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---
license: mit
tags:
- thispersondoesnotexist
- stylegan
- stylegan2
- mesh
- model
- 3d
- asset
- generative
pretty_name: HeadsNet
size_categories:
- 1K<n<10K
---
# HeadsNet
This dataset uses the [thispersondoesnotexist_to_triposr_6748_3D_Heads](https://huggingface.co/datasets/tfnn/thispersondoesnotexist_to_triposr_6748_3D_Heads) dataset as a foundation.
The heads dataset was collecting using the scraper [Dataset_Scraper.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/Dataset_Scraper.7z?download=true) based on [TripoSR](https://github.com/VAST-AI-Research/TripoSR) with [this marching cubes improvement](https://github.com/VAST-AI-Research/TripoSR/issues/22#issuecomment-2010318709) by [thatname/zephyr](https://github.com/thatname) which converts the 2D images from [ThisPersonDoesNotExist](https://thispersondoesnotexist.com/) to 3D meshes.
Vertex Normals need to be generated before we can work with this dataset, the easiest method to achieve this was with a simple [Blender](https://www.blender.org/) script:
```
import bpy
import glob
import pathlib
if not isdir(outputDir): mkdir(outputDir)
importDir = "ply/"
outputDir = "ply_norm/"
if not isdir(outputDir): mkdir(outputDir)
for file in glob.glob(importDir + "*.ply"):
model_name = pathlib.Path(file).stem
if pathlib.Path(outputDir+model_name+'.ply').is_file() == True: continue
bpy.ops.wm.ply_import(filepath=file)
bpy.ops.wm.ply_export(
filepath=outputDir+model_name+'.ply',
filter_glob='*.ply',
check_existing=False,
ascii_format=False,
export_selected_objects=False,
apply_modifiers=True,
export_triangulated_mesh=True,
export_normals=True,
export_uv=False,
export_colors='SRGB',
global_scale=1.0,
forward_axis='Y',
up_axis='Z'
)
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete(use_global=False)
bpy.ops.outliner.orphans_purge()
bpy.ops.outliner.orphans_purge()
bpy.ops.outliner.orphans_purge()
```
_Importing the PLY without normals causes Blender to automatically generate them._
At this point the PLY files now need to be converted to training data, for this I wrote a C program [DatasetGen_2_6.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/DatasetGen_2_6.7z?download=true) using [RPLY](https://w3.impa.br/~diego/software/rply/) to load the PLY files and convert them to binary data which I have provided here [HeadsNet-2-6.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet-2-6.7z?download=true).
It's always good to NAN check your training data after generating it so I have provided a simple Python script for that here [nan_check.py](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/nan_check.py?download=true).
This binary training data can be loaded into Python using:
```
load_x = []
with open("train_x.dat", 'rb') as f:
load_x = np.fromfile(f, dtype=np.float32)
load_y = []
with open("train_y.dat", 'rb') as f:
load_y = np.fromfile(f, dtype=np.float32)
```
The data can then be reshaped and saved back out as a numpy array which makes for faster loading:
```
inputsize = 2
outputsize = 6
train_x = np.reshape(load_x, [tss, inputsize])
train_y = np.reshape(load_y, [tss, outputsize])
np.save("train_x.npy", train_x)
np.save("train_y.npy", train_y)
``` |