Spaces:
Build error
Build error
UI Changes
Browse files- .gitignore +2 -1
- app.py +44 -34
.gitignore
CHANGED
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.idea
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__pycache__
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models
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.idea
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__pycache__
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models
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.vscode
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app.py
CHANGED
@@ -3,35 +3,37 @@ import builtins
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import math
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import streamlit as st
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import gdown
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from demo.src.models import load_trained_model
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from demo.src.utils import render_predict_from_pose, predict_to_image
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st.set_page_config(page_title="DietNeRF")
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def select_model():
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obj_select = st.selectbox("Select an object to render", (
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if obj_select ==
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FILE_ID = "17dj0pQieo94TozFv-noSBkXebduij1aM"
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MODEL_DIR =
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MODEL_NAME =
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elif obj_select ==
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FILE_ID = "1D9I-qIVMPaxuCHfUWPWMHaoLYtAmCjwI"
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MODEL_DIR =
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MODEL_NAME =
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elif obj_select ==
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FILE_ID = "14ZeJ86ETQr8dtu6CFoxU-ifvniHKo_Dt"
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MODEL_DIR =
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MODEL_NAME =
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elif obj_select ==
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FILE_ID = "11vNlR4lMvV_AVFgVjZmKMrMWGVG7qhNu"
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MODEL_DIR =
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MODEL_NAME =
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return MODEL_DIR,MODEL_NAME,FILE_ID
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MODEL_DIR,MODEL_NAME,FILE_ID = select_model()
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@st.cache
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def download_model():
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@@ -40,9 +42,9 @@ def download_model():
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# gdd.download_file_from_google_drive(file_id=FILE_ID,
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# dest_path=_model_path,
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# unzip=True)
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url = f
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gdown.download(url, _model_path, quiet=False)
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print(f
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@st.cache(show_spinner=False, allow_output_mutation=True)
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@@ -58,26 +60,34 @@ if not os.path.isfile(model_path):
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model, state = fetch_model()
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pi = math.pi
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st.sidebar.image("images/diet-nerf.png", width=310)
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st.sidebar.header(
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theta = st.sidebar.slider(
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st.markdown("")
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with st.spinner("Rendering Image, it may take 2-3 mins. So, why don't you read our report in the meantime"):
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pred_color, _ = render_predict_from_pose(state, theta, phi, radius)
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im = predict_to_image(pred_color)
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w, _ = im.size
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new_w = int(2*w)
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im = im.resize(size=(new_w, new_w))
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st.image(im, use_column_width=True)
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import math
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import streamlit as st
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import gdown
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# from google_drive_downloader import GoogleDriveDownloader as gdd
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from demo.src.models import load_trained_model
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from demo.src.utils import render_predict_from_pose, predict_to_image
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# from demo.src.config import MODEL_DIR, MODEL_NAME, FILE_ID
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st.set_page_config(page_title="DietNeRF")
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def select_model():
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obj_select = st.selectbox("Select an object to render", ("Chair", "Lego", "Ship", "Hotdog"))
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if obj_select == "Chair":
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FILE_ID = "17dj0pQieo94TozFv-noSBkXebduij1aM"
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MODEL_DIR = "models"
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MODEL_NAME = "diet_nerf_chair"
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elif obj_select == "Lego":
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FILE_ID = "1D9I-qIVMPaxuCHfUWPWMHaoLYtAmCjwI"
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MODEL_DIR = "models"
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MODEL_NAME = "diet_nerf_lego"
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elif obj_select == "Ship":
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FILE_ID = "14ZeJ86ETQr8dtu6CFoxU-ifvniHKo_Dt"
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MODEL_DIR = "models"
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MODEL_NAME = "diet_nerf_ship"
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elif obj_select == "Hotdog":
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FILE_ID = "11vNlR4lMvV_AVFgVjZmKMrMWGVG7qhNu"
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MODEL_DIR = "models"
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MODEL_NAME = "diet_nerf_hotdog"
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return MODEL_DIR, MODEL_NAME, FILE_ID
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@st.cache
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def download_model():
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# gdd.download_file_from_google_drive(file_id=FILE_ID,
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# dest_path=_model_path,
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# unzip=True)
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url = f"https://drive.google.com/uc?id={FILE_ID}"
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gdown.download(url, _model_path, quiet=False)
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print(f"Model downloaded from google drive: {_model_path}")
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@st.cache(show_spinner=False, allow_output_mutation=True)
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model, state = fetch_model()
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pi = math.pi
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st.sidebar.image("images/diet-nerf.png", width=310)
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st.sidebar.header("SELECT YOUR VIEW DIRECTION")
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theta = st.sidebar.slider(
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"Theta", min_value=-pi, max_value=pi, step=0.5, value=0.0, help="Rotational angle in Horizontal direction"
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)
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phi = st.sidebar.slider(
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"Phi", min_value=0.0, max_value=0.5 * pi, step=0.1, value=1.0, help="Rotational angle in Vertical direction"
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)
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radius = st.sidebar.slider(
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"Radius", min_value=2.0, max_value=6.0, step=1.0, value=3.0, help="Distance between object and the viewer"
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)
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st.title("DietNeRF")
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caption = (
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"Diet-NeRF achieves SoTA few-shot learning capacity in 3D model reconstruction. "
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"Thanks to the 2D supervision by CLIP (aka semantic loss), "
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"it can render novel and challenging views with ONLY 8 training images, "
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"outperforming original NeRF!"
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)
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st.markdown(caption)
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st.markdown("")
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MODEL_DIR, MODEL_NAME, FILE_ID = select_model()
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st.markdown("")
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with st.spinner("Rendering Image, it may take 2-3 mins. So, why don't you read our report in the meantime"):
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pred_color, _ = render_predict_from_pose(state, theta, phi, radius)
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im = predict_to_image(pred_color)
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w, _ = im.size
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new_w = int(2 * w)
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im = im.resize(size=(new_w, new_w))
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st.image(im, use_column_width=True)
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