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--- |
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license: mit |
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tags: |
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- thispersondoesnotexist |
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- stylegan |
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- stylegan2 |
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- mesh |
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- model |
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- 3d |
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- asset |
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- generative |
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pretty_name: HeadsNet |
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size_categories: |
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- 1K<n<10K |
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--- |
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# HeadsNet |
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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. |
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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. |
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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: |
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``` |
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import bpy |
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import glob |
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import pathlib |
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if not isdir(outputDir): mkdir(outputDir) |
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importDir = "ply/" |
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outputDir = "ply_norm/" |
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if not isdir(outputDir): mkdir(outputDir) |
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for file in glob.glob(importDir + "*.ply"): |
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model_name = pathlib.Path(file).stem |
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if pathlib.Path(outputDir+model_name+'.ply').is_file() == True: continue |
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bpy.ops.wm.ply_import(filepath=file) |
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bpy.ops.wm.ply_export( |
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filepath=outputDir+model_name+'.ply', |
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filter_glob='*.ply', |
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check_existing=False, |
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ascii_format=False, |
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export_selected_objects=False, |
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apply_modifiers=True, |
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export_triangulated_mesh=True, |
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export_normals=True, |
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export_uv=False, |
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export_colors='SRGB', |
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global_scale=1.0, |
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forward_axis='Y', |
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up_axis='Z' |
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) |
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bpy.ops.object.select_all(action='SELECT') |
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bpy.ops.object.delete(use_global=False) |
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bpy.ops.outliner.orphans_purge() |
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bpy.ops.outliner.orphans_purge() |
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bpy.ops.outliner.orphans_purge() |
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``` |
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_Importing the PLY without normals causes Blender to automatically generate them._ |
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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). |
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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). |
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This binary training data can be loaded into Python using: |
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``` |
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load_x = [] |
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with open("train_x.dat", 'rb') as f: |
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load_x = np.fromfile(f, dtype=np.float32) |
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load_y = [] |
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with open("train_y.dat", 'rb') as f: |
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load_y = np.fromfile(f, dtype=np.float32) |
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``` |
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The data can then be reshaped and saved back out as a numpy array which makes for faster loading: |
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``` |
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inputsize = 2 |
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outputsize = 6 |
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train_x = np.reshape(load_x, [tss, inputsize]) |
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train_y = np.reshape(load_y, [tss, outputsize]) |
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np.save("train_x.npy", train_x) |
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np.save("train_y.npy", train_y) |
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``` |
