app.py

import os
import builtins
import math
import json

import streamlit as st
import gdown

from demo.src.models import load_trained_model
from demo.src.utils import render_predict_from_pose, predict_to_image

st.set_page_config(page_title="DietNeRF")

with open("config.json") as f:
    cfg = json.loads(f.read())

MODEL_DIR = "models"


def select_model():
    obj_select = st.selectbox("Select a Scene", ("Mic", "Chair", "Lego", "Ship", "Hotdog"))
    DIET_NERF_MODEL_NAME = cfg[obj_select]["DIET_NERF_MODEL_NAME"]
    DIET_NERF_FILE_ID = cfg[obj_select]["DIET_NERF_FILE_ID"]

    NERF_MODEL_NAME = cfg[obj_select]["NERF_MODEL_NAME"]
    NERF_FILE_ID = cfg[obj_select]["NERF_FILE_ID"]

    return DIET_NERF_MODEL_NAME, DIET_NERF_FILE_ID, NERF_MODEL_NAME, NERF_FILE_ID


st.title("DietNeRF")
caption = (
    "DietNeRF achieves SoTA few-shot learning capacity in 3D model reconstruction. "
    "Thanks to the 2D supervision by CLIP (aka. _Semantic Consisteny Loss_), "
    "it can render novel and challenging views with ONLY 8 training images, "
    "outperforming original NeRF!"
)
st.markdown(caption)
st.markdown("")
DIET_NERF_MODEL_NAME, DIET_NERF_FILE_ID, NERF_MODEL_NAME, NERF_FILE_ID = select_model()


@st.cache(show_spinner=False)
def download_diet_nerf_model():
    os.makedirs(MODEL_DIR, exist_ok=True)
    diet_nerf_model_path = os.path.join(MODEL_DIR, DIET_NERF_MODEL_NAME)
    url = f"https://drive.google.com/uc?id={DIET_NERF_FILE_ID}"
    gdown.download(url, diet_nerf_model_path, quiet=False)
    print(f"Model downloaded from google drive: {diet_nerf_model_path}")


def download_nerf_model():
    nerf_model_path = os.path.join(MODEL_DIR, NERF_MODEL_NAME)
    url = f"https://drive.google.com/uc?id={NERF_FILE_ID}"
    gdown.download(url, nerf_model_path, quiet=False)
    print(f"Model downloaded from google drive: {nerf_model_path}")


@st.cache(show_spinner=False, allow_output_mutation=True)
def fetch_diet_nerf_model():
    model, state = load_trained_model(MODEL_DIR, DIET_NERF_MODEL_NAME)
    return model, state


@st.cache(show_spinner=False, allow_output_mutation=True)
def fetch_nerf_model():
    model, state = load_trained_model(MODEL_DIR, NERF_MODEL_NAME)
    return model, state


diet_nerf_model_path = os.path.join(MODEL_DIR, DIET_NERF_MODEL_NAME)
if not os.path.isfile(diet_nerf_model_path):
    download_diet_nerf_model()

nerf_model_path = os.path.join(MODEL_DIR, NERF_MODEL_NAME)
if not os.path.isfile(nerf_model_path):
    download_nerf_model()

diet_nerf_model, diet_nerf_state = fetch_diet_nerf_model()
nerf_model, nerf_state = fetch_nerf_model()

pi = math.pi

st.sidebar.markdown(
    """
<style>
.aligncenter {
    text-align: center;
}
</style>
<p class="aligncenter">
    <img src="https://user-images.githubusercontent.com/77657524/126361638-4aad58e8-4efb-4fc5-bf78-f53d03799e1e.png" width="420" height="400"/>
</p>
""",
    unsafe_allow_html=True,
)
st.sidebar.markdown(
    """
<p style='text-align: center'>
<a href="https://github.com/codestella/putting-nerf-on-a-diet" target="_blank">GitHub</a> | <a href="https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745" target="_blank">Project Report</a>
</p>
    """,
    unsafe_allow_html=True,
)
st.sidebar.header("SELECT YOUR VIEW DIRECTION")
theta = st.sidebar.slider(
    "Theta", min_value=-pi, max_value=pi, step=0.5, value=0.0, help="Rotational angle in Horizontal direction"
)
phi = st.sidebar.slider(
    "Phi", min_value=0.0, max_value=0.5 * pi, step=0.1, value=1.0, help="Rotational angle in Vertical direction"
)
radius = st.sidebar.slider(
    "Radius", min_value=2.0, max_value=6.0, step=1.0, value=3.0, help="Distance between object and the viewer"
)

st.markdown("")

with st.spinner("Rendering View..."):
    with st.spinner("It may take 2-3 mins. So, why don't you read our report in the meantime"):
        dn_pred_color, _ = render_predict_from_pose(diet_nerf_state, theta, phi, radius)
        dn_im = predict_to_image(dn_pred_color)
        dn_w, _ = dn_im.size
        dn_new_w = int(2 * dn_w)
        dn_im = dn_im.resize(size=(dn_new_w, dn_new_w))

        n_pred_color, _ = render_predict_from_pose(nerf_state, theta, phi, radius)
        n_im = predict_to_image(n_pred_color)
        n_w, _ = n_im.size
        n_new_w = int(2 * n_w)
        n_im = n_im.resize(size=(n_new_w, n_new_w))

        diet_nerf_col, nerf_col = st.beta_columns([1, 1])
        diet_nerf_col.markdown(
            """<h4 style='text-align: center'>DietNeRF</h4>""", unsafe_allow_html=True
        )
        diet_nerf_col.image(dn_im, use_column_width=True)

        nerf_col.markdown(
            """<h4 style='text-align: center'>NeRF</h4>""", unsafe_allow_html=True
        )
        nerf_col.image(n_im, use_column_width=True)