updates for hf space

app.py

@@ -1,9 +0,0 @@
-"""
-Hugging Face Space entry point for SHeaP demo.
-This file imports and runs the gradio demo.
-"""
-
-from gradio_demo import demo
-
-if __name__ == "__main__":
-    demo.launch()

convert_flame.py

@@ -1,68 +0,0 @@
-"""
-Converts FLAME pickle files to PyTorch .pt files.
-"""
-
-import argparse
-from pathlib import Path
-from typing import Union
-
-import torch
-
-from sheap.load_flame_pkl import load_pkl_format_flame_model
-
-
-def convert_flame(flame_base_dir: Union[str, Path], overwrite: bool) -> None:
-    """Convert FLAME pickle files to PyTorch .pt files.
-
-    Searches for all .pkl files in the FLAME base directory and converts them to
-    PyTorch .pt format, skipping certain mask files.
-
-    Args:
-        flame_base_dir: Path to the FLAME model directory containing pickle files.
-        overwrite: Whether to overwrite existing .pt files if they already exist.
-
-    Raises:
-        AssertionError: If flame_base_dir does not exist.
-    """
-    flame_base_dir = Path(flame_base_dir)
-    assert flame_base_dir is not None  # for mypy
-    assert flame_base_dir.exists(), (
-        f"FLAME_BASE_DIR not found at {flame_base_dir}. "
-        "Please set arg flame_base_dir to the FLAME model directory, "
-        " or set the FLAME_BASE_DIR environment variable."
-    )
-    pickle_files = list(flame_base_dir.glob("**/**/*.pkl"))
-    skip_files = ["FLAME_masks.pkl"]
-    for model_path in pickle_files:
-        if model_path.name in skip_files:
-            continue
-        print(f"Converting {model_path}...")
-        data = load_pkl_format_flame_model(model_path)
-        new_path = model_path.with_suffix(".pt")
-        if new_path.exists() and not overwrite:
-            print(f"Skipping {new_path} because it already exists.")
-            continue
-        torch.save(data, new_path)
-        print(f"Saved {new_path}")
-
-
-def main() -> None:
-    """Parse command-line arguments and convert FLAME pickle files to PyTorch format."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--flame_base_dir",
-        type=str,
-        help="Path to the FLAME model directory. "
-        "Defaults to the FLAME_BASE_DIR environment variable.",
-        default="FLAME2020/",
-    )
-    parser.add_argument(
-        "--overwrite",
-        action="store_true",
-        help="Overwrite existing files if they already exist.",
-    )
-    convert_flame(**vars(parser.parse_args()))
-
-
-if __name__ == "__main__":
-    main()

demo.py

@@ -32,7 +32,12 @@ def create_rendering_image(
         PIL Image with three views side-by-side (original, mesh, blended)
     """
     # Render the mesh
-    color, depth = render_mesh(verts=verts, faces=faces, c2w=c2w)
+    try:
+        color, depth = render_mesh(verts=verts, faces=faces, c2w=c2w)
+    except Exception as e:
+        print(f"WARNING: Rendering failed ({e}), returning original image only", flush=True)
+        # Return just the original image if rendering fails
+        return original_image.convert("RGB").resize((output_size, output_size))
 
     # Resize original to match output size
     original_resized = original_image.convert("RGB").resize((output_size, output_size))

gradio_demo.py

@@ -4,6 +4,14 @@ Accepts video or image input and renders the SHEAP output overlayed.
 """
 
 import os
+
+# CRITICAL: Set PYOPENGL_PLATFORM before any OpenGL/pyrender imports
+# This must happen before importing demo.py or sheap.render
+# Note: HF Spaces with GPU will have EGL available, use osmesa for CPU-only environments
+if "PYOPENGL_PLATFORM" not in os.environ:
+    # Default to egl (works on HF Spaces with GPU)
+    os.environ["PYOPENGL_PLATFORM"] = "egl"
+
 import shutil
 import subprocess
 import tempfile
@@ -30,8 +38,6 @@ except ImportError:
     )
 from sheap.fa_landmark_utils import detect_face_and_crop
 
-os.environ["PYOPENGL_PLATFORM"] = "egl"
-
 # Global variables for models (load once)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 sheap_model = None

packages.txt

@@ -0,0 +1,9 @@
+ffmpeg
+libsm6
+libxext6
+libxrender1
+libgomp1
+libglib2.0-0
+libgl1
+libegl1
+libgles2

pyproject.toml

@@ -1,52 +0,0 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[project]
-name = "sheap"
-version = "0.1.0"
-description = "SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians"
-readme = "README.md"
-requires-python = ">=3.11"
-license = { file = "LICENSE.txt" }
-authors = [
-    { name = "Liam Schoneveld" }
-]
-keywords = ["3d", "face", "flame", "head", "mesh", "reconstruction"]
-classifiers = [
-    "Development Status :: 3 - Alpha",
-    "Intended Audience :: Developers",
-    "Intended Audience :: Science/Research",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-]
-dependencies = [
-    "chumpy @ git+https://github.com/nlml/chumpy.git",
-    "numpy>=1.20.0",
-    "pillow>=9.0.0",
-    "pyrender>=0.1.45",
-    "roma>=1.5.4",
-    "scipy>=1.16.3",
-    "torch>=2.0.0",
-    "torchaudio>=2.0.0",
-    "torchvision>=0.15.1",
-    "tqdm>=4.67.1",
-    "trimesh>=4.9.0",
-]
-
-[project.urls]
-Homepage = "https://nlml.github.io/sheap"
-Repository = "https://github.com/nlml/sheap"
-
-[dependency-groups]
-dev = [
-    "pre-commit>=4.3.0",
-]
-
-[tool.hatch.metadata]
-allow-direct-references = true
-
-[tool.hatch.build.targets.wheel]
-packages = ["sheap"]