443 lines
14 KiB
Python
Executable File
443 lines
14 KiB
Python
Executable File
import mimetypes
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import os
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import secrets
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import subprocess
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import tempfile
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import uuid
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from datetime import datetime
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from io import BytesIO
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import imageio
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import numpy as np
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import trimesh
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import litserve as ls
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from minio import Minio
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from glb2svg import glb_to_obj, obj_to_step, step_to_svg
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from trellis.pipelines import TrellisImageTo3DPipeline
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from trellis.utils import render_utils, postprocessing_utils
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from utils.new_oss_client import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, minio_get_image, upload_local_file, download_from_minio
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minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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def generate_unique_name(original_name: str) -> str:
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stem, ext = os.path.splitext(original_name)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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random_part = secrets.token_hex(4) # 8位随机十六进制
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return f"{stem}_{timestamp}_{random_part}{ext}"
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def load_mesh(file_path):
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"""
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加载.obj或.glb文件，返回顶点数据
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Args:
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file_path: 模型文件路径（支持.obj和.glb格式）
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Returns:
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numpy.ndarray: 顶点数组，shape为(N, 3)
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"""
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file_ext = os.path.splitext(file_path)[1].lower()
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if file_ext == '.obj':
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# 使用trimesh加载obj文件
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mesh = trimesh.load(file_path, file_type='obj')
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elif file_ext == '.glb' or file_ext == '.gltf':
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# 使用trimesh加载glb/gltf文件
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mesh = trimesh.load(file_path, file_type='glb')
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else:
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raise ValueError(f"不支持的文件格式: {file_ext}，仅支持.obj和.glb/.gltf")
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# 获取顶点数据
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if isinstance(mesh, trimesh.Scene):
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# 如果是场景，合并所有几何体的顶点
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vertices = []
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for geom in mesh.geometry.values():
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vertices.append(geom.vertices)
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vertices = np.vstack(vertices)
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else:
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# 如果是单个几何体
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vertices = mesh.vertices
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if len(vertices) == 0:
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raise ValueError("文件中未找到顶点数据")
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return vertices
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def analyze_mesh(file_path):
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"""
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分析3D模型文件，计算质心、边界框、尺寸等信息
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Args:
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file_path: 模型文件路径（支持.obj和.glb格式）
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Returns:
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dict: 包含模型分析信息的字典
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"""
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# 加载模型并获取顶点
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vertices = load_mesh(file_path)
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# 边界框（每个轴的最小/最大值）
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min_coords = vertices.min(axis=0)
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max_coords = vertices.max(axis=0)
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# 质心
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centroid = vertices.mean(axis=0)
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# 尺寸 = 边界框维度
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size = max_coords - min_coords
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# 计算尺寸比例（每个轴占总尺寸的比例）
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total_size = np.sum(size)
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size_ratio = size / total_size if total_size != 0 else [0, 0, 0]
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info = {
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# "file_path": file_path,
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"file_format": os.path.splitext(file_path)[1].lower(),
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"vertex_count": len(vertices),
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"centroid": centroid.tolist(),
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"bounding_box_min": min_coords.tolist(),
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"bounding_box_max": max_coords.tolist(),
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"size": size.tolist(),
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"size_ratio": size_ratio.tolist(),
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"size_ratio_percentage": (size_ratio * 100).tolist()
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}
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return info
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def render_glb_preview(glb_path, output_path):
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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cmd = [
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"blender",
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"--background",
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"--python",
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"render_model.py",
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"--",
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glb_path,
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output_path
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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raise RuntimeError(
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f"Blender render failed\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}"
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)
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return output_path
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class TrellisAPI(ls.LitAPI):
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def setup(self, device):
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os.environ.setdefault("SPCONV_ALGO", "native")
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self.pipeline = TrellisImageTo3DPipeline.from_pretrained(
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"microsoft/TRELLIS-image-large"
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)
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self.pipeline.to(device)
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def decode_request(self, request):
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image_paths = request["image_paths"]
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bucket_name = request["bucket_name"]
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user_id = request["user_id"]
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images = []
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for path in image_paths:
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bucket, object_name = path.split('/', 1)
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image = minio_get_image(minio_client, bucket, object_name)
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images.append(image)
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params = {
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"file_name": uuid.uuid4().hex,
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"model": request.get("model", "single"),
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"seed": request.get("seed", 1),
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"steps_sparse": request.get("steps_sparse", 12),
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"cfg_sparse": request.get("cfg_sparse", 7.5),
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"steps_slat": request.get("steps_slat", 12),
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"cfg_slat": request.get("cfg_slat", 3.0),
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"simplify": request.get("simplify", 0.95),
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"texture_size": request.get("texture_size", 1024),
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"fps": request.get("fps", 30),
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"bucket_name": bucket_name,
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"user_id": user_id
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}
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return images, params
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def predict(self, inputs):
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images, params = inputs
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if params["model"] == "single":
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outputs = self.pipeline.run(
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images[0],
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seed=params["seed"],
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sparse_structure_sampler_params={
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"steps": params["steps_sparse"],
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"cfg_strength": params["cfg_sparse"],
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},
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slat_sampler_params={
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"steps": params["steps_slat"],
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"cfg_strength": params["cfg_slat"],
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},
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)
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else:
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outputs = self.pipeline.run_multi_image(
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images,
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seed=params['seed'],
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sparse_structure_sampler_params={
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"steps": params['steps_sparse'],
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"cfg_strength": params['cfg_sparse'],
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},
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slat_sampler_params={
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"steps": params['steps_slat'],
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"cfg_strength": params['cfg_slat'],
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},
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)
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# video_path = self.upload_video(outputs, params)
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minio_glb_path, local_glb_path = self.upload_glb(outputs, params)
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glb_info = analyze_mesh(local_glb_path)
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static_model_image = self.get_static_model_image(model_path=local_glb_path, params=params)
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return {
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"glb_path": minio_glb_path,
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"glb_static_img_path": static_model_image,
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"glb_info": glb_info,
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}
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def encode_response(self, output):
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return output
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# def upload_video(self, outputs, params):
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# gaussian_name = f"3d_result/video/{params['file_name']}-gaussian.mp4"
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# radiance_field_name = f"3d_result/video/{params['file_name']}-radiance_field.mp4"
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# mesh_name = f"3d_result/video/{params['file_name']}-mesh.mp4"
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#
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# # gaussian video
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# video = render_utils.render_video(outputs["gaussian"][0])["color"]
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# buffer = BytesIO()
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# imageio.mimsave(buffer, video, format="mp4", fps=params['fps'])
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# gaussian_video_path = upload_bytes(
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# buffer.getvalue(),
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# gaussian_name,
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# "video/mp4",
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# )
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#
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# # radiance field video
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# video = render_utils.render_video(outputs["radiance_field"][0])["color"]
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# buffer = BytesIO()
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# imageio.mimsave(buffer, video, format="mp4", fps=params['fps'])
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# radiance_field_video_path = upload_bytes(
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# buffer.getvalue(),
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# radiance_field_name,
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# "video/mp4",
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# )
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#
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# # mesh video
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# video = render_utils.render_video(outputs["mesh"][0])["normal"]
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# buffer = BytesIO()
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# imageio.mimsave(buffer, video, format="mp4", fps=params['fps'])
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# mesh_path = upload_bytes(
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# buffer.getvalue(),
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# mesh_name,
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# "video/mp4",
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# )
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#
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# return {
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# "gaussian": gaussian_video_path,
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# "radiance_field": radiance_field_video_path,
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# "mesh": mesh_path
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# }
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def upload_glb(self, outputs, params):
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minio_path = f"{params['bucket_name']}/{params['user_id']}/3d_result/{params['file_name']}.glb"
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local_glb_path = os.path.join("glb_output", generate_unique_name("sample.glb"))
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out_dir = os.path.dirname(local_glb_path)
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if out_dir:
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os.makedirs(out_dir, exist_ok=True)
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glb = postprocessing_utils.to_glb(
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outputs["gaussian"][0],
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outputs["mesh"][0],
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simplify=params['simplify'],
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texture_size=params['texture_size'],
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)
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glb.export(
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file_obj=local_glb_path,
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file_type="glb"
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)
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glb_path = upload_local_file(
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file_path=local_glb_path,
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minio_path=minio_path,
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content_type="application/octet-stream"
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)
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return glb_path, local_glb_path
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# def upload_ply(self, outputs, params):
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# file_name = f"3d_result/ply/{params['file_name']}.ply"
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#
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# with tempfile.NamedTemporaryFile(suffix=".ply") as tmp:
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# outputs["gaussian"][0].save_ply(tmp.name)
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# tmp.seek(0)
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#
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# ply_path = upload_bytes(
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# tmp.read(),
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# file_name,
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# "application/octet-stream",
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# )
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# return {"ply": ply_path}
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def get_static_model_image(self, model_path, params):
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local_static_model_image_path = os.path.join("glb_output", generate_unique_name("static_model_image.png"))
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print(f"model_path : {model_path}")
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print(f"local_static_model_image_path :{local_static_model_image_path}")
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output_path = render_glb_preview(model_path, local_static_model_image_path)
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static_model_image = self.upload_local_file(
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output_path,
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params['bucket_name'],
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params['user_id'],
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)
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print(f"Saved to {static_model_image}")
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return static_model_image
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def upload_local_file(self, local_path, bucket_name, user_id):
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"""
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通用上传函数：支持 SVG, PNG, OBJ 等
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"""
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object_name = f"{user_id}/3d_result/{uuid.uuid4().hex}.png"
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if not os.path.exists(local_path):
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print(f"错误: 文件 {local_path} 不存在")
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return None
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# 自动根据后缀名识别 Content-Type
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# 例如: .svg -> image/svg+xml, .png -> image/png
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content_type, _ = mimetypes.guess_type(local_path)
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if content_type is None:
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content_type = "application/octet-stream"
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try:
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minio_client.fput_object(
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bucket_name=bucket_name,
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object_name=object_name,
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file_path=local_path,
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content_type=content_type
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)
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print(f"成功上传 [{content_type}]: {object_name}")
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return f"{bucket_name}/{object_name}"
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except Exception as e:
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print(f"上传失败: {e}")
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return None
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class ModelToThreeViews(ls.LitAPI):
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def setup(self, device):
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pass
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def upload_local_file(self, local_path, type, bucket_name, user_id):
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"""
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通用上传函数：支持 SVG, PNG, OBJ 等
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"""
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object_name = f"{user_id}/3d_result/{type}/{uuid.uuid4().hex}.{type}"
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if not os.path.exists(local_path):
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print(f"错误: 文件 {local_path} 不存在")
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return None
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# 自动根据后缀名识别 Content-Type
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# 例如: .svg -> image/svg+xml, .png -> image/png
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content_type, _ = mimetypes.guess_type(local_path)
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if content_type is None:
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content_type = "application/octet-stream"
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try:
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minio_client.fput_object(
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bucket_name=bucket_name,
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object_name=object_name,
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file_path=local_path,
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content_type=content_type
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)
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print(f"成功上传 [{content_type}]: {object_name}")
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return f"{bucket_name}/{object_name}"
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except Exception as e:
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print(f"上传失败: {e}")
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return None
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def predict(self, request):
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minio_glb_path = request['minio_glb_path']
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bucket_name = request["bucket_name"]
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user_id = request["user_id"]
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work_dir = f"glb_to_obj"
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os.makedirs(work_dir, exist_ok=True)
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glb_path = os.path.join(work_dir, f"model{uuid.uuid4().hex}.glb")
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step_dir = os.path.join(work_dir, "step")
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svg_dir = os.path.join(work_dir, "svg")
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os.makedirs(step_dir, exist_ok=True)
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os.makedirs(svg_dir, exist_ok=True)
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print(f"""
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入参阶段:
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input glb-obj minio-path:{minio_glb_path},\n
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work_dir : {work_dir},glb_path : {glb_path},step_dir : {step_dir},svg_dir : {svg_dir}\n
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""")
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print("=" * 10)
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print(f" 第一阶段 下载glb文件: ")
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# 1 下载
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glb_result = download_from_minio(object_path=minio_glb_path, local_path=glb_path)
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print(f" 下载结果 : {glb_result} \n")
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print("=" * 10)
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print(f" 第二阶段 glb -> obj: ")
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# 2 glb -> obj
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obj_result = glb_to_obj(glb_result)
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print(f" glb -> obj 结果 : {obj_result} \n")
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print("=" * 10)
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print(f" 第三阶段 obj -> step: ")
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# 3 obj -> step
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step_result = obj_to_step(
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input_obj=obj_result,
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output_dir=step_dir,
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script_path="1_obj_to_step.py"
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)
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print(f" obj -> step 结果 : {step_result} \n")
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print("=" * 10)
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print(f" 第四阶段 step -> svg: ")
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# 4 step -> svg
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combined_svg, combined_png = step_to_svg(
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step_path=step_result,
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out_dir=svg_dir
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)
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print(f" step -> svg 结果 : {combined_svg} \n")
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print("=" * 10)
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# 5 上传
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minio_svg_path = self.upload_local_file(combined_png, "svg", bucket_name, user_id)
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return {"minio_svg_path": minio_svg_path}
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if __name__ == "__main__":
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trellis_api = TrellisAPI(api_path="/canvas/img_to_3D")
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model_to_three_api = ModelToThreeViews(api_path="/canvas/3d_to_3views")
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server = ls.LitServer([
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trellis_api,
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model_to_three_api])
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server.run(port=8122)
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