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LeMilenovLeMilenov
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Implement new renderers and utilitary methods
1 parent cd279b5 commit c37a688

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‎.gitattributes

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*.h5 filter=lfs diff=lfs merge=lfs -text

‎.gitignore

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.vscode/settings.json
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__pycache__/*
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output/*
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input/*
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baselMorphableModel/*
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faceNext/*
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drjit/*
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mitsuba/*
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*.pyc
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ext/*
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chi2_data.py

‎INSTALL

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@@ -5,4 +5,5 @@ pip install redner-gpu
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conda install -c conda-forge opencv
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conda install -c 1adrianb face_alignment=1.2.0
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conda install -c anaconda h5py
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pip install mediapipe
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pip install mediapipe
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pip install mitsuba

‎README.md

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‎commands_demo.ipynb

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Generate"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Reconstruction of a face from a single image ( Vertex / Redner / Mitsuba)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! py -u ./optimizer.py --input input/test/s1.png --output output/test/mitsuba_blank_text"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Batch reconstruction of a faces from a folder ( Vertex / Redner)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! py ./optimizer.py --input input/test/ --output output/test/all_images/vertex_reg "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Batch reconstruction of faces in loop (Mitsuba)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"py ./optimizer.py --input input/test/s1.png --output output/test/mitsuba_rednermat_clip\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import subprocess\n",
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"# Directory path containing all images\n",
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"imageFolderPath = './input/test/'\n",
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"\n",
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"outputDir = './output/test/mitsuba_rednermat_clip'\n",
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"#setup\n",
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"if not os.path.exists(outputDir):\n",
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" os.makedirs(outputDir) # Create the output directory if it doesn't exist\n",
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"# Get the names of all files in the specified directory\n",
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"image_names = [f for f in os.listdir(imageFolderPath) if os.path.isfile(os.path.join(imageFolderPath, f))]\n",
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"\n",
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"# Filter only image files by extensions (if required)\n",
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"image_paths = [os.path.join(imageFolderPath, image_name) for image_name in image_names if image_name.endswith(('.png', '.jpg', '.jpeg'))]\n",
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"\n",
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"# Print the command for each image\n",
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"for image_path in image_paths:\n",
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" # Removing the './' from the image path\n",
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" image_path = image_path.replace('./', '')\n",
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" outputDir = outputDir.replace('./', '')\n",
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" command = f'py ./optimizer.py --input {image_path} --output {outputDir}'\n",
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" print(command)\n",
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" # Execute the command using os.system(command) if desired\n",
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" # os.system(command)\n",
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" result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n",
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" print(result.stdout.decode())\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"^C\n"
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]
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}
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],
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"source": [
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"! py ./optimizer.py --input input/test/s3.png --output output/test/mitsuba_rednermat_clip"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Display images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"import matplotlib.pyplot as plt\n",
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"def display_images_in_folder(folder_path):\n",
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" # Iterate through the folder and subfolders\n",
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" for root, dirs, files in os.walk(folder_path):\n",
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" # Look for the file \"render0.png\" in the current directory\n",
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" if \"render_0.png\" in files:\n",
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" image_path = os.path.join(root, \"render_0.png\")\n",
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" # Open the image using PIL\n",
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" image = Image.open(image_path)\n",
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" # Display the image\n",
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" # Plot and display the image using Matplotlib\n",
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" plt.imshow(image)\n",
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" plt.axis('off') # Turn off axis\n",
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" plt.title(f\"Image from: {root}\")\n",
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" plt.show()\n",
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"\n",
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"# Path to the output directory\n",
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"output_folder_path = outputDir\n",
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"\n",
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"# Call the function to find and display the images\n",
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"display_images_in_folder(output_folder_path)\n"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "demo.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎config.py

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#morphable model
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self.path = 'baselMorphableModel'
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self.textureResolution = 256 #256 or 512
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self.textureResolution = 512 #256 or 512
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self.trimPca = False # if True keep only a subset of the pca basis (eigen vectors)
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#spherical harmonics
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self.weightRoughnessSmoothnessReg = 0.002 # smoothness regularizer weight for roughness texture (at step 3)
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self.debugFrequency = 10 #display frequency during optimization
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self.debugOutput = False # generate outputs at each debugFrequency
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self.saveIntermediateStage = False #if True the output of stage 1 and 2 are saved. stage 3 is always saved which is the output of the optim
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self.saveTime = False # if True will save the time taken in the txt
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self.verbose = False #display loss on terminal if true
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self.rtSamples = 500 #the number of ray tracer samples to render the final output
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self.rtTrainingSamples = 8 # number of ray tracing to use during training
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self.bounces = 2 # number of bounces
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self.smoothing = True #should we smooth the face when doing diff
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self.rendererName = 'redner' #default renderer name
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def fillFromDicFile(self, filePath):
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'''
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overwrite default config

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