"""Video deduplication module - compute fingerprints and detect duplicates.""" import hashlib import logging import os import tempfile from dataclasses import dataclass from typing import Optional import cv2 import numpy as np from celery import Task from sqlalchemy.orm import Session from worker_app.celery_app import celery_app from worker_app.db import SessionLocal from packages.adapters.sqlalchemy_impl.generated_video_repository import SQLAlchemyGeneratedVideoRepository from packages.shared.storage import get_storage_service logger = logging.getLogger(__name__) def compute_phash(image: np.ndarray, hash_size: int = 8) -> str: """计算图像的感知哈希（pHash），基于 DCT（离散余弦变换）。 算法步骤： 1. 将图像缩放到 hash_size*4 × hash_size*4（默认 32×32） 2. 转为灰度图，应用 2D DCT 提取频率分量 3. 取左上角 hash_size×hash_size 的低频分量（默认 8×8 = 64 bit） 4. 排除 DC 分量（[0,0] 位置），计算中位数 5. 每个分量与中位数比较，生成二值 hash Args: image: BGR 格式的 numpy 图像数组 hash_size: 哈希边长，默认 8（生成 64-bit hash） Returns: 十六进制字符串表示的感知哈希 """ # Resize to 32x32 for DCT resized = cv2.resize(image, (hash_size * 4, hash_size * 4)) gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY).astype(np.float32) # Apply 2D DCT dct = cv2.dct(gray) # Take top-left 8x8 low-frequency components dct_low = dct[:hash_size, :hash_size] # Compute median (excluding DC component at [0,0]) dct_low[0, 0] = 0 median = np.median(dct_low) # Generate hash based on comparison with median diff = (dct_low > median).astype(int) hash_str = "".join(str(b) for row in diff for b in row) return hex(int(hash_str, 2))[2:] def hamming_distance(hash1: str, hash2: str) -> int: """计算两个十六进制哈希之间的汉明距离（不同 bit 位数）。 使用 XOR 异或 + bit 计数：bin(h1 ^ h2).count("1")。 例如：hamming_distance("00", "ff") = 8（8 个 bit 全不同）。 Args: hash1: 十六进制字符串 hash2: 十六进制字符串 Returns: 不同 bit 的数量 """ h1, h2 = int(hash1, 16), int(hash2, 16) return bin(h1 ^ h2).count("1") def compute_color_histogram(image: np.ndarray, bins: int = 32) -> list[float]: """Compute color histogram for an image.""" hist = [] for i in range(3): h = cv2.calcHist([image], [i], None, [bins], [0, 256]) h = cv2.normalize(h, h).flatten() hist.extend(h) return hist @dataclass class VideoFingerprint: """Video fingerprint containing multiple similarity metrics.""" md5: str keyframe_phashes: list[str] color_histograms: list[list[float]] duration: float resolution: tuple[int, int] def to_dict(self) -> dict: # 注意：color_histograms 里的值可能是 np.float32（来自 cv2.normalize）， # 直接存进 dict 后 SQLAlchemy JSON 序列化会报 "float32 is not JSON serializable"。 # 这里统一转成 Python 原生 float。 native_histograms = [[float(v) for v in hist] for hist in self.color_histograms] return { "md5": self.md5, "keyframe_phashes": self.keyframe_phashes, "color_histograms": native_histograms, "duration": float(self.duration), "resolution": [int(self.resolution[0]), int(self.resolution[1])], } class VideoDeduplicator: """Video deduplication using multiple fingerprint methods.""" PHASH_THRESHOLD = 10 HISTOGRAM_THRESHOLD = 0.85 def compute_fingerprint(self, video_path: str) -> VideoFingerprint: """Compute video fingerprint using MD5, pHash, and color histogram.""" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise RuntimeError(f"Cannot open video: {video_path}") fps = cap.get(cv2.CAP_PROP_FPS) frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) duration = frame_count / fps if fps > 0 else 0 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) md5_hash = hashlib.md5(usedforsecurity=False) keyframe_phashes = [] color_histograms = [] frame_interval = max(1, frame_count // 10) for i in range(0, frame_count, frame_interval): cap.set(cv2.CAP_PROP_POS_FRAMES, i) ret, frame = cap.read() if not ret: continue _, buffer = cv2.imencode(".jpg", frame) md5_hash.update(buffer) keyframe_phashes.append(compute_phash(frame)) color_histograms.append(compute_color_histogram(frame)) cap.release() return VideoFingerprint( md5=md5_hash.hexdigest(), keyframe_phashes=keyframe_phashes, color_histograms=color_histograms, duration=duration, resolution=(width, height), ) def check_duplicate(self, fingerprint: VideoFingerprint, project_id: str, session: Session) -> Optional[dict]: """检查视频是否与项目中已有视频重复。 判定逻辑（按优先级）： 1. MD5 精确匹配：完全一致则 similarity=1.0，立即返回 2. pHash 相似度：计算新视频每帧 phash 与已有视频每帧 phash 的最小汉明距离， 取所有帧的平均值 avg_distance。若 avg_distance < PHASH_THRESHOLD(10)， 则判定为重复，similarity = 1.0 - (avg_distance / 64) 注意：返回第一个通过阈值的匹配（非最优匹配）。 Args: fingerprint: 待检测视频的指纹 project_id: 项目 ID，仅在同一项目内搜索 session: 数据库会话 Returns: 重复信息字典（含 duplicate, duplicate_of, reason, similarity）， 或 None 表示未找到重复。 """ video_repo = SQLAlchemyGeneratedVideoRepository(session) existing_videos = video_repo.list_by_project(project_id) for existing in existing_videos: if not existing.video_fingerprint: continue ef = existing.video_fingerprint # 精确匹配：MD5 完全一致 if fingerprint.md5 == ef.get("md5"): return {"duplicate": True, "duplicate_of": existing.id, "reason": "exact_md5_match", "similarity": 1.0} # 感知哈希相似度 existing_phashes = ef.get("keyframe_phashes", []) if not existing_phashes: continue # 计算每个新关键帧到已有关键帧的最小汉明距离，取平均 min_distances = [] for phash in fingerprint.keyframe_phashes: distances = [hamming_distance(phash, ep) for ep in existing_phashes] min_distances.append(min(distances)) avg_distance = sum(min_distances) / len(min_distances) if min_distances else 100 if avg_distance >= self.PHASH_THRESHOLD: continue phash_similarity = 1.0 - (avg_distance / 64) return { "duplicate": True, "duplicate_of": existing.id, "reason": "phash_similar", "similarity": phash_similarity, } return None def check_batch_duplicate( self, fingerprint: VideoFingerprint, batch_id: str, current_video_id: str, session: Session, ) -> Optional[dict]: """检查视频是否与同批次内其他视频重复。 逻辑与 check_duplicate 一致（MD5 + pHash），但搜索范围限定为同 batch_id 的视频。 Args: fingerprint: 待检测视频的指纹 batch_id: 批次 ID current_video_id: 当前视频 ID（排除自身） session: 数据库会话 Returns: 重复信息字典，或 None 表示未找到重复 """ video_repo = SQLAlchemyGeneratedVideoRepository(session) batch_videos = video_repo.list_by_batch(batch_id) for existing in batch_videos: if existing.id == current_video_id: continue if not existing.video_fingerprint: continue ef = existing.video_fingerprint if fingerprint.md5 == ef.get("md5"): return { "duplicate": True, "duplicate_of": existing.id, "reason": "batch_exact_md5_match", "similarity": 1.0, } existing_phashes = ef.get("keyframe_phashes", []) if not existing_phashes: continue min_distances = [] for phash in fingerprint.keyframe_phashes: distances = [hamming_distance(phash, ep) for ep in existing_phashes] min_distances.append(min(distances)) avg_distance = sum(min_distances) / len(min_distances) if min_distances else 100 if avg_distance >= self.PHASH_THRESHOLD: continue phash_similarity = 1.0 - (avg_distance / 64) return { "duplicate": True, "duplicate_of": existing.id, "reason": "batch_phash_similar", "similarity": phash_similarity, } return None @staticmethod def _average_histogram_similarity(histograms_a: list[list[float]], histograms_b: list[list[float]]) -> float: """ 计算两组颜色直方图之间的平均余弦相似度。 对每组直方图对取最小长度对齐，计算余弦相似度后取平均。 Args: histograms_a: 第一组直方图（每帧一个 list） histograms_b: 第二组直方图 Returns: 平均余弦相似度，范围 [0, 1] """ if not histograms_a or not histograms_b: return 0.0 similarities = [] for ha in histograms_a: best = 0.0 vec_a = np.array(ha, dtype=np.float64) norm_a = np.linalg.norm(vec_a) if norm_a == 0: continue for hb in histograms_b: vec_b = np.array(hb, dtype=np.float64) # 对齐长度 min_len = min(len(vec_a), len(vec_b)) va, vb = vec_a[:min_len], vec_b[:min_len] norm_b = np.linalg.norm(vb) if norm_b == 0: continue sim = float(np.dot(va, vb) / (norm_a * norm_b)) best = max(best, sim) similarities.append(best) return sum(similarities) / len(similarities) if similarities else 0.0 def compute_duplicate_rate( self, fingerprint: VideoFingerprint, project_id: str, current_video_id: str | None, session: Session, *, user_id: str = "", ) -> float: """计算当前视频与用户库内已有视频的最高相似度百分比。 优先按 user_id 全局比较（跨项目），user_id 为空时回退到项目级比较。 遍历最近 200 个其他有指纹的视频，对每个计算相似度： - MD5 精确匹配 → 100% - pHash 相似度 → (1.0 - avg_distance / 64) * 100 取最高值作为 duplicate_rate（0~100）。 如果没有其他视频可比较，返回 0.0。 Args: fingerprint: 当前视频的指纹 project_id: 项目 ID（user_id 为空时的回退范围） current_video_id: 当前视频 ID（排除自身，可为 None） session: 数据库会话 user_id: 用户 ID（优先按用户全局比较） Returns: duplicate_rate: 0~100 的浮点数 """ # 限制查询最近 200 个视频，避免大库内存溢出 from packages.adapters.sqlalchemy_impl.models import GeneratedVideoModel # 优先按 user_id 全局比较（跨项目），否则回退到项目级 if user_id: query = session.query(GeneratedVideoModel).filter( GeneratedVideoModel.user_id == user_id, ) logger.debug("compute_duplicate_rate: user-level scope user_id=%s", user_id) else: query = session.query(GeneratedVideoModel).filter( GeneratedVideoModel.project_id == project_id, ) logger.debug("compute_duplicate_rate: project-level fallback project_id=%s", project_id) # 排除当前视频自身（记录可能已写入 DB，必须在查询层排除） if current_video_id: query = query.filter(GeneratedVideoModel.id != current_video_id) recent_models = query.order_by(GeneratedVideoModel.generated_at.desc()).limit(200).all() video_repo = SQLAlchemyGeneratedVideoRepository(session) existing_videos = [video_repo._to_domain(m) for m in recent_models] max_similarity = 0.0 for existing in existing_videos: if current_video_id and existing.id == current_video_id: continue if not existing.video_fingerprint: continue ef = existing.video_fingerprint # MD5 精确匹配 → 100% if fingerprint.md5 == ef.get("md5"): return 100.0 # pHash 相似度 existing_phashes = ef.get("keyframe_phashes", []) if not existing_phashes or not fingerprint.keyframe_phashes: continue min_distances = [] for phash in fingerprint.keyframe_phashes: distances = [hamming_distance(phash, ep) for ep in existing_phashes] min_distances.append(min(distances)) avg_distance = sum(min_distances) / len(min_distances) if min_distances else 64 similarity = (1.0 - avg_distance / 64) * 100 max_similarity = max(max_similarity, similarity) return round(max(max_similarity, 0.0), 2) @celery_app.task(bind=True, max_retries=3, name="worker.check_duplicate") def check_duplicate_task(self: Task, generated_video_id: str) -> dict: """Celery task to check if generated video is a duplicate.""" session = SessionLocal() temp_dir = tempfile.mkdtemp() try: video_repo = SQLAlchemyGeneratedVideoRepository(session) storage_service = get_storage_service() deduplicator = VideoDeduplicator() video = video_repo.get(generated_video_id) if video is None: raise ValueError(f"Generated video {generated_video_id} not found") local_path = os.path.join(temp_dir, f"{generated_video_id}.mp4") storage_service.download_file( f"projects/{video.project_id}/generated/{generated_video_id}/{generated_video_id}.mp4", local_path ) fingerprint = deduplicator.compute_fingerprint(local_path) duplicate_result = deduplicator.check_duplicate(fingerprint, video.project_id, session) video.video_fingerprint = fingerprint.to_dict() if duplicate_result: video.is_duplicate = True video.duplicate_of = duplicate_result["duplicate_of"] else: video.is_duplicate = False video.duplicate_of = None video_repo.update(video) session.commit() logger.info(f"Duplicate check completed for video {generated_video_id}: is_duplicate={video.is_duplicate}") return { "ok": True, "video_id": generated_video_id, "is_duplicate": video.is_duplicate, "duplicate_of": video.duplicate_of, "fingerprint": fingerprint.to_dict(), } except Exception as e: logger.error(f"Duplicate check failed for {generated_video_id}: {str(e)}") session.rollback() raise self.retry(exc=e, countdown=60) from e finally: session.close() import shutil shutil.rmtree(temp_dir, ignore_errors=True)