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import gradio as gr
from huggingface_hub import hf_hub_download, HfFolder
from PIL import Image
import requests, torch
import numpy as np
from io import BytesIO
import plotly.graph_objects as go
import os
def load_ScanNet_sample(data_path):
all_data = torch.load(data_path)
point = np.array(all_data['coord'])
color = np.array(all_data['color'])
point = point - point.min(axis=0)
point = point / point.max(axis=0)
color = color / 255.
return point, color
def show_logo():
repo_id = "ZiyuG/Cache"
filename = "scene0000_00.pth"
token = os.getenv('HF_TOKEN')
print("token:", token)
try:
file_path = hf_hub_download(repo_id=repo_id, filename=filename, use_auth_token=token, repo_type='dataset')
point, color = load_ScanNet_sample(file_path)
if point.shape[0] > 100000:
indices = np.random.choice(point.shape[0], 100000, replace=False)
point = point[indices]
color = color[indices]
except Exception as e:
print(e)
point = np.random.rand(8000, 3)
color = np.random.rand(8000, 3)
fig = go.Figure(
data=[
go.Scatter3d(
x=point[:,0], y=point[:,1], z=point[:,2],
mode='markers',
marker=dict(size=1, color=color, opacity=0.5),
)
],
layout=dict(
scene=dict(
xaxis=dict(visible=False),
yaxis=dict(visible=False),
zaxis=dict(visible=False),
aspectratio=dict(x=1, y=1, z=1),
camera=dict(eye=dict(x=1.5, y=1.5, z=1.5))
)
)
)
return fig
iface = gr.Interface(fn=show_logo, inputs=[], outputs=gr.Plot(), title="Display Logo")
iface.launch(share=True)
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