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@@ -77,3 +77,9 @@ hf_demo/lesions/221-m-u/model_highres_0_normalized_pasted_lesion_15.png filter=l
 hf_demo/lesions/221-m-u/model_highres_0_normalized_pasted_lesion_2.png filter=lfs diff=lfs merge=lfs -text
 hf_demo/lesions/221-m-u/model_highres_0_normalized_pasted_lesion_30.png filter=lfs diff=lfs merge=lfs -text
 hf_demo/lesions/221-m-u/model_highres_0_normalized_pasted_lesion_5.png filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/data/3dbodytex-1.1-highres/006-f-run/model_highres_0_normalized.obj filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/data/3dbodytex-1.1-highres/006-f-run/model_highres_0_normalized.png filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/data/3dbodytex-1.1-highres/221-m-u/model_highres_0_normalized.obj filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/data/3dbodytex-1.1-highres/221-m-u/model_highres_0_normalized.png filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/data/annotations/annotator1/02dfbc0e14a4655ca9190705fe1b7d56/02dfbc0e14a4655ca9190705fe1b7d56.png filter=lfs diff=lfs merge=lfs -text
+DermSynth3D/docs/preprint.pdf filter=lfs diff=lfs merge=lfs -text

DermSynth3D/.circleci/config.yml

@@ -0,0 +1,75 @@
+version: 2.1
+orbs:
+  docker: circleci/docker@2.2.0
+jobs:
+  build:
+    docker:
+      - image: cimg/base:stable
+        auth:
+          username: ${DOCKER_USERNAME}
+          password: ${DOCKER_PASSWORD}
+    environment:
+      - DOCKER_USER = ${DOCKER_USERNAME}
+      - DOCKER_PASS = ${DOCKER_PASSWORD}
+      # - UID = 301455
+      # - GID = 8088
+      # - USER = "sfu-mial"
+    steps:
+      - checkout
+      - setup_remote_docker:
+          version: 20.10.11
+          docker_layer_caching: true
+          # command: echo "Remote Docker Setup"
+      - run:
+          name: check correct path
+          command: |
+            printenv | grep "DOCKER\|UID\|GID"
+            pwd
+            ls -lkht 
+            echo 
+      - run: 
+          name: docker build
+          command: |
+            docker build --build-arg UID=$(id -u) --build-arg GID=$(id -g) -t dermsynth3d:${CIRCLE_SHA1} -f Dockerfile . 
+      - run:
+          name: Login to Docker Hub
+          command: |
+            echo "${DOCKER_PASSWORD}" | docker login -u "${DOCKER_USERNAME}" --password-stdin 
+      - run:
+          name: Push Docker image to Docker Hub
+          command: |
+            docker tag dermsynth3d:${CIRCLE_SHA1} "${DOCKER_USERNAME}/dermsynth3d"
+            docker push "${DOCKER_USERNAME}/dermsynth3d"
+
+# version: 2.1
+# orbs:
+#   docker: circleci/docker@2.2.0
+# jobs:
+#   build-and-push:
+#     executor: docker/docker
+#     docker:
+#       - image: cimg/base:stable
+#         name: docker login
+#         auth: 
+#           username: $DOCKER_USERNAME
+#           password: $DOCKER_PASSWORD
+#     steps:
+#       - setup_remote_docker
+#       - checkout
+#       - docker/check
+#       - docker/build:
+#           # image: dermsynth3d/v1
+#           image: sinashish/dermsynth3d
+#       - docker/push:
+#           digest-path: /tmp/digest.txt
+#           image: sinashish/dermsynth3d
+#       # - docker/publish:
+#       #     deploy: true
+#       #     image: dermsynth3d/v1
+#       - run:
+#           command: |
+#             echo "Digest is: $(</tmp/digest.txt)"
+# workflows:
+#   commit:
+#     jobs:
+#       - build-and-push

DermSynth3D/.devcontainer/devcontainer.json

@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

DermSynth3D/.gitattributes

@@ -0,0 +1,5 @@
+data/3dbodytex-1.1-highres filter=lfs diff=lfs merge=lfs -text
+data/annotations filter=lfs diff=lfs merge=lfs -text
+data/ filter=lfs diff=lfs merge=lfs -text
+data/fitzpatrick17k filter=lfs diff=lfs merge=lfs -text
+data/processed_textures filter=lfs diff=lfs merge=lfs -text

DermSynth3D/.gitignore

@@ -0,0 +1,133 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+**/__pycache__/
+.dockerignore
+.github/ISSUE_TEMPLATE/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+.vscode/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

DermSynth3D/.gitmodules

@@ -0,0 +1,3 @@
+[submodule "skin3d"]
+	path = skin3d
+	url = https://github.com/jeremykawahara/skin3d.git

DermSynth3D/Dockerfile

@@ -0,0 +1,73 @@
+FROM nvidia/cuda:11.3.1-cudnn8-runtime-ubuntu20.04
+
+# FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel
+
+
+RUN echo $CUDA_HOME
+# ENV LD_LIBRARY_PATH /usr/local/cuda/lib64/stubs/:$LD_LIBRARY_PATH
+# ENV CUDA_HOME /usr/local/cuda
+# ENV LD_LIBRARY_PATH /usr/local/cuda/lib64/:$LD_LIBRARY_PATH
+# ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+# ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
+# ENV PATH=/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+# ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
+#
+ENV DEBIAN_FRONTEND=noninteractive
+ARG UID=1000
+ARG GID=1000
+ARG USER=developer
+ARG GROUP=$USER
+
+ENV FORCE_CUDA=1
+RUN echo $(nvcc --version)
+
+
+
+# Install necessary packages
+RUN --mount=type=cache,target=/var/cache/apt apt update && apt install -y --no-install-recommends \
+    sudo \
+    git \
+    wget \
+    bzip2 \
+    ca-certificates \
+    libx11-6 \
+    python3-opencv \
+    vim \
+    && rm -rf /var/lib/apt/lists/*
+
+## Create a non-root user and group
+RUN addgroup --gid $GID $GROUP
+RUN adduser --disabled-password --gecos '' --uid $UID --gid $GID $USER && \
+    adduser $USER sudo && \
+    echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+# RUN useradd -D -mU ${USER} --uid=${UID}
+# Run as this user from now on
+USER $USER:$GID
+
+# Install Miniconda
+WORKDIR /home/$USER
+RUN wget -q https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh \
+    && /bin/bash ~/miniconda.sh -b -p ~/miniconda \
+    && rm ~/miniconda.sh
+ENV PATH=/home/$USER/miniconda/bin:$PATH
+
+# Set up conda environment
+COPY dermsynth3d.yml .
+RUN conda env create -f dermsynth3d.yml && conda clean -afy
+ENV CONDA_DEFAULT_ENV=dermsynth3d
+ENV CONDA_PREFIX=/home/$USER/miniconda/envs/$CONDA_DEFAULT_ENV
+ENV PATH=$CONDA_PREFIX/bin:$PATH
+
+
+RUN echo "source activate $(head -1 dermsynth3d.yml | cut -d' ' -f2)" > ~/.bashrc
+ENV PATH /home/$USER/miniconda/envs/$(head -1 dermsynth3d.yml | cut -d' ' -f2)/bin:$PATH
+
+# Copy code
+COPY data /demo_data
+# COPY . /home/$USER/DermSynth3D
+
+
+# Test imports
+# RUN git clone --recurse-submodules https://github.com/sfu-mial/DermSynth3D.git
+#, "python", "scripts/gen_data.py"]
+WORKDIR /home/$USER/DermSynth3D

DermSynth3D/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
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+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
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+and/or modify the software.
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+The GNU General Public License permits making a modified version and
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+
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DermSynth3D/README.md

@@ -0,0 +1,559 @@
+# DermSynth3D
+[![CircleCI](https://dl.circleci.com/status-badge/img/gh/sfu-mial/DermSynth3D/tree/main.svg?style=svg&circle-token=176de57353747d240e619bdf9aacf9f716e7d04f)](https://dl.circleci.com/status-badge/redirect/gh/sfu-mial/DermSynth3D/tree/main)
+![GPLv3](https://img.shields.io/static/v1.svg?label=📃%20License&message=GPL%20v3.0&color=critical&style=flat-square)
+[![arXiv](https://img.shields.io/static/v1.svg?label=📄%20arXiv&message=2305.12621&color=important&style=flat-square)](https://arxiv.org/abs/2305.12621)
+[![DOI](https://img.shields.io/static/v1.svg?label=📄%20DOI&message=DOI&color=informational&style=flat-square)](https://doi.org/10.48550/arXiv.2305.12621)
+[![request dataset](https://img.shields.io/static/v1.svg?label=Dataset&message=Request%20Dataset&style=flat-square&color=blueviolet)](https://cvi2.uni.lu/3dbodytexdermsynth/)
+[![Video](https://img.shields.io/badge/Video-Youtube-ff69b4?style=flat-square)](https://www.youtube.com/watch?v=x3gDBJCI_3k)
+
+:scroll: This is the official code repository for **DermSynth3D**.
+
+<a href="docs/preprint.pdf">
+<img src="assets/thumbnail1.png" alt="PDF thumbnail" height=auto width="100%">
+</a>
+
+
+:tv: Check out the video abstract for this work:
+[![Video Thumbnail](assets/DERMSYNTH_YOUTUBE_THUMB.png)](http://www.youtube.com/watch?v=x3gDBJCI_3k)
+
+## TL;DR
+
+A data generation pipeline for creating photorealistic _in-the-wild_  synthetic dermatological data with rich annotations such as semantic segmentation masks, depth maps, and bounding boxes for various skin analysis tasks.
+
+![main pipeline](assets/pipeline.png)
+>_The figure shows the DermSynth3D computational pipeline where 2D segmented skin conditions are blended into the texture image of a 3D mesh on locations outside of the hair and clothing regions. After blending, 2D views of the mesh are rendered with a variety of camera viewpoints and lighting conditions and combined with background images to create a synthetic dermatology dataset._
+
+## Motivation
+
+In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis.
+However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions.
+To address these shortcomings, we propose a novel framework called ${DermSynth3D}$.
+
+${DermSynth3D}$ blends skin disease patterns onto 3D textured meshes of human subjects using a differentiable renderer and generates 2D images from various camera viewpoints under chosen lighting conditions in diverse background scenes.
+Our method adheres to top-down rules that constrain the blending and rendering process to create 2D images with skin conditions that mimic *in-the-wild* acquisitions, resulting in more meaningful results.
+The framework generates photo-realistic 2D dermoscopy images and the corresponding dense annotations for semantic segmentation of the skin, skin conditions, body parts, bounding boxes around lesions, depth maps, and other 3D scene parameters, such as camera position and lighting conditions.
+${DermSynth3D}$ allows for the creation of custom datasets for various dermatology tasks.
+
+
+## Repository layout
+
+```bash
+DermSynth3D/
+┣ assets/                      # assets for the README
+┣ configs/                     # YAML config files to run the pipeline
+┣ logs/                        # experiment logs are saved here (auto created)
+┣ out/                         # the checkpoints are saved here (auto created)
+┣ data/                        # directory to store the data
+┃  ┣ ...                       # detailed instructions in the dataset.md
+┣ dermsynth3d/                 #
+┃  ┣ datasets/                 # class definitions for the datasets
+┃  ┣ deepblend/                # code for deep blending
+┃  ┣ losses/                   # loss functions
+┃  ┣ models/                   # model definitions
+┃  ┣ tools/                    # wrappers for synthetic data generation
+┃  ┗ utils/                    # helper functions
+┣ notebooks/                   # demo notebooks for the pipeline
+┣ scripts/                     # scripts for traning and evaluation
+┗ skin3d/                      # external module
+```
+
+## Table of Contents
+- [DermSynth3D](#dermsynth3d)
+  - [TL;DR](#tldr)
+  - [Motivation](#motivation)
+  - [Repository layout](#repository-layout)
+  - [Table of Contents](#table-of-contents)
+    - [Installation](#installation)
+      - [using conda](#using-conda)
+      - [using Docker](#using-docker)
+  - [Datasets](#datasets)
+    - [The folder structure of data directory should be as follows:](#the-folder-structure-of-data-directory-should-be-as-follows)
+    - [Data for Blending](#data-for-blending)
+    - [Download 3DBodyTex.v1 meshes](#download-3dbodytexv1-meshes)
+    - [Download the 3DBodyTex.v1 annotations](#download-the-3dbodytexv1-annotations)
+    - [Download the Fitzpatrick17k dataset](#download-the-fitzpatrick17k-dataset)
+    - [Download the Background Scenes](#download-the-background-scenes)
+    - [Data For Training](#data-for-training)
+    - [Download the FUSeg dataset](#download-the-fuseg-dataset)
+    - [Download the Pratheepan dataset](#download-the-pratheepan-dataset)
+    - [Download the PH2 dataset](#download-the-ph2-dataset)
+    - [Download the DermoFit dataset](#download-the-dermofit-dataset)
+    - [Creating the Synthetic dataset](#creating-the-synthetic-dataset)
+  - [How to Use DermSynth3D](#how-to-use-dermsynth3d)
+    - [Generating Synthetic Dataset](#generating-synthetic-dataset)
+    - [Post-Process Renderings with Unity](#post-process-renderings-with-unity)
+          - [Click to see the a visual comparison of the renderings obtained from Pytorch3D and Unity.](#click-to-see-the-a-visual-comparison-of-the-renderings-obtained-from-pytorch3d-and-unity)
+  - [Preparing Dataset for Experiments](#preparing-dataset-for-experiments)
+  - [Cite](#cite)
+  - [Demo Notebooks for Dermatology Tasks](#demo-notebooks-for-dermatology-tasks)
+    - [Lesion Segmentation](#lesion-segmentation)
+    - [Multi-Task Prediction](#multi-task-prediction)
+    - [Lesion Detection](#lesion-detection)
+  - [Acknowledgements](#acknowledgements)
+
+<a name="installation"></a>
+
+### Installation
+
+<a name="conda"></a>
+
+#### using conda
+
+```bash
+git clone --recurse-submodules https://github.com/sfu-mial/DermSynth3D.git
+cd DermSynth3D
+conda env create -f dermsynth3d.yml
+conda activate dermsynth3d
+```
+
+<a name="docker"></a>
+
+#### using Docker
+
+```bash
+# Build the container in the root dir
+docker build -t dermsynth3d --build-arg USER=$USER --build-arg UID=$(id -u) --build-arg GID=$(id -g) -f Dockerfile .
+# Run the container in interactive mode for using DermSynth3D
+# See 3. How to use DermSynth3D
+docker run --gpus all --user=root --runtime=nvidia -it --rm -v /path/to/downloaded/data:/data dermsynth3d
+```
+We provide some [pre-built docker images](https://hub.docker.com/r/sinashish/dermsynth3d), which can be be used as well to:
+```bash
+# pull this latest docker image with the latest code
+# you need to prepare the data following the instructions below
+docker pull sinashish/dermsynth3d:latest
+
+# pull this image for trying out the code with demo data i.e. lesions and meshes
+docker pull sinashish/dermsynth3d:demo_w_code
+
+# Run the container in interactive GPU mode for generating data and training models
+# mount the data directory to the container
+docker run --gpus all -it --user=root --runtime=nvidia --rm -v /path/to/downloaded/data:/data dermsynth3d:<tag name>
+```
+
+<span style="color: red">**NOTE**:</span> The code has been tested on Ubuntu 20.04 with CUDA 11.1, python 3.8, pytorch 1.10.0, and pytorch3d 0.7.2, and we don't know if it will work on CPU.
+
+If you face any issues installing pytorch3d, please refer to their [installation guide](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md) or this issue [link](https://github.com/facebookresearch/pytorch3d/issues/1076).
+
+
+<a name="data"></a>
+
+## Datasets
+
+Follow the instructions below to download the datasets for generating the synthetic data and training models for various tasks.
+All the datasets should be downloaded and placed in the `data` directory.
+
+<a name="tree"></a>
+
+<!-- #### The folder structure of data directory should be as follows: -->
+<details>
+
+  <a name="data_tree"></a>
+  <summary>
+
+  ### The folder structure of data directory should be as follows:
+
+  </summary>
+
+```bash
+DermSynth3D/
+┣ ...						   		          # other source code
+┣ data/                        	# directory to store the data
+┃  ┣ 3dbodytex-1.1-highres   		# data for 3DBodyTex.v1 3d models and texture maps
+┃  ┣ fitzpatrick17k/
+┃  ┃  ┣ data/               		# Fitzpatrick17k images
+┃  ┃  ┗ annotations/        		# annotations for Fitzpatrick17k lesions
+┃  ┣ ph2/
+┃  ┃  ┣ images/               	# PH2 images
+┃  ┃  ┗ labels/               	# PH2 annotations
+┃  ┣ dermofit/ 						      # Dermofit dataset
+┃  ┃  ┣ images/               	# Dermofit images
+┃  ┃  ┗ targets/               	# Dermofit annotations
+┃  ┣ FUSeg/
+┃  ┃  ┣ train/               		# training set with images/labels for FUSeg
+┃  ┃  ┣ validation/             # val set with images/labels for FUSeg
+┃  ┃  ┗ test/               		# test set with images/labels for FUSeg
+┃  ┣ Pratheepan_Dataset/
+┃  ┃  ┣ FacePhoto/              # images from Pratheepan dataset
+┃  ┃  ┗ GroundT_FacePhoto/  	 	# annotations
+┃  ┣ lesions/                   # keep the non-skin masks for 3DBodyTex.v1 meshes here
+┃  ┣ annotations/               # segmentation masks for Annotated Fitzpatrick17k lesions
+┃  ┣ bodytex_anatomy_labels/ 		# per-vertex labels for anatomy of 3DBodyTex.v1 meshes
+┃  ┣ background/               	# keep the background scenes for rendering here
+┃  ┗ synth_data/			   		    # the generated synthetic data will be stored here
+    	┣ train/               		# training set with images/labels for training on synthetic data
+	    ┣ <val/test>/ 			 	    # val and test set with images/labels for training on synthetic data
+```
+</details>
+
+The datasets used in this work can be broadly categorized into data required for blending and data necessary for evaluation.
+
+<details>
+
+  <a name="blend_data"></a>
+  <summary>
+
+  ### Data for Blending
+
+  </summary> <blockquote>
+  <!-- list of blending datasets -->
+  <details>
+  <a name="mesh_data"></a>
+  <summary>
+
+  ### Download 3DBodyTex.v1 meshes
+
+  </summary>
+
+  ![3dbodytex sample](assets/scans_blurred.png)
+  > _A few examples of raw 3D scans in sports-clothing from the 3DBodyTex.v1 dataset showing a wide range of body shapes, pose, skin-tone, and gender._
+  <!-- <img align="right" src = "assets/scans_blurred.png" width="224px" height="224px" ></img> -->
+
+
+  The `3DBodyTex.v1` dataset can be downloaded from [here](https://cvi2.uni.lu/3dbodytexv1/).
+
+  `3DBodyTex.v1` contains the meshes and texture images used in this work and can be downloaded from the external site linked above (after accepting a license agreement).
+
+  **NOTE**: These textured meshes are needed to run the code to generate the data.
+
+  We provide the non-skin texture maps annotations for 2 meshes: `006-f-run` and `221-m-u`.
+  Hence, to generate the data, make sure to get the `.obj` files for these two meshes and place them in `data/3dbodytex-1.1-highres` before excecuting `scripts/gen_data.py`.
+
+  After accepting the licence, download and unzip the data in `./data/`.
+
+  </details>
+
+  <details>
+  <a name="mesh_annot_data"></a>
+  <summary>
+
+  ### Download the 3DBodyTex.v1 annotations
+
+
+  </summary>
+
+  | _Non-skin texture maps_ | _Anatomy labels_ |
+  |:-:|:-:|
+  |<p align=left> We provide the non-skin texture map ($T_{nonskin}$) annotations for 215 meshes from the `3DBodyTex.v1` dataset [here](https://cvi2.uni.lu/3dbodytexdermsynth/). </p> | <p align=left> We provide the per-vertex labels for anatomical parts of the 3DBodyTex.v1 meshes obtained by fitting SCAPE template body model [here](https://cvi2.uni.lu/3dbodytexdermsynth/). </p>|
+  |<img src="./assets/final.png" width="320" height="240" align=center><p>_A sample texture image showing the annotations for non-skin regions._ </p>|<img src="./assets/body_part_annotation_consistent_cmap_multi.png"  width="320" height="240"  align=center> <p>_A few examples of the scans showing the 7 anatomy labels._ </p>|
+
+  The folders are organised with the same IDs as the meshes in `3DBodyTex.v1` dataset.
+
+  **NOTE**: To download the the 3DBodyTex.v1 annotations with the links referred above, you would need to request access to the 3DBodyTex.DermSynth dataset by following the instructions on this [link](https://cvi2.uni.lu/3dbodytexdermsynth/).
+
+  </details>
+
+  <details>
+  <a name="fitz17k_data"></a>
+  <summary>
+
+  ### Download the Fitzpatrick17k dataset
+
+  </summary>
+
+  ![fitz_annot_fig](./assets/readme_fitz.png)<br>
+  _An illustration showing lesions from the Fitzpatrick17k dataset in the top row, and it's corresponding manually segmented lesion annotation in the bottom row._
+  <!-- , and c) The lesion mask selected for blending._  -->
+  <!-- | _Fitzpatrick17k Images_| _Fitzpatrick17k annotations_|
+  |:-:|:-:|
+  |![image](./assets/juvenile-xanthogranuloma63.jpg) _A sample image from the `fitz17k` dataset._ | ![mask.](./data/fitzpatrick17k/annotations/test/01ed6482ab261012f398c19db7dfcc6c/lesions.png) _The manually segmented corresponding annotation showing 3 lesions._ | -->
+  <!-- |<p align=left> We used the skin conditions from [Fitzpatrick17k](https://github.com/mattgroh/fitzpatrick17k). See their instructions to get access to the Fitzpatrick17k images. <br>We provide the raw images for the Fitzpatrick17k dataset [here](https://vault.sfu.ca/index.php/s/cMuxZNzk6UUHNmX).<br>After downloading the dataset, unzip the dataset:<br>```unzip fitzpatrick17k.zip -d data/fitzpatrick17k/```</p>| <p align=left> We provide the densely annotated lesion masks from the Fitzpatrick17k dataset are given within this repository under the `data` directory. More of such annotations can be downloaded from [here](https://vault.sfu.ca/index.php/s/gemdbCeoZXoCqlS).</p>| <p align=left>We provide the densely annotated lesion masks from the Fitzpatrick17k dataset are given within this repository under the `data` directory. More of such annotations can be downloaded from [here](https://vault.sfu.ca/index.php/s/gemdbCeoZXoCqlS). </p>| -->
+  We used the skin conditions from [Fitzpatrick17k](https://github.com/mattgroh/fitzpatrick17k).
+   See their instructions to get access to the Fitzpatrick17k images.
+  We provide the raw images for the Fitzpatrick17k dataset [here](https://vault.sfu.ca/index.php/s/cMuxZNzk6UUHNmX).
+
+  After downloading the dataset, unzip the dataset:
+  ```bash
+  unzip fitzpatrick17k.zip -d data/fitzpatrick17k/
+  ```
+
+  We provide a few samples of the densely annotated lesion masks from the Fitzpatrick17k dataset within this repository under the `data` directory.
+
+  More of such annotations can be downloaded from [here](https://vault.sfu.ca/index.php/s/gemdbCeoZXoCqlS).
+
+  </details>
+
+  <details>
+  <a name="bg_data"></a>
+  <summary>
+
+  ### Download the Background Scenes
+
+  </summary>
+
+  ![bg_scenes](./assets/readme_bg.png)
+  >_A few examples of the background scenes used for rendering the synthetic data._
+<!--
+  |||
+  |:-:|:-:|
+  |![scene1](./assets/50.jpg)|![scene2](./assets/700.jpg)| -->
+
+  Although you can use any scenes as background for generating the random views of the lesioned-meshes, we used [SceneNet RGB-D](https://robotvault.bitbucket.io/scenenet-rgbd.html) for the background IndoorScenes. Specifically, we used [this split](https://www.doc.ic.ac.uk/~bjm113/scenenet_data/train_split/train_0.tar.gz), and sampled 3000 images from it.
+
+  For convenience, the background scenes we used to generate the ssynthetic dataset can be downloaded from [here](https://vault.sfu.ca/index.php/s/r7nc1QHKwgW2FDk).
+
+  </details>
+</blockquote>
+</details>
+<details>
+<a name="train_data"></a>
+<summary>
+
+### Data For Training
+
+</summary> <blockquote>
+  <details>
+  <a name="fu_data"></a>
+  <summary>
+
+   ### Download the FUSeg dataset
+
+  </summary>
+
+  ![fu_seg](./assets/fuseg.png)
+  >_A few examples from the FUSeg dataset showing the images in the top row and, it's corresponding segmentation mask in the bottom row._
+  <!-- |||
+  |:-:|:-:|
+  |![scene1](./assets/0011.png)|![scene2](./assets/0011_m.png)| -->
+
+  The Foot Ulcer Segmentation Challenge (FUSeg) dataset is available to download from [their official repository](https://github.com/uwm-bigdata/wound-segmentation/tree/master/data/Foot%20Ulcer%20Segmentation%20Challenge).
+  Download and unpack the dataset at `data/FUSeg/`, maintaining the Folder Structure shown above.
+
+  For simplicity, we mirror the FUSeg dataset [here](https://vault.sfu.ca/index.php/s/2mb8kZg8wOltptT).
+
+  </details>
+
+  <details>
+  <a name="prath_data"></a>
+  <summary>
+
+   ### Download the Pratheepan dataset
+
+  </summary>
+
+  ![prath](./assets/readme_prath.png)
+  >_A few examples from the Pratheepan dataset showing the images and it's corresponding segmentation mask, in the top and bottom row respectively._
+
+  The Pratheepan dataset is available to download from [their official website](https://web.fsktm.um.edu.my/~cschan/downloads_skin_dataset.html).
+  The images and the corresponding ground truth masks are available in a ZIP file hosted on Google Drive. Download and unpack the dataset at `data/Pratheepan_Dataset/`.
+
+  </details>
+
+  <details>
+  <a name="ph2_data"></a>
+  <summary>
+
+   ### Download the PH2 dataset
+
+  </summary>
+
+  ![ph2](./assets/readme_ph2.png)
+  >_A few examples from the PH2 dataset showing a lesion and it's corresponding segmentation mask, in the top and bottom row respectively._
+
+  The PH2 dataset can be downloaded from [the official ADDI Project website](https://www.fc.up.pt/addi/ph2%20database.html).
+  Download and unpack the dataset at `data/ph2/`, maintaining the Folder Structure shown below.
+
+  </details>
+
+  <details>
+  <a name="dermfit_data"></a>
+  <summary>
+
+   ### Download the DermoFit dataset
+
+  </summary>
+
+  ![dermo](./assets/readme_df.png)
+  >_An illustration of a few samples from the DermoFit dataset showing the skin lesions and it's corresponding binary mask, in the top and bottom row respectively._
+
+  The DermoFit dataset is available through a paid perpetual academic license from the University of Edinburgh. Please access the dataset following the instructions for [the DermoFit Image Library](https://licensing.edinburgh-innovations.ed.ac.uk/product/dermofit-image-library) and unpack it at `data/dermofit/`, maintaining the Folder Structure shown above.
+
+  </details>
+
+  <details>
+  <a name="syn_data"></a>
+  <summary>
+
+   ###  Creating the Synthetic dataset
+
+  </summary>
+
+  ![synthetic data](./assets/fig_1-min.png)
+  >_Generated synthetic images of multiple subjects across a range of skin tones in various skin conditions, background scene, lighting, and viewpoints._
+
+  <!-- ![annots](./assets/AnnotationOverview.png)
+   > _A few examples of annotated data synthesized using DermSynth3D. The rows from top to bottom show respectively: the rendered images with blended skin conditions, bounding boxes around the lesions, GT semantic segmentation masks, grouped anatomical labels, and the monocular depth maps produced by the renderer._ -->
+  For convenience, we provide the generated synthetic data we used in this work for various downstream tasks [here](https://cvi2.uni.lu/3dbodytexdermsynth/).
+
+  If you want to train your models on a different split of the synthetic data, you can download a dataset generated by blending lesions on 26 3DBodyTex scans from [here](https://cvi2.uni.lu/3dbodytexdermsynth/).
+  To prepare the synthetic dataset for training. Sample the `images`, and `targets` from the path where you saved this dataset and then organise them into `train/val`.
+
+  **NOTE**: To download the synthetic 3DBodyTex.DermSynth dataset referred in the links above, you would need to request access by following the instructions on this [link](https://cvi2.uni.lu/3dbodytexdermsynth/).
+
+  Alternatively, you can use the provided script `scripts/prep_data.py` to create it.
+
+  Even better, you can generate your own dataset, by following the instructions [here](./README.md#generating-synthetic-dataset).
+
+
+
+  </details>
+  </blockquote>
+</details>
+
+<a name="usage"></a>
+
+## How to Use DermSynth3D
+
+<a name='gen'></a>
+
+### Generating Synthetic Dataset
+
+![annots](./assets/AnnotationOverview.png)
+  > _A few examples of annotated data synthesized using DermSynth3D. The rows from top to bottom show respectively: the rendered images with blended skin conditions, bounding boxes around the lesions, GT semantic segmentation masks, grouped anatomical labels, and the monocular depth maps produced by the renderer._
+
+Before running any code to synthesize a densely annotated data as shown above, make sure that you have downloaded the data necessary for blending as mentioned in [datasets](#data-for-blending) and folder structure is as described above.
+If your folder structure is different from ours, then update the paths, such as `bodytex_dir`, `annot_dir`, etc., accordingly in `configs/blend.yaml`.
+
+<!-- ```yml
+bodytex_dir: './data/3dbodytex-1.1-highres/' # Name of the mesh to blend
+mesh_name: '006-f-run'                       # Path to FitzPatrick17k lesions
+fitz_dir: './data/fitzpatrick17k/data/finalfitz17k/' # Path to annotated Fitz17k lesions with masks
+annot_dir: './data/annotations/' # Path to save the new texture maps
+tex_dir: './data/lesions/'
+``` -->
+
+Now, to *generate* the synthetic data with the default parameters, simply run the following command to generate 2000 views for a specified mesh:
+
+```bash
+python -u scripts/gen_data.py
+```
+
+To change the blending or synthesis parameters only, run using:
+```bash
+# Use python scripts/gen_data.py -h for full list of arguments
+python -u scripts/gen_data.py --lr <learning rate> \
+            -m <mesh_name> \
+            -s <path to save the views> \
+            -ps <skin threshold> \
+            -i <blending iterations> \
+            -v <number of views> \
+            -n <number of lesions per mesh>
+```
+
+Feel free to play around with other `random` parameter in `configs/blend.yaml` to control lighting, material and view points.
+
+<a name="post_proc_data"></a>
+
+### Post-Process Renderings with Unity
+
+We use Pytorch3D as our choice of differential renderer to generate synthetic data.
+However, Pytorch3D is not a Physically Based Renderer (PBR) and hence, the renderings are not photorealistic or may not look photorealistic.
+To achieve photorealistic renderings, we use Unity to post-process the renderings obtained from Pytorch3D.
+
+<details>
+<summary>
+
+###### Click to see the a visual comparison of the renderings obtained from Pytorch3D and Unity.
+
+</summary>
+
+![renderer_comp](./assets/media_rendComp.png)
+> _A visual comparison of the renderings obtained from Pytorch3D and Unity (Point Lights and Mixed Lighting)._
+</details>
+
+<span style="color:red">NOTE</span>: This is an optional step. If you are not interested in creating photorealistic renderings, you can skip this step and use the renderings obtained from Pytorch3D directly. We didn't observe a **significant** difference in the performance of the models trained on the renderings obtained from Pytorch3D and Unity.
+
+<!-- We provide detailed instructions to post-process the renderings obtained in the previous step to achieve _photorealistic_ renderings using Unity. -->
+
+<!-- ![synthetic data](./assets/fig_1-min.png) -->
+Follow the detailed instructions outlined [here](./docs/unity.md) to create photorealistic renderings using Unity. Alternatively, download the renders that we created using Unity [here](https://cvi2.uni.lu/3dbodytexdermsynth/).
+
+<a name='train_prep'></a>
+
+## Preparing Dataset for Experiments
+<!--
+![synthetic data](./assets/fig_1-min.png)
+>_Generated synthetic images of multiple subjects across a range of skin tones in various skin conditions, background scene, lighting, and viewpoints._ -->
+
+After creating the syntheic dataset in the previous step, it is now the time to evaluate the utility of the dataset on some real-world tasks.
+
+Before, you start with any experiments, ideally you would want to organize the generated data into `train/val/test` sets.
+We provide a utility script to do the same:
+```bash
+python scripts/prep_data.py
+```
+
+You can look at `scripts/prep_data.py` for more details.
+
+<a name="cite"></a>
+
+## Cite
+If you find this work useful or use any part of the code in this repo, please cite our paper:
+```bibtext
+@misc{sinha2023dermsynth3d,
+      title={DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images},
+      author={Ashish Sinha and Jeremy Kawahara and Arezou Pakzad and Kumar Abhishek and Matthieu Ruthven and Enjie Ghorbel and Anis Kacem and Djamila Aouada and Ghassan Hamarneh},
+      year={2023},
+      eprint={2305.12621},
+      archivePrefix={arXiv},
+      primaryClass={eess.IV}
+}
+```
+
+<a name="repro"></a>
+
+## Demo Notebooks for Dermatology Tasks
+
+![Qualitative Results](./assets/results.png)
+>_Qualitative results for (a) foot ulcer bounding box detection on FUSeg dataset, (b) multi-class segmentation (lesions,skin, and background) and in-the-wild body part prediction, \(c\) skin segmentation and body part prediction on Pratheepan dataset, and (d) multi-class segmentation (lesions, skin, and background) on dermoscopy images from PH2 dataset._
+
+<a name='seg'></a>
+<a name='train'></a>
+
+### Lesion Segmentation
+**Note**: Update the paths to relevant datasets in `configs/train_mix.yaml`.
+
+To train a lesion segmentation model with default parameters, on a combination of Synthetic and Real Data, simply run:
+
+```bash
+python -u scripts/train_mix_seg.py
+```
+
+Play around with the following parameters for a combinatorial mix of datasets.
+```yaml
+real_ratio: 0.5                 # fraction of real images to be used from real dataset
+real_batch_ratio: 0.5           # fraction of real samples in each batch
+pretrain: True                  # use pretrained DeepLabV3 weights
+mode: 1.0                       # Fraction of the number of synthetic images to be used for training
+```
+
+You can also look at [this notebook](./notebooks/train_segmentation.ipynb) for a quick overview for training lesion segmention model.
+
+For inference of pre-trained models/checkpoints, look at [this notebook](./notebooks/inference_segmentation.ipynb).
+
+<a name='multi'></a>
+
+### Multi-Task Prediction
+
+We also train a multi-task model for predicting lesion, anatomy and depth, and evaluate it on multiple datasets.
+
+For a quick overview of multi-task prediction task, checkout [this notebook](./notebooks/inference_multitask.ipynb).
+
+For performing inference on your trained models for this task. First update the paths in `configs/multitask.yaml`. Then run:
+```bash
+python -u scripts/infer_multi_task.py
+```
+
+<a name='det'></a>
+
+### Lesion Detection
+
+For a quick overview for training lesion detection models, please have a look at [this notebook](./notebooks/train_detection.ipynb).
+
+For doing a quick inference using the pre-trained detection models/ checkpoints, have a look at [this notebook](./notebooks/inference_detection.ipynb).
+
+<a name="ack"></a>
+
+## Acknowledgements
+
+We are thankful to the authors of [Skin3D](https://github.com/jeremykawahara/skin3d) for making their code and data public for the task of lesion detection on 3DBodyTex.v1 dataset.

DermSynth3D/app.py

@@ -0,0 +1,533 @@
+import streamlit as st
+
+st.set_page_config(
+    page_title="DermSynth3D",
+    page_icon="🧊",
+    layout="centered",
+    initial_sidebar_state="auto",
+    menu_items={
+        "Get Help": "https://github.com/sfu-mial/DermSynth3D",
+        "Report a bug": "https://github.com/sfu-mial/DermSynth3D/issues",
+        "About": "This is the demo app to try out the pipeline proposed in the paper DermSynth3D: A Dermatological Image Synthesis Framework for 3D Skin Lesions",
+    },
+)
+# from stpyvista import stpyvista
+import pandas as pd
+import numpy as np
+from glob import glob
+import os, sys
+from PIL import Image
+import torch
+import torch.nn as nn
+import trimesh
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from pytorch3d.renderer import (
+    look_at_view_transform,
+    FoVPerspectiveCameras,
+    PointLights,
+    DirectionalLights,
+    Materials,
+    RasterizationSettings,
+    MeshRenderer,
+    MeshRasterizer,
+    SoftPhongShader,
+    TexturesUV,
+    TexturesVertex,
+)
+import math
+from trimesh import transformations as tf
+import os
+
+import streamlit.components.v1 as components
+from math import pi
+from IPython.display import display
+import matplotlib.pyplot as plt
+
+import matplotlib.pyplot as plt
+from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
+import plotly
+
+import plotly.graph_objects as go
+from skimage import io
+from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
+
+init_notebook_mode(connected=True)
+
+view_width = 400
+view_height = 400
+
+import mediapy as mpy
+
+sys.path.append("./dermsynth3d")
+sys.path.append("./skin3d/")
+from pytorch3d.io import load_objs_as_meshes
+from pytorch3d.structures import Meshes
+
+st.title("DermSynth3D - Dermatological Image Synthesis Framework")
+# st.write('A dermatological image synthesis framework for 3D skin lesions')
+
+
+def setup_paths():
+    # get the meshes
+    mesh_paths = glob("./data/3dbodytex-1.1-highres/*/*.obj")
+    mesh_names = [os.path.basename(os.path.dirname(x)) for x in mesh_paths]
+
+    # get the textures
+    all_textures = glob("./data/3dbodytex-1.1-highres/*/*.png")
+
+    get_no_lesion_path = lambda x, y: os.path.join(
+        "./data/3dbodytex-1.1-highres", x, "model_highres_0_normalized.png"
+    )
+    get_mesh_path = lambda x: os.path.join(
+        "./data/3dbodytex-1.1-highres", x, "model_highres_0_normalized.obj"
+    )
+    # get the textures with the lesions
+    get_mask_path = lambda x, y: os.path.join(
+        "./data/processed_textures/", x, "model_highres_0_normalized.png"
+    )
+    get_dilated_lesion_path = lambda x, y: os.path.join(
+        "./data/processed_textures/",
+        x,
+        f"model_highres_0_normalized_dilated_lesion_{y}.png",
+    )
+    get_blended_lesion_path = lambda x, y: os.path.join(
+        "./data/processed_textures/",
+        x,
+        f"model_highres_0_normalized_blended_lesion_{y}.png",
+    )
+    get_pasted_lesion_path = lambda x, y: os.path.join(
+        "./data/processed_textures/",
+        x,
+        f"model_highres_0_normalized_pasted_lesion_{y}.png",
+    )
+    get_texture_module = lambda x: getattr(
+        sys.modules[__name__],
+        f"get_{x.lower().replace(' ', '_')}_path",
+    )
+    # Update the global namespace with the functions
+    global_namespace = globals()
+    global_namespace.update(
+        {
+            "mesh_paths": mesh_paths,
+            "mesh_names": mesh_names,
+            "all_textures": all_textures,
+            "get_no_lesion_path": get_no_lesion_path,
+            "get_mesh_path": get_mesh_path,
+            "get_mask_path": get_mask_path,
+            "get_dilated_lesion_path": get_dilated_lesion_path,
+            "get_blended_lesion_path": get_blended_lesion_path,
+            "get_pasted_lesion_path": get_pasted_lesion_path,
+            "get_texture_module": get_texture_module,
+        }
+    )
+
+
+@st.cache_data
+def set_texture_map_py3d(mesh_name, texture_name, num_lesion=1, device="cpu"):
+    mesh_path = get_mesh_path(mesh_name)
+    texture_path = get_texture_module(texture_name)(mesh_name)
+    mesh = load_objs_as_meshes([mesh_path], device=device)
+    texture_img = Image.open(texture_path).convert("RGB")
+    texture_tensor = torch.from_numpy(np.array(texture_img))
+
+    tmap = TexturesUV(
+        maps=texture_tensor.float().to(device=mesh.device).unsqueeze(0),
+        verts_uvs=mesh.textures.verts_uvs_padded(),
+        faces_uvs=mesh.textures.faces_uvs_padded(),
+    )
+    new_mesh = Meshes(
+        verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tmap
+    )
+    return new_mesh, texture_img
+
+
+import tempfile
+
+
+def render_images(mesh_name, texture_name, num_lesion=1, device="cuda"):
+    raise NotImplementedError
+
+
+@st.cache_data
+def get_trimesh_attrs(mesh_name):
+    mesh_path = get_mesh_path(mesh_name)
+    tri_mesh = trimesh.load(mesh_path)
+    angle = -math.pi / 2
+    direction = [0, 1, 0]
+    center = [0, 0, 0]
+    rot_matrix = tf.rotation_matrix(angle, direction, center)
+    tri_mesh = tri_mesh.apply_transform(rot_matrix)
+    tri_mesh.apply_transform(tf.rotation_matrix(math.pi, [0, 0, 1], [-1, -1, -1]))
+
+    verts, faces = tri_mesh.vertices, tri_mesh.faces
+    uvs = tri_mesh.visual.uv
+    colors = tri_mesh.visual.to_color()
+    vc = colors.vertex_colors  # / 255.0
+    timg = tri_mesh.visual.material.image
+
+    return verts, faces, vc, mesh_name
+
+
+@st.cache_data
+def plotly_image(image):
+    fig = go.Figure()
+    fig.add_trace(go.Image(z=image))
+    fig.update_layout(
+        width=view_width,
+        height=view_height,
+        margin=dict(l=0, r=0, b=0, t=0, pad=0),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+    )
+    fig.update_xaxes(showticklabels=False)
+    fig.update_yaxes(showticklabels=False)
+    return fig
+
+
+@st.cache_data
+def plotly_mesh(verts, faces, vc, mesh_name):
+    fig = go.Figure(
+        data=[
+            go.Mesh3d(
+                x=verts[:, 0],
+                y=verts[:, 1],
+                z=verts[:, 2],
+                i=faces[:, 0],
+                j=faces[:, 1],
+                k=faces[:, 2],
+                vertexcolor=vc,
+            )
+        ]
+    )
+    fig.update_layout(scene_aspectmode="manual", scene_aspectratio=dict(x=1, y=1, z=1))
+    fig.update_layout(scene=dict(xaxis=dict(visible=False), yaxis=dict(visible=False)))
+    fig.update_layout(scene=dict(zaxis=dict(visible=False)))
+    fig.update_layout(scene=dict(camera=dict(up=dict(x=1, y=0, z=1))))
+    fig.update_layout(scene=dict(camera=dict(eye=dict(x=-2, y=-2, z=-1))))
+    # fig.update_layout(scene=dict(camera=dict(center=dict(x=0, y=0, z=0))))
+
+    return fig
+
+
+@st.cache_data
+def load_mesh_and_texture(mesh_name, texture_name, num_lesion=1, device="cuda"):
+    mesh_path = get_mesh_path(mesh_name)
+    texture_path = get_texture_module(texture_name)(mesh_name, num_lesion)
+    # glb_mesh = convert_to_glb(mesh_path)
+    mesh = load_objs_as_meshes([mesh_path], device=device)
+    verts = mesh.verts_packed().detach().cpu().numpy()
+    faces = mesh.faces_packed().detach().cpu().numpy()
+    normals = mesh.verts_normals_packed().detach().cpu().numpy()
+    # tri_mesh = trimesh.load(mesh_path)
+
+    texture_img = Image.open(texture_path).convert("RGB")
+    texture_tensor = torch.from_numpy(np.array(texture_img))
+
+    tmap = TexturesUV(
+        maps=texture_tensor.float().to(device=mesh.device).unsqueeze(0),
+        verts_uvs=mesh.textures.verts_uvs_padded(),
+        faces_uvs=mesh.textures.faces_uvs_padded(),
+    )
+    new_mesh = Meshes(
+        verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tmap
+    )
+    pl_mesh = plotly_mesh(*get_trimesh_attrs(mesh_name))
+    # print(tri_mesh, new_mesh, texture_img.resize((256, 256)))
+    return pl_mesh, new_mesh, texture_img  # .resize((256, 256))
+
+
+@st.cache_resource
+def display_mesh(mesh_name, texture_name, num_lesion=1, device="cuda"):
+    tri_mesh, render_mesh, texture_img = load_mesh_and_texture(
+        mesh_name, texture_name, num_lesion, device
+    )
+
+    plotter = pv.Plotter(border=True, window_size=[view_width, view_width])
+    pv_mesh = pv.wrap(tri_mesh)
+    plotter.add_mesh(pv_mesh)
+    plotter.background_color = "white"
+    plotter.view_isometric()
+    return plotter, render_mesh, texture_img
+
+
+@st.cache_data
+def setup_cameras(dist, elev, azim, device="cuda"):
+    R, T = look_at_view_transform(dist, elev, azim, degrees=True)
+    cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
+    return cameras
+
+
+@st.cache_data
+def setup_lights(
+    light_pos, ambient_color, diffuse_color, specular_color, device="cuda"
+):
+    lights = PointLights(
+        device=device,
+        location=[[light_pos, light_pos, light_pos]],
+        ambient_color=[[ambient_color, ambient_color, ambient_color]],
+        diffuse_color=[[diffuse_color, diffuse_color, diffuse_color]],
+        specular_color=[[specular_color, specular_color, specular_color]],
+    )
+    return lights
+
+
+@st.cache_data
+def setup_materials(shininess, specularity, device="cuda"):
+    materials = Materials(
+        device=device,
+        specular_color=[[specularity, specularity, specularity]],
+        shininess=[shininess],
+    )
+    return materials
+
+
+# @st.cache_data
+def setup_renderer(cameras, lights, materials, device="cuda"):
+    raster_settings = RasterizationSettings(
+        image_size=256,
+        blur_radius=0.0,
+        faces_per_pixel=10,
+        # max_faces_per_bin=100,
+        bin_size=0,
+        perspective_correct=True,
+    )
+    renderer = MeshRenderer(
+        rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
+        shader=SoftPhongShader(
+            device=device, cameras=cameras, lights=lights, materials=materials
+        ),
+    )
+    return renderer
+
+
+# @st.cache_resource
+def render_images(renderer, mesh, lights, cameras, materials, nviews, device="cuda"):
+    images = renderer(mesh, lights=lights, cameras=cameras, materials=materials)
+    return images
+
+
+def main():
+    st.sidebar.title("Mesh")
+    selected_mesh = st.sidebar.selectbox(
+        "Select the mesh to be used for the synthesis", mesh_names
+    )
+
+    # set the texture
+    st.sidebar.title("Texture Map")
+    selected_texture = st.sidebar.selectbox(
+        "Select the texture map to view",
+        ["No Lesion", "Pasted Lesion", "Blended Lesion", "Dilated Lesion"],
+    )
+
+    st.sidebar.title("Lesion Count")
+    num_lesion = st.sidebar.slider(
+        "Set the number of lesions to be added to the mesh",
+        min_value=0,
+        max_value=30,
+        value=1,
+        step=1,
+    )
+    if num_lesion not in [0, 1, 2, 5, 10]:
+        st.sidebar.error("The number of lesions is not in the default list!")
+        st.sidebar.warning("We currently only support 1, 5, 15, 30.")
+        num_lesion = 1
+
+    # load the texture
+    texture_img = Image.open(
+        get_texture_module(selected_texture)(selected_mesh, num_lesion)
+    )
+
+    # display the mesh and texture
+    # based on the  selected parameters
+    with st.spinner(text="Loading Mesh with texture..."):
+        mesh_place, texture_place = st.columns(2)
+        tmesh, render_mesh, texture_img = load_mesh_and_texture(
+            selected_mesh, selected_texture, num_lesion
+        )
+        with mesh_place and st.spinner("Loading Mesh..."):
+            # mesh_place.info("click on reset camera, if unable to see the whole mesh!")
+            mesh_place.plotly_chart(tmesh, use_container_width=True, theme=None)
+            # mesh_place.info("The mesh will be displayed here. Please wait...")
+            # st.sidebar.success("Mesh with texture loaded!")
+            # stpyvista(tmesh, key="mesh")
+        with texture_place and st.spinner("Loading texture..."):
+            pl_img = plotly_image(texture_img.resize((512, 512)))
+            texture_place.plotly_chart(pl_img, use_container_width=True, theme=None)
+            # texture_place.info("The texture will be displayed here. Please wait...")
+            # texture_place.image(
+            #     texture_img,
+            #     caption=f"Texture map with {selected_texture} for {selected_mesh}",
+            #     use_column_width="auto",
+            #     # width=view_width,
+            #     clamp=True,
+            #     channels="RGB",
+            # )
+        st.sidebar.success("Mesh with texture loaded!", icon="👏")
+        finished_loading = True
+        # randomize the rendering parameters
+    st.sidebar.title("Randomize View Parameters")
+    activated = st.sidebar.toggle("Randomize?", value=False)
+    # default = False
+    st.session_state["randomized"] = False
+    if activated and finished_loading:
+        with st.spinner("Randomizing..."):
+            # set the camera parameters
+            dist = np.random.uniform(0.0, 1.0)
+            elev = np.random.uniform(0.0, 1.0)
+            azim = np.random.uniform(0.0, 1.0)
+            # set the lighting parameters
+            light_pos = np.random.uniform(0.0, 1.0)
+            ambient_color = np.random.uniform(0.0, 1.0)
+            diffuse_color = np.random.uniform(0.0, 1.0)
+            specular_color = np.random.uniform(0.0, 1.0)
+            # set the material parameters
+            shininess = np.random.uniform(0.0, 1.0)
+            specularity = np.random.uniform(0.0, 1.0)
+            camera_values = setup_cameras(dist, elev, azim)
+            light_values = setup_lights(
+                light_pos, ambient_color, diffuse_color, specular_color
+            )
+            material_values = setup_materials(shininess, specularity)
+            renderer = setup_renderer(camera_values, light_values, material_values)
+            st.session_state["camera_values"] = camera_values
+            st.session_state["light_values"] = light_values
+            st.session_state["material_values"] = material_values
+            st.session_state["renderer"] = renderer
+            st.session_state["randomized"] = True
+            if (
+                "camera_values" in st.session_state
+                and "light_values" in st.session_state
+                and "material_values" in st.session_state
+                and "renderer" in st.session_state
+            ):
+                st.sidebar.success("Randomization done!", icon="👏")
+    else:
+        if not st.session_state["randomized"]:
+            st.sidebar.warning(
+                "Randomization is disabled!\nDefine the rendering parameters!"
+            )
+        with st.spinner("Set View Parameters"):
+            # with st.sidebar.expander("Set View Parameters", expanded=True) as view_cont:
+            # camera parameters
+            # st.sidebar.header("View Parameters")
+            cam = st.sidebar.form("Change camera parameters")
+            cam.subheader("Camera Parameters")  # , expanded=False)
+            dist = cam.slider(
+                "Distance", min_value=0.0, max_value=10.0, value=0.5, step=0.5
+            )
+            elev = cam.slider(
+                "Elevation", min_value=-90, max_value=90, value=0, step=10
+            )
+            azim = cam.slider("Azimuth", min_value=-90, max_value=90, value=90, step=10)
+            # camera_values = cam.form_submit_button(
+            #     "Update Camera Parameters",
+            #     on_click=setup_cameras,
+            #     args=(dist, elev, azim),
+            # )
+            # cam.form_submit_button("Update Camera Parameters")
+            # camera_values = setup_cameras(dist, elev, azim)
+
+            # print(camera_values)
+            # cam.warning(
+            #     "*Note:* The camera parameters are set to the default values used in the paper"
+            # )
+
+            # lighting parameters
+            # st.sidebar.subheader("Lighting Parameters")
+            # light = st.sidebar.expander("Change lighting parameters", expanded=False)
+            # light = st.sidebar.form("Change lighting parameters")  # , expanded=False)
+            # light_pos = light.slider(
+            #     "Light Position", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            # )
+            # light_ac = light.slider(
+            #     "Ambient Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            # )
+            # light_dc = light.slider(
+            #     "Diffuse Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            # )
+            # light_sc = light.slider(
+            #     "Specular Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            # )
+            cam.subheader("Lighting Parameters")  # , expanded=False)
+            light_pos = cam.slider(
+                "Light Position", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            )
+            light_ac = cam.slider(
+                "Ambient Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            )
+            light_dc = cam.slider(
+                "Diffuse Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            )
+            light_sc = cam.slider(
+                "Specular Color", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            )
+            # light.form_submit_button("Update Lighting Parameters")
+            # light_values = setup_lights(light_pos, light_ac, light_dc, light_sc)
+            # light.warning(
+            #     "*Note:* The lighting parameters are set to the default values used in the paper"
+            # )
+            # print(light_values)
+            # material parameters
+            # st.sidebar.write("Material Parameters")
+            # mat = st.sidebar.expander("Change material parameters", expanded=False)
+            # mat = st.sidebar.form("Change material parameters")  # , expanded=False)
+
+            # mat_sh = mat.slider(
+            #     "Shininess", min_value=0, max_value=100, value=50, step=10
+            # )
+            # mat_sc = mat.slider(
+            #     "Specularity", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            # )
+            cam.subheader("Material Parameters")  # , expanded=False)
+            mat_sh = cam.slider(
+                "Shininess", min_value=0, max_value=100, value=50, step=10
+            )
+            mat_sc = cam.slider(
+                "Specularity", min_value=0.0, max_value=1.0, value=0.5, step=0.1
+            )
+            # mat.form_submit_button("Update Material Parameters")
+            # material_values = setup_materials(mat_sh, mat_sc)
+            # mat.warning(
+            #     "*Note:* The material parameters are set to the default values used in the paper"
+            # )
+            # update_button = st.form_submit_button("Update Parameters")
+            cam.form_submit_button("Update View Parameters")
+            camera_values = setup_cameras(dist, elev, azim)
+            light_values = setup_lights(light_pos, light_ac, light_dc, light_sc)
+            material_values = setup_materials(mat_sh, mat_sc)
+            renderer = setup_renderer(camera_values, light_values, material_values)
+
+            st.session_state["selected_camera_values"] = camera_values
+            st.session_state["selected_light_values"] = light_values
+            st.session_state["updated_renderer"] = renderer
+            st.session_state["selected_material_values"] = material_values
+    # Rendered Views
+    st.header("Rendered Views")
+    st.info("The rendered views will be displayed here. Click on the button to render!")
+    with st.form("Render Views"):
+        number_of_views = st.slider(
+            "Number of views to be rendered", 2, 16, 4, 2
+        )  # , key="num_views")
+        render_button = st.form_submit_button("Render Views")
+        st.session_state["number_of_views"] = number_of_views
+        st.session_state["render_button"] = render_button
+    if st.session_state["render_button"]:
+        with st.spinner("Rendering..."):
+            images = render_images(
+                renderer,
+                render_mesh,
+                light_values,
+                camera_values,
+                material_values,
+                number_of_views,
+            )
+            images = images.detach().cpu().numpy()
+            rendered_img = plotly_image(images[0][..., :3])
+            silhouette_img = plotly_image(images[0][..., 3:])
+            st.plotly_chart(rendered_img, use_container_width=True, theme=None)
+
+
+if __name__ == "__main__":
+    setup_paths()
+    main()

DermSynth3D/configs/blend.yaml

@@ -0,0 +1,72 @@
+name: DermSynth3d
+
+# Settings for pasting and blending lesions
+
+blending:
+  # Path to 3DBodyTex v1
+  # bodytex_dir: "/local-scratch2/asa409/data/dermSynth/3dbodytex-1.1-highres"
+  bodytex_dir: './data/3dbodytex-1.1-highres/'
+  # Name of the mesh to blend
+  mesh_name: '006-f-run'
+  # Path to FitzPatrick17k lesions
+  # fitz_dir: "/local-scratch2/asa409/data/dermSynth/data/finalfitz17k"
+  fitz_dir: './data/fitzpatrick17k/data/finalfitz17k/'
+  # Path to annotated Fitz17k lesions with masks
+  annot_dir: './data/annotations/'
+  # annot_dir: "/local-scratch2/asa409/data/dermSynth/annotations"
+  # Path to save the new texture maps
+  tex_dir: './data/lesions/'
+  # Name of extension to add to new texture maps
+  ext: 'latest'
+  # Number of lesions to paste/blend on each mesh
+  num_paste: 50
+  # Number of iterations for blending the lesion
+  num_iter: 400
+  # Learning rate for optimization
+  lr: 0.005
+  # Image size to render
+  img_w: 64
+  img_h: 64
+  view_size: (64, 64)
+
+# Settings for generating 2D views from blended texture maps
+generate:
+  # Number of images to render per mesh
+  num_views: 2000
+  # Name of the mesh to blend
+  mesh_name: '006-f-run'
+  # Path to save the rendering and ground truth annotations
+  save_dir: './out/blended_lesions/'
+  # Path to anatomy labels for 3DBodyTex v1.
+  anatomy_dir: './data/bodytex_anatomy_labels/'
+  # Path to the background scenes
+  # background_dir: "/local-scratch2/asa409/data/dermSynth/IndoorScene"
+  background_dir: './data/background/IndoorScene/'
+
+  # Whether to paste lesions for cheap blending
+  paste: False
+  # Image size to render
+  img_w: 64
+  img_h: 64
+  view_size: (64, 64)
+  # Threshold amount of lesion to be present to generate a view
+  percent_skin: 0.1
+  # Paths to skin3d repo
+  skin3d: './skin3d/data/3dbodytex-1.1-highres/bodytex.csv'
+  # Paths to skin3d nevi annotations
+  skin3d_annot: './skin3d/data/3dbodytex-1.1-highres/annotations/'
+
+  random:
+    surface_offset_bounds: (0.1, 1.3)
+    ambient_bounds: (0.3, 0.99)
+    specular_bounds: (0, 0.1)
+    diffuse_bounds : (0.3, 0.99)
+    mat_diffuse_bounds : (0.3, 0.99)
+    mat_specular_bounds : (0., 0.05)
+    znear: 0.01
+    light_pos: None
+    shininess: (30, 60)
+    sphere_pos: False
+    elev_bounds : (0, 180)
+    azim_bounds : (-90, 90)
+    background_blur_radius_bounds : (0, 3)

DermSynth3D/configs/default.yaml

@@ -0,0 +1,6 @@
+device: 'cuda'
+img_w: 64
+img_h: 64
+extension:
+  image_extension: '.jpg'
+  target_extension: '.png'

DermSynth3D/configs/multitask.yaml

@@ -0,0 +1,36 @@
+name: 'Infer MultiTask'
+
+infer:
+  multi_head: False
+  backbone: False
+  model_path: '/path/to/trained/model/'
+
+  img_size: (320, 320)
+
+  # ImageNet
+  mean: (0.485, 0.456, 0.406)
+  std: (0.229, 0.224, 0.225)
+
+  # Test Type
+  mode: 1 # Choose to test on Fitz17k/ Ph2/ DermoFit
+  
+  # Data paths
+  fitz_imgs: './data/fitzpatrick17k/data/finalfitz17k/'
+  fitz_annot: './data/fitzpatrick17k/annotations/annotations/'
+  fitz_test_skin: './data/fitzpatrick17k/annotations/test/skin/'
+  fitz_test_anatomy: './data/fitzpatrick17k/annotations/test/anatomy/'
+  fitz_test_imgs: './data/fitzpatrick17k/annotations/test/images/'
+
+  ph2_imgs: './data/ph2/images'
+  ph2_test_skin: './data/ph2/preds/skin/'
+  ph2_test_images: './data/ph2/preds/images/'
+  ph2_test_targets: './data/ph2/preds/targets/'
+
+  derm_imgs: './data/dermofit/images/'
+  derm_targets: './data/dermofit/targets/'
+  derm_preds: './data/dermofit/predictions/'
+
+  prath_imgs: './data/Pratheepan_Dataset/FacePhoto/'
+  prath_tgts: './data/Pratheepan_Dataset/GroundT_FacePhoto/'
+  prath_preds_skin: './data/Pratheepan_Dataset/predictions/skin/'
+  prath_preds_anatomy: './data/Pratheepan_Dataset/predictions/anatomy/'

DermSynth3D/configs/renderer.yaml

@@ -0,0 +1,33 @@
+name: Renderer params
+
+fov: 30
+faces_per_pixel: 1
+
+# Specific Light Source properties
+light:
+  ambient_color: (0.5, 0.5, 0.5)
+  specular_color: (0.025, 0.025, 0.025)
+  diffuse_color: (0.5, 0.5, 0.5)
+  light_location: None
+
+# Specific Material Properties
+material: 
+  ambient_color: (0.5, 0.5, 0.5)
+  specular_color: (0.025, 0.025, 0.025)
+  diffuse_color: (0.5, 0.5, 0.5)
+  shininess: 50.
+
+# Range for randomizing views/light/material
+random:
+  dists: (0.3, 3) # Distance of subject from camera
+  elevs: (90, 270) # Elevation
+  azims: (0,270) # Azimuth
+  ats: (0.25, 1.75) # camera position
+  ambients: (0.2, 0.99) # Ambient light 
+  speculars: (0, 0.1) # Specularity
+  diffuses: (0.2, 0.99) # Diffusivity
+  shininess: (30., 70.) # Shininess of the material
+  surface_offset_weight: (0.1, 1.2) # offset camera by a dsitance
+  znear: 1
+
+

DermSynth3D/configs/train_mix.yaml

@@ -0,0 +1,42 @@
+name: Mixed Training
+
+# Path to generated data
+root: './data/synth_data/'
+# Path to real data
+real: './data/FUSeg/'
+# Name of folder to save the models
+save_name: 'run1' # checkpoints are saved in '../data/exp/'
+save_dir: './out'
+
+train:
+  batch_size: 16 # batch size
+  lr: 0.00005 # learning rate
+  epochs: 200 # number of epochs
+  weight_decay: 0.00005
+  num_classes: 1 # number of output classes
+  real_ratio: 0.5 # fraction of real images to be used from real dataset
+  real_batch_ratio: 0.5 # fraction of real samples in each batch
+  pretrain: True # use pretrained DeepLabV3 weights
+  mode: 1.0 # Fraction of the number of synthetic images to be used for training
+  resume: False # resume training from last saved model
+  root: './data/synth_data/'
+  # Path to real data
+  real: './data/FUSeg/'
+
+  # ImageNet
+  mean: (0.485, 0.456, 0.406)
+  std: (0.229, 0.224, 0.225)
+  # Augmentations
+  min_v: 0.8
+  max_v: 1.2
+  img_size: (256, 256)
+  save_every: 5
+
+val:
+  batch_size: 1
+  log_every: 100
+  val_every: 500 # Validate every N iterations
+  
+test:
+  batch_size: 1
+  test_every: 500 # Test every N iterations

DermSynth3D/data/3dbodytex-1.1-highres/006-f-run/model_highres_0_normalized.mtl

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+Ka 0.200000 0.200000 0.200000
+Kd 1.000000 1.000000 1.000000
+Ks 1.000000 1.000000 1.000000
+Tr 0.000000
+illum 2
+Ns 0.000000
+map_Kd model_highres_0_normalized.png

DermSynth3D/data/3dbodytex-1.1-highres/006-f-run/model_highres_0_normalized.obj

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a6143380968215e1e181f7bf54ea119f04c3b2eebe01bbd1fdc83f5867ba9194
+size 56175506

DermSynth3D/data/3dbodytex-1.1-highres/221-m-u/model_highres_0_normalized.mtl

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+newmtl texture
+Ka 0.200000 0.200000 0.200000
+Kd 1.000000 1.000000 1.000000
+Ks 1.000000 1.000000 1.000000
+Tr 0.000000
+illum 2
+Ns 0.000000
+map_Kd model_highres_0_normalized.png

DermSynth3D/data/3dbodytex-1.1-highres/221-m-u/model_highres_0_normalized.obj

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+size 56024466