| import json, os, hashlib, logging
|
| from pathlib import Path
|
|
|
| log = logging.getLogger(__name__)
|
|
|
|
|
| TV_DATASET = 'UCF101'
|
| HF_CANDIDATES = []
|
| IMAGE_FIELD = None
|
| TEXT_FIELD = None
|
| LABEL_FIELD = 'label'
|
| PROMPT_TEMPLATE = 'a surveillance scene showing {label}'
|
| DATASET_URL = 'https://www.crcv.ucf.edu/projects/real-world/'
|
|
|
| def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
|
|
|
| out = []
|
| if TV_DATASET is not None:
|
| try:
|
| import torchvision
|
| ctor = getattr(torchvision.datasets, TV_DATASET)
|
| try:
|
| ds = ctor(root=cache_dir, split='train', download=True)
|
| except TypeError:
|
| try:
|
| ds = ctor(root=cache_dir, train=True, download=True)
|
| except TypeError:
|
| ds = ctor(root=cache_dir, download=True)
|
| classes = getattr(ds, 'classes', None)
|
| os.makedirs(os.path.join(cache_dir, 'tv'), exist_ok=True)
|
| for i, item in enumerate(ds):
|
| if len(out) >= max_samples:
|
| break
|
| img, label = item[0], item[1]
|
| name = classes[label] if classes else str(label)
|
| p = os.path.join(cache_dir, 'tv', str(i) + '.png')
|
| try:
|
| img.save(p)
|
| except Exception:
|
| continue
|
| out.append({'image': p, 'text': PROMPT_TEMPLATE.format(label=name)})
|
| if out:
|
| return out
|
| except Exception as e:
|
| print('torchvision load failed:', e)
|
| for repo in HF_CANDIDATES:
|
| try:
|
| from datasets import load_dataset
|
| try:
|
| ds = load_dataset(repo, split='train', streaming=True)
|
| except Exception:
|
| ds = load_dataset(repo, split='train')
|
| img_dir = os.path.join(cache_dir, 'hf_images')
|
| os.makedirs(img_dir, exist_ok=True)
|
| for i, ex in enumerate(ds):
|
| if len(out) >= max_samples:
|
| break
|
| txt = None
|
| if TEXT_FIELD is not None and TEXT_FIELD in ex:
|
| v = ex[TEXT_FIELD]
|
| txt = v if isinstance(v, str) else ' '.join(map(str, v if isinstance(v, (list, tuple)) else [v]))
|
| if txt is None and LABEL_FIELD in ex:
|
| txt = PROMPT_TEMPLATE.format(label=ex[LABEL_FIELD])
|
| if txt is None:
|
| continue
|
| out.append({'text': txt})
|
| if out:
|
| return out
|
| except Exception as e:
|
| print('HF load failed for', repo, ':', e)
|
| print('Automatic download failed. Please get the data manually from:')
|
| print(' ' + DATASET_URL)
|
| return out
|
|
|
|
|
| def load(data_dir):
|
|
|
| files = list(Path(data_dir).glob('*.jsonl'))
|
| if not files:
|
| files = list(Path(data_dir).glob('*.json'))
|
| samples = []
|
| for f in files:
|
| with open(f) as fp:
|
| if f.suffix == '.json':
|
| d = json.load(fp)
|
| samples.extend(d if isinstance(d, list) else [d])
|
| else:
|
| samples.extend(json.loads(l) for l in fp if l.strip())
|
| return samples
|
|
|
| def filter_quality(samples, min_score=0.5):
|
|
|
| results = []
|
| for s in samples:
|
| text = s.get("security", s.get("text", ""))
|
| if len(text.split()) >= 3:
|
| results.append(s)
|
| return results
|
|
|
| def dedup(samples):
|
| seen = set()
|
| out = []
|
| for s in samples:
|
| fp = s.get("image", s.get("audio", ""))
|
| if fp and os.path.exists(fp):
|
| h = hashlib.md5(open(fp, 'rb').read()).hexdigest()
|
| if h in seen:
|
| continue
|
| seen.add(h)
|
| out.append(s)
|
| return out
|
|
|
| def preprocess(samples, out_dir, img_size=224):
|
|
|
| os.makedirs(out_dir, exist_ok=True)
|
| processed = []
|
| for s in samples:
|
| item = {}
|
| if "image" in s:
|
| try:
|
| from PIL import Image as IM
|
| img = IM.open(s["image"]).convert("RGB")
|
| img = img.resize((img_size, img_size))
|
| p = os.path.join(out_dir, os.path.basename(s["image"]))
|
| img.save(p, "JPEG", quality=95)
|
| item["image"] = p
|
| except Exception:
|
| continue
|
| text = s.get("security", s.get("text", ""))
|
| item["text"] = text
|
| item["domain"] = "security"
|
| processed.append(item)
|
| return processed
|
|
|
| def save_jsonl(data, path):
|
| with open(path, 'w') as f:
|
| for d in data:
|
| f.write(json.dumps(d, ensure_ascii=False) + '\n')
|
|
|
| def main():
|
| import sys
|
| data_dir = sys.argv[1] if len(sys.argv) > 1 else './data'
|
| out = sys.argv[2] if len(sys.argv) > 2 else './output'
|
| samples = load(data_dir) or fetch_real_samples()
|
| samples = filter_quality(samples)
|
| samples = dedup(samples)
|
| result = preprocess(samples, out)
|
| save_jsonl(result, os.path.join(out, 'dataset.jsonl'))
|
| print(f'Done: {len(result)} samples')
|
|
|
| if __name__ == '__main__':
|
| main()
|
|
|
