import json import argparse import os import base64 import io import numpy as np import imageio.v3 as iio from PIL import Image from collections import defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed from tqdm import tqdm def parse_args(): parser = argparse.ArgumentParser(description="Build MS-Swift SFT Dataset (BBox + Start/End Points)") parser.add_argument("--traj_path", type=str, required=True, help="Path to processed_trajectories.jsonl") parser.add_argument("--task_path", type=str, required=True, help="Path to task.jsonl") parser.add_argument("--origin_bbox_path", type=str, required=True, help="Path to original bbox.jsonl") parser.add_argument("--output_path", type=str, required=True, help="Output path for swift_sft.jsonl") parser.add_argument("--workers", type=int, default=8, help="Number of worker threads") return parser.parse_args() def load_auxiliary_data(task_path, bbox_path): print("Loading auxiliary metadata (Tasks & Labels)...") meta_map = defaultdict(dict) print(f"Reading tasks from {task_path}...") with open(task_path, 'r', encoding='utf-8') as f: for line in f: if not line.strip(): continue try: item = json.loads(line) v_path = item.get('video_path') task = item.get('object_bbox', {}).get('task', "") if v_path and task: meta_map[v_path]['task'] = task except Exception: continue print(f"Reading labels from {bbox_path}...") count_labels = 0 with open(bbox_path, 'r', encoding='utf-8') as f: for line in f: if not line.strip(): continue try: item = json.loads(line) v_path = item.get('video_path') label = item.get('object_bbox', {}).get('label', "target_object") if v_path: meta_map[v_path]['label'] = label count_labels += 1 except Exception: continue print(f"Metadata loaded. Found labels for {count_labels} entries.") return meta_map def numpy_to_base64(img_np): if img_np.dtype != np.uint8: img_np = (img_np).astype(np.uint8) img = Image.fromarray(img_np) buffer = io.BytesIO() img.save(buffer, format="JPEG", quality=90) img_bytes = buffer.getvalue() img_base64 = base64.b64encode(img_bytes).decode('utf-8') return f"data:image/jpeg;base64,{img_base64}" def process_single_video(video_path, samples, meta_info): results = [] task_desc = meta_info.get('task', "") obj_label = meta_info.get('label', "target_object") if not task_desc: return [] try: if not os.path.exists(video_path): return [] samples.sort(key=lambda x: x['frame_idx']) for sample in samples: frame_idx = sample['frame_idx'] bbox = sample['bbox'] full_traj = sample['traj'] if not full_traj or len(full_traj) == 0: continue start_point = [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2] end_point = full_traj[-1] try: frame = iio.imread(video_path, index=frame_idx, plugin="pyav") image_base64 = numpy_to_base64(frame) response_data = { "box_2d": bbox, "label": obj_label, "start_point": start_point, "end_point": end_point } response_str = "```json\n" + json.dumps(response_data, ensure_ascii=False) + "\n```" user_content = ( f"\n" f"Task: {task_desc}\n" f"Detect the target object and predict its trajectory start point and end point. " f"Output the result in JSON format with the following keys: \"box_2d\", \"label\", \"start_point\", and \"end_point\"." ) swift_item = { "messages": [ { "role": "user", "content": user_content }, { "role": "assistant", "content": response_str } ], "images": [image_base64] } results.append(swift_item) except IndexError as e: # print(f"Error reading frame {frame_idx} of video {video_path}: {e}") continue except Exception as e: # print(f"Error reading frame {frame_idx} of video {video_path}: {e}") continue except Exception as e: print(f"Error opening video {video_path}: {e}") return [] return results def main(): args = parse_args() meta_map = load_auxiliary_data(args.task_path, args.origin_bbox_path) print(f"Loading trajectory data from {args.traj_path}...") video_groups = defaultdict(list) total_traj_samples = 0 with open(args.traj_path, 'r', encoding='utf-8') as f: for line in f: if not line.strip(): continue item = json.loads(line) v_path = item['video_path'] if v_path in meta_map and 'task' in meta_map[v_path]: video_groups[v_path].append(item) total_traj_samples += 1 print(f"Grouped {total_traj_samples} samples into {len(video_groups)} videos.") print(f"Starting processing with {args.workers} workers...") final_dataset = [] with ThreadPoolExecutor(max_workers=args.workers) as executor: future_to_video = {} for video_path, samples in video_groups.items(): meta_info = meta_map[video_path] future = executor.submit(process_single_video, video_path, samples, meta_info) future_to_video[future] = video_path for future in tqdm(as_completed(future_to_video), total=len(video_groups), desc="Processing Videos"): res = future.result() if res: final_dataset.extend(res) print(f"Saving {len(final_dataset)} samples to {args.output_path}...") os.makedirs(os.path.dirname(os.path.abspath(args.output_path)), exist_ok=True) with open(args.output_path, 'w', encoding='utf-8') as f: for item in final_dataset: f.write(json.dumps(item, ensure_ascii=False) + '\n') print("Done.") if __name__ == "__main__": main() ''' python sft_data/v3/build_v3_swift_data.py \ --traj_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/traj_outputs/agilex_top_video_000k_034k_bbox_traj_results.jsonl \ --task_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/agilex_top_video_000k_034k_task.jsonl \ --origin_bbox_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/agilex_top_video_000k_034k_bbox.jsonl \ --output_path sft_data/v3/agilex_top_video_000k_034k_bbox_traj_sft_data_2pts.jsonl \ --workers 64 & python sft_data/v3/build_v3_swift_data.py \ --traj_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/traj_outputs/franka_base_video_000k_100k_bbox_traj_results.jsonl \ --task_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/franka_base_video_000k_100k_task.jsonl \ --origin_bbox_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/franka_base_video_000k_100k_bbox.jsonl \ --output_path sft_data/v3/franka_base_video_000k_100k_bbox_traj_sft_data_2pts.jsonl \ --workers 64 & python sft_data/v3/build_v3_swift_data.py \ --traj_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/traj_outputs/franka_base_video_100k_200k_bbox_traj_results.jsonl \ --task_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/franka_base_video_100k_200k_task.jsonl \ --origin_bbox_path /storage/v-xiangxizheng/dataset/InternData-M1/annotaions/franka_base_video_100k_200k_bbox.jsonl \ --output_path sft_data/v3/franka_base_video_100k_200k_bbox_traj_sft_data_2pts.jsonl \ --workers 64 & hf upload sftv3 . --repo-type dataset --private & '''