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from pathlib import Path
import torch
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.misc.image_io import save_interpolated_video
from src.model.ply_export import export_ply
from src.model.model.anysplat import AnySplat
from src.utils.image import process_image
def main():
# Load the model from Hugging Face
model = AnySplat.from_pretrained("lhjiang/anysplat")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
for param in model.parameters():
param.requires_grad = False
# Load Images
image_folder = "examples/vrnerf/riverview"
images = sorted([os.path.join(image_folder, f) for f in os.listdir(image_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg'))])
images = [process_image(img_path) for img_path in images]
images = torch.stack(images, dim=0).unsqueeze(0).to(device) # [1, K, 3, 448, 448]
b, v, _, h, w = images.shape
# Run Inference
gaussians, pred_context_pose = model.inference((images+1)*0.5)
# Save the results
pred_all_extrinsic = pred_context_pose['extrinsic']
pred_all_intrinsic = pred_context_pose['intrinsic']
save_interpolated_video(pred_all_extrinsic, pred_all_intrinsic, b, h, w, gaussians, image_folder, model.decoder)
export_ply(gaussians.means[0], gaussians.scales[0], gaussians.rotations[0], gaussians.harmonics[0], gaussians.opacities[0], Path(image_folder) / "gaussians.ply")
if __name__ == "__main__":
main() |