "l": 2, "r": 3}
self._calibs = self._load_calibs(self.data_path, self.image_size, cfg.dataset.keep_aspect_ratio)
self._sequences = self._get_sequences(self.data_path)
self.pose_path = cfg.dataset.pose_path
self.depth_path = cfg.dataset.depth_path
self.gt_depths = True if self.depth_path is not None else False
self.gt_poses = True if self.pose_path is not None else False
if self.pose_path is not None:
self._poses = self._load_poses(self.pose_path, self._sequences)
def __len__(self):
return len(self.filenames)
def get_color(self, folder, frame_index, side, do_flip):
image_path = self.get_image_path(folder, frame_index, side)
color = self.loader(image_path)
if do_flip:
color = color.transpose(Image.FLIP_LEFT_RIGHT)
return color
def get_depth_anything(self, folder, frame_index, side, do_flip):
f_str = f"{frame_index:010d}.npy"
depth_path = os.path.join(
self.data_path, folder, "image_0{}/data".format(self.side_map[side]), f_str)
depth_gt = np.squeeze(np.load(depth_path))
if do_flip:
depth_gt = np.fliplr(depth_gt)
return depth_gt
def get_image_path(self, folder, frame_index, side):
f_str = "{:010d}{}".format(frame_index, self.img_ext)
image_path = os.path.join(
self.data_path, folder, "image_0{}/data".format(self.side_map[side]), f_str)
return image_path
def preprocess(self, inputs, color_aug):
"""Resize colour images to the required scales and augment if required
We create the color_aug object in advance and apply the same augmentation to all
images in this item. This ensures that all images input to the pose network receive the
same augmentation.
"""
for k in list(inputs):
frame = inputs[k]
if "color" in k:
n, im, i = k
for i in range(self.num_scales):
inputs[(n, im, i)] = self.resize[i](inputs[(n, im, i - 1)])
for k in list(inputs):
f = inputs[k]
if "color" in k:
n, im, i = k
inputs[(n, im, i)] = self.to_tensor(f)
if self.cfg.dataset.pad_border_aug != 0:
inputs[(n + "_aug", im, i)] = self.to_tensor(self.pad_border_fn(color_aug(f)))
else:
inputs[(n + "_aug", im, i)] = self.to_tensor(color_aug(f))
@staticmethod
def _get_sequences(data_path):
all_sequences = []
data_path = Path(data_path)
for day in data_path.iterdir():
if not day.is_dir():
continue
day_sequences = [seq for seq in day.iterdir() if seq.is_dir()]
# lengths = [len(list((seq / "image_02" / "data").iterdir())) for seq in day_sequences]
# day_sequences = [(day.name, seq.name, length) for seq, length in zip(day_sequences, lengths)]
day_sequences = [(day.name, seq.name) for seq in day_sequences]
all_sequences.extend(day_sequences)
return all_sequences
@staticmethod
def _load_poses(pose_path, sequences):
poses = {}
# for day, seq, _ in sequences:
for day, seq in sequences:
pose_file = Path(pose_path) / day / f"{seq}.txt"
poses_seq = []
try:
with open(pose_file, 'r') as f:
lines = f.readlines()
for line in lines:
T_w_cam0 = np.fromstring(line, dtype=float, sep=' ')
T_w_cam0 = T_w_cam0.reshape(3, 4)
T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1]))
poses_seq.append(T_w_cam0)
except FileNotFoundError:
pass
# print(f'Ground truth poses are not available for sequence {seq}.')
poses_seq = np.array(poses_seq, dtype=np.float32)
poses[(day, seq)] = poses_seq
return poses
@staticmethod
def _load_calibs(data_path, target_image_size, keep_aspect_ratio):
calibs = {}
for day in BASE_SIZES.keys():
day_folder = Path(data_path) / day
cam_calib_file = day_folder / "calib_cam_to_cam.txt"
velo_calib_file = day_folder / "calib_velo_to_cam.txt"
cam_calib_file_data = {}
with open(cam_calib_file, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
try:
cam_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32)
except ValueError:
pass
velo_calib_file_data = {}
with open(velo_calib_file, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
try:
velo_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32)
except ValueError:
pass
im_size = BASE_SIZES[day]
# Create 3x4 projection matrices
P_rect_l = np.reshape(cam_calib_file_data['P_rect_02'], (3, 4))
P_rect_r = np.reshape(cam_calib_file_data['P_rect_03'], (3, 4))
R_rect = np.eye(4, dtype=np.float32)
R_rect[:3, :3] = cam_calib_file_data['R_rect_00'].reshape(3, 3)
T_v2c = np.hstack((velo_calib_file_data['R'].reshape(3, 3), velo_calib_file_data['T'][..., np.newaxis]))
T_v2c = np.vstack((T_v2c, np.array([0, 0, 0, 1.0], dtype=np.float32)))
P_v2cl = P_rect_l @ R_rect @ T_v2c
P_v2cr = P_rect_r @ R_rect @ T_v2c
# Compute the rectified extrinsics from cam0 to camN
T_l = np.eye(4, dtype=np.float32)
T_l[0, 3] = P_rect_l[0, 3] / P_rect_l[0, 0]
T_r = np.eye(4, dtype=np.float32)
T_r[0, 3] = P_rect_r[0, 3] / P_rect_r[0, 0]
K = P_rect_l[:3, :3]
if keep_aspect_ratio:
r_orig = im_size[0] / im_size[1]
r_target = target_image_size[0] / target_image_size[1]
if r_orig >= r_target:
new_height = r_target * im_size[1]
crop_height = im_size[0] - ((im_size[0] - new_height) // 2) * 2
box = ((im_size[0] - new_height) // 2, 0, crop_height, int(im_size[1]))
c_x = K[0, 2] / im_size[1]
c_y = (K[1, 2] - (im_size[0] - new_height) / 2) / new_height
rescale = im_size[1] / target_image_size[1]
else:
new_width = im_size[0] / r_target
crop_width = im_size[1] - ((im_size[1] - new_width) // 2) * 2
box = (0, (im_size[1] - new_width) // 2, im_size[0], crop_width)
c_x = (K[0, 2] - (im_size[1] - new_width) / 2) / new_width
c_y = K[1, 2] / im_size[0]
rescale = im_size[0] / target_image_size[0]
f_x = (K[0, 0] / target_image_size[1]) / rescale
f_y = (K[1, 1] / target_image_size[0]) / rescale
box = tuple([int(x) for x in box])
else:
f_x = K[0, 0] / im_size[1]
f_y = K[1, 1] / im_size[0]
c_x = K[0, 2] / im_size[1]
c_y = K[1, 2] / im_size[0]
box = None
# Replace old K with new K
K[0, 0] = f_x * 2.
K[1, 1] = f_y * 2.
K[0, 2] = c_x * 2 - 1
K[1, 2] = c_y * 2 - 1
K_raw = np.eye(4, dtype=np.float32)
K_raw[0, 0] = f_x
K_raw[1, 1] = f_y
K_raw[0, 2] = c_x
K_raw[1, 2] = c_y
# Invert to get camera to center transformation, not center to camera
T_r = np.linalg.inv(T_r)
T_l = np.linalg.inv(T_l)
calibs[day] = {
"K": K,
"K_raw": K_raw,
"T_l": T_l,
"T_r": T_r,
"P_v2cl": P_v2cl,
"P_v2cr": P_v2cr,
"crop": box
}
return calibs
def __getitem__(self, index):
"""Returns a single training item from the dataset as a dictionary.
Values correspond to torch tensors.
Keys in the dictionary are either strings or tuples:
("color", <frame_id>, <scale>) for raw colour images,
("color_aug", <frame_id>, <scale>) for augmented colour images,
("K_tgt", scale) for camera intrinsics when projecting,
("inv_K", scale) for camera intrinsics when unprojecting,
"stereo_T" for camera extrinsics, and
"depth_gt" for ground truth depth maps.
<frame_id> is either:
an integer (e.g. 0, -1, or 1) representing the temporal step relative to 'index',
or
"s" for the opposite image in the stereo pair.
<scale> is an integer representing the scale of the image relative to the fullsize image:
-1 images at native resolution as loaded from disk
0 images resized to (self.width, self.height )
1 images resized to (self.width // 2, self.height // 2)
2 images resized to (self.width // 4, self.height // 4)
3 images resized to (self.width // 8, self.height // 8)
"""
cfg = self.cfg
inputs = {}
do_color_aug = cfg.dataset.color_aug and self.is_train and random.random() > 0.5
do_flip = cfg.dataset.flip_left_right and self.is_train and random.random() > 0.5
line = self.filenames[index].split()
folder = line[0]
day, sequence = folder.split("/")
calibs = self._calibs[day]
if len(line) == 3:
frame_index = int(line[1])
else:
frame_index = 0
stereo_flip = {"r": "l", "l": "r"}
if len(line) == 3:
side = line[2]
flip_stereo = self.is_train and random.random() > 0.5
if flip_stereo:
side = stereo_flip[side]
else:
side = None
frame_idxs = list(self.frame_idxs).copy()
for f_id in frame_idxs:
if type(f_id) == str and f_id[0] == "s": # stereo frame
the_side = stereo_flip[side]
i = int(f_id[1:])
else:
the_side = side
i = f_id
inputs[("color", f_id, -1)] = self.get_color(folder, frame_index + i, the_side, do_flip)
inputs[("frame_id", 0)] = \
f"{os.path.split(folder)[1]}+{side}+{frame_index:06d}"
# adjusting intrinsics to match each scale in the pyramid
for scale in range(self.num_scales):
if self.cfg.dataset.precise_intrinsics:
K = calibs["K_raw"]
else:
K = self.K
K_tgt = K.copy()
K_src = K.copy()
assert not do_flip
if self.cfg.dataset.precise_intrinsics and do_flip:
K[0, 2] = 1.0 - K[0, 2]
K_tgt[0, :] *= self.image_size[1] // (2 ** scale)
K_tgt[1, :] *= self.image_size[0] // (2 ** scale)
K_src[0, :] *= self.image_size[1] // (2 ** scale)
K_src[1, :] *= self.image_size[0] // (2 ** scale)
# principal points change if we add padding
K_src[0, 2] += self.cfg.dataset.pad_border_aug // (2 ** scale)
K_src[1, 2] += self.cfg.dataset.pad_border_aug // (2 ** scale)
inv_K_src = np.linalg.pinv(K_src)
inputs[("K_tgt", scale)] = torch.from_numpy(K_tgt)[..., :3, :3]
inputs[("K_src", scale)] = torch.from_numpy(K_src)[..., :3, :3]
inputs[("inv_K_src", scale)] = torch.from_numpy(inv_K_src)[..., :3, :3]
if do_color_aug:
raise NotImplementedError
color_aug = random_color_jitter(
self.brightness, self.contrast, self.saturation, self.hue)
else:
color_aug = (lambda x: x)
self.preprocess(inputs, color_aug)
for i in frame_idxs:
del inputs[("color", i, -1)]
del inputs[("color_aug", i, -1)]
if self.gt_depths:
depth_gt = self.get_depth_anything(folder, frame_index, side, do_flip)
depth_gt = np.expand_dims(depth_gt, 0)
depth_gt = torch.from_numpy(depth_gt.astype(np.float32))
depth_gt = self.resize_depth(depth_gt)
inputs[("depth_gt", 0, 0)] = depth_gt
if self.gt_poses:
# Load "GT" poses
for f_id in frame_idxs:
if type(f_id) == str and f_id[0] == "s": # stereo frame
the_side = {"r": "l", "l": "r"}[side]
i = int(f_id[1:])
else:
the_side = side
i = f_id
id = frame_index + i
T_side = calibs[f"T_{the_side}"]
pose = self._poses[(day, sequence)][id, :, :] @ T_side
inputs[("T_c2w", f_id)] = pose
return inputs
================================================
FILE: datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0001_sync.txt
================================================
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================================================
FILE: datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0002_sync.txt
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FILE: datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0005_sync.txt
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================================================
FILE: datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0009_sync.txt
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================================================
FILE: datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0011_sync.txt
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gitextract_gv2hn7lw/ ├── .gitignore ├── AUTHORS ├── README.md ├── configs/ │ ├── config.yaml │ ├── dataset/ │ │ ├── kitti.yaml │ │ ├── nyuv2.yaml │ │ └── re10k.yaml │ ├── experiment/ │ │ ├── layered_kitti.yaml │ │ ├── layered_nyuv2.yaml │ │ └── layered_re10k.yaml │ ├── hydra/ │ │ ├── cluster.yaml │ │ └── defaults.yaml │ ├── loss/ │ │ ├── reconstruction.yaml │ │ └── regularization.yaml │ └── model/ │ ├── backbone/ │ │ └── resnet.yaml │ ├── depth/ │ │ └── unidepth.yaml │ └── gaussian.yaml ├── datasets/ │ ├── colmap_misc.py │ ├── colmap_utils.py │ ├── data.py │ ├── download_realestate10k.py │ ├── download_realestate10k_colmap.sh │ ├── kitti.py │ ├── kitti_raw/ │ │ └── orb-slam_poses/ │ │ ├── 2011_09_26/ │ │ │ ├── 2011_09_26_drive_0001_sync.txt │ │ │ ├── 2011_09_26_drive_0002_sync.txt │ │ │ ├── 2011_09_26_drive_0005_sync.txt │ │ │ ├── 2011_09_26_drive_0009_sync.txt │ │ │ ├── 2011_09_26_drive_0011_sync.txt │ │ │ ├── 2011_09_26_drive_0013_sync.txt │ │ │ ├── 2011_09_26_drive_0014_sync.txt │ │ │ ├── 2011_09_26_drive_0015_sync.txt │ │ │ ├── 2011_09_26_drive_0017_sync.txt │ │ │ ├── 2011_09_26_drive_0018_sync.txt │ │ │ ├── 2011_09_26_drive_0019_sync.txt │ │ │ ├── 2011_09_26_drive_0020_sync.txt │ │ │ ├── 2011_09_26_drive_0022_sync.txt │ │ │ ├── 2011_09_26_drive_0023_sync.txt │ │ │ ├── 2011_09_26_drive_0027_sync.txt │ │ │ ├── 2011_09_26_drive_0028_sync.txt │ │ │ ├── 2011_09_26_drive_0029_sync.txt │ │ │ ├── 2011_09_26_drive_0032_sync.txt │ │ │ ├── 2011_09_26_drive_0035_sync.txt │ │ │ ├── 2011_09_26_drive_0036_sync.txt │ │ │ ├── 2011_09_26_drive_0039_sync.txt │ │ │ ├── 2011_09_26_drive_0046_sync.txt │ │ │ ├── 2011_09_26_drive_0048_sync.txt │ │ │ ├── 2011_09_26_drive_0051_sync.txt │ │ │ ├── 2011_09_26_drive_0052_sync.txt │ │ │ ├── 2011_09_26_drive_0056_sync.txt │ │ │ ├── 2011_09_26_drive_0057_sync.txt │ │ │ ├── 2011_09_26_drive_0059_sync.txt │ │ │ ├── 2011_09_26_drive_0060_sync.txt │ │ │ ├── 2011_09_26_drive_0061_sync.txt │ │ │ ├── 2011_09_26_drive_0064_sync.txt │ │ │ ├── 2011_09_26_drive_0070_sync.txt │ │ │ ├── 2011_09_26_drive_0079_sync.txt │ │ │ ├── 2011_09_26_drive_0084_sync.txt │ │ │ ├── 2011_09_26_drive_0086_sync.txt │ │ │ ├── 2011_09_26_drive_0087_sync.txt │ │ │ ├── 2011_09_26_drive_0091_sync.txt │ │ │ ├── 2011_09_26_drive_0093_sync.txt │ │ │ ├── 2011_09_26_drive_0095_sync.txt │ │ │ ├── 2011_09_26_drive_0096_sync.txt │ │ │ ├── 2011_09_26_drive_0101_sync.txt │ │ │ ├── 2011_09_26_drive_0104_sync.txt │ │ │ ├── 2011_09_26_drive_0106_sync.txt │ │ │ ├── 2011_09_26_drive_0113_sync.txt │ │ │ └── 2011_09_26_drive_0117_sync.txt │ │ ├── 2011_09_28/ │ │ │ ├── 2011_09_28_drive_0001_sync.txt │ │ │ └── 2011_09_28_drive_0002_sync.txt │ │ ├── 2011_09_29/ │ │ │ ├── 2011_09_29_drive_0004_sync.txt │ │ │ ├── 2011_09_29_drive_0026_sync.txt │ │ │ └── 2011_09_29_drive_0071_sync.txt │ │ ├── 2011_09_30/ │ │ │ ├── 2011_09_30_drive_0016_sync.txt │ │ │ ├── 2011_09_30_drive_0018_sync.txt │ │ │ ├── 2011_09_30_drive_0020_sync.txt │ │ │ ├── 2011_09_30_drive_0027_sync.txt │ │ │ ├── 2011_09_30_drive_0028_sync.txt │ │ │ ├── 2011_09_30_drive_0033_sync.txt │ │ │ └── 2011_09_30_drive_0034_sync.txt │ │ └── 2011_10_03/ │ │ ├── 2011_10_03_drive_0027_sync.txt │ │ ├── 2011_10_03_drive_0034_sync.txt │ │ ├── 2011_10_03_drive_0042_sync.txt │ │ └── 2011_10_03_drive_0047_sync.txt │ ├── nyu/ │ │ ├── camera.py │ │ ├── compute_colmap.py │ │ └── dataset.py │ ├── preprocess_realestate10k.py │ ├── re10k.py │ ├── tardataset.py │ └── util.py ├── evaluate.py ├── evaluate.sh ├── evaluation/ │ └── evaluator.py ├── misc/ │ ├── depth.py │ ├── download_pretrained_models.py │ ├── localstorage.py │ ├── logger.py │ ├── util.py │ └── visualise_3d.py ├── models/ │ ├── decoder/ │ │ ├── gauss_util.py │ │ ├── gaussian_decoder.py │ │ └── resnet_decoder.py │ ├── encoder/ │ │ ├── layers.py │ │ ├── resnet_encoder.py │ │ └── unidepth_encoder.py │ └── model.py ├── pyproject.toml ├── requirements-torch.txt ├── requirements.txt ├── splits/ │ ├── eldar/ │ │ ├── test_files.txt │ │ ├── train_files.txt │ │ └── val_files.txt │ ├── nyuv2/ │ │ └── val_files.txt │ ├── re10k_latentsplat/ │ │ ├── test_closer_as_src.txt │ │ ├── test_first_as_src.txt │ │ └── test_second_as_src.txt │ ├── re10k_mine_filtered/ │ │ ├── test_files.txt │ │ └── val_files.txt │ ├── re10k_pixelsplat/ │ │ ├── preprocess_2_frames_split.py │ │ ├── test_closer_as_src.txt │ │ ├── test_first_as_src.txt │ │ └── test_second_as_src.txt │ └── tulsiani2/ │ ├── test_files.txt │ ├── train_files.txt │ └── val_files.txt ├── train.py ├── train.sh └── trainer.py
SYMBOL INDEX (213 symbols across 30 files)
FILE: datasets/colmap_misc.py
function is_computed (line 11) | def is_computed(sparse_dir):
function read_colmap_pose (line 20) | def read_colmap_pose(image):
function read_camera_params (line 30) | def read_camera_params(camera):
function load_sparse_pcl_colmap (line 42) | def load_sparse_pcl_colmap(dir_recon):
function get_sparse_depth (line 65) | def get_sparse_depth(T_w2c, img_size, crop_margin, sparse_pcl, frame_idx):
FILE: datasets/colmap_utils.py
class Image (line 50) | class Image(BaseImage):
method qvec2rotmat (line 51) | def qvec2rotmat(self):
function read_next_bytes (line 74) | def read_next_bytes(fid, num_bytes, format_char_sequence, endian_charact...
function write_next_bytes (line 86) | def write_next_bytes(fid, data, format_char_sequence, endian_character="...
function read_cameras_text (line 102) | def read_cameras_text(path):
function read_cameras_binary (line 128) | def read_cameras_binary(path_to_model_file):
function write_cameras_text (line 157) | def write_cameras_text(cameras, path):
function write_cameras_binary (line 174) | def write_cameras_binary(cameras, path_to_model_file):
function read_images_text (line 194) | def read_images_text(path):
function read_images_binary (line 225) | def read_images_binary(path_to_model_file):
function write_images_text (line 260) | def write_images_text(images, path):
function write_images_binary (line 288) | def write_images_binary(images, path_to_model_file):
function read_points3D_text (line 309) | def read_points3D_text(path):
function read_points3d_binary (line 336) | def read_points3d_binary(path_to_model_file):
function write_points3D_text (line 366) | def write_points3D_text(points3D, path):
function write_points3d_binary (line 391) | def write_points3d_binary(points3D, path_to_model_file):
function detect_model_format (line 410) | def detect_model_format(path, ext):
function read_model (line 419) | def read_model(path, ext=""):
function write_model (line 441) | def write_model(cameras, images, points3D, path, ext=".bin"):
function qvec2rotmat (line 453) | def qvec2rotmat(qvec):
function rotmat2qvec (line 466) | def rotmat2qvec(R):
function main (line 480) | def main():
FILE: datasets/data.py
function process_projs (line 6) | def process_projs(proj):
function pose_to_4x4 (line 17) | def pose_to_4x4(w2c):
function data_to_c2w (line 24) | def data_to_c2w(w2c):
function pil_loader (line 30) | def pil_loader(path):
function get_sparse_depth (line 38) | def get_sparse_depth(pose_data, orig_size, sparse_pcl, frame_idx):
FILE: datasets/download_realestate10k.py
class Data (line 17) | class Data:
method __init__ (line 18) | def __init__(self, url, seqname, list_timestamps):
method add (line 26) | def add(self, seqname, list_timestamps):
method __len__ (line 30) | def __len__(self):
function process (line 34) | def process(data, seq_id, videoname, output_root):
function wrap_process (line 60) | def wrap_process(list_args):
class DataDownloader (line 64) | class DataDownloader:
method __init__ (line 65) | def __init__(self, data_path: Path, out_path: Path, tmp_path: Path, mo...
method run (line 102) | def run(self):
method show (line 143) | def show(self):
function main (line 157) | def main():
FILE: datasets/kitti.py
function readlines (line 23) | def readlines(filename):
class KITTIDataset (line 30) | class KITTIDataset(data.Dataset):
method __init__ (line 31) | def __init__(self,
method __len__ (line 113) | def __len__(self):
method get_color (line 116) | def get_color(self, folder, frame_index, side, do_flip):
method get_depth_anything (line 125) | def get_depth_anything(self, folder, frame_index, side, do_flip):
method get_image_path (line 137) | def get_image_path(self, folder, frame_index, side):
method preprocess (line 143) | def preprocess(self, inputs, color_aug):
method _get_sequences (line 168) | def _get_sequences(data_path):
method _load_poses (line 184) | def _load_poses(pose_path, sequences):
method _load_calibs (line 212) | def _load_calibs(data_path, target_image_size, keep_aspect_ratio):
method __getitem__ (line 326) | def __getitem__(self, index):
FILE: datasets/nyu/camera.py
function camera_params (line 4) | def camera_params():
function make_K (line 21) | def make_K(fx, fy, cx, cy):
FILE: datasets/nyu/compute_colmap.py
function get_sub_dirs (line 20) | def get_sub_dirs(dir_colmap):
function get_sub_dirs (line 27) | def get_sub_dirs(dir_colmap):
function get_nyu_test_sequences (line 34) | def get_nyu_test_sequences():
function run_colmap (line 42) | def run_colmap(dir_colmap, cuda_device):
function add_camera (line 115) | def add_camera(db):
function compute_sfm (line 139) | def compute_sfm(seq_name):
function main (line 191) | def main():
FILE: datasets/nyu/dataset.py
class NYUv2Dataset (line 23) | class NYUv2Dataset(data.Dataset):
method __init__ (line 24) | def __init__(self,
method _load_split_indices (line 73) | def _load_split_indices(index_path):
method load_image (line 89) | def load_image(self, seq_key, filename, color_aug_fn):
method get_colmap_dir (line 135) | def get_colmap_dir(self, seq_key):
method get_image_files (line 138) | def get_image_files(self, seq_key):
method get_full_sequence (line 144) | def get_full_sequence(self, seq_key, src_idx):
method get_inputs_and_targets (line 150) | def get_inputs_and_targets(self, seq_key, src_and_tgt_frame_idxs, fram...
method __getitem__ (line 219) | def __getitem__(self, index):
method __len__ (line 229) | def __len__(self) -> int:
FILE: datasets/preprocess_realestate10k.py
function main (line 10) | def main():
FILE: datasets/re10k.py
function load_seq_data (line 21) | def load_seq_data(data_path, split):
class Re10KDataset (line 28) | class Re10KDataset(data.Dataset):
method __init__ (line 29) | def __init__(self,
method __len__ (line 131) | def __len__(self) -> int:
method _load_seq_data (line 134) | def _load_seq_data(self, split):
method _full_index (line 137) | def _full_index(self, seq_keys, seq_data, left_offset, extra_frames):
method _load_sparse_pcl (line 153) | def _load_sparse_pcl(self, seq_key):
method _load_image (line 165) | def _load_image(self, key, id):
method _load_depth (line 176) | def _load_depth(self, key, id):
method _load_split_indices (line 193) | def _load_split_indices(index_path):
method get_frame_data (line 210) | def get_frame_data(self, seq_key, frame_idx, pose_data, color_aug_fn):
method __getitem__ (line 257) | def __getitem__(self, index):
FILE: datasets/tardataset.py
class TarDataset (line 15) | class TarDataset(Dataset):
method __init__ (line 39) | def __init__(self, archive, transform=to_tensor, extensions=('.png', '...
method filter_samples (line 67) | def filter_samples(self, is_valid_file=None, extensions=('.png', '.jpg...
method __getitem__ (line 87) | def __getitem__(self, index):
method __len__ (line 110) | def __len__(self):
method get_image (line 119) | def get_image(self, name, pil=False):
method get_text_file (line 135) | def get_text_file(self, name, encoding='utf-8'):
method get_file (line 148) | def get_file(self, name):
method __del__ (line 167) | def __del__(self):
method __getstate__ (line 173) | def __getstate__(self):
FILE: datasets/util.py
function create_datasets (line 10) | def create_datasets(cfg, split="val"):
function custom_collate (line 40) | def custom_collate(batch):
FILE: evaluate.py
function get_model_instance (line 21) | def get_model_instance(model):
function evaluate (line 27) | def evaluate(model, cfg, evaluator, dataloader, device=None, save_vis=Fa...
function main (line 134) | def main(cfg: DictConfig):
FILE: evaluation/evaluator.py
class Evaluator (line 10) | class Evaluator(nn.Module):
method __init__ (line 11) | def __init__(self, crop_border=True):
method norm (line 28) | def norm(img):
method metric_names (line 31) | def metric_names(self):
method forward (line 34) | def forward(self, img_pred, img_gt):
FILE: misc/depth.py
function estimate_depth_scale_kitti (line 10) | def estimate_depth_scale_kitti(depth, depth_gt):
function estimate_depth_scale (line 26) | def estimate_depth_scale(depth, sparse_depth):
function estimate_depth_scale_ransac (line 53) | def estimate_depth_scale_ransac(depth, sparse_depth, num_iterations=1000...
function gray2rgb (line 99) | def gray2rgb(im, cmap=CMAP_DEFAULT):
function normalize_depth_for_display (line 107) | def normalize_depth_for_display(depth, pc=95, crop_percent=0, normalizer...
FILE: misc/download_pretrained_models.py
function main (line 6) | def main():
FILE: misc/localstorage.py
function get_local_dir (line 9) | def get_local_dir():
function local_storage_path (line 18) | def local_storage_path(filename):
function copy_to_local_storage (line 21) | def copy_to_local_storage(filename, rank=None):
function extract_tar (line 35) | def extract_tar(fn, unzip_dir):
FILE: misc/logger.py
function get_all_keys (line 12) | def get_all_keys(d):
function setup_logging_dir (line 19) | def setup_logging_dir():
class NeptuneLogger (line 29) | class NeptuneLogger:
method __init__ (line 30) | def __init__(self, cfg):
method _setup (line 49) | def _setup(cfg):
method log (line 68) | def log(self, values, step):
method log3d (line 72) | def log3d(self, kv, step):
method upload_file (line 75) | def upload_file(self, key, filename):
method upload_image (line 78) | def upload_image(self, key, image):
method log_image (line 81) | def log_image(self, key, image, step=0):
function setup_logger (line 85) | def setup_logger(cfg):
FILE: misc/util.py
function get_source_frame_ids (line 3) | def get_source_frame_ids():
function add_source_frame_id (line 6) | def add_source_frame_id(novel_frames):
function sec_to_hm (line 9) | def sec_to_hm(t):
function sec_to_hm_str (line 20) | def sec_to_hm_str(t):
FILE: misc/visualise_3d.py
function depth_to_img (line 16) | def depth_to_img(d):
function vis_2d_offsets (line 22) | def vis_2d_offsets(model, inputs, outputs, out_dir, frame_id):
function construct_list_of_attributes (line 80) | def construct_list_of_attributes(num_rest: int) -> list[str]:
function export_ply (line 94) | def export_ply(
function save_ply (line 162) | def save_ply(outputs, path, gaussians_per_pixel=3):
FILE: models/decoder/gauss_util.py
function getProjectionMatrix (line 18) | def getProjectionMatrix(znear, zfar, fovX, fovY, pX=0.0, pY=0.0):
function fov2focal (line 46) | def fov2focal(fov, pixels):
function focal2fov (line 50) | def focal2fov(focal, pixels):
function K_to_NDC_pp (line 53) | def K_to_NDC_pp(Kx, Ky, H, W):
function render_predicted (line 63) | def render_predicted(cfg,
FILE: models/decoder/gaussian_decoder.py
function get_splits_and_inits (line 6) | def get_splits_and_inits(cfg):
class GaussianDecoder (line 39) | class GaussianDecoder(nn.Module):
method __init__ (line 41) | def __init__(self, cfg):
method forward (line 51) | def forward(self, x, split_dimensions=[3,1,3,4,3,9]):
FILE: models/decoder/resnet_decoder.py
class ResnetDecoder (line 10) | class ResnetDecoder(nn.Module):
method __init__ (line 12) | def __init__(self, cfg, num_ch_enc, use_skips=True):
method forward (line 54) | def forward(self, input_features):
class ResnetDepthDecoder (line 70) | class ResnetDepthDecoder(nn.Module):
method __init__ (line 72) | def __init__(self, cfg, num_ch_enc, use_skips=True):
method forward (line 114) | def forward(self, input_features):
FILE: models/encoder/layers.py
function disp_to_depth (line 6) | def disp_to_depth(disp, min_depth, max_depth):
function upsample (line 18) | def upsample(x, mode="nearest"):
class Conv3x3 (line 24) | class Conv3x3(nn.Module):
method __init__ (line 27) | def __init__(self, in_channels, out_channels, use_refl=True):
method forward (line 36) | def forward(self, x):
class ConvBlock (line 42) | class ConvBlock(nn.Module):
method __init__ (line 45) | def __init__(self, in_channels, out_channels):
method forward (line 51) | def forward(self, x):
class BackprojectDepth (line 57) | class BackprojectDepth(nn.Module):
method __init__ (line 60) | def __init__(self, batch_size, height, width, shift_rays_half_pixel=0):
method forward (line 84) | def forward(self, depth, inv_K):
class Project3D (line 92) | class Project3D(nn.Module):
method __init__ (line 95) | def __init__(self, batch_size, height, width, eps=1e-7):
method forward (line 103) | def forward(self, points, K, T=None):
class Project3DSimple (line 121) | class Project3DSimple(nn.Module):
method __init__ (line 124) | def __init__(self, batch_size, height, width, eps=1e-7):
method forward (line 132) | def forward(self, points, K):
class SSIM (line 143) | class SSIM(nn.Module):
method __init__ (line 146) | def __init__(self):
method forward (line 159) | def forward(self, x, y):
FILE: models/encoder/resnet_encoder.py
class ResNetMultiImageInput (line 18) | class ResNetMultiImageInput(models.ResNet):
method __init__ (line 22) | def __init__(self, block, layers, num_classes=1000, num_input_images=1):
function resnet_multiimage_input (line 43) | def resnet_multiimage_input(num_layers, pretrained=False, num_input_imag...
class ResnetEncoder (line 64) | class ResnetEncoder(nn.Module):
method __init__ (line 67) | def __init__(self, num_layers, pretrained, bn_order, num_input_images=1):
method forward (line 85) | def forward(self, input_image):
FILE: models/encoder/unidepth_encoder.py
class UniDepthExtended (line 9) | class UniDepthExtended(nn.Module):
method __init__ (line 10) | def __init__(self, cfg):
method get_parameter_groups (line 48) | def get_parameter_groups(self):
method forward (line 52) | def forward(self, inputs):
FILE: models/model.py
function default_param_group (line 14) | def default_param_group(model):
function to_device (line 18) | def to_device(inputs, device):
class GaussianPredictor (line 25) | class GaussianPredictor(nn.Module):
method __init__ (line 26) | def __init__(self, cfg):
method set_backproject (line 45) | def set_backproject(self):
method target_frame_ids (line 70) | def target_frame_ids(self, inputs):
method all_frame_ids (line 73) | def all_frame_ids(self, inputs):
method set_train (line 76) | def set_train(self):
method set_eval (line 83) | def set_eval(self):
method is_train (line 90) | def is_train(self):
method forward (line 93) | def forward(self, inputs):
method compute_gauss_means (line 106) | def compute_gauss_means(self, inputs, outputs):
method process_gt_poses (line 130) | def process_gt_poses(self, inputs, outputs):
method render_images (line 189) | def render_images(self, inputs, outputs):
method checkpoint_dir (line 281) | def checkpoint_dir(self):
method save_model (line 284) | def save_model(self, optimiser, step, ema=None):
method load_model (line 307) | def load_model(self, weights_path, optimiser=None, device="cpu", ckpt_...
FILE: splits/re10k_pixelsplat/preprocess_2_frames_split.py
function transform_json_to_txt (line 4) | def transform_json_to_txt(json_data, seq_data, src_idx=0):
function main (line 38) | def main():
FILE: train.py
function run_epoch (line 21) | def run_epoch(fabric,
function main (line 83) | def main(cfg: DictConfig):
FILE: trainer.py
class Trainer (line 17) | class Trainer(nn.Module):
method __init__ (line 18) | def __init__(self, cfg):
method set_logger (line 30) | def set_logger(self, logger):
method forward (line 33) | def forward(self, inputs):
method compute_reconstruction_loss (line 38) | def compute_reconstruction_loss(self, pred, target, losses):
method compute_losses (line 66) | def compute_losses(self, inputs, outputs):
method log_time (line 113) | def log_time(self, batch_idx, duration, loss):
method log_scalars (line 125) | def log_scalars(self, mode, outputs, losses, lr):
method log (line 145) | def log(self, mode, inputs, outputs):
method validate (line 203) | def validate(self, model, evaluator, val_loader, device):
Copy disabled (too large)
Download .json
Condensed preview — 129 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (11,354K chars).
[
{
"path": ".gitignore",
"chars": 3165,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "AUTHORS",
"chars": 51,
"preview": "Eldar Insafutdinov\nStan Szymanowicz\nChuanxia Zheng\n"
},
{
"path": "README.md",
"chars": 4048,
"preview": "[](https://arxiv.org/abs/2406.043"
},
{
"path": "configs/config.yaml",
"chars": 657,
"preview": "defaults:\n - _self_\n - hydra: defaults\n - model: gaussian\n - dataset: re10k\n - loss: [reconstruction]\n\nconfig:\n ex"
},
{
"path": "configs/dataset/kitti.yaml",
"chars": 494,
"preview": "name: kitti\ndata_path: /scratch/shared/nfs1/eldar/data/kitti_raw\npose_path: /users/cxzheng/code/facilitate4d/datasets/ki"
},
{
"path": "configs/dataset/nyuv2.yaml",
"chars": 376,
"preview": "name: nyuv2\nsplit: original\ndata_path: /scratch/shared/beegfs/eldar/data/nyuv2_raw\ncolmap_path: /work/eldar/data/dataset"
},
{
"path": "configs/dataset/re10k.yaml",
"chars": 522,
"preview": "name: re10k\nsplit: original\ndata_path: data/RealEstate10K \ndepth_path:\nunpack_pcl_tar: false\npreload_depths: false\nransa"
},
{
"path": "configs/experiment/layered_kitti.yaml",
"chars": 473,
"preview": "# @package _global_\nconfig:\n exp_name: debug\n\ndefaults:\n - override /dataset: kitti\n - override /model: gaussian\n - "
},
{
"path": "configs/experiment/layered_nyuv2.yaml",
"chars": 477,
"preview": "# @package _global_\nconfig:\n exp_name: debug\n\ndefaults:\n - override /dataset: nyuv2\n - override /model: gaussian\n - "
},
{
"path": "configs/experiment/layered_re10k.yaml",
"chars": 477,
"preview": "# @package _global_\nconfig:\n exp_name: debug\n\ndefaults:\n - override /dataset: re10k\n - override /model: gaussian\n - "
},
{
"path": "configs/hydra/cluster.yaml",
"chars": 588,
"preview": "---\nrun:\n dir: exp\nsweep:\n dir: exp\n subdir: ${hydra.job.override_dirname}\njob:\n chdir: True\nlauncher:\n submitit_fo"
},
{
"path": "configs/hydra/defaults.yaml",
"chars": 161,
"preview": "---\nrun:\n dir: exp/${now:%Y-%m-%d}/${now:%H-%M-%S}\nsweep:\n dir: exp/${now:%Y-%m-%d}/${now:%H-%M-%S}\n subdir: ${hydra."
},
{
"path": "configs/loss/reconstruction.yaml",
"chars": 100,
"preview": "mse:\n weight: 1.0\n type: l1\n\nssim:\n weight: 0.85\n\nlpips:\n weight: 0.01\n apply_after_step: 50000"
},
{
"path": "configs/loss/regularization.yaml",
"chars": 86,
"preview": "gauss_scale:\n weight: 0.001\n thresh: 2.0\n\ngauss_offset:\n weight: 0.01\n thresh: 1.0"
},
{
"path": "configs/model/backbone/resnet.yaml",
"chars": 190,
"preview": "name: resnet\nnum_layers: 50 # 18, 34, 50, 101, 152\nnum_ch_dec: [32,32,64,128,256]\nresnet_bn_order: pre_bn # monodepth, p"
},
{
"path": "configs/model/depth/unidepth.yaml",
"chars": 28,
"preview": "version: v1\nbackbone: vitl14"
},
{
"path": "configs/model/gaussian.yaml",
"chars": 593,
"preview": "defaults:\n - depth: unidepth\n - backbone: resnet\n\nname: unidepth\nframe_ids: [0, -1, 1]\nscales: [0]\ngauss_novel_frames:"
},
{
"path": "datasets/colmap_misc.py",
"chars": 2658,
"preview": "import numpy as np\nimport torch\n\nfrom datasets.colmap_utils import \\\n read_images_binary, \\\n read_points3d_binary,"
},
{
"path": "datasets/colmap_utils.py",
"chars": 21363,
"preview": "# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.\n# All rights reserved.\n#\n# Redistribution and use in source and bi"
},
{
"path": "datasets/data.py",
"chars": 1937,
"preview": "from PIL import Image\nimport numpy as np\nimport torch\n\n\ndef process_projs(proj):\n # pose in dataset is normalised by "
},
{
"path": "datasets/download_realestate10k.py",
"chars": 6537,
"preview": "\"\"\"\nAuthor: Felix Wimbauer\nSource: https://github.com/Brummi/BehindTheScenes/blob/main/datasets/realestate10k/download_r"
},
{
"path": "datasets/download_realestate10k_colmap.sh",
"chars": 330,
"preview": "#!/bin/sh\n\nDATA_PATH=data/RealEstate10K\nfile_list=(\n test.pickle.gz\n train.pickle.gz\n pcl.test.tar\n pcl.trai"
},
{
"path": "datasets/kitti.py",
"chars": 17298,
"preview": "import os\nimport random\nimport torch\nimport numpy as np\nimport torch.utils.data as data\nimport torchvision.transforms as"
},
{
"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0001_sync.txt",
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"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0002_sync.txt",
"chars": 11435,
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{
"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0005_sync.txt",
"chars": 22961,
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"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0009_sync.txt",
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"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0013_sync.txt",
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{
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"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0091_sync.txt",
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{
"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0095_sync.txt",
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{
"path": "datasets/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0096_sync.txt",
"chars": 71614,
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"path": "datasets/nyu/camera.py",
"chars": 594,
"preview": "import numpy as np\n\n\ndef camera_params():\n # RGB Intrinsic Parameters\n fx = 5.1885790117450188e+02\n fy = 5.1946"
},
{
"path": "datasets/nyu/compute_colmap.py",
"chars": 5345,
"preview": "import math\nimport os\nimport shutil\nimport subprocess\nfrom pathlib import Path\nimport numpy as np\nfrom imageio.v3 import"
},
{
"path": "datasets/nyu/dataset.py",
"chars": 8781,
"preview": "from pathlib import Path\nfrom typing import Optional\n\nimport cv2\nimport numpy as np\nfrom imageio import imread\nfrom PIL "
},
{
"path": "datasets/preprocess_realestate10k.py",
"chars": 793,
"preview": "from pathlib import Path\nimport gzip\nimport pickle\nimport argparse\nfrom tqdm import tqdm\n\nfrom datasets.re10k import loa"
},
{
"path": "datasets/re10k.py",
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"preview": "import os\nimport random\nimport pickle\nimport gzip\nimport torch\nimport numpy as np\nimport torch.utils.data as data\nimport"
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{
"path": "datasets/tardataset.py",
"chars": 6375,
"preview": "\nimport tarfile\nfrom io import BytesIO\nfrom PIL import Image, ImageFile\n\nfrom torch.utils.data import Dataset, get_worke"
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{
"path": "datasets/util.py",
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"preview": "import torch\nimport logging\nfrom torch.utils.data import DataLoader\n\nfrom packaging.version import Version\nfrom datasets"
},
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"path": "evaluate.py",
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"preview": "import os\nimport json\nimport hydra\nimport torch\nimport numpy as np\n\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom "
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"preview": "#!/bin/sh\n\n# re10k testing\npython evaluate.py \\\n hydra.run.dir=$1 \\\n hydra.job.chdir=true \\\n +experiment=layere"
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"preview": "import math\nimport torch\nimport torch.nn as nn\n\nfrom torchmetrics.image import \\\n LearnedPerceptualImagePatchSimilari"
},
{
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"chars": 4059,
"preview": "import random\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom eino"
},
{
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"chars": 542,
"preview": "from pathlib import Path\nimport argparse\nfrom huggingface_hub import hf_hub_download\n\n\ndef main():\n parser = argparse"
},
{
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"chars": 1353,
"preview": "import os\nimport shutil\nimport logging\nimport tarfile\nfrom pathlib import Path\n\nproject_name = \"monosplat\"\n\ndef get_loca"
},
{
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"chars": 2562,
"preview": "import os\nimport tempfile\nfrom pathlib import Path\n\nfrom omegaconf import OmegaConf\nimport neptune as neptune\nfrom neptu"
},
{
"path": "misc/util.py",
"chars": 537,
"preview": "\n\ndef get_source_frame_ids():\n return [0]\n\ndef add_source_frame_id(novel_frames):\n return get_source_frame_ids() +"
},
{
"path": "misc/visualise_3d.py",
"chars": 5711,
"preview": "import torch\nimport numpy as np\nfrom pathlib import Path\nfrom einops import rearrange, einsum\nfrom matplotlib import pyp"
},
{
"path": "models/decoder/gauss_util.py",
"chars": 5381,
"preview": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# Thi"
},
{
"path": "models/decoder/gaussian_decoder.py",
"chars": 4165,
"preview": "import torch\nimport torch.nn as nn\nimport numpy as np\n\n\ndef get_splits_and_inits(cfg):\n split_dimensions = []\n sca"
},
{
"path": "models/decoder/resnet_decoder.py",
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"preview": "import torch\nimport numpy as np\nfrom einops import rearrange\nfrom collections import OrderedDict\n\nfrom models.encoder.la"
},
{
"path": "models/encoder/layers.py",
"chars": 5565,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\ndef disp_to_depth(disp, min_depth"
},
{
"path": "models/encoder/resnet_encoder.py",
"chars": 4476,
"preview": "# Copyright Niantic 2019. Patent Pending. All rights reserved.\n#\n# This software is licensed under the terms of the Mono"
},
{
"path": "models/encoder/unidepth_encoder.py",
"chars": 4678,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom einops import rearrange\nfrom models.encoder.res"
},
{
"path": "models/model.py",
"chars": 13934,
"preview": "import torch\nimport logging\nimport time\nimport torch.nn as nn\n\nfrom pathlib import Path\nfrom einops import rearrange\n\nfr"
},
{
"path": "pyproject.toml",
"chars": 126,
"preview": "[tool.basedpyright]\n\"exclude\" = [\n \".git\",\n \".venv\",\n \"__pycache__\",\n \"experiments_out\",\n \"exp\",\n \"dat"
},
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"preview": "numpy==1.26.4\ntorch==2.2.2\ntorchvision\ntorchaudio\nxformers==0.0.25.post1\n\n"
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"preview": "numpy==1.26.4\ntorch==2.2.2\ntorchvision\ntorchaudio\neinops\nhuggingface-hub>=0.22.0\nimageio\nmatplotlib\nninja\nopencv-python\n"
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"preview": "2011_09_26/2011_09_26_drive_0022_sync 247 r\n2011_09_26/2011_09_26_drive_0022_sync 278 r\n2011_09_26/2011_09_26_drive_0022"
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"preview": "2011_09_26/2011_09_26_drive_0022_sync 473 r\n2011_09_26/2011_09_26_drive_0022_sync 668 r\n2011_09_26/2011_09_26_drive_0022"
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{
"path": "splits/nyuv2/val_files.txt",
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"preview": "bathroom_0003 301 306 311 277\nbathroom_0004 180 185 190 202\nbathroom_0008 30 35 40 15\nbathroom_0009 30 35 40 48\nbathroom"
},
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"preview": "5aca87f95a9412c6 133 13 135 141\n322261824c4a3003 82 4 11 84\n17d9303ee77c3a3d 66 10 12 13\ndebc3490ba0bd84b 73 0 1 8\ne124d"
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"preview": "249fd0890d439aa9 141 146 151 151\n249fd0890d439aa9 81 86 91 60\n249fd0890d439aa9 113 118 123 142\n249fd0890d439aa9 125 130 "
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"preview": "fc966f25afa3659f 52 57 62 65\nfc966f25afa3659f 77 82 87 50\nfc966f25afa3659f 130 135 140 146\nfc966f25afa3659f 194 199 204 "
},
{
"path": "splits/re10k_pixelsplat/preprocess_2_frames_split.py",
"chars": 2264,
"preview": "import json\nimport pickle\n\ndef transform_json_to_txt(json_data, seq_data, src_idx=0):\n result = []\n total_excluded"
},
{
"path": "splits/re10k_pixelsplat/test_closer_as_src.txt",
"chars": 564304,
"preview": "5aca87f95a9412c6 58 84 84 84\n5aca87f95a9412c6 133 102 102 102\n5aca87f95a9412c6 133 129 129 129\n322261824c4a3003 33 38 38"
},
{
"path": "splits/re10k_pixelsplat/test_first_as_src.txt",
"chars": 186307,
"preview": "5aca87f95a9412c6 58 84 102 129\n322261824c4a3003 33 38 60 61\n17d9303ee77c3a3d 17 26 28 38\ndebc3490ba0bd84b 34 53 66 81\ne1"
},
{
"path": "splits/re10k_pixelsplat/test_second_as_src.txt",
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"preview": "5aca87f95a9412c6 133 84 102 129\n322261824c4a3003 78 38 60 61\n17d9303ee77c3a3d 63 26 28 38\ndebc3490ba0bd84b 81 53 66 81\ne"
},
{
"path": "splits/tulsiani2/test_files.txt",
"chars": 47035,
"preview": "2011_09_26/2011_09_26_drive_0001_sync 0 l\n2011_09_26/2011_09_26_drive_0001_sync 1 l\n2011_09_26/2011_09_26_drive_0001_syn"
},
{
"path": "splits/tulsiani2/train_files.txt",
"chars": 522865,
"preview": "2011_09_26/2011_09_26_drive_0005_sync 0 l\n2011_09_26/2011_09_26_drive_0005_sync 1 l\n2011_09_26/2011_09_26_drive_0005_syn"
},
{
"path": "splits/tulsiani2/val_files.txt",
"chars": 54329,
"preview": "2011_09_26/2011_09_26_drive_0048_sync 0 l\n2011_09_26/2011_09_26_drive_0048_sync 1 l\n2011_09_26/2011_09_26_drive_0048_syn"
},
{
"path": "train.py",
"chars": 5206,
"preview": "import os\nimport time\nimport logging\nimport torch\nimport hydra\nimport torch.optim as optim\n\nfrom ema_pytorch import EMA\n"
},
{
"path": "train.sh",
"chars": 196,
"preview": "python train.py \\\n hydra=cluster \\\n hydra/launcher=submitit_slurm \\\n +hydra.job.tag=gaussian2_unidepthv1 \\\n +experim"
},
{
"path": "trainer.py",
"chars": 9605,
"preview": "import time\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom einops import rearrange\n\nfrom models.model impor"
}
]
About this extraction
This page contains the full source code of the eldar/flash3d GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 129 files (10.7 MB), approximately 2.8M tokens, and a symbol index with 213 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.