| import os |
| import json |
| import numpy as np |
| from PIL import Image |
|
|
| def convert_scene(scene_path): |
| print(f"\n⚙️ 转换场景: {scene_path}") |
| rgb_dir = os.path.join(scene_path, "rgb") |
| if not os.path.exists(rgb_dir): return |
| |
| all_files = sorted([f for f in os.listdir(rgb_dir) if f.endswith(".png")]) |
| if not all_files: return |
| |
| sample_img_path = os.path.join(rgb_dir, all_files[0]) |
| with Image.open(sample_img_path) as img: |
| w, h = img.size |
|
|
| with open(os.path.join(scene_path, "intrinsics.txt"), "r") as f: |
| line = f.readline().strip() |
| focal = float(line.split()[0]) |
| |
| camera_angle_x = 2 * np.arctan(w / (2 * focal)) |
| pose_dir = os.path.join(scene_path, "pose") |
| frames = [] |
| |
| for f in all_files: |
| name = os.path.splitext(f)[0] |
| pose_path = os.path.join(pose_dir, f"{name}.txt") |
| if not os.path.exists(pose_path): continue |
| |
| c2w = np.loadtxt(pose_path).reshape(4, 4) |
| |
| |
| |
| c2w[:, 1:3] *= -1 |
| |
| frames.append({ |
| "file_path": f"./rgb/{name}", |
| "transform_matrix": c2w.tolist() |
| }) |
|
|
| num_train = int(len(frames) * 0.8) |
| base_json = {"camera_angle_x": camera_angle_x} |
| |
| with open(os.path.join(scene_path, "transforms_train.json"), "w") as f: |
| json.dump({**base_json, "frames": frames[:num_train]}, f, indent=4) |
| |
| with open(os.path.join(scene_path, "transforms_test.json"), "w") as f: |
| json.dump({**base_json, "frames": frames[num_train:]}, f, indent=4) |
|
|
| print(f" ✅ 成功生成修复了坐标系的 transforms_*.json") |
|
|
| target_scenes = ["Chair", "Drums", "Ficus", "Hotdog", "Lego", "Materials", "Mic", "Ship"] |
| base_path = "/root/autodl-tmp/dataset/Synthetic_NeRF_Verified/Synthetic_NeRF" |
| for s in target_scenes: |
| p = os.path.join(base_path, s) |
| if os.path.exists(p): |
| convert_scene(p) |
|
|
