Spaces:
Runtime error
Runtime error
| import os | |
| import builtins | |
| import math | |
| import streamlit as st | |
| import gdown | |
| #from google_drive_downloader import GoogleDriveDownloader as gdd | |
| from demo.src.models import load_trained_model | |
| from demo.src.utils import render_predict_from_pose, predict_to_image | |
| from demo.src.config import MODEL_DIR, MODEL_NAME, FILE_ID | |
| st.set_page_config(page_title="DietNeRF Demo") | |
| def download_model(): | |
| os.makedirs(MODEL_DIR, exist_ok=True) | |
| _model_path = os.path.join(MODEL_DIR, MODEL_NAME) | |
| # gdd.download_file_from_google_drive(file_id=FILE_ID, | |
| # dest_path=_model_path, | |
| # unzip=True) | |
| url = f'https://drive.google.com/uc?id={FILE_ID}' | |
| gdown.download(url, _model_path, quiet=False) | |
| print(f'model downloaded from google drive: {_model_path}') | |
| def fetch_model(): | |
| model, state = load_trained_model(MODEL_DIR, MODEL_NAME) | |
| return model, state | |
| model_path = os.path.join(MODEL_DIR, MODEL_NAME) | |
| if not os.path.isfile(model_path): | |
| download_model() | |
| model, state = fetch_model() | |
| pi = math.pi | |
| st.sidebar.image("images/diet-nerf.png", width=310) | |
| st.sidebar.header('SELECT YOUR VIEW DIRECTION') | |
| theta = st.sidebar.slider("Theta", min_value=0., max_value=2.*pi, | |
| step=0.5, value=0.) | |
| phi = st.sidebar.slider("Phi", min_value=0., max_value=0.5*pi, | |
| step=0.1, value=1.) | |
| radius = st.sidebar.slider("Radius", min_value=2., max_value=6., | |
| step=1., value=3.) | |
| caption = "Diet-NeRF achieves SoTA few-shot learning capacity in 3D model reconstruction. " \ | |
| "Thanks to the 2D supervision by CLIP (aka semantic loss), " \ | |
| "it can render novel and challenging views with ONLY 8 training images, " \ | |
| "outperforming original NeRF!" | |
| st.markdown(f""" <h4> {caption} </h4> """, | |
| unsafe_allow_html=True) | |
| with st.spinner("Rendering Image (may take 2-3 mins)..."): | |
| pred_color, _ = render_predict_from_pose(state, theta, phi, radius) | |
| im = predict_to_image(pred_color) | |
| st.image(im, use_column_width=False) | |