| import os |
| import json |
| import glob |
| from pathlib import Path |
| from collections import defaultdict |
|
|
| |
| DATA_ROOT = "/opt/liblibai-models/user-workspace2/users/dxy/robotic/data" |
| REF_IMG_ROOT = "/opt/liblibai-models/user-workspace2/users/dxy/robotic/robot_ref_img" |
|
|
| |
| OUTPUT_DIR = "./json_outputs" |
|
|
| VIDEO_EXTS = {".mp4", ".mov", ".avi", ".mkv"} |
| IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"} |
| |
|
|
| def generate_strict_paired_metadata(): |
| |
| os.makedirs(OUTPUT_DIR, exist_ok=True) |
|
|
| |
| |
| |
| ref_images_map = {} |
| if os.path.exists(REF_IMG_ROOT): |
| print(f"Scanning reference images in {REF_IMG_ROOT}...") |
| for img_file in os.listdir(REF_IMG_ROOT): |
| name, ext = os.path.splitext(img_file) |
| if ext.lower() in IMAGE_EXTS: |
| ref_images_map[name] = os.path.abspath(os.path.join(REF_IMG_ROOT, img_file)) |
| else: |
| print(f"Warning: Reference image directory '{REF_IMG_ROOT}' not found.") |
|
|
| |
| |
| |
| context_map = defaultdict(list) |
| print(f"\nScanning videos in {DATA_ROOT}...") |
| |
| for root, dirs, files in os.walk(DATA_ROOT): |
| videos = [f for f in files if os.path.splitext(f)[1].lower() in VIDEO_EXTS] |
| if not videos: |
| continue |
| |
| rel_path = os.path.relpath(root, DATA_ROOT) |
| path_parts = Path(rel_path).parts |
| |
| if len(path_parts) < 4: |
| continue |
| |
| scene = path_parts[0] |
| location = path_parts[1] |
| camera = path_parts[2] |
| |
| character = path_parts[-1] |
|
|
| for vid in videos: |
| motion_name = os.path.splitext(vid)[0] |
| full_path = os.path.join(root, vid) |
| unique_key = (scene, location, camera, motion_name) |
| |
| context_map[unique_key].append({ |
| "char": character, |
| "path": full_path, |
| "motion": motion_name |
| }) |
|
|
| print(f"Found {len(context_map)} unique action contexts.") |
| |
| |
| |
| |
| |
| results_by_char = defaultdict(list) |
| |
| for key, video_list in context_map.items(): |
| scene, loc, cam, motion = key |
| |
| if len(video_list) < 2: |
| continue |
| |
| |
| for target_item in video_list: |
| target_char = target_item["char"] |
| target_path = target_item["path"] |
| |
| if target_char not in ref_images_map: |
| continue |
| ref_img_path = ref_images_map[target_char] |
| |
| |
| for source_item in video_list: |
| source_char = source_item["char"] |
| |
| |
| if source_char == target_char: |
| continue |
| |
| control_path = source_item["path"] |
| |
| |
| prompt_path = os.path.splitext(target_path)[0] + ".txt" |
| prompt = "" |
| if os.path.exists(prompt_path): |
| try: |
| with open(prompt_path, 'r', encoding='utf-8') as f: |
| prompt = f.read().strip() |
| except: |
| pass |
| |
| if not prompt: |
| safe_motion = motion.replace("_", " ") |
| prompt = f"A robot {target_char} is {safe_motion} in {scene} at {loc}" |
|
|
| entry = { |
| "video": target_path, |
| "control_video": control_path, |
| "prompt": prompt, |
| "appearance_id": target_char, |
| "ref_image_path": ref_img_path, |
| } |
| |
| |
| results_by_char[target_char].append(entry) |
|
|
| |
| |
| |
| print(f"\nGeneration complete! Writing files to {OUTPUT_DIR}...") |
| |
| total_files = 0 |
| for char_name, entries in results_by_char.items(): |
| if not entries: |
| continue |
| |
| |
| |
| safe_char_name = char_name.replace("/", "_").replace("\\", "_") |
| filename = f"data_{safe_char_name}.jsonl" |
| file_path = os.path.join(OUTPUT_DIR, filename) |
| |
| with open(file_path, 'w', encoding='utf-8') as f: |
| for item in entries: |
| f.write(json.dumps(item, ensure_ascii=False) + "\n") |
| |
| print(f" -> Saved {filename} ({len(entries)} samples)") |
| total_files += 1 |
| |
| print(f"\nAll done! Created {total_files} JSONL files.") |
|
|
| if __name__ == "__main__": |
| generate_strict_paired_metadata() |
