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19 KiB
19 KiB
Nerfstudio: A collaboration friendly studio for NeRFs
This colab shows how to train and view NeRFs from Nerfstudio both on pre-made datasets or from your own videos/images.
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Credit to NeX for Google Colab format.
In [ ]:
#@markdown <h1>Install Nerfstudio and Dependencies (~8 min)</h1>
%cd /content/
!pip install --upgrade pip
!pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
# Installing TinyCuda
%cd /content/
!gdown "https://drive.google.com/u/1/uc?id=1-7x7qQfB7bIw2zV4Lr6-yhvMpjXC84Q5&confirm=t"
!pip install tinycudann-1.7-cp310-cp310-linux_x86_64.whl
# Installing COLMAP
%cd /content/
!gdown "https://drive.google.com/u/0/uc?id=15WngFRNar_b8CaPR5R-hvQ3eAnlyk_SL&confirm=t"
!sudo apt-get install \
build-essential \
libboost-program-options-dev \
libboost-filesystem-dev \
libboost-graph-dev \
libboost-system-dev \
libboost-test-dev \
libeigen3-dev \
libflann-dev \
libfreeimage-dev \
libmetis-dev \
libgoogle-glog-dev \
libgflags-dev \
libsqlite3-dev \
libglew-dev \
qtbase5-dev \
libqt5opengl5-dev \
libcgal-dev \
libceres-dev
!unzip local.zip -d /usr/
!chmod +x /usr/local/bin/colmap
# Install nerfstudio
%cd /content/
!pip install git+https://github.com/nerfstudio-project/nerfstudio.gitIn [ ]:
#@markdown <h1> Downloading and Processing Data</h1>
#@markdown <h3>Pick the preset scene or upload your own images/video</h3>
import os
import glob
from google.colab import files
from IPython.core.display import display, HTML
scene = '\uD83D\uDDBC poster' #@param ['🖼 poster', '🚜 dozer', '🌄 desolation', '📤 upload your images' , '🎥 upload your own video', '🔺 upload Polycam data', '💽 upload your own Record3D data']
scene = ' '.join(scene.split(' ')[1:])
if scene == "upload Polycam data":
%cd /content/
!mkdir -p /content/data/nerfstudio/custom_data
%cd /content/data/nerfstudio/custom_data/
uploaded = files.upload()
dir = os.getcwd()
if len(uploaded.keys()) > 1:
print("ERROR, upload a single .zip file when processing Polycam data")
dataset_dir = [os.path.join(dir, f) for f in uploaded.keys()][0]
!ns-process-data polycam --data $dataset_dir --output-dir /content/data/nerfstudio/custom_data/
scene = "custom_data"
elif scene == 'upload your own Record3D data':
display(HTML('<h3>Zip your Record3D folder, and upload.</h3>'))
display(HTML('<h3>More information on Record3D can be found <a href="https://docs.nerf.studio/en/latest/quickstart/custom_dataset.html#record3d-capture" target="_blank">here</a>.</h3>'))
%cd /content/
!mkdir -p /content/data/nerfstudio/custom_data
%cd /content/data/nerfstudio/custom_data/
uploaded = files.upload()
dir = os.getcwd()
preupload_datasets = [os.path.join(dir, f) for f in uploaded.keys()]
record_3d_zipfile = preupload_datasets[0]
!unzip $record_3d_zipfile -d /content/data/nerfstudio/custom_data
custom_data_directory = glob.glob('/content/data/nerfstudio/custom_data/*')[0]
!ns-process-data record3d --data $custom_data_directory --output-dir /content/data/nerfstudio/custom_data/
scene = "custom_data"
elif scene in ['upload your images', 'upload your own video']:
display(HTML('<h3>Select your custom data</h3>'))
display(HTML('<p/>You can select multiple images by pressing ctrl, cmd or shift and click.<p>'))
display(HTML('<p/>Note: This may take time, especially on higher resolution inputs, so we recommend to download dataset after creation.<p>'))
!mkdir -p /content/data/nerfstudio/custom_data
if scene == 'upload your images':
!mkdir -p /content/data/nerfstudio/custom_data/raw_images
%cd /content/data/nerfstudio/custom_data/raw_images
uploaded = files.upload()
dir = os.getcwd()
else:
%cd /content/data/nerfstudio/custom_data/
uploaded = files.upload()
dir = os.getcwd()
preupload_datasets = [os.path.join(dir, f) for f in uploaded.keys()]
del uploaded
%cd /content/
if scene == 'upload your images':
!ns-process-data images --data /content/data/nerfstudio/custom_data/raw_images --output-dir /content/data/nerfstudio/custom_data/
else:
video_path = preupload_datasets[0]
!ns-process-data video --data $video_path --output-dir /content/data/nerfstudio/custom_data/
scene = "custom_data"
else:
%cd /content/
!ns-download-data nerfstudio --capture-name=$scene
print("Data Processing Succeeded!")In [ ]:
#@markdown <h1>Set up and Start Viewer</h1>
%cd /content
# Install localtunnel
# We are using localtunnel https://github.com/localtunnel/localtunnel but ngrok could also be used
!npm install -g localtunnel
# Tunnel port 7007, the default for
!rm url.txt 2> /dev/null
get_ipython().system_raw('lt --port 7007 >> url.txt 2>&1 &')
import time
time.sleep(3) # the previous command needs time to write to url.txt
with open('url.txt') as f:
lines = f.readlines()
websocket_url = lines[0].split(": ")[1].strip().replace("https", "wss")
# from nerfstudio.utils.io import load_from_json
# from pathlib import Path
# json_filename = "nerfstudio/nerfstudio/viewer/app/package.json"
# version = load_from_json(Path(json_filename))["version"]
url = f"https://viewer.nerf.studio/?websocket_url={websocket_url}"
print(url)
print("You may need to click Refresh Page after you start training!")
from IPython import display
display.IFrame(src=url, height=800, width="100%")/content [K[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js [K[?25h+ localtunnel@2.0.2 added 22 packages from 22 contributors in 2.07s https://viewer.nerf.studio/?websocket_url=wss://cyan-facts-matter-34-91-1-218.loca.lt You may need to click Refresh Page after you start training!
In [ ]:
#@markdown <h1>Start Training</h1>
%cd /content
if os.path.exists(f"data/nerfstudio/{scene}/transforms.json"):
!ns-train nerfacto --viewer.websocket-port 7007 nerfstudio-data --data data/nerfstudio/$scene --downscale-factor 4
else:
from IPython.core.display import display, HTML
display(HTML('<h3 style="color:red">Error: Data processing did not complete</h3>'))
display(HTML('<h3>Please re-run `Downloading and Processing Data`, or view the FAQ for more info.</h3>'))In [ ]:
#@title # Render Video { vertical-output: true }
#@markdown <h3>Export the camera path from within the viewer, then run this cell.</h3>
#@markdown <h5>The rendered video should be at renders/output.mp4!</h5>
base_dir = "/content/outputs/unnamed/nerfacto/"
training_run_dir = base_dir + os.listdir(base_dir)[0]
from IPython.core.display import display, HTML
display(HTML('<h3>Upload the camera path JSON.</h3>'))
%cd $training_run_dir
uploaded = files.upload()
uploaded_camera_path_filename = list(uploaded.keys())[0]
config_filename = training_run_dir + "/config.yml"
camera_path_filename = training_run_dir + "/" + uploaded_camera_path_filename
camera_path_filename = camera_path_filename.replace(" ", "\\ ").replace("(", "\\(").replace(")", "\\)")
%cd /content/
!ns-render camera-path --load-config $config_filename --camera-path-filename $camera_path_filename --output-path renders/output.mp4[2;36m[19:48:48][0m[2;36m [0mSkipping [1;36m0[0m files in dataset split train. ]8;id=527413;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py\[2mnerfstudio_dataparser.py[0m]8;;\[2m:[0m]8;id=243595;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py#91\[2m91[0m]8;;\
[2;36m [0m[2;36m [0mSkipping [1;36m0[0m files in dataset split test. ]8;id=109270;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py\[2mnerfstudio_dataparser.py[0m]8;;\[2m:[0m]8;id=464675;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py#91\[2m91[0m]8;;\
[2KLoading data batch [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [35m100%[0m [36m0:00:00[0m
[?25h/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
[2KLoading data batch [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [35m100%[0m [36m0:00:00[0m
[?25h/usr/local/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
/usr/local/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Loading latest checkpoint from load_dir
✅ Done loading checkpoint from
outputs/data-nerfstudio-poster/nerfacto/[1;36m2022[0m-[1;36m10[0m-29_192844/nerfstudio_models/step-[1;36m000014000.[0mckpt
[1;32mCreating trajectory video[0m
[2K🎥 Rendering 🎥 [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [35m100%[0m [31m0.14 fps[0m [33m11:47[0m
[2K[32m( ● )[0m [33mSaving video[0m
[1A[2K[92m────────────────────────────────────────────── [0m[32m 🎉 🎉 🎉 Success 🎉 🎉 🎉[0m[92m ──────────────────────────────────────────────[0m
[32mSaved video to renders/output.mp4[0m
[0m