Tripo AI：文本图像秒变3D模型

import numpy as npfrom enum import Enumfrom typing import List, Dict, Optional, Tupleimport math  class MeshFormat(Enum):    """3D模型导出格式枚举"""    OBJ = "obj"    FBX = "fbx"    GLB = "glb"  class TripoGenerator:    """模拟 Tripo AI 3D模型生成流程"""     def __init__(self):        self.supported_formats = [MeshFormat.OBJ, MeshFormat.FBX, MeshFormat.GLB]        self.generation_history: List[Dict] = []     def text_to_3d(self, prompt: str, style: str = "realistic",                   detail_level: float = 0.8) -> Dict:        """        模拟文生3D流程：文本 -> 图像 -> 3D模型        """        print(f"[文生3D] 提示词: '{prompt}'")        print(f"  风格: {style}, 细节级别: {detail_level}")         # 阶段1: 文本 -> 图像 (模拟)        print("  阶段1: 文本转图像...")        image_features = self._extract_text_features(prompt)        generated_views = self._synthesize_multiview(image_features, num_views=4)        print(f"    生成 {len(generated_views)} 个视角的2D图像")         # 阶段2: 图像 -> 3D (模拟)        print("  阶段2: 多视图3D重建...")        mesh = self._reconstruct_3d(generated_views, detail_level)        print(f"    生成网格: {mesh['num_vertices']} 顶点, {mesh['num_faces']} 面")         result = {            "input_type": "text",            "prompt": prompt,            "style": style,            "mesh": mesh,            "format": MeshFormat.GLB.value,        }        self.generation_history.append(result)        return result     def image_to_3d(self, image_desc: str, resolution: int = 512) -> Dict:        """        模拟图生3D流程：图片 -> 3D模型        """        print(f"[图生3D] 图片描述: '{image_desc}'")        print(f"  分辨率: {resolution}x{resolution}")         # 阶段1: 分析图片特征        print("  阶段1: 分析图片内容...")        image_features = {            "shape_embedding": np.random.randn(512).astype(np.float32),            "texture_embedding": np.random.randn(256).astype(np.float32),            "depth_map": np.random.rand(64, 64).astype(np.float32),        }        print("    提取形状、纹理、深度特征")         # 阶段2: 单视图3D重建        print("  阶段2: 单视图3D重建...")        mesh = self._reconstruct_3d_from_depth(image_features)        print(f"    生成网格: {mesh['num_vertices']} 顶点, {mesh['num_faces']} 面")         result = {            "input_type": "image",            "image_desc": image_desc,            "mesh": mesh,            "format": MeshFormat.GLB.value,        }        self.generation_history.append(result)        return result     def _extract_text_features(self, prompt: str) -> Dict:        """模拟从文本提取特征"""        words = prompt.lower().split()        feature_dim = 512        np.random.seed(hash(prompt) % (2**31))        return {            "text_embedding": np.random.randn(feature_dim).astype(np.float32),            "num_objects": max(1, len([w for w in words if w not in ('a', 'the', 'of')])),            "keywords": words[:5],        }     def _synthesize_multiview(self, features: Dict, num_views: int = 4) -> List[np.ndarray]:        """模拟多视角图像合成"""        views = []        for i in range(num_views):            angle = 2 * math.pi * i / num_views            view = np.random.rand(64, 64, 3).astype(np.float32)            views.append(view)        return views     def _reconstruct_3d(self, views: List[np.ndarray], detail: float) -> Dict:        """模拟从多视图重建3D网格"""        base_vertices = 500        base_faces = 1000        num_vertices = int(base_vertices * (0.5 + detail))        num_faces = int(base_faces * (0.5 + detail))        return {            "num_vertices": num_vertices,            "num_faces": num_faces,            "has_uv": True,            "has_normals": True,            "texture_size": 1024,        }     def _reconstruct_3d_from_depth(self, features: Dict) -> Dict:        """模拟从深度图重建3D网格"""        depth_map = features["depth_map"]        valid_depth = np.sum(depth_map > 0.1)        num_vertices = int(valid_depth * 2)        num_faces = num_vertices * 2        return {            "num_vertices": num_vertices,            "num_faces": num_faces,            "has_uv": True,            "has_normals": True,            "texture_size": 1024,        }     def export_model(self, result: Dict, fmt: MeshFormat) -> str:        """模拟导出3D模型"""        mesh = result["mesh"]        filename = f"model_{len(self.generation_history)}.{fmt.value}"        size_kb = (mesh['num_vertices'] * 12 + mesh['num_faces'] * 16) // 1024        print(f"[导出] {filename}: {mesh['num_vertices']} 顶点, "              f"{mesh['num_faces']} 面, 约 {size_kb}KB")        return filename  # ---- 主流程演示 ----if __name__ == "__main__":    print("=" * 55)    print("Tripo AI 3D模型生成流程模拟")    print("=" * 55)     gen = TripoGenerator()     # 场景1: 文生3D - 生成游戏道具    print("\n--- 场景1: 文生3D ---")    result1 = gen.text_to_3d(        prompt="medieval iron broadsword, battle-worn, game asset",        style="game_ready",        detail_level=0.85    )    gen.export_model(result1, MeshFormat.FBX)     # 场景2: 图生3D - 从参考图生成    print("\n--- 场景2: 图生3D ---")    result2 = gen.image_to_3d(        image_desc="wooden chess knight piece, smooth surface",        resolution=512    )    gen.export_model(result2, MeshFormat.GLB)     # 场景3: 批量生成    print("\n--- 场景3: 批量生成游戏资产 ---")    prompts = [        "stone potion bottle, fantasy style",        "golden treasure chest, ornate design",        "iron shield, round, medieval",    ]    for p in prompts:        r = gen.text_to_3d(prompt=p, detail_level=0.75)        gen.export_model(r, MeshFormat.OBJ)     print(f"\n{'=' * 55}")    print(f"生成完成! 共 {len(gen.generation_history)} 个模型")    print(f"支持格式: {[f.value for f in gen.supported_formats]}")    print("=" * 55)