render.py

import numpy as np
import torch
import os
from pathlib import Path
from PIL import Image
import json
from tqdm import tqdm
import sys

# 评估指标
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
import lpips
from torchvision import transforms
from scipy import linalg

# 导入gaussian-splatting渲染库
try:
    from scene.gaussian_model import GaussianModel
    from utils.graphics_utils import focal2fov
    from scene.cameras import Camera
    from gaussian_renderer import render
    from argparse import Namespace
except ImportError as e:
    print(f"错误: 无法导入gaussian-splatting模块: {e}")
    print("请确保gaussian-splatting仓库在Python路径中")
    sys.exit(1)


class GaussianRenderer:
    """
    基于原版gaussian-splatting的渲染器包装类
    """
    def __init__(self, ply_path, sh_degree=3, device='cuda'):
        """
        初始化渲染器
        
        Args:
            ply_path: .ply文件路径
            sh_degree: 球谐函数阶数
            device: 计算设备
        """
        self.ply_path = ply_path
        self.sh_degree = sh_degree
        self.device = device
        
        # 初始化GaussianModel
        self.gaussians = GaussianModel(sh_degree)
        self.gaussians.load_ply(ply_path)
        
        # 设置渲染参数
        self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device=device)
        
        # 创建pipeline和background参数
        self.pipe = Namespace()
        self.pipe.convert_SHs_python = False
        self.pipe.compute_cov3D_python = False
        self.pipe.debug = False
        
        print(f"加载模型: {ply_path}")
        print(f"  - 高斯点数: {len(self.gaussians.get_xyz)}")
        print(f"  - SH阶数: {sh_degree}")
    
    def render(self, camera):
        """
        渲染单个视角
        
        Args:
            camera: Camera对象
            
        Returns:
            rendered_image: numpy array, shape (H, W, 3), 值域[0, 1]
        """
        with torch.no_grad():
            rendering = render(camera, self.gaussians, self.pipe, self.bg_color)
            image = rendering["render"]
            
            # 转换为numpy数组 CHW -> HWC
            image = image.cpu().numpy()
            image = np.transpose(image, (1, 2, 0))
            
            # 确保值域在[0, 1]
            image = np.clip(image, 0, 1)
        
        return image


class MetricsCalculator:
    """评估指标计算器"""
    
    def __init__(self, device='cuda'):
        self.device = device
        
        # LPIPS模型
        self.lpips_fn = lpips.LPIPS(net='alex').to(device)
        
        # 图像预处理
        self.transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        ])
    
    def calculate_psnr(self, img1, img2):
        """
        计算PSNR
        
        Args:
            img1, img2: numpy arrays, shape (H, W, 3), 值域[0, 1]
        """
        return psnr(img1, img2, data_range=1.0)
    
    def calculate_ssim(self, img1, img2):
        """
        计算SSIM
        
        Args:
            img1, img2: numpy arrays, shape (H, W, 3), 值域[0, 1]
        """
        return ssim(img1, img2, data_range=1.0, channel_axis=2, multichannel=True)
    
    def calculate_lpips(self, img1, img2):
        """
        计算LPIPS
        
        Args:
            img1, img2: numpy arrays, shape (H, W, 3), 值域[0, 1]
        """
        # 转换为torch tensor
        img1_tensor = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
        img2_tensor = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
        
        # 归一化到[-1, 1]
        img1_tensor = img1_tensor * 2 - 1
        img2_tensor = img2_tensor * 2 - 1
        
        with torch.no_grad():
            lpips_value = self.lpips_fn(img1_tensor, img2_tensor)
        
        return lpips_value.item()
    
    def calculate_niqe(self, img):
        """
        计算NIQE (无参考图像质量评估)
        
        Args:
            img: numpy array, shape (H, W, 3), 值域[0, 1]
        """
        try:
            import pyiqa
            niqe_metric = pyiqa.create_metric('niqe', device=self.device)
            img_tensor = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
            score = niqe_metric(img_tensor).item()
            return score
        except ImportError:
            print("警告: pyiqa未安装，无法计算NIQE。请运行: pip install pyiqa")
            return None
    
    def calculate_fid_features(self, img):
        """
        提取FID特征（使用InceptionV3）
        
        Args:
            img: numpy array, shape (H, W, 3), 值域[0, 1]
        """
        from torchvision.models import inception_v3
        
        if not hasattr(self, 'inception_model'):
            self.inception_model = inception_v3(pretrained=True, transform_input=False).to(self.device)
            self.inception_model.eval()
            # 移除最后的全连接层
            self.inception_model.fc = torch.nn.Identity()
        
        # 调整大小到299x299 (InceptionV3要求)
        img_pil = Image.fromarray((img * 255).astype(np.uint8))
        img_pil = img_pil.resize((299, 299), Image.BILINEAR)
        img_array = np.array(img_pil) / 255.0
        
        # 转换为tensor并归一化
        img_tensor = torch.from_numpy(img_array).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
        img_tensor = (img_tensor - 0.5) / 0.5
        
        with torch.no_grad():
            features = self.inception_model(img_tensor)
        
        return features.cpu().numpy().flatten()
    
    @staticmethod
    def calculate_fid(features1, features2):
        """
        计算FID分数
        
        Args:
            features1, features2: numpy arrays of shape (N, D), 特征向量
        """
        mu1, sigma1 = features1.mean(axis=0), np.cov(features1, rowvar=False)
        mu2, sigma2 = features2.mean(axis=0), np.cov(features2, rowvar=False)
        
        # 计算均值差的平方
        diff = mu1 - mu2
        
        # 计算协方差矩阵的平方根
        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
        
        # 处理数值误差
        if np.iscomplexobj(covmean):
            covmean = covmean.real
        
        fid = diff.dot(diff) + np.trace(sigma1 + sigma2 - 2 * covmean)
        return fid


def load_cameras(camera_path, device='cuda'):
    """
    从cameras.json加载相机参数
    
    Args:
        camera_path: cameras.json文件路径
        device: 计算设备
        
    Returns:
        cameras: Camera对象列表
    """
    with open(camera_path, 'r') as f:
        camera_data = json.load(f)
    
    cameras = []
    
    for cam_info in camera_data:
        # 提取相机参数
        uid = cam_info['id']
        colmap_id = cam_info['id']
        img_name = cam_info['img_name']
        width = cam_info['width']
        height = cam_info['height']
        
        # 焦距
        fx = cam_info['fx']
        fy = cam_info['fy']
        
        # 相机位置和旋转（这里是相机到世界的变换）
        position = np.array(cam_info['position'])  # 相机在世界坐标系中的位置
        rotation = np.array(cam_info['rotation'])  # 3x3旋转矩阵（相机到世界）
        
        # 转换为世界到相机的变换
        # 对于相机到世界的旋转矩阵R_c2w和位置t_c2w:
        # 世界到相机: R_w2c = R_c2w^T, t_w2c = -R_c2w^T @ t_c2w
        R_w2c = rotation.T
        T_w2c = -R_w2c @ position
        
        # 转换为torch tensor
        R_tensor = torch.from_numpy(R_w2c).float()
        T_tensor = torch.from_numpy(T_w2c).float()
        
        # 计算FoV
        FovX = focal2fov(fx, width)
        FovY = focal2fov(fy, height)
        
        # 创建Camera对象
        # 注意：不同版本的gaussian-splatting可能有不同的Camera构造函数
        # 如果遇到错误，可能需要调整参数
        try:
            camera = Camera(
                colmap_id=colmap_id,
                R=R_tensor,
                T=T_tensor,
                FoVx=FovX,
                FoVy=FovY,
                image=torch.zeros((3, height, width)),  # 占位图像
                gt_alpha_mask=None,
                image_name=img_name,
                uid=uid,
                data_device=device
            )
        except TypeError:
            # 如果上面的构造函数不工作，尝试另一种形式
            camera = Camera(
                colmap_id=colmap_id,
                R=R_tensor,
                T=T_tensor,
                FoVx=FovX,
                FoVy=FovY,
                image=torch.zeros((3, height, width)),
                gt_alpha_mask=None,
                image_name=img_name,
                uid=uid
            )
        
        cameras.append(camera)
    
    return cameras


def render_and_evaluate(original_ply, compressed_ply, camera_path, output_dir, 
                       ground_truth_dir=None, sh_degree=3, device='cuda'):
    """
    渲染并评估压缩前后的3DGS
    
    Args:
        original_ply: 原始3DGS的.ply文件路径
        compressed_ply: 压缩后3DGS的.ply文件路径
        camera_path: 相机参数文件路径
        output_dir: 输出目录
        ground_truth_dir: 真实图像目录（如果有的话，用于计算与GT的指标）
        sh_degree: 球谐函数阶数
        device: 计算设备
    """
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # 创建子目录
    original_render_dir = output_dir / "original"
    compressed_render_dir = output_dir / "compressed"
    original_render_dir.mkdir(exist_ok=True)
    compressed_render_dir.mkdir(exist_ok=True)
    
    # 初始化渲染器
    print("初始化渲染器...")
    original_renderer = GaussianRenderer(original_ply, sh_degree=sh_degree, device=device)
    compressed_renderer = GaussianRenderer(compressed_ply, sh_degree=sh_degree, device=device)
    
    # 初始化评估器
    metrics_calc = MetricsCalculator(device=device)
    
    # 加载相机
    print("加载相机参数...")
    cameras = load_cameras(camera_path, device=device)
    print(f"加载了 {len(cameras)} 个相机视角")
    
    # 存储所有指标
    results = {
        'psnr': [],
        'ssim': [],
        'lpips': [],
        'niqe_original': [],
        'niqe_compressed': []
    }
    
    # 如果有真实图像，也计算与GT的指标
    if ground_truth_dir:
        results['psnr_vs_gt_original'] = []
        results['psnr_vs_gt_compressed'] = []
        results['ssim_vs_gt_original'] = []
        results['ssim_vs_gt_compressed'] = []
        results['lpips_vs_gt_original'] = []
        results['lpips_vs_gt_compressed'] = []
    
    # FID特征收集
    original_features = []
    compressed_features = []
    
    print("开始渲染和评估...")
    for i, camera in enumerate(tqdm(cameras, desc="渲染进度")):
        # 渲染
        original_img = original_renderer.render(camera)
        compressed_img = compressed_renderer.render(camera)
        
        # 保存渲染图像
        Image.fromarray((original_img * 255).astype(np.uint8)).save(
            original_render_dir / f"render_{i:04d}.png"
        )
        Image.fromarray((compressed_img * 255).astype(np.uint8)).save(
            compressed_render_dir / f"render_{i:04d}.png"
        )
        
        # 计算压缩前后的对比指标
        results['psnr'].append(metrics_calc.calculate_psnr(original_img, compressed_img))
        results['ssim'].append(metrics_calc.calculate_ssim(original_img, compressed_img))
        results['lpips'].append(metrics_calc.calculate_lpips(original_img, compressed_img))
        
        # NIQE（无参考）
        niqe_orig = metrics_calc.calculate_niqe(original_img)
        niqe_comp = metrics_calc.calculate_niqe(compressed_img)
        if niqe_orig is not None:
            results['niqe_original'].append(niqe_orig)
            results['niqe_compressed'].append(niqe_comp)
        
        # 提取FID特征
        original_features.append(metrics_calc.calculate_fid_features(original_img))
        compressed_features.append(metrics_calc.calculate_fid_features(compressed_img))
        
        # 如果有真实图像
        if ground_truth_dir:
            # 尝试多种可能的文件名格式
            possible_names = [
                f"image_{i:04d}.png",
                f"image_{i:04d}.jpg",
                f"{i:04d}.png",
                f"{i:04d}.jpg",
                camera.image_name
            ]
            
            gt_img = None
            for name in possible_names:
                gt_path = Path(ground_truth_dir) / name
                if gt_path.exists():
                    gt_img = np.array(Image.open(gt_path).convert('RGB')) / 255.0
                    break
            
            if gt_img is not None:
                results['psnr_vs_gt_original'].append(
                    metrics_calc.calculate_psnr(gt_img, original_img)
                )
                results['psnr_vs_gt_compressed'].append(
                    metrics_calc.calculate_psnr(gt_img, compressed_img)
                )
                results['ssim_vs_gt_original'].append(
                    metrics_calc.calculate_ssim(gt_img, original_img)
                )
                results['ssim_vs_gt_compressed'].append(
                    metrics_calc.calculate_ssim(gt_img, compressed_img)
                )
                results['lpips_vs_gt_original'].append(
                    metrics_calc.calculate_lpips(gt_img, original_img)
                )
                results['lpips_vs_gt_compressed'].append(
                    metrics_calc.calculate_lpips(gt_img, compressed_img)
                )
    
    # 计算FID
    print("计算FID...")
    original_features = np.array(original_features)
    compressed_features = np.array(compressed_features)
    fid_score = MetricsCalculator.calculate_fid(original_features, compressed_features)
    
    # 汇总结果
    print("\n" + "="*50)
    print("评估结果 (压缩后 vs 原始)")
    print("="*50)
    print(f"PSNR: {np.mean(results['psnr']):.2f} ± {np.std(results['psnr']):.2f} dB")
    print(f"SSIM: {np.mean(results['ssim']):.4f} ± {np.std(results['ssim']):.4f}")
    print(f"LPIPS: {np.mean(results['lpips']):.4f} ± {np.std(results['lpips']):.4f}")
    if results['niqe_original']:
        print(f"NIQE (原始): {np.mean(results['niqe_original']):.4f} ± {np.std(results['niqe_original']):.4f}")
        print(f"NIQE (压缩): {np.mean(results['niqe_compressed']):.4f} ± {np.std(results['niqe_compressed']):.4f}")
    print(f"FID: {fid_score:.4f}")
    
    if ground_truth_dir and results['psnr_vs_gt_original']:
        print("\n" + "="*50)
        print("与Ground Truth对比")
        print("="*50)
        print("原始模型 vs GT:")
        print(f"  PSNR: {np.mean(results['psnr_vs_gt_original']):.2f} ± {np.std(results['psnr_vs_gt_original']):.2f} dB")
        print(f"  SSIM: {np.mean(results['ssim_vs_gt_original']):.4f} ± {np.std(results['ssim_vs_gt_original']):.4f}")
        print(f"  LPIPS: {np.mean(results['lpips_vs_gt_original']):.4f} ± {np.std(results['lpips_vs_gt_original']):.4f}")
        print("\n压缩模型 vs GT:")
        print(f"  PSNR: {np.mean(results['psnr_vs_gt_compressed']):.2f} ± {np.std(results['psnr_vs_gt_compressed']):.2f} dB")
        print(f"  SSIM: {np.mean(results['ssim_vs_gt_compressed']):.4f} ± {np.std(results['ssim_vs_gt_compressed']):.4f}")
        print(f"  LPIPS: {np.mean(results['lpips_vs_gt_compressed']):.4f} ± {np.std(results['lpips_vs_gt_compressed']):.4f}")
    
    # 保存详细结果
    results_summary = {
        'compression_comparison': {
            'psnr_mean': float(np.mean(results['psnr'])),
            'psnr_std': float(np.std(results['psnr'])),
            'ssim_mean': float(np.mean(results['ssim'])),
            'ssim_std': float(np.std(results['ssim'])),
            'lpips_mean': float(np.mean(results['lpips'])),
            'lpips_std': float(np.std(results['lpips'])),
            'fid': float(fid_score)
        }
    }
    
    if results['niqe_original']:
        results_summary['compression_comparison']['niqe_original_mean'] = float(np.mean(results['niqe_original']))
        results_summary['compression_comparison']['niqe_original_std'] = float(np.std(results['niqe_original']))
        results_summary['compression_comparison']['niqe_compressed_mean'] = float(np.mean(results['niqe_compressed']))
        results_summary['compression_comparison']['niqe_compressed_std'] = float(np.std(results['niqe_compressed']))
    
    if ground_truth_dir and results['psnr_vs_gt_original']:
        results_summary['vs_ground_truth'] = {
            'original': {
                'psnr_mean': float(np.mean(results['psnr_vs_gt_original'])),
                'psnr_std': float(np.std(results['psnr_vs_gt_original'])),
                'ssim_mean': float(np.mean(results['ssim_vs_gt_original'])),
                'ssim_std': float(np.std(results['ssim_vs_gt_original'])),
                'lpips_mean': float(np.mean(results['lpips_vs_gt_original'])),
                'lpips_std': float(np.std(results['lpips_vs_gt_original']))
            },
            'compressed': {
                'psnr_mean': float(np.mean(results['psnr_vs_gt_compressed'])),
                'psnr_std': float(np.std(results['psnr_vs_gt_compressed'])),
                'ssim_mean': float(np.mean(results['ssim_vs_gt_compressed'])),
                'ssim_std': float(np.std(results['ssim_vs_gt_compressed'])),
                'lpips_mean': float(np.mean(results['lpips_vs_gt_compressed'])),
                'lpips_std': float(np.std(results['lpips_vs_gt_compressed']))
            }
        }
    
    # 保存结果
    with open(output_dir / "metrics.json", 'w') as f:
        json.dump(results_summary, f, indent=2)
    
    # 保存详细数据
    with open(output_dir / "detailed_metrics.json", 'w') as f:
        # 转换numpy类型为Python原生类型
        results_for_json = {}
        for key, value in results.items():
            if isinstance(value, list) and len(value) > 0:
                results_for_json[key] = [float(v) if not isinstance(v, (list, dict)) else v for v in value]
        json.dump(results_for_json, f, indent=2)
    
    print(f"\n结果已保存到: {output_dir}")
    print(f"  - 原始渲染图像: {original_render_dir}")
    print(f"  - 压缩渲染图像: {compressed_render_dir}")
    print(f"  - 评估指标摘要: {output_dir / 'metrics.json'}")
    print(f"  - 详细指标数据: {output_dir / 'detailed_metrics.json'}")
    """
    渲染并评估压缩前后的3DGS
    
    Args:
        original_ply: 原始3DGS的.ply文件路径
        compressed_ply: 压缩后3DGS的.ply文件路径
        camera_path: 相机参数文件路径
        output_dir: 输出目录
        ground_truth_dir: 真实图像目录（如果有的话，用于计算与GT的指标）
    """
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # 创建子目录
    original_render_dir = output_dir / "original"
    compressed_render_dir = output_dir / "compressed"
    original_render_dir.mkdir(exist_ok=True)
    compressed_render_dir.mkdir(exist_ok=True)
    
    # 初始化渲染器
    print("初始化渲染器...")
    original_renderer = GaussianRenderer(original_ply)
    compressed_renderer = GaussianRenderer(compressed_ply)
    
    # 初始化评估器
    metrics_calc = MetricsCalculator()
    
    # 加载相机
    print("加载相机参数...")
    cameras = load_cameras(camera_path)
    
    # 存储所有指标
    results = {
        'psnr': [],
        'ssim': [],
        'lpips': [],
        'niqe_original': [],
        'niqe_compressed': []
    }
    
    # 如果有真实图像，也计算与GT的指标
    if ground_truth_dir:
        results['psnr_vs_gt_original'] = []
        results['psnr_vs_gt_compressed'] = []
        results['ssim_vs_gt_original'] = []
        results['ssim_vs_gt_compressed'] = []
        results['lpips_vs_gt_original'] = []
        results['lpips_vs_gt_compressed'] = []
    
    # FID特征收集
    original_features = []
    compressed_features = []
    
    print("开始渲染和评估...")
    for i, camera in enumerate(tqdm(cameras)):
        # 渲染
        original_img = original_renderer.render(camera)
        compressed_img = compressed_renderer.render(camera)
        
        # 保存渲染图像
        Image.fromarray((original_img * 255).astype(np.uint8)).save(
            original_render_dir / f"render_{i:04d}.png"
        )
        Image.fromarray((compressed_img * 255).astype(np.uint8)).save(
            compressed_render_dir / f"render_{i:04d}.png"
        )
        
        # 计算压缩前后的对比指标
        results['psnr'].append(metrics_calc.calculate_psnr(original_img, compressed_img))
        results['ssim'].append(metrics_calc.calculate_ssim(original_img, compressed_img))
        results['lpips'].append(metrics_calc.calculate_lpips(original_img, compressed_img))
        
        # NIQE（无参考）
        niqe_orig = metrics_calc.calculate_niqe(original_img)
        niqe_comp = metrics_calc.calculate_niqe(compressed_img)
        if niqe_orig is not None:
            results['niqe_original'].append(niqe_orig)
            results['niqe_compressed'].append(niqe_comp)
        
        # 提取FID特征
        original_features.append(metrics_calc.calculate_fid_features(original_img))
        compressed_features.append(metrics_calc.calculate_fid_features(compressed_img))
        
        # 如果有真实图像
        if ground_truth_dir:
            gt_path = Path(ground_truth_dir) / f"image_{i:04d}.png"  # 根据实际命名调整
            if gt_path.exists():
                gt_img = np.array(Image.open(gt_path)) / 255.0
                
                results['psnr_vs_gt_original'].append(
                    metrics_calc.calculate_psnr(gt_img, original_img)
                )
                results['psnr_vs_gt_compressed'].append(
                    metrics_calc.calculate_psnr(gt_img, compressed_img)
                )
                results['ssim_vs_gt_original'].append(
                    metrics_calc.calculate_ssim(gt_img, original_img)
                )
                results['ssim_vs_gt_compressed'].append(
                    metrics_calc.calculate_ssim(gt_img, compressed_img)
                )
                results['lpips_vs_gt_original'].append(
                    metrics_calc.calculate_lpips(gt_img, original_img)
                )
                results['lpips_vs_gt_compressed'].append(
                    metrics_calc.calculate_lpips(gt_img, compressed_img)
                )
    
    # 计算FID
    original_features = np.array(original_features)
    compressed_features = np.array(compressed_features)
    fid_score = MetricsCalculator.calculate_fid(original_features, compressed_features)
    
    # 汇总结果
    print("\n" + "="*50)
    print("评估结果 (压缩后 vs 原始)")
    print("="*50)
    print(f"PSNR: {np.mean(results['psnr']):.2f} ± {np.std(results['psnr']):.2f} dB")
    print(f"SSIM: {np.mean(results['ssim']):.4f} ± {np.std(results['ssim']):.4f}")
    print(f"LPIPS: {np.mean(results['lpips']):.4f} ± {np.std(results['lpips']):.4f}")
    if results['niqe_original']:
        print(f"NIQE (原始): {np.mean(results['niqe_original']):.4f}")
        print(f"NIQE (压缩): {np.mean(results['niqe_compressed']):.4f}")
    print(f"FID: {fid_score:.4f}")
    
    if ground_truth_dir:
        print("\n" + "="*50)
        print("与Ground Truth对比")
        print("="*50)
        print("原始模型 vs GT:")
        print(f"  PSNR: {np.mean(results['psnr_vs_gt_original']):.2f} dB")
        print(f"  SSIM: {np.mean(results['ssim_vs_gt_original']):.4f}")
        print(f"  LPIPS: {np.mean(results['lpips_vs_gt_original']):.4f}")
        print("\n压缩模型 vs GT:")
        print(f"  PSNR: {np.mean(results['psnr_vs_gt_compressed']):.2f} dB")
        print(f"  SSIM: {np.mean(results['ssim_vs_gt_compressed']):.4f}")
        print(f"  LPIPS: {np.mean(results['lpips_vs_gt_compressed']):.4f}")
    
    # 保存详细结果
    results_summary = {
        'compression_comparison': {
            'psnr_mean': float(np.mean(results['psnr'])),
            'psnr_std': float(np.std(results['psnr'])),
            'ssim_mean': float(np.mean(results['ssim'])),
            'ssim_std': float(np.std(results['ssim'])),
            'lpips_mean': float(np.mean(results['lpips'])),
            'lpips_std': float(np.std(results['lpips'])),
            'fid': float(fid_score)
        }
    }
    
    if results['niqe_original']:
        results_summary['compression_comparison']['niqe_original'] = float(np.mean(results['niqe_original']))
        results_summary['compression_comparison']['niqe_compressed'] = float(np.mean(results['niqe_compressed']))
    
    if ground_truth_dir:
        results_summary['vs_ground_truth'] = {
            'original': {
                'psnr': float(np.mean(results['psnr_vs_gt_original'])),
                'ssim': float(np.mean(results['ssim_vs_gt_original'])),
                'lpips': float(np.mean(results['lpips_vs_gt_original']))
            },
            'compressed': {
                'psnr': float(np.mean(results['psnr_vs_gt_compressed'])),
                'ssim': float(np.mean(results['ssim_vs_gt_compressed'])),
                'lpips': float(np.mean(results['lpips_vs_gt_compressed']))
            }
        }
    
    # 保存结果
    with open(output_dir / "metrics.json", 'w') as f:
        json.dump(results_summary, f, indent=2)
    
    # 保存详细数据
    with open(output_dir / "detailed_metrics.json", 'w') as f:
        json.dump(results, f, indent=2)
    
    print(f"\n结果已保存到: {output_dir}")
    print(f"  - 原始渲染图像: {original_render_dir}")
    print(f"  - 压缩渲染图像: {compressed_render_dir}")
    print(f"  - 评估指标: {output_dir / 'metrics.json'}")


if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="评估3DGS压缩前后的渲染质量")
    parser.add_argument("--original_ply", type=str, required=True, help="原始.ply文件路径")
    parser.add_argument("--compressed_ply", type=str, required=True, help="压缩后.ply文件路径")
    parser.add_argument("--cameras", type=str, required=True, help="cameras.json文件路径")
    parser.add_argument("--output_dir", type=str, default="evaluation_results", help="输出目录")
    parser.add_argument("--ground_truth_dir", type=str, default=None, help="真实图像目录（可选）")
    parser.add_argument("--sh_degree", type=int, default=3, help="球谐函数阶数")
    parser.add_argument("--device", type=str, default="cuda", help="计算设备")
    
    args = parser.parse_args()
    
    # 检查文件是否存在
    if not os.path.exists(args.original_ply):
        print(f"错误: 找不到原始PLY文件: {args.original_ply}")
        sys.exit(1)
    
    if not os.path.exists(args.compressed_ply):
        print(f"错误: 找不到压缩PLY文件: {args.compressed_ply}")
        sys.exit(1)
    
    if not os.path.exists(args.cameras):
        print(f"错误: 找不到相机参数文件: {args.cameras}")
        sys.exit(1)
    
    render_and_evaluate(
        original_ply=args.original_ply,
        compressed_ply=args.compressed_ply,
        camera_path=args.cameras,
        output_dir=args.output_dir,
        ground_truth_dir=args.ground_truth_dir,
        sh_degree=args.sh_degree,
        device=args.device
    )