| """Check a video against the dataset to guess whether it matches something already stored.""" |
|
|
| import imagehash |
|
|
| import storage |
| from fingerprint import extract_fingerprint |
|
|
| |
| DEFAULT_HAMMING_THRESHOLD = 10 |
| DEFAULT_MATCH_RATIO = 0.5 |
|
|
|
|
| def _hex_to_hash(h: str): |
| return imagehash.hex_to_hash(h) |
|
|
|
|
| def compare_to_video(query_hashes: list, stored_hashes: list, hamming_threshold: int) -> float: |
| """Fraction of query frames that have a close match somewhere in the stored video.""" |
| if not query_hashes or not stored_hashes: |
| return 0.0 |
|
|
| stored_decoded = [_hex_to_hash(f["hash"]) for f in stored_hashes] |
| hits = 0 |
| for qf in query_hashes: |
| qh = _hex_to_hash(qf["hash"]) |
| best = min(qh - sh for sh in stored_decoded) |
| if best <= hamming_threshold: |
| hits += 1 |
| return hits / len(query_hashes) |
|
|
|
|
| def check_video( |
| query_path: str, |
| hamming_threshold: int = DEFAULT_HAMMING_THRESHOLD, |
| match_ratio_threshold: float = DEFAULT_MATCH_RATIO, |
| samples_per_second: float = 1.0, |
| ) -> dict: |
| query_hashes = extract_fingerprint(query_path, samples_per_second=samples_per_second) |
|
|
| candidates = [] |
| for meta in storage.list_videos(): |
| video_id = meta["id"] |
| stored_hashes = storage.load_all_hashes(video_id) |
| ratio = compare_to_video(query_hashes, stored_hashes, hamming_threshold) |
| candidates.append({"id": video_id, "title": meta["title"], "match_ratio": round(ratio, 3)}) |
|
|
| candidates.sort(key=lambda c: c["match_ratio"], reverse=True) |
| best = candidates[0] if candidates else None |
| is_match = bool(best and best["match_ratio"] >= match_ratio_threshold) |
|
|
| return { |
| "query_path": query_path, |
| "is_likely_copyrighted": is_match, |
| "best_match": best, |
| "top_candidates": candidates[:5], |
| } |
|
|
