腾讯混元3D生成入门

import numpy as npfrom typing import List, Dict, Tuple, Optional  class Hunyuan3DPromptOptimizer:    """混元3D提示词优化器：分析并优化3D生成提示词"""    def __init__(self):        self.style_presets = {            'cartoon': '卡通风格，简化几何，鲜艳色彩',            'realistic': '写实风格，高细节，自然材质',            'lowpoly': '低多边形风格，平面着色，几何感强',            'cyberpunk': '赛博朋克风格，霓虹色彩，金属质感',        }        self.three_d_keywords = ['symmetrical', 'full 360 degree', '3D model', 'volumetric']        self.avoid_keywords = ['transparent', 'reflective', 'glass-like']     def analyze_prompt(self, prompt: str) -> Dict:        has_subject = len(prompt) > 5        has_style = any(s in prompt.lower() for s in self.style_presets)        has_3d_features = any(k in prompt.lower() for k in self.three_d_keywords)        has_avoids = any(k in prompt.lower() for k in self.avoid_keywords)        score = sum([has_subject, has_style, has_3d_features, not has_avoids])        return {            'has_subject': has_subject,            'has_style': has_style,            'has_3d_features': has_3d_features,            'has_avoids': has_avoids,            'quality_score': score * 25,            'needs_optimization': score < 3,        }     def optimize_prompt(self, prompt: str, style: str = 'realistic') -> str:        parts = [prompt]        if style in self.style_presets:            parts.append(self.style_presets[style])        parts.append('symmetrical design')        parts.append('full 360 degree view')        parts.append('high detail 3D model')        return ', '.join(parts)     def predict_quality(self, prompt: str, style: str) -> Dict:        analysis = self.analyze_prompt(prompt)        base_score = analysis['quality_score']        geometry = min(95, base_score + np.random.randint(5, 15))        texture = min(90, base_score + np.random.randint(3, 12))        usability = min(88, (geometry + texture) // 2 - np.random.randint(0, 5))        return {            'geometry_completeness': geometry,            'texture_quality': texture,            'overall_usability': usability,            'needs_post_processing': usability < 85,        }  class ModelQualityChecker:    """3D模型自动质检：检测拓扑、UV、面数、纹理问题"""    def __init__(self):        self.target_face_count = 5000        self.checks = ['topology', 'uv', 'face_count', 'texture']     def check_topology(self, model_data: Dict) -> Dict:        non_manifold = model_data.get('non_manifold_faces', 0)        overlapping = model_data.get('overlapping_faces', 0)        zero_area = model_data.get('zero_area_triangles', 0)        issues = non_manifold + overlapping + zero_area        return {            'passed': issues == 0,            'non_manifold': non_manifold,            'overlapping': overlapping,            'zero_area': zero_area,            'needs_remesh': issues > 0,        }     def check_uv(self, model_data: Dict) -> Dict:        seams = model_data.get('uv_seams', 0)        stretching = model_data.get('uv_stretching', 0.0)        overlap = model_data.get('uv_overlap', False)        issues = seams + (1 if overlap else 0)        return {            'passed': issues == 0 and stretching < 0.1,            'seams': seams,            'stretching': stretching,            'overlap': overlap,            'needs_reunwrap': issues > 0 or stretching > 0.1,        }     def check_face_count(self, model_data: Dict) -> Dict:        faces = model_data.get('face_count', 0)        return {            'passed': faces <= self.target_face_count,            'face_count': faces,            'target': self.target_face_count,            'needs_decimate': faces > self.target_face_count,        }     def check_texture(self, model_data: Dict) -> Dict:        resolution = model_data.get('texture_resolution', 1024)        has_seams = model_data.get('texture_seams', False)        pbr_complete = model_data.get('pbr_complete', True)        return {            'passed': not has_seams and pbr_complete,            'resolution': resolution,            'has_seams': has_seams,            'pbr_complete': pbr_complete,            'needs_texture_fix': has_seams or not pbr_complete,        }     def run_all_checks(self, model_data: Dict) -> Dict:        return {            'topology': self.check_topology(model_data),            'uv': self.check_uv(model_data),            'face_count': self.check_face_count(model_data),            'texture': self.check_texture(model_data),        }  class PostProcessingOrchestrator:    """后处理编排器：根据质检结果编排自动化后处理流程"""    def __init__(self):        self.pipeline = []        self.steps = {            'decimate': {'tool': 'Blender CLI', 'time_per_model': 0.5},            'remesh': {'tool': 'Blender CLI', 'time_per_model': 1.0},            'reunwrap': {'tool': 'Blender CLI', 'time_per_model': 1.5},            'texture_fix': {'tool': 'Manual', 'time_per_model': 12.0},        }     def plan_pipeline(self, check_results: Dict) -> List[Dict]:        self.pipeline = []        if check_results['face_count']['needs_decimate']:            self.pipeline.append({'step': 'decimate', 'action': 'reduce_faces'})        if check_results['topology']['needs_remesh']:            self.pipeline.append({'step': 'remesh', 'action': 'fix_topology'})        if check_results['uv']['needs_reunwrap']:            self.pipeline.append({'step': 'reunwrap', 'action': 'fix_uv'})        if check_results['texture']['needs_texture_fix']:            self.pipeline.append({'step': 'texture_fix', 'action': 'manual_fix'})        return self.pipeline     def estimate_time(self, pipeline: List[Dict], num_models: int) -> float:        total_time = 0        for step in pipeline:            step_info = self.steps.get(step['step'], {})            total_time += step_info.get('time_per_model', 0) * num_models        return total_time     def generate_report(self, check_results: Dict, pipeline: List[Dict],                        num_models: int) -> Dict:        auto_steps = [s for s in pipeline if s['step'] != 'texture_fix']        manual_steps = [s for s in pipeline if s['step'] == 'texture_fix']        return {            'total_models': num_models,            'auto_steps': len(auto_steps),            'manual_steps': len(manual_steps),            'estimated_hours': self.estimate_time(pipeline, num_models),            'pipeline': pipeline,        }  if __name__ == '__main__':    optimizer = Hunyuan3DPromptOptimizer()    raw_prompt = 'magic staff'    analysis = optimizer.analyze_prompt(raw_prompt)    optimized = optimizer.optimize_prompt(raw_prompt, 'realistic')    quality = optimizer.predict_quality(optimized, 'realistic')    print(f'Raw quality: {analysis["quality_score"]}/100')    print(f'Optimized prompt: {optimized[:60]}...')    print(f'Predicted geometry: {quality["geometry_completeness"]}/100')     checker = ModelQualityChecker()    model_data = {'face_count': 8000, 'non_manifold_faces': 2, 'overlapping_faces': 1,                  'zero_area_triangles': 0, 'uv_seams': 1, 'uv_stretching': 0.05,                  'uv_overlap': False, 'texture_resolution': 1024,                  'texture_seams': True, 'pbr_complete': True}    results = checker.run_all_checks(model_data)    print(f'Topology passed: {results["topology"]["passed"]}')    print(f'Face count passed: {results["face_count"]["passed"]}')     orchestrator = PostProcessingOrchestrator()    pipeline = orchestrator.plan_pipeline(results)    report = orchestrator.generate_report(results, pipeline, 50)    print(f'Pipeline steps: {len(report["pipeline"])}')    print(f'Estimated hours: {report["estimated_hours"]:.1f}')