import gradio as gr
import subprocess
import os
import shutil
import uuid
import base64
import numpy as np
from plyfile import PlyData, PlyElement
from urllib.parse import quote
STATIC_DIR = "/tmp/sharp_static"
os.makedirs(STATIC_DIR, exist_ok=True)
# 库文件路径（Docker 镜像中已下载）
GAUSSIAN_SPLATS_JS = "/app/static/gaussian-splats-3d.module.js"
def convert_to_binary_ply(input_path, output_path):
"""将 PLY 转为二进制格式，并统一属性为 float32"""
plydata = PlyData.read(input_path)
vert = plydata['vertex']
dtype = vert.data.dtype
names = dtype.names
new_dtype = [(name, np.float32) for name in names]
old_data = vert.data
new_data = np.zeros(old_data.shape, dtype=new_dtype)
for name in names:
new_data[name] = old_data[name].astype(np.float32)
new_vert = PlyElement.describe(new_data, 'vertex')
elements = [new_vert]
for el in plydata.elements:
if el.name != 'vertex':
elements.append(el)
PlyData(elements, text=False).write(output_path)
def sharp_predict(image, enable_3d_preview):
input_dir = "/tmp/sharp_input"
output_dir = "/tmp/sharp_output"
os.makedirs(input_dir, exist_ok=True)
os.makedirs(output_dir, exist_ok=True)
input_path = os.path.join(input_dir, "input.png")
image.save(input_path)
result = subprocess.run(
["sharp", "predict", "-i", input_dir, "-o", output_dir,
"-c", "/app/checkpoints/sharp_2572gikvuh.pt"],
cwd="/app/ml-sharp",
capture_output=True, text=True, timeout=1800
)
if result.returncode != 0:
raise gr.Error(f"SHARP 运行失败：{result.stderr}")
ply_files = [f for f in os.listdir(output_dir) if f.endswith(".ply")]
if not ply_files:
raise gr.Error("未生成 .ply 文件")
orig_ply = os.path.join(output_dir, ply_files[0])
unique_name = f"model_{uuid.uuid4().hex[:8]}.ply"
dest_path = os.path.join(STATIC_DIR, unique_name)
convert_to_binary_ply(orig_ply, dest_path)
# 公网文件 URL（用于 SuperSplat 跳转）
space_host = "satosakura-sharp.hf.space" # 请确认是你的域名
file_url = f"/file={dest_path}"
full_url = f"https://{space_host}{file_url}"
supersplat_url = f"https://playcanvas.com/supersplat/editor?content={quote(full_url)}"
# 准备 HTML 说明（总是显示）
info_html = f"""
"""
# 如果启用了 3D 预览，生成自包含 HTML 文件
viewer_html_path = None
if enable_3d_preview:
# 读取模型数据
with open(dest_path, "rb") as f:
ply_bytes = f.read()
ply_b64 = base64.b64encode(ply_bytes).decode("utf-8")
# 读取渲染库
if os.path.exists(GAUSSIAN_SPLATS_JS):
with open(GAUSSIAN_SPLATS_JS, "rb") as f:
js_bytes = f.read()
js_code = js_bytes.decode("utf-8")
else:
js_code = ""
# 构建自包含 HTML
viewer_html_content = f"""
3D Gaussian Splatting Viewer
加载中...
"""
# 保存自包含 HTML 文件
viewer_name = f"viewer_{uuid.uuid4().hex[:8]}.html"
viewer_html_path = os.path.join(STATIC_DIR, viewer_name)
with open(viewer_html_path, "w", encoding="utf-8") as f:
f.write(viewer_html_content)
# 更新提示信息
info_html += """
📁 下方“下载交互式查看器”可下载自包含的 HTML 文件，
双击即可在本地浏览器中实时旋转/缩放模型（利用本地 GPU）。
"""
# 返回：.ply 文件、说明文本、可能的查看器 HTML 文件
outputs = [dest_path, info_html]
if viewer_html_path:
outputs.append(viewer_html_path)
else:
outputs.append(None) # 占位，Gradio 支持 None 输出
return tuple(outputs)
# 界面
with gr.Blocks(title="SHARP - 3DGS Dual Preview") as demo:
gr.Markdown("""
# SHARP: Image to 3D Gaussian Splatting
上传一张图片，生成 3D 高斯泼溅模型。CPU 推理约需 10-20 分钟。
""")
with gr.Row():
input_img = gr.Image(type="pil", label="上传图片")
with gr.Column():
enable_3d = gr.Checkbox(label="生成交互式查看器（可下载 .html 文件）", value=True)
output_file = gr.File(label="下载 .ply 模型文件")
output_html = gr.HTML(label="操作面板")
output_viewer = gr.File(label="下载交互式查看器 (.html)", visible=True)
btn = gr.Button("开始生成", variant="primary")
btn.click(
sharp_predict,
inputs=[input_img, enable_3d],
outputs=[output_file, output_html, output_viewer]
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
allowed_paths=[STATIC_DIR]
)