Upload render.py

render.py

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+import os
+import torch
+import json
+import numpy as np
+from tqdm import tqdm
+from argparse import ArgumentParser
+import torchvision
+from pathlib import Path
+
+# 假设你已有这些模块
+from gaussian_renderer import render, GaussianModel
+from scene.cameras import Camera
+from utils.graphics_utils import getWorld2View2, getProjectionMatrix
+
+
+def load_cameras_from_json(json_path):
+    """从cameras.json加载相机参数"""
+    with open(json_path, 'r') as f:
+        camera_data = json.load(f)
+    
+    cameras = []
+    for cam in camera_data:
+        # 根据你的cameras.json格式构建Camera对象
+        # 这里需要根据实际的json结构调整
+        camera = Camera(
+            colmap_id=cam.get('id', 0),
+            R=np.array(cam['rotation']),
+            T=np.array(cam['position']),
+            FoVx=cam['fov_x'],
+            FoVy=cam['fov_y'],
+            image=None,  # 渲染时不需要GT图像
+            gt_alpha_mask=None,
+            image_name=cam.get('img_name', f"frame_{cam['id']:04d}"),
+            uid=cam['id'],
+            data_device='cuda'
+        )
+        cameras.append(camera)
+    
+    return cameras
+
+
+def load_gaussian_model(ply_path, sh_degree=3):
+    """加载训练好的高斯模型"""
+    gaussians = GaussianModel(sh_degree)
+    gaussians.load_ply(ply_path)
+    return gaussians
+
+
+def render_cameras(cameras, gaussians, output_dir, gpu_id=0, bg_color=[0, 0, 0]):
+    """渲染指定相机视角"""
+    # 设置GPU
+    device = f'cuda:{gpu_id}'
+    torch.cuda.set_device(device)
+    
+    # 创建输出目录
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    # 设置背景颜色
+    background = torch.tensor(bg_color, dtype=torch.float32, device=device)
+    
+    # 渲染每个相机视角
+    rendered_images = []
+    for idx, camera in enumerate(tqdm(cameras, desc="Rendering")):
+        # 渲染
+        with torch.no_grad():
+            rendering = render(camera, gaussians, pipeline=None, background=background)["render"]
+        
+        # 保存图像
+        img_name = f"{camera.image_name}.png" if hasattr(camera, 'image_name') else f"render_{idx:04d}.png"
+        save_path = output_path / img_name
+        torchvision.utils.save_image(rendering, save_path)
+        
+        rendered_images.append(rendering)
+    
+    print(f"渲染完成！图像保存至: {output_path}")
+    return rendered_images
+
+
+def main():
+    parser = ArgumentParser(description="简化版3DGS渲染脚本")
+    
+    # 核心参数
+    parser.add_argument("--cameras_json", type=str, required=True, help="相机参数JSON文件路径")
+    parser.add_argument("--ply_file", type=str, required=True, help="训练好的PLY模型文件路径")
+    parser.add_argument("--output_dir", type=str, required=True, help="渲染结果保存路径")
+    parser.add_argument("--gpu_id", type=int, default=0, help="使用的GPU ID")
+    
+    # 可选参数
+    parser.add_argument("--sh_degree", type=int, default=3, help="球谐函数阶数")
+    parser.add_argument("--white_background", action="store_true", help="使用白色背景")
+    
+    args = parser.parse_args()
+    
+    # 设置背景颜色
+    bg_color = [1, 1, 1] if args.white_background else [0, 0, 0]
+    
+    print(f"加载相机参数: {args.cameras_json}")
+    cameras = load_cameras_from_json(args.cameras_json)
+    
+    print(f"加载高斯模型: {args.ply_file}")
+    gaussians = load_gaussian_model(args.ply_file, args.sh_degree)
+    
+    print(f"开始渲染，使用GPU {args.gpu_id}")
+    render_cameras(
+        cameras=cameras,
+        gaussians=gaussians,
+        output_dir=args.output_dir,
+        gpu_id=args.gpu_id,
+        bg_color=bg_color
+    )
+
+
+if __name__ == "__main__":
+    main()
+
+
+# 使用示例:
+# python render.py \
+#     --cameras_json /path/to/cameras.json \
+#     --ply_file /path/to/model.ply \
+#     --output_dir /path/to/output \
+#     --gpu_id 0 \
+#     --white_background