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README.md

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+---
+license: cc-by-4.0
+tags:
+- active
+- huggingface
+- image-depth
+- manual
+- minimal
+- movie-posters
+- none
+- strict
+- temporal
+---
+
+# loader.py
+
+## Dataset Summary
+
+A **movie posters** dataset with **image depth** modality, stored in **huggingface** format.
+
+## Preprocessing & Augmentation
+
+- **Preprocessing**: minimal
+- **Augmentation**: none
+
+## Splits & Sampling
+
+- **Split strategy**: temporal
+- **Sampling**: active
+
+## Quality & Labeling
+
+- **Quality filtering**: strict
+- **Labeling**: manual
+
+## Files
+
+- `loader.py` — main artifact of this repository
+
+## License
+
+See the license field above.

loader.py

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+import json, os, hashlib, logging
+from pathlib import Path
+
+log = logging.getLogger(__name__)
+
+# --- real data source: movie_posters ---
+TV_DATASET = None
+HF_CANDIDATES = []
+IMAGE_FIELD = 'image'
+TEXT_FIELD = None
+LABEL_FIELD = 'label'
+PROMPT_TEMPLATE = 'a movie poster for a {label} film'
+DATASET_URL = 'https://www.kaggle.com/datasets/neha1703/movie-genre-from-its-poster'
+
+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
+                if IMAGE_FIELD not in ex or ex[IMAGE_FIELD] is None:
+                    continue
+                p = os.path.join(img_dir, str(i) + '.jpg')
+                try:
+                    ex[IMAGE_FIELD].convert('RGB').save(p)
+                except Exception:
+                    continue
+                out.append({'image': p, '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("movie_posters", 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("movie_posters", s.get("text", ""))
+        item["text"] = text
+        item["domain"] = "movie_posters"
+        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()