sha2nkt · PSContainer · Jul 9, 2024
diff --git a/README.md b/README.md
@@ -1,5 +1,6 @@
 # DECO: Dense Estimation of 3D Human-Scene Contact in the Wild [ICCV 2023 (Oral)]
 
+
 > Code repository for the paper:  
 > [**DECO: Dense Estimation of 3D Human-Scene Contact in the Wild**](https://openaccess.thecvf.com/content/ICCV2023/html/Tripathi_DECO_Dense_Estimation_of_3D_Human-Scene_Contact_In_The_Wild_ICCV_2023_paper.html)  
 > [Shashank Tripathi](https://sha2nkt.github.io/), [Agniv Chatterjee](https://ac5113.github.io/), [Jean-Claude Passy](https://is.mpg.de/person/jpassy), [Hongwei Yi](https://xyyhw.top/), [Dimitrios Tzionas](https://ps.is.mpg.de/person/dtzionas), [Michael J. Black](https://ps.is.mpg.de/person/black)<br />
@@ -153,6 +154,103 @@ To train on other datasets, please follow these steps:
 5. Add the path(s) to the dataset npz(s) to ```common/constants.py``` ([these lines](https://github.com/sha2nkt/deco/blob/d5233ecfad1f51b71a50a78c0751420067e82c02/common/constants.py#L19)).
 6. Finally, change ```TRAINING.DATASETS``` and ```VALIDATION.DATASETS``` in the config file and you're good to go!
 
+
+## Docker/Singularity Installation
+
+The Images are available on DockerHub at: ```https://hub.docker.com/repository/docker/pscontainer/deco```.
+
+### 1. Make the Singularity/Docker Environment:
+
+You can launch with Singularity:
+```
+singularity build --sandbox /tmp/conda_sandbox docker://pscontainer/deco:cuda117
+```
+
+You can launch with Docker:
+```
+docker run --gpus all -it --rm pscontainer/deco:cuda117
+```
+
+
+### 2. Enter the Singularity Environment
+```
+singularity shell --writable --nv -f -c --pwd=/tmp/home -H /tmp/home /tmp/conda_sandbox/
+```
+
+In order to save the code and models downloaded, you can use the ```--writable-tmpfs``` and move to a path where the models persist out of a temporary folder.
+
+```
+singularity shell --writable-tmpfs --nv --pwd=/tmp/home -H /tmp/home /tmp/conda_sandbox/
+```
+
+With Docker:
+
+```
+docker run --gpus all -it --rm \
+  -v deco_data:/persistent_data \
+  -w /persistent_data \
+  -e HOME=/persistent_data \
+  pscontainer/deco:cuda117
+```
+
+### 3. Starting the download of all environment
+```
+git clone https://github.com/sha2nkt/deco.git
+```
+
+You need to register on "https://deco.is.tue.mpg.de/" to access the weights of the model, and on “https://hot.is.tue.mpg.de/" to have access to the data. 
+
+You can now launch the starter script that will install all of the needed to launch deco on your device.
+```bash
+bash starter.sh
+```
+
+
+### 4. Launching the Model from the Apptainer Container
+We must first leave the container.
+``` exit ```
+
+
+#### Make a .sif file
+
+```
+singularity build ./deco.sif /tmp/conda_sandbox/
+```
+
+
+You can verify that the installation was done correctly by launching the following script on the container. 
+We make an output directory:
+``` mkdir outputDECO```
+
+You can verify that the installation was done correctly by launching the following script on the container.
+we make the ```DecoLaunch.sh```
+
+```bash
+cd /path/to/deco
+
+eval "$(conda shell.bash hook)"
+
+conda activate deco
+
+python inference.py \
+    --img_src example_images \
+    --out_dir /container/output/
+```
+
+### Run the demo on a video
+
+```
+singularity exec --bind ./outputDECO:/container/output deco.sif ./DecoLaunch.sh
+```
+
+### Important Notes
+
+- Ensure you have the necessary GPU drivers and CUDA installed on your host system.
+- The Docker version requires Docker with GPU support to be installed.
+- The Singularity version requires Singularity to be installed on your system.
+- Make sure to adjust paths and directories as needed for your specific setup.
+
+
 ## Citing
 If you find this code useful for your research, please consider citing the following paper:
 
@@ -179,4 +277,4 @@ We sincerely thank Alpar Cseke for his contributions to DAMON data collection an
 
 For technical questions, please create an issue. For other questions, please contact `deco@tue.mpg.de`.
 
-For commercial licensing, please contact `ps-licensing@tue.mpg.de`.
+For commercial licensing, please contact `ps-licensing@tue.mpg.de`.
diff --git a/starter.sh b/starter.sh
@@ -0,0 +1,149 @@
+#!/bin/bash
+
+eval "$(conda shell.bash hook)"
+apt-get install libnvidia-gl-515-server -y
+apt-get install nvidia-utils-515 -y
+
+
+conda create -n deco python=3.9 -y
+
+conda activate deco
+# pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
+conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7  -c pytorch -c nvidia
+pip install fvcore
+# git clone https://github.com/facebookresearch/pytorch3d.git
+# cd pytorch3d 
+# pip install .
+# cd ..
+
+# pip install -r requirements.txt
+pip install opencv-python loguru monai==1.0.1  pyrender==0.1.33 smplx==0.1.28 scikit-learn scikit-image jpeg4py matplotlib pandas configargparse flatten_dict chumpy numpy==1.23.1 yacs networkx==2.2 charset-normalizer==3.1.0
+bash fetch_data.sh
+
+echo "Please register and agree to the license of DECO on: “https://deco.is.tue.mpg.de/"
+
+# Prompt for username
+echo "Enter username for DECO:"
+read username
+
+# Prompt for password without showing it
+echo "Enter password for DECO:"
+read -s password
+
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=deco&resume=1&sfile=Release_Damon.tar.gz' -O Release_Damon.tar.gz
+
+tar -xvf Release_Damon.tar.gz  && rm -r Release_Damon.tar.gz
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=deco&resume=1&sfile=damon_segmentations.tar.gz' -O damon_segmentations.tar.gz
+
+tar -xvf damon_segmentations.tar.gz && rm -r damon_segmentations.tar.gz
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=deco&resume=1&sfile=hot_polygon_contact.tar.gz' -O hot_polygon_contact.tar.gz
+
+tar -xvf hot_polygon_contact.tar.gz && rm -r hot_polygon_contact.tar.gz
+
+###############
+
+echo "Please register and agree to the license of the HOT dataset on: “https://hot.is.tue.mpg.de/"
+# Prompt for username
+echo "Enter username for the HOT dataset:"
+read username
+
+# Prompt for password without showing it
+echo "Enter password for the HOT dataset:"
+read -s password
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=hot&resume=1&sfile=HOT-Annotated.zip' -O HOT-Annotated.zip
+
+
+
+# we install SMPL-X
+pip install opencv-python loguru monai==1.0.1  pyrender==0.1.33 smplx==0.1.28 scikit-learn scikit-image jpeg4py matplotlib pandas configargparse flatten_dict chumpy numpy==1.23.1 yacs networkx==2.5 charset-normalizer==3.1.0
+
+
+mv Release_Datasets/damon/ datasets/Release_Datasets/damon
+
+# we install MKL
+conda install  mkl mkl-service
+
+# Pytorch3d is needed
+conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia 
+git clone https://github.com/facebookresearch/pytorch3d.git
+cd pytorch3d
+pip install .
+cd ..
+pip install configargparse
+pip install chardet==4.0.0 idna==2.10 requests==2.25.1 urllib3==1.26.19
+pip install networkx==2.5
+
+# Login to SMPL-X Website
+########### SMPL-X ###########
+
+######
+#
+#
+# We need the last SMPL-X
+#
+cd ..
+urle () { [[ "${1}" ]] || return 1; local LANG=C i x; for (( i = 0; i < ${#1}; i++ )); do x="${1:i:1}"; [[ "${x}" == [a-zA-Z0-9.~-] ]] && echo -n "${x}" || printf '%%%02X' "'${x}"; done; echo; }
+
+
+read -p "Username (SMPl-X Website - https://smpl-x.is.tue.mpg.de/):" username
+read -p "Password (SMPl-X Website - https://smpl-x.is.tue.mpg.de/):" -s password
+
+username=$(urle $username)
+password=$(urle $password)
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=smplx&sfile=models_smplx_v1_1.zip&resume=1' -O 'models_smplx_v1_1.zip' --no-check-certificate --continue
+unzip models_smplx_v1_1.zip
+
+
+# we need to move this into the data
+cp models/smplx/SMPLX_NEUTRAL.npz deco/data/smplx/SMPLX_NEUTRAL.npz
+
+########### SMPL ###########
+echo "Downloading SMPL Models"
+
+urle () { [[ "${1}" ]] || return 1; local LANG=C i x; for (( i = 0; i < ${#1}; i++ )); do x="${1:i:1}"; [[ "${x}" == [a-zA-Z0-9.~-] ]] && echo -n "${x}" || printf '%%%02X' "'${x}"; done; echo; }
+
+username=$(urle $username)
+password=$(urle $password)
+
+# Login to SMPL Website
+# If you don't have an account, create one here: https://smpl.is.tue.mpg.de/
+read -p "Username (SMPL Website - https://smpl.is.tue.mpg.de/):" username
+read -p "Password (SMPL Website - https://smpl.is.tue.mpg.de/):" -s password
+
+wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=smpl&sfile=SMPL_python_v.1.1.0.zip&resume=1' -O 'models_smpl.zip' --no-check-certificate --continue
+unzip models_smpl.zip
+mv SMPL_python_v.1.1.0 smpl
+cp smpl/smpl/models/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl deco/data/smpl/SMPL_NEUTRAL.pkl
+
+
+####################################
+# 
+# we launch the code.
+#
+
+cd deco
+
+# we move the HOT datasets in the proper paths
+unzip HOT-Annotated.zip
+mv HOT-Annotated datasets/HOT-Annotated
+mv polygon_contact datasets/HOT-Annotated/polygon_contact
+mv parts datasets/HOT-Annotated/parts
+mv segmentation_masks datasets/HOT-Annotated/segmentation_masks
+
+
+# we launch the the inference
+
+echo "Part 1: inference launch"
+
+python inference.py \
+    --img_src example_images \
+    --out_dir demo_out
+
+echo "Part 2: Tester Script"
+
+python tester.py --cfg configs/cfg_test.yml