File size: 2,467 Bytes
98442a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import os
import sys
from PIL import Image
import torch
# Add root folder to python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from formaai import FormaAi
def main():
print("=" * 80)
print("FormaAi Unified Hybrid 3D Neural Network Test")
print("=" * 80)
# 1. Initialize hybrid model on GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
model = FormaAi(device=device)
# 2. Load test image
image_path = "TRELLIS-main/assets/example_image/typical_misc_lantern.png"
full_image_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), image_path)
if not os.path.exists(full_image_path):
print(f"Error: Test image not found at {full_image_path}")
return
print(f"Loading test image: {full_image_path}")
image = Image.open(full_image_path).convert("RGBA")
# 3. Preprocess
print("Preprocessing image (removing background, centering)...")
processed_image = model.preprocess(image)
# Save processed image to check
output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "scratch/formaai_test")
os.makedirs(output_dir, exist_ok=True)
processed_image.save(os.path.join(output_dir, "preprocessed.png"))
print(f"Saved preprocessed image to: {output_dir}/preprocessed.png")
# 4. Run unified forward pass
print("Running unified forward pass (Sparse Structure + Latent Flow + Multi-decoder inference)...")
aligned_outputs = model(
processed_image,
seed=42,
ss_steps=12,
ss_cfg=7.5,
slat_steps=12,
slat_cfg=3.0,
formats=['mesh', 'gaussian', 'radiance_field'],
preprocess=False, # Already preprocessed
refine_gs=False, # Disabled by default
refine_steps=100
)
# 5. Print alignment metadata
center = aligned_outputs['center']
scale = aligned_outputs['scale']
print(f"\nCoordinate Alignment Metadata:")
print(f" - Center offset translated to origin: {center.cpu().numpy().tolist()}")
print(f" - Scale multiplier: {scale}")
# 6. Save hybrid assets
print("\nSaving aligned hybrid assets (GLB + PLY + Metadata)...")
model.save_hybrid_asset(aligned_outputs, output_dir, prefix="forma_hybrid", upscale_factor=2)
print("\n" + "=" * 80)
print("FormaAi verification completed successfully!")
print("=" * 80)
if __name__ == "__main__":
main()
|