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import gradio as gr
import torch
import numpy as np
from huggingface_hub import hf_hub_download
from depth_anything_v2.dpt import DepthAnythingV2
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model_configs = {
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
}
encoder = 'vitl'
model = DepthAnythingV2(**model_configs[encoder])
model_path = hf_hub_download(
repo_id="depth-anything/Depth-Anything-V2-Large",
filename=f"depth_anything_v2_{encoder}.pth",
repo_type="model"
)
state_dict = torch.load(model_path, map_location="cpu")
model.load_state_dict(state_dict)
model = model.to(DEVICE).eval()
def infer(img):
with torch.no_grad():
depth = model.infer_image(img[:, :, ::-1]) # BGR to RGB if needed
# Normalize to 0-255 and convert to uint8
depth_norm = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
return depth_norm.astype(np.uint8)
iface = gr.Interface(fn=infer, inputs=gr.Image(type="numpy"), outputs=gr.Image())
iface.launch()
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