Spaces:
Runtime error
Runtime error
File size: 2,855 Bytes
de5aa49 b6fb00d 0b11f48 b6fb00d de5aa49 2cdf85d de5aa49 2e25e60 9270e76 de5aa49 e89796e de5aa49 e89796e de5aa49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
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/)") |