app.py

import streamlit as st
import tensorflow as tf
import numpy as np

from transformations import *
from rendering import *

# Setting random seed to obtain reproducible results.
tf.random.set_seed(42)

# Initialize global variables.
AUTO = tf.data.AUTOTUNE
BATCH_SIZE = 1
NUM_SAMPLES = 32
POS_ENCODE_DIMS = 16
EPOCHS = 30
H = 25
W = 25
focal = 0.6911112070083618

def show_rendered_image(r,theta,phi):
    # Get the camera to world matrix.
    c2w = pose_spherical(theta, phi, r)

    ray_oris, ray_dirs = get_rays(H, W, focal, c2w)
    rays_flat, t_vals = render_flat_rays(
        ray_oris, ray_dirs, near=2.0, far=6.0, num_samples=NUM_SAMPLES, rand=False
    )

    rgb, depth = render_rgb_depth(
        nerf_loaded, rays_flat[None, ...], t_vals[None, ...], rand=False, train=False
    )
    return(rgb[0], depth[0])


# app.py text matter starts here
st.title('NeRF:3D volumetric rendering with NeRF')
st.markdown("Authors: [Aritra Roy Gosthipathy](https://twitter.com/ariG23498) and [Ritwik Raha](https://twitter.com/ritwik_raha)")
st.markdown("## Description")
st.markdown("[NeRF](https://arxiv.org/abs/2003.08934) proposes an ingenious way to synthesize novel views of a scene by modelling the volumetric scene function through a neural network.")
st.markdown("## Interactive Demo")

# download the model:

# from huggingface_hub import snapshot_download
# snapshot_download(repo_id="Alesteba/your-model-name", local_dir="./nerf")

from huggingface_hub import from_pretrained_keras

nerf_loaded = from_pretrained_keras("Alesteba/NeRF_ficus")

# load the pre-trained model
# nerf_loaded = tf.keras.models.load_model("nerf", compile=False)

# set the values of r theta phi
r = 4.0
theta = st.slider("Enter a value for Θ:", min_value=0.0, max_value=360.0)
phi = -30.0
color, depth = show_rendered_image(r, theta, phi)

col1, col2= st.columns(2)

with col1:
    color = tf.keras.utils.array_to_img(color)
    st.image(color, caption="Color Image", clamp=True, width=300)

with col2:
    depth = tf.keras.utils.array_to_img(depth[..., None])
    st.image(depth, caption="Depth Map", clamp=True, width=300)

st.markdown("## Tutorials")  
st.markdown("- [Keras](https://keras.io/examples/vision/nerf/)")
st.markdown("- [PyImageSearch NeRF 1](https://www.pyimagesearch.com/2021/11/10/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-1/)")
st.markdown("- [PyImageSearch NeRF 2](https://www.pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/)")
st.markdown("- [PyImageSearch NeRF 3](https://www.pyimagesearch.com/2021/11/24/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-3/)")

st.markdown("## Credits")  
st.markdown("- [PyImageSearch](https://www.pyimagesearch.com/)")
st.markdown("- [JarvisLabs.ai GPU credits](https://jarvislabs.ai/)")