File size: 4,521 Bytes
53b4cfb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import json, os, hashlib
from pathlib import Path
# security dataset processor
# modality: pointcloud_text, preprocessing: adaptive
# --- real data source: security ---
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'):
# 本地???有数据时自动下载真实公开数据集: torchvision -> HuggingFace -> 手动说明
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 build_dataset(src, dst, sz=224):
samples = []
for f in Path(src).glob('*.jsonl'):
with open(f) as fp:
for line in fp:
if line.strip():
samples.append(json.loads(line))
if not samples:
samples = fetch_real_samples()
# dedup
seen = set()
unique = []
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 not in seen:
seen.add(h)
unique.append(s)
else:
unique.append(s)
os.makedirs(dst, exist_ok=True)
out = []
for s in unique:
item = {}
if 'image' in s:
from PIL import Image
img = Image.open(s['image']).convert('RGB').resize((sz, sz))
p = os.path.join(dst, os.path.basename(s['image']))
img.save(p, 'JPEG', quality=95)
item['image'] = p
item['text'] = s.get('security', s.get('text', ''))
item['domain'] = 'security'
out.append(item)
with open(os.path.join(dst, 'dataset.jsonl'), 'w') as f:
for d in out:
f.write(json.dumps(d, ensure_ascii=False) + '\n')
return out
if __name__ == '__main__':
import sys
result = build_dataset(sys.argv[1], sys.argv[2])
print(f'Processed {len(result)} samples')
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