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import gradio as gr
import glob
import os
import time
import random
# 生成6到12之间的随机整数
delay = random.randint(6, 12)
def generate(front_view: gr.Image, seed=-1):
origin_file = selected_file_path.split('/')[-1].split('.')[0]
# print(origin_file)
# print(seed)
# print(os.path.join("examples", f'{origin_file}.ply'))
# 随机延时
time.sleep(delay)
return os.path.join("examples", f'{origin_file}.obj')
# return "./examples/processed_xxy_v2.obj"
def get_png_paths(directory='examples'):
"""
获取指定目录下所有jpg图片的路径列表。
参数:
directory (str): 要搜索的目录路径，默认为 'examples'。
返回:
list[str]: 包含jpg图片文件路径的列表。
"""
# 使用glob模块匹配目录下所有的jpg文件
jpg_paths = glob.glob(os.path.join(directory, '*.png'))
return jpg_paths
selected_file_path = ""
def on_select(evt: gr.SelectData):
global selected_file_path
selected_file_path = evt.value['image']['path']
return selected_file_path
with gr.Blocks() as demo:
gr.Markdown(
"""
# 3D Avatar Head Reconstruction based on 2-3DGS
👋Welcome to use!
- Easy to use
- Run fast
- Impressive output
""")
with gr.Row():
with gr.Column(scale=1):
frontview = gr.Image(label="FrontView")
examples = gr.Gallery(get_png_paths(), columns=6, height=200, object_fit="contain", allow_preview=False)
examples.select(fn=on_select, inputs=None, outputs=frontview)
with gr.Column(scale=1):
meshmodel = gr.Model3D(label="Mesh Model")
seed = gr.Slider(-1, 10000, value=0, label="Seed", info="random seed")
btn = gr.Button("Generate 3D Model")
btn.click(fn=generate, inputs=[frontview, seed], outputs=meshmodel)
if __name__ == "__main__":
demo.launch()
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